Agriculture-Computing Ontology (AgriComO)
type: Ontology
creator: Quoc Hung Ngo, Tahar Kechadi, Nhien-An Le-Khac
creator: UCD CONSUS Research Group
description: Agriculture-Computing Ontology (AgriComO) - A ontology developed for knowledge representation systems and handling mined knowledge in digital agriculture. Keywords: Agriculture, Computing, Knowledge Map, Data Mining, Cropping, Disease, Transsformation.

Abstract
type: AnnotationProperty
comment: A brief summary of the resource.

Contributor
type: AnnotationProperty
comment: The guidelines for using names of persons or organizations as creators also apply to contributors. Typically, the name of a Contributor should be used to indicate the entity.

Created
type: AnnotationProperty
comment: The Dublin Core term "created" is the annotation to be used to define the date that the creator added the RD to their work space (e.g. YYYY-MM-DD).

Creator
type: AnnotationProperty
comment: The Dublin Core term "creator" is the annotation to be used to define the person and organization who added the class to the RD Library. The form of the dc:creator value should be first name, last name, comma, and then organization name (e.g Rob Bodington, Eurostep).

Date
type: AnnotationProperty
comment: Date may be used to express temporal information at any level of granularity. Recommended practice is to express the date, date/time, or period of time according to ISO 8601-1 [ISO 8601-1] or a published profile of the ISO standard, such as the W3C Note on Date and Time Formats [W3CDTF] or the Extended Date/Time Format Specification [EDTF]. If the full date is unknown, month and year (YYYY-MM) or just year (YYYY) may be used. Date ranges may be specified using ISO 8601 period of time specification in which start and end dates are separated by a '/' (slash) character. Either the start or end date may be missing.

Description
type: AnnotationProperty
comment: Description may include but is not limited to: an abstract, a table of contents, a graphical representation, or a free-text account of the resource.

Identifier
type: AnnotationProperty
comment: Recommended practice is to identify the resource by means of a string conforming to an identification system. Examples include International Standard Book Number (ISBN), Digital Object Identifier (DOI), and Uniform Resource Name (URN). Persistent identifiers should be provided as HTTP URIs.
range: anyURI

Language
type: AnnotationProperty
comment: The Dublin Core term "language" is the annotation to be used to define the natural language in which a label, definition or comment is made (e.g. "en" for English).

Licence
type: AnnotationProperty

Modified
type: AnnotationProperty
comment: The Dublin Core term "modified" is the annotation to be used to define the date of the most recent change to the Reference Data item (e.g. YYYY-MM-DD).

Publisher
type: AnnotationProperty
comment: The Dublin Core term "publisher" is the annotation to be used to define the entity, which makes resource be available.

References
type: AnnotationProperty
title: is Defined By
comment: The Dublin Core term "references" is the annotation to be used to define any reference to another standard or document that appears as part of the definition of the RD item. (e.g. "ISO 10303-41").

Rights
type: AnnotationProperty
comment: The Dublin Core term "rights" is the annotation to be used to define the Intellectual Property Rights, etc. related to the RD. It is used in conjunction with "rightsHolder".

Source
type: AnnotationProperty
comment: The Dublin Core term "source" is the annotation to be used to define any standard or organization that originally created the class and is responsible for publishing it and controlling its development. A unique name for any dc:source must be agreed by the RD developers.

Subject
type: AnnotationProperty
comment: dc:subject is the annotation to be used to define the keywords or classifications codes (e.g. class of class) related to an RD item. Each keyword or classification code should be created in separate instances of "subject". (e.g. "Aircraft maintenance")

Title
type: AnnotationProperty
comment: Typically, the subject will be represented using keywords, key phrases, or classification codes. Recommended best practice is to use a controlled vocabulary.

Acronym
type: AnnotationProperty
comment: This property is intended to be used with non-literal values. This property is an inverse property of Is Referenced By.

Comment
type: AnnotationProperty
description: An rdfs:comment is a human readable text definition for an RD item where more than one are allowed to support definitions in different natural languages.

Label
type: AnnotationProperty
comment: An rdfs:label is a human readable name for an RD item where more than one are allowed to support labels in different natural languages (e.g. 'Trigo' in Spanish and 'Wheat' in English)

See Also

affected by
type: ObjectProperty
sub property of: opCrop
inverse of: affects
domain: Crop
domain: Person
domain: Product
range: Crop Problem

affects
type: ObjectProperty
equivalent property: influences
sub property of: opCropDisease
domain: Crop Problem
range: Crop
range: Person
range: Product

affects on stage
type: ObjectProperty
sub property of: opCropDisease

applied to
type: ObjectProperty
domain: Adjuvants
domain: Biopesticides
domain: Crop Protection
domain: Fungicides
domain: Growth Regulator
domain: Herbicides
domain: Insecticide
domain: Molluscicides
domain: Pesticides
range: Cereal
range: Crop
range: Farm
range: Field
range: Fruit Tree
range: Vegetable

belongs to
type: ObjectProperty
inverse of: has country
domain: Country
range: Sub-Country

biological control
type: ObjectProperty
sub property of: controls
type: DatatypeProperty

caused by
type: ObjectProperty
sub property of: opCropDisease
domain: Crop Problem
range: Microoganism

causes
type: ObjectProperty
sub property of: disease property
sub property of: opCropDisease
domain: Bacterial
domain: Bacterial Disease
domain: Crop Disease
domain: Crop Pest
domain: Crop Problem
domain: Crop Stress
domain: Fungal Disease
domain: Fungus
domain: Microoganism
domain: Pathogen
domain: Protozoa
domain: Protozoal Disease
domain: Viral Disease
domain: Virus
domain: Weeds
range: Bacterial Disease
range: Crop Disease
range: Crop Injure
range: Crop Pest
range: Crop Problem
range: Crop Stress
range: Fungal Disease
range: Protozoal Disease
range: Viral Disease
range: Weeds
type: DatatypeProperty
range: Literal

chemical control
type: ObjectProperty
sub property of: controls
type: DatatypeProperty

controls
type: ObjectProperty
sub property of: disease property
sub property of: opCropDisease
inverse of: isControlledBy
domain: Bacterial Disease
domain: Crop Disease
domain: Crop Pest
domain: Crop Problem
domain: Crop Stress
domain: Fungal Disease
domain: Protozoal Disease
domain: Treatment
domain: Viral Disease
domain: Weeds
range: Bacterial Disease
range: Crop Disease
range: Crop Injure
range: Crop Pest
range: Crop Problem
range: Crop Stress
range: Fungal Disease
range: Protozoal Disease
range: Viral Disease
range: Weeds
type: DatatypeProperty
range: Literal

Conventional Management
type: ObjectProperty
sub property of: management
type: DatatypeProperty

defined in
type: ObjectProperty
domain: Agriculture Transformation
domain: Association Rule
domain: Classification
domain: Clustering
domain: Data Selection
domain: Evaluation Metric
domain: Mining Algorithm
domain: Regression
domain: Transformation
range: Article
range: Document
range: Policy
range: Report
range: Toolkit

defines
type: ObjectProperty
domain: Article
domain: Document
domain: Policy
domain: Report
range: Association Rule
range: Classification
range: Clustering
range: Data Selection
range: Regression

definition
type: ObjectProperty
sub property of: disease property
domain: Abstract Entity
domain: Physical Entity
type: DatatypeProperty
range: Literal

development
type: ObjectProperty
sub property of: disease property
domain: Bacterial Disease
domain: Crop Disease
domain: Crop Pest
domain: Crop Problem
domain: Crop Stress
domain: Fungal Disease
domain: Protozoal Disease
domain: Viral Disease
domain: Weeds
type: DatatypeProperty
range: Literal

diagnosis
type: ObjectProperty
sub property of: disease property
domain: Bacterial Disease
domain: Crop Disease
domain: Crop Pest
domain: Crop Problem
domain: Crop Stress
domain: Fungal Disease
domain: Protozoal Disease
domain: Viral Disease
domain: Weeds
type: DatatypeProperty
range: Literal

disease property
type: ObjectProperty
domain: Bacterial Disease
domain: Crop Disease
domain: Crop Problem
domain: Crop Stress
domain: Fungal Disease
domain: Protozoal Disease
domain: Viral Disease
type: DatatypeProperty
range: Literal

distribution
type: ObjectProperty
sub property of: disease property
domain: Bacterial Disease
domain: Crop Disease
domain: Crop Pest
domain: Crop Problem
domain: Crop Stress
domain: Fungal Disease
domain: Protozoal Disease
domain: Viral Disease
domain: Weeds
type: DatatypeProperty
range: Literal

evaluated by
type: ObjectProperty
sub property of: opDataMining
inverse of: is evaluation of
domain: Association
domain: Association Rule
domain: Classification
domain: Classifier
domain: Cluster
domain: Clustering
domain: Regression
domain: Regressor
range: Evaluation

grows in
type: ObjectProperty
domain: Crop
range: Country
range: Sub-Country
range: Location

has algorithm
type: ObjectProperty
sub property of: opDataMining
inverse of: is algorithm of
domain: Association Rule
domain: Classification
domain: Clustering
domain: Data Mining
domain: Data Selection
domain: Evaluation
domain: Regression
domain: Toolkit
range: Computer System
range: Computing Algorithm
range: Evaluation Metric
range: Mining Algorithm
range: Spectral Vegetation Indices

has boundary
type: ObjectProperty
inverse of: is boundary of
domain: Farm
domain: Field
range: Boundary

has cause
type: ObjectProperty
domain: Person
domain: Product
range: Crop Problem

has classifier
type: ObjectProperty
sub property of: opDataMining
domain: Classification
range: Classifier

has climate
type: ObjectProperty
sub property of: opLocation
domain: Area
domain: Continent
domain: Country
domain: Location
domain: Sub-Country
range: Climate Type

has cluster
type: ObjectProperty
sub property of: opDataMining
domain: Clustering
range: Cluster

has component
type: ObjectProperty
inverse of: is component of
domain: Weather
range: Air Pollution
range: Atmospheric Pressure
range: Climate Type
range: Cloud Cover
range: Humidity
range: Precipitation
range: Solar Radiation
range: Temperature
range: Wind Type

has condition
type: ObjectProperty
sub property of: opDataMining
inverse of: is condition of
inverse of: used by
domain: Association
domain: Classifier
domain: Cluster
domain: Crop
domain: Field
range: Fertilizer

has corner point
type: ObjectProperty
sub property of: opLocation
domain: Boundary
range: Point
range: Location

has country
type: ObjectProperty
domain: Farm
domain: Sub-Country
range: Country

has dataset
type: ObjectProperty
sub property of: opDataMining
inverse of: is dataset of
domain: Association Rule
domain: Classification
domain: Clustering
domain: Regression
range: Dataset

has disease
type: ObjectProperty
sub property of: opCrop
sub property of: opCropDisease
inverse of: is disease of
domain: Almond
domain: Aloe vera
domain: Apple
domain: Apricot
domain: Artichoke
domain: Avocado
domain: Banana
domain: Barley
domain: Basil
domain: Bean
domain: Beet
domain: Bergamot
domain: Blackberry
domain: Cabbage
domain: Canola
domain: Cantaloupe
domain: Cashew
domain: Cassava
domain: Cauliflower
domain: Celery
domain: Cereal
domain: Cherry
domain: Chestnut
domain: Chickpea
domain: Citrus
domain: Cocoa
domain: Coconut
domain: Coffee
domain: Corn
domain: Cotton
domain: Crabapple
domain: Cranberry
domain: Crop
domain: Crop Pest
domain: Cucumber
domain: Eggplant
domain: Flower
domain: Fruit Tree
domain: Garlic
domain: Gourd
domain: Grape
domain: Grapevines
domain: Grass
domain: Guava
domain: Hazelnut
domain: Kiwi
domain: Lemon
domain: Lettuce
domain: Limes
domain: Maize
domain: Mint
domain: Mushrooms
domain: Mustard
domain: Oak
domain: Oats
domain: Oil palm
domain: Okra
domain: Onion
domain: Orange
domain: Papaya
domain: Peach
domain: Peanut
domain: Pear
domain: Pepper
domain: Pomegranate
domain: Pumpkin
domain: Quince
domain: Rapeseed
domain: Raspberry
domain: Rice
domain: Rye
domain: Soybean
domain: Spinach
domain: Squash
domain: Sugar beet
domain: Sugarcane

has evaluation metric
type: ObjectProperty
sub property of: opDataMining
inverse of: is evaluation metric of
domain: Association Rule
domain: Classification
domain: Clustering
domain: Data Selection
domain: Regression
range: Evaluation Metric

has fertiliser
type: ObjectProperty
inverse of: is fertiliser of
domain: Field
range: Fertilizer

has field
type: ObjectProperty
inverse of: is field of
domain: Farm
range: Field

has center
type: ObjectProperty
domain: Farm
domain: Field
range: Location

has growth stage
type: ObjectProperty
domain: Crop
range: Growth Stage

has knowledge model
type: ObjectProperty
sub property of: opDataMining
domain: Document
range: Association Rule
range: Classification
range: Classifier
range: Cluster
range: Clustering
range: Data Mining
range: Data Selection
range: Regression
range: Regressor

has link
type: ObjectProperty
inverse of: is link of
domain: Abstract Entity
domain: Data Mining
domain: Information Object
domain: Physical Entity
domain: Transformation
range: Link

has location
type: ObjectProperty
inverse of: is location of
domain: Association Rule
domain: Classification
domain: Clustering
domain: Farm
domain: Organization
domain: Regression
range: Location

has observation value
type: ObjectProperty
inverse of: is observation of
domain: Observe System
domain: Sensor
domain: Sensor Station
domain: SubSystem
range: Observation Value
range: Sensor Output

has organizer
type: ObjectProperty
domain: Event
range: Organization
range: Person

has owner
type: ObjectProperty
inverse of: owns
domain: Farm
range: Land Owner

has part
type: ObjectProperty
domain: Physical Entity
range: Physical Entity
References: http://www.loa-cnr.it/ontologies/DUL.owl#hasPart
comment: A schematic relation between any entities, e.g. 'the human body has a brain as part'. Subproperties and restrictions can be used to specialize hasPart for objects, events, etc.

has phenomenon
type: ObjectProperty
domain: Crop
domain: Farm
domain: Field
domain: Location
range: Phenomenon

has plan
type: ObjectProperty
inverse of: is plan of
domain: Field
range: Plan

has product
type: ObjectProperty
inverse of: is product of
domain: Country
domain: Organization
domain: Person
domain: Location
range: Product

has regressor
type: ObjectProperty
sub property of: opDataMining
domain: Regression
range: Regressor

has association rule
type: ObjectProperty
domain: Association Rule
range: Regressor

has setting
type: ObjectProperty
domain: Observe System
domain: Sensor
domain: Sensor Station
domain: SubSystem

has soil condition
type: ObjectProperty
sub property of: has condition
inverse of: is soil condition of
domain: Field
range: Soil

has spray
type: ObjectProperty
inverse of: sprays on
domain: Field
range: Spraying

has state
type: ObjectProperty
inverse of: is state of
domain: Abstract Entity
domain: Data Mining
domain: Physical Entity
range: State

has sub country
type: ObjectProperty
inverse of: is sub-country of
domain: Country
range: Sub-Country

has sub-system
type: ObjectProperty
domain: Observe System
range: Sensor
range: Sensor Station
range: SubSystem
References: http://purl.oclc.org/NET/ssnx/ssn#hasSubSystem

has supplier
type: ObjectProperty
inverse of: is supplier of
domain: Product
range: Supplier

has symptom
type: ObjectProperty
sub property of: opCropDisease
domain: Crop Problem
range: Symptom

has transformation
type: ObjectProperty
sub property of: opDataMining
inverse of: is transformation of
domain: Abstract Entity
domain: Physical Entity
range: Agriculture Transformation
range: Agroclimatic Indices
range: Agronomic Indices
range: Data Transformation
range: Spectral Vegetation Indices

has value
type: ObjectProperty
sub property of: opDataMining
domain: Observe System
domain: Sensor
domain: Sensor Station
domain: SubSystem
range: Observation Value

has variety
type: ObjectProperty
inverse of: is variety of
domain: Crop
range: Crop

has water condition
type: ObjectProperty
sub property of: has condition
inverse of: is water condition of
domain: Field
range: Water

has weather condition
type: ObjectProperty
sub property of: has condition
inverse of: is weather condition of
domain: Field
range: Weather

image
type: ObjectProperty
sub property of: disease property
domain: Almond
domain: Aloe vera
domain: Apple
domain: Apricot
domain: Artichoke
domain: Avocado
domain: Bacterial Disease
domain: Banana
domain: Barley
domain: Basil
domain: Bean
domain: Beet
domain: Bergamot
domain: Blackberry
domain: Cabbage
domain: Canola
domain: Cantaloupe
domain: Cashew
domain: Cassava
domain: Cauliflower
domain: Celery
domain: Cereal
domain: Cherry
domain: Chestnut
domain: Chickpea
domain: Citrus
domain: Cocoa
domain: Coconut
domain: Coffee
domain: Corn
domain: Cotton
domain: Cranberry
domain: Crop
domain: Crop Disease
domain: Crop Injure
domain: Crop Pest
domain: Crop Problem
domain: Crop Stress
domain: Cucumber
domain: Eggplant
domain: Flower
domain: Fruit Tree
domain: Fungal Disease
domain: Garlic
domain: Gourd
domain: Grape
domain: Grapevines
domain: Grass
domain: Guava
domain: Hazelnut
domain: Kiwi
domain: Lemon
domain: Limes
domain: Maize
domain: Mint
domain: Mushrooms
domain: Mustard
domain: Oats
domain: Oil palm
domain: Okra
domain: Onion
domain: Orange
domain: Papaya
domain: Peach
domain: Peanut
domain: Pear
domain: Pepper
domain: Physical Entity
domain: Pomegranate
domain: Protozoal Disease
domain: Pumpkin
domain: Quince
domain: Rapeseed
domain: Raspberry
domain: Rice
domain: Rye
domain: Soybean
domain: Spinach

important
type: ObjectProperty
sub property of: disease property
domain: Bacterial Disease
domain: Crop Disease
domain: Crop Pest
domain: Crop Problem
domain: Crop Stress
domain: Fungal Disease
domain: Protozoal Disease
domain: Viral Disease
domain: Weeds
type: DatatypeProperty
range: Literal

includes event
type: ObjectProperty
domain: Physical Entity
range: Event
References: http://www.loa-cnr.it/ontologies/DUL.owl#includesEvent
comment: A relation between situations and events, e.g. This month, our farm organize a workshop.

inherits
type: ObjectProperty
domain: Agriculture Transformation
domain: Agroclimatic Indices
domain: Computer System
domain: Evaluation Metric
domain: Mining Algorithm
domain: Spectral Vegetation Indices
domain: Transformation
range: Agriculture Transformation
range: Agroclimatic Indices
range: Computer System
range: Evaluation Metric
range: Mining Algorithm
range: Spectral Vegetation Indices

is algorithm of
type: ObjectProperty
sub property of: opDataMining
domain: Computer System
domain: Computing Algorithm
domain: Evaluation Metric
domain: Mining Algorithm
domain: Spectral Vegetation Indices
range: Association Rule
range: Classification
range: Clustering
range: Data Mining
range: Data Selection
range: Evaluation
range: Regression

is boundary of
type: ObjectProperty
domain: Boundary
range: Farm
range: Field

is component of
type: ObjectProperty
domain: Air Pollution
domain: Atmospheric Pressure
domain: Climate Type
domain: Cloud Cover
domain: Humidity
domain: Precipitation
domain: Solar Radiation
domain: Temperature
domain: Wind Type
range: Weather

is condition of
type: ObjectProperty
sub property of: opDataMining
domain: Air
domain: Climate Type
domain: Imagery
domain: Soil
domain: Topography
domain: Water
domain: Weather
range: Field

is dataset of
type: ObjectProperty
sub property of: opDataMining
domain: Dataset
range: Association
range: Association Rule
range: Classification
range: Classifier
range: Cluster
range: Clustering
range: Data Mining
range: Data Selection
range: Knowledge Model
range: Regression
range: Regressor

is disease of
type: ObjectProperty
sub property of: opCropDisease
domain: Crop Problem
range: Crop

is evaluation metric of
type: ObjectProperty
sub property of: opDataMining
domain: Evaluation Metric
range: Association Rule
range: Classification
range: Clustering
range: Data Mining
range: Data Selection
range: Regression

is evaluation of
type: ObjectProperty
sub property of: opDataMining
domain: Evaluation
range: Association
range: Association Rule
range: Classification
range: Classifier
range: Cluster
range: Clustering
range: Regression
range: Regressor

is fertiliser of
type: ObjectProperty
domain: Fertilizer
range: Field

is field of
type: ObjectProperty
domain: Field
range: Farm

is link of
type: ObjectProperty
domain: Link
range: Abstract Entity
range: Physical Entity

is location of
type: ObjectProperty
sub property of: opLocation
domain: Sub-Country
domain: Location
range: Country
range: Sub-Country

is observation of
type: ObjectProperty
domain: Sensor Output
range: Observe System
range: Sensor
range: Sensor Station
range: SubSystem

is plan of
type: ObjectProperty
domain: Plan
range: Field

is product of
type: ObjectProperty
domain: Product
range: Country
range: Organization
range: Person
range: Sub-Country
range: Location

is soil condition of
type: ObjectProperty
sub property of: is condition of
range: Field

is state of
type: ObjectProperty
sub property of: opDataMining
domain: State
range: Abstract Entity
range: Data Mining
range: Physical Entity

is sub-country of
type: ObjectProperty
domain: Sub-Country
range: Country

is supplier of
type: ObjectProperty
domain: Supplier
range: Product

is target of
type: ObjectProperty
sub property of: opDataMining
inverse of: predicts

is transformation of
type: ObjectProperty
sub property of: opDataMining
domain: Agriculture Transformation
domain: Agroclimatic Indices
domain: Agronomic Indices
domain: Data Transformation
domain: Spectral Vegetation Indices
range: Abstract Entity
range: Physical Entity

is variety of
type: ObjectProperty
domain: Crop
range: Crop

is water condition of
type: ObjectProperty
sub property of: is condition of
range: Field

is weather condition of
type: ObjectProperty
sub property of: is condition of
range: Field

life cycle
type: ObjectProperty
sub property of: disease property
domain: Bacterial Disease
domain: Crop Disease
domain: Crop Pest
domain: Crop Problem
domain: Crop Stress
domain: Fungal Disease
domain: Protozoal Disease
domain: Viral Disease
domain: Weeds
type: DatatypeProperty
range: Literal

made from
type: ObjectProperty
sub property of: opLocation
domain: Product
range: Farm
range: Field
range: Location

management
type: ObjectProperty
sub property of: disease property
domain: Bacterial Disease
domain: Crop Disease
domain: Crop Pest
domain: Crop Problem
domain: Crop Stress
domain: Fungal Disease
domain: Protozoal Disease
domain: Viral Disease
domain: Weeds
type: DatatypeProperty
range: Literal

mentioned in
type: ObjectProperty
domain: Agriculture Transformation
domain: Computer System
domain: Evaluation Metric
domain: Mining Algorithm
domain: Spectral Vegetation Indices
range: Article
range: Document
range: Policy
range: Report

observed by
type: ObjectProperty
inverse of: observes
domain: Observation
range: Observe System

observes
type: ObjectProperty
domain: Observe System
domain: Sensor
domain: Sensor Station
domain: SubSystem
range: Observation

opCrop
type: ObjectProperty
comment: Object properties of crops

opCropDisease
type: ObjectProperty
comment: Object properties of diseases

opDataMining
type: ObjectProperty
comment: Object properties of data mining

opLocation
type: ObjectProperty
comment: Object properties of locations

orders
type: ObjectProperty
domain: Supplier
range: Order

Organic Management
type: ObjectProperty
sub property of: management
type: DatatypeProperty

owned by
type: ObjectProperty
inverse of: owns
domain: Farm
domain: Field
range: Farmer
range: Land Owner

owns
type: ObjectProperty
domain: Farmer
domain: Land Owner
range: Farm
range: Field

plants
type: ObjectProperty
domain: Field
range: Crop

plants on
type: ObjectProperty
domain: Crop
range: Farm
range: Field

predicts
type: ObjectProperty
sub property of: opDataMining
domain: Classification
domain: Classifier
domain: Data Mining
domain: Regression
domain: Regressor
range: Abstract Entity
range: Physical Entity

produced by
type: ObjectProperty
inverse of: produces
domain: Product
range: Organization
range: Supplier

produces
type: ObjectProperty
sub property of: opCropDisease
domain: Company
domain: Customer
domain: Person
range: Product

related to
type: ObjectProperty
domain: Association
domain: Association Rule
domain: Classification
domain: Classifier
domain: Cluster
domain: Clustering
domain: Regression
domain: Regressor
range: Abstract Entity
range: Data Mining
range: Physical Entity

sale by
type: ObjectProperty
domain: Sale
range: Organization
range: Person

sales
type: ObjectProperty
domain: Sale
range: Crop
range: Product

sprays on
type: ObjectProperty
domain: Spraying
range: Field

symptoms
type: ObjectProperty
sub property of: disease property
domain: Bacterial Disease
domain: Crop Disease
domain: Crop Pest
domain: Crop Problem
domain: Crop Stress
domain: Fungal Disease
domain: Protozoal Disease
domain: Viral Disease
domain: Weeds
type: DatatypeProperty
range: Literal

trained on
type: ObjectProperty
sub property of: opDataMining
domain: Association Rule
domain: Classification
domain: Clustering
domain: Data Selection
domain: Regression
range: Dataset

transmission
type: ObjectProperty
sub property of: disease property
domain: Bacterial Disease
domain: Crop Disease
domain: Crop Pest
domain: Crop Problem
domain: Crop Stress
domain: Fungal Disease
domain: Microoganism
domain: Protozoal Disease
domain: Viral Disease
domain: Weeds
type: DatatypeProperty
range: Literal

used by
type: ObjectProperty
domain: Fertilizer
range: Crop
range: Field

used by
type: ObjectProperty
domain: Transformation
range: Document

uses
type: ObjectProperty
domain: Crop
domain: Farm
domain: Field
domain: Fruit Tree
domain: Vegetable
range: Fertilizer
type: DatatypeProperty
sub property of: crop property
range: Literal

topObjectProperty
domain: Field
domain: Thing
range: Thing

ISO code
type: DatatypeProperty
sub property of: location property
domain: Country
domain: Sub-Country
range: string

abstract
type: DatatypeProperty
sub property of: article property
domain: Article
domain: Dataset
domain: Document
domain: Policy
domain: Report
range: Literal
comment: An abstract is a short summary of a longer work (such as a report or research paper). The abstract concisely reports the aims and outcomes of your research so that readers know exactly what the paper is about.

address
type: DatatypeProperty
sub property of: contact
domain: Farmer
domain: Supplier
range: Literal
comment: The particulars of the place where someone lives or an organization is situated.

agricultural land
type: DatatypeProperty
sub property of: location property
domain: Country
domain: Sub-Country

application area
type: DatatypeProperty
sub property of: fertiliser property
domain: Fertilizer
range: float

application code
type: DatatypeProperty
sub property of: treatment property
domain: Treatment
range: Literal

application description
type: DatatypeProperty
sub property of: treatment property
domain: Treatment
range: Literal

applied area
type: DatatypeProperty
sub property of: spray property
domain: Spraying
range: float

applied area unit
type: DatatypeProperty
sub property of: spray property
domain: Spraying
range: string

applied date
type: DatatypeProperty
sub property of: spray property
domain: Spraying
range: dateTime

area
type: DatatypeProperty
sub property of: field property
sub property of: inspection property
sub property of: location property
domain: Field
domain: Inspection
range: double

area unit
type: DatatypeProperty
sub property of: field property
sub property of: inspection property
domain: Field
domain: Inspection
range: Literal

article property
type: DatatypeProperty
domain: Article
domain: Document
domain: Policy
domain: Report
range: Literal

atmospheric pressure
type: DatatypeProperty
sub property of: weather property
domain: Weather
range: float

authors
type: DatatypeProperty
sub property of: article property
domain: Article
domain: Dataset
domain: Document
domain: Policy
domain: Report
range: string
comment: An author is the creator or originator of any written work such as a report, a document, or a scientific article.

block
type: DatatypeProperty
sub property of: field property
domain: Field
range: Literal

business id
type: DatatypeProperty
sub property of: business property
domain: Company
domain: Customer
domain: Organization
domain: Supplier
range: Literal
comment: Business identification number is a proposed identification number to replace eighteen different registration numbers required to be obtained by a company to operate.

business name
type: DatatypeProperty
sub property of: business property
domain: Company
domain: Customer
domain: Organization
domain: Supplier
range: string
comment: A business name is the name your business is known by. It might be your own personal name, or it might be something entirely different and unique to your business.

business property
type: DatatypeProperty
domain: Company
domain: Customer
domain: Organization
domain: Supplier
range: string

clay
type: DatatypeProperty
sub property of: soil property
domain: Soil
domain: Soil Clay
domain: Soil Texture

clay range
type: DatatypeProperty
sub property of: soil range property

climate
type: DatatypeProperty
sub property of: location property
domain: Country
domain: Sub-Country
range: Literal

comments
type: DatatypeProperty
sub property of: treatment property
sub property of: topDataProperty
domain: Abstract Entity
domain: Physical Entity
domain: Treatment
range: Literal
comment: A comment is something that you say which gives an explanation of concept.

common name
type: DatatypeProperty
sub property of: pest property
domain: Crop Pest
range: string

contact
type: DatatypeProperty
sub property of: topDataProperty
domain: Organization
domain: Person

contact person
type: DatatypeProperty
sub property of: contact
domain: Supplier
range: string
comment: A person who provides a link for information or representation between two parties.

coordinate
type: DatatypeProperty
sub property of: location property

coverage
type: DatatypeProperty
sub property of: pest property
domain: Crop Pest
range: string

coverage unit
type: DatatypeProperty
sub property of: pest property
domain: Crop Pest
range: string

crop property
type: DatatypeProperty
domain: Cereal
domain: Crop
domain: Fruit Tree
domain: Vegetable
range: Literal

cropping year
type: DatatypeProperty
sub property of: fertiliser property
sub property of: yield property
domain: Fertilizer
range: int

density
type: DatatypeProperty
sub property of: location property
sub property of: pest property
domain: Country
domain: Crop Pest
domain: Sub-Country
domain: Location
range: float

density unit
type: DatatypeProperty
sub property of: location property
domain: Country
domain: Sub-Country
range: string

description
type: DatatypeProperty
sub property of: crop property
sub property of: inspection property
sub property of: treatment property
domain: Crop
domain: Fruit Tree
domain: Vegetable
range: Literal
comment: Description is an act of describing; specifically : discourse intended to give a mental image of something experienced.

direction
type: DatatypeProperty
sub property of: weather property
domain: Weather
range: int

do date
type: DatatypeProperty
sub property of: plan property
domain: Order
domain: Plan
domain: Sale
range: dateTime

drilling date
type: DatatypeProperty
sub property of: field property
sub property of: yield property
domain: Farm
domain: Field
range: dateTime

elevation
type: DatatypeProperty
sub property of: location property
domain: Country
domain: Sub-Country
range: decimal

email
type: DatatypeProperty
sub property of: contact
domain: Farmer
domain: Supplier
range: string
comment: An email address is a unique identifier for an email account. It is used to both send and receive email messages over the Internet.

estimate yield
type: DatatypeProperty
sub property of: crop property
domain: Crop
domain: Fruit Tree
domain: Vegetable
range: Literal

example
type: DatatypeProperty
domain: Abstract Entity
domain: Data Mining
domain: Information Object
domain: Physical Entity
domain: Transformation
range: Literal

farm name
type: DatatypeProperty
sub property of: farm property
domain: Farm
range: Literal

farm property
type: DatatypeProperty
domain: Farm

fertiliser name
type: DatatypeProperty
sub property of: fertiliser property
domain: Fertilizer
range: string

fertiliser property
type: DatatypeProperty
domain: Fertilizer
range: Literal

fertiliser unit
type: DatatypeProperty
sub property of: fertiliser property
domain: Fertilizer
range: string

fertiliser year
type: DatatypeProperty
sub property of: fertiliser property
domain: Fertilizer
range: integer

field name
type: DatatypeProperty
sub property of: field property
domain: Field
range: string

field property
type: DatatypeProperty
domain: Field
range: Literal

form type
type: DatatypeProperty
sub property of: treatment property
domain: Treatment
range: Literal

grade
type: DatatypeProperty
domain: Article
domain: Association
domain: Classification
domain: Clustering
domain: Data Selection
domain: Dataset
domain: Document
domain: Knowledge Model
domain: Policy
domain: Regression
domain: Report
range: float

harvest date
type: DatatypeProperty
sub property of: field property
sub property of: yield property
domain: Farm
domain: Field
range: dateTime

harvesting
type: DatatypeProperty
sub property of: crop property
domain: Crop
domain: Fruit Tree
domain: Vegetable
range: Literal
comment: Harvesting is the process of gathering a ripe crop from the fields. Reaping is the cutting of grain or pulse for harvest, typically using a scythe, sickle, or reaper.

head category name
type: DatatypeProperty
sub property of: product property
domain: Product
range: string

head group name
type: DatatypeProperty
sub property of: product property
domain: Product
range: string

head type name
type: DatatypeProperty
sub property of: product property
domain: Product
range: string

heading name
type: DatatypeProperty
sub property of: product property
domain: Fertilizer
domain: Product
range: string

humidity
type: DatatypeProperty
sub property of: weather property
domain: Weather
range: float

inspection id
type: DatatypeProperty
sub property of: inspection property
domain: Inspection
range: Literal

inspection property
type: DatatypeProperty
domain: Inspection
range: Literal

latitude
type: DatatypeProperty
equivalent property: latitude
sub property of: farm property
sub property of: field property
sub property of: location property
domain: Country
domain: Farm
domain: Field
domain: Sub-Country
domain: Location
range: decimal

level no
type: DatatypeProperty
sub property of: treatment property
domain: Treatment
range: int

location property
type: DatatypeProperty
domain: Country
domain: Sub-Country

longitude
type: DatatypeProperty
equivalent property: longitude
sub property of: farm property
sub property of: field property
sub property of: inspection property
sub property of: location property
domain: Country
domain: Farm
domain: Field
domain: Sub-Country
domain: Location
range: decimal

lot code
type: DatatypeProperty
sub property of: treatment property
domain: Treatment
range: Literal

maintenance
type: DatatypeProperty
sub property of: crop property
domain: Crop
domain: Fruit Tree
domain: Vegetable
range: Literal
comment: General care and maintenance

map image
type: DatatypeProperty
sub property of: location property
domain: Continent
domain: Country
domain: Location
domain: Sub-Country
domain: Point

mean yield
type: DatatypeProperty
sub property of: yield property
range: float

measurement unit
type: DatatypeProperty
sub property of: plan property
sub property of: product property
sub property of: spray property
domain: Order
domain: Plan
domain: Sale
domain: Spraying
range: string

mobile phone
type: DatatypeProperty
sub property of: contact
domain: Farmer
domain: Supplier
range: string
comment: String of specific numbers that a cell phone user can dial to reach another telephone or mobile phone

notes
type: DatatypeProperty
sub property of: inspection property
sub property of: order property
sub property of: plan property
sub property of: topDataProperty
domain: Farm
domain: Field
domain: Inspection
domain: Order
domain: Plan
domain: Sale
range: Literal

nutrient group
type: DatatypeProperty
sub property of: fertiliser property
sub property of: nutrient property
domain: Essential Nutrient
domain: Fertilizer
domain: Macronutrient
domain: Micronutrient
domain: Mineral
domain: Nutrient
range: string

nutrient id
type: DatatypeProperty
sub property of: nutrient property
domain: Essential Nutrient
domain: Fertilizer
domain: Macronutrient
domain: Micronutrient
domain: Mineral
domain: Nutrient
range: Literal

nutrient name
type: DatatypeProperty
sub property of: fertiliser property
sub property of: nutrient property
domain: Essential Nutrient
domain: Fertilizer
domain: Macronutrient
domain: Micronutrient
domain: Mineral
domain: Nutrient
range: string

nutrient property
type: DatatypeProperty
domain: Essential Nutrient
domain: Macronutrient
domain: Micronutrient
domain: Mineral
domain: Nutrient
range: Literal

order property
type: DatatypeProperty
domain: Order
range: Literal

original name
type: DatatypeProperty
sub property of: business property
domain: Company
domain: Customer
domain: Organization
domain: Supplier
range: string
comment: A originam name is the name your business is known at starting time. It might be your own personal name, or it might be something entirely different and unique to your business.

pest id
type: DatatypeProperty
sub property of: pest property
domain: Crop Pest
range: Literal

pest number
type: DatatypeProperty
sub property of: pest property
domain: Crop Pest
range: string

pest property
type: DatatypeProperty
domain: Crop Pest
range: Literal

pest type
type: DatatypeProperty
sub property of: pest property
domain: Crop Pest
range: string

phone
type: DatatypeProperty
sub property of: contact
domain: Farmer
domain: Supplier
range: string
comment: String of specific numbers that a telephone user can dial to reach another telephone or mobile phone

plan name
type: DatatypeProperty
sub property of: plan property
domain: Plan
range: string

plan number
type: DatatypeProperty
sub property of: plan property
domain: Plan
range: string

plan property
type: DatatypeProperty
domain: Plan
range: Literal

population
type: DatatypeProperty
sub property of: location property
domain: Country
domain: Sub-Country
domain: Location
range: long

population notes
type: DatatypeProperty
sub property of: location property
domain: Country
domain: Sub-Country
range: Literal

postcode
type: DatatypeProperty
sub property of: contact
domain: Farm
domain: Field
range: string
comment: A group of numbers or letters and numbers which are added to a postal address to assist the sorting of mail.

precipitation
type: DatatypeProperty
sub property of: weather property
domain: Weather
range: float

problem notes
type: DatatypeProperty
sub property of: inspection property
domain: Inspection
range: Literal

problem type
type: DatatypeProperty
sub property of: inspection property
domain: Inspection
range: Literal

product name
type: DatatypeProperty
sub property of: plan property
sub property of: product property
sub property of: spray property
domain: Order
domain: Plan
domain: Product
domain: Sale
domain: Spraying
range: string

product property
type: DatatypeProperty
domain: Product
range: Literal

product rate
type: DatatypeProperty
sub property of: plan property
sub property of: product property
sub property of: spray property
domain: Order
domain: Plan
domain: Sale
domain: Spraying
range: float

propagation
type: DatatypeProperty
sub property of: crop property
domain: Crop
domain: Fruit Tree
domain: Vegetable
range: Literal
comment: The breeding of specimens of a plant by natural processes from the parent stock.

publish year
type: DatatypeProperty
sub property of: article property
domain: Article
domain: Dataset
domain: Document
domain: Policy
domain: Report
range: int
comment: The year on which a document, article or paper is or is published.

publisher
type: DatatypeProperty
sub property of: article property
domain: Article
domain: Dataset
domain: Document
domain: Policy
domain: Report
comment: Individual or corporation responsible for the printing and distribution of digital or printed publications. Publishers also handle the marketing efforts for the publications and usually are not the creator of the material. Publishers simply serve as the intermediary between the author of the publication and the consumer market.

quantity
type: DatatypeProperty
sub property of: fertiliser property
sub property of: nutrient property
sub property of: order property
sub property of: sale property
sub property of: spray property
sub property of: topDataProperty
domain: Fertilizer
domain: Nutrient
domain: Order
domain: Sale
domain: Spraying
range: float
range: int

rainfall
type: DatatypeProperty
sub property of: weather property
domain: Weather
range: int

reading time
type: DatatypeProperty
sub property of: soil property
sub property of: weather property
domain: Air
domain: Imagery
domain: Soil
domain: Water
domain: Weather
range: dateTime

reference
type: DatatypeProperty
sub property of: farm property
sub property of: field property
sub property of: order property
domain: Field
domain: Order
range: Literal

registration no
type: DatatypeProperty
sub property of: plan property
domain: Plan
range: string

requirements
type: DatatypeProperty
sub property of: crop property
domain: Crop
domain: Fruit Tree
domain: Vegetable
range: Literal
comment: Basic requirements

sale property
type: DatatypeProperty
domain: Sale
range: Literal

sand
type: DatatypeProperty
sub property of: soil property
domain: Soil
domain: Soil Sand
domain: Soil Texture

sand range
type: DatatypeProperty
sub property of: soil range property

scientific name
type: DatatypeProperty
sub property of: pest property
domain: Crop Pest
range: string

season end
type: DatatypeProperty
sub property of: crop property
domain: Crop
domain: Fruit Tree
domain: Vegetable
range: string

season start
type: DatatypeProperty
sub property of: crop property
domain: Crop
domain: Fruit Tree
domain: Vegetable
range: string

seed rate
type: DatatypeProperty
sub property of: yield property
range: float

sensor property
type: DatatypeProperty
domain: Sensor
domain: Sensor Station
domain: SubSystem
range: Literal

sensor station id
type: DatatypeProperty
sub property of: sensor property
domain: Sensor Station
range: Literal

severity
type: DatatypeProperty
sub property of: inspection property
domain: Inspection
range: Literal

silt
type: DatatypeProperty
sub property of: soil property
domain: Soil
domain: Soil Silt
domain: Soil Texture

silt range
type: DatatypeProperty
sub property of: soil range property
domain: Soil Texture
range: range

soil property
type: DatatypeProperty
domain: Soil
domain: Soil Chemical
domain: Soil Composition
domain: Soil Conductivity
domain: Soil pH
domain: Soil Physical
domain: Soil Spatial
domain: Soil Temperature
domain: Soil Type

soil range property
type: DatatypeProperty
domain: Soil
domain: Soil Structure
domain: Soil Texture

sowing rate
type: DatatypeProperty
sub property of: crop property
comment: Sowing Rate Advice

spray property
type: DatatypeProperty
domain: Spraying
range: Literal

stage maximum
type: DatatypeProperty
sub property of: pest property
domain: Crop Pest
range: string

stage minimum
type: DatatypeProperty
sub property of: pest property
domain: Crop Pest
range: string

state
type: DatatypeProperty
domain: Abstract Entity
domain: Association
domain: Association Rule
domain: Biological
domain: Classification
domain: Classifier
domain: Cluster
domain: Clustering
domain: Crop
domain: Crop Part
domain: Crop Problem
domain: Crop Protection
domain: Cropping
domain: Data Mining
domain: Data Selection
domain: Environment
domain: Evaluation
domain: Farm
domain: Fertilizer
domain: Location
domain: Machine
domain: Market
domain: Microoganism
domain: Nutrient
domain: Observe System
domain: Organization
domain: Person
domain: Phenomenon
domain: Physical Entity
domain: Process
domain: Product
domain: Regression
domain: Regressor
domain: Sensor Output
domain: Time
range: Literal

station name
type: DatatypeProperty
sub property of: soil property
sub property of: weather property
domain: Sensor Station
range: string

stock date
type: DatatypeProperty
sub property of: fertiliser property
domain: Fertilizer
range: dateTime

supplier name
type: DatatypeProperty
sub property of: supplier property
domain: Company
domain: Organization
domain: Supplier
range: string

supplier property
type: DatatypeProperty
domain: Company
domain: Organization
domain: Supplier

temperature
type: DatatypeProperty
sub property of: weather property
domain: Weather
range: int

texture label
type: DatatypeProperty
sub property of: soil property
domain: Soil Texture
range: Datatype
  one of: Clay
  one of: Clay loam
  one of: Loam
  one of: Loamy sand
  one of: Sand
  one of: Sandy clay
  one of: Sandy clay loam
  one of: Sandy loam
  one of: Silt
  one of: Silty clay
  one of: Silty clay loam
  one of: Silty loam

time
type: DatatypeProperty
sub property of: order property
sub property of: sale property
domain: Observation Value
domain: Order
domain: Sale
range: dateTime

treatment name
type: DatatypeProperty
sub property of: treatment property
domain: Treatment
range: string

treatment property
type: DatatypeProperty
domain: Treatment
range: Literal

unit
type: DatatypeProperty
sub property of: crop property
sub property of: nutrient property
sub property of: order property
sub property of: sale property
sub property of: topDataProperty
domain: Abstract Entity
domain: Crop
domain: Fertilizer
domain: Fruit Tree
domain: Nutrient
domain: Observation Value
domain: Order
domain: Physical Entity
domain: Sale
domain: Vegetable
range: string

value
type: DatatypeProperty
sub property of: order property
sub property of: sale property
domain: Observation Value
domain: Order
domain: Sale
range: double

variety
type: DatatypeProperty
sub property of: crop property
domain: Crop
domain: Fruit Tree
domain: Vegetable
range: string

volume unit
type: DatatypeProperty
sub property of: plan property
sub property of: spray property
domain: Order
domain: Plan
domain: Sale
domain: Spraying
range: string

water volume
type: DatatypeProperty
sub property of: plan property
sub property of: spray property
domain: Plan
domain: Spraying
range: float

weather property
type: DatatypeProperty
domain: Weather
range: Literal

wind
type: DatatypeProperty
sub property of: weather property
domain: Weather
range: float

working area
type: DatatypeProperty
sub property of: field property
sub property of: yield property
domain: Field
range: double

working area unit
type: DatatypeProperty
sub property of: field property
domain: Field
range: Literal

year
type: DatatypeProperty
sub property of: nutrient property
domain: Fertilizer
domain: Nutrient
range: int

yield property
type: DatatypeProperty
domain: Cropping

topDataProperty
domain: Thing
range: Literal

latitude
type: DatatypeProperty
sub property of: location property
domain: Country
domain: Sub-Country
domain: Location
range: decimal

longitude
type: DatatypeProperty
sub property of: location property
domain: Country
domain: Sub-Country
domain: Location
range: decimal

AGROVOC
type: Class
subclass of: Link
comment: AGROVOC is a controlled vocabulary covering all areas of interest of the Food and Agriculture Organization (FAO) of the United Nations, including food, nutrition, agriculture, fisheries, forestry, environment etc. It is published by FAO and edited by a community of experts. It is widely used in specialized libraries as well as digital libraries and repositories to index content and for the purpose of text mining. It is also used as a specialized tagging resource for content organization by FAO and third-party stakeholders. AGROVOC consists of +37,000 concepts and +750,000 terms in up to 37 languages. Currently, AGROVOC is an SKOS-XL concept scheme, and a Linked Open Data (LOD) set edited by VocBench. AGROVOC is aligned with 18 other multilingual knowledge organization systems.

Airborne Laser Scanning (ALS)
type: Class
subclass of: Sensor Station
type: NamedIndividual
type: Sensor Station
Acronym: ALS

Abiotic Stress
type: Class
subclass of: Crop Stress
type: NamedIndividual
type: Crop Stress
comment: Abiotic stress is the negative impact of non-living factors on the living organisms in a specific environment. The non-living variable must influence the environment beyond its normal range of variation to adversely affect the population performance or individual physiology of the organism in a significant way.

Abstract Entity
type: Class
comment: Abstract entity is a general concept formed by extracting common features from specific examples.

Acquisition
type: Class
subclass of: Planned Process
comment: Acquire within the project the data (or access to the data) listed in the project resources. This initial collection includes data loading if necessary for data understanding. For example, if you apply a specific tool for data understanding, it makes perfect sense to load your data into this tool. This effort possibly leads to initial data preparation steps. Note: if you acquire multiple data sources, integration is an additional issue, either here or in the later data preparation phase. Output List the dataset (or datasets) acquired, together with their locations within the project, the methods used to acquire them and any problems encountered. Record problems encountered and any solutions achieved to aid with future replication of this project or with the execution of similar future projects."

Adjuvants
type: Class
subclass of: Crop Protection
comment: An adjuvant is a pharmacological or immunological agent that modifies the effect of other agents. Adjuvants may also be used to enhance the efficacy of a vaccine by helping to modify the immune response to particular types of immune system cells: for example, by activating T cells instead of antibody-secreting B cells depending on the purpose of the vaccine. Adjuvants are also used in the production of antibodies from immunized animals.

Agriculture Transformation
type: Class
subclass of: Data Transformation
comment: Agriculture Transformations are transformations, which include a set of functions used to transform agricuture attributes in pre-processing step in data mining.

Agricultural Machinery
type: Class
subclass of: Machine
comment: Agricultural machinery is machinery used in farming or other agriculture.

Agroclimatic Indices
type: Class
subclass of: Data Transformation
comment: Agroclimatic Indices are transformations, which include a set of functions used to transform agroclimatic attributes in the pre-processing step in data mining.

Agronomic Indices
type: Class
subclass of: Data Transformation
comment: Agronomic Indices are pre-defined transformations, include a set of functions used to transform agricuture attributes in pre-processing step in data mining.

Air
type: Class
subclass of: Environment
type: NamedIndividual
type: Environment
comment: Air (also the atmosphere of Earth) is the layer of gases, commonly known as air, that surrounds the planet Earth and is retained by Earth's gravity. The atmosphere protects life on Earth by absorbing ultraviolet solar radiation, warming the surface through heat retention, and reducing temperature extremes between day and night.

Air Density
type: Class
subclass of: Air
comment: The density of air or atmospheric density, denoted ρ (Greek: rho), is the mass per unit volume of Earth's atmosphere. Air density, like air pressure, decreases with increasing altitude. It also changes with variation in atmospheric pressure, temperature and humidity.

Air Humidity
type: Class
equivalent class: Humidity
subclass of: Air
type: NamedIndividual
type: Air
unit: %
comment: Humidity is the concentration of water vapor present in the air. Water vapor, the gaseous state of water, is generally invisible to the human eye.[1] Humidity indicates the likelihood for precipitation, dew, or fog to be present. Humidity depends on temperature and the pressure of the system of interest. The same amount of water vapor results in higher humidity in cool air than warm air. A related parameter is the dew point. The amount of water vapor needed to achieve saturation increases as the temperature increases. As the temperature of a parcel of air decreases it will eventually reach the saturation point without adding or losing water mass. The amount of water vapor contained within a parcel of air can vary significantly.

Air Pollution
type: Class
subclass of: Air
comment: The current air pollution represented as European Air Quality Index value. The index value is based on 3 pollutants of major concern in Europe: PM10, NO2, O3 and will be able to take into account to 3 additional pollutants (CO, PM2.5 and SO2) where data are also available.

Air Pressure
type: Class
equivalent class: Pressure
subclass of: Weather
type: NamedIndividual
type: Weather
comment: Air Pressure varies smoothly from the Earth's surface to the top of the mesosphere.

Air Temperature
type: Class
equivalent class: Temperature
subclass of: Air
type: NamedIndividual
type: Air
unit: oC
comment: Temperature is a physical quantity that expresses hot and cold. It is the manifestation of thermal energy, present in all matter, which is the source of the occurrence of heat, a flow of energy, when a body is in contact with another that is colder. Temperature is measured with a thermometer. Thermometers are calibrated in various temperature scales that historically have used various reference points and thermometric substances for definition. The most common scales are the Celsius scale (formerly called centigrade, denoted °C), the Fahrenheit scale (denoted °F), and the Kelvin scale (denoted K)

Air Viscosity
type: Class
subclass of: Air

Aircraft
type: Class
subclass of: Vehicle
comment: An aircraft is a vehicle that is able to fly by gaining support from the air. Common examples of aircraft include airplanes, helicopters, airships (including blimps), gliders, paramotors and hot air balloons.

Alfalfa
type: Class
subclass of: OtherCrops
type: NamedIndividual
type: OtherCrops
comment: Alfalfa, also called lucerne, is a perennial flowering plant in the legume family Fabaceae. It is cultivated as an important forage crop in many countries around the world. It is used for grazing, hay, and silage, as well as a green manure and cover crop.

Almond
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
description: The almond tree, Prunus dulcis, is a deciduous tree in the family Rosaceae which is grown for its edible seeds (nuts). The tree has brown or gray bark and either an erect or weeping growth habit depending on the variety. The trunk can reach 30 cm (12 in) in diameter. Almond leaves are 7.5-13 cm (3-5 in) long with a serrated edge and grow alternately on the branches. The tree produces white to pale pink flowers and hairy green fruits which are oblong in shape. The fruit is a drupe, containing a single seed. The seed is protected by a hard brown shell. At maturity, the flesh of the fruit becomes leathery and splits to reveal the nut inside. Nuts generally measure 3.5 to 6 cm (1.4-2.4 in) in length. Almond trees can reach heights between 4 and 10 m (13-33 ft) and have a commercial lifespan of between 30 and 40 years. Almond may be referred to by variety and this includes bitter almond almond nuts are generally about long and may also be referred to as sweet or bitter almond depending on variety and originates from wild species found in Central and Southwest Asia.
maintenance: Almond trees will benefit from a layer of mulch around the base to prevent the growth of weeds and conserve soil moisture. Mulch should be spread around the tree in a 1 m (3 ft) radius. Leave a gap between the mulch and the trunk to prevent rotting the trunk. In addition, an application of fertilizer should be made once in Spring before any new growth and again in the Fall.
propagation: Almond trees are most commonly propagated by budding. Dormant wood is collected in winter when the trees are dormant and stored until Spring. T-budding is usually carried out in Spring and involves joining a bud from one variety to the rootstock of another. The bud is taken from a parent with desirable characteristics and grows to produce a new tree. Trees may also be propagated by grafting. Cuttings are taken from trees during dormancy and grafted to a suitable rootstock in the Spring. Planting Once trees have been acquired from a nursery, it is important to plant as soon as possible and keep the roots moist in the meantime. Almond trees should be planted by digging a hole just deep enough to accommodate the root ball. The tree should be planted by carefully backfilling the soil into the hole around the tree after it has been properly positioned. Planting depth should not exceed the height of the graft union. The soil around the newly planted tree should then be tamped and watered deeply but not excessively. If planting multiple trees then they should be spaced in rows 6-7 m (20-23 ft) apart with 5-6 m (16-23 ft) between each tree. Pruning Almond trees should be pruned in the first year and every subsequent year to help thin the canopy and prevent disease. The first pruning is critical in establishing the canopy shape. Three limbs should be selected to form the basis of the trees canopy and all others removed, including any growth below the lowest limb. In the second year, two scaffolds (lateral branches which grow to form a Y with the primary limbs) should be selected and the rest removed. Aim for the remaining scaffolds to be evenly spaced around the canopy.
requirements: Almond grows best in Mediterranean climates with warm, dry summers and mild, wet winters. The optimal temperature for their growth is between 15 and 30oC (60-85oF) and the tree buds have a chilling requirement of between 300 and 600 hours below 7.2oC (45oF) to break dormancy. Almond trees will grow best when planted in deep, well-draining loam although they can withstand drought and grow in poor soils. The trees benefit from being planted in areas sheltered from frost and wind as trees bloom early and can therefore be susceptible to damage from late frosts. Trees will generally bear nuts after 3 to 4 years with the nut crop developing after blossom, in the fall.
uses: The almond nut is eaten raw or processed into butter, flour, extract, oil, paste, syrup, and milk. Almond oil is used as a flavoring agent in baked goods, perfumery and medicines. Sweet almond oil is used for cosmetic creams and lotions.
References: https://plantvillage.psu.edu/topics/almond/infos
comment: The almond tree, Prunus dulcis, is a deciduous tree in the family Rosaceae which is grown for its edible seeds (nuts). The tree has brown or gray bark and either an erect or weeping growth habit depending on the variety. The trunk can reach 30 cm (12 in) in diameter. Almond leaves are 7.5-13 cm (3-5 in) long with a serrated edge and grow alternately on the branches. The tree produces white to pale pink flowers and hairy green fruits which are oblong in shape. The fruit is a drupe, containing a single seed. The seed is protected by a hard brown shell. At maturity, the flesh of the fruit becomes leathery and splits to reveal the nut inside. Nuts generally measure 3.5 to 6 cm (1.4-2.4 in) in length. Almond trees can reach heights between 4 and 10 m (13-33 ft) and have a commercial lifespan of between 30 and 40 years. Almond may be referred to by variety and this includes bitter almond almond nuts are generally about long and may also be referred to as sweet or bitter almond depending on variety and originates from wild species found in Central and Southwest Asia.

Aloe vera
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
maintenance: Aloe plant are generally very easy to care for but care should be taken to avoid overwatering. The plants should be watered deeply but allowed to dry out before the next watering. Check the soil prior to watering. Allow the soil to dry down to a depth of 7.5 to 10 cm (3-4 in) for older, well-established plants or 3.5-5 cm (1-2 in) for younger plants. In addition, although Aloe plants require lots of lights, sitting them in full sun can be harmful and it is best to position potted Aloes in a bright window. If the plant is receiving too much sun, the leaves will begin to turn brown.
propagation: Aloe vera can be grown from seed, but is most easily propagated from suckers which are readily produced by the mother plant. The suckers are commonly referred to as "pups". The pups should be cut from the main root by gently uprooting the mother plant and finding the point of attachment. The young plant should be cut from the parent using a sharp knife. Pups can be safely removed when they have several sets of leaves. The young plants should be planted in their own pot and watered deeply. Refrain from overwatering to force the growth of new roots. If multiple plants are being planted, provide them with individual pots or plant at least 60 cm (24 in) apart outdoors.
requirements: Aloe vera can be grown in a wide range of soils providing that they are well-draining. Aloe is commonly found growing in very nutrient poor soil in their natural range. Aloes are very drought tolerant but they will not tolerate frosts due to their succulent leaves. Aloe vera plants do well in pots and can be kept indoors if positioned in a bright, sunny spot. Plants grown in pots should be planted in a well-draining potting medium such as those used for cacti.
uses: Aloe vera plants are grown for the extraction of the gel inside the leaves. The gel is used in desserts, yogurts and beverages. The gel is also commonly used as a home remedy for treatment of burns.
comment: Aloe vera is an herbaceous perennial in the family Liliaceae grown for its succulent leaves which have a variety of culinary and medicinal uses. The Aloe vera plant has a short, stout stem and a rosette of fleshy, lanceolate leaves which have a serrated margin of small white teeth. The leaves may be flecked with white and are pale green or gray-green in color. Aloe vera plants produce a conspicuous inflorescence composed of densely packed pendulous yellow flowers on a spike which can be up to 90 cm (35 in) in height. The plant itself can grow to be 1 m (3 ft) in height and can live for up to 100 years if well cared for. Aloe vera may also be called Aloe barbadensis or Aloe perfoliata and is indigenous to eastern and southern Africa.
References: https://plantvillage.psu.edu/topics/aloe-vera/infos

Altitude
type: Class
subclass of: Terrain
type: NamedIndividual
type: Terrain
comment: Altitude, like elevation, is the distance above sea level, used for object in the air.

Aluminum (Al)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
Acronym: Al
comment: Aluminum is one of the few elements capable of making soil more acidic. This is achieved by aluminum taking hydroxide ions out of water, leaving hydrogen ions behind. As a result, the soil is more acidic, which makes it unlivable for many plants. Another consequence of aluminum in soils is aluminum toxicity, which inhibits root growth.

Amino Acid
type: Class
subclass of: Essential Nutrient
type: NamedIndividual
type: Biomolecule
comment: Amino acids are organic compounds that contain amine (–NH2) and carboxyl (–COOH) functional groups, along with a side chain (R group) specific to each amino acid. The key elements of an amino acid are carbon (C), hydrogen (H), oxygen (O), and nitrogen (N), although other elements are found in the side chains of certain amino acids.

Stamen
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: An androecium is the third whorl of most flowers. It is made up of male reproductive units called stamens. A stamen has a long stalk called a filament. At the top of the filament is a cluster of microsporangia called the anther. Androecium is polyandrous, sometimes coherent in bundles while Gynoecium is light green, has capitate stigma and a terminal style.
label: Androecium

Animal Manure
type: Class
subclass of: Organic Mature
type: NamedIndividual
type: Organic Mature

Anthocyanin
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Anthocyanins are water-soluble vacuolar pigments that, depending on their pH, may appear red, purple, blue or black.

Apple
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
has disease: Apple Scab
description: Apple, Malus domestica, is a deciduous tree in the family Rosaceae which is grown for its fruits, known as apples. Apple fruits are one of the most widely cultivated fruits in the world, are round (pome) in shape and range in color from green to red. When planted from a seed, an apple tree can take six to ten years to mature and produce fruit of its own. Apple trees are small to medium sized trees reaching heights of 5-10 m (16.4-32.8 ft), with a central trunk which divides into several branches. The leaves of the tree are oval in shape and can reach up to 13 cm (5.1 in) in length and 7 cm (2.8 in) in width. Apple may also be referred to as mela or appel and the domestic tree is believed to have originated from Western Asia and the Mediterranean from several wild ancestors.
propagation: Apple trees grow best in the tropics and at higher latitudes they require a mild growing season and a cold winter to break their dormancy. At these latitudes, the tree will flower in spring and fruit will ripen in the fall. In the tropics, the leaves will remain on the tree longer so that it is essentially evergreen and flowering and fruiting will happen sporadically throughout the year unless the tree is managed to enforce a uniform cycle across the entire tree by bending shoots to create a wide tree. The standard method of propagating apple is by budding. When planting an apple nursery or orchard it is highly advisable to plant seedlings budded from root stock to prevent an increase in dormancy of the buds. Budded trees should be pruned in the first year to encourage new shoot growth. In the tropics, apple trees require careful management in order to make the heavy crop loads sustainable. This includes bending shoots, pruning the tips and also defoliating the trees. Flowers are also removed to promote growth until the first fruit production, generally after 2 years. Apple trees can also be propagated by grafting and mound layering. Grafting involves joining the lower part of one plant (root stock) with the upper part (scion) of another. Grafting is usually done during the dormant season and must be done on dormant scion and stock wood. Mound layering is used to propagate apple clonal root stocks. Soil is mounded around shoots that have been cut back, thereby stimulating roots to grow at the base of the shoots. A year before propagation begins, 8-10 mm (0.3-0.4 in) diameter stock plants are planted in rows and then cut back to 45-60 cm (17.7-23.6 in). They are then grown for one year. In the spring, the plants are again cut back, this time to 2.5 cm (1 in) above the ground. New shoots gradually form and more soil and bark are added in mounds around the plants. This cycle can continue on through the growing season. Then the shoots are harvested by cutting close to t…
uses: Apples are most commonly eaten fresh but can also be used for baking and cooking. Apples can also be processed into apple sauce, cider, vinegar, juice or butter while slices can be dried for later consumption. Apples can also be used for the extraction of useful compounds such as fructose and pectin
References: https://plantvillage.psu.edu/topics/apple/infos
comment: Apple, Malus domestica, is a deciduous tree in the family Rosaceae which is grown for its fruits, known as apples. Apple fruits are one of the most widely cultivated fruits in the world, are round (pome) in shape and range in color from green to red. When planted from a seed, an apple tree can take six to ten years to mature and produce fruit of its own. Apple trees are small to medium sized trees reaching heights of 5-10 m (16.4-32.8 ft), with a central trunk which divides into several branches. The leaves of the tree are oval in shape and can reach up to 13 cm (5.1 in) in length and 7 cm (2.8 in) in width. Apple may also be referred to as mela or appel and the domestic tree is believed to have originated from Western Asia and the Mediterranean from several wild ancestors.

Apricot
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
has disease: Armillaria root rot
maintenance: Apricots should be pruned annually and are generally trained to an open center. Annual pruning encourages new fruit spurs. When the tree is bearing fruit, it is important to thin the fruits to leave 3 or 4 per cluster. This allows fruits to become larger and prevents the tree from reducing production the following year. Trees should be watered regularly during the growing season to aid with fruit development. During dry periods, water trees every 10 to 14 days. Apply water deeply and widely, to at least the width of the canopy. Trees will also benefit from the application of a nitrogen fertilizer in Spring.
propagation: Apricot trees are usually propagated vegetatively to maintain the desirable genetic characteristic of the parent. Trees can be propagated from cuttings or by budding and grafting. Cuttings are lengths of stem usually taken from the previous years growth of an established tree. Cuttings are taken in late winter or early spring and rooted so that they produce a whole new tree. Budding and grafting involves joining two genetically distinct plants one is used for the lower part called the rootstock and another is used for the upper part, known as the scion. The scion is attached by inserting a bud from the desired variety under the bark of the rootstock so that it produces a new tree.
requirements: Apricots have a high genetic variability and as a result, they also have a wide range of growing conditions. The trees tend to bloom early compared with other stone fruits and are therefore susceptible to damage from late frosts. Apricots will grow best in deep, well-draining soils and will not tolerate water saturating. Apricots have a chilling requirement (period of cold required to break dormancy) of between 250 and 1200 hours below 7°C (45°F) depending on the variety. In addition, most apricot trees do not require a second variety for cross-pollination.
uses: Apricots can be consumed fresh or dried. They may also be processed into jams and jellies, syrup or juice.
comment: Apricot, Prunus armeniaca is a deciduous tree in the family Rosaceae grown for its edible fruit. The apricot tree is has an erect growth habit and a spreading canopy. The leaves of the tree are ovate with a rounded base, pointed tip and serrated margin. The tree produces white to pink flowers, singly or in pairs, and a fleshy yellow to orange fruit. The apricot fruit is a drupe with skin that can be smooth or covered in tiny hairs depending on the variety and a single seed enclosed within a protective outer shell (stone). Apricot trees can reach 8–12 m (26–39 ft) and can live anywhere between 20 and 40 years depending on variety and growth conditions. Apricots may have as many as three centers of origin in China, Central Asia and the Near East.
References: https://plantvillage.psu.edu/topics/apricot/infos

Area
type: Class
subclass of: Crop Attribute
subclass of: Field
type: NamedIndividual
type: Crop Attribute
type: Field
unit: Ha
comment: Area is the size of arable field, usually measured in hectares
label: Working Area
comment: Working Area is the size of planting field, usually measured in hectares

Artichoke
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
harvesting: Globe artichoke flower buds should be harvested before they begin to open. Small, immature heads are more tender than older heads close to opening. Use a sharp knife to cut the buds from the plant leaving a 2.5 to 7.5 cm (1-3 in) section of stem with each bud. Once all buds have been harvested from the plant, the plant should be cut down to the ground.
maintenance: Globe artichokes will benefit from the application of a balanced fertilizer once every month during the growing season. Plants should not be allowed to dry out and soil should be kept moist. A covering of thick mulch will help to conserve soil moisture. Remove the mulch when the plants begin to bud and replace with a generous covering of compost. After harvest, cut the stems of the plant back to ground level or slightly below the soil line. Protect the plants over winter by mulching over the roots with plenty organic mulch (e.g. about 20cm/8 in of straw and/or leaves). this protective layer can be supplemented by further covering the plants with a cardboard or styrofoam box filled with straw and leaves.
propagation: Vegetative propagation Globe artichokes are usually vegetatively propagated from underground shoots known as 'ovoli' or from the suckers, stumps or dried shoots from the previous growing season although it can be grown as an annual plant from seed. Existing globe artichoke plants should be divided in Fall or Winter when the plant is dormant. Shoots should be selected from plants that produce well and should be removed when they are about 20 cm (8 in) in height. The shoots are removed by cutting the attachment to the mother plant at the root with a sharp knife and gently loosening it from the soil and existing root ball. The separated shoots should be planted in a freshly prepared bed. The roots should be set to a depth of 15 to 20 cm (6-8 in) and spaced 1.2 to 1.8 m (4-6 ft) apart. Do not harvest any flower heads in the first year while the new plants establish. Seeds Globe artichoke seeds should be started indoors approximately 2 month prior to the last frost date for your area. Seeds should be sown 2.5 cm (1 in) deep in seeds starting mix in small 4 in pots. Seeds should be sown generously as successful germination is approximately 70% and not all of the emerging seedling will produce high quality plants. Seedlings should be transplanted outdoors after hardening and after all danger of frost has passed. Space the plants 0.9 m (3 ft) apart in rows spaced 0.9 m (3 ft). AFter the first year, remove any plants that are not producing well to achieve a final spacing of 1.2 to 1.8 m (4-6 ft) between plants.
requirements: Globe artichokes are cool season crops and grow best in deep, fertile and well drained soils with a pH between 6.0 and 8.0. The prevailing climatic conditions are extremely important to successful growth of high quality flowerheads. The optimum daytime temperature is 20–22°C (68–71.6°F) and optimum nighttime temperature is 12–14°C (53.6–57.2°F). The plants will tolerate both cold and high temperatures but this will reduce the tenderness of the head. Frost can cause the flower heads to blister and may kill the buds. In addition, globe artichokes require frequent irrigation to ensure growth of good quality heads. However, the plants will not tolerate a waterlogged soil.
uses: Artichokes are grown primarily for consumption and can be eaten raw or cooked. Globe artichokes may also be canned or pickled or processed to make tea, liqueur or for extraction of secondary metabolties such as cynarin and chlorogenic acid which can be used in the preparation of alcoholic beverages. The globe artichoke has one of the highest antioxidant contents of any vegetable and is therefore valued as a health food with roles in lowering cholesterol and regulating blood sugar levels.
comment: Artichoke is an herbaceous perennial thistle in the family Asteraceae grown for its edible fleshy flowerhead, or heart, which is considered a delicacy. The globe artichoke has arched, irregularly lobed leaves which are silvery green in color, reaching 50–82 cm (19.7–32.3 in) in length and possessing a few spines. The flowerhead is 4–8 cm (1.6–3.1 in) in diameter with numerous triangular scales and individual florets which are purple in color. Artichokes mature in 150 to 180 days after sowing and can reach heights of 1.4–2 m (4.6–6.6 ft). The globe artichoke may also be referred to as leaf artichoke, artichoke, artisjok, artichaut, carciofo, alcachofra, alacachofa or kharsuf and it originates from Southern Europe and the Mediterranean.
References: https://plantvillage.psu.edu/topics/artichoke/infos

Article
type: Class
subclass of: Document
comment: A scientific article, known as paper, is an academic work that is usually published in an academic journal or conference.
label: Scientific Article

Ash
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Ash or ashes are the solid remnants of fires. Specifically, ash refers to all non-aqueous, non-gaseous residues that remain after something/plant burns. In analytical chemistry, to analyse the mineral and metal content of chemical samples, ash is the non-gaseous, non-liquid residue after complete combustion.

Aspect
type: Class
subclass of: Terrain
type: NamedIndividual
type: Terrain

Association
type: Class
subclass of: Data Mining
type: NamedIndividual
type: Data Mining
comment: Association rule is a data mining method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.

Association Rule
type: Class
subclass of: Association
type: NamedIndividual
type: Data Mining
comment: Association rule is a data mining method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness.

Atmospheric Pressure
type: Class
equivalent class: Pressure
subclass of: Weather
comment: Atmospheric Pressure varies smoothly from the Earth's surface to the top of the mesosphere.

Atomic Absorption Spectroscopy (AA)
type: Class
subclass of: Spectrophotometer

Atomic Emission Spectroscopy (AE)
type: Class
subclass of: Spectrophotometer

Avocado
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
has disease: Algal Leaf Spot
maintenance: Newly planted young trees should be mulched after planting with several inches of straw or woodchips. Young trees will also benefit from staking which will help to prevent wind damage. Wood stakes should be driven into the ground outside the root ball allowing 2 stakes per tree. The tree should then be tied loosely to the stakes to provide support while it establishes. Young trees should be irrigated and the root ball should not be allowed to dry out. Trees should be watered every few days. Water trees at and around the base to ensure the root ball is wetted. Trees are usually fertilized at around 4 weeks after planting. Add half a cup of urea every 4 to 6 weeks during the growing season.
propagation: Avocados are commonly propagated from seeds but the seeds will not breed true to type and this should be taken into consideration before planting. Clonal plants are obtained from budding and grafting from a parent tree to ensure the offspring are of the same high quality of the parent. In plantations, seeds can be sown directly in the soil. 2 to 3 seeds are usually sown and thinned later to leave the strongest seedling for grafting. Seeds may also be sown in containers and grown for 2 to 3 months before planting at the final site. Planting Avocado seedling should ideally be planted in the Spring when the soil has warmed through. Choose a location that receives full sun and has protection from the wind. The trees should be planted by digging a hole a little wider than the root ball and gently easing the tree into the hole. Slow release fertilizer can be added to the hole at planting but is not necessary. Care should be taken not to disturb the roots as much as possible and the hole should be carefully backfilled and the soil and tamped to prevent dislodging. Trees should be planted 4.5 to 6 m (15-20 ft) apart in rows spaced 6 m (20 ft) apart.
requirements: Avocados thrive in subtropical or tropical climates but can also be grown successfully in cooler areas of the world. The optimum temperature for growing avocado is between 25 and 33oC (77-91.4oF) with moderate humidity levels. Once established, trees can tolerate temperatures down to around -2oC (28oF) with minimal damage but young trees will not tolerate freezing temperatures. Avocado requires a well draining, aerated soil and they produce a shallow root system which require a warm soil for efficient water and nutrient uptake. Although trees will tolerate low rainfall, irrigation, particularly during flowering and fruit set, will ensure high fruit yields.
uses: The avocado is usually consumed fresh as a fruit or as an ingredient in salads or savory dishes. It has a markedly higher fat content than other fruits and is a staple in diets that have limited access to foods with high contents of monounsaturated fats. It is the main ingredient in guacamole, a popular Mexican dip. In Asia, avocados are used in desserts and dessert drinks.
comment: Avocado, Persea americana, is an evergreen tree in the family Lauraceae which grown for its nutritious fruit, the avocado. The avocado tree is large and dome shaped with oval or elliptical leaves arranged in a spiral on the tips of branches. The leaves have a red pigmentation when they first emerge and turn green as they mature. Avocado trees produce clusters of small, green-yellow flowers at the end of twigs and a large, fleshy, pear-shaped fruit with a single large seed. The fruits can be purple to green in color with smooth or warty skin depending on variety. The flesh of the fruit is yellow-green in color and has the consistency of butter. Each fruit contains one large seed. Avocado trees grown from seed can take 4-6 years to produce fruit whereas grafted plants may produce fruit within 1-2 years. The tree can reach a height of 20 m (65.6 ft) and originated in the rainforests of Central America.
References: https://plantvillage.psu.edu/topics/avocado/infos

Bacterial
type: Class
subclass of: Microoganism
comment: Bacteria are tiny microorganisms that are made up of a single cell. They're very diverse and can have a large variety of shapes and structural features. Bacteria can live in almost every conceivable environment, including in or on the human body. Only a handful of bacteria cause infections in humans.

Bacterial Disease
type: Class
subclass of: Crop Disease
comment: Infectious plant diseases are caused by bacteria and can range in severity from mild leaf or plant damage to death.

Banana
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
has disease: Banana Speckle
description: The fruit is the most widely used part of the plant and can be eaten fresh or cooked or processed into starch, chips, puree, beer, vinegar or dehydrated to produce dried fruit. The fruit may also be processed into flour which is used in baking, soups or beverages. The flowers of the plant may be used as a vegetable. Fresh leaves have a high protein content and can be fed to cattle. Other uses for leaves include polishing floors, lining pots or wrapping food.
propagation: Banana plants are vegetatively propagated, most often from suckers (shoots that grow from a bud at the base of the plant) or from corms (underground bulbs known as rhizomes). The use of whole corms is very laborious so it is more common to grow from small pieces of corm. There are three different types of banana suckers which are produced by the mother plant; maidenheads, sword suckers and water suckers. Maidenheads have a large pseudostem which does not produce fruit. Sword suckers have a narrow base, short pseudostem and narrow, blade-like leaves. They produce healthy, fruitful pseudostems when they mature Water suckers have short pseudostems and broad leaves. Water suckers are not strongly attached to the rhizome and generally produce weaker plants and less fruit. Maidenheads and large sword suckers are preferred over water suckers. Planting The desired pieces of the plant are usually planted 30-60 cm (11.8-23.6 in) deep in the soil and should generally be planted at the end of the dry season or the beginning of the wet season. Plant spacing is dependent on the cultivar being planted. Frequent weeding is required until plants are tall enough to shade out competing plants and should be started about 6 weeks after planting. The banana plants are fast growing and require the frequent addition of nutrients as well as additional irrigation in the dry season. Banana is often grown alongside other crop plants with similar requirements, indeed, the young banana plants make excellent 'nurses' for other crops such as papaya or cocoa which can be grown very close to the young banana.
requirements: Bananas grow best in hot and humid climates, require a rainfall of at least 1000 mm (39.4 in) per year to survive and have a high light requirement. Banana plants grow optimally at 27oC (98.6oF) and require a deep soil, rich in organic matter which is well draining and well aerated. The plants will grow optimally in soil with a pH between 5.5 and 7.0. Young banana plants are very susceptible to wind damage and it is recommended that they are planted in sufficient shelter or in a block so that the plants will protect one another.
References: https://plantvillage.psu.edu/topics/banana/infos
comment: The banana plant, Musa paradisiaca, is the world's largest herbaceous perennial plant and belongs to the family Musaceae. It is grown for it's fleshy, curved banana fruit. The plant is tall, tropical and tree-like with a sturdy main pseudostem (not a true stem as it is made of rolled leaf bases) with the leaves arranged spirally at the top. The leaves are large blades with a pronounced central midrib and obvious veins. They can reach up to 2.7 m (8.9 ft) in length and up to 0.6 m (2.0 ft) in width. Each pseudostem produces a group of flowers which may also be called the 'banana heart' from which the fruits develop in an hanging cluster. The banana fruits are comprised of a protective outer layer, or skin, with numerous long, thin strings that run between the skin and the edible inner portion. The seeds are tiny black specks running through the center of the fruit. In commercial plantations, the parent banana plant dies after harvest and is replaced with a daughter plant. However, a plantation can grow for 25 years or more if managed properly. The trees can reach heights between 2 and 9 m (6.6-29.5 ft). Bananas may also be referred to as plantains and the cultivated varieties are derived from ancestors which originated from the Malaysian peninsula, New Guinea and South-East Asia.

Barley
type: Class
subclass of: Cereal
type: NamedIndividual
type: Cereal
type: Crop
has disease: Bacterial Leaf Blight
has disease: Black Point
has disease: Common root rot
has disease: Downy mildew
has disease: Black Rust
description: Barley, Hordeum vulgare, is an edible annual grass in the family Poaceae grown as a cereal grain crop. It is a tall grass with a hairy stem which stands erect and produces spikelets at the head. The stem is made up of nodes and internodes. The internodes are solid, whereas the internodes are hollow. The stem supports the inflorescence, or spike, where the grain is produced. Barley seeds heads are cylindrical spikes composed of rachis each with 3 spikelets. Each spike produces 20-60 grains. Barley plants are freely tillering and typically possesses 1-6 stems. The tillers do not produce seed heads. Barley is an annual plant which is harvested each year and it can range in height from 80 to 100 cm (31.5-39.4 in). Barley may be referred to as spring barley or winter barley and it was first domesticated in the Fertile Crescent of the Middle East.
harvesting: Barley is ready to harvest when the stalks and heads have turned from green to yellow and the seed heads are drooping towards the ground. Check the seeds for ripeness before harvest. The should be firm and crunchy and not doughy in texture. Commercially produced wheat is usually harvested using a combine. Smaller plots can be harvested by hand using a scythe or sickle. Small plots can be harvested by snipping off the heads with a pair of scissors.
sowing rate: 75.0kg/acre (187.5kg/ha) (Recommended by www.cotswoldseeds.com)
uses: Barley is cultivated as a food cereal in the tropics and subtropics in India, Nepal, Tibet, Afghanistan, Russia, Ethiopia, North Africa and the Andean region of South America. The straw produced is used as an animal feed, bedding and to cover roofs of houses. In temperate regions, barley is used in malt production to brew beer and make other distilled alcoholic beverages, particularly whisky.
uses: Barley varieties Like wheat, barley can be grown in Spring or Fall. Barley can be classed as either six-rox or two-row. Two-row barley has two rows of seeds on each spike whereas six-row barley has six rows of seeds on each spike. Two-row barley varieties tend to be spring grown whereas six-row barley varieties include both spring and winter grown types. Planting Winter barley varieties should be planted in the Fall approximately 6 to 8 weeks before the first frost date. Spring varieties should be planted as soon as the soil can be worked in the Spring. Barley requires temperatures to have reached a minimum of 1-2oC (34-36oF) for successful germination. Seeds should be sown in prepared seed bed in rows or by broadcasting. Commercially grown barley is usually mechanically drilled using a machine that creates a furrow and drops the seed in before covering it back up. Seeds are usually sown to depths ranging from 2 to 12 cm (0.8-4.7 in) depending on soil conditions (seed must be sown deeper in drier soil) and the soil should be raked lightly after planting. Spring barley varieties usually reach maturity in 60 to 80 days and winter barley in 60 days.
comment: Barley, Hordeum vulgare, is an edible annual grass in the family Poaceae grown as a cereal grain crop. It is a tall grass with a hairy stem which stands erect and produces spikelets at the head. The stem is made up of nodes and internodes. The internodes are solid, whereas the internodes are hollow. The stem supports the inflorescence, or spike, where the grain is produced. Barley seeds heads are cylindrical spikes composed of rachis each with 3 spikelets. Each spike produces 20-60 grains. Barley plants are freely tillering and typically possesses 1-6 stems. The tillers do not produce seed heads. Barley is an annual plant which is harvested each year and it can range in height from 80 to 100 cm (31.5-39.4 in). Barley may be referred to as spring barley or winter barley and it was first domesticated in the Fertile Crescent of the Middle East. Barley varieties Like wheat, barley can be grown in Spring or Fall. Barley can be classed as either six-rox or two-row. Two-row barley has two rows of seeds on each spike whereas six-row barley has six rows of seeds on each spike. Two-row barley varieties tend to be spring grown whereas six-row barley varieties include both spring and winter grown types.
References: https://plantvillage.psu.edu/topics/barley/infos

Basil
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
harvesting: Basil leaves can begin to be harvested any time after the plants have reached a height of 15–20 cm (6–8 in). Harvest leaves by pinching the leaves from the tips of the stems to encourage the more branching. Leaves should be pinched regularly to keep the plants productive and prevent them from going to seed.
propagation: Basil seeds should be sown indoors 6–8 weeks before planting outside. Sow seeds in a sterile seed starting mix in seed trays or pots 0.2–1.0 cm (0.08–0.4 in) deep and water gently. Ensure the temperature remains between 15.5 and 27°C (60–80°F). Seeds should germinate in about 5 days at 21°C (70°F). Basil seedlings can be transplanted to the garden when they are between 6 and 8 weeks old, about 2 weeks after the last frost date. Plants should be spaced approximately 30 cm (12 in) apart, allowing 45 cm (18 in) between rows. Pinching back the growing tip of the plants after transplanting will encourage the growth of new shoots.
requirements: Basil is a warm season crop which will grow optimally in areas where daytime temperatures are consistently above 21°C (70°F) and nighttime temperatures stay above 10°C (50°F). Basil is very sensitive to frost and will need protected if a late cold snap is forecast. The plant will grow best in a fertile, moist soil with a pH between 6 and 7. Basil requires around 6–8 hours of sun every day and benefits from some shade in the afternoon.
uses: Basil is commonly used as a fresh or dried herb in cooking and is popularly used in beverages in Southeast Asia. Essential oil can be extracted from the leaves and used in cosmetics, dental products and perfume.
comment: Basil, Ocimum basilicum, is a short lived annual or perennial plant in the family Lamiaceae grown for its leaves which are used as a herb. The basil plant grows from a thick taproot and has silky green opposite (paired) oval leaves which grow to be 3–11 cm (1.2–4.3 in) long and 1–6 cm (0.4–2.4 in), branching out from the central stem. The plant produces small white flowers which are clustered on a single spike at the top of the plant. Basil plants are often grown as annuals but may survive for several seasons with some care and can reach heights between 30 and 130 cm (11.8 and 51.2 in) depending on the variety. Basil may also be referred to as sweet basil, St. Joseph's wort, thai basil, lemon basil or holy basil depending on the variety and is native to India and other tropical regions of Asia.
References: https://plantvillage.psu.edu/topics/basil/infos

Bean
type: Class
subclass of: Cereal
type: NamedIndividual
type: Crop
type: Vegetable
description: The common bean, Phaseolus vulgaris, is an herbaceous annual plant in the family Fabaceae which is grown as a pulse and green vegetable. The common bean can be bushy, vine-like or climbing depending on the variety being grown. The leaves grow alternately on the stems, are green or purple in color and are divided into 3 oval leaflets with smooth edges. The leaves can grow 6-15 cm (2.4-5.9 in) long and 3-11 cm (1.2-4.3 in) wide. The common bean produces white, pink, lilac or purple flowers which are approximately 1 cm (0.4 in) in diameter, and bean pods 8-20 cm (3.1-7.9 in) long and 1-1.5 cm (0.4-0.6 in) wide which can range in color from green to yellow or black to purple. Each pod contains 4-6 smooth, kidney-shaped beans. Common bean plants are annual plants and last only one growing season and range greatly in size from the bushy varieties 20-60 cm (7.9-24 in) in height; to vines or runner beans which can reach 2-3 m (6 ft 7 in-9 ft 10 in) in length. The common bean includes many varieties and may be referred to as bush bean, dry bean, dwarf bean, field bean, French bean, snap bean, garden bean, haricot bean, kidney bean, pole bean or string bean. The plant originates from the Americas.
harvesting: Beans are generally ready for harvest approximately two weeks after bloom. The beans should be harvested just before the seeds are mature and before they form bumps on the pod. The pods should be firm and snap when they are bent. Pick beans every 2-3 days to ensure the plants remain productive. Pinch beans rather than pulling to avoid damaging the plant. Cut pole beans from the plant using scissors.
propagation: Beans should be direct seeded in the garden in when the soil has reached a temperature of at least 15.5oC (60oF), with the optimum temperature for germination being between 15.5 and 29oC (60-85oF). Planting at cooler temperatures leads to slow germination and promotes seed rotting. Seeds should be planted 2.5-3.5 cm (1-1.5 in) deep. Bush beans should be planted 5-10 cm (2-4 in) apart allowing 0.6-0.9 m ( 2-3 ft) between rows. Pole beans can be planted in both row and hills. In rows, seeds should be spaced 15-25 cm (6-10 in) apart allowing 0.9-1.2 m (3-4 ft) between rows. For a continuous harvest of beans over the summer months, make new plantings every 2-3 weeks. Pole beans should be provided with a pole or trellis to climb on to support the weight of the pods and allow light to penetrate to all parts of the plant, helping to prevent disease. Bean poles should have a rough surface to help the plant to grip and should be 1.8-2.1 m (6-7 ft) long. Three or four poles can be used to form a tripod onto which the plants can be trained. Bean trellises can be constructed easily using posts (or a tripod arrangement of poles), wire and twine. Position a post at either end of the area in which you wish to plant a row of beans and connect with two lengths of wire. The first wire should be approximately 13 cm (5 in) from the ground, and the second 1.5-1.8 m (5-6 ft) from the ground. Finally, use the twine to create a V-shaped trellis by tying the twine to the bottom wire, bringing it up to the top wire and looping it back down around the bottom wire. Continue to zigzag the string all the way along the wires to the second support before tying the twine off. A rough textured twine is best as it will encourage the plant to climb and twine around it. Pinch back the growing tips of the plants once they reach the top support to encourage the plant to branch.
requirements: Common beans are warm-season crops and should be planted after all danger of frost has passed and the soil has warmed. Beans will grow best at soil temperatures between 15.5 and 29oC (60-85oF) and are sensitive to cold temperatures and frosts. Beans will grow best in a fertile, well-draining soil with a pH between 6.0 and 6.75 Beans will perform best in full sunlight.
uses: The common bean is used as a pulse and green vegetable eaten fresh or cooked. The beans can be dried, cooked in sauce and canned.
variety: Common beans can either be bush or pole varieties. Bush beans will grow erect without any support and require less care than pole varieties. Pole beans grow vertically on a support and are generally easier to harvest. Most type of bean are now available as both bush and pole varieties. Some green bean varieties may be referred to as “stringâ€ beans. This name is a reference to older varieties which produced a stringy fibre along the seam of the pod. Modern cultivars were bred which did not produce this substance and which are commonly referred to as “snapâ€ beans due to the ease with which the green pods can be snapped in two.
References: https://plantvillage.psu.edu/topics/bean/infos
comment: The common bean, Phaseolus vulgaris, is an herbaceous annual plant in the family Fabaceae which is grown as a pulse and green vegetable. The common bean can be bushy, vine-like or climbing depending on the variety being grown. The leaves grow alternately on the stems, are green or purple in color and are divided into 3 oval leaflets with smooth edges. The leaves can grow 6-15 cm (2.4-5.9 in) long and 3-11 cm (1.2-4.3 in) wide. The common bean produces white, pink, lilac or purple flowers which are approximately 1 cm (0.4 in) in diameter, and bean pods 8-20 cm (3.1-7.9 in) long and 1-1.5 cm (0.4-0.6 in) wide which can range in color from green to yellow or black to purple. Each pod contains 4-6 smooth, kidney-shaped beans. Common bean plants are annual plants and last only one growing season and range greatly in size from the bushy varieties 20-60 cm (7.9-24 in) in height; to vines or runner beans which can reach 2-3 m (6 ft 7 in-9 ft 10 in) in length. The common bean includes many varieties and may be referred to as bush bean, dry bean, dwarf bean, field bean, French bean, snap bean, garden bean, haricot bean, kidney bean, pole bean or string bean. The plant originates from the Americas.

Beet
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
harvesting: Young beet greens can be harvested for salads when they are 2.5–5.0 cm (1–2 in) high and older greens before they reach 15 cm (6 in) in length. The roots are ready for harvest when they have reached 2.5 cm (1 in) in diameter and are most tender before the exceed 7.5–10 cm (3–4 in). Watering the soil the day before harvest or the day after rainfall makes pulling the beets easier. Pull beets out of the soil by firmly grasping the top and pulling the root out of the soil vertically. Alternatively, use a garden fork to dig the beets out of the soil. Cut the tops of the beets to 1.25–2.5 cm (0.5–1.0 in) above the root before storing the root. This helps keep the beets fresh.
maintenance: eets require plenty of moisture to develop optimally. Even watering will promote the development of good quality roots and prevent the formation of rings in the root. Soil moisture can be conserved by applying a layer of mulch around the plants. In addition to moisture, beets also require an adequate supply of nutrients, particularly phosphorus. Apply a complete fertilizer at planting to ensure optimum development. Remove any weeds around plants by cultivating shallowly to avoid damaging the developing roots.
propagation: Beets are direct seeded and can be planted as soon as the soil is workable in Spring. The soil should be prepared for planting by first removing any large rocks and stones and then working in 2–3 inches of compost or well-rotted manure. Plant one seed every 2.5 cm (1 in) at a depth of 13 mm (0.5 in) in rows spaced 30–40 cm (12–16 in) apart. Keep the seedbed well watered. Seedlings should emerge in 5 to 17 days at temperatures between 10 and 24°C (50–75°F). When seedlings have reached between 7 and 10 cm (3–4 in) in height, thin to a final spacing of 7–10 cm (3–4 in) between plants. For a continuous harvest, plant seeds every 2–3 weeks as long as the daytime temperature stays below 26.6°C (80°F). Most beet varieties mature in 55 to 70 days.
requirements: Beets are cool season vegetables with a long growing season. They grow best in cool climates but can tolerate some heat as well as some freezing. The optimum temperature for their growth is between 15.5 and 18.3°C (60–65°F). Beets grow best in a loose, well draining soil with a pH between 6.2 and 6.8 and should be planted in full sun for optimum development.
uses: The roots are consumed after boiling and may be pickled in vinegar. The leaves of the spinach beet plant are consumed as a herb in Indonesia and Japan. Chemicals in the roots can be extracted and used as food coloring.
References: https://plantvillage.psu.edu/topics/beet/infos
comment: Beets, Beta vulgaris, are herbacious biennial root vegetables in the family Chenopodiaceae grown for their edible root. The plant is usually erect with a long main root and a rosette of leaves growing on stems. The leaves are oval in shape, arranged alternately on the stem and grow 20–40 cm (7.9–15.7 in) in length. The roots are usually red in color. The plant produces sessile green flowers and can reach 1–2 m (3.3–6.6 ft) in height. Beets are usually grown as annual plants, harvested after one growing season. Beets may also be referred to as beetroot, garden beet or spinach beet and originated from the Mediterranean.

Bergamot
type: Class
subclass of: Citrus
type: NamedIndividual
type: Crop
maintenance: Newly planted trees require proper irrigation to ensure they become established. During the first year, water should be applied at the base of the trunk so that the root ball is kept moist to allow the roots to establish in the soil. Newly planted trees should be provided with water every 3–7 days. The soil should be moist, but not wet. Trees planted in sandy soils will require water more frequently. Young trees will also require a light application of fertilizer every month in the first year.
propagation: Bergamot seedlings are usually produced by grafting or budding to an appropriate rootstock as seeds will not produce fruit true to type. Grafting is the process by which a scion from one plant is joined to the rootstock of another to produce a new tree. Budding is a special type of grafting where the scion that is joined to the rootstock consists of a single bud. Budding is commonly used in citrus propagation as it is the easier of the two processes and works very well. Bergamot trees can be purchased as seedlings which have already been grafted and only require planting in the garden or orchard. The best time to plant citrus trees is in Spring after all danger of frost has passed in your area. Standard sized trees should be spaced 3.7–7.6 m (12–25 ft) apart in an area that receives full sunlight, but is protected from strong winds which can damage the trees. Planting against a south facing wall will help protect the tree in cooler climates.
requirements: Bergamot oranges grow best in regions with a pronounced change in season. They will grow best at temperatures between 12.8 and 37.8°C (55–100°F) during the growing season and 1.7 to 10°C (35–50°F) during dormancy. Mature trees can survive short periods of freezing, whereas young trees will be killed. Trees should be protected from frosts and freezing conditions to prevent damage. The trees will also tolerate drought conditions but perform poorly in water-logged soil. Trees will grow best when planted in a well-draining sandy loam with a pH between 6.0 and 7.5. Soil must be deep enough to permit adequate root development. Trees require full sun and should be protected from wind which can cause damage to the trees.
uses: The oil produced by the flowers, leaves and rind of the fruit is used in eau de toilette, cologne and soap. The bitter fruit is used to make marmalade and to flavor liqueurs.
References: https://plantvillage.psu.edu/topics/bergamot/infos
comment: Bergamot, Citrus bergamia, is an evergreen tree grown for its fruit, flowers and leaves which are used primarily for the extraction of its lemon scented oil. Bergamot is an erect branching tree with oval leaves which are alternately arranged. The tree produced clusters of white flowers and a fruit that resembles a pear-shaped orange. The fruit is green and turns to yellow when ripe. Bergamot trees can reach up to 4 m (13.1 ft) in height and will remain productive for up to 60 years if managed well. Bergamot may also be referred to as bergamot orange or sour orange and its origin is unknown although it is almost exclusively grown in the coastal plains of Southern Italy.

BioFertilizer
type: Class
subclass of: Fertilizer
comment: Biofertilizer (also bio-fertilizer) is a substance which contains living micro-organisms which, when applied to seeds, plant surfaces, or soil, colonize the rhizosphere or the interior of the plant and promotes growth by increasing the supply or availability of primary nutrients to the host plant. Biofertilizers add nutrients through the natural processes of nitrogen fixation, solubilizing phosphorus, and stimulating plant growth through the synthesis of growth-promoting substances.

Biological
type: Class
subclass of: Abstract Entity
comment: Biological is used to describe processes and states that occur in the bodies and cells of crops or plants.

Biological Control
type: Class
subclass of: Treatment

Biological Day
type: Class
subclass of: Cropping
type: NamedIndividual
type: Cropping
References: Soltani, A., & Sinclair, T. R. (2012). "Modeling Physiology of Crop Development". Cambridge, MA: Growth and Yield. CAB International.

Biomass
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
unit: kg/m2
comment: Biomass is plant material used for energy production (electricity or heat), or in various industrial processes as raw substance for a range of products.

Biomolecule
type: Class
subclass of: Nutrient
type: NamedIndividual
type: Nutrient
comment: A biomolecule or biological molecule is a loosely used term for molecules present in organisms that are essential to one or more typically biological processes, such as cell division, morphogenesis, or development

Biopesticides
type: Class
subclass of: Crop Protection
comment: Biopesticides, a contraction of 'biological pesticides', include several types of pest management intervention: through predatory, parasitic, or chemical relationships. The term has been associated historically with biological control - and by implication - the manipulation of living organisms.

Biotic Stress
type: Class
subclass of: Crop Stress
type: NamedIndividual
type: Crop Stress
affects: Coffee
comment: Biotic stress is stress that occurs as a result of damage done to an organism by other living organisms, such as bacteria, viruses, fungi, parasites, beneficial and harmful insects, weeds, and cultivated or native plants.
label: Leaf Biotic Stress

Blackberry
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
description: Blackberry, is a perennial shrub in the family Rosaceae that is grown for its aggregate black fruit of the same name. The name blackberry is used to describe several species, including Rubus fruticosis (wild blackberry), Rubus ursinus and Rubus argutus, two species native to North America. Blackberries have three stem types: erect, arching, and trailing. They often have thorns, but some varieties are thornless. The leaves alternate along the stem with each group of leaves consisting of 3-5 leaflets. The leaves are prickly and bright green, and are toothed along the edges. The life-span of blackberry shrubs is variable, but they usually live for less than ten years reaching heights of up to 3 m (10 ft). Blackberry may also be referred to as bramble and the shrubs grow native on every continent except Australia and Antarctica.
propagation: Preparation Soil may need prepared up to two years in advance of planting if major amendments are required. Acidic soil can be amended with lime to bring the pH up to a level suitable for blackberries. Organic content can be increased by planting a cover crop or by the addition of manure or compost. Avoid planting blackberries where peppers, eggplant, tomatoes or potatoes have been grown previously as these plants are host to Verticillium fungi which can cause root rot in blackberries. Choose a variety which is suited to your region. Planting and trellising Many blackberry varieties are very vigorous and using a support system such as a trellis will help to protect the canes from wind damage while also supporting the weight of the fruit crop. The trellis should be constructed before or at planting to avoid damaging the young plants after they are in the ground. The traditional method of supporting red blackberry canes is a post and wire system. This method involves running two wires about 60 cm (2 ft) apart vertically between wooden posts staked into the ground. The lower wire should be positioned 90 cm (3 ft) from the ground and the upper 1.5 m (5 ft) from the ground. The blackberry canes can then be tied to the wires. A second option is a T-trellis which is similar to the post and wire but the vertical wooden posts each have two cross bars to attach the wire. Two sets of wires run parallel to one another, one above the other. The vertical posts should be spaced 3.6-4.6 m (12-15 ft) apart with the lower wire positioned 90 cm (3 ft) from the ground and the upper 1.5 m (5 ft) from the ground. Blackberry plants in the home garden are usually grown from bare root plants or from tissue-cultured plants and should be planted in early Spring when the danger of any severe frosts has passed. The plants are usually planted in a row and the suckers will fill in the spaces to produce a hedge. Plant approximately 70 cm (27.5 in) apart, allowing 2.4-3 m (8-10 ft) between rows. Pr…
requirements: Blackberries grow best in temperate regions with cool summers and mild winters as they are susceptible to cold weather. They grow best when daytime temperatures are around 25oC (77oF). Blackberries prefer full sun (minimum six hours of direct sunlight) and they need to be planted in soil that drains well, is high in organic matter, and has a pH range of 6-6.5. Drainage it critical in blackberry propagation as the plants are susceptible to root rot. Blackberries should not be planted in low lying areas where water may build up and they require a post support system or trellis to support the weight of the fruit on the plants. Blackberry canes are biennial and produce fruit in the second year of growth. Canes in their first year of growth are called primocanes and those in the second year of growth are called fruiting canes or floricanes. The young canes are green in color, whereas the older floricanes are tougher and have a woody covering making them easy to tell apart.
uses: Blackberries are consumed as a fresh fruit. They can also be frozen or canned for later use in ice creams, juices, pies, jellies, preserves and jams and other sweet desserts. Blackberries are high in antioxidants and vitamins and are a good sources of potassium, phosphorus, iron and calcium.
References: https://plantvillage.psu.edu/topics/blackberry/infos
comment: Blackberry, is a perennial shrub in the family Rosaceae that is grown for its aggregate black fruit of the same name. The name blackberry is used to describe several species, including Rubus fruticosis (wild blackberry), Rubus ursinus and Rubus argutus, two species native to North America. Blackberries have three stem types: erect, arching, and trailing. They often have thorns, but some varieties are thornless. The leaves alternate along the stem with each group of leaves consisting of 3-5 leaflets. The leaves are prickly and bright green, and are toothed along the edges. The life-span of blackberry shrubs is variable, but they usually live for less than ten years reaching heights of up to 3 m (10 ft). Blackberry may also be referred to as bramble and the shrubs grow native on every continent except Australia and Antarctica.

Blooming
type: Class
subclass of: Plant Phenomenon
type: NamedIndividual
type: Plant Phenomenon
comment: Blooming is the act or state of producing flowers : the period during which a plant produces flowers the flowering of the iris a plant that should be pruned only when flowering ends

Boll
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Boll is a rounded seed vessel or pod of a plant, as of flax or cotton.

Borage
type: Class
subclass of: OilSeeds
type: NamedIndividual
type: Crop
type: OilSeeds
comment: Borage is an annual herb in the flowering plant family Boraginaceae. It is native to the Mediterranean region, and has naturalized in many other locales. It grows satisfactorily in gardens in most of Europe, such as Denmark, France, Germany, and the United Kingdom, remaining in the garden from year to year by self-seeding. The leaves are edible and the plant is grown in gardens for that purpose in some parts of Europe.The plant is also commercially cultivated for borage seed oil extracted from its seeds. The plant contains pyrrolizidine alkaloids, some of which are hepatotoxic, mutagenic, and carcinogenic..

Borecole
type: Class
equivalent class: Kale
subclass of: Vegetable
type: NamedIndividual
type: Vegetable
comment: Borecole, also known as Kale, is green leafy plant, that is good alternative for cabbage varieties. It is juiced for green smoothies. It has become a very popular and versatile vegetable. It comes in a wide variety leaf shapes and colours. Although mostly grown as a late summer or Autumn/Fall crop it can be grown almost all year in some zones as it is frost hardy. It tends to run to seed in hot weather. Best grown in partial shade, out of direct hot sun during the warmer months in the hotter areas. The small, tender Kale leaves can be eaten uncooked and added to salads.

Boron (B)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
comment: Boron is absorbed by plants in the form of the anion BO3^ - 3. It is available to plants in moderately soluble mineral forms of Ca, Mg and Na borates and the highly soluble form of organic compounds. It is available to plants over a range of pH, from 5.0 to 7.5. It is mobile in the soil, hence, it is prone to leaching.
Acronym: B

Boron Deficiency
type: Class
subclass of: Plant Disorder
type: NamedIndividual
type: Plant Disorder
related to: Boron (B)
comment: Boron deficiency is a common deficiency of the micronutrient boron in plants. It is the most widespread micronutrient deficiency around the world and causes large losses in crop production and crop quality. Boron deficiency affects vegetative and reproductive growth of plants, resulting in inhibition of cell expansion, death of meristem, and reduced fertility.

Boundary
type: Class
subclass of: Crop Part
subclass of: Field
type: NamedIndividual
type: Field
comment: Boundary is the line which marks the limits of a field.
comment: Object Boundary can be the boundary of leaf, fruits, or flower,...
alternative label: Object Boundary

Broadleaf
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: A broad-leaved, broad-leaf, or broadleaf tree is any tree within the diverse botanical group of angiosperms that has flat leaves and produces seeds inside of fruits.

Bud
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: A small lateral or terminal protuberance on the stem of a plant that may develop into a flower, leaf, or shoot.

Bulk Density
type: Class
subclass of: Soil Spatial
type: NamedIndividual
type: Soil

Business Event
type: Class
subclass of: Event
comment: A business event is a definable occurrence in a business scenario. It can be a common high-level occurrence, such as a customer placing an order.

Methane (CH4)
type: Class
subclass of: Gas
type: NamedIndividual
type: Gas
comment: Methane (CH4) is the second most important greenhouse gas (GHG) in terms of global warming. It is 28 times more potent to the environment than carbon dioxide (CO2). Agriculture accounts for 34% of Irish GHGs emissions, with CH4 responsible for nearly 60% of agri emissions.
Acronym: CH4

Carbon Dioxide (CO2)
type: Class
subclass of: Gas
type: NamedIndividual
type: Gas
Acronym: CO2
comment: Carbon dioxide (chemical formula CO. 2) is a colorless gas with a density about 53% higher than that of dry air. Carbon dioxide molecules consist of a carbon atom covalently double bonded to two oxygen atoms. It occurs naturally in Earth's atmosphere as a trace gas.

Cabbage
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
harvesting: Cabbages are ready to harvest when the head is fully formed and feels firm and well-packed when squeezed. Cut the head away from the stalk with a sharp knife. Leaving the stalks in the ground will result in the formation of several smaller heads which can also be harvested and eaten.
maintenance: Cabbage should be kept evenly watered to ensure the development of tight heads. Uneven watering can cause heads to crack. Application of mulch around plants helps to conserve soil moisture. Cabbage plants have shallow roots and in order to avoid damaging them, it is preferable to hand pull any weeds growing around the plants. Cabbages are heavy feeders and require the addition of fertilizer to meet their growth requirements and develop optimally. Fertilize the plants when they are beginning to form new leaves and starting to develop heads.
propagation: Cabbage can be direct seeded or started indoors to produce transplants. The optimum soil temperature for germination is between 12 and 24°C (55–75°F). Cabbage seeds can be planted outdoors 6–8 weeks before the last spring frost date in a cold frame and transplanted to their final location approximately 4 weeks before the last frost. If planting for a fall harvest, cabbage can be direct seeded 6–8 weeks before the first frost date. Prepare the soil for planting through the addition of nitrogen in the form of bone meal or composted manure. Plant cabbage seeds 6 mm (0.25 in) deep allowing 10–15 cm (4–6 in) between plants in the row and a 0.6 to 1.2 m (2–4 ft) between rows. Thin seedlings to a final within row spacing of 45–60 cm (18–24 in). Keep soil moist during germination to prevent a crust from forming on the soil surface as this will cause uneven germination. Seedlings started indoors or in a cold frame are ready to be transplanted when they have 3–4 leaves and the daytime temperature has reached 10°C (50°F). Seedling should be planted at the final spacing for seeds (45–60 cm/18–24 in between plants and 0.6 to 1.2 m/2–4 ft between rows). Plant each seedling slightly deeper than it was previously. The plantings can be staggered in 2 week intervals to prolong the harvest.
requirements: Cabbage is cool season crop that grows best in cool, moist conditions. The plant will grow best at temperatures between 4 and 21°C (40–50°F) allowing it to be grown in both Spring and Fall. Cabbage will grow optimally in a rich, moist, well draining soil With a pH of 6.5. The plant requires at least six hours of direct sunlight every day.
uses: Cabbage is primarily grown for consumption as a vegetable, eaten after boiling or steaming. Some cultivars are grown as fodder for animals. Red cabbage is commonly pickled.
comment: Cabbage, Brassica oleracea, is an herbaceous annual or biennial vegetable in the family Brassicaceae grown for its edible head. There are many different varieties of cabbage which include the white and red cabbage (Brassica oleracea var. capitata) and the savoy cabbage (Brassica oleracea var. sabauda. The head of the cabbage is round and forms on a short thick stem. The leaves are thick and alternating with wavy or lobed edges and the roots are are fibrous and shallow. The plant produces large yellow flowers. The densely leaved heads can range in size from 0.5 to 3.6 kg (1-8 lb) depending on variety. The plant is usually grown as an annual. Brassica oleracea may be referred to as cabbage, Shetland cabbage, Savoy cabbage, white cabbage or red cabbage and is believed to have originated from a wild cabbage ancestor in ancient Asia minor.
References: https://plantvillage.psu.edu/topics/cabbage-red-white-savoy/infos

Cadmium (Cd)
type: Class
subclass of: Other Chemical
type: NamedIndividual
type: Other Chemical
comment: Cadmium is a chemical element with the symbol Cd and atomic number 48. This soft, silvery-white metal is chemically similar to the two other stable metals in group 12, zinc and mercury.
Acronym: Cd

Calcium (Ca)
type: Class
subclass of: Macronutrient
type: NamedIndividual
type: Macronutrient
comment: Calcium is essential for root health, growth of new roots and root hairs, and the development of leaves. It is generally in short supply in the North Coast's acid soils. Lime, gypsum, dolomite and superphosphate (a mixture of calcium phosphate and calcium sulfate) all supply calcium. Lime is the cheapest and most suitable option for the North Coast; dolomite is useful for magnesium and calcium deficiencies, but if used over a long period will unbalance the calcium/magnesium ratio. Superphosphate is useful where calcium and phosphorus are needed. Calcium regulates transport of other nutrients into the plant and is also involved in the activation of certain plant enzymes. Calcium deficiency results in stunting. This nutrient is involved in photosynthesis and plant structure. Blossom end rot is also a result of inadequate calcium.
Acronym: Ca

Canal
type: Class
subclass of: Field Part
type: NamedIndividual
type: Field Part
comment: Canal is one of canals in the irrigation systems which are used primarily to irrigate paddy fields.

Canola
type: Class
equivalent class: Rapeseed
subclass of: OilSeeds
type: NamedIndividual
type: Cereal
type: Crop
comment: Canola oil is a vegetable oil derived from a variety of rapeseed that is low in erucic acid, as opposed to colza oil. There are both edible and industrial forms produced from the seed of any of several cultivars of the plant family Brassicaceae, namely cultivars of Brassica napus, Brassica rapa subsp. oleifera (syn. B. campestris), or Brassica juncea, which are also referred to as "canola".

Canopy
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: The canopy is the aboveground portion of a plant community or crop, formed by the collection of individual plant crowns.

Cantaloupe
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
harvesting: Cantaloupes are ready to be harvested when the the surface becomes netted and the color underneath turns from green to yellow. When the melon is ready to picked the stem will come away easily from the fruit.
maintenance: Cantaloupe vines are sprawling and require plenty space to grow. Vines can be trained to grow on a trellis or fence to save space. Cantaloupe plants also require a regular supply of water while growing, blooming and setting fruit and where drip irrigation is not being used, plants should be watered deeply once per week, providing at least an inch of water. Mulches can be used to conserve soil moisture and black polyethylene mulch has the advantage of warming the soil.
propagation: Cantaloupe can be direct seeded in areas with a long, warm growing season but in more Northern climates it should be sown indoors and transplanted. If direct seeding,seeds should be sown after the last frosts and when the soil has warmed to at least 18.4°C (65°F). Allow 90–120 cm (~3–4 ft) between seeds in a row and 150–180 cm (~5–6 ft) between rows. If transplanting, seeds should be sown approximately 3–4 weeks before the last frost date in your area and transplanted after the plants develop their first set of true leaves. Seeds sown both indoors and out require lightly moist soil for germination, care should be taken to avoid overwatering as seeds are prone to rotting. Seeds should germinate in 3–10 days depending on the soil temperature. Cantaloupe seedlings should be transplanted when all danger of frost has passed and the soil has warmed to at least 18.4°C (65°F). Covering soil with dark plastic or mulch a week prior to planting outdoors can help bring the soil temperature up more quickly in colder regions, allowing earlier planting. Beginning approximately 7–10 days before transplanting, plants should be set outside to harden off (see https://www.plantvillage.com/posts/264). The planting site should be prepared by incorporating plenty of organic matter to encourage vegetative growth. When transplanting seedlings, allow 90–120 cm (~3–4 ft) between plants and 150–180 cm (~5–6 ft) between rows. Drip or soaker irrigation is preferred to overhead irrigation and plants should be watered evenly to keep them moist.
requirements: Cantaloupe is a warm-season crop, requiring lots of sun and good drainage to develop optimally and growing best at temperatures between 18 and 28°C (65–82°F). Melon will yield best if grown in a light, well-draining soil, rich in organic matter and with a pH between 6.0 and 7.0. Cantaloupe should be planted in full sun and are heavy feeders and water users. They need to be provided with ample soil moisture and fertilized regularly. Vining varieties can grow to very large sizes and require a good deal of space.
uses: Melon is primarily consumed as a fresh fruit. The pulp may be turned into a fresh beverage and the seeds can be roasted and eaten.The unripe fruit of some species are cooked as a vegetable.
References: https://plantvillage.psu.edu/topics/cantaloupe/infos
comment: Cantaloupe, Cucumis melo, is a vining plant in the family Cucurbitaceae, grown for its large, sweet fruit of the same name. Cantaloupe vines are trailing and are slightly hairy with simple oval leaves arranged alternately on the stem. The plant produces small yellow flowers which are 1.2–3.0 cm (0.5–1.2 in) in diameter and large oval to round fruit with green to orange flesh. Melon is an annual plant and a vine can grow up to 3 m (9.8 ft) in length. Cantaloupe may be referred to as muskmelon or melon and likely originate from East and Northeast Africa.

Carbon (C)
type: Class
subclass of: Essential Nutrient
type: NamedIndividual
type: Essential Nutrient
Acronym: C
comment: Carbon is a chemical element with the symbol C and atomic number 6.

Carotenoid
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Carotenoids, also called tetraterpenoids, are yellow, orange, and red organic pigments that are produced by plants and algae, as well as several bacteria, and fungi.

Carts
type: Class
subclass of: Vehicle
comment: A strong open vehicle with two or four wheels, typically used for carrying loads and pulled by a horse.

Cashew
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
harvesting: Young trees do not produce high yields of cashews and the yield increases as the trees mature.
maintenance: Young trees should be provided with supplemental irrigation to help them establish. Establishment of trees is slow but mature trees can survive dry periods as their extensive root systems can access moisture deep in the soil. The area around the trunk of the tree should be kept free from weeds to prevent competition for water and nutrients, particularly during establishment. Cashews require little pruning. Pruning is limited to the removal of limbs to facilitate movement of machinery in commercial plantations and is only practices on trees that are 6 years old or greater.
propagation: Trees are propagated from mature seeds which can be directly sown at the final planting site or planted in polyethylene bags to produce seedlings for transplanting. Only seeds that sink when floated in water should be selected for planting. Newly emerging seedling should be grown under approximately 45% shade in the nursery and hardened off in sunlight before final planting. As the seeds have a poor germination rate 2–3 seeds should be sown per planting hole and then thinned as necessary. Seedlings should be thinned to a final spacing of 10 m (33 ft) allowing a further 10 m (33 ft) between rows.
requirements: Cashew trees grow only in regions with consistently high temperatures, typically between 24 and 28°C (69.8–82.4°F). The trees cannot be grown in frost prone areas as they will be killed by the low temperatures. Cashew will grow optimally in deep, well draining soils with a pH between 4.2 and 8.7. The trees have the ability to grow in sandy soils where other fruit trees will not survive. In cooler areas, they can be grown in containers and moved indoors in winter to prevent damage to the plant.
uses: Cashew nuts are usually eaten after roasting either as a snack or as in ingredient in other dishes. Cashew apples are also edible and can be eaten fresh or cooked in a variety of dishes. It may also be used to flavor drinks or be fermented to produce vinegar or alcoholic beverages.
comment: Cashew, Anacardium occidentale, is an evergreen tree in the family Anacardiaceae grown for its edible fruits (nuts). The cashew tree has a branching main trunk and characteristic domed crown. The thin foliage of the tree is limited to the ends of the branches and is made up of oval-oblong leathery, shiny dark green leaves. The leaves are smooth with pronounced veins and midrib and possess petioles which are swollen at their base. The tree produces numerous pinkish-white flowers on drooping panicles and a kidney shaped true fruit (nut) which is approximately 3 cm (1.2 in) long with a gray-brown shell and develops from a fleshy accessory fruit, sometimes referred to as the 'cashew apple'. The cashew apple is pear shaped and red to yellow in color. Cashew trees can reach a height of 12 m (39.4 ft) and have an economic lifespan of 25 years after which time they are replaced in commercial plantations. Cashew originates from Brazil.
References: https://plantvillage.psu.edu/topics/cashew-nuts/infos

Cassava
type: Class
subclass of: Cereal
type: NamedIndividual
type: Crop
has disease: Mites
has disease: White leaf spot
harvesting: Cassava is ready to harvest about a year after planting depending on the variety being grown. Some early maturing varieties may be ready to harvest in around nine months. In colder regions, cassava tubers can remain in the ground for up to 2 years before harvesting but can become fibrous so this is not recommended where cassava is being grown for consumption. Cassava tubers are harvested by digging. The roots should be dug up carefully to prevent damage.
propagation: Cassava is propagated from stem cuttings as the tubers do not produce buds. Stem cuttings should only be taken from plants which are free from disease, are at least 10 months old and havee borne tubers. The cuttings should be taken from hardened stems leaving at least 30 cm (11.8 in) of stem intact in the ground. The stem can be severed using a sharp knife, secateurs or saw and each cutting should have 1-2 nodes and be approximately 20 cm (7.9 in) long. Plants propagated from stem cuttings are ready to be transplanted after approximately 4-6 weeks. Prepare the field for planting by cultivating the soil and removing weeds. Space transplants 75–100 cm (2.5–3.2 ft) apart in rows spaced 1–-5 m (3.2–16.4 ft) apart. Fertilize the plants as appropriate. Manure or poultry droppings can be used. Cultivate the soil to remove weeds and break up the soil around the plants
requirements: Cassava thrives in tropical and subtropical regions of the world as it requires warm temperatures for optimal growth. The plants require at least 8 months of warm weather, thriving in regions with warm, moist climates with regular rainfall. Cassava can be grown in many types of soil, producing even in poor soil but but will be optimally productive in well-draining, sandy clay loam with a pH between 5.5 and 6.5. Cassava is drought resistant but will not tolerate water-logging. Root production is maximized when temperatures are between 25 and 32°C (77–90°F). Cassava should be planted in full sun and is very sensitive to shading, which leads to low yields.
uses: Cassava root is eaten as a vegetable and is considered to be toxic in raw form which is why it must be cooked before being consumed. The root has a variety of applications, some of which include the production of flour, starch, or ethanol. Cassava leaves can supply a good source of vitamins and protein which can also be consumed after cooking. Cassava hay is used as animal feed and it plays a role in the production of adhesives, textiles, and cosmetics.
References: https://plantvillage.psu.edu/topics/cassava-manioc/infos
comment: Cassava root is eaten as a vegetable and is considered to be toxic in raw form which is why it must be cooked before being consumed. The root has a variety of applications, some of which include the production of flour, starch, or ethanol. Cassava leaves can supply a good source of vitamins and protein which can also be consumed after cooking. Cassava hay is used as animal feed and it plays a role in the production of adhesives, textiles, and cosmetics.

Cation Exchange Capacity
type: Class
subclass of: Soil Composition
type: NamedIndividual
type: Soil
has transformation: Cation Exchange Capacity by Default
Acronym: CEC
comment: Cation exchange capacity (CEC) is the total capacity of a soil to hold exchangeable cations. CEC is an inherent soil characteristic and is difficult to alter significantly. It influences the soil's ability to hold onto essential nutrients and provides a buffer against soil acidification.

Cauliflower
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
harvesting: Cauliflower heads are ready for harvest when the head reaches 15 to 20 cm in diameter (6-8 in), usually about 7 to 12 days after blanching begins. Harvest the head by cutting the main stem with a sharp knife, including some of the central leaves which will protect the head. The heads can quickly become overmature, leading to reduced quality. Be sure to harvest the heads while they are still firm, before individual florets can be distinguished.
propagation: Cauliflower is most successfully grown from transplants started in the glasshouse or indoors. Peat pots work well for growing seedlings as damage to the roots during transplanting can be avoided. Plant seeds in a sterile seed starting mix to a depth of 0.6-1.3 cm (0.25-0.5 in) deep and keep moist. Seedlings can be transplanted when they are approximately 6 weeks old after hardening. Plant spacing will greatly affect the final size of the cauliflower head and the plants should be spaced at least 46 cm (18 in) apart leaving approximately 75 cm (30 in) between rows.
requirements: Cauliflower is a cool season crop and grows best in well draining, organic soil at a pH of 6.5 or above. A high amount of organic matter in the soil will help to hold moisture. The plant requires consistent cool temperatures to prevent ‘buttoning’ - the formation of several small heads instead of one large one. Cauliflower is less cold hardy than its relatives and it should be planted after the last frost as sub-freezing temperatures are likely to damage the plant. It is best grown as a Fall crop in cooler areas. Only mature plants will survive frost in the Fall.
uses: Cauliflower is grown for consumption as a vegetable, either fresh or cooked. May be processed for freezing or pickling.
comment: Cauliflower, Brassica oleracea var. botrytis, is an herbaceous annual or biennial vegetable plant in the family Brassicaceae grown for its edible head. The head is actually a mass of abortive flowers (flowers which are unable to produce fruit or seed as they possess only female reproductive organs; the male organs are either underdeveloped or totally lacking). Cauliflower plants are shallow rooted with a small, thickened stem. The ribbed leaves branch off the top of the stem and are light green in color. The plant can reach a height of 1–1.5 m (3.3–4.9 ft) and is most commonly grown as an annual, harvested between 60 and 100 days after planting. The origin of cauliflower has not been definitively determined but its ancestor, wild cabbage, is thought to have originated in ancient Asia Minor.
References: https://plantvillage.psu.edu/topics/cauliflower/infos

Celery
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
harvesting: Celery can begin to be harvested when the stalks are about 20 cm (8 in) tall. Unblanched celery can be harvested as needed by cutting stalks from the outside in. Blanched celery can be harvested by cutting the plant just below the soil line. The plants can be kept in the garden until Fall and will tolerate a light frost but not continuous heavy frosts. If uncovered, the stalks will grow darker in color and the resultant texture will be more fibrous than the young stalks.
maintenance: Celery is a very heavy feeder and should be fed every 10–14 days with a balanced fertilizer. The plants require constant moisture and should be provided with at least 1 inch of water per week. A layer of mulch spread around the plants helps to conserve the moisture in the soil and prevents weeds. Any weeds should be gently removed from around the plants. If temperatures are forecast to drop below 12.7°C (55°F) then the plants should be protected with cloches to prevent them developing weak stalks. Prolonged exposure to temperatures below this threshold will result in bolting.
propagation: For best results celery should usually be started indoors or in a glasshouse and transplanted to the garden or field. Germination of celery seeds is slow and the seeds should be planted 10 to 12 weeks before the last Spring frost. Soaking seeds in warm water overnight prior to planting can increase germination success. Plant seeds in seedling flats containing a good quality sterile seed starting mix by lately sprinkling a pinch of seeds across the soil surface and covering with a thin layer of starting mix or vermiculite. Place trays in a warm location inside the house or in the glasshouse and keep well-watered. Do not allow the trays to dry out. It is important to keep the soil at the temperature between 12.7 and 21°C (55 and 70°F). The optimum temperature for germination is 60° to 70° (15° to 21° C) and the seedlings should emerge within 2–3 weeks. The seedlings can be potted into individual pots when the are about 5 cm (2 in) tall. Seedlings are ready to transplant when they are 15 cm (6 in) tall and have been hardened off. Prepare the transplant site in advance of planting by working fertilizer or compost into the soil and plant the seedlings when the weather has settled after the last frost. Seedling should be planted at the same depth as their previous container and should be spaced 15–20 cm (6–8 in) apart in rows spaced 60–90 cm (2–3 ft) apart.
requirements: Celery is a cool season vegetable which grows best in cool climates in well-draining, fertile soils with a pH between 6.0 and 6.8.. Celery plants grow optimally in cool, moist locations and will tolerate some shade but will not tolerate heat. The plants require constant moisture and can tolerate temperatures between 5 and 27°C (41–80.6°F) with the optimum for growth being between 15–21°C (59–69.8°F). If temperatures drop below 12.7°C (55°F) then the plants will benefit from protective coverings to prevent the plants from bolting (going to seed).
uses: Celery is eaten around the world as a vegetable. In North America the crisp petiole is used. In Europe the hypocotyl is used as a root vegetable. The leaves are strongly flavored and are used less often, either as a flavoring in soups and stews or as a dried herb. Celery, onions, and bell peppers are the "holy trinity" of Louisiana Creole and Cajun cuisine. Celery, onions, and carrots make up the French mirepoix, often used as a base for sauces and soups. Celery is a staple in many soups, such as chicken noodle soup.
comment: Celery is a marshland plant in the family Apiaceae that has been cultivated as a vegetable since antiquity. Celery has a long fibrous stalk tapering into leaves. Depending on location and cultivar, either its stalks, leaves or hypocotyl are eaten and used in cooking. Celery seed is also used as a spice and its extracts have been used in herbal medicine.
References: https://plantvillage.psu.edu/topics/celery/infos

Cellulose
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Cellulose is a molecule, consisting of hundreds – and sometimes even thousands – of carbon, hydrogen and oxygen atoms. Cellulose is the main substance in the walls of plant cells, helping plants to remain stiff and upright. Humans cannot digest cellulose, but it is important in the diet as fibre.

Cereal
type: Class
subclass of: Crop
comment: A cereal is any grass cultivated for the edible components of its grain, composed of the endosperm, germ, and bran. Cereal grain crops are grown in greater quantities and provide more food energy worldwide than any other type of crop and are therefore staple crops.

Chemical Control
type: Class
subclass of: Treatment

Chemical Fertilizer
type: Class
subclass of: Fertilizer
comment: Chemical fertilizers are generally classified according to their NPK content, representing the concentrations of the three principal plant nutrients, nitrogen (wt.%), phosphorus (wt.% P2O5 equivalent), and potassium (wt.% K2O equivalent).

Cherry
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
description: Cherry tree, Prunus avium, is a perennial tree in the family Rosaceae grown for its fruit, the cherry. Cherry trees have alternating simple oval leaves which often have serrated margins and approximately eight pairs of veins, 5-13 cm (2-5 in) long. The flowers are white and appear in clusters of about 3-5. They appear in early spring and are about one inch long. The fruit is dark red, about half an inch to a full inch, matures in early to mid summer. The bark is gray-brown, smooth and glossy and it often peels. Cherry trees can live up to 60 years, growing to a height of over 15 m (50 ft). Cherry may also be referred to as sweet cherry, gean, mazzard or wild cherry and originates from the Caspian-Black Sea region and Asia. Sour cherry, Prunus cerasus, is closely related to Prunus avium but with fruit that is more acidic. The tree has twiggy branches with bright red to near black cherries. The tree reaches a height of 4-10 m (13.1-16.4 ft) and is believed to have originated as a natural hybrid of wild cherry (Prunus avium) and ground cherry (Prunus fruticosa) somewhere in the Caucasus Mountains, Turkey or Eastern Europe where the two species' geographic ranges overlap.
maintenance: The area around the base of the tree should be kept free from weeds either by cultivating or using a chemical weed control. In the home garden, trees are usually surrounded by turf with only a small area of soil around the trunk which is easy to keep weed free by hand. Unless grown in fertile soils, cherry trees may require the addition of some nitrogen fertilizer, the volume of which will vary with the age of the tree. The fertilizer should be applied in a band around the trunk of the tree.
propagation: Cherry nurseries are mainly propagated by budding and grafting onto 1-2 year old seedling grown from seed. In order to grow from seed, pits should be planted in the fall (after first being chilled) and seedlings will emerge in the spring. A cherry tree will take three-four years to produce its first crop of fruit and seven years to attain full maturity. Planting Cherry trees should be planted in the Spring when all danger of frost has passed and the soil has warmed. If bare root plants have been purchased, be sure to keep the root ball moist prior to planting. The tree should be planted in a hole which is large enough to accommodate the root ball without bending any of the roots. Broken or damaged roots can be removed but try to avoid pruning the roots. Place the tree in the planting hole and carefully backfill with soil. The tree should be planted at the same depth as it was in the nursery. Carefully firm or tamp the soil to prevent air pockets and when the planting hole is about 2/3 filled, water the roots thoroughly before filling the remainder of the hole with top soil. The tree should be watered as needed throughout the first year of growth. Pruning Cherry trees are usually trained to a modified central leader system where the lateral limbs branch from a central trunk. Sweet cherries have a more upright growth habit than sour cherries which tend to have a more open and spreading canopy. Trees should only pruned lightly while the tree matures and reaches maximum productivity. Any dead or damaged branches should be removed.
requirements: Cherries grow optimally in mild, dry climates and also have a chilling requirement to break dormancy so require cold winters. Because of the cold weather requirement, none of the Prunus family, including cherries, can grow in tropical climates. Cherry trees will grow in a variety of soils provided that they are well-draining. Soils which remain wet for prolonged periods of time and soils that become excessively dry should be avoided. Trees will perform best if planted in sandy soil with a pH of 5.5-7.5. Planting on a gentle slope is beneficial for cherry as cold air can drain away from the trees to lower areas, helping to prevent damage. Sweet cherries bloom earlier than sour cherries and can be susceptible to damage from late frosts.
uses: Cherries are mainly consumed as a fresh fruit or processed into jellies and juices. The wood of the cherry tree is a distinctive honey color and is used in furniture making and for musical instruments. Sour cherries are used mainly for canning and processing.
References: https://plantvillage.psu.edu/topics/cherry-including-sour/infos
comment: Cherry tree, Prunus avium, is a perennial tree in the family Rosaceae grown for its fruit, the cherry. Cherry trees have alternating simple oval leaves which often have serrated margins and approximately eight pairs of veins, 5-13 cm (2-5 in) long. The cherry flowers are white and appear in clusters of about 3-5. They appear in early spring and are about one inch long. The fruit is dark red, about half an inch to a full inch, matures in early to mid summer. The bark is gray-brown, smooth and glossy and it often peels. Cherry trees can live up to 60 years, growing to a height of over 15 m (50 ft). Cherry may also be referred to as sweet cherry, gean, mazzard or wild cherry and originates from the Caspian-Black Sea region and Asia. Sour cherry, Prunus cerasus, is closely related to Prunus avium but with fruit that is more acidic. The tree has twiggy branches with bright red to near black cherries. The tree reaches a height of 4-10 m (13.1-16.4 ft) and is believed to have originated as a natural hybrid of wild cherry (Prunus avium) and ground cherry (Prunus fruticosa) somewhere in the Caucasus Mountains, Turkey or Eastern Europe where the two species' geographic ranges overlap.

Chestnut
type: Class
subclass of: Nuts
type: NamedIndividual
type: Crop
type: Nuts
maintenance: After planting, the areas around the trees should be kept free from weeds. A layer of mulch spread around the trunks will help suppress weeds and conserve soil moisture. You may have to protect the young trees from pests such as deer which can cause severe damage to the trees. The most effective method of protecting trees is to erect an 8 ft high metal deer fence around the area where the new trees have been planted.
propagation: Chestnut trees can be grown from seeds or can be propagated by grafting onto a seedling rootstock. Seedling grown for use as a rootstock for grafting are usually ready to receive a scion when they are two years old. The seedling must have reached a suitable thickness in order to receive a graft (usually when it reaches a thickness similar to that of a pencil). Chestnut seeds germinate readily but not all will produce a seedling suitable for planting and/or grafting. Seeds can be obtained from breeders or collected from chestnut trees in the Fall. Seeds should be kept refrigerated until planting and can then be directed seeded outdoors or grown in containers inside. Seeds should be planted in early Spring as soon as the soil can be worked. The seed should be planted in a hole 7.5 to 15.0 cm (3-6 in) deep with the radicle (embryo emerging from the seed) facing downwards. If planting in a container, be sure to choose one that is at least 30 cm (12 in) deep and 10 cm (4 in) in diameter. The seeds can be planted earlier if the containers are to be kept indoors. Seedlings started in containers can be transplanted after all danger of frost has passed and after hardening. The seedling should be planted in a hole that is at least 1.5 times the size of the existing root ball. Gently remove the remnants of the nut from the roots and place the seedling in the hole. Backfill the hole with soil and tamp to remove air pockets. Seedlings should be planted 10–12 m (33–39 ft) apart, allowing an additional 10–12 m (33–39 ft) between rows. After planting, the seedlings should be watered thoroughly. Continue watering the seed for the first month after planting to help the seedling establish.
requirements: Chestnuts are hardy trees which will grow in a variety of conditions. The trees grow best in well-draining, acid loam soils and require a pH between 4.5 and 6.5. The trees perform well when planted on a gentle slope which allows cold air to drain to a lower area as well as aiding in soil drainage. Chestnuts should be planted in full sunlight and open fields are preferred for maximum nut production.Chestnut has poor self compatibility and at least 2 trees are required for nut production.
uses: Chestnuts are commonly consumed after roasting or may be processed to produce chestnut creams and purees or for canning.
comment: Chestnut (Castanea spp.) is a deciduous tree in the family Fagaceae grown for its edible seeds (nuts). The chestnut tree has a thick trunk covered in gray bark. The trunk has an erect growth habit and can grow 2 m (6.6 ft) in diameter. When the tree stands alone, the canopy can spread 15 m (49 ft) across and is made up of glossy leaves with serrated margins and pointed tips. The leaves are hairy with visible glands on the underside. The chestnut tree produces flowers on long catkins and the seeds are produced in clusters of 1–3. The seeds are covered by a thick, spiny bur which is approximately 10 cm (4 in) in diameter. The kernel within is protected by a thin, dark brown shell. Chestnut trees can reach 40–60 m (131–198 ft) in height and can live for in excess of 150 years. Chestnut may also be referred to by variety and includes European, American, Japanese and Spanish chestnut. The tree originates from Asia.

Chickpea
type: Class
subclass of: Cereal
type: NamedIndividual
type: Crop
harvesting: Chickpeas are ready to harvest approximately 100 days after planting. in the home garden, chickpeas can be collected and eaten when green but for dried seeds, the leaves should be allowed to turn brown before collecting the pods. The pods should be allowed to dry and split open before collecting seeds. In commercial production, chickpeas can be harvested by swathing and/or combine.
maintenance: In the home garden, chickpea seedlings will benefit from the addition of a layer of mulch after they have emerged to suppress weeds and conserve soil moisture. Plants should be provided with additional irrigation during periods of dry weather but care should be taken not to overwater as this can cause the plants to drop their flowers and/or pods. Application of fertilizer is generally not necessary as chickpeas are leguminous and fix their own nitrogen with the aid of micro-organisms but requirements vary from field to field and management should be based on the results of a soil test. Like other pulses, chickpeas require phosphorus, potassium and micronutrients for growth and application should be based on the results of soil testing. Suppression of weeds in commercial fields is important as chickpeas do not compete well and weed will reduce productivity. Pre- and post-emergence herbicide applications can be helpful for managing weed. If a field has a history of weed problems then it is not suitable for chickpea production.
propagation: Chickpeas are propagated directly from seed with timing of planting depending on the variety being grown and geographic location. Spring crops should be planted when the soil has warmed to at least 5°C (41°F). Chickpeas are leguminous and when planting in a site which has not previously been used for chickpea cultivation, seed should be inoculated with an appropriate type of Rhizobacteria prior to planting. Seed is usually spread by broadcasting or by drilling in rows. Desi types require a seed rate of 30 to 40 kg per hectare whereas Kabuli types require 80 to 100 kg per hectare. The seed should be planted 2 to 12 cm (0.8–4.7 in) deep and when planting in rows, seed should be spaced 10 cm (4 in) apart allowing 25 to 60 cm (9.8 to 23.6 in) between rows. Seedlings usually emerge between 7 and 15 days after sowing depending on temperature.
requirements: Chickpeas are cool-season legumes which are best grown as a winter crop in the tropics or as a spring or summer crop in temperate regions. Chickpeas are classified into two distinct types based on seed characteristics. Desi chickpeas have a thick, colored seed coat and the surface of the seed is rough and angular whereas the Kabuli type have white or beige seeds with a smooth surface. Kabuli chickpeas are best grown in temperate areas whereas Desi chickpeas are best suited to semi-arid areas of the tropics. Chickpeas will grow optimally at temperatures between 15 and 29°C (59–84.2°F) with extremes of temperature above 35°C (95°F) and below 15°C (59°F) causing flowers to drop, reducing production. Plants will perform best when planted in well-draining sandy loam soils with a pH between 5.0 and 7.0 in areas where the annual rainfall is between 600 and 1000 mm.
uses: Chickpea is primarily consumed as a dry pulse. The shelled peas are eaten as snack or vegetable. The seed husks can be used as a feed for animals. Chickpeas are also commonly cooked and ground into a paste to produce the popular dish, hummus.
comment: Chickpea, Cicer arietinum, is a leguminous annual plant in the family Fabaceae grown for its edible seeds. The plant has a branched, straight or bending stem with small feathery leaves arranged alternately on the stem. The leaves are composed of 11–15 individual leaflets which are oval in shape. The flowers are produced singly or in pairs and can be white, pink, purple or blue in color. The seed pod is rhomboid or ellipsoid and contains 1–4 cream, brown, green or black seeds. The chickpea plant can range in height from 20 cm (7.9 in) up to 100 cm (39.4 in) and as an annual, grows over only one growing season. Chickpea may also be referred to as gram pea, garbanzo bean or ceci bean and originate from south-eastern Turkey.
References: https://plantvillage.psu.edu/topics/chickpea-gram-pea/infos

Chlorine (Cl)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
Acronym: Cl
comment: Chlorine, as compounded chloride, is necessary for osmosis and ionic balance; it also plays a role in photosynthesis.

Chlorophyll
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Chlorophyll (also chlorophyl) is any of several related green pigments found in the mesosomes of cyanobacteria, as well as in the chloroplasts of algae and plants. Chlorophyll is essential in photosynthesis, allowing plants to absorb energy from light.

Chromium (Cr)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
Acronym: Cr
comment: Chromium (Cr) is a chemical element with the symbol Cr and atomic number 24.

Citrus
type: Class
subclass of: Crop
type: NamedIndividual
type: Crop
related to: Lemon
related to: Limes
related to: Orange
comment: Citrus is a genus of flowering trees and shrubs in the rue family, Rutaceae. Plants in the genus produce citrus fruits, including important crops such as oranges, lemons, grapefruits, pomelos, and limes.

Classification
type: Class
subclass of: Data Mining
type: NamedIndividual
type: Data Mining
comment: Classification is learning method where the output variable is a category, such as “Highâ€ or “Mediumâ€ or “Lowâ€ and “Noneâ€ based on the basis of a training set of data containing observations (or instances) whose category membership is known. Examples are assigning a given email to the "High yieldâ€ or “Medium yield" or "Low yield" class, and assigning a diagnosis to a given patient based on observed characteristics of the cropping records (crop name, seed rate, soil characteristics, etc.). Classification is an example of pattern recognition.

Classifier
type: Class
subclass of: Classification
type: NamedIndividual
type: Data Mining

Climate
type: Class
subclass of: Environment
type: NamedIndividual
type: Environment
comment: Climate is the long-term average of weather, typically averaged over a period of 30 years. Some of the meteorological variables that are commonly measured are temperature, humidity, atmospheric pressure, wind, and precipitation.

Climate Type
type: Class
subclass of: Climate
comment: There are several ways to classify climates into similar regimes. Originally, climes were defined in Ancient Greece to describe the weather depending upon a location's latitude. Modern climate classification methods can be broadly divided into genetic methods, which focus on the causes of climate, and empiric methods, which focus on the effects of climate.

Cloud
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather

Cloud Cover
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
unit: %

Cloud Density
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather

Cluster
type: Class
subclass of: Clustering
type: NamedIndividual
type: Data Mining

Clustering
type: Class
subclass of: Data Mining
type: NamedIndividual
type: Data Mining
comment: Clustering is a learning method used to discover the inherent groupings in the data. For example: Grouping fields on the basis of their cropping records which are further used to segment the fields. And then the agronomists can use the appropriate tactics to generate more profits. Example of clustering algorithms: K-Means, hierarchical clustering, etc.

Cobalt (Co)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
Acronym: Co
comment: Cobalt has proven to be beneficial to at least some plants although it does not appear to be essential for most species.

Cocoa
type: Class
subclass of: Nuts
type: NamedIndividual
type: Crop
type: Nuts
maintenance: The cocoa nursery should be kept weed free while the seedlings established but generally do not require weeding after the trees have formed a closed canopy as the lack of light under the trees prevent any further growth. Cocoa should be supplied with additional nutrients by fertilizing, particularly when trees are grown on poor soils or without shade. Organic fertilizers are generally preferable to inorganic ones as they do not deplete the soil organic content and conserve soil structure. The amount of fertilizer required is dependent on many factors, such as the age of the tree and the amount of shading but mature cocoa generally requires at least 50–100 kg/ha of nitrogen, 25 kg/ha of phosphorus, 75 kg/ha of potassium and 15 kg/ha of magnesium each year.
propagation: Cocoa can also be vegetatively propagated via cuttings, marcotting and budding. Cuttings should have 2–5 leaves and 1 or 2 buds. Leaves should be cut in half before placing the cutting in a pot and covering with polyethylene to allow roots to develop. Marcotting, is achieved by removing a strip of bark from a tree branch and covering the area with a layer of sawdust before covering it with polyethylene. The covered area will develop roots and can then be removed and planted. The final method, called budding, can be used to rejuvenate older plantings and involves excising a bud and positioning it under a flap of bark on another tree. The join is then sealed with raffia and waxed tape. Once the bud begins to grow the tree above the new growth should be removed. Seedlings are usually planted in the ground when they are 4–6 months old. The young trees are delicate and require some protection from strong sunlight and wind damage. Protection is usually provided by planting seedlings next to mother trees. This shading also helps to prevent the trees from growing too tall, keeping them at a manageable size for maintenance and harvest. Shade trees are usually other crops such as banana, plantain coconut or rubber. Cocoa seedlings should be planted 3–4 m (10–13 ft) apart and 3–6 m (10–20 ft) from the shade trees. The shading can be reduced once the cocoa trees have formed a closed canopy but some should be retained to reduce water stress and insect damage.
requirements: Cocoa trees are tropical plants and grow best in humid conditions at temperatures between 18 and 32°C (65–90°F). It is typically grown in regions where daytime humidity reaches up to 100% and night time humidity is between 70 and 80%. The plants require a deep, fertile and well-draining soil with a pH of 5.0–7.5 for optimum development. Cocoa is usually grown in tropical lowland areas is sensitive to drought. It should typically not be grown in regions which experience less than 1 cm of rainfall for periods in excess of 3 months.
uses: Cocoa beans are primarily used in the production of chocolate, cocoa powder and cocoa butter for consumption. Cocoa butter is also used in the cosmetic industry.
comment: Cocoa, Theobroma cacao, is an evergreen tree in the family Malvaceae, grown for its seeds (beans) which are used primarily in the manufacture of chocolate. The cocoa plant is a branching tree with with simple, pointed (lanceolate) leaves which can measure up to 61 cm (24 in) long and 10 cm (4 in) wide. The tree produces clusters of pale yellow flowers each with five petals and sepals. The cocoa pods (drupes) can be green-white, yellow, purplish or red in color each of which contains 20–50 seeds, usually arranged in five distinct rows. The cocoa tree can reach 4–20 m (13-66 ft) in height and can live for up to 40 years although the commercial life of a cocoa tree is usually about 25 years. Cocoa may also be referred to as cacao, koko or Kacao and originates from upper Amazon region of South America.
References: https://plantvillage.psu.edu/topics/cocoa-cacao/infos

Coconut
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
description: Coconut palm, Cocos nucifera, is an erect palm in the family Arecaceae which is grown its fruits, used primarily for the extraction of coconut oil for use in cooking. The coconut palm has an erect or slightly curved stem which grows from a swollen base. The stem is smooth, light gray in color and has prominent leaf scars. The stem is topped with a crown of 60-70 spirally arranged leaves. The leaves are long (up to 7 m/23 ft), pinnately divided and composed of 200-250 tapering leaflets. The inflorescence is a spike produced at the leaf axil with 20-60 branches, each with a female flower at the base and many male flowers. The fruit is a drupe containing a single seed. It is ovoid in shape with three sides divided by ridges. The exocarp and the mesocarp make up the husk of the coconut. The seed is protected by a thick, stony shell, or endocarp, and is partially filled with a liquid known as coconut water. The edible endosperm is white and meaty and can be between 1.0 and 2.5 cm (0.4-1.0 in) thick. Coconut palms can reach a height of 30 m (98 ft), produce up to 75 fruits a year, and live for up to 90 years. The origin of the coconut is unknown although the center of genetic diversity lies in Southeast Asia.
propagation: Coconut palms are propagated exclusively from seed. The seeds are ready for planting when the coconut milk can be heard sloshing around inside the seed when it is shaken. Seeds are germinated by planting in seed beds before transferring to a polythene bag or nursery beds after germination. Seeds should be planted on their sides in a shallow hole with enough soil to cover about one third of the seed. The seed should be watered regularly to prevent it drying out. Germination usually occurs after about 3 months but may take up to 6 months. Coconut seedling can be transplanted from 6 months onwards or transferred to pots and grown further in the nursery. Trees require a wide spacing and are typically planted 8-9 m (26-30 ft) apart allowing a further 8-9 m (26-30 ft) between rows. Dwarf varieties can be spaced closer together and are typically planted 7.5 m (25 ft) apart allowing another 7.5 m (25 ft) between rows
uses: Coconuts are primarily cultivated for their oil. The oil is extracted from the endosperm and is used in cooking. Low grade oils are used in the production of soaps. The endosperm can be consumed fresh or dry and is commonly grated for use in baking. Young coconuts, termed waternuts, are commonly sold in tropical resorts, where the liquid is drunk directly from the shell. Coconuts may also be used to produce coconut milk by squeezing the liquid from the grated endosperm. Coconut milk is popularly used in many dishes from Southeast Asia.
References: https://plantvillage.psu.edu/topics/coconut/infos
comment: Coconut palm, Cocos nucifera, is an erect palm in the family Arecaceae which is grown its fruits, used primarily for the extraction of coconut oil for use in cooking. The coconut palm has an erect or slightly curved stem which grows from a swollen base. The stem is smooth, light gray in color and has prominent leaf scars. The stem is topped with a crown of 60-70 spirally arranged leaves. The leaves are long (up to 7 m/23 ft), pinnately divided and composed of 200-250 tapering leaflets. The inflorescence is a spike produced at the leaf axil with 20-60 branches, each with a female flower at the base and many male flowers. The fruit is a drupe containing a single seed. It is ovoid in shape with three sides divided by ridges. The exocarp and the mesocarp make up the husk of the coconut. The seed is protected by a thick, stony shell, or endocarp, and is partially filled with a liquid known as coconut water. The edible endosperm is white and meaty and can be between 1.0 and 2.5 cm (0.4-1.0 in) thick. Coconut palms can reach a height of 30 m (98 ft), produce up to 75 fruits a year, and live for up to 90 years. The origin of the coconut is unknown although the center of genetic diversity lies in Southeast Asia.

Coffee
type: Class
subclass of: Nuts
type: NamedIndividual
type: Crop
type: Fruit Tree
has disease: Bacterial Blight
has disease: Black Twig Borer
has disease: Cercospora Leaf Spot
has disease: Coffee Berry Borer
description: Coffee is the name give to several species of plant in the genus Coffea (family Rubiaceae), including C. arabica and C. canephora which are cultivated for their beans (seeds) that are used to make the stimulatory drink. Coffee plants are small evergreen trees or shrubs often with multiple stems and smooth leaves. The leaves are oval in shape and dark, glossy green. Coffee plants produce clusters of cream-white flowers and a fruit, commonly referred to as a berry, which normally possesses two seeds. The fruit is green to begin with but ripens to a crimson red and turns black when dry. C. canephora can reach a height of 15 m (49 ft), but C. arabica is smaller, reaching only 4-5 m (13-16 ft). The trees can live for 20-30 years. Coffee may also be referred to as Arabian coffee (C. arabica) or robusta coffee (C.cenephora) and originates from Africa.
propagation: Most coffee varieties are self pollinating and can be propagated from seed. Coffee seeds are usually pre-germinated in sand beds before planting in a nursery. The seeds are spread in the sand and covered to keep them moist. The seeds usually germinate within 4-5 weeks. When shoots begin to emerge, they are removed and planted in polyethylene bags or prepared nursery beds. The seedlings are grown in shade and are are ready to be transplanted when they are 6 to 12 months old. Young coffee plants are planted in the field in large, pre-dug holes. (50cm × 50cm × 50cm). Various spacings are used in different regions of the world and coffee can be grown as hedgerows or in high density squares depending on the variety. Coffee will usually grow best if planted in rows and plants should be spaced 3 m (9.8 ft) apart. Young trees are delicate and require protection by shading. Shade trees are usually planted before the coffee trees are transplanted (up to a year in advance).
requirements: Coffee plants grow best in warm, humid environments. Arabica coffee varieties grow best at temperatures between 18 and 22oC (64-72oF), while Robusta coffee grows best in slightly warmer temperatures of 22-26oC (72-79oF). The plants do not tolerate cold and freezing temperatures will kill the leaves instantly. The plants can grow in a wide range of soils but generally prefer a deep, well-draining loam with a pH between 5 and 6.
uses: Coffee beans are usually cured, roasted and ground before being brewed with hot water to produce the coffee beverage. The ground beans are often dehydrated to produce instant coffee.
References: https://plantvillage.psu.edu/topics/coffee/infos
comment: Coffee is the name give to several species of plant in the genus Coffea (family Rubiaceae), including C. arabica and C. canephora which are cultivated for their beans (seeds) that are used to make the stimulatory drink. Coffee plants are small evergreen trees or shrubs often with multiple stems and smooth leaves. The leaves are oval in shape and dark, glossy green. Coffee plants produce clusters of cream-white flowers and a fruit, commonly referred to as a berry, which normally possesses two seeds. The fruit is green to begin with but ripens to a crimson red and turns black when dry. C. canephora can reach a height of 15 m (49 ft), but C. arabica is smaller, reaching only 4-5 m (13-16 ft). The trees can live for 20-30 years. Coffee may also be referred to as Arabian coffee (C. arabica) or robusta coffee (C.cenephora) and originates from Africa.

Cold Stress
type: Class
subclass of: Crop Stress
type: NamedIndividual
type: Crop Stress
related to: Temperature
comment: Cold stress also severely hampers the reproductive development of plants. The major negative effect of cold stress is that it induces severe membrane damage.

Color
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute
alternative label: Crop Color
comment: Color is the characteristic of visual perception described through color categories (with names such as red, orange, yellow, green, blue, or purple) or value.

Computer System
type: Class
subclass of: Computing Algorithm
comment: Computer System is a computing system , which is used to annalyse data and mining knowledge.

Company
type: Class
subclass of: Organization
comment: A company is a commercial business.

Compost
type: Class
subclass of: Fertilizer
comment: Compost is organic matter that has been decomposed in a process called composting. This process recycles various organic materials otherwise regarded as waste products and produces a soil conditioner (the compost). Compost is rich in nutrients. It is used, for example, in gardens, landscaping, horticulture, urban agriculture and organic farming. The compost itself is beneficial for the land in many ways, including as a soil conditioner, a fertilizer, addition of vital humus or humic acids, and as a natural pesticide for soil. Compost is useful for erosion control, land and stream reclamation, wetland construction, and as landfill cover.

Computing Algorithm
type: Class
subclass of: Transformation

Conference
type: Class
subclass of: Event
comment: Conference is a formal meeting of people with a shared interest, typically one that takes place over several days.

Continent
type: Class
subclass of: Location
comment: A continent is one of several very large landmasses. Ordered from largest in area to smallest, these seven regions are: Asia, Africa, North America, South America, Antarctica, Europe, and Australia.

Copper (Cu)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
Acronym: Cu
comment: Copper is important for photosynthesis. Symptoms for copper deficiency include chlorosis. It is involved in many enzyme processes; necessary for proper photosynthesis; involved in the manufacture of lignin (cell walls) and involved in grain production. It is also hard to find in some soil conditions.

Corn
type: Class
subclass of: Cereal
type: NamedIndividual
type: Cereal
type: Crop
description: Corn is an annual grass in the family Poaceae and is a staple food crop grown all over the world. The maize plant possesses a simple stem of nodes and internodes. A pair of large leaves extend off of each internode and the leaves total 8-21 per plant. The leaves are linear or lanceolate (lance-like) with an obvious midrib (primary vein) and can grow from 30 to 100 cm (11.8-39.4 in) in length. The male and female inflorescences (flower bearing region of the plant) are positioned separately on the plant. The male inflorescence is known as the 'tassel' while the female inflorescence is the 'ear'. The ear of the maize is a modified spike and there may be 1-3 per plant. The maize grains, or 'kernels', are encased in husks and total 30-1000 per ear. The kernels can be white, yellow, red, purple or black. Maize is an annual plant, surviving for only one growing season and can reach 2-3 m (7-10 ft) in height. Maize may also be referred to as corn or Indian corn and is believed to originate from Mexico and Central America.
harvesting: Each maize stalk should produce 1 large ear of maize. Under ideal conditions, the stalk will produce a second, slightly smaller ear which reaches maturity slightly later than the first. Maize ears should be harvested at the “milk stageâ€ of development, when the kernels within the husk are well packed and produce a milky substance when the kernel is punctured. Check ears for ripeness by gently peeling back a small portion of the husk. Be sure to check the ears frequently for ripeness and harvest as required as ears can quickly become over-ripe and lose their sweetness. Remove the ears from the stalk by pulling quickly downward while twisting and then refrigerate until consumption.
maintenance: Maize plants are heavy feeders, particularly of nitrogen (N) and care should be taken to provide them with adequate nutrients by applying fertilizer. Maize undergoes a rapid growth period between 30 and 40 days after planting and should be fertilized just before this. All fertilizer applications should be made before the tasseling period to ensure the plant maximizes nitrogen use. Be aware of symptoms of nutrient deficiency, plants should be a deep green color. Purple tinged leaves indicate that the plants are suffering from a lack of phosphorous, whereas light green leaves indicate a lack of nitrogen. Apply fertilizer. Plants also require adequate soil moisture throughout the growing period in order to tassel and form silks. Soaker hoses can be used to great effect in small to mid-size plantings. Pollination occurs when pollen is transferred from the male tassel to the female silk by the wind. Each silk produced a single kernel of corn and partially filled ears are usually a result of poor pollination.
propagation: Planting dates for maize depend on the variety being grown. Standard varieties should be planted when the soil has warmed to at least 12.7oC (55oF) and supersweet varieties when the soil reaches 18.3oC (65oF). Soil can be brought up to temperature faster by laying black plastic mulches approximately 1 week prior to planting. Seeds should be sown about 2.5 cm (1 in) deep and 10-15 cm (~3-4 in) apart allowing 76-91 cm (~30-36 in) between rows. Maize should be planted in blocks (numerous rows) rather than in a single long row as it is wind pollinated and pollen can transfer between plants much more efficiently. Seedlings should be thinned to a final spacing 20-30 cm (8-12 in) when they are approximately 7.5-10.0 cm (3-4 in) in height. It is common to stagger maize plantings to ensure a continuous harvest over the summer months.
requirements: Maize is best grown in warm, tropical and sub-tropical regions as it requires warm soils to develop optimally. One of the most important requirements for growing maize is a high quality soil which is deep, fertile and well-draining with a pH between 6.0 and 6.8. Maize plants are very heavy feeders and even the most fertile of soils may need to supplemented with nutrients as the plants develop, particularly nitrogen. Maize also requires plenty of space as it grows and is pollinated by wind. It should be planted where it will receive full sunlight for most of the day and provided with ample moisture.
uses: Corn and cornmeal (dried, ground maize) are staple foods in countries all over the world. The ears can be cooked and eaten from the cob as a vegetable or the kernels can be removed and either eaten as is or used to produce a wide variety of foodstuffs including cereals and flour. Cor is also a major source of starch, which can be processed into oils and high fructose corn syrup. Maize is also commonly grown as feed for livestock.
References: https://plantvillage.psu.edu/topics/corn-maize/infos
comment: Corn is an annual grass in the family Poaceae and is a staple food crop grown all over the world. The maize plant possesses a simple stem of nodes and internodes. A pair of large leaves extend off of each internode and the leaves total 8-21 per plant. The leaves are linear or lanceolate (lance-like) with an obvious midrib (primary vein) and can grow from 30 to 100 cm (11.8-39.4 in) in length. The male and female inflorescences (flower bearing region of the plant) are positioned separately on the plant. The male inflorescence is known as the 'tassel' while the female inflorescence is the 'ear'. The ear of the maize is a modified spike and there may be 1-3 per plant. The maize grains, or 'kernels', are encased in husks and total 30-1000 per ear. The kernels can be white, yellow, red, purple or black. Maize is an annual plant, surviving for only one growing season and can reach 2-3 m (7-10 ft) in height. Maize may also be referred to as corn or Indian corn and is believed to originate from Mexico and Central America.

Corpus
type: Class
subclass of: Dataset
comment: A corpus may contain texts in a single language (monolingual corpus) or text data in multiple languages (multilingual corpus).

Cotton
type: Class
subclass of: OtherCrops
type: NamedIndividual
type: Crop
description: Cotton is the collective name given to four species of plants in the genus Gossypium, Gossypium hirsutum, Gossypium barbadense, Gossypium arboreum and Gossypium herbaceum which are perennial shrubs in the family Malvaceae grown for the fluffy fiber which protects the seeds of the plant. G. hirsutum accounts for approximately 90% of world wide cotton production today. Cotton plants possess a main stem giving rise to several branches at the top. The leaves of the plant are spirally arranged on the branches, have long petioles and have 3-5 triangular lobes. The plant produces a single flower on each axillary branch which can be red-purple, yellow or white in color and forms a leathery, oval seed capsule, or 'boll' which is 2-6 cm (0.8-2.4 in) long. Mature bolls will usually split open to reveal the characteristic white cotton fibers and the seed. The cotton plant can reach heights of 1-1.5 m (3.3-4.9 ft) and is usually cultivated as an annual, surviving only one growing season. Cotton may also be referred to as tree cotton and its center of origin is unknown although the plant has diversified from Mexico, north-east Africa and Arabia and Australia.
harvesting: Cotton is still picked by hand in many areas of the world where the crop is grown. In the US cotton is machine harvested. Cotton in ready to harvest approximately 4 months after sowing when the bolls split open to reveal the white cotton fibers. Fields are usually picked once every 3 to 4 weeks to prevent the fibers remaining in the field too long where they are susceptible to pests. The entire field is usually harvested with 2 to 3 pickings.
maintenance: The cotton field should be kept free from weeds and where the crop does not receive an adequate amount of water from rainfall, additional irrigation should be provided. Demand for nutrients is dependent on the type of soil on which the cotton is being grown. Cotton growing in acidic soils the plants will have a greater demand for nitrogen and phosphorus, whereas in sandy soil, potassium will be of greater importance. It is not recommended to grow cotton in the same field for more than three years and the crop should be rotated to prevent the build-up of diseases in the soil.
propagation: Cotton is propagated from seed by planting directly in a prepared field when growing conditions are favorable (suitable temperature, adequate rainfall etc). Cotton should only be planted when the soil has warmed to at least 18.3oC (65oF). Generally, seeds should be sown at a depth of 0.25 cm (1 in), with 3-6 seed sown in each hole. Ridging the soil is recommended as it helps to drain the plant in wet conditions and also to conserve water in dry conditions. Plant spacing depends on the variety but generally 20-100 cm (7.9-39.4 in) should be left between plants.
requirements: Cotton is best grown in desert conditions using irrigation. The seeds will germinate optimally at 34oC (93.2oF), while the seedlings requires a temperature between 24 and 29oC (75.2-84.2oF) to grow and develop properly. Cotton will grow on a variety of soils including sandy soil and heavy clay as long as it is water permeable and will grow optimally in a soil with a pH of 5.5-8.5. In addition cotton has a high tolerance for salt.
uses: The major use of cotton today is in the textile industry, the fibers or 'lints' of the cotton plant are harvested and woven into fabric for the production of clothing, towels, bed sheets and many other textiles. Cotton fiber may also be used in the production of yarn and twine. The cotton seeds can be used to extract oil for use in the production of shortening or cooking oil and the manufacture of soaps and lubricants. The seed may be used as a feed for livestock. The fuzz produced as a byproduct of the ginning process can be used in the upholstery industry.
comment: Cotton is the collective name given to four species of plants in the genus Gossypium, Gossypium hirsutum, Gossypium barbadense, Gossypium arboreum and Gossypium herbaceum which are perennial shrubs in the family Malvaceae grown for the fluffy fiber which protects the seeds of the plant. G. hirsutum accounts for approximately 90% of world wide cotton production today. Cotton plants possess a main stem giving rise to several branches at the top. The leaves of the plant are spirally arranged on the branches, have long petioles and have 3-5 triangular lobes. The plant produces a single flower on each axillary branch which can be red-purple, yellow or white in color and forms a leathery, oval seed capsule, or 'boll' which is 2-6 cm (0.8-2.4 in) long. Mature bolls will usually split open to reveal the characteristic white cotton fibers and the seed. The cotton plant can reach heights of 1-1.5 m (3.3-4.9 ft) and is usually cultivated as an annual, surviving only one growing season. Cotton may also be referred to as tree cotton and its center of origin is unknown although the plant has diversified from Mexico, north-east Africa and Arabia and Australia.
References: https://plantvillage.psu.edu/topics/cotton/infos

Country
type: Class
subclass of: Location
type: NamedIndividual
type: Location
comment: A country is a political state, nation, or controlled territory. It is often referred to as the land of an individual's birth, residence, or citizenship.

Crabapple
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
inherits: Apple
comment: Crabapple (plural crabapples) Any of the wild species of apple tree, genus Malus, which generally yield small, bitter fruit (in comparison to domestic apples, Malus domestica). The fruit of this tree. An unpleasant person; a person who is crabby.
References: https://en.wikipedia.org/wiki/Malus

Cranberry
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
description: Cranberry is the name given to a group of long-lived woody perennial shrubs or trailing vines in the genus Vaccinium, including Vaccinium macrocarpon, the most popular commercial species, grown for their edible berries. The cranberry bush possesses a slender, wiry stems with small elliptical waxy evergreen leaves 7–10 mm (0.28–0.45 in) in length. The plant produces distinctive dark pink flowers with reflexed petals that leave the style and stamens exposed. The fruit is produced on the new plant growth produced that season and are initially white and ripen to dark red, waxy berries measuring 9–14 mm (0.4–0.6 in) in diameter. The cranberry plant can reach 5–20 cm (2.0–7.9 in) in height, with the vines stretching up to 2 m (6.6 ft) long.
requirements: Cranberries are wetland plants which grow best in bogs, swamps and poorly draining soils. The plants grow best in acidic, organic or sandy soils with a pH between 4.0 and 5.5 where there is a plentiful water supply. The bed should have a base material which is impermeable to water such as clay or peat. Commercially grown cranberries are grown in large beds surrounded by dykes that hold water in the beds when flooded for harvest. Cranberry is very susceptible to frost and flooding is also used to protect the plants along with sprinkler irrigation. This means that it is the water that will freeze and the resultant heat produced by the change of liquid to solid (latent heat of fusion) protects the plants from freezing themselves. It is common for cranberry beds to be covered with up to 25 cm (9.8 in) of ice.
uses: Cranberries are usually processed into juice, sauce, jam and sweetened dried cranberries. They may also eaten raw. Cranberries have long been used as a remedy to prevent recurrent urinary tract infections, research has demonstrated the benefit to women, but the effect on other groups is less clear.
comment: Cranberry is the name given to a group of long-lived woody perennial shrubs or trailing vines in the genus Vaccinium, including Vaccinium macrocarpon, the most popular commercial species, grown for their edible berries. The cranberry bush possesses a slender, wiry stems with small elliptical waxy evergreen leaves 7–10 mm (0.28–0.45 in) in length. The plant produces distinctive dark pink flowers with reflexed petals that leave the style and stamens exposed. The fruit is produced on the new plant growth produced that season and are initially white and ripen to dark red, waxy berries measuring 9–14 mm (0.4–0.6 in) in diameter. The cranberry plant can reach 5–20 cm (2.0–7.9 in) in height, with the vines stretching up to 2 m (6.6 ft) long.
References: https://plantvillage.psu.edu/topics/cranberry/infos

Crop
type: Class
subclass of: Physical Entity
type: NamedIndividual
comment: A crop is a plant or animal product that can be grown and harvested extensively for profit or subsistence. Crop may refer either to the harvested parts or to the harvest in a more refined state. Most crops are cultivated in agriculture or aquaculture. A crop may include macroscopic fungus (e.g. mushrooms), or alga (algaculture). Most crops are harvested as food for humans or fodder for livestock.

Crop Attribute
type: Class
subclass of: Physical Entity

Crop Cover
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute

Crop Disease
type: Class
subclass of: Crop Problem
type: NamedIndividual
type: Crop Problem
comment: Crop disease involves any harmful deviation or alteration from the normal functioning of the physiological processes.

Crop Imagery
type: Class
subclass of: Imagery
type: NamedIndividual
type: Imagery
comment: Crop Images are images of plants or plant parts

Crop Injure
type: Class
subclass of: Crop Problem
type: NamedIndividual
type: Crop Problem
comment: Crop Injure case in general.

Crop Name
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute
comment: Name of plant

Crop Part
type: Class
subclass of: Physical Entity
comment: Crop part contains parts of plants.

Crop Pest
type: Class
subclass of: Crop Problem
type: NamedIndividual
type: Crop Problem
comment: Insect pests are those insects that feed on, compete for food with, or transmit diseases to plants.
label: Insect Pest

Crop Problem
type: Class
subclass of: Physical Entity
comment: Crop problem involves any harmful deviation or alteration from the normal functioning of the physiological processes. It includes disease, stress, pests or weeds.

Crop Protection
type: Class
subclass of: Physical Entity
comment: Crop protection is the aspects of managing plant diseases, weeds and other pests (both vertebrate and invertebrate) that damage agricultural crops and forestry.

Crop Rotation
type: Class
subclass of: Cropping
type: NamedIndividual
type: Cropping
comment: Crop rotation is the practice of growing a series of different types of crops in the same area across a sequence of growing seasons.

Crop Row
type: Class
subclass of: Cropping
type: NamedIndividual
type: Cropping
comment: A row crop is a crop that can be planted in rows wide enough to allow it to be tilled or otherwise cultivated by agricultural machinery, machinery tailored for the seasonal activities of row crops. Such crops are sown by drilling rather than broadcasting.

Crop Stress
type: Class
subclass of: Crop Problem
type: NamedIndividual
type: Crop Problem
comment: Crop stresses include abiotic viz., drought, water logging, salinity, extremes of temperature, high variability in radiation, subtle but perceptible changes in atmospheric gases and biotic viz., insects, birds, other pests, weeds, pathogens (viruses and other microbes).

Cropping
type: Class
subclass of: Abstract Entity
type: NamedIndividual
comment: Cropping is activities in agriculture, such as crop growing and harvesting and their related figures.

Crude Oil Content
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
label: Crude oil content in plant

Cucumber
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Vegetable
description: Cucumber, Cucumis sativus, is a warm season, vining, annual plant in the family Cucurbitaceae grown for its edible cucumber fruit. The cucumber plant is a sprawling vine with large leaves and curling tendrils. The plant may have 4 or 5 main stems from which the tendrils branch. The leaves of the plant are arranged alternately on the vines, have 3-7 pointed lobes and are hairy. The cucumber plant produces yellow flowers that are 4 cm (1.6 in) in diameter. The cucumber fruit varies in shape but is generally a curved cylinder rounded at both ends that can reach up to 60 cm (24 in) in length 10 cm (3.9 in) in diameter. Cucumber plants are annual plants, surviving only one growing season and the vines can reach up to 5 m (16.4 ft) in length. Cucumber may also be referred to as gherkin and originates from the foothills of the Himalayas, likely in India.
harvesting: Cucumbers should be harvested from the plant when they are still immature and green in color. Mature fruits are yellow and the flesh is often tough with woody seeds. The size of cucumbers at harvest depends on the variety of the cucumber being grown and what it is to be used for. Cucumbers for pickling are generally picked when they are less than 5 cm (2 in) long whereas burpless cucumbers for slicing should be allowed to reach approximately 4 cm (~1.5 in) in diameter. It is important to remove any fruits nearing maturity to ensure the plant remains productive. Their rapid growth means that cucumbers may need harvested every couple of days.
maintenance: Cucumber vines are sprawling and require plenty space to grow. Vines can be trained to grow on a trellis or fence. Providing burpless varieties with vertical support allows the fruits to hang loose and grow straight. Cucumbers also require a continuous supply of water and where drip irrigation is not being used, plants should be watered deeply once per week, providing at least an inch of water. Shallow watering or watering less frequently will reduce fruit yields. Mulches can be used to conserve soil moisture and black plastic mulch has the advantage of warming the soil.
propagation: Direct seeding is the preferred method for sowing cucumbers as they do not transplant well. Seeds should be sown after the last frosts and when the soil has warmed to at least 15.6oC (60oF). Sow seeds 1.3-2.5 cm (0.5-1.0 in) deep, thinning to a spacing of at least 30 cm (12 in) between plants after germination. Cucumbers can also be seeded on hills or mounds of soil to encourage warm soil and better drainage. In this instance, seeds should be sown on hills in groups of 4-6 seeds, allowing 1.2 m (4 ft) between each group in all directions. After emergence, thin the seedlings to 1 or 2 plants per hill. Cucumber seeds should germinate in 4-13 days depending on the soil temperature.
requirements: Cucumbers require warm, dry conditions to develop optimally, preferring both warm days and warm nights and growing best at a temperature of 30oC (86oF). Cucumbers will yield best if grown in a fertile, well-draining soil, rich in organic matter and with a pH between 6.5 and 7.5. Cucumbers are very sensitive to cold and should be planted in full sun and provided with ample soil moisture due to their shallow root system.
uses: Although technically a fruit, cucumbers are used as a fresh vegetable, consumed fresh in salads. Some varieties are grown specifically for pickling. Yellow varieties are generally cooked before consumption.
variety: One of the biggest considerations when choosing a cucumber variety is whether or not it requires pollinating. Many newer cucumber varieties are gynoecious which means that they produce only, or mostly, female flowers. Some gynoecious varieties require pollinating with male flowers, in which case a proportion of the seeds in the packet will be pollinator plants which produce the male flowers. Some gynoecious varieties are parthenocarpic which means that they do not need the male plants to produce fruit. These types are recommended for growing in glasshouses as they do not require the presence of insect pollinators.
comment: Cucumber, Cucumis sativus, is a warm season, vining, annual plant in the family Cucurbitaceae grown for its edible cucumber fruit. The cucumber plant is a sprawling vine with large leaves and curling tendrils. The plant may have 4 or 5 main stems from which the tendrils branch. The leaves of the plant are arranged alternately on the vines, have 3-7 pointed lobes and are hairy. The cucumber plant produces yellow flowers that are 4 cm (1.6 in) in diameter. The cucumber fruit varies in shape but is generally a curved cylinder rounded at both ends that can reach up to 60 cm (24 in) in length 10 cm (3.9 in) in diameter. Cucumber plants are annual plants, surviving only one growing season and the vines can reach up to 5 m (16.4 ft) in length. Cucumber may also be referred to as gherkin and originates from the foothills of the Himalayas, likely in India.
References: https://plantvillage.psu.edu/topics/cucumber/infos

Cultivated Row
type: Class
subclass of: Field
type: NamedIndividual
type: Field
is transformation of: Boundary
comment: A cultivated row is a rown on the field, in which crop can be planted in rows wide enough to allow it to be tilled or otherwise cultivated by agricultural machinery, machinery tailored for the seasonal activities of row crops.

Cultivator
type: Class
subclass of: Agricultural Machinery
type: NamedIndividual
type: Machine
comment: A cultivator is any of several types of farm implement used for secondary tillage.

Cultural Measures
type: Class
subclass of: Treatment

Customer
type: Class
subclass of: Organization
comment: A customer is a company or person who buys goods or services from a shop or business.

DNA
type: Class
type: NamedIndividual
type: Crop Part

Damping off
type: Class
subclass of: Symptom
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
life cycle: The fungi that cause damping-off are ubiquitous and can multiply and readily infect seedling when conditions favor their development. There is no recommended cure for damping off. Survivors are generally weak and non-productive. However, there are actions which can reduce the likelihood of the condition reoccurring. These include elimination of pathogens and control of environmental conditions.
management: Management Strategies 1. Sanitation. Dispose of all plants and soil in the affected containers and washing all containers and tools with soapy water and soak/rinse with a 1:10 solution of bleach to water. 2. Start clean. When starting seeds always use a sterile soil-less growing mix. Do not use garden soil or reuse contaminated soil. 3. Use seeds treated with a fungicide, if available, or apply a layer of milled sphagnum moss on the soil surface. Milled sphagnum moss retards the growth of damping-off fungi. Placing a 1/8 inch layer on the soil after the seeds are planted can provide some protection to the young developing seedlings. 4. Provide an environment that is not conducive to the development of the disease. After seeds germinate place plants in an area with good air circulation. Remove plastic or glass coverings after half of the seeds have germinated. Bottom water seedlings and do not overwater. Thin out and fertilize seedlings after the first true leaves have developed.
symptoms: Damping-off is caused by a number of soil fungi in several genera including: Rhizoctonia, Pythium, Botrytis, Phytophthora, and Fusarium and is often accompanied by low rates of germination. Affected stem tissue on the seedlings may have black water-soaked lesions at the soil line which constrict the stem causing the seedlings to topple. Occasionally, white/pink/gray cottony fungus may appear at the base of the stem.
comment: When apparently healthy seedlings die suddenly soon after germination the likely cause is "damping-off". Species of Pythium and Rhizoctonia are most important among the fungi which cause damping off disease of seedlings. Either the stem is attacked near the soil level or the crown of roots. The attacked region becomes weak and thus is not able to bear the load of the upper portion of the seedling. Consequently the seedling collapses. It topples down and dies. The seedling of many plants such as chilli, tobacco, tomato and mustard are prone to the damping-off disease.

Data Cleaning Report
type: Class
subclass of: Report
References: CRISP-DM
comment: Activities : - Reconsider how to deal with observed type of noise. - Correct, remove or ignore noise. - Decide how to deal with special values and their meaning. The area of special values can give rise to many strange results and should be carefully examined. Examples of special values could arise through taking results of a survey where some questions were not asked or nor answered. This might result in a value of ‘99’ for unknown data. For example, 99 for marital status or political affiliation. Special values could also arise when data is truncated – e.g. ‘00’ for 100 year old people or all cars with 100,000 km on the clock. - Reconsider Data Selection Criteria (See Task 2.1) in light of experiences of data cleaning (i.e. one may wish include/exclude other sets of data). Good idea! Remember that some fields may be irrelevant to the data mining goals and therefore noise in those fields has no significance. However, if noise is ignored for these reasons, it should be fully documented as the circumstances may change later!"
comment: Describe the decisions and actions that were taken to address the data quality problems reported during the Verify Data Quality Task. The report should also address what data quality issues are still outstanding if the data is to be used in the data mining exercise and what possible affects that could have on the results.

Data Collection Report
type: Class
subclass of: Report
References: CRISP-DM
comment: This report describes how the different data sources identified in the inventory were captured and extracted. Topics to be covered: - Background of data. - List of data sources with broad area of required data covered by each. - For each data source, method of acquisition or extraction. - Problems encountered in data acquisition or extraction.

Data Description Report
type: Class
subclass of: Report
References: CRISP-DM
comment: Each dataset acquired is described. Topics to be covered: - Each data source described in detail. - List of tables (may be only one) or other database objects. - Description of each field including units, codes used, etc.

Data Exploration Report
type: Class
subclass of: Report
References: CRISP-DM
comment: Describes the data exploration and its results. Topics to be covered: - Background including broad goals of data exploration. For each area of exploration undertaken: - Expected regularities or patterns. - Method of detection. - Regularities or patterns found, expected and unexpected. - Any other surprises. - Conclusions for data transformation, data cleaning and any other pre-processing. - Conclusions related to data mining goals or business objectives. - Summary of conclusions.

Data Mining
type: Class
comment: Data mining is the process of finding anomalies, patterns and correlations (knowledge) within large data sets to predict outcomes.

Data Quality Report
type: Class
subclass of: Report
References: CRISP-DM
comment: This report describes the completeness and accuracy of the data. Topics to be covered: - Background including broad expectations about data quality. For each dataset: - Approach taken to assess data quality. - Results of data quality assessment. - Summary of data quality conclusions.

Data Selection
type: Class
subclass of: Data Mining
type: NamedIndividual
type: Data Mining

Data Transformation
type: Class
subclass of: Transformation

Dataset
type: Class
subclass of: Information Object
type: NamedIndividual
comment: A data set (or dataset) is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as height and weight of an object, for each member of the data set.

Dataset Description Report
type: Class
subclass of: Report
References: CRISP-DM
comment: Each dataset acquired is described. Topics to be covered: - Each data source described in detail. - List of tables (may be only one) or other database objects. - Description of each field including units, codes used, etc.

Day Of Year
type: Class
subclass of: Time
comment: The day of the year

Daytime
type: Class
subclass of: Time
comment: The daytime is the part of a day between the time when it gets light and the time when it gets dark.

Deep Electrical Conductivity (ECdp)
type: Class
subclass of: Soil Conductivity
type: NamedIndividual
type: Soil
unit: mS/m
Acronym: ECdp
comment: Soil conductivity measures the total conductivity in the sample at the deep layer 20‐ to 40‐cm.

Deep Soil
type: Class
subclass of: Soil Conductivity
type: NamedIndividual
type: Soil
has transformation: Deep Soil by Default

Density
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute
comment: Crop density or plant density is such that competition between plants does not restrict yield and the potential plant population is available to produce the maximum yield for the conditions under which the crop is being grown.
alternative label: Crop Density

Diameter
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute

Diesel Fuel
type: Class
subclass of: Energy
type: NamedIndividual
type: Energy
comment: Diesel fuel in general is any liquid fuel specifically designed for use in diesel engines, whose fuel ignition takes place, without any spark, as a result of compression of the inlet air mixture and then injection of fuel. Therefore, diesel fuel needs good compression ignition characteristics.

Digital Camera
type: Class
subclass of: Sensor
type: NamedIndividual
type: Sensor
comment: A camera is an optical instrument used to record images.

Dimension Reduction
type: Class
subclass of: Data Selection
comment: Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension.

Disaster
type: Class
subclass of: Event
comment: Disaster is a sudden accident or a natural catastrophe that causes great damage or loss of environment or plants.

Dissolved Oxygen
type: Class
subclass of: Water
type: NamedIndividual
type: Water
Acronym: DO

Document
type: Class
subclass of: Information Object
comment: A collection of information content entities intended to be understood together as a whole. Document can be printed or electronic matter that provides information or evidence or that serves as an official record.

Documenting
type: Class
subclass of: Planned Process
References: CRISP-DM

Drilling
type: Class
subclass of: CultivatingProcess
comment: Drilling is the action used for the mechanized sowing of an agricultural crop.

Drilling Date
type: Class
subclass of: Cropping
type: NamedIndividual
type: Cropping
comment: The time of drilling or sowing.

Drone
type: Class
subclass of: Sensor Station
type: NamedIndividual
type: Sensor Station
type: Vehicle
comment: A drone is a type of aircraft that does not have a pilot and is controlled by someone on the ground.

Drought
type: Class
subclass of: Planetary Phenomena
type: NamedIndividual
type: Planetary Phenomena
comment: A drought or drouth is an event of prolonged shortages in the water supply, whether atmospheric (below-average precipitation), surface water or ground water. A drought can last for months or years, or may be declared after as few as 15 days. It can have a substantial impact on the ecosystem and agriculture of the affected region and harm to the local economy. Annual dry seasons in the tropics significantly increase the chances of a drought developing and subsequent bush fires. Periods of heat can significantly worsen drought conditions by hastening evaporation of water vapour. Drought is a recurring feature of the climate in most parts of the world.

Drought Stress
type: Class
subclass of: Crop Stress
type: NamedIndividual
type: Crop Disease
type: Crop Stress
related to: Temperature
related to: Water
comment: Drought stress defined as naturally occurring water deficit is one of the main causes of crop losses within the agricultural world. This is due to water's necessity in so many fundamental processes in plant growth.

Dry Weight
type: Class
subclass of: Weight
comment: The weight of any plant (or other organism) part after all its water content has been removed by drying. The dry weight is an estimate of a body or of a body samples weight when all water has been removed, means the weight of the sample, excluding the weight of the water in the sample.

Ear
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: An ear is the grain-bearing tip part of the stem of a cereal plant, such as wheat or maize. It can also refer to "a prominent lobe in some leaves.

Earthquake
type: Class
subclass of: Planetary Phenomena
type: NamedIndividual
type: Planetary Phenomena
comment: An earthquake (also known as a quake, tremor or temblor) is the shaking of the surface of the Earth resulting from a sudden release of energy in the Earth's lithosphere that creates seismic waves. Earthquakes can range in size from those that are so weak that they cannot be felt to those violent enough to propel objects and people into the air, and wreak destruction across entire cities.

Eggplant
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Watermelon
description: Eggplant, Solanum melongena, is a tropical, herbaceous, perennial plant, closely related to tomato, in the family Solanaceae which is grown for its edible fruit. The plants has a branching stem and simple, long, flat. coarsely lobed leaves which are green in color and are arranged alternately on the branches. The leaves can measure 10 to 20 cm (4–8 in) long and 5 to 10 cm (2–4 in) broad. The plant produces purple flowers which are 3–5 cm (1.2–2.0 in) in diameter. The fruit is a large, fleshy ovoid berry which can reach 40 cm (15.7 in) in length, with glossy smooth skin and numerous small seeds. The color of the fruit is variable and can be white, green, yellow, purple or black. Eggplants can reach up to 1.5 m (4.9 ft) in height and although they are perennial plants, they are most commonly grown as annuals. Eggplant may also be referred to as aubergine or guinea squash and originates from the Indian subcontinent.
harvesting: Eggplant fruits are ready to harvest while the flesh is still firm and seeds are small and tightly packed. The skin of the fruit should be firm, glossy and dark purple. Over ripe fruit will contain darker seeds and will taste bitter. Harvest the fruit as soon as it is ripe to ensure maximum productivity. The fruit should be removed from the plant by cutting the calyx (green stem above the fruit) with a sharp knife.
maintenance: Eggplant may benefit from the addition of mulch to conserve soil moisture and maintain a higher soil temperature. Row covers will help to increase the temperature around the plants in cooler climates and during cool spells in otherwise warm areas. The row covers should be removed to allow pollinators to access the plants during flowering. Eggplant should be provided with a steady water supply for optimum development of fruit and the soil around the plants should not be allowed to dry out but should also not be wet. Plants can be laden with numerous fruit and the use of stakes and supports can help to support the plants before harvest.
propagation: In cold areas and regions with a short growing season it is necessary to start eggplant indoors or in a glasshouse. In addition, eggplant will often perform much better in colder areas if planted in containers or grown under row covers as this helps to keep the soil warm. Seeds should be sown indoors 6 to 8 weeks before the last frost date. Sow seeds thickly in seed trays containing good quality sterile seed starting mix to a depth of 6 mm (0.25 in). Keep the trays moist and provide bottom heat by placing on a heat mat or in a warm area of the house. Seedling should be potted into larger pots when they have two sets of true leaves. Eggplant seedlings can be transplanted after hardening-off and when all danger of frost has passed in your area. Seedlings should be spaced 45–60 cm (18–24 in) apart, depending on variety, allowing a further 60–90 cm (24–36 in) between rows.
requirements: Eggplants are warm-season crops which require a long growing season.They grow best in regions where the daytime temperature is between 26 and 32°C (80–90°F) and night time temperatures around 21°C (70°F). In addition, eggplant is a sun loving plant and should be positioned in an area that receives full sunlight. Plants will perform best when planted in a fertile soil with a pH between 6.3 and 6.8.
uses: Eggplant is usually cooked before consumption and is eaten as a vegetable.
comment: Eggplant, Solanum melongena, is a tropical, herbaceous, perennial plant, closely related to tomato, in the family Solanaceae which is grown for its edible fruit.

Electrical Conductivity
type: Class
subclass of: Soil Conductivity
type: NamedIndividual
type: Soil
has transformation: Electrical Conductivity by Default
unit: dS/m
Acronym: EC
comment: Electric Conductivity of Soil. Soil electrical conductivity (EC) is a measurement that correlates with soil properties that affect crop productivity, including soil texture, cation exchange capacity (CEC), drainage conditions, organic matter level, salinity, and subsoil characteristics.

Electricity
type: Class
subclass of: Energy
type: NamedIndividual
type: Energy
comment: Electricity is the set of physical phenomena associated with the presence and motion of matter that has a property of electric charge. Electricity is related to magnetism, both being part of the phenomenon of electromagnetism, as described by Maxwell's equations. Various common phenomena are related to electricity, including lightning, static electricity, electric heating, electric discharges and many others.

Elevation
type: Class
subclass of: Terrain
type: NamedIndividual
type: Terrain
comment: Elevation is the height above a sea level, computing by the distance above sea level.

Energy
type: Class
subclass of: Environment
comment: Energy is the quantitative property that must be transferred to an object in order to perform work on, or to heat, the object.

Environment
type: Class
subclass of: Physical Entity
comment: Environment is surroundings or conditions in which plants grow. It effects different farming practices which has on the ecosystems around them, and how those effects can be traced back to those practices.

Essential Nutrient
type: Class
subclass of: Nutrient
comment: A nutrient in which an organism must obtain from the environment or from a dietary source since the organism is unable to synthesize it (or can produce but inadequately)

Essential Oil
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: An essential oil is a concentrated hydrophobic liquid containing volatile (easily evaporated at normal temperatures) chemical compounds from plants. Essential oils are also known as volatile oils, ethereal oils, aetheroleum, or simply as the oil of the plant from which they were extracted, such as oil of clove. An essential oil is "essential" in the sense that it contains the "essence of" the plant's fragrance—the characteristic fragrance of the plant from which it is derived.

Evaluation
type: Class
subclass of: Data Mining
type: NamedIndividual
type: Data Mining

Evaluation Metric
type: Class
subclass of: Computing Algorithm
comment: Evaluation metrics are used to measure the quality of the statistical or machine learning model.

Evaluation Report
type: Class
subclass of: Report
References: CRISP-DM
comment: Assessment of data mining results with respect to business success criteria. This report compares the data mining results with the business objectives and the business success criteria. Topics to be covered: - Review of Business Objectives and Business Success Criteria (which may havechanged during and/or as a result of data mining). For each Business Success Criterion: - Detailed comparison between success criterion and data mining results. - Conclusions about achievability of success criterion and suitability of data mining process. - Review of Project Success; has the project achieved the original Business Objectives? - Are there new business objectives to be addressed later in the project or in new projects? - Conclusions for future data mining projects. Review of process This section assesses the effectiveness of the project and identifies any factors that may have been overlooked that should be taken into consideration if the project is repeated. List of possible actions This section makes recommendations regarding the next steps in the project."

Evaporation
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
comment: Evaporation is a type of vaporization that occurs on the surface of a liquid as it changes into the gas phase. The surrounding gas must not be saturated with the evaporating substance. When the molecules of the liquid collide, they transfer energy to each other based on how they collide with each other. When a molecule near the surface absorbs enough energy to overcome the vapor pressure, it will escape and enter the surrounding air as a gas.[2] When evaporation occurs, the energy removed from the vaporized liquid will reduce the temperature of the liquid, resulting in evaporative cooling.

Evapotranspiration
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
Acronym: ET
comment: Evapotranspiration (ET) is the sum of evaporation and plant transpiration from the Earth's land and ocean surface to the atmosphere. Evaporation accounts for the movement of water to the air from sources such as the soil, canopy interception, and water bodies. Transpiration accounts for the movement of water within a plant and the subsequent loss of water as vapor through stomata in its leaves in vascular plants and phyllids in non-vascular plants. Evapotranspiration is an important part of the water cycle. An element (such as a tree) that contributes to evapotranspiration can be called an evapotranspirator. Evapotranspiration (ET) is a key component of water balance and is closely linked to ecosystem productivity.

Event
type: Class
subclass of: Information Object
References: http://www.loa-cnr.it/ontologies/DUL.owl#Event
comment: Event is a thing that happens or takes place, especially one of importance. Any physical, social, or mental process, event, or state. More theoretically, events can be classified in different ways, possibly based on 'aspect' (e.g. stative, continuous, accomplishement, achievement, etc.), on 'agentivity' (e.g. intentional, natural, etc.), or on 'typical participants' (e.g. human, physical, abstract, food, etc.). Here no special direction is taken, and the following explains why: events are related to observable situations, and they can have different views at a same time. If a position has to be suggested here anyway, the participant-based classification of events seems the most stable and appropriate for many modelling problems.

Exchangeable Calcium (Caex)
type: Class
subclass of: Soil Composition
type: NamedIndividual
type: Soil
unit: Meq/kg
unit: meq l−1
Acronym: Caex
comment: Ca_ex

Exchangeable Magnesium (Mgex)
type: Class
subclass of: Soil Composition
type: NamedIndividual
type: Soil
unit: Meq/kg
unit: meq l−1
comment: Mg_ex
Acronym: Mgex

Exchangeable Potassium (Kex)
type: Class
subclass of: Soil Composition
type: NamedIndividual
type: Soil
unit: Meq/kg
unit: meq l−1
Acronym: Kex

Exchangeable Sodium (Naex)
type: Class
subclass of: Soil Composition
type: NamedIndividual
type: Soil
unit: Meq/kg
unit: meq l−1
Acronym: Naex

Exhibition Event
type: Class
subclass of: Event
comment: An exhibition is an organized event in which a collection of special objects can be viewed by the public.

Farm
type: Class
subclass of: Physical Entity
comment: A farm is an area of land that is devoted primarily to agricultural processes with the primary objective of producing food and other crops; it is the basic facility in food production. The name is used for specialised units such as arable farms, vegetable farms, fruit farms, dairy, pig and poultry farms, and land used for the production of natural fibres, biofuel and other commodities. It includes ranches, feedlots, orchards, plantations and estates, smallholdings and hobby farms, and includes the farmhouse and agricultural buildings as well as the land

Farmer
type: Class
subclass of: Person
comment: A farrmer is a person who works on a farm.

Farmyard Manure
type: Class
subclass of: Organic Mature
type: NamedIndividual
type: Organic Mature

Fat
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Fat means any ester of fatty acids, or a mixture of such compounds, most commonly those that occur in living beings or in food.

Fatty Acid
type: Class
subclass of: Essential Nutrient
type: NamedIndividual
type: Essential Nutrient
comment: A fatty acid is a carboxylic acid with a long aliphatic chain, which is either saturated or unsaturated. Most naturally occurring fatty acids have an unbranched chain of an even number of carbon atoms, from 4 to 28.[1] Fatty acids are usually not found in organisms in their standalone form, but instead exist as three main classes of esters: triglycerides, phospholipids, and cholesteryl esters. In any of these forms, fatty acids are both important dietary sources of fuel for animals and they are important structural components for cells.

Feature Selection
type: Class
subclass of: Data Selection
comment: Feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction.

Fertilizer
type: Class
subclass of: Physical Entity
type: NamedIndividual
comment: A fertilizer is any material of natural or synthetic origin (other than liming materials) that is applied to soil or to plant tissues to supply one or more plant nutrients essential to the growth of plants

Festival
type: Class
subclass of: Event
comment: A festival is an event ordinarily celebrated by a community and centering on some characteristic aspect of that community and its religion or cultures.

Field
type: Class
subclass of: Farm
type: NamedIndividual
type: Farm
comment: Field is an area of land, enclosed or otherwise, used for agricultural purposes such as cultivating crops or as a paddock or other enclosure for livestock.

Field Capacity (FC)
type: Class
subclass of: Soil Physical
type: NamedIndividual
type: Soil Physical
Acronym: FC

Field Imagery
type: Class
subclass of: Imagery
type: NamedIndividual
type: Imagery
comment: Images of fields, which are taken by Drones, UAV, or satelites from at least 50 m above ground level.

Field Part
type: Class
subclass of: Farm

Flower
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
has transformation: Flower by Flowering
comment: A flower, sometimes known as a bloom or blossom, is the reproductive structure found in flowering plants. The biological function of a flower is to facilitate reproduction, usually by providing a mechanism for the union of sperm with eggs. Flowers may facilitate outcrossing (fusion of sperm and eggs from different individuals in a population) resulting from cross pollination or allow selfing when self-pollination occurs.

Flowering
type: Class
subclass of: Plant Phenomenon
type: NamedIndividual
type: Growth Stage
type: Plant Phenomenon
related to: Wheat
comment: Stage 6: Flowering, anthesis 61 Beginning of flowering: first anthers visible 65 Full flowering: 50% of anthers mature 69 End of flowering: all spikelets have completed flowering but some dehydrated anthers may remain Growth Stages of Sorghum: Seeding, Emergence, Three-leaf, Five-leaf, Growing point differentation, Flag leaf visible, Booting, Flowering, Soft Dough, Hard Dough, Physiological maturity
comment: Flowering is the act or state of producing flowers : the period during which a plant produces flowers the flowering of the iris a plant that should be pruned only when flowering ends For sorghum plants, flowering or blooming is the most critical stage in the life of the sorghum plant. A plant begins flowering from the top of the panicle and progresses downward. A field of sorghum is in the flowering stage when blooming has progressed halfway down the panicle in 50 percent of the plants. At this stage, the peduncle is still elongating, and it typically takes four to nine days for a single panicle to complete the flowering process. Hybrids are rated on their maturity largely based on the length of time the plant takes to reach the flowering stage.

Flower
type: Class
subclass of: Crop
comment: A flower is the part of a plant which is often brightly coloured, grows at the end of a stem, and only survives for a short time.

Forest Tree
type: Class
subclass of: Crop

Fresh Weight
type: Class
subclass of: Weight
comment: Fresh weight = weight recorded immediately after the plant or part of a plant is harvested. For instance, the total yield of a tomato plant would be measured in fresh weight, if you want to know how much money you can get for the crop of tomatoes.

Frost
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
comment: Frost is a thin layer of ice on a solid surface, which forms from water vapor in an above-freezing atmosphere coming in contact with a solid surface whose temperature is below freezing, and resulting in a phase change from water vapor (a gas) to ice (a solid) as the water vapor reaches the freezing point. Plants that have evolved in warmer climates suffer damage when the temperature falls low enough to freeze the water in the cells that make up the plant tissue. The tissue damage resulting from this process is known as "frost damage". Farmers in those regions where frost damage is known to affect their crops often invest in substantial means to protect their crops from such damage.

Fruit
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: In botany, a fruit is the seed-bearing structure in flowering plants (also known as angiosperms) formed from the ovary after flowering.

Fruit Tree
type: Class
subclass of: Crop
type: NamedIndividual
type: Crop
comment: Fruits are the means by which angiosperms disseminate seeds. Edible fruits, in particular, have propagated with the movements of humans and animals in a symbiotic relationship as a means for seed dispersal and nutrition;

Fungal Disease
type: Class
subclass of: Crop Disease
type: NamedIndividual
type: Crop Disease
comment: Infectious plant diseases are caused by fungi and can range in severity from mild leaf or plant damage to death.

Fungicides
type: Class
subclass of: Crop Protection
comment: Fungicides are biocidal chemical compounds or biological organisms used to kill parasitic fungi or their spores.

Fungus
type: Class
subclass of: Microoganism
comment: A fungus (plural: fungi or funguses) is any member of the group of eukaryotic organisms that includes microorganisms such as yeasts and molds, as well as the more familiar mushrooms. These organisms are classified as a kingdom, which is separate from the other eukaryotic life kingdoms of plants and animals.

Garlic
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
has disease: Downy mildew
has disease: Mosaic
harvesting: Garlic is ready to harvest when the plants begin to turn yellow or brown and begin to fall over. Dig the plants while there are still some green leaves remaining on the plant. Harvest the bulbs by digging the plant carefully and lifting the bulbs using a fork. The garlic can be used straight away or it can be cured for longer storage. Garlic can be cured by hanging the plants in bunches or by spreading them out on a rack or screen. The plants should be kept intact while they cure, do not remove the tops until the garlic is dry. Curing should be carried out in a cool, dry place with good ventilation such as a barn, attic or garage. Once dry, the bulbs will keep for up to a year.
maintenance: When the ground begins to freeze, it is good practice to cover the garlic plants with a layer of straw mulch. This helps to protect the plants over winter, prevents frost heaving and helps to suppress weeds in the Spring. Garlic requires additional irrigation during dry periods but watering should be ceased a few weeks prior to harvest to allow the papery skin around the bulb to dry and to prevent the development of disease. Garlic benefits from the addition of fertilizer during the growing season. Nitrogen should be applied in early Spring, later applications may delay bulb development. Hardneck garlic should be pruned when the flowering stalks (scapes) begin to straighten. Removal of the flower head directs the plants energy to bulb production. Softneck garlic does not require pruning.
propagation: Most garlic varieties do not produce fertile seed so the plant is propagated from the cloves. Individual cloves are obtained by breaking apart the bulb. Generally, garlic should be planted in the Fall around the same time spring bulbs such as daffodils are planted. Planting in the Spring does not allow sufficient time for the roots system to develop and the garlic may not form heads. The soil should be prepared for planting by digging with a fork to loosen it and break up any large clumps. Cloves should be planted 5–8 cm (2-3 in) deep, leaving 8–10 cm (3.1–3.9 in) between individual plants and 15–20 cm (5.9–7.9 in) between rows. The cloves should be planted pointed side up with the basal plate positioned downwards. The roots will grow from the basal plate. Each clove will produce a whole head of garlic which can be harvested and cured before use.
requirements: Garlic is a hardy perennial which can be grown in a variety of soil types. The plants perform best when planted in a light, well draining, organic loam with a pH between 6.0 and 7.0. The plant grows well in cool weather but will tolerate a range from 9–28°C (48.2–82.4°F). Garlic should be planted in an area that receives full sun for most of the day. Garlic requires a period of cold followed by a period of light and heat in order to develop properly. The plants will perform best when they have 6-8 weeks below 4.4ºC (40ºF). It may be beneficial to chill bulbs prior to use if planting in area that does not fulfil these temperature requirements.
comment: Garlic, Allium sativum, is a an herbaceous, annual, bulbous plant in the family Amaryllidaceae grown for its pungent, edible bulb of the same name. The garlic plant can either have a short, woody central stem (hardneck) or a softer pseudostem made up of overlapping leaf sheaths (softneck). Hardneck varieties produce a false flower stock which is termed a 'scape' and produce larger garlic cloves but in smaller numbers. Softneck garlic is the most popular variety of garlic grown in the US. The bulb can be up to 7 cm (2.8 in) in diameter and is made up of 1–15 cloves. The stem is very short and flattened and gives way to a pseudostem, The garlic plant can possess 6–12 flat, blade-like leaves which can stretch up to 50 cm (19.7 in) long. The plant can reach 60 cm (23.6 in) in height and is an annual, surviving only one growing season. Garlic is believed to originate from Asia.
References: https://plantvillage.psu.edu/topics/garlic/infos

Gas
type: Class
subclass of: Climate
comment: Gas
comment: Gas refer to substances in a form like air used for energy (fire air) or greenhouse gases.

Genotype
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: A genotype is an individual's collection of genes. The term also can refer to the two alleles inherited for a particular gene. The genotype is expressed when the information encoded in the genes' DNA is used to make protein and RNA molecules.

Germination
type: Class
subclass of: Plant Phenomenon
type: NamedIndividual
type: Growth Stage
type: Plant Phenomenon
related to: Wheat
comment: Germination refers to the process by which an organism grows from a seed or a spore. The most common forms of germination include a seed sprouting to form a seedling and the formation of a sporeling from a spore. Thus, germination occurs primarily in plant and fungal species.
comment: Germination Stage 00 Dry seed 01 Start of imbibition 03 Imbibition complete 05 Radicle emerged from seed 07 Coleoptile emerged from seed 09 Leaf just at coleoptile tip
label: Germination Stage

Ginger
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Vegetable
comment: Ginger is a plant with leafy stems and yellowish green flowers. The ginger spice comes from the roots of the plant.

Glume
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: A glume is a bract (leaf-like structure) below a spikelet in the inflorescence (flower cluster) of grasses (Poaceae) or the flowers of sedges (Cyperaceae). There are two other types of bracts in the spikelets of grasses: the lemma and palea.

Gourd
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
description: Gourds are annual trailing or climbing vines in the family Cucurbitaceae grown for their fruit of the same name. The two most commonly grown types of gourd are Lagenaria species and Cucurbita species. Gourd plants produce long vines with long-stemmed, large, oval or triangular lobed leaves. The Cucurbita gourds produce yellow flowers and unusually shaped fruit which can be smooth or warty, plain or patterned. The Lagenaria gourds produce white flowers and smooth, knobly or ridged fruit which can range in size from 7 cm (3 in) up to 1 m (3 ft). Gourd vines are capable of climbing over 3.5 m (12 ft) and as annual plants, survive only one growing season. Gourds may also be referred to as dudi, cucuzzi, spaghetti squash or calabash and are believed to have originated in Africa.
maintenance: Gourd plants sprawling and require plenty space to grow. Vines can be trained to grow on a trellis or fence. Plants also require a continuous supply of water and where drip irrigation is not being used, plants should be watered deeply once per week, providing at least an inch of water. Shallow watering or watering less frequently encourages a shallow root system. Mulches can be used to conserve soil moisture and black polyethylene mulch has the advantage of warming the soil. Gourds produce both male and female flowers (monoecious) and are pollinated by insects such as bees. Poor fruit set is often caused by poor pollination.
propagation: Gourds can be direct seeded or sown indoors and transplanted. If direct seeding,seeds should be sown after the last frosts and when the soil has warmed to at least 15.6°C (60°F). Sow 1–2 seeds 1.3–2.5 cm (0.5–1.0 in) deep, at least 90 cm (~3 ft) apart if growing bush varieties and 120–150 cm (4–5 ft) apart if growing vining varieties. Allow a further 1–3 m (6–10 ft) between rows depending on the variety. If transplanting, seeds should be sown 3–4 weeks before the last frost date in your area and transplanted before the plants develop their second set of true leaves. Sowing seeds in 3–4 inch pots help to minimize disturbance to the roots prior to transplanting. Peat pots can be transplanted with the seedlings eliminating the need to disturb the roots entirely. Seeds in sown both indoors and out require lightly moist soil for germination, care should be taken to avoid overwatering. Seeds should germinate in 5–10 days depending on the soil temperature.
requirements: Gourds are warm-season crops, requiring lots of sun and good drainage to develop optimally. Plants will grow best at temperatures between 18 and 25°C (65–75°F) in a fertile, well-draining soil, rich in organic matter and with a pH between 6.5 and 7.5. Gourd plants should be planted in full sun and provided with ample soil moisture. Vining varieties can grow to very large sizes and require a good deal of space.
uses: Young gourds can be cooked and eaten as a vegetable while the mature gourds are used to make decorative items such as bottles, containers and utensils. In the US, gourds have become a very popular household decoration in the Fall.
comment: Gourds include the fruits of some flowering plant species in the family Cucurbitaceae, particularly Cucurbita and Lagenaria. The term refers to a number of species and subspecies, many with hard shells, and some without.

Grape
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
description: Grapevine (Vitis vinifera) is a long stemmed, woody vine (liana) which produces high value berries, or grapes. The vines can reach lengths in excess of 30 m and can live for many years with proper management. The leaves of the grape vine are alternately arranged on the stem and are long and broad with 5-7 lobes, typically reaching sizes of 5-20 cm (2.0-7.9 in). Flowers are produced in clusters and fruit. The fruit is a berry known as a grape and grows in clusters from the vine. In wild species, the fruit is 6 mm (1/5 in) in diameter and ripens to dark purple to black with a pale wax bloom. In cultivated plants, the berry is usually much larger, up to 3 cm (1.2 in) long and can be green, red or purple. Vitis vinifera is native to the Mediterranean region, central Europe, and southwestern Asia but is cultivated on every continent except Antarctica. Most grape cultivation centers on the use of Vitis vinifera, however, in North America the related species Vitis labrusca, Vitis riparia and Vitis rotundifolia are also grown. Vitis amurensis is native to Asia and has been hybridized with Vitis vinifera to produce cold tolerant grapevine varieties.
propagation: Preparation Grape vines are usually planted as dormant bare root vines in Spring. Young plants can be purchased from nurseries and garden centres for planting in the home garden. Grape vines require a trellis and this should be built before the vines are planted in the ground. For information on constructing a suitable trellis see: https://www.plantvillage.com/posts/192-grape-how-to-build-a-trellis-for-grape-vines?locale=en. The trellis helps support the weight of the fruit and protects the vines from damage while aslo increasing air circulation and reducing diseases in the canopy. You may also consider a more decorative method of supporting the vines, such as an arbor. Planting New vines should be planted out in Spring after all danger of frost has passed. Dig a hole for each plant approximately 30 cm (12 in) deep and 30 cm (12 in) wide, spaced 1.8- 3.0 m (6-10 ft) apart and plant the vine at the same level as the nursery. It is important not to cover the graft union in soil. Tamp the soil around the plants and add any remaining soil. The newly planted vines should be cut back to have only 2 or 3 new buds and watered lightly. Pruning Pruning is an essential component of healthy grape production and should be carried out annually in early Spring while the vines are still dormant and before the buds begin to swell. From the third year onwards, most of the previous years growth should be removed. The more buds that are left on each shoot, the more fruit it will produce but care must be taken to ensure that too many are not left as the fruit may not ripen as a result. Fruit clusters can be removed as required throughout the growing season.
requirements: The first consideration when attempting to cultivate grape is to select a variety based on the prevailing local climate, with the best production occurring in hot, dry regions. American varieties tend to be the most cold hardy while the European hybrids perform best in hotter, drier regions. Generally, vines should be grown in full sun, in a well draining soil and in a location where there is good circulating air to reduce incidence of disease. Low lying areas should be avoided when selecting a planting site as this can lead to water accumulation during periods of wet weathe Vines prefer a soil with a slightly acidic to neutral pH between 6.0 and 7.0 and require a trellis system to support the weight of the fruit on the vines.
uses: Grapes are the most widely produced commercial fruit crop in the world. They are often eaten fresh but are also commonly used to produce wine. Grapes can also be processed into jams, and preserves, juices, grape seed oil, grape seed extract, raisins and vinegar.
References: https://plantvillage.psu.edu/topics/grape/infos
comment: Grapevine (Vitis vinifera) is a long stemmed, woody vine (liana) which produces high value berries, or grapes. The vines can reach lengths in excess of 30 m and can live for many years with proper management. The leaves of the grape vine are alternately arranged on the stem and are long and broad with 5-7 lobes, typically reaching sizes of 5-20 cm (2.0-7.9 in). Flowers are produced in clusters and fruit. The fruit is a berry known as a grape and grows in clusters from the vine. In wild species, the fruit is 6 mm (1/5 in) in diameter and ripens to dark purple to black with a pale wax bloom. In cultivated plants, the berry is usually much larger, up to 3 cm (1.2 in) long and can be green, red or purple. Vitis vinifera is native to the Mediterranean region, central Europe, and southwestern Asia but is cultivated on every continent except Antarctica. Most grape cultivation centers on the use of Vitis vinifera, however, in North America the related species Vitis labrusca, Vitis riparia and Vitis rotundifolia are also grown. Vitis amurensis is native to Asia and has been hybridized with Vitis vinifera to produce cold tolerant grapevine varieties.

Grapevines
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
comment: Grapevines are actually part of the "espalier fruit" family and traditionally play a major role in the greening of facades. Due to the abundance of information, grapevines are treated separately at FassadeGrün: here you will find extensive information on grapevines-- for the house and in the garden. You can select climbing (trellis) systems, compare table grapes, learn how to prune and how to recognize diseases. FassadenGrün sells several grapevine varieties.

Grass
type: Class
subclass of: OtherCrops
type: NamedIndividual
type: Crop
type: OtherCrops

Grassland
type: Class
subclass of: Farm
type: NamedIndividual
type: Farm
type: Topography

Green Manure
type: Class
subclass of: Organic Mature
type: NamedIndividual
type: Organic Mature

Greenhouse
type: Class
subclass of: Farm
type: NamedIndividual
type: Farm
comment: A greenhouse (also called a glasshouse, or, if with sufficient heating, a hothouse) is a structure with walls and roof made chiefly of transparent material, such as glass, in which plants requiring regulated climatic conditions are grown.

Greenhouse Effect
type: Class
subclass of: Planetary Phenomena
type: NamedIndividual
type: Planetary Phenomena
comment: The greenhouse effect is the process by which radiation from a planet's atmosphere warms the planet's surface to a temperature above what it would be without this atmosphere.

Greenness
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Green is the color between blue and yellow on the visible spectrum. It is evoked by light which has a dominant wavelength of roughly 495–570 nm. By far the largest contributor to green in nature is chlorophyll, the chemical by which plants photosynthesize and convert sunlight into chemical energy.

Growing Season
type: Class
subclass of: Cropping
type: NamedIndividual
type: Cropping
comment: A growing season is the period of the year when crops and other plants grow successfully. The length of a growing season varies from place to place.

Growth Regulator
type: Class
subclass of: Crop Protection
comment: Growth Regulators are defined as small, simple chemicals produced naturally by plants to regulate their growth and development. Growth Regulators can be of a diverse chemical composition such as gases (ethylene), terpenes (gibberellic acid) or carotenoid derivates (abscisic acid). They are also referred to as plant growth substances, phytohormones or plant hormones.

Growth Stage
type: Class
subclass of: Cropping
type: NamedIndividual
type: Cropping
comment: In agronomy, the BBCH-scale for cereals' describes the phenological development of cereals using the BBCH-scale. Principal growth stages 0: Germination, sprouting, bud development 1: Leaf development 2: Formation of side shoots, tillering 3: Stem elongation or rosette growth, shoot development 4: Development of harvestable vegetative plant parts, bolting 5: Inflorescene emergence, heading 6: Flowering 7: Development of fruit 8: Ripening or maturity of fruit and seed 9: Senescence, beginning of dormancy

Guava
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
harvesting: Trees grown from seed may not come into production for anywhere between 3 to 8 years. Guava does not ripen off the tree and it can be difficult to distinguish when the fruits are ready for harvest. The best indication is a color change from dark to light green and the development of some yellowing on the fruits. Fruit should be harvested every 2-3 days to prevent fruit becoming overripe.
maintenance: Newly planted guava trees should be watered every two days after planting for the first week and then once a week for the following few months to allow the root system to develop and become established. A 0.6 to 1.5 m (2.0-5.0 ft) area around the trunk should be maintained to be free from grass and weeds. Guava trees benefit from the application of a layer or organic mulch such as bark or wood chips around the base. This helps to suppress weeds and conserve moisture in the soil. Do not mound the mulch around the trunk, allow a gap of 20 to 30 cm (8-10 in) between the trunk and the mulch layer. Young trees benefit from fertilizer application with the type an amount varying with area and soil type.
propagation: Guavas grown for processing can be grown from seed as approximately 70% of the seedlings will retain the genetic characteristics of the parent tree. Guavas that are grown for fresh fruit are usually vegetatively propagated by air layering or budding. Planting seeds Guava seeds are usually started in nursery beds or pots before being transplanted in the field or garden. Only seeds from healthy, vigorous trees with the desired characteristics should be planted. Seeds should be planted in flats containing sandy soil and covered to a depth of 6 mm (0.25 in). Seeds usually germinate within 15 to 20 days of planting. When the seedlings reach 3.8 cm (1.5 in) in height they should be transplanted into individual pots. Seedling are ready to be moved to the field after about 6 to 7 months when they have reached approximately 30.5 cm (12 in) in height. Transplanting seedlings Guava trees should be planted in full sun and should be spaced 4.5–7.5 m (15–25 ft) away from other trees and buildings to prevent shading. A hole should be dug which is slightly larger than the existing root ball and the addition of a layer of compost or rotted manure at the bottom of the planting hole. The tree should be planted at the same depth as it was in the nursery by placing the seedling upright into the planting hole and backfilling the soil around the plant. The soil should be tamped by hand around the tree to eliminate any air pockets. Water the newly planted seedlings immediately unless the soil is already damp. Young trees should be pruned to encourage the development of laterals. This is achieved by cutting back existing laterals at 30 to 60 cm (1 to 2 ft). During the first year of growth, 3 to 4 of the lateral branches should be allowed to grow 60 to 90 cm (24-36 in) before the tips are cut to encourage further branching. any new shoots formed by this process should also be tipped when they reach 60 to 90 cm (24-36 in). Pruning of established trees should be carried out to retain a managea…
requirements: Guava is mainly grown in the tropics and will tolerate temperatures between 15 and 45°C (59–113°F). Guava will grow optimally between 23 and 28°C (73–82°F) but established trees can tolerate short periods at -3 to -2°C (27-28°F) although temperatures below 15°C (60°F) can cause the tree to cease producing fruit. Guava is also amenable to a wide range of soils and will grow in both sandy or rocky soils in addition to loams, preferring a pH of 4.5–7 but tolerating alkaline soil to to pH 8.5. Guava is more resistant to drought than most tropical fruits and can withstand long periods of dry weather by ceasing vegetative growth until conditions improve.
uses: Guava fruits may be eaten fresh or processed to produce paste, jellies or preserves. Dehydrated fruit is used to make guava powder.
comment: Guava, Psidium guajava, is an evergreen shrub or small tree in the family Myrtaceae grown for its edible fruits. Guava has a slender trunk with smooth green to red-brown bark. The trunk may be branched at the base and the branches droop low to the ground. The plant possesses oval or elliptical leaves which are smooth on the upper surface and hairy on the lower surface. Guava produces solitary white flowers and a berry fruit. The fruit is oval in shape and green to yellow in color. The flesh inside can be white, yellow, pink or red in color and contains numerous yellowish seeds. Guava can reach grow to 10 m (33 ft) in height and lives for approximately 40 years. Guava may also be referred to as common guava and its origin is unknown although it grows native in parts of tropical America.
References: https://plantvillage.psu.edu/topics/guava/infos

Gynoecium
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: GynoeciumGynoecium is most commonly used as a collective term for the parts of a flower that produce ovules and ultimately develop into the fruit and seeds.

Harrow
type: Class
subclass of: Agricultural Machinery
type: NamedIndividual
type: Machine
comment: In agriculture, a harrow (often called a set of harrows in a plurale tantum sense) is an implement for breaking up and smoothing out the surface of the soil.

Harvest Date
type: Class
subclass of: Cropping
type: NamedIndividual
type: Cropping
comment: The time or season of the year for the gathering of ripened crops.

Harvest Index
type: Class
subclass of: Cropping
type: NamedIndividual
type: Cropping
comment: Harvest index is a measure of the efficiency of plants in producing seed. It is defined as the ratio of grain yield to total aboveground biomass. Since elevated temperature decreases grain yield while either increasing or not affecting vegetative growth, an increase in temperature will result in a lower harvest index.

Harvester
type: Class
subclass of: Agricultural Machinery
type: NamedIndividual
type: Machine
comment: Harvester, in farming, any of several machines for harvesting; the design and function of harvesters varies widely according to crop.

Harvesting
type: Class
subclass of: CultivatingProcess
type: NamedIndividual
type: Growth Stage
comment: Harvesting is the process of gathering a ripe crop from the fields. Reaping is the cutting of grain or pulse for harvest, typically using a scythe, sickle, or reaper.

Hazelnut
type: Class
subclass of: Nuts
type: NamedIndividual
type: Crop
type: Nuts
harvesting: Newly planted trees usually bear nuts within two to three years after planting although full production may not be reached until twenty five years after planting. The nuts are usually harvested two to three times over the season. The nuts can be spread out on the ground to dry or, as with commercially produced nuts, dried artificially at a temperature of 90 to 100°C (176-212°F).
propagation: While it is possible to propagate filberts from cuttings, the success rate for obtaining rooted cuttings is usually only between 20 and 50%. The most successful method of propagating filberts is by layering. The most successful and widely used method of layering for commercial filbert production is tip layering. Tip layering involves bending shoots into a V-shape and burying the lower parts in the soil to a depth of 20 to 25 cm (8-10 in) while the tips of the shoot, which will form the new tree tops, are kept upright. The soil is kept moist to promote root development above the V bend on the tip and roots should ideally be congregated in a 5 to 10 cm (2-4 in) section of the shoot. Trees should be planted in early winter while dormant. The trees should be planted at least 6 m (20 ft) apart. Before planting, remove as much as possible of the old layered shoot and prune back the ends of any broken roots. Plant the tree in a hole large enough to accommodate the roots but avoid deep planting. Fill in the hole around the roots with fine soil, pressing down with your hands to eliminate any air pockets around the roots. Add soil on top of the roots and tamp down to set the tree. Fill in the remainder of the hole with loosely packed soil. Once planted, the tree should be headed back in order to compensate for reduced water uptake. the tree should be cut back to a height of 45 to 76 cm (18-30 in). Hazelnut trees produce suckers which should be removed from the tree at, or close to, their point of origin on the trunk. This can be achieved by gently removing the soil from around the sucker. While the trunk is exposed, any other buds which are beginning to show should also be removed. Cutting the sucker at or just below the soil line will encourage more suckers so it is important to make the cut as close to the trunk as possible.
requirements: Filberts are hardy plants which can survive adverse growing conditions. They should be grown in a soil which is at least 2.4 to 3 m (8-10 ft) deep and will grow optimally in well-draining, fertile loams with a pH between 5.5 and 7.5 in full sun or partial shade. Filberts will grow well in areas where wild hazel grows large and vigorous.
uses: The kernel of the hazelnut is edible and can be eaten raw or toasted. The kernels can be processed to produce praline or as an ingredient in confectionery and baked goods.
References: https://plantvillage.psu.edu/topics/hazelnut-filbert/infos
comment: Hazelnut, belongs to the family Corylus, which includes C. avellana (Common or European hazelnut) and the closely related species C. maxima (filbert). Hazels are deciduous trees or shrubs in the family Betulaceae grown for their edible nuts. Hazelnuts are large multi-stemmed shrubs or small trees with rounded leaves which possess a doubly serrated margin (each tooth bears another tooth). They produces flowers very late in the winter prior to the emergence of any leaves. The female flowers are small and only the bright red stigmas are visible protruding from the bud. The male flower is a catkin which is pale yellow in color and measures 6–12 cn (2–5 in) in length. The fruit of the hazelnut is a classic nut which grows in clusters of 1–5, each protected by a leafy husk which covers most (common) or all of the nut (filbert). The nut is oval in shape and yellow to brown in colour. Each has a pale scale at its base. When ripe, the nut falls from the husk to the ground. Hazenut can reach a height of 3–8 m (10–26 ft) and can live for many years, although its commercial lifespan is usually about 40 years. Hazelnut originates from Europe and South East Asia.

Heat Stress
type: Class
subclass of: Crop Stress
type: NamedIndividual
type: Crop Stress
related to: Temperature
comment: Plant Heat Stress (PHS)

Height
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute
comment: Crop height is the shortest distance between the upper boundary of the main photosynthetic tissues (excluding inflorescences) on a plant and the ground level, expressed in metres.
alternative label: Crop Height

Helicopter
type: Class
subclass of: Vehicle
type: NamedIndividual
type: Vehicle
comment: A helicopter is a type of rotorcraft in which lift and thrust are supplied by horizontally-spinning rotors. This allows the helicopter to take off and land vertically, to hover, and to fly forward, backward, and laterally.

Herbicides
type: Class
subclass of: Crop Protection
comment: Herbicides, also commonly known as weedkillers, are chemical substances used to control unwanted plants.

Human Labor
type: Class
subclass of: Person
type: NamedIndividual
type: Person

Human Manure
type: Class
subclass of: Organic Mature
type: NamedIndividual
type: Organic Mature

Humidity
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
unit: %
comment: Humidity is the concentration of water vapor present in the air. Water vapor, the gaseous state of water, is generally invisible to the human eye. Humidity indicates the likelihood for precipitation, dew, or fog to be present. The amount of water vapor needed to achieve saturation increases as the temperature increases. As the temperature of a parcel of air decreases it will eventually reach the saturation point without adding or losing water mass. The amount of water vapor contained within a parcel of air can vary significantly. For example, a parcel of air near saturation may contain 28 grams of water per cubic metre of air at 30 °C, but only 8 grams of water per cubic metre of air at 8 °C.

Hydration
type: Class
subclass of: Plant Phenomenon
type: NamedIndividual
type: Plant Phenomenon
comment: Hydration is widely used as a pre-treatment for legumes and cereals before some processing operations such as extraction, cooking, germination, tempering and wet milling. It is necessary to know water absorption behavior in legumes and cereals during hydration because it affects following processing operations and the quality of the final product (Turhan et al., 2002).

Hydraulic Conductivity (HC)
type: Class
subclass of: Soil Conductivity
type: NamedIndividual
type: Soil
Acronym: SHC
alternative label: Soil Hydraulic Conductivity (SHC)

Hydrogen (H)
type: Class
subclass of: Essential Nutrient
type: NamedIndividual
type: Essential Nutrient
comment: Hydrogen is the chemical element with the symbol H and atomic number 1. With a standard atomic weight of 1.008, hydrogen is the lightest element in the periodic table. Hydrogen is the most abundant chemical substance in the universe, constituting roughly 75% of all baryonic mass.
Acronym: H

Hypertrophy
type: Class
subclass of: Symptom
type: NamedIndividual
type: Symptom
comment: It is an abnormal increase in size of one or more organs of a plant in response to the attack of a pathogen. It results in abnormal growth causing distortions, swellings, leaf curls and galls. The excessive growth may be due to two processes, hyperplasia and hypertrophy. The former consists in rapid cell division and thus increase in the number of cells of which the organ is composed. Hypertrophy leads to abnormal increase in the size of cells only. The attack of Com smut stimulates abnormal and rapid cell division (hyperplasia) and enlargement of individual cells (hypertrophy) of the infected organs resulting in the formation of overgrowths called the corn galls or tumours (Fig. 22.6 E). Witches broom is another result of hypertrophy. It is a compact cluster of fine slender branches generally developing from an enlarged axis and the whole looking like a broom (Witches broom of Cherry). The hypertrophied parts become inedible.

Hypoplasia
type: Class
subclass of: Symptom
type: NamedIndividual
type: Symptom
comment: It is a subnormal cell production in response to the attack of the pathogen. It results in the subnormal growth of the parts attacked resulting in stunted growth and dwarfing of the host plant.

IRRadiation
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather

Identification
type: Class
subclass of: Planned Process
References: CRISP-DM

Imagery
type: Class
subclass of: Environment
type: NamedIndividual
comment: Imagery is a part of the figurative language in a literary work, whereby the author uses vivid images to describe a phenomenon. In this context, imagery pertains to graphics, visual scenes, pictures, or the sense of sight.

Information Object
type: Class
References: http://www.loa-cnr.it/ontologies/DUL.owl#InformationObject
comment: A piece of information, such as a musical composition, a text, a word, a picture, independently from how it is concretely realized.

Information Processing
type: Class
subclass of: Planned Process
References: CRISP-DM

Infrared Spectroscopy (IR)
type: Class
subclass of: Spectrophotometer

Input Condition
type: Class
subclass of: State
comment: State is a value or result of attributes / concepts as input of the knowledge model.

Insecticide
type: Class
subclass of: Crop Protection
comment: Insecticides are substances used to kill insects. They include ovicides and larvicides used against insect eggs and larvae, respectively. Insecticides are used in agriculture, medicine, industry and by consumers.

Inspection
type: Class
subclass of: Observation

Interpreting Data
type: Class
subclass of: Planned Process
References: CRISP-DM

Investigation
type: Class
subclass of: Planned Process
References: CRISP-DM

Iodine (I)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
comment: Iodine is a chemical element with the symbol I and atomic number 53. The heaviest of the stable halogens, it exists as a lustrous, purple-black non-metallic solid at standard conditions that melts to form a deep violet liquid at 114 degrees Celsius, and boils to a violet gas at 184 degrees Celsius. However, it sublimes easily with gentle heat, resulting in a widespread misconception even taught in some science textbooks that it does not melt.
Acronym: I

Iron (Fe)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
Acronym: Fe
comment: Iron is necessary for photosynthesis and is present as an enzyme cofactor in plants. Iron deficiency can result in interveinal chlorosis and necrosis. Iron is not a structural part of chlorophyll but very much essential for its synthesis. Copper deficiency can be responsible for promoting an iron deficiency.

Iron Deficiency
type: Class
subclass of: Plant Disorder
type: NamedIndividual
type: Plant Disorder
related to: Iron (Fe)
comment: Iron (Fe) deficiency is a plant disorder also known as "lime-induced chlorosis". It can be confused with manganese deficiency. Soil iron concentration is high, but can become unavailable for absorption if soil pH is higher than 6.5.

Irrigation
type: Class
subclass of: Cropping
type: NamedIndividual
type: Cropping
comment: Irrigation is the process of artificially applying water to soil to allow plant growth. This term is preferably used when large amounts of water is applied to dry, arid regions in order to facilitate plant growth. The process of irrigation not only increases the growth rate of the plant bust also increments the yield amount.

Potassium oxide (K2O)
type: Class
subclass of: Mineral
type: NamedIndividual
type: Mineral
unit: Kg/Ha
comment: Potassium oxide (K2O) is an ionic compound of potassium and oxygen. This pale yellow solid, the simplest oxide of potassium, is a rarely encountered, highly reactive compound. Some materials of commerce, such as fertilizers and cements, are assayed assuming the percent composition that would be equivalent to the chemical compound mixture K2O.

Kale
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Vegetable
harvesting: Kale leaves can be harvested when they are 20–25 cm (8–10 in) long. Harvest the outer leaves to avoid damaging the growing tip of the plant. Kale leaves are sweeter when they are harvested after frost.
maintenance: Kale should be kept evenly watered, application of mulch around plants helps to conserve soil moisture. The plants have shallow roots and in order to avoid damaging them, it is preferable to hand pull any weeds growing around the plants. Kale plants are heavy feeders, requiring plentiful nitrogen to meet their growth requirements and develop optimally. Apply an appropriate complete fertilizer when thinning seedlings.
propagation: Kale can be direct seeded or started indoors for transplants. The optimum soil temperature for germination is between 12 and 24°C (55–75°F). Kale seeds should be planted after any danger of hard frost or in a cold frame for transplanting to their final location. For a Fall harvest, try to time seeding so that the plants will mature in cool weather. This often means sowing in July but may be as late as October in warmer Southern regions. Prepare the soil for planting through the addition of nitrogen in the form of bone meal or composted manure. Plant seeds 12 mm (0.5 in) deep allowing 3.5 cm (1 in) between plants in the row. Thin seedlings to a final within row spacing of 45–60 cm (18–24 in). Keep soil moist during germination to prevent a crust from forming on the soil surface as this will cause uneven germination. Seedlings started indoors or in a cold frame are ready to be transplanted when they have 3–4 leaves and the daytime temperature has reached 10°C (50°F). Seedlings should be planted at the final spacing for seeds (45–60 cm/18–24 in between plants and 0.6 to 1.2 m/2–4 ft between rows). Plant each seedling slightly deeper than it was previously. The plantings can be staggered in 2 week intervals to prolong the harvest.
requirements: Kale is cool season crop that grows best in cool, moist conditions. The plant will grow best at temperatures between 4 and 21°C (40–50°F) allowing it to be grown in both Spring and Fall. Kale is a hardy plant, tolerating frost, and will grow optimally in a rich, moist, well draining soil with a pH of 6.5. Kale requires at least six hours of direct sunlight every day.
uses: Kale is used as a green vegetable and is usually cooked before consumption. Some varieties of kale are grown as decorative ornamental plants due to their attractive, brightly colored foliage.
comment: Kale, Brassica oleracea variant acephala, is a leafy herbaceous biennial or perennial plant in the family Brassicaeae grown as a leafy green vegetable. The kale plant is a non-heading, cabbage like plant with curly or straight, loose blue-green or purple leaves. Kale is usually grown as as an annual plant, harvested after one growing season and can reach a height of 1 m (3.3 ft). Kale may also be referred to as borecole or non-heading cabbage or broccoli and its exact origins are unknown, although it grows native in regions of the eastern Mediterranean and Asia.
References: https://plantvillage.psu.edu/topics/kale/infos

Kiwi
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
maintenance: Kiwis have a high nutrient demand, particularly for nitrogen and potassium. after the first year of planting, plants should be fertilized in the Spring and as required throughout the growing season with the type and amount of fertilizer depending on the particular soil type and the age of the vines.
propagation: Kiwis can be propagated by a variety of methods, including growing from seed or from hard or softwood cuttings or root cuttings. The easiest method of propagation is the use of softwood cuttings. The cuttings should be taken during the summer months when the tree is actively growing. Each cutting should be taken from the current season’s growth and should possess 2-3 nodes (leaves). The leaves must be fully expanded in order for the cutting to be successful, cuttings should not be taken from immature growth such as at the end of shoots. once the cuttings have been obtained, they should be stripped of all but the top-most leaf. Rooting hormone can be used to aid the cuttings in developing new roots. Those containing indole-3-butyric acid have been shown to be most effective in kiwi propagation. After treatment with rooting hormone, the kiwi cuttings should be planted in beds or small pots, preferably in a glasshouse, and kept moist. When the cuttings have began to develop new shoots, they should be transferred into 1 gallon pots containing a high quality potting soil and watered daily. The potted plants should be fed with a slow release fertilizer at least twice over the growing season. If the kiwi vines are to be grown on a trellis or pergola, this should be constructed prior to planting. Kiwi should be planted by digging a planting hole which is at least three times the width of the current pot that the plant is in. Individual plants should typically be spaced 5 m (16.4 ft) apart when using a T-trellis system but can be planted closer together when vines are managed by pruning. Approximately 6 m (19.6 ft) should be left between rows. This distance is again dependent on the training system being utilized. Almost all kiwi species are dioecious and require the presence of both a male and female tree in order to set fruit. The recommended planting ratio is 1 male to every 8 females but a higher density of male trees will increase productivity.
requirements: Kiwi plants are native to warm, forest regions of China and require a long growing season with 8 to 9 month with no frost. The optimal temperature during the growing season is between 14 and 24°C (57.2–75.2°F). The plant can tolerate cold but is damaged by temperatures below -8°C (17.6°F). Some varieties of A. deliciosa, such as the Hayward cultivar will tolerate temperatures down to -10°C (14°F). In general, kiwifruit vines will grow best when planted in deep, well-draining, sandy loams as they are extremely sensitive to standing water. The optimum pH for the soil is between 5.5 and 6.0. In addition, kiwifruits require a plentiful supply of water during production and the vines require the support of a trellis to support the weight of the growing fruit. Kiwi should be planted in full sunlight to ensure optimal productivity.
uses: Kiwis are primarily consumed as a fresh fruit.
References: https://plantvillage.psu.edu/topics/kiwi/infos
comment: Kiwi, Actinidia deliciosa, is a deciduous climbing vine or shrub in the the family Actinidiaceae grown for its edible fruits. The plant is vigorous and woody with nearly circular leaves which have long petioles and are alternately arranged on the stems. Young leaves and shoots are covered in tiny red hairs, while mature leaves are smooth and dark green on the upper surface and white and downy on lower surface. The kiwi plant produces fragrant white-yellow flowers singly or in clusters of 3 at the leaf axils (area between leaf and stem). The fruit is oval or ovoid in shape with green-brown skin covered in stiff brown hairs. The flesh of the fruit is bright green and juicy with many tiny black seeds. Kiwi plants can reach a height of 9 m (30 ft) and have an economic lifespan of 3 years, after which time fruit production begins to decline. Kiwi may also be referred to as kiwifruit or Chinese gooseberry and originates from China.

Knowledge Model
type: Class
subclass of: Data Mining

LWRadiation
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
unit: W/m^2

Land Owner
type: Class
subclass of: Person
comment: Land owner is a person who owns a farm.

Lead
type: Class
subclass of: Other Chemical
type: NamedIndividual
type: Other Chemical
Acronym: Lead (Pd)
Acronym: Pd

Leaf
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: A leaf (plural leaves) is the principal lateral appendage of the vascular plant stem, usually borne above ground and specialized for photosynthesis. The leaves and stem together form the shoot. Leaves are collectively referred to as foliage, as in "autumn foliage".

Lemon
type: Class
subclass of: Citrus
type: NamedIndividual
type: Citrus
type: Crop
type: Fruit Tree
has disease: Anthracnose
has disease: Aphids
has disease: Bacterial Canker
has disease: Black Root Rot
has disease: Citrus Greening Disease
has disease: Citrus Leaf Miner
has disease: Huanglongbing
has disease: Black scale
has disease: Thrips
maintenance: Newly planted trees require proper irrigation to ensure they become established. During the first year, water should be applied at the base of the trunk so that the root ball is kept moist to allow the roots to establish in the soil. Newly planted trees should be provided with water every 3–7 days. The soil should be moist, but not wet. Trees planted in sandy soils will require water more frequently. Young trees will also require a light application of fertilizer every month in the first year. Lemon trees will need protected from cold temperatures to prevent damage. Soil can be mounded up around the trunk during the winter and removed in the Spring. Young trees can also be protected from frosts by covering them with tarps or blankets as required.
propagation: Budding should be carried out when seedling stems have reached roughly the diameter of a pencil (6–9 mm/0.25–0.36 in) and at a time when the bark of the rootstock tree is slipping (this is the term used to describe a period of active growth when the bark can be easily peeled from the plant). Twigs (budwood) should be collected from the previous growth flush or the current flush so long as the twig has begun to harden. The twigs should have well developed buds and should be as close as possible to the diameter of the rootstock onto which it will be joined. It is extremely important to only collect budwood from disease-free trees. The use of diseased budwood can cause the spread of many serious citrus diseases which can kill trees. The budwood to be used for propagation should be trimmed to create budsticks which are 20–25 cm (8–10 in) by removing any unwanted wood and leaves. These budsticks can be stored for 2–3 months under the correct conditions but it is best to use them as soon as possible after cutting. The simplest way to join the budwood the the rootstock is by T-budding. The area to be joined should be pruned to remove any thorns or twigs and the cut made approximately 15 cm (6 in) from the ground. Using a sharp knife, a 2.5–3.8 cm (1–1.5 in) vertical cut should be made in the stem of the rootstock, through the bark. A horizontal cut should be made at either the top or the bottom of the vertical cut to produce a "T-shape" The horizontal cut should be made a slightly upward-pointing angle and should reach through the bark. Remove a bud from a budstick by slicing a thin, shield-shaped piece of bark and wood from the stem, beginning about 1.25 cm (0.5 in) above the bud. This piece should measure 1.9–2.5 cm(0.75–1.0 in) in length. Immedietely insert the piece of bud into the cut on the rootstock by sliding it under the opened bark so that the cut surface lies flat against the wood of the rootstock plant. Finish the join by wrapping the bud with budding tape. Aft…
requirements: Lemon is a subtropical plant and the trees grow best in regions with a pronounced change in season. They will grow best at temperatures between 26–28°C (79–82°F) and are very sensitive to cold. Trees and fruit will be damaged or killed by freezing conditions without protection.The trees will tolerate drought conditions but perform poorly in water-logged soil. Trees will grow best when planted in a well-draining sandy loam with a pH between 6.0 and 7.5. Soil must be deep enough to permit adequate root development. Lemon trees will grow best when positioned in full sunlight.
uses: Due to their bitter taste, lemon fruit is not usually consumed fresh. It is used widely to make juices such as lemonade, as garnishes in cooking and as a flavoring in cooking and baking.
comment: Lemon, Citrus limon, is a small evergreen tree in the family Rutaceae grown for its edible fruit which, among other things, are used in a variety of foods and drinks. The tree has a spreading, upright growth habit, few large branches and stiff thorns. The tree possesses large, oblong or oval, light green leaves and produces purple-white flowers in clusters. The lemon fruit is an ellipsoid berry surrounded by a green rind, which ripens to yellow, protecting soft yellow segmented pulp. Lemon trees can reach 3–6 m (10–20 ft) in height and can live for many years, reaching full fruit bearing capacity in approximately 40 years. Lemon may also be referred to as bush lemon or Persian apple and likely originated from the eastern Himalaya of India.
References: https://plantvillage.psu.edu/topics/lemon/infos

Length
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute

Lettuce
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Vegetable
comment: Lettuce is an annual plant of the daisy family, Asteraceae. It is most often grown as a leaf vegetable, but sometimes for its stem and seeds. Lettuce is most often used for salads, although it is also seen in other kinds of food, such as soups, sandwiches and wraps; it can also be grilled.

Light
type: Class
subclass of: Environment
type: NamedIndividual
type: Environment
comment: Light is a key determinant of agricultural productivity. In agriculture, there are two sides to sunshine. On the positive side, it contributes to the growth of plants by allowing photosynthesis and by providing them with a healthy environment. On the negative side, excessive sunshine exposes plants to thermal and water stress that impairs their growth, can endanger their survival, and increases water consumption.

Light Stress
type: Class
subclass of: Crop Stress
type: NamedIndividual
type: Crop Stress
related to: Light

Limes
type: Class
subclass of: Citrus
type: NamedIndividual
type: Citrus
type: Crop
type: Fertilizer
type: Fruit Tree
maintenance: Newly planted trees require proper irrigation to ensure they become established. During the first year, water should be applied at the base of the trunk so that the root ball is kept moist to allow the roots to establish in the soil. Newly planted trees should be provided with water every 3–7 days. The soil should be moist, but not wet. Trees planted in sandy soils will require water more frequently. Young trees will also require a light application of fertilizer every month in the first year. Lime trees will need protected from cold temperatures to prevent damage. Soil can be mounded up around the trunk during the winter and removed in the Spring. Trees can also be protected by stringing christmas lights in the branches and covering the leaves with tarps. In areas with cold winters, lime trees are best grown in containers which allows them to be moved indoors during the winter months.
propagation: Unlike many other fruit bearing trees, limes are commonly propagated from seed as the will produce offspring that is true to the parent plant. In some cases, trees are propagated by removing offshoots from the main trunk (called suckers) and replanting although, if the tree is grafted, it should be noted that the suckers will be the same variety of the rootstock. The best time to plant citrus trees is in Spring after all danger of frost has passed in your area. Trees should be planted at or higher than the level of the nursery pot. Once the tree is positioned in the planting hole, backfill the soil by about half and water to allow the soil to settle around the lower roots before filling in the hole. The newly planted tree should be watered every few days.
requirements: Lime is a tropical plant and the trees grow best in regions with hot summers and warm winters where temperatures are typically between 25 and 30°C (77–86°F). Limes are more sensitive to cold than lemons and trees are killed by freezing conditions without protection. Lime trees are the most drought tolerant of all citrus trees conditions and will grow best when planted in a well-draining sandy loam with a pH between 6.0 and 7.5. Soil must be deep enough to permit adequate root development. Lime trees will grow best when positioned in full sunlight.
uses: Lime fruit is commonly used to make juices and cordials or for extraction of citric acid. It may be used as a flavoring in cooking. Lime oil can be extracted from the peel of the fruit.
References: https://plantvillage.psu.edu/topics/lime-sour/infos
comment: Lime, Citrus aurantifolia, is a small perennial evergreen tree in the family Rutaceae grown for its sour fruit. The lime tree is irregularly branched and possesses sharp spines. The leaves of the tree are elliptical with small rounded teeth around the edge. The leaves can grow 4–8 cm (1.6–3.2 in) in length. The tree produces small, cupped white flowers and yellowish-green fruit which is round or egg-like in shape. Lime trees can reach 5 m (16 ft) in height and can produce fruit for many years. Lime may also be referred to as sour lime, key lime, Mexican lime, acid lime or West Indian lime and originated in southeast Asia.
comment: Lime raises pH and is usually added as ground limestone, commonly called ‘garden lime’. Lime is usually added in winter for annual crops, such as vegetables, just prior to digging, as the lime can take effect over the winter months and will not damage young growth.

Link
type: Class
subclass of: Information Object
comment: A link is a web address which refers to the address of a document or resource on the World Wide Web.

Location
type: Class
equivalent class: Location
subclass of: Physical Entity
type: NamedIndividual
comment: A location is the place where plants grow, something happens or is situated.
References: http://purl.org/dc/terms/Location

Magnesium oxide (MgO)
type: Class
subclass of: Mineral
type: NamedIndividual
type: Mineral
unit: Kg/Ha
comment: Magnesium oxide (MgO), or magnesia, is a white hygroscopic solid mineral that occurs naturally as periclase and is a source of magnesium (see also oxide). It has an empirical formula of MgO and consists of a lattice of Mg2+ ions and O2− ions held together by ionic bonding.

MODIS
type: Class
subclass of: Sensor
type: NamedIndividual
type: Sensor
comment: The Moderate Resolution Imaging Spectroradiometer (MODIS) is a payload imaging sensor built by Santa Barbara Remote Sensing that was launched into Earth orbit by NASA in 1999 on board the Terra (EOS AM) satellite, and in 2002 on board the Aqua (EOS PM) satellite. The instruments capture data in 36 spectral bands ranging in wavelength from 0.4 μm to 14.4 μm and at varying spatial resolutions (2 bands at 250 m, 5 bands at 500 m and 29 bands at 1 km).
comment: Moderate Resolution Imaging Spectroradiometer (MODIS)

Machine
type: Class
subclass of: Physical Entity
comment: Machine is an apparatus using mechanical power and having several parts, each with a definite function and together performing a particular task.

Macronutrient
type: Class
subclass of: Nutrient
comment: Macronutrient is a chemical element or substance (such as potassium or protein) that is essential in relatively large amounts to the growth and health. Macronutrients provide the bulk energy an organism's metabolic system needs to function while micronutrients provide the necessary cofactors for metabolism to be carried out. Both types of nutrients can be acquired from the environment.

Magnesium (Mg)
type: Class
subclass of: Macronutrient
type: NamedIndividual
type: Macronutrient
Acronym: Mg
comment: Magnesium is a key component of chlorophyll, the green colouring material of plants, and is vital for photosynthesis (the conversion of the sun's energy to food for the plant). Deficiencies occur mainly on sandy acid soils in high rainfall areas, especially if used for intensive horticulture or dairying. Heavy applications of potassium in fertilisers can also produce magnesium deficiency, so banana growers need to watch magnesium levels because bananas are big potassium users. Magnesium deficiency can be overcome with dolomite (a mixed magnesium-calcium carbonate), magnesite (magnesium oxide) or epsom salts (magnesium sulfate).

Maize
type: Class
subclass of: Cereal
type: NamedIndividual
type: Cereal
type: Crop
has growth stage: Flowering
has growth stage: Fruit Development stage
has growth stage: Germination
has growth stage: Inflorescence Emergence stage
has growth stage: Leaf Development stage
has growth stage: Ripening stage
has growth stage: Senescence stage
has growth stage: Stem Elongation stage
comment: Maize, also known as corn, is a cereal grain first domesticated by indigenous peoples in southern Mexico about 10,000 years ago. The leafy stalk of the plant produces pollen inflorescences and separate ovuliferous inflorescences called ears that yield kernels or seeds, which are fruits. Maize has become a staple food in many parts of the world, with the total production of maize surpassing that of wheat or rice. However, little of this maize is consumed directly by humans: most is used for corn ethanol, animal feed and other maize products, such as corn starch and corn syrup. The six major types of maize are dent corn, flint corn, pod corn, popcorn, flour corn, and sweet corn. Maize is widely cultivated throughout the world, and a greater weight of maize is produced each year than any other grain. In 2014, total world production was 1.04 billion tonnes.

Manganese (Mn)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
comment: Manganese is necessary for photosynthesis, including the building of chloroplasts. Manganese deficiency may result in coloration abnormalities, such as discolored spots on the foliage.
Acronym: Mn

Market
type: Class
subclass of: Abstract Entity
comment: A regular gathering of people for the purchase and sale of plants, agriculture products.

Marketing
type: Class
subclass of: InUse
comment: A series of steps that allow organizations to identify customer problems, analyze market opportunities, and create marketing materials to reach the desired audience or buyers.

Maturity
type: Class
subclass of: Plant Phenomenon
type: NamedIndividual
type: Growth Stage
type: Plant Phenomenon
comment: At this stage, grain has achieved its maximum dry weight. Physiological maturity is recognized by a dark spot or black layer on the bottom of the kernel. Grain moisture content typically ranges between 25 and 35 percent. Dessicants can be safely used to aid in harvest without reducing grain yield. Total time from flowering to physiological maturity is approximately 40 to 45 days.

Meeting
type: Class
subclass of: Event
comment: Meeting is an assembly of people for a particular purpose, especially for formal discussion.

Micronutrient
type: Class
subclass of: Nutrient
comment: Micronutrients are essential elements required by plants in varying quantities throughout life to orchestrate a range of physiological functions to maintain health. Micronutrients are used to build and repair tissues and to regulate body processes while macronutrients are converted to, and used for, energy.

Microoganism
type: Class
subclass of: Physical Entity
comment: A microorganism, or microbe, is a microscopic organism, which may exist in its single-celled form or a colony of cells.

Mildews
type: Class
subclass of: Symptom
type: NamedIndividual
type: Symptom
comment: These are a group of important fungal diseases of seed plants in which the parasite is seen as a superficial growth on the host surface (leaves, green stems and fruits) in the form of patches of varying sizes and colours. The mildews are of two kinds, downy mildews and powdery mildews. The downy mildews are all internal obligate parasites. They are characterised by superficial downy growth consisting of conidiophores and conidia on the host lesions in damp and warm weather. The powdery mildews are external parasites in which the mycelium forms whitish patches on the surface of the leaves of the host plant. The patches appear dusty or powdery with the formation of numerous white conidia which form a coating on the host surface.

Millets
type: Class
subclass of: Cereal
type: NamedIndividual
type: Cereal
type: Crop
comment: Millets are a group of highly variable small-seeded grasses, widely grown around the world as cereal crops or grains for fodder and human food. Millets are important crops in the semiarid tropics of Asia and Africa (especially in India, Mali, Nigeria, and Niger), with 97% of millet production in developing countries. The crop is favored due to its productivity and short growing season under dry, high-temperature conditions.

Mineral
type: Class
subclass of: Nutrient
type: NamedIndividual
type: Nutrient
comment: A mineral is a naturally occurring chemical compound. Most often, they are crystalline and abiogenic in origin. A mineral is different from a rock, which can be an aggregate of minerals or non-minerals and does not have one specific chemical composition, as a mineral does. A mineral is, broadly speaking, a solid chemical compound that occurs naturally in pure form.

Mineral Stress
type: Class
subclass of: Crop Stress
type: NamedIndividual
type: Crop Stress

Mining Algorithm
type: Class
subclass of: Computing Algorithm
comment: Algorithm includes a set of algorithms used to process data mining tasks (including data selection, clustering, claasification, regression, and association rule) on datasets.

Mining Result
type: Class
subclass of: State
comment: State is a result of attributes / concepts in knowledge model.

Mint
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Watermelon
harvesting: Mint leaves can be harvested as soon as the plants have reached 8 to 10 cm (3-4 in) in height. Cut leaves and stems with a sharp knife or scissors. If harvesting whole stems, cut the stem at about 2.5 cm (1 in) from the soil line.
maintenance: Mint is very vigorous and should be pruned regularly to keep the plants in check. Remove any unwanted runners and pinch the tips of the plants back regularly. Mint may be fertilized in the Spring with a slow release fertilizer to supply it with nutrients throughout the growing season. Pinch of any flowers that form to conserve the flavor of the leaves. Essential oil content is reduced during bloom. In areas with mild winters, mint can be moved to a sheltered area of the garden to overwinter, otherwise the plant can be cut to the ground in the Fall. Container grown mint plants can be brought indoors.
propagation: Mint is readily propagated from seeds, cuttings or by dividing an established plant. Seeds should be planted in the Spring or in the Fall in areas that are free from frost. Seeds should be sown to a depth of 6 mm (0.25 in). Seedling should be thinned after emergence such that the plants are spaced 46 to 61 cm (18 to 24 in) apart. Established mint can be easily divided for transplanting by taking some branches along with a portion of root. Many people choose to keep mint in containers or sink the containers into the ground when planting to prevent mint from spreading uncontrollably.
requirements: int is a rapidly growing plant which is very easy to grow. It is best grown in partial shade to full sun and is generally very hardy, tolerating temperatures down to -29°C (-20°F). Care should be taken with variegated varieties which may scorch in full sun. Mint is very fast growing which can lead to it invading gardens quickly unless controlled. The best soils for planting mint are rich and moist with a slightly acidic pH between 6.0 and 7.0.
uses: Mint leaves are used fresh or dried to make teas, jams and desserts. Essentila oil can be extracted from the leaves and is used as a flavoring.
comment: Mints belong to the genus Mentha and comprise approximately 20 species in the plant family Lamiaceae and are growm for their leaves which are widely used as a flavoring. Mint plants are mainly aromatic perennials and they possess erect, branching stems and oblong to ovate or lanceolate leaves arranged in opposing pairs on the stems. The leaves are often covered in tiny hairs and have a serrated margin. Mint plants produce a terminal flower spike and the flowers can be white or purple in color depending on variety. Mint plants are fast growing and can become very invasive. They can reach heights of 60–90 cm and will cpntinue to grow for many years once established. Mint may also be referred to by species and these include, but are not limited to peppermint, spearmint, water mint and Japanese mint.
References: https://plantvillage.psu.edu/topics/mint/infos

Model State
type: Class
subclass of: State
comment: Model state is the state of knowledge model from the mining process.

Modeling Report
type: Class
subclass of: Report
References: CRISP-DM
comment: The outputs produced during the Modeling phase can be combined into one report. We suggest the following sections: Modeling assumption This section defines explicitly any assumptions made about the data and any assumptions that are implicit in the modeling technique to be used. Test design This section describes how the models are built, tested and evaluated. Topics to be covered: - Background – outlines the modeling undertaken and its relation to the data mining goals. For each modeling task: - Broad description of the type of model and the training data to be used. - Explanation of how the model will be tested or assessed. - Description of any data required for testing. - Plan for production of test data if any. - Description of any planned examination of models by domain or data experts. - Summary of test plan. Model description This report describes the delivered models and overviews the process by which they were produced. Topics to be covered: - Overview of models produced. For each model: - Type of model and relation to data mining goals. - Parameter settings used to produce the model. - Detailed description of the model and any special features. For example: - For rule-based models, list the rules produced plus any assessment of per-rule or overall model accuracy and coverage. - For opaque models, list any technical information about the model (such as neural network topology) and any behavioral descriptions produced by the modeling process (such as accuracy or sensitivity). - Description of Model’s behavior and interpretation. - Conclusions regarding patterns in the data (if any); sometimes the model will reveal important facts about the data without a separate assessment process (e.g. that the output or conclusion is duplicated in one of the inputs). - Summary of conclusions. Model assessment This section describes the results of testing the models according to the test design. Topics to be covered: - Overview of assessment process and results including an…

Moisture
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Moisture is the presence of a liquid, especially water, often in trace amounts. Small amounts of water may be found, for example, in the air (humidity), in foods, and in some commercial products. Moisture also refers to the amount of water vapor present in the air.

Molluscicides
type: Class
subclass of: Crop Protection
comment: Molluscicides, also known as snail baits, snail pellets or slug pellets, are pesticides against molluscs, which are usually used in agriculture or gardening, in order to control gastropod pests specifically slugs and snails which damage crops or other valued plants by feeding on them.

Molybdenum (Mo)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
comment: Molybdenum is a cofactor to enzymes important in building amino acids and is involved in nitrogen metabolism. Molybdenum is part of the nitrate reductase enzyme (needed for the reduction of nitrate) and the nitrogenase enzyme (required for biological nitrogen fixation).
Acronym: Mo

Month Of Year
type: Class
subclass of: Time
comment: The month of the year

Mortality
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute

Multi-sensor platform (MSP)
type: Class
subclass of: Sensor Station

Multispectral Image
type: Class
subclass of: Imagery
type: NamedIndividual
type: Imagery

Mushrooms
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Watermelon
harvesting: Harvest the mushrooms at soil level by carefully twisting them or by cutting them at soil level with sharp knife. Pulling can damage other surrounding mushrooms. Harvest flushes of mushrooms daily. The beds will continue to produce for up to 6 months.
propagation: Substrates for growing Agaricus mushrooms are either agricultural by-products such as straw bedded horse manure or synthetic composts specially formulated for mushroom growing. Horse manure substrates should have a nitrogen content of 1.5–1.7%, while synthetic composts should contain between 1.7 and 1.9%. Additional nitrogen can be added to the substrate if required using nitrogen supplements or through the addition of poultry manure. Gypsum is commonly added to the mix to buffer the pH and to give the compost the desired structure for optimum mushroom growth. The growing substrate then must be composted to make it suitable for growing mushrooms. Horse manure is composted by turning every 4–5 days and watering after each turning to keep the substrate moist. The compost is ready to use when it turns a rich brown color, usually after 4 or 5 turnings. Other compost mixes should be turned after 5–6 days and kept moist by watering. The compost should be turned a second time and should be ready for use after another week. the compost is then set out in trays for planting the spawn. In commercial mushroom production, the compost is pasteurized to remove and fungal, bacterial or insect contaminants from the medium before the spawn is planted. Spawn Mushroom spawn can be purchased from many commercial seed companies or online suppliers. Spawn should be planted when the compost reaches 24°C (75°F) and should be planted in the trays at a depth of 5 cm (2 in) allowing 20–25 cm (8–10 in) between plantings. The temperature should be kept at 21°C (70°F) for the next 21 days before lowering to 15.6°C (60°F) and covering with a 2.5 cm (1 in) layer of good quality soil. The growing mushrooms should be kept moist throughout by watering with a gentle spray of water whenever the top of the soil begins to dry. Tiny white spots should begin to appear on the soil surface after approximately 3 weeks and will begin to be ready for harvest approximately 10 days later.
requirements: Mushrooms grow from a filamentous mass (mycelia or 'spawn') that grows on dead or decaying plant material. Agaricus mushrooms can be cultivated in a dark, moist, cool space such as a basement. Agaricus mushrooms require a rich organic material in which to grow such as horse manure. Oyster mushrooms are cultivated on hardwood logs by introducing spawn into pre-drilled holes in the wood or or by sawing a section through the log and covering the exposed wood in spawn before nailing the log back together. The logs are then stored in watertight bags containing sand and soaked with water. Oyster mushrooms can also be grown in straw or sawdust by inoculating the sterile substrate with spawn and sealing in plastic bags. Straw mushrooms can be grown on wheat straw, rice straw or sawdust inoculated with spawn and stored at temperatures between 28 and 36°C and relative humidity of 75–85%.
uses: Mushrooms are widely used after cooking and are incorporated into many dishes worldwide.
comment: Mushrooms are the fleshy fruiting bodies of fungi and include edible species in the genus Agaricus (button mushrooms, portabellas and criminis), Pleurotus (oyster mushrooms), and Volvariella, (straw mushrooms). Mushrooms are highly variable in appearance depending on their stage of development and variety. Agaricus mushrooms have a cap and stem, the cap is a pale grey or brown in color and is rounder while immature, but flattens out as it reaches maturity and can reach a diameter of 5–10 cm (2–4 in). Pleurotus mushrooms may not have a stem and instead may be laterally attached to a growing substrate such as the bark of a a tree. Pleurotus mushrooms are smooth and elongated and can reach 4–15 cm (1.5–6.0 in) in diameter. Volvariella mushrooms are small, with pink gills and a characteristic sac-like covering (volva) at the base of the stem. The cap can reach 5–15 cm (2–6 in) in diameter.

Mustard
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: OilSeeds
harvesting: Mustard is generally ready to harvest 80 to 85 days after planting. The crop should be harvested promptly to reduce losses to shattering. In commercially grown fields the recommended method of harvest is swathing. The plants should be cut just below the height of the lowest seed pods. In the home garden, mustard can be harvested by hand by cutting the plants. The seeds can then be recovered from the pods and dried for storage.
maintenance: Weeds can cause huge losses in mustard cultivation as they quickly out compete seedlings and are difficult to eradicate once established. The best method of preventing weed growth in a mustard field is to plant seeds at the appropriate depth. Shallow planted seeds germinate rapidly, allowing the development of a uniform plant stand. Established plants are less susceptible to competition from weeds. soil should be tested to determine nutrient requirements prior to planting. Mustard will benefit from the addition of supplemental nitrogen and this is generally applied at a rate of 100–120 lb per acre.
propagation: Mustards is propagated from seed and due to the small size of the seeds, should be planted in a well prepared seed bed which is firm and flat. Mustard seeds should be sown in Spring once soils have warmed to 4.4–7.2°C (40–45°F) and should be planted at a depth of 1.25–2.5 cm (0.5–1.0 in) at a density of 8–14 lbs of seeds per acre. The seed can be spread by broadcasting in the home garden or, in the case of commercial production, using a seed drill. Mustard generally matures in 80–85 days.
requirements: Mustard grows very well in cool climates with short growing seasons, at temperatures as low as 4.4°C (40°F). Seedlings can tolerate some light frost but severe frost will kill the plants. The mustard plant can be grown in sandy, loamy or clay soils with a pH between 4.9 and 8.2 and prefers moist soil. The plant will tolerate partial shade.
uses: The leaves and shoots of the mustard plant are consumed as a vegetable in some countries. Mustard seed is incorporated into seasonings and dressings. Mustard may be grown as a cover crop in rotation with other vegetables.
comment: Mustards, (Brassica spp.) are herbaceous annual plants in the family Brassicaceae grown for their seeds which are used as a spice. Mustard plants are thin herbaceous herbs with yellow flowers. The leaves of the plant are toothed, lobed, and occasionally have the larger terminal lobes. Plants can reach 16 cm (6.3 in) in length. The yellow flowers grow in spike like clusters of 2–12 flowers and individual flowers are 8 mm (0.3 in) in diameter. The seeds are red to brown in color and produced from each flower. Mustard can grow 1.2-2 m (4–6.6 ft) in height and as an annual plant, survives only one growing season. Mustard may also be referred to as mustard, brown mustard, red mustard, yellow mustard or wedlock and is believed to have originated in the temperate regions of Europe.

Nitrous Oxide (N2O)
type: Class
subclass of: Gas
type: NamedIndividual
type: Gas
comment: Nitrous oxide, commonly known as laughing gas or nitrous, is a chemical compound, an oxide of nitrogen with the formula N2O. At room temperature, it is a colourless non-flammable gas, with a slight metallic scent and taste. At elevated temperatures, nitrous oxide is a powerful oxidiser similar to molecular oxygen.
Acronym: N2O
comment: https://en.wikipedia.org/wiki/Nitrous_oxide

Ammonia (NH3)
type: Class
subclass of: Gas
type: NamedIndividual
type: Gas
comment: Ammonia is a compound of nitrogen and hydrogen with the formula NH3. A stable binary hydride, and the simplest pnictogen hydride, ammonia is a colourless gas with a characteristic pungent smell. It is a common nitrogenous waste, particularly among aquatic organisms, and it contributes significantly to the nutritional needs of terrestrial organisms by serving as a precursor to food and fertilizers. Ammonia, either directly or indirectly, is also a building block for the synthesis of many pharmaceutical products and is used in many commercial cleaning products. It is mainly collected by downward displacement of both air and water.
Acronym: NH3

Natrium (Na)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient

Natural Phenomenon
type: Class
subclass of: Phenomenon
comment: Natural phhenomenon is an observable fact or event related to environment, such as planet, soil, weather.

Near-infrared (NIR) Spectroscopy
type: Class
subclass of: Spectrophotometer

Necrosis
type: Class
subclass of: Symptom
comment: Death or killing of the host tissues induced by the attack of a pathogen is called necrosis. It may be caused by rots, blights, wilts, die back and cankers and may be general or local, gradual or rapid. When necrosis is general it is called decay and rotting. The causal organism may be a fungus or a bacterium. Rotting is universal in plants. It takes a heavy toll of useful plants. The rots are of many kinds. The common ones are dry rots, wet rots, soft rots, hard rots, brown rots, and black rots. These rots cause general necrosis and progress slowly or rapidly.

Network
type: Class
subclass of: Observe System

Nickel (Ni)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
Acronym: Ni
comment: In higher plants, nickel is absorbed by plants in the form of Ni2+ ion. Nickel is essential for activation of urease, an enzyme involved with nitrogen metabolism that is required to process urea. Without nickel, toxic levels of urea accumulate, leading to the formation of necrotic lesions. In lower plants, nickel activates several enzymes involved in a variety of processes, and can substitute for zinc and iron as a cofactor in some enzymes.

Nighttime
type: Class
subclass of: Time
comment: The time between dusk and dawn; also, the time between sunset and sunrise.

Nitrogen (N)
type: Class
subclass of: Essential Nutrient
type: NamedIndividual
type: Essential Nutrient
unit: Kg/Ha
comment: Nitrogen is a key element in plant growth. It is found in all plant cells, in plant proteins and hormones, and in chlorophyll. Atmospheric nitrogen is a source of soil nitrogen. Some plants such as legumes fix atmospheric nitrogen in their roots; otherwise fertiliser factories use nitrogen from the air to make ammonium sulfate, ammonium nitrate and urea. When applied to soil, nitrogen is converted to mineral form, nitrate, so that plants can take it up. Soils high in organic matter such as chocolate soils are generally higher in nitrogen than podzolic soils. Nitrate is easily leached out of soil by heavy rain, resulting in soil acidification. You need to apply nitrogen in small amounts often so that plants use all of it, or in organic form such as composted manure, so that leaching is reduced.
Acronym: N

Nitrogen Deficiency
type: Class
subclass of: Plant Disorder
type: NamedIndividual
type: Plant Disorder
related to: Nitrogen (N)
comment: All plants require sufficient supplies of macronutrients for healthy growth, and nitrogen (N) is a nutrient that is commonly in limited supply. Nitrogen deficiency in plants can occur when organic matter with high carbon content, such as sawdust, is added to soil. Soil organisms use any nitrogen to break down carbon sources, making N unavailable to plants.

Nuclear Magnetic Resonance (NMR)
type: Class
subclass of: Spectrophotometer

Nutrient
type: Class
subclass of: Physical Entity
type: NamedIndividual
comment: A nutrient is a component in foods that an organism uses to survive and grow. A nutrient is also a substance used by an organism to survive, grow, and reproduce. Macronutrients provide the bulk energy an organism's metabolic system needs to function while micronutrients provide the necessary cofactors for metabolism to be carried out. Both types of nutrients can be acquired from the environment. Micronutrients are used to build and repair tissues and to regulate body processes while macronutrients are converted to, and used for, energy.

Nuts
type: Class
subclass of: Crop
comment: A nut is a fruit consisting of a hard or tough nutshell protecting a kernel which is usually edible. In general usage and in a culinary sense, a wide variety of dry seeds are called nuts, but in a botanical context "nut" implies that the shell does not open to release the seed.

Oak
type: Class
subclass of: Nuts
type: NamedIndividual
type: Crop
type: Fruit Tree
type: Nuts
comment: Oak are one of the most popular shade trees, and provide excellent habitat for wildlife. Oak are hardwood trees, and a mature oak may grow well over 200 years. Oaks are commonly used in native forestry plantations and their wood is very popular for furniture and flooring.

Oats
type: Class
subclass of: Cereal
type: NamedIndividual
type: Cereal
type: Crop
has disease: Rice Weevil
description: Oats are annual grasses belonging to the genus Avena, in the family Poaceae and are grown primarily for use as livestock feed. Several species of oats are grown commercially in different regions of the world, including Avena sativa (common oat), Avena byzantina (Algerian or red oat), Avena nuda (naked oat) and Avena sterilis (the sterile or animated oat). Like wheat, oats generally have been developed into different varieties that are adapted to planting at different times of the year. Spring oats are planted for a late summer harvest, whereas Winter oats are planted for harvesting in early to mid summer. Overwintering varieties are more commonly grown in regions with mild winters. Oats generally possess an upright stem and fibrous root system. The inflorescence consists of a number of branches, or racemes, and spikelets (20-150 per plant) which usually contain three florets or flowers (naked oats produce 3-7 flowers per spikelet). Generally, two seeds (kernels) are produced per spikelet, but sometimes only one develops. Ancestors of the common oat, Avena sativa and the closely related Avena byzantina originated from the Fertile Crescent of the Near East.
estimate yield: Yields range from 60 to 80 US bushels per acre (5.2-7.0 m3/ha) on marginal land, to 100 to 150 US bushels per acre (8.7-13.1 m3/ha) on high-producing land. The average production is 100 bushels per acre, or 3.5 tonnes per hectare.
harvesting: Oats are ready to harvest when the kernels are at the “hard doughâ€ stage when the grain moisture content drops below 14%. The plants will have lost their green color and appear yellow-brown. The kernels harden and cannot be dented with a fingernail. Commercially produced oats are harvested by combine. Small-scale productions can be harvested by hand cutting. After harvest, the grain must be dried before being stored.
maintenance: Soil should be tested prior to planting oats in order to establish the rate of fertilizer and lime application required. Lime is often applied to raise the pH of the soil. if pH is too low, yields may be reduced. Nitrogen should be applied to oats after seedlings emerge at a rate of 20 lb per acre. Further applications are made throughout the growing season based on the specific soil and rotation schedule. In addition to nitrogen, oats require phosphorus and potassium for optimal growth. The rates of application of these nutrients should be based on the results of a soil test.
propagation: Oats are propagated from seed which can be sown in Spring or in Fall depending on the variety, prevailing climate and the intended use. Commercially produced oats are drilled into a prepared seedbed. Seedbeds should be firm and free of any weeds or vegetation. If oats are being grown to harvest grain then the recommended seeding rate is 60-90 lb of seed per acre. This should be increased slightly if the seeds is being sown by broadcasting as is commonly the case in small-scale production or home gardens. Seeds should be sown at depths of 3-7 cm (1.2-2.8 in) allowing 15-17 cm (5.9-6.7 in) between rows.
requirements: Oats grow best in cool, moist climates, with the optimum temperature for growth being between 20 and 21oC (68-70oF). The plants will thrive in well drained soils but are adapted to grow in many soil types, requiring a pH between 5.5 and 7.0. Oats are less tolerant of drought and heat than other cereals but, like other small grains, tolerate acidic soils well.
sowing rate: 70.0kg/acre (175.0kg/ha) (Recommended by www.cotswoldseeds.com)
uses: Oats can be rolled or crushed to produce oatmeal or ground to produce oat flour. Oats may also be used in the production of several baked goods such as oat cakes or oat bread. The vast majority of commercially grown oats are used as livestock feed.
References: CABI Crop Protection Compendium. (2010). Avena sativa (oats) datasheet.
comment: Oats are annual grasses belonging to the genus Avena, in the family Poaceae and are grown primarily for use as livestock feed. Several species of oats are grown commercially in different regions of the world, including Avena sativa (common oat), Avena byzantina (Algerian or red oat), Avena nuda (naked oat) and Avena sterilis (the sterile or animated oat). Like wheat, oats generally have been developed into different varieties that are adapted to planting at different times of the year. Spring oats are planted for a late summer harvest, whereas Winter oats are planted for harvesting in early to mid summer. Overwintering varieties are more commonly grown in regions with mild winters. Oats generally possess an upright stem and fibrous root system. The inflorescence consists of a number of branches, or racemes, and spikelets (20–150 per plant) which usually contain three florets or flowers (naked oats produce 3–7 flowers per spikelet). Generally, two seeds (kernels) are produced per spikelet, but sometimes only one develops. Ancestors of the common oat, Avena sativa and the closely related Avena byzantina originated from the Fertile Crescent of the Near East.
References: Paid subscription required. Mask, P. L., van Reissen, H. W. & Ball, D. (1994). Production guide for oats. Alabama Cooperative Extension System.
References: https://plantvillage.psu.edu/topics/oats/infos

Object Image
type: Class
subclass of: Imagery
type: NamedIndividual
type: Imagery
comment: Object image can be Crop Images, which are images of plants or plant parts

Observation
type: Class
subclass of: Process
References: http://www.w3.org/ns/sosa/Observation
comment: Observation - Act of carrying out an (observation) procedure to estimate or calculate a value of a property.

Observation Value
type: Class
subclass of: Sensor Output
References: https://www.w3.org/2005/Incubator/ssn/ssnx/ssn#term-ObservationValue
Source: skos:exactMatch 'measured quantity value' [VIM 2.10] http://www.bipm.org/utils/common/documents/jcgm/JCGM_200_2008.pdf skos:exactMatch 'observed value' [SensorML OGC-0700] http://www.opengeospatial.org/standards/sensorml skos:closeMatch 'observation result' [O&M] http://www.opengeospatial.org/standards/om O&M conflates what we have as SensorOutput and ObservationValue into observation result, though the OGC standard does say "result contains a value" and "a result which has a value", which fits naturally with the model here.
comment: The observation value is generally a signal that is converted to human-readable display at the sensor location or transmitted electronically over a network for reading or further processing.

Observe System
type: Class
subclass of: Physical Entity
References: http://purl.oclc.org/NET/ssnx/ssn#System

Oil
type: Class
subclass of: Product
comment: Oil is a smooth, thick liquid made from plants and is often used for cooking.

Oil palm
type: Class
subclass of: OilSeeds
type: NamedIndividual
type: Cereal
type: Crop
description: Oil palm, includes the palm species native to Africa, Elaeis guineensis, and the species native to South and Central America, Elaeis oleifera. Both species are perennial tropical trees in the family Arecaceae which are grown for their oil which is used in cooking and in industry. Oil palm trees are unbranched with a long stout single stem, or trunk, terminating in a crown of 7-100 leaves. The leaves are pinnate and can reach 3-5 m (9.8-16.4 ft) in length. The tree produces large, spherical red fruits in bunches. Up to 200 fruits can be produced per bunch and and the oil is extracted from the pulp and kernel. Oil palm can reach heights of 20-30 m (65.6-98.4 ft) and has an economic lifespan of 25-30 years, at which point they become too tall to be managed efficiently and are cut down. Left alone, oil palm has been known to live for periods up to 200 years. Oil palm may also be referred to as African oil palm and originated from West Africa.
harvesting: Harvesting of oil palms generally begins 2.5 years after the trees are planted in the field. Harvesting takes place throughout the year, usually at 2-3 week intervals, as the bunches ripen. Bunches are harvested by cutting from the tree using a sharp knife or sickle.
maintenance: Oil palms are frequently planted along with a leguminous cover crop which helps provide nitrogen to the palms while also suppressing weeds. When bunches are harvested, it is usual for fronds to be pruned. The number of fronds per palm is maintained at between 35 and 40. Oil palm plantations are rarely, if ever, irrigated. Due to the moisture requirements of the palms, supplemental irrigation is recommended during dry spells.
propagation: Trees are propagated from seed and are germinated prior to planting by moistening them and and placing them in a small box within a polyethylene bag placed in a germinator. Once the seeds have germinated they are allowed to grow further in a pre-nursery (a prepared bed, tray or container with fertile soil). The seedlings are then planted on into a nursery where they will be allowed to grow for a further 6-12 months before they are planted in the field. Field seedlings are planted in the field allowing 9 m (29.5 ft) between trees and an additional 9 m (29.5 ft) between rows. Trees will begin to bear fruit after 3-4 years.
requirements: Oil palm is typically grown in tropical lowland regions over large areas. The tree requires deep soil and stable high temperatures (30-32oC/86-89.6oF for at least 80 days) for optimum productivity. Temperatures below 20oC (68oF) will severely reduce the growth of both species, while the African oil palm will also suffer a reduction in growth at temperatures above 40oC (104oF). Palms will grow in a variety of deep tropical soils and will tolerate a pH between 4.0 and 8.0. Oil palms have a requirement for continuous moisture throughout the year. The trees can tolerate 2-3 months of dry conditions but it can affect yields.
uses: Palm oil is widely used as a cooking oil, as an ingredient in many processed foods and as a substitute for butter. Palm oil can be used to produce biodiesel. Palm oil is also used as a binding agent in cosmetics.
References: https://plantvillage.psu.edu/topics/oil-palm/infos
comment: Oil palm, includes the palm species native to Africa, Elaeis guineensis, and the species native to South and Central America, Elaeis oleifera. Both species are perennial tropical trees in the family Arecaceae which are grown for their oil which is used in cooking and in industry. Oil palm trees are unbranched with a long stout single stem, or trunk, terminating in a crown of 7-100 leaves. The leaves are pinnate and can reach 3-5 m (9.8-16.4 ft) in length. The tree produces large, spherical red fruits in bunches. Up to 200 fruits can be produced per bunch and and the oil is extracted from the pulp and kernel. Oil palm can reach heights of 20-30 m (65.6-98.4 ft) and has an economic lifespan of 25-30 years, at which point they become too tall to be managed efficiently and are cut down. Left alone, oil palm has been known to live for periods up to 200 years. Oil palm may also be referred to as African oil palm and originated from West Africa.

OilSeeds
type: Class
subclass of: Crop
type: NamedIndividual
comment: Oil Seeds
comment: Seed oil is a vegetable oil that is obtained from the seed (endosperm) of some plant, rather than the fruit. Most vegetable oils are seed oils. Some common examples are sunflower oil, canola oil, and sesame oil.

Okra
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
harvesting: Pods are usually ready for harvesting 2 months after planting. Okra pods are generally ready to harvest 4 to 6 days after flowering and pods should be harvested every 2–3 days when they have reached 7.6–15.2 cm (3–5 in) in length. Pods can be removed from the plant by cutting with a sharp knife or by snapping from the plant.
maintenance: Okra requires a moist soil for optimum development. Water should be applied at a rate of 1.5 inches every 10 days is recommended in hotter areas. In cooler climates, plants require less water as it tends to cool the plants and restrict their growth. Okra benefits from the addition of nitrogen and phosphorous. Growers generally apply ammonium phosphate fertilizer that contain both nutrients at a rate of 100 lb per acre prior to planting seed. Chicken manure may be spread in place of chemical fertilizer 1 week prior to planting. An additional side dressing of 40–60 lb of nitrogen can be applied 6 weeks after planting. Due to the long period over which okra is harvested, weeds can become a problem and require management. in the home garden, weeds can be removed by careful cultivation of the soil around the plants. Applications of appropriate herbicides may be required in commercial production.
propagation: Okra is typically propagated from seed. Soaking seeds in water overnight prior to planting helps the plants to germinate. In the home garden, seeds should be sown at a depth of 2.5 cm (1 in) leaving 25–45 cm (10–18 in) between rows only after the soil has reached a temperature of 18°C (65°F). In commercial okra production, seeds are planted in rows spaced 0.65–1.0 m (26–40 in) apart. Okra seed is commonly planted at a rate of 10 lb per acre but this quantity is vastly reduced by the use of precision planting methods. Seedlings are thinned to a final spacing of 15.0–22.5 cm (6–9 in) when they are 4–6 weeks old to produce the final plant stand.
requirements: Okra is a heat loving plant, growing best in southern climates in a well draining, light sandy to medium loams. Soils should be high in organic matter with a pH between 5.8 to 6.8. If planting in clay soil, transplants are recommended due to the difficulty encountered in seedling emerging from the heavy soil. The plant should be grown in an area of high sunlight and grows best in hot temperatures. Soil temperatures should be at least 18.3°C (65°F) with optimal growth of the plants occurring at soil temperatures between 23.9–32.3°C (75–90°F).
uses: The young seed pods are eaten fresh or cooked as a vegetable. The seeds can be used to extract oil. Okra fiber can be used in paper production.
comment: Okra, Abelmoschus esculentus, is an herbaceous annual plant in the family Malvaceae which is grown for its edible seed pods. Okra plants have small erect stems that can be bristly or hairless with heart-shaped leaves. The leaves are 10–20 cm (4–8 in) long with 5–7 lobes The plant produces flowers with five white to yellow petals which are 4–8 cm (1.6–3.1 in) in diameter. The seed pod is a capsule up to 25 cm (10 in) long, containing numerous seeds. Okra can grow 1.2–1.8 m (4–6 ft) tall and as an annual plant, survives only one growing season. Okra may also be referred to as lady's fingers and is believed to have originated in Ethiopia.
References: https://plantvillage.psu.edu/topics/okra/infos

Olive
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
comment: The olive, known by the botanical name Olea europaea, meaning "European olive", is a species of small tree in the family Oleaceae, found traditionally in the Mediterranean Basin. The olive's fruit, also called an "olive", is of major agricultural importance in the Mediterranean region as the source of olive oil;

On-line vis-NIR sensor
type: Class
subclass of: Sensor
type: NamedIndividual
type: Sensor
References: Mouazen, A. M., Maleki, M. R., De Baerdemaeker, J., & Ramon, H. (2007). On-line measurement of some selected soil properties using a VIS-NIR sensor. Soil and Tillage Research, 93, 13e27.

Onion
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
description: Onion, Allium cepa, is an herbaceous biennial in the family Liliaceae grown for its edible bulb. The stem of the plant is a flattened disc at the base and the tubular leaves form a pseudostem where their sheaths overlap. The leaves are either erect or oblique and there are 3–8 per plant. The onion plant produces pink or white flowers clustered on stalks. The bulbs are formed just above the flattened stem of the plant by overlapping leaves. The bulb is made up of several layers, each corresponding to a leaf. They are are generally oval but shape can be variable and occur in clusters of 3–18 to a plant. The bulb is protected by a membrane which turns to a papery coat. Onion plants can reach a height of 50 cm (20 in) and are grown as annuals, harvested after one growing season. Onion may also be referred to by cultivar and these include red or purple onion, shallots and spring onions or scallions. The origin of the onion has not been conclusively determined although it is likely to be somewhere in South East Asia where the gene pool is most diverse.
harvesting: When the soil at the base of the plants begins to crack, this indicates that the bulbing process has begun. Fertilizer applications should be stopped at this point. Avoid hilling the soil over the bulbs as onions mature best if they are on top of the soil. When the onion bulbs are mature, the leaves will begin to yellow and fall over. The tops can be bent right back to speed maturation. After a few days, the bulbs can be pulled and left on the soil surface to begin curing. Any damaged onions should be cooked and consumed right away as they will not store well. Onions should be cured for several weeks before storing. This can be done outdoors by placing them on a plastic sheet off of the ground, under cover if the weather is wet.
maintenance: Onions should be watered thoroughly after planting and once every week thereafter, applying approximately 1 inch of water each time. Be careful not to allow the soil to become dry and cracked as onions have a shallow root system and this indicates a lack of water. Water adequately but do not overwater. Remove any weeds around young plants by hoeing shallowly so as not to damage the roots of the onions. Plants should be fertilized avery few weeks with a high nitrogen fertilizer.
propagation: In milder climates, onion seeds can be direct seeded as soon as the soil is workable in the Spring, 4–6 weeks before the last frost date, or even earlier if starting seeds indoors to produce transplants. The planting site should be cultivated deeply and be free of stones. Work some compost into the soil and ensure that soil has a soft crumbly texture before planting. Seeds should be sown 2.5 cm (1 in) deep allowing 10–13 cm between plants and 30–45 cm (12–18 in) between rows. Onion transplants which have been started indoors can be transplanted to the garden at a similar time to planting seeds. Transplants are best produced in cell trays by planting 2 seeds per cell. Once the seeds have germinated, the seedlings should be thinned to one plant per cell. Transplants are ready to be planted when the root system has developed sufficiently to bind the soil in the cell together. Transplants should be spaced 10–13 cm allowing 30–45 cm (12–18 in) between rows. Onion sets are small, immature onion bulbs which can be purchased from seed companies and garden centers for planting in place of seeds. Select the smaller sets for planting as they are less prone to bolting. Sets should be planted 2 cm (0.75 in) deep allowing 5–7.5 cm between sets and 25 cm (10 in) between rows.
requirements: Onions are hardy, cool season vegetables that grow best at temperatures of 12 to 24 °C (55–75 °F), growing particularly well in areas with cool spring weather and drier, hotter summer weather. They require a fertile, well-draining soil such as clay or silt loams with a pH of 5.5–6.5. The plants do not do well in acidic soils. Onions should be set out in full sun for optimum bulb development. Onions are biennial vegetables and if they are left in the ground for a second year, they will produce flowers and set seed.
uses: The bulb is an edible vegetable and is the most commonly used part of the onion, usually consumed after cooking although it can be eaten fresh. The stems and leaves are also edible.
comment: Onion also known as the bulb onion or common onion, is a vegetable that is the most widely cultivated species of the genus Allium. Its close relatives include the garlic, scallion, shallot, leek, chive,[2] and Chinese onion.
References: https://en.wikipedia.org/wiki/Onion

Online Marrket
type: Class
subclass of: Market
comment: An onlinemarket (online marketplace or online e-commerce marketplace) is a type of e-commerce site where product or service information is provided by multiple third parties. Online marketplaces are the primary type of multichannel ecommerce and can be a way to streamline the production process.

Orange
type: Class
subclass of: Citrus
type: NamedIndividual
type: Citrus
type: Crop
type: Fruit Tree
description: Orange, Citrus sinensis, is an evergreen tree in the family Rutaceae grown for its edible fruit. The orange tree is branched with a rounded crown and possesses elliptical or oval leaves which are alternately arranged on the branches. The leaves have narrowly winged petioles, a feature that distinguishes it from bitter orange, which has broadly winged petioles. The tree produces white flowers singly or clustered on a raceme. The fruit is a spherical berry with a green-yellow to orange skin covered in indented glands and a segmented pulpy flesh and several seeds. Orange trees can grow to a height of 6-15 m (16-49 ft) and can live for periods in excess of 100 years. Most plantations have an economic lifespan of around 30 years. Orange may also be referred to as sweet orange or navel orange and is believed to have originated from a wild ancestor in the border between Vietnam and China.
maintenance: Newly planted trees require proper irrigation to ensure they become established. During the first year, water should be applied at the base of the trunk so that the root ball is kept moist to allow the roots to establish in the soil. Newly planted trees should be provided with water every 3-7 days. The soil should be moist, but not wet. Trees planted in sandy soils will require water more frequently. Young trees will also require a light application of fertilizer every month in the first year.
propagation: Orange seedlings are usually produced by grafting or budding to an appropriate rootstock as seeds will not produce fruit true to type. Grafting is the process by which a scion from one plant is joined to the rootstock of another to produce a new tree. Budding is a special type of grafting where the scion that is joined to the rootstock consists of a single bud. Budding is commonly used in citrus propagation as it is the easier of the two processes and works very well. Planting Orange trees can be purchased as seedlings which have already been grafted and only require planting in the garden or orchard. The best time to plant citrus trees is in Spring after all danger of frost has passed in your area. Standard sized trees should be spaced 3.7-7.6 m (12-25 ft) apart in an area that receives full sunlight, but is protected from strong winds which can damage the trees. Planting against a south facing wall will help protect the tree in cooler climates.
requirements: Orange is a subtropical plant and the trees grow best in regions with a pronounced change in season. They will grow best at temperatures between 12.8 and 37.8oC (55-100oF) during the growing season and 1.7 to 10oC (35-50oF) during dormancy. Mature orange trees can survive short periods of freezing, whereas young trees will be killed. Trees should be protected from frosts and freezing conditions to prevent damage. The trees will also tolerate drought conditions but perform poorly in water-logged soil. Trees will grow best when planted in a well-draining sandy loam with a pH between 6.0 and 7.5. Soil must be deep enough to permit adequate root development. Orange trees require full sun and should be protected from wind which can cause damage to the trees.
uses: Oranges can be consumed as a fresh fruit and are commonly pressed or squeezed to produce orange juice.
comment: Orange, Citrus sinensis, is an evergreen tree in the family Rutaceae grown for its edible fruit. The orange tree is branched with a rounded crown and possesses elliptical or oval leaves which are alternately arranged on the branches. The leaves have narrowly winged petioles, a feature that distinguishes it from bitter orange, which has broadly winged petioles. The tree produces white flowers singly or clustered on a raceme. The fruit is a spherical berry with a green-yellow to orange skin covered in indented glands and a segmented pulpy flesh and several seeds. Orange trees can grow to a height of 6-15 m (16-49 ft) and can live for periods in excess of 100 years. Most plantations have an economic lifespan of around 30 years. Orange may also be referred to as sweet orange or navel orange and is believed to have originated from a wild ancestor in the border between Vietnam and China.
References: https://plantvillage.psu.edu/topics/orange/infos

Order
type: Class
subclass of: InUse

Organic Carbon
type: Class
subclass of: Soil Composition
type: NamedIndividual
type: Soil
has transformation: Organic Carbon by Default
Acronym: OC
comment: Organic carbon (OC) or Total Organic carbon (TOC) is the amount of carbon found in an organic compound and is often used as a non-specific indicator of water quality or cleanliness of pharmaceutical manufacturing equipment.
alternative label: Soil Organic Carbon (SOC)

Organic Fertilizer
type: Class
equivalent class: Organic Mature
subclass of: Fertilizer
comment: Organic fertilizers are fertilizers derived from animal matter, animal excreta (manure), human excreta, and vegetable matter (e.g. compost and crop residues). Naturally occurring organic fertilizers include animal wastes from meat processing, peat, manure, slurry, and guano.

Organic Mature
type: Class
subclass of: Fertilizer
type: NamedIndividual
type: Fertilizer
Acronym: OM
label: Organic Manure
comment: Manure is organic matter that is used as organic fertilizer in agriculture. Most manure consists of animal feces; other sources include compost and green manure. Manures contribute to the fertility of soil by adding organic matter and nutrients, such as nitrogen, that are utilised by bacteria, fungi and other organisms in the soil. Higher organisms then feed on the fungi and bacteria in a chain of life that comprises the soil food web.

Organic Matter
type: Class
subclass of: Soil Composition
type: NamedIndividual
type: Soil
has transformation: Organic Matter by Default
unit: %
comment: Organic matter, organic material, or natural organic matter refers to the large source of carbon-based compounds found within natural and engineered, terrestrial and aquatic environments. It is matter composed of organic compounds that have come from the remains of organisms such as plants and animals and their waste products in the environment.
Acronym: OM

Organization
type: Class
subclass of: Physical Entity
comment: An organization, or organisation is an entity, such as a company, an institution, or an association, comprising one or more people and having a particular purpose.

Other Chemical
type: Class
subclass of: Nutrient
comment: Other common chemicals related to agriculture.

Oxygen (O2)
type: Class
subclass of: Essential Nutrient
type: NamedIndividual
type: Essential Nutrient
type: Gas
Acronym: O2
comment: Oxygen is the chemical element, which is necessary for cellular respiration; plants use oxygen to store energy in the form of ATP.
comment: Oxygen is an important element for plant growth. Reducing its concentration in the soil affects plant physiological processes such as nutrient and water uptake as well as respiration, the redox potential of soil elements and the activity of microorganisms.

Phosphorus Pentoxide (P2O5)
type: Class
equivalent class: Phosphate (P2O5)
subclass of: Mineral
type: NamedIndividual
type: Mineral
unit: Kg/Ha
comment: Phosphorus pentoxide is a chemical compound with molecular formula P4O10 (with its common name derived from its empirical formula, P2O5). This white crystalline solid is the anhydride of phosphoric acid. It is a powerful desiccant and dehydrating agent.
label: P2O5

Papaya
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
description: Papaya, Carica papaya, is an herbaceous perennial in the family Caricaceae grown for its edible fruit. The papaya plant is tree-like,usually unbranched and has hollow stems and petioles. The leaves are palmately lobed, spirally arranged and clustered at the growing tip of the trunk. Papaya trees can be male, female or hermaphrodite and the type of inflorescence produced is reflective of this. Male trees produce many flowers on long, pendulous panicles while female trees produce either solitary flowers of clusters of a few flowers which are yellow-green in color. Hermaphrodite trees produce bisexual flowers. The papaya fruit is a large fleshy berry with smooth green skin that ripens to yellow or orange. The flesh of the fruit is thick and succulent and ranges in color from yellow to red or orange. The fruit contains many black wrinkled seeds. Papaya trees range in height from 2–10 m (6.6–33 ft) and can live for up to 25 years. Plantations are usually replaced every 3 years to ensure maximum productivity. Papaya may also be referred to as pawpaw and is believed to originate from the Caribbean region on Central America.
harvesting: Papaya fruits generally require 22–26 weeks to mature. The fruits can be picked when 1/5 of the fruit surface has turned from green to yellow but leaving them on the tree longer will increase the sugar content of the fruit. Fruit can be twisted or snapped from the tree or cut using a sharp knife.
maintenance: Papaya seedlings are very susceptible to competition from weeds and the areas around the trees should be kept weed-free. A layer of mulch around the plants can successfully suppress weeds. Papaya requires regular fertilizer applications to meet the nutrient requirements for fruit production. In commercial plantations, fertilizer is usually applied 2–4 times per year. In the home garden, the addition of 1/4 cup of a balanced fertilizer every 14 days is usually sufficient. As the trees mature, the amount of fertilizer should be increased. When trees reach 7 to 8 months, 1–2 lbs of a complete fertilizer should be provided every 2 months. Papaya trees should be watered regularly, particularly during hot, dry periods. Papaya trees do not require pruning but it is good practice to remove any dead leaves from the tree.
propagation: Papaya is propagated from seed due to the labor involved in producing cuttings. Seeds are usually sown in small containers or nursery beds in sterilized soil. It is usual to sow 3–4 seeds per container and seeds can take 3–4 weeks to germinate depending on temperature.. Seedlings are transplanted after approximately 2 months when the reach approximately 20 cm (8 in) in height and possess 3–4 leaves. Seedlings are commonly planted on hills or ridges to aid drainage and should be spaced 2–3 m (6.6–9.8 ft) apart.
requirements: Papaya is a tropical plant and will grow optimally at temperatures between 21 and 33°C (69.8–91.4°F) in areas with no frost. Papaya can be grown in a range of soils as long as there is adequate drainage but will grow optimally in light, well-draining soils with a pH between 5.5 and 6.5. Papaya requires well distributed rainfall of about 4 inches per month. In areas with low rainfall, trees should be provided with supplemental irrigation. Trees are very sensitive to flooding and water-logged soils should be be avoided. Papaya is also sensitive to high winds and the tall trees can be easily toppled. Windy areas should be avoided for planting.
uses: Papaya fruits are commonly eaten fresh. The may also be processed into jams, jellies and juices are dried and candied. Green fruits and young leaves can be cooked and eaten as a vegetable.
comment: Papaya is native to Central America and exists in tropical and subtropical areas throughout the world. There are three different tree types, female plants, male plants and bisexual plants. The female and bisexual plants are the only ones that produce fruit. Depending on the tree type, this fruit is small to medium round or medium to large oblong shape. Fruit flesh is generally yellow, although some red and orange types exist as well.
References: https://plantvillage.psu.edu/topics/papaya-pawpaw/infos

Pathogen
type: Class
subclass of: Microoganism
comment: A pathogen is anything that can produce disease; Typically the term is used to describe an infectious agent such as a virus, bacterium, prion, a fungus, or even another micro-organism. There are several substrates including pathways where the pathogens can invade a host. The principal pathways have different episodic time frames, but soil contamination has the longest or most persistent potential for harboring a pathogen. Diseases caused by organisms in humans are known as pathogenic diseases.

Pea
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Vegetable
comment: The pea is most commonly the small spherical seed or the seed-pod of the pod fruit Pisum sativum. Each pod contains several peas, which can be green or yellow. Botanically, pea pods are fruit since they contain seeds and develop from the ovary of a (pea) flower. The name is also used to describe other edible seeds from the Fabaceae such as the pigeon pea (Cajanus cajan), the cowpea (Vigna unguiculata), and the seeds from several species of Lathyrus.

Peach
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
maintenance: Peaches should be pruned annually, including the year of planting and are best trained to an open center. When the tree is bearing fruit, it is important to thin the fruits to prevent the tree from over-bearing. Aim to have 1 fruit every 8 inches. This allows fruits to become larger and prevents the tree from reducing production the following year. Trees should be watered regularly during the growing season to aid with fruit development. During dry periods, water trees every 10 to 14 days. Apply water deeply and widely, to at least the width of the canopy. Trees will also benefit from the application of a nitrogen fertilizer in Spring.
propagation: Peach trees are propagated vegetatively to maintain the desirable genetic characteristic of the parent. Commercial trees are usually propagated by T-budding in the summer. T-budding involves joining a bud from a desired variety onto an appropriate rootstock. Buds can also be collected late in the summer for budding during dormancy. The budwood should be collected from healthy shoots of current season growth, leaves removed and the budwood kept moist until the union with the rootstock is made. The budwood is joined to the rootstock by inserting the bud stick into a T-shaped cut on the bark of the rootstock and allowing it to grow. The rest of the tree is pruned to force the growth of the new bud which has the desired characteristics.
requirements: Peaches grow best in areas with warm summers and require a summer temperature between 20 and 30°C (68-86°F) for the fruit to mature. The trees also have a chilling requirement to break dormancy but fruit buds can be damaged by prolonged periods below -15°C (5°F) and as such, the majority of production takes place in countries which are relatively close to the equator. In addition, peach trees bloom early and can be damaged by late frosts. Trees grow best in well-draining sandy loams in areas that receive full sun but can will grow in a variety of soils as long as water does not sit on the surface after heavy rainfall. It can be beneficial to plant the trees on elevated land to allow cold air to drain away. Trees will grow optimally in soils with a pH of 6.5.
uses: Peach trees are primarily grown for their fruit which is consumed fresh. Peach trees are also grown as ornamental plants.
comment: Peach, Prunus persica, is a deciduous tree or shrub in the family Rosaceae grown for its edible fruit of the same name. The peach tree is relatively short with slender and and supple branches. The leaves are alternately arranged, slender and pointed. The tree produces pink flowers which have five petals and emerge in January and February. The fruit is a velvety, soft, fleshy red to yellow-orange fruit which is oval in shape and 3-8 cm in diameter. Peach trees can grow up to 8 m (19 ft) in height and produce fruit for 10 to 20 years. Peach may also be referred to as nectarine, the two fruits belonging to the same species, although nectarines have smooth skin, and are believed to have originated in China.
References: https://plantvillage.psu.edu/topics/peach/infos

Peanut
type: Class
subclass of: Nuts
type: NamedIndividual
type: Crop
type: Nuts
harvesting: Peanuts are ready for harvest between 85 and 130 days after sowing, depending on the variety. Pods can be harvested by hand or by mechanical means. Plants should be pulled from the ground and the pods removed. After harvest, pods are dried in the sun for 2–10 days until their moisture content is about 10%.
maintenance: To achieve maximum yields, peanut fields should be kept as free as possible from competing weeds. Plants should be supplied with additional irrigation if dry conditions coincide with flowering and pod fill. Peanuts do not generally require the addition of supplemental nitrogen as their roots form symbioses bacteria which are capable of fixing atmospheric nitrogen in the soil. Peanuts require calcium in the soil for good pod fill. the calcium in the soil is absorbed directly by the pods in the pegging zone. Calcium deficient soils may result in empty or poorly filled pods.
propagation: Peanut is usually propagated from seed. Seeds should be planted in a well prepared seedbed in soil that is loose and crumbly with no large clumps. The seedbed should be free from weeds which will compete with the peanut seedlings. Weeds may be removed by hand cultivating or through the use of an appropriate herbicide. Peanut seeds should be planted by hand to a depth of 3–5 cm (1–2 in). It is best to ridge the soil or use flat beds as this will make harvesting the peanuts easier. Peanuts can be grown a sole crop or intercropped with other crops such as corn (maize), cassava or soybean.
requirements: Peanuts grow very well in tropical and subtropical climates, requiring warm temperatures and a long growing season. Plants grow optimally at a temperatures between 30 and 34°C (86–93°F) although they will tolerate a range between 15 and 45°C (59–113°F). Temperatures above 34°C may damage flowers. The peanut plant grows best in a light, well draining sandy loam, but may also grow in heavier soils. The optimal pH range for peanut growth is with a pH of 6.0-6.5. Peanuts are resistant to drought and can withstand long periods without water albeit with a reduction in yield. For optimal production, a well distributed rainfall of between 500 and 600 mm of water over the course of the growing season is ideal.
uses: Most commercially grown peanuts are used for the extraction of their oil which is used in cooking. The by product of oil extraction is a pressed cake which is used as an animal feed and also in the production of peanut flour. Raw kernels are also commonly roasted and eaten as a snack food.
comment: Peanut, also called groundnut, earthnut, or goober, legume of the pea family, grown for its edible seeds. Native to tropical South America, the peanut was at an early time introduced to the Old World tropics. The seeds are a nutritionally dense food, rich in protein and fat.
References: https://plantvillage.psu.edu/topics/peanut-groundnut/infos

Pear
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
harvesting: Pear fruits are usually harvested when mature, but are are ripened off of the tree. Pear allowed to ripen on the tree tend to ripen from the core outwards, resulting in fruit with mushy centers. It can be difficult to determine the correct time to harvest pears and so a variety of factors should be taken into consideration prior to picking. One of the most common indicators of maturity is the firmness of the fruit. Commercial plantations use devices called penetrometers to determine fruit firmness. Pears should be harvested when the reading is between 16 and 19 pounds.
maintenance: The amount of fertilizer that should be supplied to pear trees depends on the soil type and composition and should be checked with the aid of a soil test. Less nitrogen is supplied to pear trees than to apple as it promotes vigorous growth, increasing susceptibility to fire blight. in the first year of growth, phosphorous and potassium may be required but in subsequent years, only nitrogen is generally added to the soil. The area around the base of the pear tree should be kept free from weeds which compete for water and nutrients. In commercial plantations, appropriate herbicides may be used for weed control. In the home garden, weeds can be removed by hand. Pear trees may require fruit to be thinned to prevent over production which can lead to reduced yields in subsequent years or cause damage to the tree through excess weight. fruits should be thinned early in the season to a final density of approximately 1 fruit every 15 cm (6 in).
propagation: Pear trees are propagated by budding onto suitable rootstocks. Rootstocks are usually also pear but quince is also used in warmer growing regions. Pear trees are usually acquired from the nursery as bare-rooted seedlings. They are planted by digging a whole which is large enough to accommodate the outstretched roots of the tree without bending. The graft union should be at least 10–15 cm (4–6 in) above the soil line.Trees should be spaced 4.8 m (16 ft) apart, leaving 7.5 m (25 ft) between rows. The best time for planting is in early Spring or Fall while the trees are still dormant. Young trees are susceptible to wind damage and should be provided with a wind break.
requirements: Pears grow very well in areas that have a late frost and a cool, dry summer and will grow at temperatures between -26 and 45°C (-14.8–133°F). Pear trees have a chilling requirement of between 1000 and 1500 hours between 0 and 7°C (32–44.6°F) to break dormancy depending on the particular variety being grown. Generally, trees must cross pollinate with a different variety in order to successfully set fruit. Pear trees require a deep, well draining soil with a pH of 6–7 and will grow in sandy, medium or heavy soil. Pear trees are the most tolerant of all fruit trees of wet soils but roots should not be waterlogged for more than a few days at a time.
uses: The pear fruit is eaten fresh or can be cooked in a range of sweet dishes. The fruit may also be pressed for juice. The leaves of the tree can be used to produce dyes and the wood can be used in carpentry and is very durable.
References: https://plantvillage.psu.edu/topics/pear/infos
comment: Pear, Pyrus communis, is a perennial deciduous tree in the family Rosaceae, grown for its fruit. The tree is a short deciduous tree with a tall and narrow crown and alternately arranged, simple leaves. The leaves are elliptical with finely serrated margins and defined tips and can reach 2–12 cm (0.7–4.7 in) in length. The tree produces white flowers which are 2.5 cm (1 in) in diameter and a fleshy green pyriform fruit. Pear trees can reach 9 m (30ft) and will produce fruit for about 20 years . The European pear may be referred to by name of its cultivars which include the Bartlett, d'Anjou, Kaiser Alexander and Comice pears. The European pear occurs only as cultivated trees and does not grow wild in nature. It is descendant of wild European and Asian pear trees.

Pepper
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Vegetable
description: Peppers are varieties of several plant species in the genus Capsicum, including Capsicum annuum (e.g. jalapeños and cayennes), C. frutescens (e.g. tabasco and piri piri), C. chinense (e.g. habaneros and Scotch bonnet), C. pubescens (e.g. rocoto and locoto) and C. baccatum (e.g. aji peppers) and their hybrids which include some of the hottest peppers known. Unsurprisingly, the plants are most commonly grown for the harvest of their fruits, which are used as a spice. Hot peppers are perennial plants with varying structure. They can be herbaceous or shrub-like but are generally branching with green-brown stems and simple oval leaves. The plants produces flowers with five teeth (petals) which are usually white in color. Chilli pepper plants can reach heights in the range of 0.5 to 1.5 m (1.6–4.9 ft) tall and although they are perennial species, they are commonly grown as annuals, with fruit being harvested for one growing season.
maintenance: Peppers will benefit from applications of fertilizer to keep them supplied with essential nutrients. A timed release fertilizer can be applied at bloom or the plants can be fed with a liquid fertilizer at planting and every two weeks thereafter. In addition, pepper plants can produce a heavy load of fruit and it can be helpful to provide the plants with some support using stakes or cages to prevent branches from breaking under the weight. Use a soft material to tie plants to stakes or cages.
requirements: Peppers are warm-season crops and grow best at temperatures between 18 and 30°C (65–86°F). They can be grown in many soil types although sandy soils warm faster in Spring and are good for early planting. The soil should have a pH between 6 and 7. Peppers will not tolerate water saturated soil and should be planted in a well draining soil or raised bed. Peppers should be planted in an area that receives full sun for most of the day.
uses: Hot peppers can be eaten fresh or cooked as a vegetable, seasoning or garnish.
comment: Peppers are varieties of several plant species in the genus Capsicum, including Capsicum annuum (e.g. jalapeños and cayennes), C. frutescens (e.g. tabasco and piri piri), C. chinense (e.g. habaneros and Scotch bonnet), C. pubescens (e.g. rocoto and locoto) and C. baccatum (e.g. aji peppers) and their hybrids which include some of the hottest peppers known. Unsurprisingly, the plants are most commonly grown for the harvest of their fruits, which are used as a spice. Hot peppers are perennial plants with varying structure. They can be herbaceous or shrub-like but are generally branching with green-brown stems and simple oval leaves. The plants produces flowers with five teeth (petals) which are usually white in color. Chilli pepper plants can reach heights in the range of 0.5 to 1.5 m (1.6–4.9 ft) tall and although they are perennial species, they are commonly grown as annuals, with fruit being harvested for one growing season.

Perimeter
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute
comment: The perimeter is the distance all the way around the outside of a 2D shape. To work out the perimeter, add up the lengths of all the sides.

Person
type: Class
subclass of: Physical Entity
References: foaf:Person
comment: Person is a person considered alone, rather than as belonging to a group of people while person is an individual; usually a human being.

Pesticides
type: Class
subclass of: Crop Protection
comment: Pesticides are substances that are meant to control pests, including weeds.[1] The term pesticide includes all of the following: herbicide, insecticides nematicide, molluscicide, piscicide, avicide, rodenticide, bactericide, insect repellent, animal repellent, antimicrobial, fungicide and disinfectant. The most common of these are herbicides which account for approximately 80% of all pesticide use. Most pesticides are intended to serve as plant protection products, which in general, protect plants from weeds, fungi, or insects.

Phenomenon
type: Class
subclass of: Abstract Entity
comment: Phenomenon is a fact, occurrence, or circumstance observed or observable.

Phosphate (P2O5)
type: Class
subclass of: Other Chemical
type: NamedIndividual
type: Chemical Fertilizer
Acronym: P2O5

Phospholipid
type: Class
subclass of: Biomolecule
type: NamedIndividual
type: Biomolecule
References: Waldron, Keith W., ed. Advances in Biorefineries: Biomass and Waste Supply Chain Exploitation. Elsevier, 2014.
comment: Phospholipids are mostly made from glycerides by substituting one of the three fatty acids by a phosphate group with some other molecule attached to its end.

Phosphorus (P)
type: Class
subclass of: Essential Nutrient
type: NamedIndividual
type: Essential Nutrient
Acronym: P
comment: Phosphorus helps transfer energy from sunlight to plants, stimulates early root and plant growth, and hastens maturity. Very few Australian soils have enough phosphorus for sustained crop and pasture production and the North Coast is no exception. The most common phosphorus source on the North Coast is superphosphate, made from rock phosphate and sulfuric acid. All manures contain phosphorus; manure from grain-fed animals is a particularly rich source.

Phosphorus Deficiency
type: Class
subclass of: Plant Disorder
type: NamedIndividual
type: Plant Disorder
related to: Phosphorus (P)
comment: Phosphorus Deficiency
comment: Phosphorus deficiency is a plant disorder associated with insufficient supply of phosphorus. Phosphorus refers here to salts of phosphates (PO43−), monohydrogen phosphate (HPO42−), and dihydrogen phosphate (H2PO4−). These anions readily interconvert, and the predominant species is determined by the pH of the solution or soil. Phosphates are required for the biosynthesis of genetic material as well as ATP, essential for life. Phosphorus deficiency can be controlled by applying sources of phosphorus such as bone meal, rock phosphate, manure, and phosphate-fertilizers.

Photosynthetic
type: Class
subclass of: Plant Phenomenon
type: NamedIndividual
type: Plant Phenomenon
comment: Photosynthesis, the process by which green plants and certain other organisms transform light energy into chemical energy. During photosynthesis in green plants, light energy is captured and used to convert water, carbon dioxide, and minerals into oxygen and energy-rich organic compounds.

Physical Control
type: Class
subclass of: Treatment

Physical Entity
type: Class
References: http://www.loa-cnr.it/ontologies/DUL.owl#PhysicalObject
comment: A physical entity is an entity that has physical existence

Pigment
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: The primary function of pigments in plants is photosynthesis, which uses the green pigment chlorophyll and several colorful pigments that absorb as much light energy as possible. Pigments are also known to play a role in pollination where pigment accumulation or loss can lead to floral color change, signaling to pollinators which flowers are rewarding and contain more pollen and nectar. Plant pigments include many molecules, such as porphyrins, carotenoids, anthocyanins and betalains. All biological pigments selectively absorb certain wavelengths of light while reflecting others. The principal pigments responsible are: + Chlorophyll is the primary pigment in plants; + Carotenoids are red, orange, or yellow tetraterpenoids.

Plan
type: Class
subclass of: InUse

Planetary Phenomena
type: Class
subclass of: Natural Phenomenon
comment: Planetary phhenomenon is an observable fact or event related to soil, such as Drought, sinkhole, thundersnow.

Planned Process
type: Class
subclass of: Data Mining
References: CRISP-DM

Planning
type: Class
subclass of: Planned Process
References: CRISP-DM

Plant Density
type: Class
subclass of: Cropping
type: NamedIndividual
type: Cropping
comment: Plant density refers to the NUMBER of plants per unit area of land. Plant spacing, on the other hand, refers to the ARRANGEMENT of plants on the area planted.

Plant Disorder
type: Class
subclass of: Crop Problem
comment: Plant disorders are caused by non-pathological conditions such as poor light, adverse weather, water-logging, phytotoxic compounds or a lack of nutrients, and affect the functioning of the plant system.

Plant Phenomenon
type: Class
subclass of: Phenomenon
comment: Plant phhenomenon is an observable fact or event related to plants, such as germination, pollination, blooming.

Planting
type: Class
subclass of: CultivatingProcess

Plum
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Fruit Tree

Pods
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: A pod is a case that holds a plant's seeds. In many plants, seeds grow in groups, nestled within a pod. Peas are one such plant, and many other legumes and flowers have seed pods as well. The expression "two peas in a pod" refers to the way seeds are clustered together within the pod, and means "very similar to each other." Pod comes from the fifteenth century term podware, "seed of legumes or grain."

Policy
type: Class
subclass of: Document
comment: Policy is principle of action which is written as document and adopted or proposed by an organization or individual.

Pollination
type: Class
subclass of: Plant Phenomenon
type: NamedIndividual
type: Plant Phenomenon
comment: The process of pollination refers to the transfer of pollen to the female organs of the plant. Optimum factor for ideal pollination are: relative humidity rate of 50-70% and temperature of 24.4 degrees Celsius. If the humidity rate is higher than 90%, the pollen would not shed. Increasing air circulation is a favourable method of keeping humidity levels under control.

Polyphenol
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
comment: Polyphenols are micronutrients that we get through certain plant-based foods. They're packed with antioxidants and potential health benefits. It's thought that polyphenols can improve or help treat digestion issues, weight management difficulties, diabetes, neurodegenerative disease, and cardiovascular diseases. Polyphenols are a large family of naturally occurring organic compounds characterized by multiples of phenol units. They are abundant in plants and structurally diverse. Polyphenols include flavonoids, tannic acid, and ellagitannin, some of which have been used historically as dyes and for tanning garments.

Pomegranate
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
harvesting: Pomegranate fruits are generally ready to harvest between 6 and 7 months after flowering, fruits should be allowed to mature fully on the branch prior to harvest as they will not continue to ripen off of the tree. Fully mature fruit turn bright red in color and make a metallic sound when tapped. Fruits should be harvested by cutting from the tree.
maintenance: Pomegranates have a similar water requirement to citrus trees and should be provided with additional irrigation during dry periods. Pomegranate orchards usually utilize drip, furrow or sprinkler irrigation systems to promote optimal yields. Pomegranates can be pruned to a single stemmed tree or allowed to grow as a multi-stemmed bush. Suckers should be removed from around the central trunks as they develop. Pomegranates will benefit from the addition of nitrogen. Nitrogen should be applied at a rate of 0.2–0.5 kg per tree each year. Pomegranate fruits are usually thinned to promote the production of larger fruits
propagation: Commercial pomegranate trees are propagated from softwood and hardwood cuttings as seeds will not breed true to type. Hardwood cutting are generally preferred over softwood due to the ease with which they root. Hardwood cuttings are taken from shoots or suckers from the previous season and are rooted in nursery beds after treatment with a rooting hormone. Cuttings are grown in the nursery for one season before being planted out in the orchard. Rooted cuttings are best planted in winter or early spring and are usually spaced 3.5–5.5 m (11.5–18 ft) apart. The young trees are headed back to a height of 60–70 cm (23–28 in) after planting to promote branching.
requirements: Pomegranates grow best in temperate or semi-arid climates with a cool winter and warm summer. They are less hardy than many other deciduous fruit trees but more hardy than citrus. Pomegranates will suffer severe damage when temperatures drop below -10°C (14°F). Pomegranate can be grown successfully on a range of soil types, including calcareous soils and acidic loam but will grow optimally in deep, well-draining loam.
uses: Pomegranate is primarily eaten as a fresh fruit by splitting open the rind and consuming the seeds. The seeds may be used in salads. The fruit may also be used to produce juice, either by removing and pressing the seeds or by pressing the whole fruit.
References: https://plantvillage.psu.edu/topics/pomegranate/infos
comment: Pomegranate, Punica granatum, is a deciduous or evergreen tree or shrub in the family Punicaceae grown for its edible fruits. The pomegranate tree is branched and spiny with glossy, leathery, oval to oblong leaves that grow in whorls of five or more on the branches. The tree produces bright red flowers singly at the tips of the branches and a rounded hexagonal fruit with a thick pink-red skin. The fruit has a thick, leathery rind which protects the pulp[ and seeds inside. The inside of the fruit is separated into compartments by white spongy tissue. Each compartment contains seeds and pulp. Each pomegranate fruit may contain as many as 600 seeds. Pomegranate trees can reach a height of 10 m (33 ft) and can be very long lived, although their economic lifespan is usually between 12 and 15 years. Pomegranate may also be referred to as grenadine or Chinese apple and originated from Central Asia, likely in Iran.

Population
type: Class
subclass of: Cropping
type: NamedIndividual
type: Cropping
comment: Plant population refers to the NUMBER of plants on the area of land. Plant spacing, on the other hand, refers to the ARRANGEMENT of plants on the area planted.

Porosity
type: Class
subclass of: Soil Physical
type: NamedIndividual
type: Soil

Potassium (K)
type: Class
subclass of: Essential Nutrient
type: NamedIndividual
type: Essential Nutrient
Acronym: K
comment: Potassium increases vigour and disease resistance of plants, helps form and move starches, sugars and oils in plants, and can improve fruit quality. Potassium is low or deficient on many of the sandier soils of the North Coast. Also, heavy potassium removal can occur on soils used for intensive grazing and intensive horticultural crops (such as bananas and custard apples). Muriate of potash and sulfate of potash are the most common sources of potassium.

Potassium Deficiency
type: Class
subclass of: Plant Disorder
type: NamedIndividual
type: Plant Disorder
related to: Potassium (K)
comment: Potassium deficiency, also known as potash deficiency, is a plant disorder that is most common on light, sandy soils, because potassium ions (K+) are highly soluble and will easily leach from soils without colloids. Potassium deficiency is also common in chalky or peaty soils with a low clay content. It is also found on heavy clays with a poor structure.

Potato
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Vegetable
description: Potato, Solanum tuberosum, is an herbaceous perennial plant in the family Solanaceae which is grown for its edible tubers. The potato plant has a branched stem and alternately arranged leaves consisting of leaflets which are both of unequal size and shape. The leaflets can be oval to oblong in shape and the leaves can reach 10-30 cm (4-12 in) in length and 5-15 cm (2-6 in) wide. The potato plant produces white or blue flowers and yellow-green berries. The potato tubers grow underground and generally located in the top 25 cm (10 in) of the soil. The tubers can range in color from yellow to red or purple depending on the variety. Potato plants can reach in excess of 1 m (3.3 ft) in height and are grown as annual plants, surviving only one growing season. Potato may also be referred to as, spud, Irish potato, white potato or Spanish potato and originates from South America.
harvesting: The time potato tubers take to reach maturity is variable and depends on the variety being grown, although it is usually about 2-3 weeks after the plants have bloomed. All tubers should be harvested when the vines have died or before a frost which will kill the plants. Harvest the tubers by gently digging them up with a fork or with your hands if the soil is loose enough. Harvesting is easier when the soil is dry. Do not wash the tubers prior to storing.
maintenance: Potatoes are sensitive to soil moisture and grow best when soil moisture is consistent. The plants generally require about 1 inch of water a week from rainfall or irrigation. Water-satureated soil should be avoided as it can poorly formed tubers and rot. In addition, potatoes are heavy feeders and the addition of a balnced fertilizer every two weeks can help increase tuber yields.
propagation: Potatoes are usually grown from seed potatoes. Small sections of a large tuber known as “seed piecesâ€ or small seed potatoes can be used. Seed potatoes can be purchased from garden centers when in season and through seed companies. Each piece of planting material should have at least two eyes. The eyes are the area from which a shoot will sprout and after cutting should be allowed to cure for a few days prior to planting in the soil. Curing helps to protect the seed pieces from rotting as well as reducing the likelihood of a pathogen invading. Curing is very simple and can be achieved by laying out the seed pieces on paper towels and allowing to dry out for 3 to 4 days. Seed potatoes and pieces can be planted 0-2 weeks after the last frost or as soon as the soil is workable in early Spring bearing in mind that the plants will be killed by a frost. Prepare soil for planting by working in compost or well-rotted manure. Potatoes are commonly grown in hilled rows. This involves digging shallow trenches 60-90 cm (2-3 ft) apart after the addition of the compost or manure. Space the seed pieces 30 cm (12 in) apart and cover with 7.5 cm (3 in) of soil. When the plants reach approximately 25 cm (10 in) in height mound the soil up around the stems of the plants so that they are covered up to about half of their height. This protects shallow tubers from turning green from exposure to sunlight. Allow the plants to grow another 25 cm (10 in) and again mound the soil. Continue this process for the duration of the crops growth. Straw can be used around the plants instead of soil which eliminates the need to dig for the tubers but care must be taken to add fresh the straw regularly as it will break down over time. Alternatively, potatoes can be grown in containers or burlap bags.
requirements: Potatoes are cool season crops which grow best in cooler climates or as a winter crop in areas with warm summers. They are sensitive to heat but can tolerate a light frost. Potatoes require a deep, fertile, loose, well-draining soil with a pH between 5.8 and 6.5 and will grow optimally at daytime temperatures between 18 and 27oC (65-80oF) and night time temperatures between 12 and 18oC (55-65oF). Plants can also be successfully grown outdoors in burlap bags or large containers.
uses: Potato tubers are a staple food source in temperate regions and are eaten after cooking. They may be cut or sliced and made into potato chips or fries. Potatoes can also be processed into starch, alcohol or flour.
References: https://plantvillage.psu.edu/topics/potato/infos
comment: Potato, Solanum tuberosum, is an herbaceous perennial plant in the family Solanaceae which is grown for its edible tubers. The potato plant has a branched stem and alternately arranged leaves consisting of leaflets which are both of unequal size and shape. The leaflets can be oval to oblong in shape and the leaves can reach 10-30 cm (4-12 in) in length and 5-15 cm (2-6 in) wide. The potato plant produces white or blue flowers and yellow-green berries. The potato tubers grow underground and generally located in the top 25 cm (10 in) of the soil. The tubers can range in color from yellow to red or purple depending on the variety. Potato plants can reach in excess of 1 m (3.3 ft) in height and are grown as annual plants, surviving only one growing season. Potato may also be referred to as, spud, Irish potato, white potato or Spanish potato and originates from South America.

Precipitation
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
unit: mm
comment: Precipitation is the quality or state of being precipitate, atmospheric water vapor that falls under gravity from clouds. The main forms of precipitation include drizzle, rain, sleet, snow, ice pellets, graupel and hail.

Presentation
type: Class
subclass of: Information Object

Pressure
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
comment: Pressure varies smoothly from the Earth's surface to the top of the mesosphere.

Pretrained Model
type: Class
subclass of: Computing Algorithm
comment: Pre-trained models means the models which have been already trained on some sort of data with different number of classes. For Example : ImageNet which contains 1.2 million images with 1000 categories.

Prevention
type: Class
subclass of: Biological
comment: The act or method of preventing or hindering of disease infection

Process
type: Class
subclass of: Abstract Entity
comment: A process is a series of actions which are carried out in order to achieve a particular result during farrming agricultural production.

Product
type: Class
subclass of: Physical Entity
comment: Product is a substance that is manufactured or refined from agricultural things for sale.

Project Plan
type: Class
subclass of: Information Object
comment: List the stages to be executed in the project, together with duration, resources required, inputs, outputs and dependencies. Wherever possible make explicit the large-scale iterations in the data mining process, for example, repetitions of the modeling and evaluation phases. As part of the project plan, it is also important to analyze dependencies between time schedule and risks. Mark results of these analyses explicitly in the project plan, ideally with actions and recommendations for actions if the risks appear. Remember that, although this is the only task in which the Project Plan is directly named, nevertheless it should be continually consulted and reviewed throughout the project. It should at least be consulted whenever a new Task is started or a further iteration of a task or activity is begun.

Project Report
type: Class
subclass of: Report
References: CRISP-DM
comment: The final report is used to summarize the project and its results. Contents: - Summary of Business Understanding: background, objectives and success criteria. - Summary of data mining process. - Summary of data mining results. - Summary of results evaluation. - Summary of deployment and maintenance plans. - Cost/benefit analysis. - Conclusions for the business. - Conclusions for future data mining.

Protein
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Protein content

Protozoa
type: Class
subclass of: Microoganism
comment: Protozoa (also protozoan, plural protozoans) is an informal term for a group of single-celled eukaryotes, either free-living or parasitic, which feed on organic matter such as other microorganisms or organic tissues and debris. Historically, protozoans were regarded as "one-celled animals", because they often possess animal-like behaviours, such as motility and predation, and lack a cell wall, as found in plants and many algae. Although the traditional practice of grouping protozoa with animals is no longer considered valid, the term continues to be used in a loose way to describe single-celled protists (that is, eukaryotes that aren't animals, plants, or fungi) that feed by heterotrophy.

Protozoal Disease
type: Class
subclass of: Crop Disease
comment: Infectious plant diseases are caused by flagellate protozoa and can range in severity from mild leaf or plant damage to death. Protozoa are not animals, plants, algae, fungi or virus. They are, however, parasitic.

Pumpkin
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Vegetable
harvesting: Pumpkins are harvested by hand when the fruit reaches full maturity. The fruit is ripe when the color changes uniformly and the rind becomes hard. Pumpkins should be harvested by cutting the stem of the fruit with a sharp knife. it is best to harvest the fruit with a portion of stem as the fruit will keep better.
maintenance: Pumpkin vines are sprawling and require plenty space to grow. Vines can be trained to grow on a trellis or fence. Pumpkin also require a continuous supply of water and where drip irrigation is not being used, plants should be watered deeply once per week, providing at least an inch of water. Shallow watering or watering less frequently encourages a shallow root system. Mulches can be used to conserve soil moisture and black polyethylene mulch has the advantage of warming the soil. All pumpkin varieties produce both male and female flowers (monoecious) and are pollinated by insects such as bees.
propagation: Pumpkin can be direct seeded or sown indoors and transplanted. If direct seeding,seeds should be sown after the last frosts and when the soil has warmed to at least 15.6°C (60°F). Sow 1–2 seeds 1.3–2.5 cm (0.5–1.0 in) deep, at least 90 cm (~3 ft) apart if growing bush varieties and 120–150 cm (4–5 ft) apart if growing vining varieties. Allow a further 1–3 m (6–10 ft) between rows depending on the variety. If transplanting, seeds should be sown 3–4 weeks before the last frost date in your area and transplanted before the plants develop their second set of true leaves. Seeds in sown both indoors and out require lightly moist soil for germination, care should be taken to avoid overwatering. Seeds should germinate in 5–10 days depending on the soil temperature.
requirements: Pumpkin is a warm-season crop, requiring lots of sun and good drainage to develop optimally and growing best at temperatures between 18 and 25°C (65–75°F). Pumpkin will yield best if grown in a fertile, well-draining soil, rich in organic matter and with a pH between 6.5 and 7.5. Pumpkin should be planted in full sun and provided with ample soil moisture due to their shallow root system. Vining varieties can grow to very large sizes and require a good deal of space. Smaller bush varieties are available for more modest spaces.
uses: Pumpkin flesh can be cooked and eaten in a variety of dishes. The seeds are also edible and are commonly roasted and eaten as a snack.
References: https://plantvillage.psu.edu/topics/pumpkin/infos
comment: Pumpkin is the name given to a group of plant species in the genus Cucurbita, including Cucurbita pepo, Cucurbita mixta, Cucurbita maxima, and Cucurbita moschata. Pumpkin belongs to the family Cucurbitaceae and is grown primarily as a vegetable or ornamental plant. Pumpkin plants are short lived annual or perennial vines with branching tendrils and broad lobed leaves. The plant produces large yellow or orange flowers and a pepo fruit (berry with a thick rind) known as a pumpkin. The fruit can range greatly in size, from miniature pumpkins weighing a few ounces to giant pumpkins which can reach over 75 lbs (34 kg). The skin of the pumpkin is usually ribbed and is usually orange on color although some varieties are green, grey, yellow or red in color. Pumpkin plants are usually grown as annuals, surviving one growing season and the vines are capable of reaching 15 m (50 ft) in length if vines are allowed to root. Pumpkin may also be referred to as squash or marrow and is believed to have originated in Mexico and South America.

Quality
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute

Quince
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
harvesting: Quince fruits should be harvested when they are mature but not full ripe. Fruits will continue to ripen off of the tree and by harvesting in this manner, losses from fruit dropping from the tree are reduced. Fruit should be cut from the tree using a sharp knife as pulling them can cause damage to the skin.
maintenance: Quince trees are susceptible to fire blight and should not be fertilized excessively with nitrogen as this promotes vegetative growth which makes the trees more susceptible to the bacterium. Suckers should be removed by pruning from the tree in winter or early spring. Trees should be pruned in winter to thin out fruiting wood and stimulate new growth. If fruiting wood is pruned properly then fruits should not require thinning when the trees are bearing. Quince will benefit from regular deep watering during the summer months.
propagation: Quince trees are usually propagated from hardwood cuttings or by budding onto a quince rootstock. Seeds can be grown but will not breed true to type and should not be used for commercial purposes where fruit quality is very important. Some varieties are not self compatible and require another variety for successful pollination and fruit production. Quince trees should be spaced 5–6 m apart (16–20 ft) apart, allowing 4–5 m (13–16 ft) between rows.
requirements: Quince trees grow well in a wide range of climates and soils but the plants shallow root system make it susceptible to drought or a low availability of water and additional irrigation may be required. Quince should be planted in full sun and will grow optimally at temperatures averaging 15°C (59°F). Quince trees are more tolerant of wet soils and drought than apple trees and, compared with other fruit trees, has good cold tolerance, being hardy to temperatures between -15 and -25°C (5 and -13°F respectively). Quince trees are easily damaged by strong winds and should be planted in a sheltered location which will also protect them from rapid temperature fluctuations which is also detrimental to their growth.
uses: Quince fruits are consumed fresh or may be used to make jams and jellies.
References: https://plantvillage.psu.edu/topics/quince/infos
comment: Quince, Cydonia oblonga, is a bush-like deciduous tree in the family Rosaceae grown for its edible fruits. The tree has crowded, gnarled branches and a crooked growth habit. The leaves have a smooth upper surface and hairy lower surface. The tree produces single white-light pink colored flowers on tiny shoots and a large, fragrant, pome fruit. The fruit is initially covered in dense gray-white hairs but these disappear as the fruit ripens. The ripe fruit is a golden yellow color and resembles a pear or apple depending on variety. It has a soft, yellow pulp with a grainy texture protected by a thin skin. Quince trees can reach heights of 5–8 m (16–26 ft) and can live for periods in excess of 50 years and have an economic lifespan of approximately 25 years. Quince originates from Asia Minor.

Radiation
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
comment: In physics, radiation is the emission or transmission of energy in the form of waves or particles through space or through a material medium. Radiation is often categorized as either ionizing or non-ionizing depending on the energy of the radiated particles. Ionizing radiation carries more than 10 eV, which is enough to ionize atoms and molecules and break chemical bonds. This is an important distinction due to the large difference in harmfulness to living organisms. A common source of ionizing radiation is radioactive materials that emit α, β, or γ radiation, consisting of helium nuclei, electrons or positrons, and photons, respectively. Other sources include X-rays from medical radiography examinations and muons, mesons, positrons, neutrons and other particles that constitute the secondary cosmic rays that are produced after primary cosmic rays interact with Earth's atmosphere.

Rainfall
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
comment: Rain is liquid water in the form of droplets that have condensed from atmospheric water vapor and then become heavy enough to fall under gravity. Rain is a major component of the water cycle and is responsible for depositing most of the fresh water on the Earth. It provides suitable conditions for many types of ecosystems, as well as water for hydroelectric power plants and crop irrigation.

Rapeseed
type: Class
subclass of: OilSeeds
type: NamedIndividual
type: Cereal
type: Crop
type: OilSeeds
description: Rapeseed, Brassica napus, is an herbaceous annual or biennial member of the family Brassicaceae primarily grown for the oil which can be extracted from its seeds. The rapeseed plant has several erect, branched stems originating from a single base, the stems are purple in color towards the base. The leaves of the plant are bluish-green and mostly smooth. The basal leaves are stalked whereas the highest leaves grow straight off of the stem. The plant produces pale to bright yellow flowers which are 11-15 mm (0.4-0.6 in) in diameter, and after pollination the plant develops pods containing a single row of seeds. Each pod can contain 20-40 dark brown to black seeds. The rapeseed plant can reach 1.0-2.5 m (3.3-8.2 ft) and is grown as an annual, harvested after one growing season. Rapeseed may also be referred to as colza, oilseed rape, canola, swede rape or fodder rape and is believed to originate from the Mediterranean.
harvesting: Rapeseed is a fast-ripening plant and is ready for harvest when the seeds have turned from green to black in color. Rapeseed is mechanically harvested by combine or by swathing. in some countries, such as China, the crop is cut by hand.
maintenance: It is important to control weed growth in rapeseed fields as they can have a significant impact on the growth and productivity of the crop. Good preparation of the seedbed helps to limit weed growth. This is achieved by tilling the soil in the fall prior to planting, followed by shallow cultivation just prior to seeding. Rapeseed has a high nutrient requirement and a soil test should be carried out prior to planting in order to prevent nutrient deficiencies from occurring. fertilizers are most successfully applied to the side of the seed furrows to prevent damage to the plants. Nitrogen fertilizer should be applied at a basic rate of 50-60 kg per hectare for spring planted rapeseed and 70 kg per hectare for winter crops. Fertilizer is commonly applied at time of sowing. in addition to nitrogen, rapeseed may also require the addition of phosphorus, potassium, sulfur and magnesium to the soil. The rate of application of these nutrients should be determined by a soil test.
propagation: Rapeseed in almost always propagated from seed which is sown in prepared fields by drilling in rows. Seeds are sown shallowly as the seeds are very small and seed should be sown at a depth of 2-3 cm (0.8-1.2 in). Spring crops of rapeseed should be drilled in rows 18-23 cm (7-9 in) apart, whereas winter crops require more space and should be drilled in rows approximately 40 cm (16 in) apart.
requirements: Rapeseed is adapted to grow in cool, moist climates, requiring a temperature range of 2-10oC (35.6-56oF), although temperatures closer to 10oC (56oF) promote the most rapid growth. As a result, rapeseed is grown as a cool season crop in sub-tropical regions and a winter crop in more temperate areas. Rapeseed can be grown on a variety of soil types but medium textured, well-draining soils work best. Rapeseed should be grown in soil with a pH between 5.5 and 8.3. Rapeseed should not be grown in soil in which other brassicas have been grown within the past 3-4 years.
uses: The seeds of the plant are used to extract rapeseed oil which can be used as a cooking oil or in the production of margarine. Some recently developed cultivars have a high erucic acid content which is extracted for the production of industrial oil. Rapeseed is also grown as fodder for livestock.
comment: Rapeseed, Brassica napus, is an herbaceous annual or biennial member of the family Brassicaceae primarily grown for the oil which can be extracted from its seeds. The rapeseed plant has several erect, branched stems originating from a single base, the stems are purple in color towards the base. The leaves of the plant are bluish-green and mostly smooth. The basal leaves are stalked whereas the highest leaves grow straight off of the stem. The plant produces pale to bright yellow flowers which are 11-15 mm (0.4-0.6 in) in diameter, and after pollination the plant develops pods containing a single row of seeds. Each pod can contain 20-40 dark brown to black seeds. The rapeseed plant can reach 1.0-2.5 m (3.3-8.2 ft) and is grown as an annual, harvested after one growing season. Rapeseed may also be referred to as colza, oilseed rape, canola, swede rape or fodder rape and is believed to originate from the Mediterranean.
References: https://plantvillage.psu.edu/topics/rapeseed-colza/infos

Raspberry
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
type: Fruit Tree
propagation: Soil may need prepared up to two years in advance of planting if major amendments are required. Acidic soil can be amended with lime to bring the pH up to a level suitable for raspberries. Organic content can be increased by planting a cover crop or by the addition of manure or compost. Avoid planting raspberries where peppers, eggplant, tomatoes or potatoes have been grown previously as these plants are host to Verticillium fungi which can cause root rot in raspberries. Choose a raspberry variety which is suited to your region. Red raspberries tend to be the most cold hardy, whereas black or yellow varieties are more sensitive.
requirements: Raspberry plants grow best in regions with cool summers and relatively mild winters. The plants are sensitive to high temperatures and grow best when daytime temperatures are around 25°C (77°F). Raspberries are best suited to well-draining sandy loams, rich in organic matter and have a pH between 5.5 and 6.5. Drainage it critical in raspberry propagation as the plants are susceptible to root rot. Plants require full sunlight and should not be planted in low lying areas where water may build up. Raspberries also require a post support system or trellis to support the weight of the fruit on the canes. Raspberry canes are biennial and produce fruit in the second year of growth. Canes in their first year of growth are called primocanes and those in the second year of growth are called fruiting canes or floricanes. The young canes are green in color, whereas the older floricanes are tougher and have a woody covering making them easy to tell apart.
uses: Raspberries are primarily consumed as a fresh fruit or may be processed into jams, jellies, juices and pulp.
comment: Raspberry is the name given to two plant species in the genus Rubus, Rubus idaeus (red raspberry) and Rubus occidentalis (black raspberry) grown for their edible fruit. Raspberry plants have perennial root systems and biennial stems which are known as canes. The canes are woody, erect and shrub-like and generally undergo a period of growth one year and fruit production the next although primocane varieties exist that produce fruit in the first year. The canes may possess spines. Raspberry plants produce white or pink flowers with five petals which are surrounded by green sepals. After the plant has been pollinated, an aggregate berry is produced which consists of numerous druplets which are held together into the familiar raspberry fruit by tiny hairs. Raspberry canes can grow from 0.5 to in excess of 2 m (1.6–6.6 ft) in height and red raspberry will produce a commercial yield of fruit for 16–20 years, while black raspberry has a shorter lifespan and will produce for 4–8 years. Red raspberry may also be referred to as European raspberry, red garden raspberry or hindberry, while black raspberry may be referred to as blackcap raspberry and may have originated in the Ide mountains of Turkey.
References: https://plantvillage.psu.edu/topics/raspberry/infos

Reference
type: Class
subclass of: Document
comment: Reference is a type of document that outlines concepts, entities, past procedures, actions or strategies as they relate to a particular activity.

Regression
type: Class
subclass of: Data Mining
type: NamedIndividual
type: Data Mining
comment: Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables (often called 'predictors', 'covariates', or 'features'). The most common form of regression analysis is linear regression, in which a researcher finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion. For example, a learning method where the output variable is a real value, such as Soil pH: 4, 5 or 7.0 or 7.5

Regressor
type: Class
subclass of: Regression
type: NamedIndividual
type: Data Mining

Remote Sensing
type: Class
subclass of: Sensing
type: NamedIndividual
type: Sensing
comment: Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft).

Report
type: Class
subclass of: Document
comment: Report is an account given of a particular matter, especially in the form of an official document, after thorough investigation or consideration by an appointed person or body.

Rice
type: Class
subclass of: Cereal
type: NamedIndividual
type: Cereal
type: Crop
has growth stage: Booting stage
has growth stage: Flowering
has growth stage: Fruit Development stage
has growth stage: Germination
has growth stage: Inflorescence Emergence stage
has growth stage: Leaf Development stage
has growth stage: Ripening stage
has growth stage: Senescence stage
has growth stage: Stem Elongation stage
has growth stage: Tillering stage
description: There are only two species of cultivated rice in the world, Oryza sativa (Asian rice) and Oryza glaberrima (African rice). Both species are annual grasses (except in the tropics, where the plant can be perennial) belonging to the family Poaceae which are cultivated for their grain which is considered a staple food in most parts of the world. Asian and African rice plants are morphologically very similar and can be difficult to tell apart. They have rounded stems (called culms) which are divided into nodes and internodes. The plant leaves are borne on the nodes of the stem and are long and slender with a pronounced midrib. The plant produces three flowers, two of which are reduced, on a spikelet on the terminal (last) internode of the stem. The rice grain is formed by the ripened ovary of the flower and is between 5 and 12 mm in length. Rice is an annual plant, harvested after one growing season and can reach a height of between 1 and 1.8 m (3.3-5.9 ft) depending on variety. Generally, African rice tends to form smaller, pear shaped grains with a red bran and olive to black seed coat and has several disadvantages compared with its Asian counterpart e.g. seed is easily scattered, the grain is difficult to mill and the crop gives a lower yield. The growing of Asian rice has therefore begun to supercede that of African rice in West Africa. Asian rice originates from China and African rice is believed to have been domesticated in areas around the Niger river in Africa.
harvesting: In many areas of the world rice is still harvested using the traditional method of cutting by hand. Rice should be cut when 80-85% of the panicles have turned from green to straw colored. When harvested by hand, the rice is cut using sharp knives or sickles. In some areas, rice is mechanically harvested using threshers or combines.
propagation: Propagation Rice is propagated directly from seed and sown on wet or dry seed beds or used to grow transplants in a nursery bed. Transplants produced in a nursery are always planted in dry seed beds when they are transferred to the field. Seeds may be sown in the field by broadcasting or by mechanical drilling. Three main methods are used for direct seeding rice in lowland areas: dry seedbeds, wet seedbeds and dapog. Dry seedbeds are prepared in close proximity to a water source. Each bed is generally 1.5 m (4.9 ft) in width and is seeded at a rate of 10 kg of seed for every 10 square meters. The seeds are covered with a light layer of soil and the bed is saturated with water until the seeds germinate. Wet seedbeds are raised nursery beds in which pre-germinated seeds are sown. The bed is puddled or wet with water and the germinated seeds are spread out uniformly and kept wet. When the seedling reach 2-3 cm (0.8-1.2 in) in height, shallow irrigation is used and the water levels in the bed are increased as the plants grow. Dapod is a method of producing transplants on a mat made of banana leaves of plastic sheeting which is used in some areas of the Philippines. Pre-germinated seeds are spread out on cement of wet soil and covered with the leaves or plastic sheeting. The cover is pushed down onto the seeds and continuously watered. After 11 days, the mat is rolled up and transferred to to the field. Cropping systems Rice can be be grown in an irrigated system, as a rainfed crop or in an upland systems in managed fields known as paddies. In irrigated systems, supplemental water is provided during the dry season alone or in both the dry and wet seasons depending on the amount of local rainfall. Irrigated systems are required in areas where rainfall is very low. Irrigated fields are bunded and the water levels are carefully managed prior to harvest. Rainfed systems are generally a poorer alternative to irrigated systems as they are unstable and prone to drought or flood…
requirements: Rice is mainly grown in warmer regions as the plants will not grow at temperatures below 10oC. Rice is considered to be a semi-aquatic annual grass and is commonly grown in paddies in wetlands or under shallow water. Several new methods of propagating rice have been developed which allow rice to be cultivated in less conventional areas e.g. drought resistant varieties are grown in upland areas.
sowing rate: 100 – 160 kg/ha
uses: After removal of the rice grain from the chaff, the rice can be used as brown rice or further processed to remove the bran to produce white rice. Brown and white rice may be consumed after cooking or may be ground into rice flour. Rice can be puffed under low pressure to produce puffed rice that is commonly used as a breakfast cereal. Rice bran oil can be produced from the inner husk and can be used as a cooking oil. Rice starch can be extracted and fermented to produce rice wine or brewed to produce sake.
comment: There are only two species of cultivated rice in the world, Oryza sativa (Asian rice) and Oryza glaberrima (African rice). Both species are annual grasses (except in the tropics, where the plant can be perennial) belonging to the family Poaceae which are cultivated for their grain which is considered a staple food in most parts of the world. Asian and African rice plants are morphologically very similar and can be difficult to tell apart. They have rounded stems (called culms) which are divided into nodes and internodes. The plant leaves are borne on the nodes of the stem and are long and slender with a pronounced midrib. The plant produces three flowers, two of which are reduced, on a spikelet on the terminal (last) internode of the stem. The rice grain is formed by the ripened ovary of the flower and is between 5 and 12 mm in length. Rice is an annual plant, harvested after one growing season and can reach a height of between 1 and 1.8 m (3.3–5.9 ft) depending on variety.
References: https://plantvillage.psu.edu/topics/rice/infos

Root
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Roots are the organs of a plant that typically lie below the surface of the soil. Roots can also be aerial or aerating, that is, growing up above the ground or especially above water.

Rose
type: Class
subclass of: Flower
type: NamedIndividual
type: Flower
comment: A rose is a woody perennial flowering plant of the genus Rosa, in the family Rosaceae, or the flower it bears. There are over three hundred species and tens of thousands of cultivars. They form a group of plants that can be erect shrubs, climbing, or trailing, with stems that are often armed with sharp prickles.

Rural Market
type: Class
subclass of: Market
comment: Rural markets are usually for direct sales of small quantities of produce by farmers to village traders and rural consumers. Rural primary markets often form part of a network arranged on a periodic basis, such as on a specific day of each week.

Rusts
type: Class
subclass of: Symptom
comment: These are fungal diseases of grasses and other plants which appear on the host surface as small, coloured pustules-red, brown, yellow, orange, or black in colour.

Rye
type: Class
subclass of: Cereal
type: NamedIndividual
type: Cereal
type: Crop
description: Rye, Secale cereale, is an herbaceous annual or biennial in the family Poaceae that is grown primarily for its grain. Rye has an erect slender stem topped with a curved spike which is 7-15 cm (3-6 in) length. The head is made up of individual spikelets each with two florets which produce 1-2 kernels. The spikelets and arranged alternately along the length of the head.The leaves of the plant grow from nodes on the stem and are lance-like blades, blue-green in color. Rye can reach 1-3 m (3.3-10 ft) in height and is either grown as an annual (spring rye) or biennial (winter rye). Modern rye does not occur naturally but is likely derived from wild ancestors found in Afghanistan, Iran and the Middle East.
harvesting: Rye is ready to harvest when the leaves are dead and the stems have turned yellow-brown in color. Rye is commonly harvested by combine but can also be harvested by hand.
maintenance: Rye fields should be kept free from weeds through the use of herbicides or by harrowing. Herbicides may be applied while the plants are at the tillering stage of growth. Rye will benefit from the addition of nitrogen which is often the biggest yield limiting factor in rye cultivation. Fertilizer should be applied when the crop begins to grow and the application may be split over two applications.
propagation: Rye is propagated directly from seed and can be sown by broadcasting or by drilling. Seeds should be sown to a depth of 2.5-5.0 cm (1-2 in) allowing 15-18 cm (6-7 in) between rows where seeds are drilled. Seeding If seeds are broadcast, they must be covered to ensure adequate germination. Seeding rates are generally between 100 to 150 kg of seed per hectare for winter rye and 150 to 200 kg per hectare for spring rye to produce a plant stand of 200-300 plants per square meter.
requirements: Rye is a cool season annual grass which can be grown in a wide range of soils and climates. Rye grows best when planted in well draining sandy or light loams but can also be grown in clay soils. The optimum pH for rye growth is between 5.6 and 6.5. Rye will germinate when soil temperatures are between 4 and 5oC (39-41oF). Daily temperatures should not exceed an average of 20oC in order for the plant to grow adequately. Winter rye should be planted in fall to mid-winter depending on the variety and be overwintered for a summer harvest, while spring rye is planted in the spring for summer harvest.
uses: Rye grain can be used to make alcoholic drinks such as whiskey, gin and beer or used as a livestock feed. Rye flour can be used to make bread. Rye is extensively grown as a winter cover crop to prevent soil erosion and the mature stems are commonly used as animal bedding.
References: https://plantvillage.psu.edu/topics/rye/infos
comment: Rye, Secale cereale, is an herbaceous annual or biennial in the family Poaceae that is grown primarily for its grain. Rye has an erect slender stem topped with a curved spike which is 7-15 cm (3-6 in) length. The head is made up of individual spikelets each with two florets which produce 1-2 kernels. The spikelets and arranged alternately along the length of the head.The leaves of the plant grow from nodes on the stem and are lance-like blades, blue-green in color. Rye can reach 1-3 m (3.3-10 ft) in height and is either grown as an annual (spring rye) or biennial (winter rye). Modern rye does not occur naturally but is likely derived from wild ancestors found in Afghanistan, Iran and the Middle East.

Sulfur Dioxide (SO2)
type: Class
subclass of: Gas
type: NamedIndividual
type: Gas
Acronym: SO2
comment: Sulfur dioxide or sulphur dioxide is the chemical compound with the formula SO2.

Sulfur Trioxide (SO3)
type: Class
subclass of: Mineral
type: NamedIndividual
type: Mineral
unit: Kg/Ha
comment: Sulfur trioxide (alternative spelling sulphur trioxide) is the chemical compound with the formula SO3, with a relatively narrow liquid range. In the gaseous form, this species is a significant pollutant, being the primary agent in acid rain.

Sulphate (SO4)
type: Class
subclass of: Gas
type: NamedIndividual
type: Gas
comment: The sulfate or sulphate ion is a polyatomic anion with the empirical formula SO2− 4. Salts, acid derivatives, and peroxides of sulfate are widely used in industry. Sulfates occur widely in everyday life. Sulfates are salts of sulfuric acid and many are prepared from that acid.

SWRadiation
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
unit: W/m^2

Safflower
type: Class
subclass of: OilSeeds
type: NamedIndividual
type: Crop
type: OilSeeds
comment: Safflower, Carthamus tinctorius, is a highly branched, herbaceous, thistle-like annual plant. It is commercially cultivated for vegetable oil extracted from the seeds and was used by the early Spanish colonies along the Rio Grande as a substitute for saffron. Plants are 30 to 150 cm (12 to 59 in) tall with globular flower heads having yellow, orange, or red flowers. Each branch will usually have from one to five flower heads containing 15 to 20 seeds per head. Safflower is native to arid environments having seasonal rain. It grows a deep taproot which enables it to thrive in such environments.

Sale
type: Class
subclass of: InUse

Sales Event
type: Class
subclass of: Event
comment: Sales event - an occasion (usually brief) for buying at specially reduced prices;

Salicylic Acid
type: Class
subclass of: Biomolecule
type: NamedIndividual
type: Biomolecule

Salinity
type: Class
subclass of: Water
type: NamedIndividual
type: Water

Salinity Stress
type: Class
subclass of: Crop Stress
type: NamedIndividual
type: Crop Stress

Satellite
type: Class
subclass of: Sensor Station
type: NamedIndividual
type: Sensor Station
comment: A satellite is an object which has been sent into space in order to collect information

Satellite Imagery
type: Class
subclass of: Imagery
type: NamedIndividual
type: Imagery
comment: Satellite images are images of Earth or fields collected by imaging satellites around the world.

Sclerotia
type: Class
subclass of: Symptom
type: NamedIndividual
type: Symptom
comment: The conspicuous phase of some fungal diseases such as Ergot of rye is the formation of sclerotia in the position of kernels in the spike. The sclerotium is a compact, hard mass of dormant fungal hyphae. It may be black, greyish violet, dark brown or purplish in colour.

Season
type: Class
subclass of: Time
comment: Season is one of the four periods of the year (spring, summer, autumn, and winter), beginning astronomically at an equinox or solstice. Each of the four divisions of the year (spring, summer, autumn, and winter) marked by particular weather patterns and daylight hours, resulting from the earth's changing position with regard to the sun.

Seed
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: A seed is an embryonic plant enclosed in a protective outer covering. The formation of the seed is part of the process of reproduction in seed plants, the spermatophytes, including the gymnosperm and angiosperm plants.

Seed Rate
type: Class
subclass of: Cropping
type: NamedIndividual
type: Cropping
unit: Kg/Ha
comment: Seed rate is the number of seeds planted per hectare (ha) to ensure normal density of sprouts and a maximum yield.

Seeding
type: Class
subclass of: Plant Phenomenon
type: NamedIndividual
type: Plant Phenomenon
comment: The fundamental factor in the process of seeding is dependent on the properties of both seed and the soil it is being planted in. The prior step associated with seeding is crop selection, which mainly consists of two techniques: sexual and asexual. Asexual technique include all forms of vegetative process such as budding, grafting and layering. Sexual technique involves growing of the plant from a seed.

Seeds
type: Class
subclass of: Crop Part
comment: A seed is the small, hard part of a plant from which a new plant grows.

Selection
type: Class
subclass of: Planned Process
References: CRISP-DM

Selenium (Se)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
comment: Selenium is probably not essential for flowering plants, but it can be beneficial; it can stimulate plant growth, improve tolerance of oxidative stress, and increase resistance to pathogens and herbivory.
Acronym: Se

Sensing
type: Class
subclass of: Observation
References: https://www.w3.org/2005/Incubator/ssn/ssnx/ssn#Sensing
Source: http://www.w3.org/2005/Incubator/ssn/
comment: Sensing is a process that results in the estimation, or calculation, of the value of a phenomenon.

Sensor
type: Class
subclass of: Observe System
References: http://www.w3.org/ns/sosa/Sensor
comment: Device, agent (including humans), or software (simulation) involved in, or implementing, a Procedure.

Sensor Output
type: Class
subclass of: Abstract Entity
References: https://www.w3.org/2005/Incubator/ssn/ssnx/ssn#SensorOutput
Source: http://www.w3.org/2005/Incubator/ssn/ skos:closeMatch 'observation result' [O&M] http://www.opengeospatial.org/standards/om See comments at ObservationValue.
comment: The sensor output is generally a signal that is converted to human-readable display at the sensor location or transmitted electronically over a network for reading or further processing.

Sensor Station
type: Class
subclass of: Observe System
comment: Sensor station is a group of sensors which detect or measure several physical properties and records, indicates, or otherwise responds to them.

Sesame
type: Class
subclass of: OilSeeds
comment: Sesame is a flowering plant in the genus Sesamum, also called benne. Numerous wild relatives occur in Africa and a smaller number in India. It is widely naturalized in tropical regions around the world and is cultivated for its edible seeds, which grow in pods.

Shallow Electrical Conductivity (ECsh)
type: Class
subclass of: Soil Conductivity
type: NamedIndividual
type: Soil
unit: mS/m
comment: Soil conductivity measures the total conductivity in the sample at the surface 0‐ to 20‐cm.
Acronym: ECsh

Shallow Soil
type: Class
subclass of: Soil Conductivity
type: NamedIndividual
type: Soil

Shape
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute
comment: Shape

Shoot
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: In botany, a plant shoot consists of any plant stem together with its appendages, leaves and lateral buds, flowering stems, and flower buds.

Slope
type: Class
subclass of: Terrain
type: NamedIndividual
type: Terrain
comment: slope is the percent change in that elevation over a certain distance.

Smartphone
type: Class
subclass of: Sensor Station
type: NamedIndividual
type: Sensor Station
comment: A smartphone is a mobile device that combines cellular and mobile computing functions into one unit. A smartphone can have a number of different types of sensing devices installed on a user's phone to gather data for various user purposes, such as camera, accelerometer, gyroscope, magnetometer, GPS, proximity sensor, etc.

Smuts
type: Class
subclass of: Symptom
type: NamedIndividual
type: Symptom
comment: These are the fungal diseases of cereals and other members of grass family that cause the ears (particularly the ovaries) to turn black or sooty. The smut symptoms may as well appear on other parts (leaves, stem or roots) of the host plant.

Sodium (Na)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
comment: Sodium is involved in the regeneration of phosphoenolpyruvate in CAM and C4 plants. Sodium can potentially replace potassium's regulation of stomatal opening and closing.
Acronym: Na

Soil
type: Class
subclass of: Environment
type: NamedIndividual
type: Environment
comment: Soil is a mixture of minerals, organic matter, gases, liquids, and countless organisms that together support life on Earth. Soil is a natural body called the pedosphere which has four important functions: it is a medium for plant growth. Soil includes soil physical properties and processes, that affect agricultural, engineering, and environmental soil functions

Soil Boron
type: Class
subclass of: Soil Chemical
type: NamedIndividual
type: Soil
has transformation: Soil Boron by Default
unit: mg/L

Soil Calcium
type: Class
subclass of: Soil Chemical
type: NamedIndividual
type: Soil
has transformation: Soil Calcium by Default
unit: mg/L
unit: mg/kg

Soil Chemical
type: Class
subclass of: Soil

Soil Clay
type: Class
subclass of: Soil Physical
type: NamedIndividual
type: Soil
unit: %
Acronym: Clay

Soil Color
type: Class
subclass of: Soil Physical
type: NamedIndividual
type: Soil
has transformation: Soil Color by Default

Soil Composition
type: Class
subclass of: Soil

Soil Conductivity
type: Class
subclass of: Soil
type: NamedIndividual
type: Soil
unit: dS/m
comment: Soil conductivity measures the total conductivity. Total conductivity includes the EC of the soil, air, and moisture in the sample.

Soil Density
type: Class
subclass of: Soil Physical

Soil Depth
type: Class
subclass of: Soil Physical
subclass of: Soil Spatial
type: NamedIndividual
type: Soil

Soil Potasium
type: Class
subclass of: Soil Chemical
type: NamedIndividual
type: Soil
has transformation: Soil Potasium by Default
unit: mg/L
unit: mg/kg

Soil MG
type: Class
subclass of: Soil Chemical
type: NamedIndividual
type: Soil
unit: mg/L

Soil Moisture
type: Class
subclass of: Soil Composition
type: NamedIndividual
type: Soil
has transformation: Moisture Content by Default
Acronym: SMC
comment: Moisture content is the amount of water present in a moist sample of a product like wood, soil or similar. Moisture content can be be expressed on wet or dry basis.
alternative label: Soil Water content

Soil Nitrogen
type: Class
subclass of: Soil Chemical
type: NamedIndividual
type: Soil
has transformation: Soil Nitrogen by Default
unit: mg/L

Soil Phosphorus
type: Class
subclass of: Soil Chemical
type: NamedIndividual
type: Soil
has transformation: Soil Phosphorus by Default
unit: mg/L
unit: mg/kg

Soil pH
type: Class
subclass of: Soil
type: NamedIndividual
type: Soil
has transformation: Soil pH by Default
comment: Soil Reaction
comment: Soil pH or soil reaction is an indication of the acidity or alkalinity of soil and is measured in pH units. Soil pH is defined as the negative logarithm of the hydrogen ion concentration. The pH scale goes from 0 to 14 with pH 7 as the neutral point, below 7 acidic and above 7 alkaline.
Acronym: pH

Soil Phenomena
type: Class
subclass of: Natural Phenomenon
comment: Soil phhenomenon is an observable fact or event related to soil, such as salt sieving, soil cracking.

Soil Physical
type: Class
subclass of: Soil

Soil Process
type: Class
subclass of: CultivatingProcess

Soil Salinity
type: Class
subclass of: Soil Composition
type: NamedIndividual
type: Soil
has transformation: Soil Salinity by Default
comment: Soil salinity is the salt content in the soil; the process of increasing the salt content is known as salinization. Salts occur naturally within soils and water.

Soil Sand
type: Class
subclass of: Soil Physical
type: NamedIndividual
type: Soil
has transformation: Soil Sand by Default
unit: %
Acronym: Sand

Soil Silt
type: Class
subclass of: Soil Physical
type: NamedIndividual
type: Soil
unit: %
Acronym: Silt

Soil Spatial
type: Class
subclass of: Soil

Soil Structure
type: Class
subclass of: Soil Physical

Soil Surface
type: Class
subclass of: Soil Spatial
type: NamedIndividual
type: Soil

Soil Temperature
type: Class
subclass of: Soil
subclass of: Weather
type: NamedIndividual
type: Soil
type: Weather
comment: Soil temperature is simply the measurement of the warmth in the soil. Ideal soil temperatures for planting most plants are 65 to 75 F. (18-24 C.). Nighttime and daytime soil temperatures are both important.

Soil Texture
type: Class
subclass of: Soil Physical
type: NamedIndividual
type: Soil
comment: Soil Texture indicates the relative content of particles of various sizes, such as sand, silt and clay in the soil. Texture influences the ease with which soil can be worked, the amount of water and air it holds, and the rate at which water can enter and move through soil. To find the texture of a soil sample, first separate the fine earth , all particles less than 2 mm, from larger particles such as gravel and stones. Fine earth is a mixture of sand, silt and clay.

Soil Type
type: Class
subclass of: Soil
type: NamedIndividual
type: Soil
comment: Soil Type

Soil Water
type: Class
type: NamedIndividual
type: Environment
comment: Soil water is the medium from which all plant nutrients are assimilated by plants. Soil water, sometimes referred to as the soil solution, contains dissolved organic and inorganic substances and transports dissolved nutrients, such as nitrogen, phosphorus, potassium, and calcium, to the plant roots for absorption.

Solar Radiation
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
unit: W/m^2

Sowing
type: Class
type: NamedIndividual
type: Growth Stage
comment: Sowing is the process of planting. An area or object that has had seeds planted in it will be described as a sowed area.

Soybean
type: Class
subclass of: Nuts
type: NamedIndividual
type: Cereal
type: Crop
type: Nuts
description: Soybean, Glycine max, is an herbaceous annual plant in the family Fabaceae grown for its edible seeds. The soybean plant is usually an erect bush with woody stems and alternately arranged leaves. The leaves possess three individual leaflets which are oval or lance-like in shape, growing to a length of 3-10 cm (1.2-4.0 in). The soybean plant produces small white or purple flowers and curved seed pods which are 3-15 cm (1.2-6 in) in length and can contain between 1 and 5 seeds. The seeds can be a variety of colors including yellow, green, brown, black or a mottled combination. Soybean is an annual plant, surviving only one growing season and can reach heights of 0.2-1.5 m (0.7-1.4 ft). Soybean may also be referred to as soyabean or soya and originates from Northeast China.
harvesting: Soybeans are ready to harvest between 70 and 160 days after planting, depending on variety. Plants are ready for harvest when the leaves and stems have turned yellow and the seed pods brown to black. Soybeans for fresh use are cut while still green. Plants may be pulled by hand or cut with the use of a combine. Once cut, the seeds are removed from the pods by threshing.
maintenance: Weeds are often a problem in soybean fields and can be reduced through good preparation of the seedbed and maintaining a weed-free seedbed for several weeks prior to planting. Weeds may be controlled with appropriate herbicides, where available, or through mechanical cultivation where necessary. Soybeans should be provided with irrigation at flowering and again at seed-set for maximum seed fill and optimal yield. Irrigation should be increased in sandy soils that do not retain as much moisture. Soybeans fix approximately half of the nitrogen they require for growth and the other half must be supplied from the soil or through fertilizer application. Soybean also requires an adequate supply of phosphorus and potassium and application rates should be based on the results of a soil test.
propagation: Soybean is propagated directly from seed. If seeds are to planted in a field where soybean has not been been grown in the 3-5 year previous, they should first be inoculated with nitrogen fixing bacteria. The seedbed should be prepared prior to planting by removing any crop residue, weeds and large stones. Tilling the soil helps to break up large clods of earth. Seeds may be sown mechanically or by hand. in tilled soils, seeds are usually planted at a depth 3.2-4.5 cm (1.25 to 1.75 in) in rows spaced 30 cm (11.8 in) apart. Alternatively, no-till planting may be used to plant seeds in the stubble of a previous crop without first cultivating the soil. With this method, seeds are drilled into the soil in rows spaced 17.8 cm (7 in) apart. No-till practices reduce soil erosion and reduces the loss of moisture in the soil.
requirements: Soybean is a short-day plant, requiring hot weather for optimum production. It can be grown year round in most parts of the tropics. Plants can be grown at ambient temperatures between 15 and 27oC (60-80oF) although temperature below 21oC (69.8oF) and above 32 (89.6oF) may reduce flowering. Temperatures exceeding 40oC (104oF) are detrimental to seed production. Soybeans are adapted to grow in a wide range of soils and climates but require adequate soil moisture for germination and seedling establishment. Plants are sensitive to waterlogging but are tolerant of drought conditions once established. Soybean grows best on a light, loose, well draining loam with a pH of 6.5.
uses: Soybean seeds can be used to make flour and dairy substitutes such as milk, margarine and yogurt and meat substitutes such as veggie burgers. Oil can be extracted from both the seeds and pods and the by product of the extraction is used as an animal feed. The oil from the plant is used in products such as paint, linoleum and soap. Soybean is also grown as a cover crop and used as animal fodder or hay.
comment: Soybean, Glycine max, is an herbaceous annual plant in the family Fabaceae grown for its edible seeds. The soybean plant is usually an erect bush with woody stems and alternately arranged leaves. The leaves possess three individual leaflets which are oval or lance-like in shape, growing to a length of 3-10 cm (1.2-4.0 in). The soybean plant produces small white or purple flowers and curved seed pods which are 3-15 cm (1.2-6 in) in length and can contain between 1 and 5 seeds. The seeds can be a variety of colors including yellow, green, brown, black or a mottled combination. Soybean is an annual plant, surviving only one growing season and can reach heights of 0.2-1.5 m (0.7-1.4 ft). Soybean may also be referred to as soyabean or soya and originates from Northeast China.
References: https://plantvillage.psu.edu/topics/soybean/infos

Spectral Image
type: Class
subclass of: Imagery
type: NamedIndividual
type: Imagery
comment: Hyperspectral images are the images in which one continuous spectrum is measured for each pixel.
label: Hyperspectral Image

Spectral Vegetation Indices
type: Class
subclass of: Data Transformation
comment: Spectral vegetation indices (VIs) are spectral transformations of two or more bands designed to enhance the contribution of vegetation properties and allow reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations. They are derived using the reflectance properties of vegetation. Each of the VIs is designed to accentuate a particular vegetation property. Usually simple algebraic formulations, SVIs are designed to enhance the vegetation signal in remotely sensed data and provide an approximate measure of live, green vegetation amount.

Spectrophotometer
type: Class
subclass of: Device
type: NamedIndividual
type: Device

Spike
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: In botany, a spike refers to a type of inflorescence (flower arrangement) that bears taller-than-average flowers, most usually on an axis that is unbranched. Similar to a raceme, the spike bears flowers that are directly attached to the plant, without any stalks whatsoever.

Spinach
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Vegetable
harvesting: Spinach leaves can be harvested as soon as they are large enough to use and may be harvested by hand or machine. Individual leaves may be harvested as required in the home garden or the whole plant can be cut. In commercial production, bunched fresh spinach is usually cut by hand. SPinach for processing may be cut by machine.
maintenance: Spinach requires high levels of moisture, and if rainfall is inadequate, 2.5 cm (1 in) of water should be applied every 7–10 days. Spinach also has high requirements for nitrogen and potassium which should be provided by applying fertilizer based on the results of a soil test. Potassium poses little environmental risk and may be applied based on the results of a soil test. Timing of nitrogen applications vary by location as there is a risk of leaching during heavy rainfall. In the home garden, fertilizer is often not required as long as spinach is planted in a fertile soil.
propagation: Spinach is propagated from seed with round seeded spinach usually being sown in early spring for a summer harvest and the prickly seeded type which is usually sown in fall for harvest in winter and spring. Seeds should be planted at a depth of 1–2 cm (0.5–1 in) leaving 33–38 cm (13-15 in) between rows. When seedlings reach about 5 cm (2 in) in height they should be thinned out to a spacing of 8–10 cm (3–4 in) between plants.
requirements: Spinach grows well in cool areas and can survive the first frost of temperate climates. It will germinates and grows optimally at temperatures between 4 and 16°C (40–60°F) but can withstand temperatures as low as -7C (20°F). Spinach grows best in a well draining loamy soil with a pH between 6.4 and 6.8. It is sensitive to acidic soil and if the pH is too high, adding lime is recommended. The soil temperature should not exceed 21°C (70°F).
uses: Spinach is eaten cooked as a vegetable and contains both large amounts iron, calcium, and other essential vitamins and minerals.
comment: Spinach, Spinacia oleracea, is a leafy herbaceous annual plant in the family Amaranthaceae grown for its leaves which are used as a vegetable. The spinach plant has simple leaves which stem from the center of the plant and measure about 2–30 cm (0.8–12.0 in) long and 1 to 15 cm (0.4–6.0 in) across. The leaves grow in a rosette and can appear crinkled or flat. The plant produces small yellow-green flowers which are 3–4 mm (0.1 in) in diameter. The flowers produce small fruit clusters which contain seeds. Spinach is an annual and survives only one growing season and can reach 30 cm (12 in) in height. Spinach originates from ancient Persia (now Iran).
References: https://plantvillage.psu.edu/topics/spinach/infos

Spore
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Spore is a unit of sexual or asexual reproduction that may be adapted for dispersal and for survival, often for extended periods of time, in unfavourable conditions. Spores form part of the life cycles of many plants, algae, fungi and protozoa. Bacterial spores are not part of a sexual cycle but are resistant structures used for survival under unfavourable conditions. Myxozoan spores release amoebulae into their hosts for parasitic infection, but also reproduce within the hosts through the pairing of two nuclei within the plasmodium, which develops from the amoebula. Spores are usually haploid and unicellular and are produced by meiosis in the sporangium of a diploid sporophyte. Under favourable conditions the spore can develop into a new organism using mitotic division, producing a multicellular gametophyte, which eventually goes on to produce gametes. Two gametes fuse to form a zygote which develops into a new sporophyte. This cycle is known as alternation of generations.

Sprayer
type: Class
subclass of: Agricultural Machinery
type: NamedIndividual
type: Machine
comment: In agriculture, a sprayer is a piece of equipment that is used to apply herbicides, pesticides, and fertilizers on agricultural crops.

Spraying
type: Class
subclass of: CultivatingProcess
comment: Spraying and dusting, in agriculture, the standard methods of applying pest-control chemicals and other compounds.

Squash
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
harvesting: Squash is ready to harvest when the rind is hard and cannot be punctured with a fingernail. The skin of mature fruits is dull and dry in appearance, especially when compared with the shiny skin of an immature fruit.
maintenance: Squash vines are sprawling and require plenty space to grow. Vines can be trained to grow on a trellis or fence. Squash also require a continuous supply of water and where drip irrigation is not being used, plants should be watered deeply once per week, providing at least an inch of water. Shallow watering or watering less frequently encourages a shallow root system. Mulches can be used to conserve soil moisture and black polyethylene mulch has the advantage of warming the soil. All squash varieties produce both male and female flowers (monoecious) and are pollinated by insects such as bees.
propagation: Squash is propagated from seed and can be direct seeded or sown indoors and transplanted. If direct seeding,seeds should be sown after the last frosts and when the soil has warmed to at least 15.6°C (60°F). Sow 1–2 seeds 1.3–2.5 cm (0.5–1.0 in) deep, at least 90 cm (~3 ft) apart if growing bush varieties and 120–150 cm (4–5 ft) apart if growing vining varieties. Allow a further 1–3 m (6–10 ft) between rows depending on the variety. If transplanting, seeds should be sown 3–4 weeks before the last frost date in your area and transplanted before the plants develop their second set of true leaves. Seeds in sown both indoors and out require lightly moist soil for germination, care should be taken to avoid overwatering. Seeds should germinate in 5–10 days depending on the soil temperature.
requirements: Squash is a warm-season crop, requiring lots of sun and good drainage to develop optimally and growing best at temperatures between 18 and 25°C (65–75°F). Squash will yield best if grown in a fertile, well-draining soil, rich in organic matter and with a pH between 6.5 and 7.5. Squash should be planted in full sun and provided with ample soil moisture due to their shallow root system. Vining varieties can grow to very large sizes and require a good deal of space. Smaller bush varieties are available for more modest spaces.
uses: Squash fruit can be cooked and eaten as a vegetable. Some varieties are grown as ornamentals.
References: https://plantvillage.psu.edu/topics/squash/infos
comment: Squash is the collective name given to several species of plant in the genus Cucurbita, including C. maxima, C. mixta , C. moschata and C. pepo, which are widely grown for their edible fruit. Squash plants are are herbaceous annual plants which are either trailing vines or bush-like in morphology. Vines generally have large, lobed leaves and long vines which can climb by attaching to surfaces with their tendrils. Bushes generally take up less space than the sprawling vine types and may have prickly leaves. Squash plants produce yellow or orange flowers and green, white or yellow fruit in a variety of shapes and sizes with smooth or ridged skin. Vining squash varieties can reach several meters in length and, as annuals, survive only one growing season. Squash originate from North and Central America and are referred to by their cultivar name e.g. acorn squash, butternut squash, spaghetti squash, zucchini, banana squash, hubbard squash and buttercup squash.

Starch
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Starch or amylum is a polymeric carbohydrate consisting of numerous glucose units joined by glycosidic bonds. This polysaccharide is produced by most green plants for energy storage. Worldwide, it is the most common carbohydrate in human diets, and is contained in large amounts in staple foods like potatoes, maize (corn), rice, wheat and cassava (manioc).

State
type: Class
comment: State is a value or result of attributes / concepts in a knowledge model.

Stem
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
has transformation: Stem by DC
comment: A stem is one of two main structural axes of a vascular plant, the other being the root. It supports leaves, flowers and fruits, transports fluids between the roots and the shoots in the xylem and phloem, stores nutrients, and produces new living tissue.

Storage
type: Class
subclass of: InUse
comment: Storage means the phase of the post-harvest system during which the products are kept in such a way as to guarantee food security other than during periods of agricultural production

Storm
type: Class
subclass of: Planetary Phenomena
type: NamedIndividual
type: Weather Phenomena
type: Wind Type
comment: A storm is any disturbed state of an environment or in an astronomical body's atmosphere especially affecting its surface, and strongly implying severe weather. It may be marked by significant disruptions to normal conditions such as strong wind, tornadoes, hail, thunder and lightning (a thunderstorm), heavy precipitation (snowstorm, rainstorm), heavy freezing rain (ice storm), strong winds (tropical cyclone, windstorm), or wind transporting some substance through the atmosphere as in a dust storm, blizzard, sandstorm, etc.

Straw
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
References: https://en.wikipedia.org/wiki/Straw
comment: Straw is an agricultural byproduct consisting of the dry stalks of cereal plants after the grain and chaff have been removed. It makes up about half of the yield of cereal crops such as barley, oats, rice, rye and wheat. It has a number of different uses, including fuel, livestock bedding and fodder, thatching and basket making.

Study Design
type: Class
subclass of: Planned Process
References: CRISP-DM
comment: A planned process that realizes the concretization of a study design

Sub-Country
type: Class
subclass of: Location
type: NamedIndividual
type: Location
comment: A sub-country is almost always an administrative division within a country, it can be a province or state.

SubSystem
type: Class
subclass of: Observe System
References: http://purl.oclc.org/NET/ssnx/ssn#hasSubSystem

Sugar beet
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Vegetable
comment: A sugar beet is a plant whose root contains a high concentration of sucrose and which is grown commercially for sugar production. In plant breeding it is known as the Altissima cultivar group of the common beet (Beta vulgaris).

Sugarcane
type: Class
subclass of: OtherCrops
type: NamedIndividual
type: Crop
has disease: Eyespot
description: Sugarcane, Saccharum officinarum, is a perennial grass in the family Poaceae grown for its stem (cane) which is primarily used to produce sucrose. Sugarcane has a thick, tillering stem which is clearly divided into nodes and internodes. The leaves of the plant grow from the nodes of the stem, arranged in two rows on either side of the stem. The leaves are tubular and blade-like, thicker in the centres than at the margins and encircle the stem. The inflorescence of sugarcane is a terminal panicle which possesses two spikelets and seeds protected by husks (glumes) covered in silky hair. Two flowers are produced on the inflorescence, one sterile and the other bisexual. Sugarcane can reach a height of up to 6 m (3.3 ft) and once harvested, the stalk will regrow allowing the plant to live for between 8 and 12 years. Sugarcane may also be referred to as nobel cane and originates from New Guinea.
maintenance: Plantations should be kept free from weeds with weeding being carried out every 3-4 weeks. Weeds can be removed by hand or through the use of machinery or appropriate chemicals. If rainfall is not sufficient to meet the growth requirements of the plants then irrigation must be supplied every 2-4 weeks through furrow or sprinkler irrigation. Soil should be mounded up around the base of the canes 1-2 times during the growing season to promote good root development, aid drainage in heavy soil or prevent lodging in light soils.
maintenance: Sugarcane is most commonly harvested by hand or cutting machine. Stalks should be cut close to the ground with a sharp knife when the canes are fully mature.
propagation: Sugarcane is vegetatively propagated by planting part of a mature cane called “settsâ€. Setts should be cut from carefully selected mature canes. A few days before the cuttings are taken, the end of the canes are removed to break the apical dominance of the cane and promote the breaking of buds. The best cuttings are taken from the upper portions of the cane and should be approximately 40 cm (16 in) in length with 2-3 buds. Sugarcane setts should be planted horizontally or at a 45o angle in furrows 15-30 cm (6-12 in) deep. Once in the ground, the setts should be covered with a thin layer of soil. Setts can be grown in a nursery bed and transplanted to the field or planted directly at the final growing site. The average planting density for sugarcane is 15,000-24,000 cuttings per hectare of land. Normally furrow method of planting is followed. A new method called pit method of planting promises two to three times more yield and more ratoon (up to 10) compared to furrow method.
requirements: Sugarcane grows best in tropical and subtropical regions as the plants require a warm, sunny and moist environment for growth. Plants will grow optimally at temperatures between 26 and 33oC (78.8-91.4oF) where there are no frosts which will damage the plants. Sugarcane can be grown successfully in a variety of soils but will perform optimally in deep, well-draining soils, rich in organic matter with a pH between 5.0 and 8.0. Sugarcane requires an average annual rainfall of between 1800 and 2500 mm per year for adequate growth. If rainfall is too low, plants should be grown with irrigation to maximize yield.
uses: Sugarcane is primarily used for the production of cane sugar (sucrose). One of the biproducts of sugarcane production is bioethanol which can be used as a fuel in place of gasoline. The dried fibre which is left over after the extraction of the sugarcane juice is called bagasse and is used in paper and textile production, as a fuel or as an organic mulch.
comment: Sugarcane, Saccharum officinarum, is a perennial grass in the family Poaceae grown for its stem (cane) which is primarily used to produce sucrose. Sugarcane has a thick, tillering stem which is clearly divided into nodes and internodes. The leaves of the plant grow from the nodes of the stem, arranged in two rows on either side of the stem. The leaves are tubular and blade-like, thicker in the centres than at the margins and encircle the stem. The inflorescence of sugarcane is a terminal panicle which possesses two spikelets and seeds protected by husks (glumes) covered in silky hair. Two flowers are produced on the inflorescence, one sterile and the other bisexual. Sugarcane can reach a height of up to 6 m (3.3 ft) and once harvested, the stalk will regrow allowing the plant to live for between 8 and 12 years. Sugarcane may also be referred to as nobel cane and originates from New Guinea.
References: https://plantvillage.psu.edu/topics/sugarcane/infos

Sulphur (S)
type: Class
subclass of: Macronutrient
type: NamedIndividual
type: Macronutrient
Acronym: S
comment: Sulfur is a constituent of amino acids in plant proteins and is involved in energy-producing processes in plants. It is responsible for many flavour and odour compounds in plants such as the aroma of onions and cabbage. Sulfur deficiency is not a problem in soils high in organic matter, but it leaches easily. On the North Coast seaspray is a major source of atmospheric sulfur. Superphosphate, gypsum, elemental sulfur and sulfate of ammonia are the main fertiliser sources.

Sunflower
type: Class
subclass of: Nuts
type: NamedIndividual
type: Crop
type: Nuts
type: OilSeeds
has growth stage: Flowering
has growth stage: Fruit Development stage
has growth stage: Germination
has growth stage: Inflorescence Emergence stage
has growth stage: Leaf Development stage
has growth stage: Ripening stage
has growth stage: Senescence stage
has growth stage: Stem Elongation stage
description: Sunflower, Helianthus annuus, is an herbaceous annual plant in the family Asteraceae, grown for its seeds. The plant has a thick, hairy, erect stem which gives rise to a large flower head. The plant has large, broad lower leaves which are oval and arranged alternately on the stem and smaller, narrower upper leaves which are attached individually to the stem. The flower head is a large disc reaching 10-30 cm (4-12 in) in diameter which is made up of 16-30 individual florets which are yellow-gold in color. The outer florets are sterile and produce the outer petals of the flower head, while the inner florets will mature into the seeds in the central disc. Sunflowers are annual plants, harvested after one growing season and can reach 1-3.5 m (3.3-11.5 ft) in height. Sunflower may also be referred to as girasole and originates from North America.
harvesting: Sunflowers are ready to harvest when the back of the flowers begin to turn yellow and the flower head droops. Cut the stem about 5 cm (2 in) below the head and hang up to dry in a warm, well ventilated place for several weeks. When the heads are thoroughly dried, remove the seeds with a brush or by hand. Allow the seeds to dry out for a further few days before storing.
maintenance: Although sunflowers are drought resistant, frequent, deep watering promotes the development of a strong root system. The plants do not require fertilizing, indeed, addition of nitrogen promotes vegetative growth and will delay flowering. The stems of the plants can easily reach in excess of 1.8 m (6 ft) in height and will benefit from some support. Bamboo stakes work well.
propagation: Sunflowers are most easily grown by direct seeding outdoors. In areas with a long growing season, they should be planted in late Spring after all danger of frost and when the soil has warmed through. However, in areas with short growing seasons, the seeds can be planted a week or two before the last frost date as the plants are tolerant to cold Prepare the soil for planting by digging to loosen the soil and break up any large clumps. Plant seeds 2.5-5.0 cm (1-2 in) deep and space them 10-15 cm (3-4 in) apart. If planting in rows, allow 0.75 m (~30 in) between each one. An application of a balanced fertilizer at planting promotes the development of a strong root system. The seeds should sprout in 7-10 days. When the seedlings are about 15 cm (6 in) tall with two pairs of true leaves, thin the plants to a final within row spacing of 60 cm (2 ft). Sow more seeds every 2-3 weeks for a continuous bloom over the summer.
requirements: Sunflowers are generally very easy to grow and thrive in areas with long hot summers which promote flowering. They grow best at temperatures between 21 and 25.5oC (70-78oF). Sunflowers can be very large and require plenty of space although there are some varieties which have been specially bred to be compact for growing in smaller spaces or for growing in containers. Sunflowers will grow in a variety of soils as long as they are not waterlogged and can be grown successfully at a pH ranging from 6.0 to 7.5. A pH above 6.0 is not recommended. They should be grown in an area that receives full sunlight and it is preferable to provide them with some shelter from wind which can damage the stems e.g. along a fence or wall. Sunflowers originate from the dry prairie regions of North America and are drought resistant once established. Their easy to grow nature makes them ideal plants for growing with children.
uses: Sunflower seeds can be eaten either fresh or cooked or used to extract oil which is widely used in cooking. The seeds are commonly harvested for bird seeds. Sunflowers are often grown as ornamental plants due to their large, attractive flower head.
comment: Sunflower, Helianthus annuus, is an herbaceous annual plant in the family Asteraceae, grown for its seeds. The plant has a thick, hairy, erect stem which gives rise to a large flower head. The plant has large, broad lower leaves which are oval and arranged alternately on the stem and smaller, narrower upper leaves which are attached individually to the stem. The flower head is a large disc reaching 10-30 cm (4-12 in) in diameter which is made up of 16-30 individual florets which are yellow-gold in color. The outer florets are sterile and produce the outer petals of the flower head, while the inner florets will mature into the seeds in the central disc. Sunflowers are annual plants, harvested after one growing season and can reach 1-3.5 m (3.3-11.5 ft) in height. Sunflower may also be referred to as girasole and originates from North America.
References: https://plantvillage.psu.edu/topics/sunflower/infos

Sunshine
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
comment: Sunlight is a portion of the electromagnetic radiation given off by the Sun, in particular infrared, visible, and ultraviolet light. On Earth, sunlight is scattered and filtered through Earth's atmosphere, and is obvious as daylight when the Sun is above the horizon. When direct solar radiation is not blocked by clouds, it is experienced as sunshine, a combination of bright light and radiant heat.
label: Sunlight

Supermarket
type: Class
subclass of: Market
comment: A supermarket is a self-service shop offering a wide variety of food, beverages and household products, organized into sections. It is larger and has a wider selection than earlier grocery stores, but is smaller and more limited in the range of merchandise than a hypermarket or big-box market.

Supplier
type: Class
subclass of: Organization
comment: A supplier is a person or organization that provides something needed such as a product or service.

Survival
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute

SwissChard
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Vegetable
comment: Chard or Swiss is a green leafy vegetable. In the cultivars of the Flavescens Group, the leaf stalks are large and often prepared separately from the leaf blade;[1] the Cicla Group is the leafy spinach beet. The leaf blade can be green or reddish in color; the leaf stalks are usually white, or a colorful yellow or red.
comment: Swiss Chard

Symptom
type: Class
subclass of: Biological
comment: A visible response of a host plant to a pathogenic organism.
comment: A visible response of a host plant to a pathogenic organism. It is the first step to identifying possible solutions to a plant health problem and understanding the problem correctly.

Syndrome
type: Class
subclass of: Biological
comment: A group of symptoms which consistently occur together, or a condition characterized by a set of associated symptoms.

Tank
type: Class
subclass of: Field Part
type: NamedIndividual
type: Field Part
comment: Tank is one of tanks in the irrigation systems which are used primarily to irrigate paddy fields.

Tea
type: Class
subclass of: OtherCrops
type: NamedIndividual
type: Crop
description: Tea, Camellia sinensis, is a tree or small shrub in the family Theaceae grown for its leaves which are used to make beverages. The tea plant is branching with alternate elliptical leaves. The leaves are leathery in texture, matte green in color and have serrated edges. The tea plant can take the form of a tree with a bowl-shaped canopy but is usually pruned under cultivation to be smaller and shrub-like. The plant produces fragrant white flower singly or in small clusters. Tea tree can reach up to 15 m (49 ft) in height and can live anywhere between 30 and 50 years. The plant originates from China.
harvesting: Tea is harvested by “pluckingâ€ which involves the removal of the youngest leaves at the shoot terminals. The terminal bud is removed along with the two leaves directly below (fine plucking) or the bud and three leaves (coarse plucking). The younger leaves have the best flavor and removing three leaves increases production but diminishes the overall quality by including an older leaf. Tea plants are plucked by hand by grasping the plant between thumb and forefinger and is usually carried out every 7-14 days depending on altitude.
maintenance: Newly planted tea plantations must be kept free from weeds while the tea plants establish. Weeding should be done by hand until the canopy closes and shades out weeds. Tea plants should be pruned to encourage the production of lateral branches. Busjes are shaped by tipping and thinning of branches to create the desired shape. Pruning should be maintained throughout the life of the plantation to keep the plants a manageable size and maintain productivity. pruning is best carried out when the plants are dormant where possible. If trees do not have a dormant period then they should be pruned in winter or after the dry period depending on geographic location. The tree clippings can be left in the centers of the rows to acts as an organic mulch. Tea plantations are usually fertilized 2-3 times per year. The amount of each nutrient added to the soil will depend on the soil’s own deficiencies and should ideally be based on the results of soil testing or by testing the leaves for nutrient deficiencies.
propagation: Tea is propagated from seed or rooted leaf cuttings. Good quality seeds are selected by immersing in water for 30 minutes and selecting the seeds which sink. Seeds are commonly germinated by placing them between wet cloths. Seeds which successfully germinate are transferred to a nursery bed to grow for 2-3 years. Before transplanting to the field, the plants are cut back to a height of 15 cm (6.0 in). Leaf cuttings are also used to establish new tea plantations. Single node cuttings are taken from the desired variety and rooted in polyethylene bags containing soil by firmly pressing the stem into the soil. The bags are then placed in a shaded area and watered regularly. New tea plants produced from rooted cuttings are usually planted out in the field after 6-9 months. Tea can be planted as single rows, double rows or as a hedge. Plantations established on slopes are planted on contour rows. Seedlings or cuttings are usually planted 60 cm (24 in) apart, allowing 1.5 m (5 ft) between rows. The tea plants are often interplanted with legumes or grasses to conserve soil moisture and prevent soil erosion. Shade trees are utilized in tea plantations grown at lower altitudes.
requirements: Tea grows best in warm and humid climates. It will tolerate temperatures between 14 and 27oC (57.2-80.6oF) with 18-20oC (64.4-68oF) being optimum. Tea plants will not tolerate frost and mean temperatures should not fall below 13oC (55.4oF) or exceed 29oC (84.2oF). Tea grows optimally in deep, well-draining, tropical red soils with an acidic pH between 4.5 and 6.0. Tea requires an average annual rainfall of 1600 mm per year distributed evenly throughout the growing season. In growing regions that experience a long dry season, tea plants require shading to increase the humidity around the plants.
uses: Tea leaves are usually infused with boiling water to produce an infused drink. In China, most tea leaves are processed for green tea (little or no fermentation of the leaves) whereas most Assam tea is processed for black tea which is manufactured by fermenting and crushing the leaves.
References: https://plantvillage.psu.edu/topics/tea/infos
comment: Tea, Camellia sinensis, is a tree or small shrub in the family Theaceae grown for its leaves which are used to make beverages. The tea plant is branching with alternate elliptical leaves. The leaves are leathery in texture, matte green in color and have serrated edges. The tea plant can take the form of a tree with a bowl-shaped canopy but is usually pruned under cultivation to be smaller and shrub-like. The plant produces fragrant white flower singly or in small clusters. Tea tree can reach up to 15 m (49 ft) in height and can live anywhere between 30 and 50 years. The plant originates from China.

Technological Control
type: Class
subclass of: Treatment

Temperature
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
unit: oC
comment: Temperature is a physical quantity that expresses hot and cold. It is the manifestation of thermal energy, present in all matter, which is the source of the occurrence of heat, a flow of energy, when a body is in contact with another that is colder. Temperature is measured with a thermometer. Thermometers are calibrated in various temperature scales that historically have used various reference points and thermometric substances for definition. The most common scales are the Celsius scale (formerly called centigrade, denoted °C), the Fahrenheit scale (denoted °F), and the Kelvin scale (denoted K)

Temperature Sensor
type: Class
subclass of: Sensor
type: NamedIndividual
type: Sensor
comment: A temperature sensor is a device, typically, a thermocouple or resistance temperature detector, that provides temperature measurement in a readable form through an electrical signal.

Terrain
type: Class
subclass of: Environment
type: NamedIndividual
type: Environment
comment: Terrain or relief (also topographical relief) is the vertical and horizontal dimension of the land surface. Terrain is used as a general term in physical geography, referring to the lay of the land. This is usually expressed in terms of the elevation, slope, and orientation of terrain features. Terrain affects surface water flow and distribution.

Thickness
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute
alternative label: Crop Thickness
comment: Crop Thickness

Time
type: Class
subclass of: Abstract Entity
type: NamedIndividual
comment: Time is what we measure in minutes, hours, days, and years.

Time Duration
type: Class
subclass of: Time
comment: Duration extent; duration of a time interval separate from its particular start position

Time Position
type: Class
subclass of: Time
comment: A temporal position described using either a (nominal) value from an ordinal reference system, or a (numeric) value in a temporal coordinate system.

Tobacco
type: Class
subclass of: OtherCrops
type: NamedIndividual
type: Crop
description: Tobacco, Nicotiana tabacum, is an herbaceous annual or perennial plant in the family Solanaceae grown for its leaves. The tobacco plant has a thick, hairy stem and large, simple leaves which are oval in shape. The tobacco plant produces white, cream, pink or red flowers which grow in large clusters, are tubular in appearance and can reach 3.5-5.5 cm (1,25-2 in) in length. Tobacco may reach 1.2-1.8 m (4-6 ft) in height and as is usually grown as an annual, surviving only one growing season. Tobacco may also be referred to as Virginia tobacco or cultivated tobacco and originates from South America.
harvesting: Tobacco is harvested by hand in most parts of the world by picking 2-3 leaves from each plant per harvest. In the USA and Canada, tobacco plants are mechanically harvested by cutting the stalks of the plants. Only fully mature leaves should be harvested when hand picking is practices and harvests should be carried out at weekly intervals. After harvest, leaves are usually tied in pairs to cure.
maintenance: The best quality tobacco leaves are produced when the the flowerheads of the plants are removed, a process known as topping. Topping plants promotes the development of suckers which should also be removed. Suckers are removed through the use of chemicals in commercial tobacco production with some hand removal also necessary. Fertilizer and irrigation requirements of tobacco vary with the variety being grown but generally, tobacco has a requirement of 40-80 kg per hectare of nitrogen, 80-90 kg per hectare of phosphorous and 50-110 kg per hectare of potassium.
propagation: Tobacco is propagated from seed on protected (covered) seed beds or in the glasshouse and transplanted to the final growing site. Seeds grown outdoors are protected for the first few weeks to prevent weather damage to the emerging young plants. seedlings are transplanted after 30-60 days when they are approximately 15 cm (6 in) in height. The young plants should be spaced 46-61 cm (18-24 in) apart.
requirements: Tobacco grows very well in a wide range of climates and will grow optimally at temperatures between 20 and 30oC (68-86oF) in areas where there is a dry period to facilitate harvest of the leaves. The type of soil depends on the variety of tobacco being grown but the best yields are usually obtained in loam to sandy loam soils. The soil should have a pH between 5.0 and 6-6. Tobacco plants are easily damaged by waterlogged soils and quality can be affected by high salinity. Plants should therefore be grown in a well draining and well aerated soil.
uses: Tobacco is a stimulant and the dried leaves of the tobacco plant can be cured and used to produces tobacco cigarettes, cigars and snuff or for pesticide production.
comment: Tobacco, Nicotiana tabacum, is an herbaceous annual or perennial plant in the family Solanaceae grown for its leaves. The tobacco plant has a thick, hairy stem and large, simple leaves which are oval in shape. The tobacco plant produces white, cream, pink or red flowers which grow in large clusters, are tubular in appearance and can reach 3.5-5.5 cm (1,25-2 in) in length. Tobacco may reach 1.2-1.8 m (4-6 ft) in height and as is usually grown as an annual, surviving only one growing season. Tobacco may also be referred to as Virginia tobacco or cultivated tobacco and originates from South America.
References: https://plantvillage.psu.edu/topics/tobacco/infos

Tomato
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Crop
type: Vegetable
description: Tomato, Lycopersicum esculentum (syn. Solanum lycopersicum and Lycopersicon lycopersicum) is an herbaceous annual in the family Solanaceae grown for its edible fruit. The plant can be erect with short stems or vine-like with long, spreading stems. The stems are covered in coarse hairs and the leaves are arranged spirally. The tomato plant produces yellow flowers, which can develop into a cyme of 3-12, and usually a round fruit (berry) which is fleshy, smoothed skinned and can be red, pink, purple, brown, orange or yellow in color. The tomato plant can grow 0.7-2 m (2.3-6.6 ft) in height and as an annual, is harvested after only one growing season. Tomato may also be referred to as love apple and originates from South America.
propagation: In most cases, tomato seeds should be started indoors 6-8 weeks before last Spring frost. Seeds can be direct seeded in areas with a long growing season. Seeds should be sown in flats or cell trays using a sterile seedling mix. Plant seeds to a depth of 0.6 cm (1/4 in) and water lightly. If cells are being used, plant several seeds in each cell and thin to 1 seedling after germination. Position trays in a bright South facing window or under fluorescent lighting. The optimum soil temperature for germination is 21-32oC (70-90oF). A heat mat can be used to warm the flats if required. Seedlings should emerge within 6-14 days and after the seedlings has developed the first set of true leaves then they can be moved to a larger (3-4 in) pot and moved to a cooler temperature (16-21oC/60-70oF). Tomato seedlings are ready to be transplanted once they are 15-25 cm (6-10 in) in height and have 3-5 true leaves assuming all danger of frost has passed. Beginning approximately 7-10 days before transplanting, plants should be set outside to harden off (see https://www.plantvillage.com/posts/264). The transplanting site should be prepared by working in a balanced fertilizer according to the guidelines on the product label. Transplants should be spaced 76-123 cm (30-48 in) apart with a between row spacing of 123 cm (48 in). It is common practice to plant tomatoes in trenches on their side (see https://www.plantvillage.com/posts/833-tomato) as tomato stems will sprout roots along their length when buried. Avoid over-fertilizing transplants, particularly with nitrogen, at this stage of growth as it will promote growth of foliage rather than fruits. Water plants lightly at base instead of overhead as wet foliage is more prone to diseases and the buried stem needs time to adapt and sprout roots. It is important that tomato plants receive even watering to prevent the development of blossom end rot, drip or soaker hoses work best and mulching around the plants helps to conserve soil moistur…
requirements: Tomatoes grow very well in warm areas at temperatures between 21 and 24oC (69.8-75.2oF). They require a deep, loamy, well-draining soil with a pH between 5.5 and 6.8. If soil drainage is a problem then tomatoes can be planted in a raised bed. Like all fruiting plants, tomatoes require full sun for most of the day.
uses: Tomato fruit can be eaten raw or cooked and is used in many dishes. The fruit may also be processed into juice, soup, ketchup, puree, paste or powder.
variety: Determinate (“bushâ€) tomatoes have been purposefully bred to grow vertically and remain relatively compact. The plant will stop growing once fruit begins developing on the terminal shoot and all the fruits ripen at around the same time. In contrast, indeterminate (“viningâ€) tomato varieties spread laterally and will continue to grow and produce fruit until frosts begin the the Fall. Indeterminate varieties can produce fruit all season and fruits will develop and ripen at different times. Heirloom tomatoes are generally open-pollinated varieties which have been conserved over many generations due to certain desirable characteristics such as flavor. Hybrid tomatoes are the product of cross-pollination between two parents with desirable characteristics such as high yield, early maturation, improved flavor or resistance to certain diseases.
comment: Tomato, Lycopersicum esculentum (syn. Solanum lycopersicum and Lycopersicon lycopersicum) is an herbaceous annual in the family Solanaceae grown for its edible fruit. The plant can be erect with short stems or vine-like with long, spreading stems. The stems are covered in coarse hairs and the leaves are arranged spirally. The tomato plant produces yellow flowers, which can develop into a cyme of 3-12, and usually a round fruit (berry) which is fleshy, smoothed skinned and can be red, pink, purple, brown, orange or yellow in color. The tomato plant can grow 0.7-2 m (2.3-6.6 ft) in height and as an annual, is harvested after only one growing season. Tomato may also be referred to as love apple and originates from South America.
References: https://plantvillage.psu.edu/topics/tomato/infos

Toolkit
type: Class
subclass of: Data Mining

Top Soil
type: Class
subclass of: Soil Conductivity
type: NamedIndividual
type: Soil
comment: Topsoil is the upper, outermost layer of soil, that is rich in organic matter and microorganisms. It is usually the top 5–10 inches (13–25 cm).

Topography
type: Class
subclass of: Terrain
type: NamedIndividual
type: Terrain

Trace Elements
type: Class
subclass of: Mineral
comment: A trace element is a chemical element whose concentration (or other measure of amount) is very low (a "trace amount"). They are classified into two groups; essential and non-essential. Essential trace elements are needed for many physiological and biochemical processes in both plants and animals.

Tractor
type: Class
subclass of: Vehicle
comment: A powerful vehicle with large rear wheels, used chiefly on farms for hauling equipment and trailers.

Transformation
type: Class
comment: Transformation is the process or function of converting data from one range, format or structure into another range, format or structure.

Transportation
type: Class
subclass of: InUse
comment: Transport, whether traditional or mechanized, is needed to move the agricultural commodities: from the harvest fields, to the threshing or drying site

Treatment
type: Class
subclass of: Biological
comment: The medical care given to plants for a disease infection.

Tree
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Tree is a perennial plant with an elongated stem, or trunk, supporting branches and leaves in most species. In some usages, the definition of a tree may be narrower, including only woody plants with secondary growth, plants that are usable as lumber or plants above a specified height.

Triacylglycerol
type: Class
subclass of: Biomolecule
type: NamedIndividual
type: Biomolecule
comment: Triacylglycerols are important storage lipids and the main constituents (∼99%) of vegetable oils and food lipids. Natural triacylglycerols contain generally a mixture of different fatty acids and their composition reflects the relative fatty acid concentrations.

Truck
type: Class
subclass of: Vehicle
comment: A truck or lorry is a motor vehicle designed to transport cargo.

Tuber
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Tubers are parts of plants, enlarged structures in some plant species used as storage organs for nutrients.

Tulip
type: Class
subclass of: Flower
type: NamedIndividual
type: Flower
comment: Tulips (Tulipa) form a genus of spring-blooming perennial herbaceous bulbiferous geophytes (having bulbs as storage organs). The flowers are usually large, showy and brightly colored, generally red, pink, yellow, or white (usually in warm colors).

Ultraviolet-Visible Spectroscopy (UV-Vis)
type: Class
subclass of: Spectrophotometer

Validation
type: Class
subclass of: Planned Process
References: CRISP-DM
comment: A planned process with objective to check that the accuracy or the quality of a claim or prediction satisfies some criteria and which is assessed by comparing with independent results.

Vanadium (V)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
comment: Vanadium may be required by some plants, but at very low concentrations. It may also be substituting for molybdenum.
Acronym: V

Vapor Pressure
type: Class
subclass of: Air
subclass of: Weather
type: NamedIndividual
type: Air
type: Weather
comment: Vapor Pressure exerted by water vapor in thermodynamic equilibrium with its condensed phase (i.e) solid or liquid form at a given temperature.
alternative label: Vapour Pressure

Vapour Pressure
type: Class
type: NamedIndividual
type: Weather
comment: Vapor Pressure exerted by water vapor in thermodynamic equilibrium with its condensed phase (i.e) solid or liquid form at a given temperature.

Variety
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute
comment: Crop Variety means a plant grouping, within a single botanical taxon of the lowest known rank, defined by the reproducible expression of its distinguishing and other genetic characteristics.
alternative label: Crop Variety

Vegetable
type: Class
subclass of: Crop
comment: Vegetable is a usually herbaceous plant (such as the cabbage, bean, or potato) grown for an edible part that is usually eaten as part of a meal;

Vegetation
type: Class
subclass of: Farm
type: NamedIndividual
type: Terrain

Vehicle
type: Class
subclass of: Machine
comment: A vehicle is a machine that transports people or cargo.

Vermi Compost
type: Class
subclass of: Organic Mature
type: NamedIndividual
type: Compost
type: Organic Mature

Viral Disease
type: Class
subclass of: Crop Disease
comment: Infectious plant diseases are caused by virus and can range in severity from mild leaf or plant damage to death.

Virus
type: Class
subclass of: Microoganism
type: NamedIndividual
type: Crop Problem
comment: A virus is a submicroscopic infectious agent that replicates only inside the living cells of an organism. Viruses can infect all types of life forms, from animals and plants to microorganisms, including bacteria and archaea. Plant viruses are viruses that affect plants. Like all other viruses, plant viruses are obligate intracellular parasites that do not have the molecular machinery to replicate without a host. Plant viruses can be pathogenic to higher plants.

Visibility
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
unit: Km
comment: In meteorology, visibility is a measure of the distance at which an object or light can be clearly discerned. It is reported within surface weather observations and METAR code either in meters or statute miles, depending upon the country. Visibility affects all forms of traffic: roads, sailing and aviation. Meteorological visibility refers to transparency of air: in dark, meteorological visibility is still the same as in daylight for the same air.
comment: https://en.wikipedia.org/wiki/Visibility

Vitamin
type: Class
subclass of: Mineral
type: NamedIndividual
type: Mineral
comment: A vitamin is an organic molecule (or a chemically closely related set of molecules, i.e. vitamers) that is an essential micronutrient which an organism needs in small quantities for the proper functioning of its metabolism.

Vitamin C
type: Class
type: NamedIndividual
type: Vitamin

Wireless Local Area Network (WLAN)
type: Class
subclass of: Network
type: NamedIndividual
type: Network
comment: A wireless LAN (WLAN) is a wireless computer network that links two or more devices using wireless communication to form a local area network (LAN) within a limited area such as a home, school, computer laboratory, campus, or office building.
label: Wireless Local Area Network

Wireless Sensor Network
type: Class
subclass of: Network
type: NamedIndividual
type: Network
comment: Wireless sensor network (WSN) refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. WSNs measure environmental conditions like temperature, sound, pollution levels, humidity, wind, and so on.
label: Wireless Sensor Network (WSN)

Walnut
type: Class
subclass of: Nuts
type: NamedIndividual
type: Crop
type: Nuts
type: OilSeeds
harvesting: Walnuts in the home garden can simply be collected when they drop from the tree. In commercial orchards, walnuts are commonly harvested with the used of a mechanical shaker. Once the nuts are collected, they must be immediately hulled (the process of removing the nut from the green flesh) and dried.
maintenance: Walnut trees should be trained to a central leader or modified leader system. Trees should be pruned in the Fall to prevent sap bleeding. Once the desired shape has been achieved, annual pruning should focus on thinning the tops of the trees to allow better air and light penetration to the canopy. In commercial orchards, lower limbs may be removed to facilitate the movement of machinery through the orchard. Newly planted walnut trees should be provided with supplemental irrigation for the first two years following planting. Trees should be watered deeply and the soil allowed to dry out between waterings. Irrigation is also required in the period after bloom in order to maximize nut production. In addition to an initial application of manure at planting, trees should be provided with 100–400 g of nitrogen per tree until the fifth year. In the sixth year, trees may be provided with up to 100 kg of nitrogen per hectare.
propagation: Walnut trees are usually propagated vegetatively by budding or grafting, while saplings for rootstock are grown from seed. The planting site should be prepared by levelling the ground and eliminating weeds. Trees are planted by digging a hole that is twice as wide as the existing root ball. Aged manure or compost may be added to the soil at time of planting by incorporating it into the backfill soil. Trees should be spaced 10–15 m (33–50 ft) apart. In commercial plantations, trees may be spaced closer together. Grafted trees will begin to bear fruit in 5 years, while non-grafted plants may take 7-8 years. Newly planted young trees require staking as the new wood is soft and the tree can be damaged by winds.
requirements: Walnut can be grown successfully in a wide variety of climates but blossoms can be damaged by frost or high humidity. Must walnut varieties have a winter chilling requirement of 800–1000 hours below 7°C (45°F) to break dormancy and require a growing season of at least 200 frost-free days. Walnut trees should be planted in areas where there is good air circulation and drainage and will grow optimally when planted in a deep, fertile, well-draining loam soil with a pH between 5.5 and 6.5. Walnut roots systems can be extensive and It is not unusual for walnut roots to grow to depths of 4.5 m (15 ft). Water-logged soils will limit root growth and should be avoided. Walnuts will usually begin to produce in the fourth or fifth year following planting.
uses: Walnut kernels can be eaten fresh or processed for use in baked goods and confectionery. The timber of the tree is a highly valued as a wood for furniture making in Europe. Kernels may also be used as an animal feed or used to extract oil which was once a popular thinner for oil based paints.
References: https://plantvillage.psu.edu/topics/walnut/infos
comment: Walnut, Juglans regia, is a deciduous tree in the Family Juglandaceae grown for its edible seeds. Walnut trees are large and vigorous with a wide-spreading canopy. The trunk of the tree can reach 2 m (6.6 ft) in diameter and mature trees possess smooth, silver-gray bark. Walnut leaves are composed of an odd number of smaller, oval shaped leaflets which are bright green in color. The tree produces male flowers on catkins and female flowers on terminal clusters where the fruit develops. The fruit of the walnut tree is a fleshy green drupe in which the nut is encased. The kernel of the nut is protected by a corrugated woody shell. Walnut trees can reach 25–35 m (82–115 ft) in height and can live for periods in excess of 200 years. Walnut may also be referred to as Persian walnut, English walnut, common walnut or European walnut and originates from central Asia.

Waste
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part

Water
type: Class
subclass of: Environment
type: NamedIndividual
type: Environment
comment: Water is an inorganic, transparent, tasteless, odorless, and nearly colorless chemical substance, which is the main constituent of Earth's hydrosphere and the fluids of all known living organisms. "Water" is the name of the liquid state of H2O at standard ambient temperature and pressure. It forms precipitation in the form of rain and aerosols in the form of fog. Water moves continually through the water cycle of evaporation, transpiration (evapotranspiration), condensation, precipitation, and runoff, usually reaching the sea.

Water Density
type: Class
subclass of: Water
type: NamedIndividual
type: Water

Water Depth
type: Class
subclass of: Water
type: NamedIndividual
type: Water

Water pH
type: Class
subclass of: Water
type: NamedIndividual
type: Water

Water Sensor
type: Class
subclass of: Sensor
comment: A water detector is an electronic device that is designed to detect the presence of water for purposes such as to provide an alert in time to allow the prevention of water leakage.

Water Stress
type: Class
subclass of: Crop Stress
type: NamedIndividual
type: Crop Disease
type: Crop Stress
comment: Water stress in plants is caused by the water deficit, as induced possibly by drought or high soil salinity. The prime consequence of water stress in plants is the disruption in the agricultural production, resulting in food shortage. The plants, however, try to adapt to the stress conditions using biochemical and physiological interventions.

Water Surface Tension
type: Class
subclass of: Water

Water Temperature
type: Class
subclass of: Water
type: NamedIndividual
type: Water

Water Viscosity
type: Class
subclass of: Water
type: NamedIndividual
type: Water

Watermelon
type: Class
subclass of: Fruit Tree
type: NamedIndividual
type: Crop
harvesting: Watermelons are ready to be harvested when the the tendrils closest to the fruit are beginning to dry out and turn brown. The underside of the melon should be beginning to turn yellow
maintenance: Watermelon vines are sprawling and require plenty space to grow. Vines can be trained to grow on a trellis or fence to save space. Watermelon plants Watermelons have a very deep root system which allows them to access soil moisture efficiently. However, if conditions are very dry for prolonged periods then additional water should be provided.. Plastic mulches are highly recommended in areas where conditions are not ideal for growth as they conserve soil moisture and black polyethylene has the advantage of warming the soil. Floating row covers can be beneficial while the plants establish to protect against insect pests.
requirements: Watermelon is a warm-season crop, requiring lots of sun and good drainage to develop optimally and growing best at temperatures between 18 and 28°C (65–82°F). Watermelon will yield best if grown in a light, well-draining soil, rich in organic matter and with a pH between 6.0 and 7.0. Watermelon should be planted in full sun and heavy feeders. They need to be provided with even soil moisture and fertilized regularly. Vining varieties can grow to very large sizes and require a good deal of space.
uses: Watermelon is usually consumed as a fresh fruit. In Africa it is sometimes cooked before eating and may also be used as an animal feed.
comment: Watermelon, Citrullus lanatus, is a vining annual plant in the family Cucurbitaceae grown for its fleshy fruit. Watermelon vines are thin, grooved and covered in tiny hairs. Vines are branching and possess deeply lobed pinnate leaves. The plant produces solitary yellow flowers and and a large spherical to oblong fruit. The fruit is a 'pepo' - a fleshy fruit protected by a thick leathery rind. The fruit is smooth, light to dark green in color and can be striped, marbled or solid green. The flesh of the fruit is usually red in color but some cultivars produce green, orange or white flesh and contains numerous seeds which are usually black or dark brown in color. Watermelon vines can reach a length of 3 m (10 ft) and as an annual, survives only one growing season. Watermelon originates from Africa.
References: https://plantvillage.psu.edu/topics/watermelon/infos

Weather
type: Class
subclass of: Environment
type: NamedIndividual
type: Environment
comment: Weather is the state of the atmosphere, to the degree that it is hot or cold, wet or dry, calm or stormy, clear or cloudy. Most weather phenomena occur in the troposphere, just below the stratosphere. Weather refers to day-to-day temperature and precipitation activity, whereas climate is the term for the statistics of atmospheric conditions over longer periods of time. When used without qualification, "weather" is generally understood to mean the weather of Earth. Weather is driven by air pressure, temperature and moisture differences between one place and another.

Weather Phenomena
type: Class
subclass of: Natural Phenomenon
comment: Weather phhenomenon is an observable fact or event related to weather, such as blizzard, snow, storm, tornado.

Weather Sensor
type: Class
subclass of: Sensor
type: NamedIndividual
type: Sensor
comment: A weather station is a facility, either on land or sea, with instruments and equipment for measuring atmospheric conditions to provide information for weather

Weeds
type: Class
subclass of: Crop Problem
type: NamedIndividual
type: Crop Problem
comment: A weed is a wild plant that grows in fields of crops and prevents the plants that farmers want from growing properly.

Weight
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute
comment: Crop weight is the weight of plant or any plant (or other organism) part. Crop weight has two types: fresh weight and dry weight.
alternative label: Crop Weight

Well
type: Class
subclass of: Field Part
type: NamedIndividual
type: Field Part
comment: Well is one of wells in the irrigation systems which are used primarily to irrigate paddy fields.

Wheat
type: Class
subclass of: Cereal
type: NamedIndividual
type: Cereal
type: Crop
has disease: Aphids
has disease: Bacterial Black Chaff
has disease: Leaf Rust
has disease: Soilborne Wheat Mosaic
has disease: Stem Elongation stage
has disease: Black Rust
has disease: Stink Bugs
has growth stage: Flowering
has growth stage: Fruit Development stage
has growth stage: Germination
has growth stage: Inflorescence Emergence stage
has growth stage: Leaf Development stage
has growth stage: Ripening stage
has growth stage: Senescence stage
has growth stage: Stem Elongation stage
description: Wheat, is the name given to several plants in the genus Triticum including Triticum aestivum, Triticum compactum, Triticum spelta and Triticum durum, which are annual or biennial grasses grown primarily for their grain. Wheat species possess an erect smooth stem with linear leaves that grow in two rows on either side of the stem with larger 'flag' leaves at the top of the stem. The stem terminates in a spike that is made up on individual spikelets, each possessing 3-9 florets. The wheat fruit develops within the spikelets, maturing to a seed (kernel). Wheat can reach 1.2 m (4 ft) in height and like other cereals, has been developed into different varieties that are adapted to planting at different times of the year. Spring wheat is planted for a late summer harvest, whereas Winter wheat is planted for harvesting in early to mid summer. Overwintering varieties are more commonly grown in regions with mild winters. Wheat may be referred to by variety and these include durum or macaroni wheat (Triticum durum), club wheat (Triticum compactum), spelt wheat (Triticum spelta) and bread wheat (Triticum aestivum). Wheat originated in the Fertile Crescent of the Middle East.
estimate yield: In 2018 the UK record stood at 16t/ha, and the average yield was just 8t/ha.
harvesting: Wheat is ready to harvest when the stalks and heads have turned from green to yellow and the seed heads are drooping towards the ground. Check the seeds for ripeness before harvest. The should be firm and crunchy and not doughy in texture. Commercially produced wheat is usually harvested using a combine. Smaller plots can be harvested by hand using a scythe or sickle. Small plots can be harvested by snipping off the heads with a pair of scissors.
propagation: Wheat varieties One of the first things to consider before planting is which type of wheat you want to grow. There are several different varieties to choose from depending on the time of year and how you want to utilize your harvest. Wheat is broadly categorized into Winter wheat and Spring wheat. Winter wheat is high yielding and is planted in the Fall and harvested in the Spring or Summer of the following year (depending on location). Spring wheat is not as high yielding but tolerated drier conditions. It is planted in the Spring and harvested in the Fall. Both Spring and Winter wheat is then further categorized as soft wheat, hard wheat, spelt or durum. Planting Winter wheat varieties should be planted in the Fall approximately 6 to 8 weeks before the first frost date. Spring wheat varieties should be planted as soon as the soil can be worked in the Spring. Commercially grown wheat is usually mechanically drilled using a machine that creates a furrow and drops the seed in before covering it back up. Wheat seeds can be sown by hand broadcasting in smaller areas, or using a hand-cranked seeder. Seeds are usually sown to at depths ranging from 2 to 12 cm (0.8-4.7 in) depending on soil conditions (seed must be sown deeper in drier soil). Once the seeds have been scattered, the soil should be raked lightly to set the seeds at the desired depth.
requirements: Wheat can be grown in a wide variety of climates but grows best in cool regions where the temperature is between 10 and 24oC (50-75oF). Wheat will not grow at temperatures above 35oC (95oF). Wheat will grow optimally in a deep, fertile, well draining and well aerated soil at a pH between 5.5 and 7.5.
sowing rate: 75 kg/acre (187.5 kg/ha) (Recommended by www.cotswoldseeds.com)
unit: mg/L
uses: Wheat is one of the most important food plants in the world. It is used primarily to produce flour for bread. It is used widely in the production of many other baked goods. Wheat grain is also used in the manufacture of alcoholic beverages and alcohol. Wheat straw is used as an animal feed and in the manufacture of carpets, baskets, packing, bedding, and paper.
References: https://plantvillage.psu.edu/topics/wheat/infos
comment: Wheat, is the name given to several plants in the genus Triticum including Triticum aestivum, Triticum compactum, Triticum spelta and Triticum durum, which are annual or biennial grasses grown primarily for their grain. Wheat species possess an erect smooth stem with linear leaves that grow in two rows on either side of the stem with larger 'flag' leaves at the top of the stem. The stem terminates in a spike that is made up on individual spikelets, each possessing 3-9 florets. The wheat fruit develops within the spikelets, maturing to a seed (kernel). Wheat can reach 1.2 m (4 ft) in height and like other cereals, has been developed into different varieties that are adapted to planting at different times of the year. Spring wheat is planted for a late summer harvest, whereas Winter wheat is planted for harvesting in early to mid summer. Overwintering varieties are more commonly grown in regions with mild winters. Wheat may be referred to by variety and these include durum or macaroni wheat (Triticum durum), club wheat (Triticum compactum), spelt wheat (Triticum spelta) and bread wheat (Triticum aestivum). Wheat originated in the Fertile Crescent of the Middle East.

Wheelbarrow
type: Class
subclass of: Vehicle
comment: A wheelbarrow is an essential product to help you move heavy loads whether at home or on a work site.

White blisters
type: Class
subclass of: Symptom
type: NamedIndividual
type: Symptom
comment: These are white, shining, blister-like pustules found on the leaves of cruciferous plants caused by Albugo Candida, the white rust. These pustules expose the white, powdery masses of spores.

Wikipedia
type: Class
subclass of: Link
comment: Wikipedia is an online free-content encyclopedia project that aims to help create a world in which everyone can freely share in the sum of all knowledge. It is supported by the Wikimedia Foundation and based on a model of openly editable content. The name "Wikipedia" is a blending of the words wiki (a technology for creating collaborative websites, from the Hawaiian word wiki, meaning "quick") and encyclopedia. Wikipedia's articles provide links designed to guide the user to related pages with additional information. Wikipedia is written collaboratively by largely anonymous volunteers who write without pay. Anyone with internet access can write and make changes to Wikipedia articles, except in limited cases where editing is restricted to prevent disruption or vandalism. Since its creation on January 15, 2001, Wikipedia has grown into the world's largest reference website, attracting 1.5 billion unique visitors monthly as of March 2020. It currently has more than 54 million articles in more than 300 languages, including 6,144,813 articles in English with 126,740 active contributors in the past month.

Wildfire
type: Class
subclass of: Planetary Phenomena
type: NamedIndividual
type: Planetary Phenomena
comment: A wildfire, wildland fire or rural fire is an uncontrolled fire in an area of combustible vegetation occurring in rural areas. Depending on the type of vegetation present, a wildfire can also be classified more specifically as a brush fire, bushfire, desert fire, forest fire, grass fire, hill fire, peat fire, vegetation fire, or veld fire.

Wilts
type: Class
subclass of: Symptom
type: NamedIndividual
type: Symptom
comment: The drooping of the entire plant due to loss of turgidity is a common symptom of disease. The causes may be varied. The wilting due to a disease is permanent and eventually leads to death of the plant.

Wind Direction
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
comment: Wind direction is reported by the direction from which it originates. For example, a north or northerly wind blows from the north to the south. Wind direction is usually reported in cardinal (or compass) direction, or in degrees. Consequently, a wind blowing from the north has a wind direction referred to as 0° (360°); a wind blowing from the east has a wind direction referred to as 90°, etc.

Wind Gust
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
comment: Wind gust is a brief increase in the speed of the wind, usually less than 20 seconds. It is of a more transient character than a squall, which lasts minutes, and is followed by a lull or slackening in the wind speed.[1] Generally, winds are least gusty over large water surfaces and most gusty over rough land and near high buildings.

Anemometer
type: Class
subclass of: Sensor
type: NamedIndividual
type: Sensor
comment: Wind Sensor is a device used for measuring wind speed and direction. It is also a common weather station instrument.
label: Wind Sensor

Wind Speed
type: Class
subclass of: Weather
type: NamedIndividual
type: Weather
unit: Km/h
comment: Wind speed, or wind flow speed, is a fundamental atmospheric quantity caused by air moving from high to low pressure, usually due to changes in temperature. Note that wind direction is usually almost parallel to isobars (and not perpendicular, as one might expect), due to Earth's rotation.

Wind Type
type: Class
subclass of: Weather

Wine
type: Class
subclass of: Product
type: NamedIndividual
type: Product
comment: Wine is an alcoholic drink which is made from grapes.

Wireless Sensor
type: Class
subclass of: Sensor
type: NamedIndividual
type: Sensor
See Also: A wireless sensor node is equipped with sensing and computing devices, radio transceivers and power components.

Wood
type: Class
subclass of: Crop Part
type: NamedIndividual
type: Crop Part
comment: Wood is a porous and fibrous structural tissue found in the stems and roots of trees and other woody plants. It is an organic material – a natural composite of cellulose fibers that are strong in tension and embedded in a matrix of lignin that resists compression. Wood is sometimes defined as only the secondary xylem in the stems of trees, or it is defined more broadly to include the same type of tissue elsewhere such as in the roots of trees or shrubs.

Workshop
type: Class
subclass of: Event
comment: Workshop is a meeting at which a group of people engage in intensive discussion and activity on a particular subject or project.

Yam
type: Class
subclass of: Vegetable
type: NamedIndividual
type: Vegetable
See Also: Yam is the common name for some plant species in the genus Dioscorea that form edible tubers. Yams are perennial herbaceous vines cultivated for the consumption of their starchy tubers in many temperate and tropical regions, especially in Africa, South America and the Caribbean, Asia, and Oceania.

Yield
type: Class
subclass of: Crop Attribute
type: NamedIndividual
type: Crop Attribute
unit: T/Ha
alternative label: Mean Yield
comment: The yield (also known as "agricultural productivity" or "agricultural output") is a measurement of the amount of a crop grown, or product such as crop yield produced, per unit area of land.
alternative label: Crop Yield

Zinc (Zn)
type: Class
subclass of: Micronutrient
type: NamedIndividual
type: Micronutrient
unit: mg/kg
comment: Zinc is required in a large number of enzymes and plays an essential role in DNA transcription. A typical symptom of zinc deficiency is the stunted growth of leaves, commonly known as "little leaf" and is caused by the oxidative degradation of the growth hormone auxin. Zinc occurs in plants as a free ion, as a complex with a variety of low molecular weight compounds, or as a component of proteins and other macromolecules.
Acronym: Zn

Zinc Deficiency
type: Class
subclass of: Plant Disorder
type: NamedIndividual
type: Plant Disorder
related to: Zinc (Zn)
comment: Zinc deficiency occurs when plant growth is limited because the plant cannot take up sufficient quantities of this essential micronutrient from its growing medium. It is one of the most widespread micronutrient deficiencies in crops and pastures worldwide and causes large losses in crop production and crop quality.

Point
type: Class
subclass of: Location
References: http://purl.org/dc/terms/Point
References: http://www.w3.org/2003/01/geo/wgs84_pos#
comment: The relation between something and the point, or other geometrical thing in space, where it is. For example, the realtionship between a radio tower and a Point with a given lat and long. Or a relationship between a park and its outline as a closed arc of points, or a road and its location as a arc (a sequence of points). Clearly in practice there will be limit to the accuracy of any such statement, but one would expect an accuracy appropriate for the size of the object and uses such as mapping.
comment: The set of points in space defined by their geographic coordinates

Location
type: Class
subclass of: Physical Entity
comment: Spatial thing is used to describe things relating to areas or locations.
label: Spatial Thing

Advanced Land Observing Satellite (ALOS)
type: NamedIndividual
type: Satellite

Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2)
type: NamedIndividual
type: Spectrophotometer

Abort tree
type: NamedIndividual
type: Symptom

Above Room Temperature
type: NamedIndividual
type: Temperature

Above Zero Temperature
type: NamedIndividual
type: Temperature

Advanced Miner
type: NamedIndividual
type: Toolkit
comment: The Advanced Miner is an upgraded version of the Miner. Unlike the Miner, it does not require Mining Pipe or a Mining Drill to function, relying solely on power. An additional feature of the Advanced Miner is a black/whitelist function. The mode of this can be toggled by a button in the GUI. In Blacklist Mode, any blocks placed inside the GUI will not be mined by the Advanced Miner, and will be ignored. In Whitelist Mode, any block inside the GUI will be the only blocks mined, and all other blocks will be ignored. The blocks specified are based on what they drop, but are also compatible with Silk Touched ores. For example, on Blacklist Mode, when Redstone Dust or Redstone Ore is in the GUI, no Redstone Ore will be mined. It is worth noting that the slots inside the GUI are not storage spaces, they are only used for the filters. A Chest or similar inventory placed above the Advanced Miner will automatically collect the items, not needing an Ejector Upgrade.

Aelia Sibirica
type: NamedIndividual
type: Crop Pest
comment: Aelia is a genus of shield bug belonging to the family Pentatomidae.
alternative label: Aelia sibirica

Afghanistan
type: NamedIndividual
type: Country
belongs to: Middle East
has climate: Continental Climate
ISO code: ISO 3166-1:AF
agricultural land: 379,100 sq. km
area: 647,500 km²
climate: arid to semiarid; cold winters and hot summers
density: 46/km²
latitude: 67.67894
longitude: 33.93045
population: 32,738,376 (2008)
comment: Afghanistan, officially the Islamic Republic of Afghanistan, is a landlocked country at the crossroads of Central and South Asia. Afghanistan is bordered by Pakistan to the east and south; Iran to the west; Turkmenistan, Uzbekistan, and Tajikistan to the north; and China to the northeast. Occupying 652,000 square kilometers, it is a mountainous country with plains in the north and southwest. Kabul is the capital and largest city. The population is around 32 million, composed mostly of ethnic Pashtuns, Tajiks, Hazaras, and Uzbeks.

Africa
type: NamedIndividual
type: Continent
has country: Algeria
has country: Angola
has country: Benin
has country: Botswana
has country: Bouvet Island
has country: Burkina Faso
has country: Burundi
has country: Cameroon
has country: Cape Verde
has country: Central African Republic
has country: Chad
has country: Comoros
has country: Congo
has country: Cote dIvoire
has country: The Democratic Republic of Congo
has country: Djibouti
has country: Equatorial Guinea
has country: Eritrea
has country: Ethiopia
has country: Gabon
has country: Gambia
has country: Ghana
has country: Guinea
has country: Guinea-Bissau
has country: Kenya
has country: Lesotho
has country: Liberia
has country: Libya
has country: Madagascar
has country: Malawi
has country: Mali
has country: Mauritania
has country: Mauritius
has country: Morocco
has country: Mozambique
has country: Namibia
has country: Niger
has country: Nigeria
has country: Reunion
has country: Rwanda
has country: Saint Helena
has country: Sao Tome and Principe
has country: Senegal
has country: Seychelles
has country: Sierra Leone
has country: Somalia
has country: South Africa
has country: Sudan
has country: Eswatini
has country: Tanzania
has country: Togo
has country: Tunisia
has country: Uganda
has country: Western Sahara
has country: Zambia
has country: Zimbabwe

Africa Eggplant
type: NamedIndividual
type: Crop
description: African eggplant, Solanum aethiopicum, is a deciduous shrub in the family Solanaceae which is grown for its edible fruits which are cooked and eaten as a vegetable. African eggplant is a highly branching plant which can grow up to 2 m (6.6 ft) in height. The leaves of the plant are arranged alternately on the stems and have smooth or lobed margins. Leaf blades may reach up to 30 cm (11.8 in) in length and 21 cm (8.3 in) in width. The leaf petioles are oval or elliptical in shape, reaching up to 11 cm (4.3 in) in length. Plants produce clusters of up to 12 white flowers which develop into egg- or spindle-shaped berries which are red to orange in color with a smooth or grooved surface depending on variety. African eggplant may also be referred to as scarlet eggplant, bitter tomato, mock tomato, garden egg or Ethiopian nightshade and is native to Africa, likely resulting from the domestication of a related species, S. anguivi.
harvesting: African eggplant is typically ready for harvest 100 to 120 days after planting. The fruit should be harvested before the skin changes color from white to pale yellow when the skin becomes tough. Fruits should be harvested regularly to encourage maximum fruit production. Young leaves may be harvested from 45-60 days of growth.
maintenance: African eggplants will benefit from frequent irrigation during the dry season, particularly when fruiting, to ensure high yields. The crop should be weeded as required to prevent competition. Addition of fertilizer in the form of cattle or chicken or cattle manure or compost will improve yields.
propagation: African eggplant seeds can be collected from fully ripe fruits. Once the seeds have been extracted, they should be laid out on a piece paper to dry in a place where they are not exposed to direct sunlight. Once dry, seeds can be stored for many years and still remain viable. Seeds should be planted in a prepared nursery bed and should be sown 15 cm (6 in) apart with a further 20 cm (8 in) between rows. Seedlings are ready for transplanting when they reach 15 to 20 cm (6–8 in) in height and have 5–7 leaves. Plants should be hardened prior to transplanting by gradually reducing the amount of water they receive. Plants should be spaced 50 cm (20 in) apart allowing 75 cm (30 in) between rows.
requirements: Growth requirements for African eggplant vary with variety. All types grow best in full sun in well-draining, deep soils with a pH between 5.5 and 6.8. Gilo types grow best at daytime temperatures between 25 and 35°C (77 and 95°F). Kumba types can grow in hotter temperatures of up to 45°C in low humidity, whereas Shum types require warm and humid conditions in order to thrive. No varieties of African eggplant tolerate very cold or water-logged conditions.
uses: African eggplant is grown primarily for its bitter orange-red fruit, which may be eaten boiled, steamed, pickled or in stews with meat and other vegetables. Young leaves are also often used in soups.
comment: Eggplant is a plant species in the nightshade family Solanaceae. Solanum melongena is grown worldwide for its edible fruit.

Agricultural land
type: NamedIndividual
type: LandUse

Average Annual Temperature (AAT)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Temperature
unit: oC
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: AAT
comment: Average Annual Temperature (AAT) is the transformation for Temperature by Average Annual Temperature
definition: AAT = min{Tminj}; j = 1; ; 365

Accumulated Frost Days (AFD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Frost
unit: Days
Acronym: AFD
comment: Accumulated Frost Days (AFD) serves as a frost index that relates to both cold stress and incidence of pest and disease.
definition: Days where Tmin < 0◦C

Air Vapor Pressure Deficit (AVPD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Vapor Pressure
Acronym: AVPD

Abundant and well-distributed rainfall (AWDR)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Rainfall
References: N. Tremblay, Y.M. Bouroubi, C. Bélec, R.W. Mullen, N.R. Kitchen, W.E. Thomason, S. Ebelhar, D.B. Mengel, W.R. Raun, D.D. Francis, E.D. Vories, I. Ortiz-Monasterio, "Corn response to nitrogen is influenced by soil texture and weather", Agron. J., 104 (2012), pp. 1658-1671, 10.2134/agronj2012.0184
Acronym: AWDR
comment: Abundant and Well-Distributed Rainfall (AWDR) parameter was proposed in Tremblay et al. (2012) as an integrator of rainfall intensity and distribution over time around N sidedressing in corn. A time window ranging from -15 days to +30 days around N sidedressing was initially proposed for maximal impact of the AWDR on corn yield response to N rate.
definition: AWDR = SDI × PPT

Biologically Effective Degree Days (BEDD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: BEDD
comment: Biologically Effective Degree Days (BEDD) index has been specifically targeted to describe grape growth (Gladstones 1992). The BEDD index is based on a growing degree days measure. Let T Xi and T Ni be the daily maximum and daily minimum temperature for day i.
definition: BEDD = min ( max ( (TXi + TNi) / 2 - b, 0), 9 ) where i = 01/04... to 30/09 and b = 10 is an appropriate value for grape growth.

Bhalme and Mooly Drought Index (BMDI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Drought
References: Bhalme, H.N., and Mooley, D.A. 1980. "Large-scale drought / floods and monsoon circulation. Mon. Weather Rev. 108 (8): 1197–1211". doi:10.1175/1520-0493(1980)108<1197:LSDAMC>2.0.CO;2.
Acronym: BMDI
comment: Bhalme and Mooly Drought Index (BMDI) considers the percent departure of monthly or annual precipitation from its long-term means (Byun and Wilhite 1999).

Mean of Daily Cloud Cover (CC)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Cloud Cover
unit: oktas
Acronym: CC
comment: Mean of daily cloud cover (oktas)
definition: Let CCij be the daily cloud cover at day i of period j. Then mean values in period j are given by: CCj = Sum(CCij / L), where i = 1....L

Mostly Sunny Days (CC2)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Cloud Cover
unit: Days
Acronym: CC2
comment: Mostly Sunny Days (cloud cover <= 2 oktas)
definition: Let CCij be the daily cloud cover at the day i of period j. Then counted is the no of days where: CCij <= 2 oktas

Mostly Cloudy Days (CC6)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Cloud Cover
unit: Days
Acronym: CC6
comment: Mostly Sunny Days (cloud cover >= 6 oktas)
definition: Let CCij be the daily cloud cover at the day i of period j. Then counted is the no of days where: CCij >= 6 oktas

Cold/Dry Days (CD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: Beniston, Martin. \"Trends in joint quantiles of temperature and precipitation in Europe since 1901 and projected for 2100.\" Geophysical Research Letters 36.7 (2009).
Acronym: CD
definition: Counted is the number of days where: TGij < TGin25 and RRwj < RRwn25

Consecutive Dry Days (CDD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
is transformation of: Temperature
unit: Days
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: CDD
comment: Days with TG < 25th percentile of daily mean temperature and RR < 25th percentile of daily precipitation sum (cold/dry days) Let T Gij be the daily mean temperature at day i of period j and let TGin25 be the calendar day 25th percentile calculated for a 5-day window centred on each calendar day in the 1961–1990 period. Let RRwj be the daily precipitation amount at wet day w (RR ≥ 1.0 mm) of period j and let RRwn25 be the 25th percentile of precipitation at wet days in the 1961–1990 period.
comment: Maximum length of dry spell: maximum number of consecutive days with RR < 1mm
definition: Let RRij be the daily precipitation amount on day i in period j. Count the largest number of consecutive days where RRij < 1mm.

Cloud Density (CD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Cloud
Acronym: CD

Maximum no of Consecutive Frost Days, TN <0oC (CFD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Frost
unit: days
Acronym: CFD
definition: Let TNij be the daily minimum temperature at day i of period j. Then counted is the largest number of consecutive days where: TNij < 0◦C

Cumulated (Corn) Heat Units (CHU)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: Mathieu, Jordane A., and Filipe Aires. "Assessment of the agro-climatic indices to improve crop yield forecasting." Agricultural And Forest Meteorology 253 (2018): 15-30.
Acronym: CHU
comment: Transformation for Temperature by Cumulated (Corn) Heat Units

Cold Spell Duration Index (CSDI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: CSDI
comment: Cold Spell Duration Index (CSDI) refers to annual count of days with at least 6 consecutive days when TN < 10th percentile
definition: Let TNij be the daily maximum temperature on day i in period j and let TNin10 be the calendar day 10th percentile centred on a 5-day window for the base period 1961-1990. Then the number of days per period is summed where, in intervals of at least 6 consecutive days, TNij < TNin10.

Cold/Wet Days (CW)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
is transformation of: Temperature
unit: days
References: Beniston, Martin. "Trends in joint quantiles of temperature and precipitation in Europe since 1901 and projected for 2100." Geophysical Research Letters 36.7 (2009).
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: CW
comment: Days with TG < 25th percentile of daily mean temperature and RR > 75th percentile of daily precipitation sum (cold/wet days) Let T Gij be the daily mean temperature at day i of period j and let TGin25be the calendar day 25th percentile calculated for a 5-day window centred on each calendar day in the 1961–1990 period. Let RRwj be the daily precipitation amount at wet day w (RR ≥ 1.0 mm) of period j and let RRwn75 be the 75th percentile of precipitation at wet days in the 1961–1990 period.
definition: Then counted is the number of days where: TGij < TGin25 and RRwj > RRwn75

Consecutive Wet Days (CWD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
unit: Days
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: CWD
comment: Maximum length of wet spell: Maximum number of consecutive days with RR ≥ 1mm
definition: Let RRij be the daily precipitation amount on day i in period j. Count the largest number of consecutive days where RRij ≥ 1mm.

Total Degree-Days (DD)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Temperature
unit: oC
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: DD
comment: Total Degree Days (DD) from an appropriate starting date are used to plan the planting of crops and management of pests and pest control timing.
definition: DD = Sum(DDj), in which j = {d1 ... d2}

Days with Easterly Winds (DDeast)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Wind Direction
unit: Days
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: DDeast
comment: Days with easterly winds (45◦ < DD ≤ 135 ◦) (days) Let DDij be the daily value of the wind direction at day i of period j
definition: Counted is the number of days where: 45 ◦ < DDij ≤ 135 ◦

Total Degree-Days in Growing Season (DDgs)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Temperature
unit: oC
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: DDgs
comment: Total Degree-Days in Growing Season (DDgs) from an appropriate starting date to ending date of growing season are used to plan the planting of crops and management of pests and pest control timing.
definition: DDgs = Sum(DDj), in which, j = {BGS...EGS}

Degree-Days (DDj)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Temperature
unit: oC
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: DDj
comment: Transformation for Temperature by Degree-Days
definition: DDj = max{0, (Tavej−Tbase)}

Days with Northerly Winds (DDnorth)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Wind Direction
unit: Days
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: DDnorth
comment: Days with northerly winds (-45 ◦ < DD ≤ 45 ◦) (days) Let DDij be the daily value of the wind direction at day i of period j.
definition: Counted is the number of days where: −45 ◦ < DDij ≤ 45 ◦

Days with Southerly Winds (DDsouth)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Wind Direction
unit: Days
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: DDsouth
comment: Days with southerly winds (135◦ < DD ≤ 225 ◦) (days) Let DDij be the daily value of the wind direction at day i of period j.
definition: Counted is the number of days where: 225 ◦ < DDij ≤ 315 ◦

Days with Westerly Winds (DDwest)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Wind Direction
unit: Days
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: DDwest
comment: Days with westerly winds (225◦ < DD ≤ 315 ◦) (days) Let DDij be the daily value of the wind direction at day i of period j.
definition: Counted is the number of days where: 225 ◦ < DDij ≤ 315 ◦

Dew Point Depression of the air (DPDa)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: DPDa
comment: Dew point depression of the air (DPDa) is calculated as the difference between the air temperature and the dew point of the air.

Daily Temperature Range (DTR)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: DTR
comment: Let TXij and TNij be the daily maximum and minimum temperature respectively on day i in period j.
definition: If i represents the number of days in j, then: DTRj = ∑ (TXij - TNij)/I i = 1....I

Extreme Degree Day (EDD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: EDD
comment: Transformation for Temperature by Extreme Degree Day

Evapotranspiration (ET)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Evapotranspiration
Acronym: ET

Agricultural Drought Index (DTx)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Drought
related to: Drought
References: Matera, A., Fontana, G., and Marletto, V. 2007. "Use of a new agricultural drought index within a regional drought observatory." In Methods and Tools for Drought Analysis and Management.
Acronym: ETDI
comment: Agricultural Drought Index (DTx) uses a water balance model and crop transpiration to calculate an integrated transpiration deficit over a period of time.

Extreme Temperature Range (ETR)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp. Download (PDF from clivar.org): Peterson et al. 2001 (PDF)
Acronym: ETR
comment: Intra-annual Extreme Temperature Range (Difference between the highest temperature observation of any given calendar year (Th) and the lowest temperature reading of the same calendar year (Tl))

Crop Evapotranspiration (ETc)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Evapotranspiration
Acronym: ETc

Evaporation Amount (Esum)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Evaporation
References: De Bruin, H.A.R., Lablans, W.N., 1998. "Reference crop evapotranspiration determined with a modified Makkink equation." Hydrol. Process. 12, 1053-1062 10.1002/(SICI) 1099-1085 (19980615)12:7
Acronym: Esum
comment: Transformation for Evaporation by Total

First Autumnal Frost (FAF)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Frost
unit: Date
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: FAF
comment: First Autumnal Frost (FAF) is a date in autumn before which the first frost of the season seldom comes, and a corresponding date in springtime after which it is unlikely to occur.

Number of Frost Days (FD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Frost
unit: Days
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: FD
comment: Number of Frost Days (FD) is the number of days with daily minimum temperature below 0°C

Number of Days When TN is below -2°C (FD-2)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Frost
Acronym: FD-2
comment: Transformation for Frost by Number of days when TN is below - 2°C (FD-2)

Freezing Degree Days (FDD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: FDD

Free Frost Period (FFP)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Frost
unit: Day
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: FFP
comment: Free Frost Period (FFP) is a part of the year when the temperature stays continuously above 0° C in an average calculation over several years. This period lasts from the long-term average date of the last spring frost to the long-term average date of the first autumn frost. In the northern regions of the USSR the frost-free period is less than 150 days. (On the Iamal and Taimyr peninsulas it is about 45 days.) In the west and south of the country it is 250 days and longer. (In the region of Batumi and on the southern coast of the Crimea it lasts 300 days and longer.) In the temperate zones it corresponds approximately to the growing season, whose duration significantly determines the composition of the local wild and cultivated vegetation.

Daily Mean Wind Speed (FG)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Wind Speed
unit: m/s
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: FG
comment: Daily Mean Wind Speed (FG) refers to whenever synoptical wind speed data, which is available (in m/s) at 00, 06, 12 and 18 UT, daily mean wind speed is calculated as the mean of the available values.
definition: Let FGij be the mean wind strength at day i of period j. Then mean values in period j are given by: FGj = Sum(FGij) / I in which i=1...I

Days with Daily Averaged Wind ≥ 6 Bf (FG6Bft)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Wind Speed
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: FG6Bft
comment: Days with daily averaged wind ≥ 6 Bft (10.8 m s−1 ) (days)
definition: Let F Gij be the daily averaged wind strength at day i of period j. Then counted is the number of days where: FGij ≥ 10.8 m/s

Calm Days (FGcalm)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Wind Speed
unit: Days
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: FGcalm
definition: Calm days (FG ≤ 2 m/s) (days) Let F Gij be the daily averaged wind strength at day i of period j. Then counted is the number of days where: FGij ≤ 2 m/s

Frost Period (FP)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Frost
Acronym: FP
comment: Frost Period (FP) covers from the date of the first to the last frost recorded for each time series.

Daily Maximum Wind Gust (FX)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Wind Gust
unit: m/s
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: FX
comment: Daily Maximum Wind Gust (FX) refers to whenever synoptical wind gust data, which is available (in m/s) at 00, 06, 12 and 18 UT, daily maximum wind gust is taken as the maximum of the available values.

Maximum Value of Daily Maximum Wind Gust (FXx)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Wind Gust
unit: m/s
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: FXx
definition: Let F Xij be the daily maximum wind gust on day i of period j. Then the maximum daily maximum wind gust for period j is: FXnj = max(FXij )

Growing Degree Days (sum of TG >4oC)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: oC
Acronym: GD4
definition: Let TGij be the daily mean temperature at the day i of period j. Then the growing degree days are: GD4j = Sum(TGij - 4 / in which TGij > 4oC) in which i = 1 .. I

Growing Degree-Days (GDD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: McMaster, G. S., & Wilhelm, W. W. (1997). "Growing degree-days: one equation, two interpretations". Agricultural and Forest Meteorology, 87, 291–300.
Acronym: GDD
comment: Growing Degree Days (GDD), or heat units, are used to estimate the growth and development of certain crops and pests during the growing season. If the mean temperature is above the base temperature, then the GDD equals the value of the mean temperature minus the base temperature.
definition: GDD = (Tmax - Tmin)/2 - Tbase

Growing Season Length (GSL)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: Days
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp. Download (PDF from clivar.org): Peterson et al. 2001 (PDF)
Acronym: GSL
comment: Annual* count between the first span of at least 6 days with daily mean temperature TG >5 °C and the first span after July 1st (Jan 1st in SH) of 6 days with TG <5 °C. GSL, important for agriculture, is expected to increase as a direct result of increasing temperatures and indirectly as a result of reductions in snow cover.
definition: Let TGij be daily mean temperature on day i in year j. Count the number of days between the first occurrence of at least 6 consecutive days with TGij > 5 °C and the first occurrence after 1st July (Jan 1st in SH) of at least 6 consecutive days with TGij < 5 °C. * Annual means Jan 1st to Dec 31st in the Northern Hemisphere (NH); July 1st to June 30th in the Southern Hemisphere (SH).

Daily Maximum Ground Surface Temperature (GTmax)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: GTmax

Daily Average Ground Surface Temperature (GTmean)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: GTmean

Daily Minimum Ground Surface Temperature (GTmin)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: GTmin

Heating Degree Day (HD17)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: HD17
comment: Heating degree days (sum of 17 ◦C - TG) (◦C)
definition: Let T Gij be the daily mean temperature at day i of period j. Then the heating degree days are: HD17j = Sum(17 ◦C − TGij ) in which i=1....I

Huglin Index (HI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: HI
comment: The Huglin Index is an index specifically aimed at grap growth (Huglin 1978) and defined using daily averaged temperature T Gi and the daily maximum temperature T Xi for day i in the period 1 April to 30 September.
definition: HI = SUM( (TGi − 10) + (TXi − 10)) * K / 2 where, i = 01/04 .... to 30/09 and K is a daylength coefficient. The daylight coefficient is a function of the latitude of the station but a clear definition is absent. The value of K is determined using table 2. Table 2: latitude | K -----------------+---- ≤ 40◦N | 1.00 40◦N-42◦N | 1.02 42◦N-44◦N | 1.03 44◦N-46◦N | 1.04 46◦N-48◦N | 1.05 48◦N-50◦N | 1.06

Daily Mean Humidity (HU)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Humidity
unit: %
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: HU
comment: Daily Mean Humidity (HU) refers to whenever synoptical humidity data, which is available (in percents) at 00, 06, 12 and 18 UT, daily mean humidity is calculated as the mean of the available values.

Heat Wave Duration Index (HWDI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: Days
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: HWDI
comment: Maximum period > 5 consecutive days with Tmax > 5 °C above the 1961-90 daily Tmax normal

Daily Maximum Humidity (HXj)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Humidity
Acronym: HXj
comment: Transformation for Humidity by Max

Humidity by Mean
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Humidity
References: Herman Mollenhorsta, Michel H.A. de Haan, Jouke Oenema, Claudia Kamphuis, "Field and crop specific manure application on a dairy farm based on historical data and machine learning", Computers and Electronics in Agriculture 175 (2020) 105599
Acronym: Hmean
comment: Transformation for Humidity by Mean

Daily Minimum Humidity (HNj)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Humidity
Acronym: HNj
Acronym: Transformation for Humidity by Minimal

Humidity by Tier4
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Humidity
unit: %
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
Acronym: Htier4
comment: Transformation for Humidity by Tier 4
definition: [<35, "Very low"], [35-44, "Low"], [45-65, "Medium"], [>65, "High"]

Number of Icing Days (ID)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: Days
Acronym: ID
comment: Number of Icing Days (ID) refers to annual count of days when TX (daily maximum temperature) < 0 °C. Let TXijbe daily maximum temperature on day i in year j.
definition: Count the number of days where TXij < 0 °C.

Irradiation Amount (Imonth)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: IRRadiation
References: Herman Mollenhorsta, Michel H.A. de Haan, Jouke Oenema, Claudia Kamphuis, "Field and crop specific manure application on a dairy farm based on historical data and machine learning", Computers and Electronics in Agriculture 175 (2020) 105599
Acronym: Imonth
comment: Cumulated Irradiation for month (SUM)

Irrigation by Tier5
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Irrigation
unit: #
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
Acronym: Itier5
comment: Transformation for Irrigation by Tier 5
definition: [<20, "Very low"], [20-24, "Low"], [25-35, "Medium"], [36-45, "High"], [>45, "Very high"]

Killing Degree Days (KDD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: KDD

Killing Freeze Free Period (KFFP)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: KFFP
comment: Killing Freeze Free Period (KFFP) is the number of consecutive days where the air temperature does not fall below 24 degrees Fahrenheit. The first frost usually occurs in the fall of the year, but it may occur during the winter months, or in some locations may not occur at all.

Last Frost Day (LFD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Frost
Acronym: LFD
comment: Last frost date (LFD) is defined as the last day in spring in which the daily minimum temperature drops below a critical temperature that can damage the flowers.

Longwave Radiation (LRAD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Radiation
Acronym: LRAD

Last Spring Frost (LSF)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Frost
unit: Date
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: LSF
comment: Last Spring Frost (LSF) is a date in springtime after which the last frost of the season seldom comes, then it is unlikely to occur

Land Surface Temperature (LST)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: LST
comment: Land Surface Temperature (LST) is the radiative skin temperature of the land surface, as measured in the direction of the remote sensor. It is estimated from Top-of-Atmosphere brightness temperatures from the infrared spectral channels of a constellation of geostationary satellites (Meteosat Second Generation, GOES, MTSAT/Himawari). Its estimation further depends on the albedo, the vegetation cover and the soil moisture. LST is a mixture of vegetation and bare soil temperatures. Because both respond rapidly to changes in incoming solar radiation due to cloud cover and aerosol load modifications and diurnal variation of illumination, the LST displays quick variations too. In turn, the LST influences the partition of energy between ground and vegetation, and determines the surface air temperature.

Leaf-air Vapor Pressure Difference (LVPD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Vapor Pressure
References: A. Nereu, "Stomatal response to water vapor pressure deficit: an unsolved issue", Rev. Bras. Agrociência., 9 (2003), pp. 317-322, 10.18539/cast.v9i4.649
References: O. Jolliet, B.J. Bailey, "The effect of climate on tomato transpiration in greenhouses: measurements and models comparison", Agric. For. Meteorol., 58 (1992), pp. 43-62
Acronym: LVPD

Multivariate Standardized Precipitation Index (MSPI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Drought
References: J. Bazrafshan, S. Hejabi, J. Rahimi, "Drought monitoring using the multivariate standardized precipitation index (MSPI)", Water Resour. Manage., 28 (4) (2014), pp. 1045-1060
Acronym: MSPI
comment: Multivariate Standardized Precipitation Index (MSPI) was first introduced by Bazrafshan et al. (2014) as a combined index. Integrating this index with SPI time windows can indicate the severity and weakness of several types of drought. The basis for integrating SPI time windows into MSPI is based on Principal Component Analysis (PCA). So far, this index has been calculated for time windows of 3–6, 6–12, 3–12, 12–24, 24–48 months and 1–12 months and it has been evaluated favorably for Iran's climates.

Frequency of Temperatures above 30°C (OCA)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: Day
Acronym: OCA
comment: Transformation for Temperature by Frequency of temperatures above 30°C

Pálfai Aridity Index (PAI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Drought
References: Pálfai, I. 1991. "Az 1990 évi aszály Magyarországon". Vízügyi Közlemények, 2: 117–132.
Acronym: PAI
comment: Characterizes drought severity using precipitation, temperature and ground water conditions data. Designed primarily for Hungarian and the Carpathian Basin climate conditions (UNFCCC 2002)

Palmer Drought Severity Index (PDSI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Drought
References: G. Van der Schrier, P.D. Jones, K.R. Briffa, "The sensitivity of the PDSI to the Thornthwaite and Penman-Monteith parameterizations for potential evapotranspiration", J. Geophys. Res.: Atmosph., 116 (D3) (2011)
Acronym: PDSI
comment: Palmer Drought Severity Index (PDSI) is based on the soil-water equilibrium equation, and the data needed to calculate it are precipitation, evapotranspiration, and soil moisture capacity. In this index, soil moisture storage is examined by dividing soil into two layers and it is assumed that 25 mm of rainfall water can be stored in the surface layer (Ss). The bottom layer of soil (Su) has the aqueous capacity that is dependent on soil properties and when the moisture of the surface layer is not removed, the moisture of bottom layer will not be removed. Reference evapotranspiration (ET0) in this index is calculated by the Penman and Thornthwaite methods. Despite the ET0 (difference) calculated from these two methods, the calculated PDSI values have only an insignificant difference so that this similarity has been proven for the whole planet

Potential Evapotranspiration (PET)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Evapotranspiration
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: PET
comment: Potential Evapotranspiration (PET) is an index which gives the FAO-endorsed potential evapotranspiration as calculated by the Penman-Monteith parametrization. Here reference crop evapotranspiration is a measure for potential evapotranspiration. Reference crop evaporation is defined as the rate of evaporation from an idealized grass reference crop with a fixed crop height of 0.12 m, an albedo of 0.23, and a surface resistance of 70 s m−1. In terms of of its evaporation rate, such a crop closely resembles the reference crop of an extensive surface of short green grass cover of uniform height, actively growing, completely shading the ground, and not short of water.
definition: The equation used for estimating the reference crop evaporation is based on the Penman-Monteith approach;

Plant High Heat Stress (PHHS)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: Days
Acronym: PHHS
comment: Plant High Heat Stress (PHHS), the number of days with daily maximum temperature above a threshold, identifies the high risk of heat stress and the associated yield decline.
definition: Days when Tmax > 30◦C

Plant Heat Stress (PHS)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: Days
Acronym: PHS
comment: Plant Heat Stress (PHS), the number of days with daily maximum temperature above a threshold, identifies the risk of heat stress and the associated yield decline. Various thresholds can be used for specific plants or crops; for example, a threshold of 29◦C corresponds to maize, a major US crop [6], or different thresholds for other crops (i.e. 30◦C for soybeans or 32◦C for cotton).
definition: Days when Tmax > 25◦C

Daily Mean Sea Level Pressure (PP)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Pressure
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: PP
comment: Daily Mean Sea Level Pressure (PP) refers to whenever sea level pressure data, which is available at 00, 06, 12 and/or 18 UT, daily mean sea level pressure is calculated as the average of the available values.

Sum of Daily Rainfall (PPT)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Rainfall
Acronym: PPT

Daily Cumulative Rainfall (PR)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Rainfall
unit: mm
Acronym: PR

Annual Total Precipitation on Wet Days (PRCPTOT)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
References: Karl, T.R., N. Nicholls, and A. Ghazi, 1999: "CLIVAR/GCOS/WMO workshop on indices and indicators for climate extremes: Workshop summary". Climatic Change, 42, 3-7.
References: Peterson, T.C., and Coauthors, "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001". WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: PRCPTOT
comment: Annual Total Precipitation on Wet Days (PRCPTOT) refers to the daily precipitation
definition: Let RRij be the daily pre If i represents the number of days in j, then: PRCPTOTj = Sum(RRij), i = 1....I

Precipitation (PRE)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
Acronym: PRE

Highest 3 Day Precipitation Amount (PX3d)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
Acronym: PX3day
comment: Transformation for Precipitation by Highest 3 Day Precipitation Amount (PX3d)
definition: Greatest RR sum for 3 day

Agroclimatic Pcp
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
Acronym: Pcp

Precipitation during the Growing Season (Pgs)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Precipitation
unit: mm
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: Pgs
comment: Transformation for Precipitation by Pgs

Total Precipitation of day j (Pj)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Precipitation
unit: mm
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: Pj
comment: Transformation for Precipitation by Pj

Cumulated Precipitation for Month (Pmonth)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Precipitation
unit: mm
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: Pmonth
comment: Cumulated precipitation for month

Number of Days Precipitation >= threshold (Prep)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: Prep
comment: Number of Days Precipitation >= user defined threshold

Precipitation ≥ 10mm (R10)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
unit: Days
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: R10
comment: No. of days with precipitation ≥ 10 mm/day

Annual count of days when PRCP ≥10mm (R10mm)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
References: Peterson, T.C., and Coauthors, "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001". WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: R10mm
comment: Annual count of days when PRCP ≥10mm (R10mm) is the daily precipitation amount on day i in period j. Count the number of days where RRij ≥ 10mm
definition: Let RRij be the daily precipitation amount for day i of period j. Then counted is the number of days where: RRij ≥ 10 mm

Annual count of days when PRCP ≥20mm (R20mm)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
References: Peterson, T.C., and Coauthors, "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: R20mm
comment: Annual count of days when PRCP ≥20mm (R20mm) is the daily precipitation amount on day i in period j. Count the number of days where RRij ≥ 20mm
definition: Let RRij be the daily precipitation amount on day i in period j. Count the number of days where RRij ≥ 20mm

Maximum 5-day Precipitation Total (R5)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: R5
comment: The maximum 5-day precipitation total (R5D) is the indicator of flood-producing events and shows a general increase throughout large areas of the globe.

Greatest 5-day Rainfall (R5d)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Rainfall
unit: mm
Acronym: R5d
comment: The Greatest 5-day Rainfall Total

Days with RR > 75th percentile of daily amounts (R75p)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
unit: Days
Acronym: R75p
comment: Days with RR > 75th percentile of daily amounts (moderate wet days) (days)
definition: Let RRwj be the daily precipitation amount at wet day w (RR ≥ 1.0 mm) of period j and let RRwn75 be the 75th percentile of precipitation at wet days in the 1961–1990 period. Then counted is the number of days where: RRwj > RRwn75

Precipitation fraction due to moderate wet days (R75pTOT)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
Acronym: R75pTOT
comment: Precipitation fraction due to moderate wet days (R75pTOT) refers to the sum of daily precipitation amount for period.
definition: Let RRj be the sum of daily precipitation amount for period j and let RRwj be the daily precipitation amount at wet day w (RR ≥ 1.0 mm) of period j and RRwn75 the 75th percentile of precipitation at wet days in the 1961–1990 period. Then R75pT OTj is determined as: R75pTOTj = 100 × Sum(RRwj)/RRj in which w=1...PW , where RRwj > RRwn75

Annual total PRCP when RR > 95th percentile (R95p)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
unit: %
References: Peterson, T.C., and Coauthors, "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: R95p
comment: Annual total PRCP when RR > 95th percentile (R95p) is the daily precipitation amount on a wet day w (RR ≥ 1.0mm) in period i and let RRwn95 be the 95th percentile of precipitation on wet days in the 1961-1990 period.

Precipitation fraction due to very wet days (R95pTOT)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
Acronym: R95pTOT
comment: Let RRj be the sum of daily precipitation amount for period j and let RRwj be the daily precipitation amount at wet day w (RR ≥ 1.0 mm) of period j and RRwn95 the 95th percentile of precipitation at wet days in the 1961–1990 period. Then R95pT OTj is determined as: R95pTOTj = 100 × Sum(RRwj)/RRj, in which w=1...PW, where RRwj > RRwn95
comment: Precipitation fraction due to very wet days (> 95th percentile) (%)

Annual total PRCP when RR > 99th percentile (R99p)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
References: Peterson, T.C., and Coauthors, "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: R99p
comment: Annual total PRCP when RR > 99th percentile (R99p) is the daily precipitation amount on a wet day w (RR ≥ 1.0mm) in period i and let RRwn99 be the 99th percentile of precipitation on wet days in the 1961-1990 period.

Rainfall Anomaly Index (RAI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Rainfall
References: Wanders, N., van Lanen, H. A. J., and van Loon, A. F. 2010. "Indicators for drought characterization on a global scale". Technical Report No. 24.
Acronym: RAI
comment: Rainfall Anomaly Index (RAI) refers to the average precipitation over weekly, monthly, or annual time periods to characterize relative drought. Re lative drought is then ranked with respect to the 10 most severe droughts in the long-term record, based on which the drought is then assigned a magnitude

Relative Humidity (RH)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Humidity
Acronym: RH
comment: Relative Humidity (RH) is the ratio of the actual water vapour pressure to the saturation water vapour pressure at the prevailing temperature. For example – if a cubic metre can hold 100ml of water at 20 degrees centigrade (273 K) and it does contain 100ml then it is said to be 100% RH.

Daily Precipitation Amount (RR)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Precipitation
is transformation of: Rainfall
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: RR
comment: Daily Precipitation Amount (RR) refers to whenever synoptical 12-hourly precipitation data, which is available at 06 and 18 UT, daily precipitation is calculated as the sum of RR of 18UT of the current day and RR of 6 UT of the next day.
definition: Let RRij be the daily precipitation amount for day i of period j. Then sum values are give by: RRj = Sum(RRij) in which i=1...I

Wet Days (RR1)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
is transformation of: Rainfall
unit: Days
Acronym: RR1
comment: Wet Days (RR1) refers to wet days with RR ≥ 1 mm (days)
definition: Let RRij be the daily precipitation amount for day i of period j. Then counted is the number of days where: RRij ≥ 1 mm

Radiation Use Efficiency (RUE)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Radiation
References: Hatfield, Jerry L., and Christian Dold. "Photosynthesis in the solar corridor system." The Solar Corridor Crop System. Academic Press, 2019. 1-33.
References: J.W. Warren-Wilson, "Ecological data on dry-matter production by plants and plant communities", E.F. Bradley, O.T. Denmead (Eds.), The Collection and Processing of Field Data, Interscience Publishers, New York (1967), pp. 77-123
Acronym: RUE
comment: Radiation use efficiency (RUE) is the relationship between the accumulation of biomass relative to the light intercepted by the crop.

Maximum Humidity (Rhmax)
type: NamedIndividual
type: Agroclimatic Indices
Acronym: Rhmax

Average Humidity (Rhmean)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Humidity
Acronym: Rhmean

Humidity Minimum (Rhmin)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Humidity
Acronym: Rhmin
comment: Humidity Minimum (Rhmin) is

Rainfall by Mean (Rmean)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Rainfall
Acronym: Rmean
comment: Transformation for Rainfall by Average

Annual count of days when PRCP≥ nnmm (Rnnmm)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
References: Peterson, T.C., and Coauthors, "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: Rnnmm
comment: Annual count of days when PRCP≥ nnmm (Rnnmm) refers to annual count of days when PRCP≥ nnmm, nn is a user defined threshold.
definition: Let RRij be the daily precipitation amount on day i in period j. Count the number of days where: RRij ≥ nnmm

Rainfall by Tier5
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Rainfall
unit: mm
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
Acronym: Rtier5
comment: Transformation for Rainfall by Tier 5
definition: [<500, "Very low"], [500-600, "Low"], [600-999, "Medium"], [1000-1500, "High"], [>1500, "Very high"]

Monthly Maximum 1-day Precipitation (Rx1day)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
References: Peterson, T.C., and Coauthors, "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: Rx1day
comment: Monthly maximum 1-day precipitation (Rx1day)
definition: Let RRij be the daily precipitation amount on day i in period j. The maximum 1-day value for period j are Rx1dayj = max (RRij)

Monthly Maximum Consecutive 5-day Precipitation (Rx5day)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
References: Peterson, T.C., and Coauthors, "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: Rx5day
comment: Let RRkj be the precipitation amount for the 5-day interval ending k, period j. Then maximum 5-day values for period j are Rx5dayj = max (RRkj)

Mean of Daily Snow Depth (SD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Snow
unit: cm
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: SD
comment: Let SDij be the daily snow depth at day i of period j. Then mean values in period j are given.
definition: Let SDij be the daily snow depth at day i of period j. Then mean values in period j are given by: SDj = Sum(SDij / I), in which i = 1 ... to I

Number of Snow Days (SD1)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Snow
unit: Days
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: SD1
comment: Snow days (SD ≥ 1 cm) (days)
definition: Let SDij be the daily snow depth for day i of period j. Then counted is the number of days where: SDij ≥ 1 cm

Number of Snow Days with ≥50cm (SD50cm)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Snow
unit: Days
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: SD50cm
comment: Let SDij be the daily snow depth for day i of period j. Then counted is the number of days where: SDij ≥ 50 cm

Number of Snow Days with ≥5cm (SD5cm)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Snow
unit: Days
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: SD5cm
comment: Let SDij be the daily snow depth for day i of period j. Then counted is the number of days where: SDij ≥ 5 cm

Shannon diversity index (SDI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Rainfall
References: N. Tremblay, Y.M. Bouroubi, C. Bélec, R.W. Mullen, N.R. Kitchen, W.E. Thomason, S. Ebelhar, D.B. Mengel, W.R. Raun, D.D. Francis, E.D. Vories, I. Ortiz-Monasterio, "Corn response to nitrogen is influenced by soil texture and weather", Agron. J., 104 (2012), pp. 1658-1671, 10.2134/agronj2012.0184
Acronym: SDI
comment: Shannon Diversity Index (SDI) is divided by the maximum diversity. Diversity indices are statistics used to summarize the diversity of a population in which each member belongs to a unique group. For example, in ecology the groups are typically species. In ecology, species richness refers to number of species and species eveness refers to homogeneity of the species. That is, the more equal the proportions for each of the groups, the more homogeneous, or even, they are. Different fields of application may use different terminology for these concepts.
definition: Shannon diversity index (SDI) = [−Σpi ln(pi)]/ln(n); where pi = Rain/PPT (daily rainfall relative to total rainfall in a given time; n = total number of days.

Simple Precipitation Intensity Index (SDII)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
unit: mm / day
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: SDII
comment: The Simple Daily Intensity Index (SDII) is defined as the mean daily intensity for events greater than or equal to 1mm per day
definition: Let RRwj be the daily precipitation amount for wet day w (RR ≥ 1.0mm) of period j. Then the mean precipitation amount of wet days is given by: SDIIj = Sum(RRwj) / W in which w=1 ... PW

Standardized Precipitation Evapotranspiration Index (SPEI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Evapotranspiration
is transformation of: Precipitation
Acronym: SPEI

Standardized Precipitation Index (SPI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
References: McKee, T.B., Doesken, N.J., Kleist, J., 1993. "The relationship of drought frequency and duration to time scales". In: Proceedings of the 8th Conference on Applied Climatology (Vol. 17, No. 22, pp. 179–183). Boston, MA: American Meteorological Society.
References: Svoboda, M., Fuchs, B.A., 2016, "Handbook of Drought Indicators and Indices". World Meteorological Organization (WMO) and Global Water Partnership (GWP), Integrated Drought Management Programme (IDMP), Integrated Drought Management Tools and Guidelines Series 2. Geneva, Switzerland.
Acronym: SPI
comment: Standardized Precipitation Index (SPI) was first introduced by McKee et al. (1993) to monitor drought in the state of Colorado and highlighted by the WMO, as a starting point for meteorological drought monitoring (Svoboda and Fuchs, 2016). This index only needs monthly rainfall to calculate.

3-Month Standardized Precipitation Index (SPi3)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Precipitation
References: Guttman, N. B. 1999, J. Amer. "Water Resources Assoc"., 35, 311
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: SPI3
comment: 3-Month Standardized Precipitation Index (SPi3) is a probability index based on precipitation. It is designed to be a spatially invariant indicator of drought. SPI3 refers to precipiation in the previous 3-month period (+ indicates wet; - indices dry).

6-Month Standardized Precipitation Index (SPi6)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Precipitation
References: Guttman, N. B. 1999, J. Amer. "Water Resources Assoc"., 35, 311
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: SPI6
comment: 6-Month Standardized Precipitation Index (SPi6) is a probability index based on precipitation. It is designed to be a spatially invariant indicator of drought. SPI6 refers to precipiation in the previous 6-month period (+ indicates wet; - indices dry).

Shortwave Radiation (SRAD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Radiation
Acronym: SRAD
comment: Shortwave radiation (SRAD) is a radiant energy produced by the sun with wavelengths ranging from infrared through visible to ultraviolet. Shortwave radiation is therefore exclusively associated with daylight hours for a particular location on the Earth's surface.

Solar Radiation by Tier3
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Solar Radiation
unit: MJ/M
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
Acronym: SRtier3
comment: Transformation for Solar radiation by Tier 3
definition: [<18, "Low"], [18-36, "Medium"], [>36, "High"]

Daily Sunshine Duration (SS)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Sunshine
unit: h (hour)
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: SS
comment: Daily Sunshine Duration (SS) refers to whenever synoptical sunshine duration, which is available (in minutes) at 00, 06, 12 and/or 18 UT, daily sunshine duration is calculated as the summation of the available values.
definition: Let SSij be the daily sunshine duration for day i of period j. Then sum values are given by: SSj = Sum (SSij), in which i = 1...I

Daily Hours of Sunshine (SSH)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Sunshine
unit: h (hour)
References: Herman Mollenhorsta, Michel H.A. de Haan, Jouke Oenema, Claudia Kamphuis, "Field and crop specific manure application on a dairy farm based on historical data and machine learning", Computers and Electronics in Agriculture 175 (2020) 105599
Acronym: SSH
comment: Daily Hours of Sunshine (SSH) refers to the number of hours of sunshine per day

Sea Surface Temperature (SST)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: SST
comment: Sea Surface Temperature (SST) is defined as the skin temperature of the ocean surface water.

Sunshine Duration Fraction with Respect to Daylength (SSp)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Sunshine
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: SSp
definition: Let SSij be the daily sunshine duration amount for day i of period j and SSij the maximum daylight hours for day i of period j. Sum values in period j are given by: SSj = Sum(SSij) in which i=1...I and SSmaxj = Sum(SSmaxij) in which i=1...I

Sunshine by Percentage
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Sunshine
References: Herman Mollenhorsta, Michel H.A. de Haan, Jouke Oenema, Claudia Kamphuis, "Field and crop specific manure application on a dairy farm based on historical data and machine learning", Computers and Electronics in Agriculture 175 (2020) 105599
Acronym: SSpct
comment: Percentage of maximum time possible of sunshine

Number of Summer Days (SU)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: SU
comment: Annual count of days when TX (daily maximum temperature) > 25°C. Let TXij be daily minimum temperature on day i in year j.
definition: Count the number of days where TXij > 25 °C.

Minimum Temperature at 10 cm above Field Level (T10min)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: Herman Mollenhorsta, Michel H.A. de Haan, Jouke Oenema, Claudia Kamphuis, "Field and crop specific manure application on a dairy farm based on historical data and machine learning", Computers and Electronics in Agriculture 175 (2020) 105599
Acronym: T10min
comment: Transformation for Temperature by Minimum temperature at 10 cm above field level.

Total Cooling Degree Days (TCD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: Day
References: Mathieu, Jordane A., and Filipe Aires. "Assessment of the agro-climatic indices to improve crop yield forecasting." Agricultural And Forest Meteorology 253 (2018): 15-30.
Acronym: TCD
comment: Total Cooling Degree Days (TCD) is a measurement designed to quantify the demand for energy needed to cool buildings. It is the number of degrees that a day's average temperature is above 65o Fahrenheit (18o Celsius).

Temperature Condition Index (TCI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: TCI
comment: Transformation for Temperature by Temperature condition index (TCI)

Cumulated Degree Days during frost period (TDD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: Mathieu, Jordane A., and Filipe Aires. "Assessment of the agro-climatic indices to improve crop yield forecasting." Agricultural And Forest Meteorology 253 (2018): 15-30.
Acronym: TDD
comment: Transformation for Temperature by Cumulated Degree Days during frost period

Mean of Daily Mean Temperature (TG)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: TG
comment: Mean of Daily Mean Temperature (TG) refers to the average daily temperature, which is calculated as TG=(TX+TN)/2. This combines the TX reading of 18 UT and that of TN of 6 UT of the current day.
definition: TG = the middle of lowest and highest temperature recorded in day j

Days with TG < 10th Percentile of Daily Mean Temperature (TG10p)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: days
Acronym: TG10p
comment: Days with TG < 10th percentile of daily mean temperature (cold days) (days)
definition: Let TGij be the daily mean temperature at day i of period j and let T Gin10 be the calendar day 10th percentile calculated for a 5-day window centred on each calendar day in the 1961–1990 period. Then counted is the number of days where: T Gij < T Gin10

Moving Average of Daily Mean Temperature During 5 Days (TMM5)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: Mathieu, Jordane A., and Filipe Aires. "Assessment of the agro-climatic indices to improve crop yield forecasting." Agricultural And Forest Meteorology 253 (2018): 15-30.
Acronym: TMM5
comment: Transformation for Temperature by Moving average of daily mean temperature during 5days

Weighted Moving Average of Daily Mean Temperature During 5 Days (TMMp5)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: Mathieu, Jordane A., and Filipe Aires. "Assessment of the agro-climatic indices to improve crop yield forecasting." Agricultural And Forest Meteorology 253 (2018): 15-30.
Acronym: TMMP5
comment: Transformation for Temperature by Weighted moving average of daily mean Temperature during 5 days

Daily Minimum Temperature (TN)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Temperature
unit: ◦C
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: TN
comment: In the synoptical report of 06 UT, the daily minimum temperature is given for that day. This daily minimum temperature is the lowest temperature recorded between 18 UT (previous day) and 06 UT (according to WMO specifications).
comment: This daily minimum temperature is the lowest temperature recorded between 18 UT (previous day) and 06 UT (according to WMO specifications)..
label: Mean of Daily Minimum Temperature (TN)
definition: Let TNij be the minimum temperature at day i of period j. Then mean values in period j are given by: TNj = SUM(TNij) / I in which i = 1...I
definition: TNj = the lowest temperature recorded in day j

Percentage of days when TN < 10th percentile (TN10p)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: % of time
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: TN10p
comment: Let TNij be the daily minimum temperature on day i in period j and let TNin10 be the calendar day 10th percentile centred on a 5-day window for the base period 1961-1990. The percentage of time for the base period is determined where: TNij < TNin10. To avoid possible inhomogeneity across the in-base and out-base periods, the calculation for the base period (1961-1990) requires the use of a bootstrap procedure.
definition: Let TNij be the daily minimum temperature at day i of period j and let TNin10 be the calendar day 10th percentile calculated for a 5-day window centred on each calendar day in the 1961–1990 period. Then counted is the number of days where: TNij < TNin10

Percentage of days when TN > 90th percentile (TN90p)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: % of time
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: TN90p
comment: Let TNij be the daily minimum temperature on day i in period j and let TNin90 be the calendar day 90th percentile centred on a 5-day window for the base period 1961-1990. The percentage of time for the base period is determined where TNij > TNin90. To avoid possible inhomogeneity across the in-base and out-base periods, the calculation for the base period (1961-1990) requires the use of a bootstrap processure.
definition: Let TNij be the daily minimum temperature at day i of period j and let TNin90 be the calendar day 90th percentile calculated for a 5-day window centred on each calendar day in the 1961–1990 period. Then counted is the number of days where: TNij > TNin90

Monthly Minimum Value of Daily Minimum Temperature (TNn)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: ◦C
Acronym: TNn
comment: Let TNn be the daily minimum temperatures in month k, period j. The minimum daily minimum temperature each month is then TNnkj=min(TNnkj)
definition: Let TNij be the daily minimum temperature on day i of period j. Then the minimum daily minimum temperature for period j is: TNnj = min(TNij )

Monthly Maximum Value of Daily Minimum Temperature (TNx)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: ◦C
Acronym: TNx
comment: Let TNx be the daily minimum temperatures in month k, period j. The maximum daily minimum temperature each month is then TNxkj = max(TNxkj).
definition: Let TNij be the daily minimum temperature on day i of period j. Then the maximum daily minimum temperature for period j is: TNxj = max(TNij )

Number of Tropical Nights (TR)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: TR
comment: Number of Tropical Nights (TR) refers to annual count of days when TN (daily minimum temperature) > 20 °C. Let TNij be daily minimum temperature on day i in year j.
definition: Count the number of days where TNij > 20 °C.

10ptile Diurnal Temperature Range (TRQ10)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: TRQ10
comment: 10ptile Diurnal Temperature Range (TRQ10) refers to 10ptile of daily TX-TN values
definition: 10ptile of daily TX-TN values

Mean of Daily Maximum Temperature (TX)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: ◦C
Acronym: TX
comment: In the synoptical report of 18 UT, the daily maximum temperature is given for that day. This daily maximum temperature is the highest temperature recorded between 06 UT and 18 UT (according to WMO specifications).
definition: TX = the highest temperature recorded

Percentage of days when TX < 10th percentile (TX10p)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: % of time
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: TX10p
comment: Let TXij be the daily maximum temperature on day i in period j and let TXin10 be the calendar day 10th percentile centred on a 5-day window for the base period 1961-1990. The percentage of time for the base period is determined where TXij < TXin10. To avoid possible inhomogeneity across the in-base and out-base periods, the calculation for the base period (1961-1990) requires the use of a bootstrap processure.

Percentage of days when TX > 90th percentile (TX90p)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: % of time
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: TX90p
comment: Let TXij be the daily maximum temperature on day i in period j and let TXin90 be the calendar day 90th percentile centred on a 5-day window for the base period 1961-1990. The percentage of time for the base period is determined where TXij > TXin90. To avoid possible inhomogeneity across the in-base and out-base periods, the calculation for the base period (1961-1990) requires the use of a bootstrap processure.
definition: Let TXij be the daily maximum temperature at day i of period j and let TXin90 be the calendar day 90th percentile calculated for a 5-day window centred on each calendar day in the 1961–1990 period. Then counted is the number of days where: TXij > TXin90

Daily Maximum Temperature (TX)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: TX
comment: This daily maximum temperature is the highest temperature recorded between 18 UT (previous day) and 06 UT (according to WMO specifications)..
definition: TXj = the highest temperature recorded in day j

Monthly Minimum Value of Daily Maximum Temperature (TXn)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: ◦C
Acronym: TXn
comment: Let TXn be the daily maximum temperatures in month k, period j. The minimum daily maximum temperature each month is then TXnkj = min(TXnkj)
definition: Let TXij be the daily maximum temperature on day i of period j. Then the minimum daily maximum temperature for period j is: TXnj = min(TXij)

Monthly Maximum Value of Daily Maximum Temperature (TXx)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: TXx
comment: Let TXx be the daily maximum temperatures in month k, period j. The maximum daily maximum temperature each month is then TXxkj = max(TXxkj).
definition: Let TXij be the daily maximum temperature on day i of period j. Then the maximum daily maximum temperature for period j is: TXxj = max(TXij)

Temperature by Tbase (Tbase)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Temperature
unit: oC
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: Tbase
comment: Transformation for Temperature by Citrus ontology development based on the eight-point charter of agriculture
definition: Tbase = Baseline growing temperature

Frost Temperature threshold (Tfrost)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Temperature
unit: oC
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: Tfrost
comment: Transformation for Temperature by Frost temperature threshold

Maximum Temperature for Day j (Tmax)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Temperature
unit: oC
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
References: P.S. Maya Gopal, R. Bhargavi, "A novel approach for efficient crop yield prediction", Computers and Electronics in Agriculture, Volume 165, October 2019, 104968, Elsevier B.V.
Acronym: Tmax
comment: Transformation for Temperature by Maximum temperature for day j
definition: Tmaxj = Maximum temperature for day j

Average Temperature for Day j (Tmean)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Temperature
unit: oC
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: Tmean
comment: Transformation for Temperature by Average temperature for day j
definition: Tmeanj = (Tminj+Tmaxj) / 2

Minimum Temperature for Day j (Tmin)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Temperature
unit: oC
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: Tmin
comment: Transformation for Temperature by Minimum temperature for day j .
definition: Tminj = Minimum temperature for day j

Average Temperature for Month (Tmonth)
type: NamedIndividual
type: Agroclimatic Indices
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Temperature
unit: oC
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: Tmonth
comment: Transformation for Temperature by Average temperature for month

Number of Days Tmin ≥ threshold (TnGE)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: Days
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: TnGE
comment: Number of Days Tmin ≥ user defined threshold

Number of Days Tmin <= threshold (TnLE)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: Days
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: TnLE
comment: Number of Days Tmin <= user defined threshold

Temperature range (Trange)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
Acronym: Trange
comment: An index based on daily temperature range (t min to t max) calculated from the planting date.

Temperature by Tier5
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: oC
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
Acronym: Ttier5
comment: Transformation for Temperature by Tier 5
definition: [<15, "Very low"], [15-24, "Low"], [25-30, "Medium"], [31-37, "High"], [>37, "Very high"]

Number of Days Tmax ≥ threshold (TxGE)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: Days
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: TxGE
comment: Number of Days Tmax ≥ user defined threshold

Number of Days Tmax <= threshold (TxLE)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
References: Peterson, T.C., et al. 2001. "Report on the Activities of the Working Group on Climate Change Detection and Related Rapporteurs 1998-2001", WMO, Rep. WCDMP-47, WMO-TD 1071, Geneve, Switzerland, 143pp.
Acronym: TxLE
comment: Number of Days Tmax <= user defined threshold

Daily Average Wind Speed at 2m (2)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Wind Speed
Acronym: U2

Mean of the Universal Thermal Climate Index (UTCI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Climate
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: UTCI
comment: The assessment of the thermophysiological effects of the atmospheric environment is one of the key issues in human biometeorology. To quantify these effect, the Universal Thermal Climate Index (UTCI) is developed in COST action 730. Input to the UTCI are mean radiant temperature, air temperature, water vapour pressure and windspeed. The algorithm for the mean radiant temperature is the one given by Fanger (1970). Documentation for the mean radiant temperature and the UTCI is available via http://www.utci.org. In ECA&D, data for daily averaged windspeed, relative humidity and sunshine duration are available which we will use as input for calculating the mean radiant temperature and the UTCI. For this purpose, sunshine duration is related to an estimate of direct solar radiation (which is required as input to the mean radiant temperature) and relative humidity is related to water vapour pressure using values of the daily maximum and minimum temperatures. The algorithms used for these conversions are documented elsewhere Allen et al. (1994b,a); Duffie & Beckman (1991). Since the UTCI relates to human activity, we use the daily maximum temperature as the air temperature since we can expect outdoor human activity to occur during the day time. In the mean radiant temperature, the projected area factor fg is included which is related to the angle of the sun. Again since outdoor activity is in the day time, we take the average of fg over the period 10:00h to 20:00h.

Vapour Pressure (VP)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Vapor Pressure
Acronym: VP
comment: Vapor Pressure (VP) or equilibrium vapor pressure is defined as the pressure exerted by a vapor in thermodynamic equilibrium with its condensed phases (solid or liquid) at a given temperature in a closed system.

Vapor Pressure Deficit (VPD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Vapor Pressure
Acronym: VPD
comment: Vapor Pressure Deficit (VPD) is the difference (deficit) between the amount of moisture in the air and how much moisture the air can hold when it is saturated. Once air becomes saturated, water will condense out to form clouds, dew or films of water over leaves. It is this last instance that makes VPD important for greenhouse regulation. If a film of water forms on a plant leaf, it becomes far more susceptible to rot. On the other hand, as the VPD increases, the plant needs to draw more water from its roots. In the case of cuttings, the plant may dry out and die.

Vapor Pressure Deficit Maximum (VPDmax)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Vapor Pressure
Acronym: VPDmax

Vapor Pressure Deficit Minimum (VPDmin)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Vapor Pressure
Acronym: VPDmin

Vapor Pressure (Vap)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Vapor Pressure
Acronym: Vap

Warm/Dry Days (WD)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Precipitation
is transformation of: Temperature
References: Beniston, Martin. "Trends in joint quantiles of temperature and precipitation in Europe since 1901 and projected for 2100." Geophysical Research Letters 36.7 (2009).
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: WD
comment: Days with TG > 75th percentile of daily mean temperature and RR < 25th percentile of daily precipitation sum (warm/dry days)
definition: Let TGij be the daily mean temperature at day i of period j and let TGin75 be the calendar day 75th percentile calculated for a 5-day window centred on each calendar day in the 1961–1990 period. Let RRwj be the daily precipitation amount at wet day w (RR ≥ 1.0 mm) of period j and let RRwn25 be the 25th percentile of precipitation at wet days in the 1961–1990 period. Then counted is the number of days where: TGij > TGin75 and RRwj < RRwn25

Wind Direction at 12 UT (WDD)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Wind Direction
unit: degrees
References: Beniston, Martin. "Trends in joint quantiles of temperature and precipitation in Europe since 1901 and projected for 2100." Geophysical Research Letters 36.7 (2009).
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: WDD
comment: Wind Direction at 12 UT (WDD) refers to whenever synoptical wind direction data, which is available (in degrees) at 12 UT, that value is taken as the wind direction.

Warm Spell Duration Index (WSDI)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Temperature
unit: Days
Acronym: WSDI
comment: Warm Spell Duration Index (WSDI) refers to annual count of days with at least 6 consecutive days when TX > 90th percentile
definition: Let TXij be the daily maximum temperature on day i in period j and let TXin90 be the calendar day 90th percentile centred on a 5-day window for the base period 1961-1990. Then the number of days per period is summed where, in intervals of at least 6 consecutive days, TXij > TXin90.

Wind Speed by Max (Wsmax)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Wind Speed
Acronym: Wsmax
comment: Transformation for Wind speed by Max

Average Wind Speed (Wsmean)
type: NamedIndividual
type: Agroclimatic Indices
is transformation of: Wind Speed
unit: m/s
Acronym: Wsmean
comment: Transformation for Wind speed by Mean

Warm/Wet Days (WW)
type: NamedIndividual
type: Agroclimatic Indices
defined in: EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
is transformation of: Precipitation
is transformation of: Temperature
References: Beniston, Martin. "Trends in joint quantiles of temperature and precipitation in Europe since 1901 and projected for 2100." Geophysical Research Letters 36.7 (2009).
References: Klein Tank, A. M. G. "EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4." Report EPJ029135 (2007).
Acronym: WW
comment: Days with TG > 75th percentile of daily mean temperature and RR >75th percentile of daily precipitation sum (warm/wet days)
definition: Let TGij be the daily mean temperature at day i of period j and let TGin75 be the calendar day 75th percentile calculated for a 5-day window centred on each calendar day in the 1961–1990 period. Let RRwj be the daily precipitation amount at wet day w (RR ≥ 1.0 mm) of period j and let RRwn75 be the 75th percentile of precipitation at wet days in the 1961–1990 period. Then counted is the number of days where: TGij > TGin75 and RRwj > RRwn75

Agronomic Efficiency (AE)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Nutrient
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: AE
comment: Agronomic Efficiency (AE) is calculated in units of yield increase per unit of nutrient applied. It more closely reflects the direct production impact of an applied fertilizer and relates directly to economic return.
definition: AE = ( Y1 - Y2) / Nu where, Y1 : Grain yield in fertilized plot (kg ha-1) Y2 : Grain yield in unfertilized plot (kg ha-1) Nu : Quanlity of nutrient applied

Absolute Frequency (AF)
type: NamedIndividual
type: Agronomic Indices
related to: Weeds
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: AF
comment: Absolute Frequency (AF) is the ratio between the number of quadrats in which a given species occurs and total number of quadrats used.
definition: AF = 100 * [ The number of quadrats in which a given species occurs ] / [ Total number of quadrats used ]

Above Ground Biomass (AGB)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Biomass
Acronym: AGB

Aridity Index (AI)
type: NamedIndividual
type: Agronomic Indices
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: AI
comment: AI = (No. of rainy dáy x Mean precipitation day) / (Mean temperature + 10) x 100
comment: Aridity Index (AI) is a measure of dryness of a region.

Apparent Recovery (AR)
type: NamedIndividual
type: Agronomic Indices
related to: Nutrient
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: AR
comment: Apparent Recovery (AR) is also known as recovery fraction and indicates the percentage of nutrien absorbed from externally applied fertilizer nutrient source. It is expressed in kg of nutrient uptake/kg of nutrient applied.
definition: AR = ( Yt - Yo) / Nt where, Yt : Uptake of nutrient in particular treatment Yo : Uptake of nutrient in unfertilizer plot Nt : Quantity of Nutrient applied for the treatment

Available Water Capacity (AWC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Soil
References: Richards, L.A.; Wadleigh, C.H. (1952). "Soil water and plant growth". In B.T. Shaw (ed.). Soil Physical Conditions and Plant Growth. American Society of Agronomy Series Monographs, Volume II. New York: Academic Press. pp. 74–251.
Acronym: AWC
comment: Available Water-holding Capacity (AWC) is a key attribute, as it quantifies the amount of water available for plants that the soil can hold. The AWC is defined as the amount of water held by the soil between field capacity (FC) and permanent wilting point (PWP)

Biological Yield (BY)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Yield
Identifier: Biological Yield (BY) = Grain Yield (GY) + Straw Yield (SY)
Acronym: BY
comment: Biological Yield (BY) refers to the total dry matter accumulation of a plant system.

Canopy Cover (CC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Canopy
Acronym: CC
comment: Canopy Cover (CC) is defined as the fraction of ground covered by the vertically projected crown envelopes.
definition: CC = Segmentation(Plant) / (Segmentation(Plant) + Segmentation(Soil)

Crop Growth Rate (CGR)
type: NamedIndividual
type: Agronomic Indices
related to: Growth Stage
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: CGR
comment: Crop Growth Rate (CGR) is measured as mass increase in crop biomass per unit ground area per unit time. It simply indicates the change in dry weight over a period' of time.
definition: CGR = (W2-W1) / (P * (t2 - t1)) where, P = Ground area, W1 = Dry weight of plant/m2 recorded at time t1, W2 = Dry weight of plant/m2 recorded at time t2, t1 and t2 were the interval of time, respectively and it is expressed in g/m2/day.

Crop Lower Limit (CLL)
type: NamedIndividual
type: Agronomic Indices
Acronym: CLL
comment: Crop Lower Limit (CLL) refers to the amount of water remaining after a particular crop has extracted all the water available to it from the soil.

Crop Resistance Index (CRI)
type: NamedIndividual
type: Agronomic Indices
related to: Weeds
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: CRI

Canopy Surface Wetness Index (CSW)
type: NamedIndividual
type: Agronomic Indices
Acronym: CSW

Canopy Surface Wetness Duration (CSWD)
type: NamedIndividual
type: Agronomic Indices
Acronym: CSWD

Consumptive Use Efficiency (CUE)
type: NamedIndividual
type: Agronomic Indices
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: CUE
comment: Consumptive Use Efficiency (CUE) is the percentage ratio of the amount of water consumptively used by the crop to the amount of the water stored in the root zone.
definition: CUE = CUW / WU * 100%

Consumptive Use of Water (CUW)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Irrigation
Acronym: CUW

Climatic Water Balance (CWB)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
References: Cao, Xinchun, Jie Ren, Mengyang Wu, Xiangping Guo, Zhenchang Wang, and Weiguang Wang. "Effective use rate of generalized water resources assessment and to improve agricultural water use efficiency evaluation index system." Ecological Indicators 86 (2018): 58-66.
Acronym: CWB
comment: Climatic Water Balance (CWB) is defined as the difference between precipitation depth and the depth of potential evapotranspiration at a given site during a certain time period.

Canopy Water Content (CWC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
unit: g m−2
Acronym: CWC

Crop Water Productivity (CWP)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Water
References: Cao, Xinchun, Jie Ren, Mengyang Wu, Xiangping Guo, Zhenchang Wang, and Weiguang Wang. "Effective use rate of generalized water resources assessment and to improve agricultural water use efficiency evaluation index system." Ecological Indicators 86 (2018): 58-66.
Acronym: CWP

Crop Water Stress Index (CWSI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Temperature
is transformation of: Water
References: R.D. Jackson, S.B. Idso, R.J. Reginato, P.J. Pinter Jr, "Canopy temperature as a drought stress indicator", Water Resour. Res., 13 (1981), pp. 651-656
Acronym: CWSI
comment: Crop Water Stress Index (CWSI) requires an estimate of canopy temperature under non-water stressed conditions (Tnws)
definition: CWSI = (Tcanopy - Tnws) / (Tdry - Tnws) where Tcanopy is the measured temperature of the vine canopy, and Tdry and Tnws are the upper and lower canopy temperature thresholds when transpiration is completely limited or non-restricted, respectively.

Canopy Weight (CWT)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Canopy
Acronym: CWT

Crop Yield (CY)
type: NamedIndividual
type: Agronomic Indices
related to: Yield
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: CY

Chlorophyll content (Cab)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Chlorophyll
unit: μg cm−2
Acronym: Cab

Chlorophyll Concentration (ChC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Chlorophyll
unit: mg g-1
Acronym: ChC
definition: ChC = ((20.3 R645 + 8.04 R663) x V) / (W x 1000) where Rw is the absorbance of the extract solution at waveban w, V (ml) is the volume of the extract solution, and W (g) is the leaf fresh weight

Chlorophyll Density (ChD)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Chlorophyll
unit: g m−2
Acronym: ChD
comment: Chlorophyll density (ChD, g m−2) is the product of leaf fresh weight per unit ground area and leaf chlorophyll concentration.
definition: ChD = ChC x LFW where ChC: Chlorophyll concentration LFW: Leaf fresh weight

Disease Index (DI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Crop Disease
is transformation of: Leaf
References: Li, G. B., Zeng, S. M., & Li, Z. Q. (1989). "Integrated Management of Wheat Pests" . Beijing: Press of Agriculture Science and Technology of China (in Chinese), (pp. 185–186).
Acronym: DI
comment: Disease Index (DI) was based on the total number of leaves of each degree of disease severity and n is the highest degree of disease severity observed.

Dry Matter Content (DMC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Biomass
Acronym: DMC
comment: Dry Matter Content (DMC) refers to a measure of dry matter concentration in plants and their parts, such as leaves, stems, roots and entire plants in relation to dry matter concentration (D, dry mass per volume of plant organ)

Dry Matter Efficiency (DME)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Biomass
related to: Organic Matter
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: DME
comment: Dry Matter Efficiency (DME) is the relative between dry matter grain yield and straw yield.
definition: DME = GrainYield / (GrainYield + StrawYield) + 100 / DurationOfCrop.

Dry Matter Yield (DMY)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Yield
Acronym: DMY

Drained Upper Limit (DUL)
type: NamedIndividual
type: Agronomic Indices
Acronym: DUL
comment: Drained Upper Limit (DUL) or field capacity refers to the amount of water a soil can hold against gravity.

Crop Development Stage (DVS)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Growth Stage
Acronym: DVS

Effective Canopy Transmittance (ECT)
type: NamedIndividual
type: Agronomic Indices
Acronym: ECT
comment: Effective Canopy Transmittance (ECT) is defined as the number of measurement points recorded in a gap within a tree crown divided by the total number of measurement points under a tree crown measured in the plot. ECT could only be obtained with the Cajanus tube in this study. If tree crowns do not overlap, this corresponds to single crown transmittance.

Economic Irrigation Efficiency (EIE)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Irrigation
is transformation of: Water
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: EIE
comment: Economic Irrigation Efficiency (EIE) is the ratio of the actual income attained with operating irrigation system, compared with income expected under ideal condition. This parameter is a measure of overall efficiency, because it relates the final return to input cost.

Economic Nutrient Use Efficiency (ENUE)
type: NamedIndividual
type: Agronomic Indices
related to: Nutrient
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: ENUE
comment: Economic Nutrient Use Efficiency (ENUE) is calculated in terms of economic produce grain or seed yield per rupee invested per hectare on particular nutrient fertilizer.
definition: ENUE = Y / Nuf where, Y : Grain yield Nuf : Amount invested on particular nutrient fertilizer (Rs.ha-1)

Effective Rainfall Use Efficiency (ERUE)
type: NamedIndividual
type: Agronomic Indices
unit: kg ha-1 mm-1
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: ERUE
comment: Effective Rainfall Use Efficiency (ERUE) is defined as the ratio between the grain yield per unit area and the effective rainfall received during cropping yield
definition: ERUE = GrainYield / EffictiveRainfall

Evapotranspiration Deficit Index (ETDI)
type: NamedIndividual
type: Agronomic Indices
References: Narasimhan, B., and R. Srinivasan. "Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring." Agricultural and forest meteorology 133.1-4 (2005): 69-88.
Acronym: ETDI
comment: Evapotranspiration Deficit Index (ETDI) were developed based on weekly evapotranspiration deficit.

Evapotranspiration Use Efficiency (ETUE)
type: NamedIndividual
type: Agronomic Indices
unit: kg ha-1 mm-1
References: Devasenapathy, P. \"Efficiency indices for agriculture management research\", New India Publishing, 2008.
Acronym: ETUE
comment: Evapotranspiration Use Efficiency (ETUE) is the ratio between the economic output (grain yield) and total evopotransporation during cropping period.
definition: ETUE = GrainYield / TotalEvapotranspiration

Equivalent Water Thickness (EWT)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Biomass
unit: g cm-2
References: Bowyer P, Danson FM (2004), "Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy level." Remote Sensing of Environment 92:297-308.
Acronym: EWT
comment: Equivalent Water Thickness (EWT) is an index for measurement of (leaf) water content.
definition: EWT= (FW - DW) / A where FM is leaf fresh biomass, DW is leaf dry biomass, and A is area

Ecologically Weighted Water Withdrawal Index (EWWWI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
Acronym: EWWWI

Fuel Moisture Content (FMC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Moisture
Acronym: FMC

Full Moisture Content (FMC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Biomass
References: Zhang J, Xu Y, Yao F, Wang P, Guo W, Li L, Yang L (2010), "Advances in estimation methods of vegetation water content based on optical remote sensing techniques." Science China Technological Sciences 53:1159-1167.
Acronym: FMC
comment: FMC (%) = 100 x (FW -DW) / FW (or DW) where, FW is the leaf fresh mass, and DW is the leaf dry mass.
comment: Full Moisture Content (FMC) is the ratio of leaf water mass to either dry or wet mass of leaf.

Flowering Rate (FR)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Flowering
related to: Flower
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: FR
comment: FR = (Fn2 - Fn1) / (t2 - t1) where, Fn2 and Fn1 are number of flowers at times t1 and t2 respectively.
comment: Flowering Rate (FR) indicates sunchronous flowering.

Fresh Yield (FY)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Yield
Acronym: FY

Gross Cropped Area (GCA)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Area
related to: Irrigation
Acronym: GCA
comment: Gross Cropped Area (GCA) refers to total area planted to crops during all growing seasons of the year.

Gross Irrigated Area (GIA)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Area
related to: Irrigation
Acronym: GIA
comment: Gross Irrigated Area (GIA) rế to the total area under crops that have received irrigation during all the growing seasons of the year

Gross Irrigation Requirement (GIR)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Irrigation
related to: Water
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: GIR
comment: Gross Irrigation Requirement (GIR) is the net irrigation requirement plus losses in water applocation and other losses means seepage, non-uniform distribution over the field, percolation below the root zone and wastage due to surface runoff etc. It is the total amount of water applied through irrigation.
definition: GIR = (NIR / FES) where, NIR : Net irrigation requirement FES : Field effeciency system

Gross Inflow Water Productivity (GIWP)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Water
Acronym: Cao, Xinchun, Jie Ren, Mengyang Wu, Xiangping Guo, Zhenchang Wang, and Weiguang Wang. "Effective use rate of generalized water resources assessment and to improve agricultural water use efficiency evaluation index system." Ecological Indicators 86 (2018): 58-66.
Acronym: GIWP

Grain N Content (GNC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Nitrogen (N)
unit: %
Acronym: GNC

Soil Gravimetric Moisture (GSM)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Moisture
related to: Biomass
related to: Moisture
related to: Soil
References: Blake, G. R., & Hartge, K. H. (1986). "Bulk Density. Methods of Soil Analysis—Part 1: Physical and Mineralogical Methods", 363–75. Madison, WI, USA: American Society of Agronomy-Soil Science Society of America.
Acronym: GSM
definition: GSM = 100 x (FressBiomass - Drybiomass) / Drybiomass

Generalized Water Productivity (GWP)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Water
Acronym: Cao, Xinchun, Jie Ren, Mengyang Wu, Xiangping Guo, Zhenchang Wang, and Weiguang Wang. "Effective use rate of generalized water resources assessment and to improve agricultural water use efficiency evaluation index system." Ecological Indicators 86 (2018): 58-66.
Acronym: GWP

Grain Yield (GY)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Yield
Acronym: GY

Herrbicide Efficiency Index (HEI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Weeds
related to: Weeds
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: HEI
comment: Herrbicide Efficiency Index (HEI) indicated the weed killing potential of different herbicide treatments and their phytotocicity on the crop.
definition: HEI = 100 * ( (Yt-Yc) / Yc ) / (DMT / DMC) where, Yc : Yield from control plot Yt : Yield from treatment plot DMC : Weed dry matter in control plot DMT : Weed dry matter in treatment plot

Harvest Index (HI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Biomass
related to: Yield
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: HI
comment: Harvest index (HI) is the ratio of grain to total shoot dry matter and is as a measure of reproductive efficiency. HI is determined by interactions between genotypes (G), environment (E), and crop management (M).
definition: HI = GrainYield / TotalDryMatter x 100

Heat Use Efficiency (HUE)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Temperature
unit: kg ha-1 oC-1
References: Devasenapathy, P. \"Efficiency indices for agriculture management research\", New India Publishing, 2008.
Acronym: HUE
Acronym: HUE = DM / GDD where, DM : Dry matter productiuon GDD: Growing degree days
comment: Heat Use Efficiency (HUE) is the amount of dry matter produced per unit of thermal time or gropwing degree day (GDD)

Irrigation Efficiency (EIE)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Irrigation
related to: Water
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: IE

Irrigation Interval (II)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Irrigation
related to: Water
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: II
comment: Irrigation Interval (II) refers to the number of days between two irrigations in the period of the highest consumptive use of the crop grown without rainfall. It depends on available soil moisture and evapotraspiration. Irrigation is usually started for most crops when 50% of the available soil moisture is depleted.
definition: II = ( Mfc - Mbi ) / Pcu where, Mfc : Moisture content at field capacity of the soil in the effective root zone (cm) Mbi : Moisture content of the same root zone at the time at before irrigation (cm) Pcu : Peak period moisture use rate of the crop (cm)

Irrigation Period (IP)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Irrigation
related to: Water
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: IP
comment: Irrigation Period (IP) iss the number of days that can be allowed for applying the irrigation to a given design area during the peak consumptive use period of the crop being irrigated. It is important to design the irrigation system so that the irrigation period is not greater than the irrigation interval.
definition: IP = In / ETpeak wherem In : Net amount opf moisture (cm) in the design moisture extraction depth of soil between starting of irrigation and lower limit of moisture depletion. ETpeak: Peak period water use rate of the crop (cm)

Irrigation Scheduling (IS)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Irrigation
related to: Water
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: IS

Importance Value (IV)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Weeds
related to: Weeds
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: IV

Inter-Crop-cum Weed Control Effeciency Index (IWEI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Weeds
related to: Weeds
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: IWEI

Irrigation Water Productivity (IWP)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Irrigation
related to: Irrigation
related to: Water
References: Cao, Xinchun, Jie Ren, Mengyang Wu, Xiangping Guo, Zhenchang Wang, and Weiguang Wang. "Effective use rate of generalized water resources assessment and to improve agricultural water use efficiency evaluation index system." Ecological Indicators 86 (2018): 58-66.
Acronym: IWP

Irrigation Water Quality Index (IWQI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
References: Muniz, Daphne HF, et al. "Proposal of an irrigation water quality index (IWQI) for regional use in the Federal District, Brazil." Environmental Monitoring and Assessment 192.9 (2020): 1-15.
Acronym: IWQI

Leaf Area Duration (LAD)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Leaf
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: LAD
comment: Leaf Area Duration (LAD) iss a measure of ability to produce leaf area on unit area of land throughout life.
definition: LAD = (L1 + L2) / 2 x (t2 - t1) where, L1 and L2 arre LAI at t1 stage and t2 stage respectively, t2 - t1 is the time interval in days.

Leaf Area Index (LAI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Spectral Image
related to: Leaf
References: Boegh, E., H. Soegaard, N. Broge, C. Hasager, N. Jensen, K. Schelde, and A. Thomsen. "Airborne Multi-spectral Data for Quantifying Leaf Area Index, Nitrogen Concentration and Photosynthetic Efficiency in Agriculture." Remote Sensing of Environment 81, no. 2-3 (2002): 179-193.
Acronym: LAI
comment: Leaf Area Index (LAI) is defined as the total one-sided leaf area per ground surfacearea. This index is used to estimate foliage cover and to forecast crop growth and yield. It is a measure of the canopy photosynthetic area. LAI is often usedas an indicator of growth stage and crop biomass. LAI measurements have also440been demonstrated to show correlations with canopy micro-climate and evap-otranspiration traits. .
definition: LAI = (3.618 * EVI - 0.118) Where EVI is the Enhanced Vegetation Index value.

Land Cover (LC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Area
Acronym: LC

Leaf Production Rate (LPR)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Leaf
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: LPR

Leaf Wetness Duration (LWD)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Leaf
is transformation of: Water
Acronym: LWD
comment: Leaf Wetness Duration (LWD) refers to duration of wet leaf per day.

Moisture Availability Index (MAI)
type: NamedIndividual
type: Agronomic Indices
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: MAI
comment: Moisture Availability Index (MAI) is the ratio between rainfall (weekly / monthly) at 50% probability level and potential evapotransporation (PET) of the corresponding period.
definition: MAI = Rainfall (at 50% probability) / PET

Multiple Cropping Index (MCI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Area
related to: Cropping
Acronym: MCI
comment: Multiple Cropping Index (MCI) measures the sum of areas planted to different crops and harveted in a single year divided by the total cultivated area times 100.
definition: MCI = 100 * Sum(ai) / A, which i = 1...n where, n : Total number of crops ai : Area occupied by the ith crop plant and harvested within one year A : Total cultivated land area

Moisture Deficit Index (MDI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Evapotranspiration
is transformation of: Precipitation
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: MDI
comment: Moisture Deficit Index (MDI) is used to estimate the dryness of a region.
definition: MDI = (P - PET) / PET x 100 where, P : Precipitation PET : Potential Evapotraspiration

Net Assimilation Rate (NAR)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Biomass
is transformation of: Yield
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: NAR
comment: Net Assimilation Rate (NAR) is the net gain in total dry matter per unit leaf area per unit time.
definition: NAR = [(W2 - W1) * (LogL2 - LogL1)] / [(t2 - t1) * (L1 - L2) ] where, L1 and L2 are total leaf area at time t1 and t2 respectively. W1 and W2 are total dry weight time t1 and t2 respectively.

Net Cropped Area (NCA)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Area
Acronym: NC

Nutrient Efficiency Ratio (NER)
type: NamedIndividual
type: Agronomic Indices
related to: Nutrient
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: NER
comment: Nutrient Efficiency Ratio (NER) describes the quantity of total biomass obtained for each unit of nutrient utilized.
definition: NER = TotalBiomass / QuantityOfUtilizedNutrient

Nutrient Harvest Index (NHI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Nutrient
related to: Nutrient
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: NHI
comment: Nutrient Harvest Index (NHI) is defined as the ration between the nutrient uptake under economic yield and biological yield. In indicates the quantity of nutrient taken by the economic produce to the total crop uptake.
definition: NHI = NUe / NUb x 100 where, Nue : Nutrient Uptake (in economic yield) Nub : Nutrient Uptake (in biological yield)

Net Irrigated Area (NIA)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Area
is transformation of: Irrigation
Acronym: NIA
comment: Net Irrigated Area (NIA) refers to the total geographic area which has received irrigation throughout the year.

Nutrient Incremental Efficiency (NIE)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Nutrient
related to: Nutrient
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: NIE
comment: Nutrient Incremental Efficiency (NIE) denotes the percentage of increment in grain yield over the subsequent levels of nutrient.
definition: NIE = (Y2 - Y1) / Y1 x 100 where, Y1 and Y2 : Grain yield in L1 and L2 plot, L2 and L1 : are two subsequent levels of particular nutrient (kg ha-1)

Net Irrigation Requirement (NIR)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Irrigation
related to: Water
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: NIR
comment: Net Irrigation Requirement (NIR) is the depth of irrigation water, exclusive of effective rainfall during the crop period, antecedent or carry over moisture, ground water contribution, which is required consumptively for crop production. It is the amount of irrigation water required to bring the soil moisture in the effective root zone depth to field capacity. It is the difference between field capacity and the soil moisture content in the root zone before starting irrigation.
definition: NIR = ((Mfc-Mbi)/100 ) * D * Db where, Mfc : Moisture content at field capacity (%) Mbi : Moisture content at before irrigation (%) Db : Bulk density of the soil (g/cm3) D : Depth of soil (cm)

Nutrient Productivity (NP)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Nutrient
related to: Nutrient
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: NP
comment: Nutrient Productivity (NP) is the amount off biomass producted by the crop per unit of time.
definition: NP = (By / Nu) / t where, By : Total biomass yield Nu : Total crop N uptake t : Total duration of crop in days

Nutrient Use Efficiency (NUE)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Nutrient
related to: Nutrient
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: NUE
comment: Nutrient Use Efficiency (NUE) is a critically important concept in the evaluation of crop production systems. It can be greatly impacted by fertilizer management as well as by soil- and plant-water management. The objective of nutrient use is to increase the overall performance of cropping systems by providing economically optimum nourishment to the crop while minimizing nutrient losses from the field.
definition: NUE = GY / FeNut where, GY : Grain yield (kg ha-1) FeNut : Quantity Of Fertilizer nutrient applied (kg ha-1)

Operational Efficiency (OE)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Water
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: OE
comment: Operational Efficiency (OE) is the ratio of the actual project efficiency compared with the operational efficiency of an ideally designed and managed system using the same irrigation methods and facilities. Low operational efficciency indicates management or system design problems or both.

Profile Available Water (PAW)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
References: Duncan, Maurice; Davoren, Anthony; Carrick, Sam; Ellery, Glenn; Stevenson, Peter; Stewart, Doug (June 2013). Soil Water Measurement: Measurement, Processing and Archiving of Soil Water Content Data (PDF) (Report). 1.0. National Environmental Monitoring Standards. p. ii.
Acronym: PAW
comment: Profile Available Water (PAW) refers to the total available water for the soil profile to a depth of 0.9 m, or to the potential rooting depth (whichever is the lesser).

Plant Available Water Capacity (PAWC)
type: NamedIndividual
type: Agronomic Indices
Acronym: PAWC
comment: Plant Available Water Capacity (PAWC) of the soil is defined as the water content between field capacity and wilting point, and has wide practical application in planning the land use. PAWC is also refered to the amount of water they can store and release to different crops.

Potential Biomass (PB)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Biomass
related to: Biomass
Acronym: PB

Physiological Efficiency (PE)
type: NamedIndividual
type: Agronomic Indices
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: PE
comment: Physiological Efficiency (PE) indicates efficiency of utilization i.e., kg of grain per kg of absorbed nutrient computed as follows. It is expressed in kg of grain / kg of nutrient uptake.
definition: PE = ( Y1 - Y2) / (Nu1 - Nu2) where, Y1 : Grain yield in fertilized plot (kg ha-1) Y2 : Grain yield in unfertilized plot (kg ha-1) Nu1 : Nutrient uptake in fertilizer plot Nu2 : Nutrient uptake in unfertilizer plot

Panicle Emergence Rate (PER)
type: NamedIndividual
type: Agronomic Indices
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: PER
comment: Panicle Emergence Rate (PER) indicates the rate at which panicle emerges from the leaf sheath. As panicle emerges due to the force of internodal elongation, thiss parametewr is helpful in moisture stress studies.
definition: PER = (PI2 - PI1) / (t2 - t1) where, PI1 and PI2 are length of emerged panicle at times t1 and t2 respectively.

Partial Factor Productivity (PFP)
type: NamedIndividual
type: Agronomic Indices
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: PFP

Plant N Content (PNC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Nitrogen (N)
unit: %
Acronym: PNC

Partial Production Efficiency (PPE)
type: NamedIndividual
type: Agronomic Indices
related to: Nutrient
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: PPE
comment: Partial Production Efficiency (PPE) is calculated fro the treatment plot by the particular level of input compared to the treatment plot by the preceding level of input.
definition: PPE = (Yn - Yn-1) / (Nn - Nn-1) x 100 where, Yn : Yield of the treatment corresponding to nutrient level Yn-1 : Yield of the treatment corresponding to preceding nutrient level Nn : Nutrient supplied to the treatment at nutrient level Nn-1: Nutrient supplied to the treatment at preceding nutrient level

Relative Agronomic Efficiency (RAE)
type: NamedIndividual
type: Agronomic Indices
related to: Nutrient
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: RAE

Relative Density (RD)
type: NamedIndividual
type: Agronomic Indices
related to: Weeds
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: RD

Relative Dry Weight (RDW)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Biomass
related to: Weeds
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: RDW

Relative Economic Efficiency (REE)
type: NamedIndividual
type: Agronomic Indices
related to: Nutrient
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: REE

Relative Frequency (RF)
type: NamedIndividual
type: Agronomic Indices
related to: Weeds
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: RF
comment: Relative Frequency (BF) is the ratio between the absolute frequency for a specie and total of the absolute frequencies for all species.
definition: RF = 100 * [ Absolute frequence for a spice ] / [ Toal of the absolute frequencies for all species ]

Relative Growth Rate (RGR)
type: NamedIndividual
type: Agronomic Indices
related to: Growth Stage
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: RGR
comment: Relative growth rate (RGR) measures the increase in dry matter with a given amount of assimilatory material at a given point of time.
definition: RGR = (InWW2 - InWW1) / (t2 - t1) where, In = Natural log, W1 = Dry weight of plant/m2 recorded at time t1, W2 = Dry weight of plant/m2 recorded at time t2, t1 and t2 were the interval of time, respectively and is expressed as g/g/day

Relative Humidity Disparity (RHD)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Humidity
References: Devasenapathy, P. "Efficiency indices for agriculture management research\\\\\\\", New India Publishing, 2008.
Acronym: RHD
comment: Relative Humidity Disparity (RHD) is the relative humidity difference between the morning and evening values in a cropping period.
definition: RHD = (MorningRH - EveningRH) / MorningRH x 100

Root Length Density (RLD)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Root
unit: cm/cm3
Acronym: RLD

Relative Soil Moisture (RSM)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Soil Moisture
Acronym: RSM
comment: Relative Soil Moisture (RSM) is calculated the water balance from various methods. Takes climate, soil, and crop variables including potential ET and precipitation; soil physical properties; and crop characteristics and crop management practices.

Relative Turgidity (RT)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Biomass
related to: Leaf
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: RT
comment: Relative Turgidity (RT) expresses the ration of turgidity of leaf tissue at a particular stage to that of when it is fully turgid.
definition: RT = (FreshLeafWeight - DryLeafWeight) / (FullyTurgidLeafWeight - DryLeafWeight)

Relative Temperature Disparity (RTD)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Temperature
References: Devasenapathy, P. "Efficiency indices for agriculture management research\\\", New India Publishing, 2008.
Acronym: RTD
comment: Relative Temperature Disparity (RTD) is a linear orr direct relationship between temperature and growth of the plans. The temperature differences between the maximum and minimum measured at 2 to 3p,m and 4 to 5:30am in a day respectively are considered as very important because all the enzymes activity of any living organism are controlled by temperature variation.
definition: RTD = (Tmax - Tmin) / Tmax x 100

Rainfall Use Efficiency (RUE)
type: NamedIndividual
type: Agronomic Indices
References: Devasenapathy, P. \"Efficiency indices for agriculture management research\", New India Publishing, 2008.
Acronym: RUE
comment: Rainfall Use Efficiency (RUE) is defined as the ratio between the grain yield per unit area and the rainfall received during cropping yield.

Relative Leaf Water Content (RWC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
References: Schonfeld, M. A., Johnson, R. C., Carver, B. F., & Mornhinweg, D. W. (1988). "Water relations in winter wheat as drought resistance indicators." Crop Science, 28(3), 526.
Acronym: RWC
definition: RWC = 100 x (InitialFreshBiomass - Drymass) / (FullTurgid Biomass - Drymass)

Rain-fed Water Productivity (RWP)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Water
References: Cao, Xinchun, Jie Ren, Mengyang Wu, Xiangping Guo, Zhenchang Wang, and Weiguang Wang. "Effective use rate of generalized water resources assessment and to improve agricultural water use efficiency evaluation index system." Ecological Indicators 86 (2018): 58-66.
Acronym: RWP

Regional Water Stress Index (RWSI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Crop Stress
is transformation of: Water
Acronym: RWSI

Relative Yield (RY)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Yield
related to: Yield
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: RY

Relative Yield Total (RYT)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Yield
References: Connor, David J., Robert S. Loomis, and Kenneth G. Cassman. Crop ecology: productivity and management in agricultural systems. Cambridge University Press, 2011.
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: RYT

Stress Day Index (SDI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Crop Stress
related to: Crop Stress
related to: Water
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: SDI
comment: Stress Day Index (SDI) was proposed by Hiler et al. (1974) for scheduling irrigation when water resources are limited. The SDI concept provides a quantitative means for determining the stress inposed on a crop during its growing season. The SDI is determined from the stress day factor (SDi) and a crop susceptibility factor (Csi). The SDi is a measure of the defree and duration of plant or soil wayet deficit. The Csi indicate the magnitude of yield reduction due to given stress at a given stage when compared with the crop without any stress. The Csi depends on the species and stage of developemnt of the given crop. SDI is studied to delineate the critical stage of the crop growth period. SDI concept of scheduling of irrigation reveals the extent of yield reduction due to imposed stress at any stage of the crop growth.
definition: SDI = Sum(Csi * SDi) which i = (1...n) where, Csi : coefficient of drought susceptibility computed as Csi = 100 * (X - Xi) / X where, X: Yield from non-stress treatment, Xi : Yield from moisture stress treatment SDi : Stress day factor indication the extent of given stress SDi = Sum(1.0 - ETactual / ETpot) which j = 1..N where, ETactual : Actual Evapotranspiration for jth day in the growing period, ETpot : Potential Evapotranspiration for jth day in the growing period, N : Number of days in the growing period.

Summed Dominance Ratio (SDR)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Weeds
related to: Weeds
Acronym: SDR

Saturated Hydraulic Conductivity (SHC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Hydraulic Conductivity (HC)
Acronym: SHC

Specific Leaf Weight (SLW)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Leaf
unit: mg/cm2
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: SLW
comment: Specific Leaf Weight (SLW) is a way to measured leaf thickness indirectly by finding out the dry weight of leaf per unit area and the values are expressed in mg/cm2. It shows the efficiency of leaves to utilize the applied fertilizer for a greater yield as higher values are obtained from thicker leaves, which will have higher net CO2 exchange, and this will natuirally give a higher grain yield.
definition: SWL = DryWeightOfLeaf / AreaOfLeaf

Soil Moisture Deficit Index (SMDI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Soil Moisture
References: Narasimhan, B., and R. Srinivasan. "Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring." Agricultural and forest meteorology 133.1-4 (2005): 69-88.
Acronym: SMDI
comment: Soil Moisture Deficit Index (SMDI) was computed at four different levels, using soil water available in the entire soil profile, then soil water available at the top 2 ft. (SMDI-2), 4 ft. (SMDI-4), and 6 ft. (SMDI-6).

Specific Root Length (SRL)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Root
Acronym: SRL

Soil Water (SW)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Soil
is transformation of: Water
Acronym: SW

Soil Water Content (SWC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Soil Water
related to: Soil
related to: Water
Acronym: SWC
comment: Soil Water Content (SWC) is a measure of the amount of water (volume or mass) contained in a unit volume or mass of soil. If the measure is the volume of water per unit volume of soil, the water content is called the 'volume wetness.'

Specific Water Density (SWD)
type: NamedIndividual
type: Agronomic Indices
unit: g cm−2
Acronym: SWD

Soil Water Retention Curve (SWRC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Soil Water
related to: Soil
related to: Water
Acronym: SWRC
comment: Soil Water Retention Curve (SWRC) defines the degree of saturation corresponding to a particular suction in a soil, it is widely used to estimate unsaturated soil properties.

Straw Yield (SY)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Yield
Acronym: SY

Total Available Water (TAW)
type: NamedIndividual
type: Agronomic Indices
Acronym: TAW
comment: Total Available Water (TAW) refers to the amount of water that a well-drained soil should hold against gravitational forces, or the amount of water remaining when downward drainage has markedly decreased.

Total Factor Productivity (TFP)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Yield
related to: Yield
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: TFP
comment: Total Factor Productivity (TFP) is ...

Thornthwaite Moisture Index (TMI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Evapotranspiration
References: Devasenapathy, P. \\\"Efficiency indices for agriculture management research\\\", New India Publishing, 2008.
Acronym: TMI
comment: Thornthwaite Moisture Index (TMI) is the ration between actual evapotranspiration and potential evapotranspiration.
definition: TMI = AE / PET x 100 where, AE : Percent probability of actual evaporation PET : Potential Evapotranspiration

Total Nitrogen (TN)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Soil Nitrogen
Acronym: TN
comment: Total Nitrogen (TN) is the sum of nitrate-nitrogen (NO3-N), nitrite-nitrogen (NO2-N), ammonia-nitrogen (NH3-N) and organically bonded nitrogen.

Total Porosity (TP)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Porosity
Acronym: TP
comment: Total porosity (TP) is the total void space and as such includes isolated pores and the space occupied by clay-bound water. It is the porosity measured by core analysis techniques that involve disaggregating the sample. Total porosity was also estimated from bulk density values.
definition: Total porosity was estimated from bulk density values by the formula: TP = (1 - pb/ps) x 100 Where, TP = total porosity, ρb = bulk density and ρs = particle density (2.65 g cm−3).

Total Rainfall Use Efficiency (TRUE)
type: NamedIndividual
type: Agronomic Indices
unit: kg ha-1 mm-1
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: TRUE
comment: Total Rainfall Use Efficiency (TRUE) is the ratio between the economic output (grain yield) per unit and total rainfall received during cropping period.
definition: Total rainfall use efficiency by using the following formula and expressed as kg ha-1 mm-1: TRUE = GrainYield / TotalRainfall

Total Water Consumption (TWC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
References: Cao, Xinchun, Jie Ren, Mengyang Wu, Xiangping Guo, Zhenchang Wang, and Weiguang Wang. "Effective use rate of generalized water resources assessment and to improve agricultural water use efficiency evaluation index system." Ecological Indicators 86 (2018): 58-66.
Acronym: TWC
comment: Total Water Consumption (TWC) is ...

Total Water Inflow (TWI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
References: Cao, Xinchun, Jie Ren, Mengyang Wu, Xiangping Guo, Zhenchang Wang, and Weiguang Wang. "Effective use rate of generalized water resources assessment and to improve agricultural water use efficiency evaluation index system." Ecological Indicators 86 (2018): 58-66.
Acronym: TWI
comment: Total Water Inflow (TWI) is ...

Total Water Requirements (TWR)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
References: Cao, Xinchun, Jie Ren, Mengyang Wu, Xiangping Guo, Zhenchang Wang, and Weiguang Wang. "Effective use rate of generalized water resources assessment and to improve agricultural water use efficiency evaluation index system." Ecological Indicators 86 (2018): 58-66.
Acronym: TWR
comment: Total Water Requirements (TWR) is ...

Vegetation Crop Area (VCA)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Area
Acronym: VCA
comment: Vegetation Crop Area (VCA) is defined as the percentage of the total groundarea covered by the vertical projection of the crop. In other words, it is thepercent of the soil surface covered by plant foliage. It is sometimes referred to as Crop Canopy Cover (CC).

Vegetation Optical Depth (VOD)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Canopy
References: D. Long, F.T. Ulaby, "Microwave Remote Sensing Active and Passive", The University of Michigan Press (2014)
Acronym: VOD
comment: Vegetation Optical Depth (VOD) is a parameter accounting for the attenuation of microwave signals naturally emitted by the soil as they pass through the vegetation canopy.

Vegetation Water Content (VWC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
unit: %
Acronym: VWC
comment: Vegetation water content (VWC) is an important variable for both agriculture and forest fire management.

Vegetation Outlook (VegOut)
type: NamedIndividual
type: Agronomic Indices
References: Tadesse, T., and Wardlow, B. 2007. "The Vegetation Outlook (VegOut): A new ool for providing outlooks of general vegetation conditions using data mining techniques". In Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), IEEE, pp. 667–672.
Acronym: VegOut
comment: Vegetation Outlook (VegOut) combines climate information and RS observations of current vegetation conditions with oceanic index data and environmental biophysical information such as land cover type, irrigation status, soils, and ecological setting to provide a future outlook of general vegetation conditions.

Water Application Efficiency (WAE)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Water
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: WAE
comment: Water Application Efficiency (WAE) is the measure of efficiency of water application in the field. The water application efficiency may be defined as the percentage ration of the amount of water stored in the crop root zone to the amount of water delivereed to the field.
definition: WAE = Ws / Wt x 100 where, Ws : Amount of water stored in the crop root zone soil Wt : Amount of water delivered to the field (at field supply channel)

Water Consumption (WC)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
Acronym: WC
comment: Water Consumption (WC) refers to ...

Water Conveyance Efficiency (WCE)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Water
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: WCE
comment: Water Conveyance Efficiency (WCE) expresses the percentage reduction in weed population due to weed management practices over control. It is worked out by using weed population present in control and treated plots.
definition: WCE = 100 * (WPc - WPt) / WPc where, WPc : Weed population (no./m2) in control plot WPt : Weed population (no./m2) in treatment plot

Weed Control Index (WCI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Weeds
related to: Weeds
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: WCI
comment: Weed Control Index (WCI) is worked by using same formula opf weed control efficiency replacing weed pipolation in terms of weed dry weight.
definition: WCE = 100 * ( DMPc - DMPt) / DMPc where, DMPc : Weed dry matter produced per m2 in control plot DMPt : Weed dry matter produced per m2 in treatment plot

Water Distribution Efficiency (WDE)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Water
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: WDE
comment: Water Distribution Efficiency (WDE) refers to ...

Water Equivalent Ratio (WER)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
Acronym: WER
comment: Water Equivalent Ratio (WER) is applied to show the difference between the practical range of the ion beam in water over the practical range of ion beam in a certain material m.

Wetland Fish Index (WFI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Wet land
Acronym: WFI
comment: Wetland Fish Index (WFI) refers to ...

Weed Index (WI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Weeds
related to: Weeds
Identifier: WI = 100 * ( X - Y ) / X where, X : Yield from minimun weed competition plot Y : Yield from treatment for which WI is to be worked out.
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: WI
comment: Weed Index (WI) is defined as the reduction in yield due to the presence of weeds in comparison with no weed plot. Experiuments which do not include the treatments having complete removal of weeds, the treatment indicating minimum weed competion has been used for calculating as no weed plots.

Water Limited Biomass (WLB)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Biomass
Acronym: WLB
comment: Water Limited Biomass (WLB) refers to ...

Water Limited Leaf Area Index (WLLAI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
Acronym: WLLAI
comment: Water Limited Leaf Area Index (WLLAI) refers to ...

Water Limited Storage Organ (WLSO)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
Acronym: WLSO
comment: Water Limited Storage Organ (WLSO) refers to ...

Water Limited Transpiration (WLTR)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Water
Acronym: WLTR
comment: Water Limited Transpiration (WLTR) refers to ...

Weed Management Index (WMI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Weeds
related to: Weeds
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: WMI
comment: Weed Management Index (WMI) is the ratio between yield increase due to weed management and proportionate control of weeds by the respective treatments.
definition: WMI = [Percent yield over control] / [Percent control of the pest]

Water Productivity (WP)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Water
References: Cao, Xinchun, Jie Ren, Mengyang Wu, Xiangping Guo, Zhenchang Wang, and Weiguang Wang. "Effective use rate of generalized water resources assessment and to improve agricultural water use efficiency evaluation index system." Ecological Indicators 86 (2018): 58-66.
Acronym: WP
comment: Water Productivity (WP) refers to ...

Weed Persistence Index (WPI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Weeds
related to: Weeds
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: WPI
comment: Weed Persistence Index (WPI) is used to indicate the resistance of weeds against various tested treatments and to confirm the effectiveness of the herbicidal efficiency of the selected treatments to kill the weeds.
definition: WPI = (DMT * WCC) / (DMT * WCT) where, DMC : Weed dry matter in control plot DMT : Weed dry matter in treatment plot" WCC : Weed count in control plot WCT : Weed count in treatment plot"

Water Quality Index (WQI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
References: Ochuko U, Thaddeus O, Oghenero OA, John EE. A comparative assessment of water quality index (WQI) and suitability of river Ase for domestic water supply in urban and rural communities in Southern Nigeria. Int J Human Soc Sci. 2014;4(1):234–45.
Acronym: WQI
comment: Water Quality Index (WQI) is considered as the most effective method of measuring water quality. A number of water quality parameters are included in a mathematical equation to rate water quality, determining the suitability of water for drinking.

Water Storage Efficiency (WSE)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Water
Identifier: WSE = Ws / Ww * 100 where Ws = Water stored in the root zone during the irrigation where Ww = Water needed in the root zone prior to irrigation
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: WSE
comment: Water Storage Efficiency (WSE) refers to the percentage ratio of the amount of water stored in the effective root zone to the amount of water needed to make up the soil water depleted in the crop root zone prior to irrigation.

Water Stress Index (WSI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Crop Stress
related to: Water
References: P.H. Gleick, "Basic water requirements for human activities: Meeting basic needs", Water International (IWRA), 21 (1996), pp. 83-92
References: Pfister, Stephan, Annette Koehler, and Stefanie Hellweg. "Assessing the environmental impacts of freshwater consumption in LCA." Environmental science & technology 43.11 (2009): 4098-4104.
Acronym: WSI
comment: Water Stress Index (WSI) developed by Gleick (1996) is often used to assess the scarcity of a country’s renewable water supply to meet all water requirements for human needs in terms of drinking and hygienic services.

Water Self-Sufficiency (WSS)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
related to: Water
References: M.M. Aldaya, P. Martinez-Santos, M.R. Llamas, "Incorporating the water footprint and virtual water into policy: Reflections from the Mancha Occidental Region, Spain", Water Resources Management, 24 (5) (2010), pp. 941-958
References: Zhao, Shengnan, Jianyi Lin, and Shenghui Cui. "Water resource assessment based on the water footprint for Lijiang City." International Journal of Sustainable Development & World Ecology 18.6 (2011): 492-497.
Acronym: WSS
comment: Water Self-Sufficiency (WSS) is used to evaluate the sustainability of water resource use in Lijiang City.

Water Use Efficiency (WUE)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Water
References: Briggs, L. J., and Shantz, H. L. (1913). "The water requirement of plants," in Bureau of Plant Industry Bulletin (Washington, DC: US Department of Agriculture), 282–285.
References: Devasenapathy, P. "Efficiency indices for agriculture management research", New India Publishing, 2008.
Acronym: WUE
comment: Water use efficiency (WUE) is defined as the amount of carbon assimilated as biomass or grain produced per unit of water used by the crop.

Wetland Zooplankton Index (WZI)
type: NamedIndividual
type: Agronomic Indices
is transformation of: Wet land
References: Lougheed, Vanessa L., and Patricia Chow-Fraser. "Development and use of a zooplankton index of wetland quality in the Laurentian Great Lakes basin." Ecological Applications 12.2 (2002): 474-486.
Acronym: WZI
comment: Wetland Zooplankton Index (WZI) is based on water quality and zooplankton associations with aquatic vegetation (emergent, submergent, and floating-leaf) that could be used to assess wetland quality, in particular in marshes of the Laurentian Great Lakes basin.

Albania
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Continental Climate
has climate: Dry summer climate
ISO code: ISO 3166-1:AL
agricultural land: 12,010 sq. km
area: 28,748 km²
climate: mild temperate; cool, cloudy, wet winters; hot, clear, dry summers; interior is cooler and wetter
density: 134/km²
latitude: 20.160294
longitude: 41.15476
population: 3,600,523 (2009)
comment: Albania, officially the Republic of Albania, is a country in Southeastern Europe. It is located on the Adriatic and Ionian Sea within the Mediterranean Sea, and shares land borders with Montenegro to the northwest, Kosovo to the northeast, North Macedonia to the east, Greece to the south; and maritime borders with Greece, Montenegro and Italy to the west. Tirana is its capital and largest city, followed by Durrës, Vlorë and Shkodër.

Alfalfa Mosaic
type: NamedIndividual
type: Crop Disease
type: Viral Disease
affects: Soybean
symptoms: Symptoms caused by Alfalfa mosaic virus (AMV) are patterns of bright yellow and dark green leaf tissue. Newly emerged leaves may be small with bright yellow spots and brown discoloration and plants may be stunted. Alfalfa mosaic is transmitted by more than 15 aphid species, including the soybean aphid. Alternate hosts of AMV include alfalfa, other legumes, and solanaceous (nightshade family) plants. Alfalfa mosaic may be more prevalent at field edges, especially bordering alfalfa.
comment: Alfalfa mosaic virus (AMV), also known as Lucerne mosaic virus or Potato calico virus, is a worldwide distributed phytopathogen that can lead to necrosis and yellow mosaics on a large variety of plant species, including commercially important crops. It is the only Alfamovirus of the Bromoviridae family.

Algal Leaf Spot
type: NamedIndividual
type: Crop Disease
type: Viral Disease
affects: Avocado
causes: Virus
management: Ensure that trees are properly pruned and fertilized to promote vigor; remove all weeds from around tree bases; employ a wider tree spacing to increase air circulation around the trees; badly infested trees can be treated with copper containing fungicides
symptoms: Raised, orange-red spots on both upper and lower surfaces of leaves; spots may coalesce to form irregularly shaped patches; spots may also be present on twigs and branches; when the surface of the spot is scraped away, a gray to dark necrotic crust is visible
comment: Disease affects many fruit trees in the tropics; infection is unsightly but often harmless

Algal spot
type: NamedIndividual
type: Crop Disease
affects: Citrus
life cycle: During wet weather, the algae produce spores that are spread by wind and splashing rain. The spores infect leaf tissue causing small, greenish circular spots that may age to light brown or reddish brown. The spots may appear raised and velvety. The algae will overwinter or survive other unfavorable environmental conditions in leaf spots including those on fallen leaves.
management: Integrated Pest Management Strategies 1. Encourage healthy plants with good cultural techniques. This disease is most damaging on plants that are already slow-growing or weakened. 2. Clean up fallen leaves and remove diseased leaves from the plant. Good sanitation practices will help control this disease. 3. Promote dry leaves by improving air circulation and drainage. If necessary, selectively prune overcrowded vegetation. Avoid spraying water on the leaves. 4. Use fungicidal sprays containing copper if chemical control is necessary Organic Strategies Strategies 1, 2, and 3 are strictly organic approaches. For an organic approach to Strategy 4, consult the Organic Materials Review Institute (OMRI™) for appropriate organic copper products.
symptoms: Algal leaf spot is characterized by grayish, green, brown or orange cushion-like blotches on the leaf surface. Some hosts may also have diseased twigs and branches that are girdled and stunted with reddish brown fruiting bodies. The spots are generally 1/2 inch or less in diameter although they may coalesce to form larger colonies. Leaf tissue may die beneath the spots and the leaves may yellow and drop prematurely.
comment: Algal leaf spot is a foliar disease most commonly seen in warm humid climates or in greenhouses. The causal organism is Cephaleuros virescens, a green parasitic alga whose usual hosts are plants with leathery leaves such as cotoneasters, magnolias, hollies, rhododendrons and viburnums.

Algeria
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:DZ
agricultural land: 413,830 sq. km
area: 2,381,741 km²
climate: arid to semiarid; mild, wet winters with hot, dry summers along coast; drier with cold winters and hot summers on high plateau; sirocco is a hot, dust/sand-laden wind especially common in summer
density: 14/km²
latitude: 1.65284
longitude: 28.02688
population: 33,769,669 (2008)
comment: Algeria, officially the People's Democratic Republic of Algeria, is a country in the Maghreb region of North Africa. The capital and most populous city is Algiers, located in the far north of the country on the Mediterranean coast. With an area of 2,381,741 square kilometres, Algeria is the tenth-largest country in the world, and the largest by area in the African Union and the Arab world. With an estimated population of over 44 million, it is the ninth-most populous country in Africa.

Ant Colony Clustering Algorithm (ACCA)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Clustering
References: WEN Zhi-qiang, CAI Zi-xing. "Global path planning approach based on ant colony optimization algorithm". J Cent South Univ Tech, 2006, 13(6): 707−712.
Acronym: ACCA
comment: Ant Colony Clustering Algorithm (ACCA) is a type of ant colony algorithm. It is a clustering algorithm based on the behavior of dead ant accumulation. It has a robust, parallel, fuzzy clustering ability with good convergence and positive feedback.

Adaptive Genetic Algorithm (AGA)
type: NamedIndividual
type: Mining Algorithm
inherits: Genetic Algorithm (GA)
is transformation of: Classification
is transformation of: Classifier
References: M. Srinivas, L. M. Patnaik. "Adaptive probabilities of crossover and mutation in Genetic Algorithm". IEEE Transactions On Systems, Man and Cybernetics, 1994, 24(4): 656~667
Acronym: AGA
comment: Adaptive Genetic Algorithm (AGA) is a solution to this calibration problem, which dynamically performs an on-line autoconfiguration of the GA-parameters

Agglomerative Hierarchical Clustering (AHC)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Clustering
Acronym: AHC
comment: Agglomerative Hierarchical Clustering (AHC) is a clustering (or classification) method which has the following advantages: It works from the dissimilarities between the objects to be grouped together. A type of dissimilarity can be suited to the subject studied and the nature of the data. One of the results is the dendrogram which shows the progressive grouping of the data. It is then possible to gain an idea of a suitable number of classes into which the data can be grouped. Agglomerative Hierarchical Clustering (AHC) is an iterative classification method whose principle is simple. + The process starts by calculating the dissimilarity between the N objects. + Then two objects which when clustered together minimize a given agglomeration criterion, are clustered together thus creating a class comprising these two objects. + Then the dissimilarity between this class and the N-2 other objects is calculated using the agglomeration criterion. The two objects or classes of objects whose clustering together minimizes the agglomeration criterion are then clustered together.

Artificial Neural Network (ANN)
type: NamedIndividual
type: Mining Algorithm
defined in: Keras
defined in: Machine Learning PYthon (mlpy)
defined in: Scikit-learn
defined in: TensorFlow
is transformation of: Classification
is transformation of: Classifier
is transformation of: Cluster
is transformation of: Regression
is transformation of: Regressor
Acronym: ANN
comment: Artificial Neural Network (ANN) is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. ANNs have an input layer and output layer. Between these two layers there are other hidden layers that perform the mathematical computations that help determine the decision or action the machine should take. Ultimately, these hidden layers are in place to transform the input data into something the output unit can use. The data is processed by each hidden layer and then moves on to the next based on connections that are weighted. Think of this process as an assembly line in a factory—raw materials as the input and different stops on the conveyor belt to add an element to the product equate to the hidden layers of an ANN that processes the data until you get to the output. Based on what the machine learns about the data when processed by one layer, it determines how to move it through to the next, more senior layer based on the value it receives when evaluated. Based on the complexity of the issue at hand, it can continue to process through more senior units until delivered to the output layer.

Attentive Recurrent Generative Adversarial Network (ARGAN)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Regression
is transformation of: Regressor
References: Ding, B., Long, C., Zhang, L. and Xiao, C., 2019. "Argan: Attentive recurrent generative adversarial network for shadow detection and removal". In Proceedings of the IEEE international conference on computer vision (pp. 10213-10222).
Acronym: ARGAN
comment: Attentive Recurrent Generative Adversarial Network (ARGAN) to detect and remove shadows in an image

Association Rule Networks (ARNs)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Association
is transformation of: Association Rule
References: Chawla, Sanjay. "Feature selection, association rules network and theory building." Feature Selection in Data Mining. 2010.
References: Pandey, G., Chawla, S., Poon, S., Arunasalam, B., Davis, J.G., 2009. "Association rules network: definition and applications Gaurav". Stat. Anal. Data Min. 1 (4), 179-260.
Acronym: ARNs
comment: Association Rules Network (ARN) are built from association rules and can be used as first step to understand the interaction amongst variables in a domain. ARNs are particular suitable in settings where the data available is observational (as opposed to experimental).

Adaptive Boosting (AdaBoost)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
References: Freund, Yoav; Schapire, Robert E (1997). "A decision-theoretic generalization of on-line learning and an application to boosting". Journal of Computer and System Sciences. 55: 119–139.
Acronym: AdaBoost
comment: AdaBoost is a boosting algorithm used to construct a classifier. A classifier is a data mining tool that takes data predicts the class of the data based on inputs. Boosting algorithm is an ensemble learning algorithm which runs multiple learning algorithms and combines them. AdaBoost is an ensemble learning method (also known as "meta-learning") which was initially created to increase the efficiency of classifiers. AdaBoost uses an iterative approach to learn from the mistakes of weak classifiers, and turn them into strong ones.

Apriori (Apriori)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Association
is transformation of: Association Rule
References: Rakesh Agrawal and Ramakrishnan Srikant, "Fast algorithms for mining association rules". Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages 487-499, Santiago, Chile, September 1994.
Acronym: Apriori
comment: Apriori algorithm works by learning association rules. Association rules are a data mining technique that is used for learning correlations between variables in a database. Once the association rules are learned, it is applied to a database containing a large number of transactions. Apriori algorithm is used for discovering interesting patterns and mutual relationships and hence is treated as an unsupervised learning approach. Thought the algorithm is highly efficient, it consumes a lot of memory, utilizes a lot of disk space and takes a lot of time.

Bayesian Additive Regression Trees (BART)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Regression
Acronym: BART
comment: Bayesian additive regression trees (BART) is a flexible prediction model/machine learning approach, a powerful predictive model that often outperforms alternative models at out-of-sample prediction. BART is especially well-suited to settings with unstructured predictor variables and substantial sources of unmeasured variation as is typical in the social, behavioral and health sciences.

Bacterial Foraging Optimization (BFO)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
Acronym: BFO
comment: Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria.

Binary Logistic Regression (BLR)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Regression
is transformation of: Regressor
Acronym: BLR
comment: Binary logistic regression is used to predict the odds of being a case based on the values of the independent variables (predictors). The odds are defined as the probability that a particular outcome is a case divided by the probability that it is a noninstance.

Back Propagation Neural Network (BPNN)
type: NamedIndividual
type: Mining Algorithm
inherits: Artificial Neural Network (ANN)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
References: D. Rumelhart, G. Hinton, and Williams R., "Learning representations by back-propagating errors''. Nature, vol. 323, pp. 533-536, 1986.
Acronym: BPNN
comment: BPNN is an Artificial Neural Network (ANN) based powerful technique which is used for detection of the intrusion activity.

Bayesian Regression (BR)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Regression
is transformation of: Regressor
References: Albert, J.H. & Chib, S. (1993). "Bayesian Analysis of Binary and Polychotomous Response Data". Journal of the American Statistical Association, Theory and Methods, 88(422), 669-679.
Acronym: BR
comment: Bayesian Regression model is obtained from a probability distribution, as compared to regular regression techniques where the output is just obtained from a single value of each attribute. The output, 'y' is generated from a normal distribution (where mean and variance are normalized).

Bayesian Ridge Regression (BRR)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
inherits: Bayesian Regression (BR)
is transformation of: Regression
is transformation of: Regressor
References: Hoerl AE, Kennard RW (1970). "Ridge Regression: Biased Estimation for Nonorthogonal Problems", Technometrics, 42(1), 80–86
Acronym: BRR
comment: Bayesian ridge regression (BRR) is equivalent to the genomic best linear unbiased predictor when the distribution of single-nucleotide polymorphism (SNP) effects is regarded as Gaussian.

Boosted Regression Trees (BRT)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Regression
is transformation of: Regressor
References: J. H. Friedman, "Stochastic gradient boosting", Computational Statistics & Data Analysis, vol. 38, no. 4, pp. 367–378, 2002.
Acronym: BRT
comment: Boosted Regression Tree (BRT) models are a combination of two techniques: decision tree algorithms and boosting methods. Like Random Forest models, BRTs repeatedly fit many decision trees to improve the accuracy of the model. One of the differences between these two methods is the way in which the data to build the trees is selected. Both techniques take a random subset of all data for each new tree that is built. All random subsets have the same number of data points, and are selected from the complete dataset.

Bagging (Bagging)
type: NamedIndividual
type: Mining Algorithm
Acronym: Bagging
comment: Bagging, also known as bootstrap aggregation, is the ensemble learning method that is commonly used to reduce variance within a noisy dataset.

Bi-direction Long Short-term Memory (BiLSTM)
type: NamedIndividual
type: Mining Algorithm
Acronym: BiLSTM
comment: Bidirectional LSTM, or biLSTM, is a sequence processing model that consists of two LSTMs: one taking the input in a forward direction, and the other in a backwards direction. BiLSTMs effectively increase the amount of information available to the network, improving the context available to the algorithm (e.g. knowing what words immediately follow and precede a word in a sentence).

C4.5 Algorithm (C45)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
References: Quinlan, J. R. "C4.5: Programs for Machine Learning". Morgan Kaufmann Publishers, 1993.
Acronym: C45
comment: C4.5 is one of the top data mining algorithms and was developed by Ross Quinlan. C4.5 is used to generate a classifier in the form of a decision tree from a set of data that has already been classified. Classifier here refers to a data mining tool that takes data that we need to classify and tries to predict the class of new data.

Classification and Regression Trees (CART)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
References: Breiman, Leo; Friedman, J. H.; Olshen, R. A.; Stone, C. J. (1984). "Classification and regression trees". Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software.
Acronym: CART
comment: Classification and Regression Trees or CART for short is a term introduced by Leo Breiman to refer to Decision Tree algorithms that can be used for classification or regression predictive modeling problems. CART stands for classification and regression trees. It is a decision tree learning algorithm that gives either regression or classification trees as an output. In CART, the decision tree nodes will have precisely 2 branches. Just like C4.5, CART is also a classifier. The regression or classification tree model is constructed by using labelled training dataset provided by the user. Hence it is treated as a supervised learning technique

Canopy Clustering (CC)
type: NamedIndividual
type: Mining Algorithm
defined in: Apache Mahout
is transformation of: Cluster
is transformation of: Clustering
References: McCallum, A.; Nigam, K.; and Ungar L.H. (2000) "Efficient Clustering of High Dimensional Data Sets with Application to Reference Matching", Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 169-178
Acronym: CC
comment: Canopy Clustering is a very simple, fast and surprisingly accurate method for grouping objects into clusters. All objects are represented as a point in a multidimensional feature space. The algorithm uses a fast approximate distance metric and two distance thresholds T1 > T2 for processing. The basic algorithm is to begin with a set of points and remove one at random. Create a Canopy containing this point and iterate through the remainder of the point set. At each point, if its distance from the first point is < T1, then add the point to the cluster. If, in addition, the distance is < T2, then remove the point from the set. This way points that are very close to the original will avoid all further processing. The algorithm loops until the initial set is empty, accumulating a set of Canopies, each containing one or more points. A given point may occur in more than one Canopy. Canopy Clustering is often used as an initial step in more rigorous clustering techniques, such as K-Means Clustering . By starting with an initial clustering the number of more expensive distance measurements can be significantly reduced by ignoring points outside of the initial canopies.

Color Content Fidelity Ratio (CCFR)
type: NamedIndividual
type: Mining Algorithm
related to: Imagery
Acronym: CCFR
comment: Color Content Fidelity Ratio (CCFR) is an algorithm, which is effective to measure the contrast loss after color-togray conversion.

Color Contrast Preservation Ratio (CCPR)
type: NamedIndividual
type: Mining Algorithm
related to: Imagery
Acronym: CCPR
comment: Color Contrast Preservation Ratio (CCPR) is an algorithm, which is based on the phenomenon of color perception—that is, when the Euclidean distance of two colors in the Lab space δ is smaller than a certain value τ , the difference is generally invisible in human visual system. The task of contrast-preserving decolorization is therefore to only maintain color contrast that is perceivable by human.

Cokriging (CK)
type: NamedIndividual
type: Mining Algorithm
References: T. Tajgardan, S. Ayoubi, S. Shataee, K.L. Sahrawat, I. Gorgan, "Soil surface salinity prediction using ASTER data: comparing statistical and geostatistical models", J Australian Journal of Basic Applied Sciences, 4 (2010), pp. 457-467
Acronym: CK
comment: Cokriging (CK) is an extension of OK from a single spatial random variable to two or more spatially correlated random variables.

Clustering Large Applications based on RANdomized Search (CLARANS)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Cluster
is transformation of: Clustering
References: Ng and J. Han (2002). "CLARANS: A method for clustering objects for spatial data mining". IEEE Transactions on Knowledge and Data Engineering. http://dx.doi.org/10.1109/TKDE.2002.1033770).
Acronym: CLARANS
comment: CLARANS is a modification of the partitioning around medoids (PAM) algorithm pam. Where the PAM algorithm is estimating all distances between SNPs and the respective medoids, CLARANS is searching a random subset of the SNPs. This is independently repeated several times and the result which minimises the average distance the most is reported. This produces results close to those of the PAM algorithm (Ng and Han 2002), though the number of runs and the subset size have to be arbitrarily chosen by the user. The algorithm has two advantages: (i) the number of distance comparisons is dramatically reduced; and (ii) parallelizing is straightforward.

Classical Least Square (CLS)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Regression
is transformation of: Regressor
Acronym: CLS
comment: Classical least squares (CLS) regression is a popular multivariate statistical method used frequently for quantitative analysis. Classical least squares provides the best unbiased estimator for uncorrelated residual errors with zero mean and equal variance.

Convolutional Neural Network (CNN)
type: NamedIndividual
type: Mining Algorithm
defined in: Keras
defined in: TensorFlow
inherits: Artificial Neural Network (ANN)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
References: LeCun, Yann; Bengio, Yoshua (1995). "Convolutional networks for images, speech, and time series". In Arbib, Michael A. (ed.). The handbook of brain theory and neural networks (Second ed.). The MIT press. pp. 276–278.
Acronym: CNN
comment: A convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. They are also known as shift invariant or space invariant artificial neural networks (SIANN), based on their shared-weights architecture and translation invariance characteristics.

Cokriging (COK)
type: NamedIndividual
type: Mining Algorithm
inherits: Ordinary Kriging (OK)
Acronym: COK
comment: Cokriging (COK) is an extension of ordinary kriging and uses one or more secondary variables, or covariables, (Z2) which are spatially correlated with the primary variable (Z1).

Counter-propagation Artificial Neural Network (CPANN)
type: NamedIndividual
type: Mining Algorithm
defined in: Keras
defined in: TensorFlow
inherits: Artificial Neural Network (ANN)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: CPANN

Condition Random Fields (CRF)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
References: Lafferty, J., McCallum, A., Pereira, F. (2001). "Conditional random fields: Probabilistic models for segmenting and labeling sequence data". Proc. 18th International Conf. on Machine Learning. Morgan Kaufmann. pp. 282–289.
Acronym: CRF
comment: Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighboring" samples, a CRF can take context into account. To do so, the prediction is modeled as a graphical model, which implements dependencies between the predictions. What kind of graph is used depends on the application. For example, in natural language processing, linear chain CRFs are popular, which implement sequential dependencies in the predictions. In image processing the graph typically connects locations to nearby and/or similar locations to enforce that they receive similar predictions.

Computer Vision (CV)
type: NamedIndividual
type: Mining Algorithm

Discriminant Analysis (DA)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
is transformation of: Data Selection
Acronym: DA
comment: Discriminant analysis (DA) is a multivariate technique used to separate two or more groups of observations (individuals) based on variables measured on each experimental unit (sample) and find the contribution of each variable in separating the groups.

Density-based Spatial Clustering Of Applications With Noise (DBSCAN)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Classifier
is transformation of: Clustering
Acronym: DBSCAN
comment: DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Finds core samples of high density and expands clusters from them. Good for data which contains clusters of similar density. DBSCAN is the acronym for Density-Based Spatial Clustering of Applications with Noise. It is a clustering algorithm that isolates different density regions by forming clusters. For a given set of points, it groups the points which are closely packed. The algorithm has two important features: 1. distance 2. the minimum number of points required to form a dense region
definition: clusterers.DBSCAN

Deep Convolutional Generative Adversarial Network (DCGAN)
type: NamedIndividual
type: Mining Algorithm
Acronym: DCGAN

Deep Convolutional Neural Network (DCNN)
type: NamedIndividual
type: Mining Algorithm
defined in: Keras
defined in: TensorFlow
inherits: Artificial Neural Network (ANN)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: DCNN
comment: Deep Convolutional Neural Network (DCNN) is a convolutional-pooling neural network that is a relatively deep learning neural network.

Deep Learning (DL)
type: NamedIndividual
type: Mining Algorithm

Dynamic Matrix Control (DMC)
type: NamedIndividual
type: Mining Algorithm
defined in: Matlab
Acronym: DMC

Deep Neural Network (DNN)
type: NamedIndividual
type: Mining Algorithm
Acronym: DNN
comment: Deep Neural Network (DNN) is a group of neural network that is a relatively deep learning neural network.

Decision Tree (DT)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Association
is transformation of: Association Rule
is transformation of: Classification
is transformation of: Classifier
is transformation of: Cluster
is transformation of: Clustering
Acronym: DT
comment: Decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning.

Decision Tree Regressor (DTR)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
inherits: Decision Tree (DT)
is transformation of: Regression
is transformation of: Regressor
Acronym: DTR
comment: Decision Tree algorithm has become one of the most used machine learning algorithm both in competitions like Kaggle as well as in business environment. Decision Tree can be used both in classification and regression problem.This article present the Decision Tree Regression Algorithm along with some advanced topics.

Extreme Learning Machine (ELM)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
References: Huang GB, Zhu QY, Siew CK. "Extreme learning machine: theory and applications". Neurocomputing 2006;70(1-3):489-501.
Acronym: ELM

Expectation-Maximization (EM)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classifier
is transformation of: Clustering
Acronym: EM
comment: Expectation-Maximization (EM) is used as a clustering algorithm, just like the k-means algorithm for knowledge discovery. EM algorithm work in iterations to optimize the chances of seeing observed data. Next, it estimates the parameters of the statistical model with unobserved variables, thereby generating some observed data. Expectation-Maximization (EM) algorithm is again unsupervised learning since we are using it without providing any labelled class information

Elastic Net (EN)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Regression
Acronym: EN
comment: Elastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.

Eclat (Eclat)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Association
is transformation of: Association Rule
Acronym: Eclat
comment: associations.Eclat

Ensemble Kalman Filter (EnKF)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Data Selection
References: Evensen, G. "Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics". J. Geophys. Res. Space Phys. 1994, 99, 10143.
Acronym: EnKF
comment: Ensemble Kalman Filter (EnKF) is an optimal recursive data assimilation method introduced by Evensen.

Factor Analysis (FA)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Association
is transformation of: Association Rule
is transformation of: Data Selection
Acronym: FA
comment: Factor Analysis (FA) is a simple linear generative model with Gaussian latent variables. The observations are assumed to be caused by a linear transformation of lower dimensional latent factors and added Gaussian noise. Without loss of generality the factors are distributed according to a Gaussian with zero mean and unit covariance. The noise is also zero mean and has an arbitrary diagonal covariance matrix. If we would restrict the model further, by assuming that the Gaussian noise is even isotropic (all diagonal entries are the same) we would obtain PPCA. FactorAnalysis performs a maximum likelihood estimate of the so-called loading matrix, the transformation of the latent variables to the observed ones, using SVD based approach.
comment: associations.FilteredAssociator
label: Filtered Associator (FA)

Filtered Clusterer (FC)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Cluster
is transformation of: Clustering
Acronym: FC

Fuzzy C-means (FCM)
type: NamedIndividual
type: Mining Algorithm
inherits: C4.5 Algorithm (C45)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Cluster
is transformation of: Clustering
References: Bezdek, J.C., 1981. "Pattern Recognition with Fuzzy Objective Function Algorithms". Plenum, New York.
Acronym: FCM
comment: Fuzzy K-Means (also referred to as soft clustering or soft k-means) is a form of clustering in which each data point can belong to more than one cluster. Clustering or cluster analysis involves assigning data points to clusters such that items in the same cluster are as similar as possible, while items belonging to different clusters are as dissimilar as possible. Clusters are identified via similarity measures. These similarity measures include distance, connectivity, and intensity. Different similarity measures may be chosen based on the data or the application.

Fuzzy Cognitive Maps (FCMs)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
References: Kosko, B. (1986). "Fuzzy cognitive maps". International journal of man-machine studies, 24(1), 65-75.
Acronym: FCMs
comment: Fuzzy cognitive maps (FCMs) are fuzzy-graph structures for representing causal reasoning. Their fuzziness allows hazy degrees of causality between hazy causal objects (concepts). Their graph structure allows systematic causal propagation, in particular forward and backward chaining, and it allows knowledge bases to be grown by connecting different FCMs.

Fully Convolutional Network (FCN)
type: NamedIndividual
type: Mining Algorithm
inherits: Artificial Neural Network (ANN)
inherits: Convolutional Neural Network (CNN)
is transformation of: Classification
is transformation of: Classifier
References: Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
Acronym: FCN
comment: Fully convolutional network (FCN) trained end-to-end, pixels-to-pixels on semantic segmentation exceeds the state-of-the-art without further machinery.

Fuzzy Counter Propagation Neural Network (FCPN)
type: NamedIndividual
type: Mining Algorithm
inherits: Artificial Neural Network (ANN)
inherits: Counter-propagation Artificial Neural Network (CPANN)
is transformation of: Classification
References: Sakhre, Vandana, Sanjeev Jain, Vilas S. Sapkal, and Dev P. Agarwal. "Fuzzy counter propagation neural network control for a class of nonlinear dynamical systems." Computational intelligence and neuroscience 2015 (2015).
Acronym: FCPN
comment: Fuzzy Counter Propagation Neural Network (FCPN) controller design is developed, for a class of nonlinear dynamical systems.

Finite Element Analysis (FEA)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
References: Strang, Gilbert; Fix, George (1973). "An Analysis of The Finite Element Method". Prentice Hall. ISBN 978-0-13-032946-2.
Acronym: FEA
comment: Finite Element Analysis (FEA) is the simulation of any given physical phenomenon using the numerical technique called Finite Element Method (FEM). Engineers use FEA software to reduce the number of physical prototypes and experiments and optimize components in their design phase to develop better products, faster while saving on expenses.

Finite Element Method (FEM)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Regression
References: Strang, Gilbert; Fix, George (1973). \"An Analysis of The Finite Element Method\". Prentice Hall. ISBN 978-0-13-032946-2.
Acronym: FEM
comment: Finite element method (FEM) is a widely used method for numerically solving differential equations arising in engineering and mathematical modeling. Typical problem areas of interest include the traditional fields of structural analysis, heat transfer, fluid flow, mass transport, and electromagnetic potential. The FEM is a particular numerical method for solving partial differential equations in two or three space variables (i.e., some boundary value problems). To solve a problem, the FEM subdivides a large system into smaller, simpler parts that are called finite elements. This is achieved by a particular space discretization in the space dimensions, which is implemented by the construction of a mesh of the object: the numerical domain for the solution, which has a finite number of points. The finite element method formulation of a boundary value problem finally results in a system of algebraic equations. The method approximates the unknown function over the domain. The simple equations that model these finite elements are then assembled into a larger system of equations that models the entire problem. The FEM then uses variational methods from the calculus of variations to approximate a solution by minimizing an associated error function.

Factorial Kriging Analysis (FKA)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
Acronym: FKA

Fisher's linear discriminant analysis (FLDA)
type: NamedIndividual
type: Mining Algorithm
Acronym: FLDA

First Derivative And Mean Centering Iteration Algorithm (FMCIA)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
Acronym: FMCIA
comment: First Derivative And Mean Centering Iteration Algorithm (FMCIA)

FPGrowth (FPGrowth)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Association
is transformation of: Association Rule
References: J. Han, H. Pei, and Y. Yin. "Mining Frequent Patterns without Candidate Generation". In: Proc. Conf. on the Management of Data (SIGMOD’00, Dallas, TX). ACM Press, New York, NY, USA 2000.
Acronym: FPGrowth
comment: FP-growth is an improved version of the Apriori Algorithm which is widely used for frequent pattern mining(AKA Association Rule Mining). It is used as an analytical process that finds frequent patterns or associations from data sets. For example, grocery store transaction data might have a frequent pattern that people usually buy chips and beer together. The Apriori Algorithm produces frequent patterns by generating itemsets and discovering the most frequent itemset over a threshold "minimal support count".

Fuzzy Support Vector Machine (FSVM)
type: NamedIndividual
type: Mining Algorithm
inherits: Support Vector Machine (SVM)
is transformation of: Classification
is transformation of: Classifier
References: Lin, C., & Wang, S. (2004). "Training algorithms for fuzzy support vector machines with noisy data". Pattern Recognition Letters, 25, 1647–1656.
Acronym: FSVM

Foreground target detection algorithm (FTDA)
type: NamedIndividual
type: Mining Algorithm
Acronym: FTDA
comment: Foreground target detection algorithm (FTDA) is a fundamental preprocessing step in computer vision and video processing. FTDA means to detect moving targets from the video sequence, achieve foreground pixel segmentation and location, provide robust and stable preprocessing results for subsequent advanced tasks such as target tracking, behavior identity, and gesture recognition.

Functional Unfold Principal Component Analysis (FUPCA)
type: NamedIndividual
type: Mining Algorithm
inherits: Principal Component Analysis (PCA)
is transformation of: Data Selection
Acronym: FUPCA

Fast Algorithm for Independent Component Analysis (FastICA)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
inherits: Principal Component Analysis (PCA)
is transformation of: Data Selection
Acronym: FastICA
comment: Independent component analysis separates a multivariate signal into additive subcomponents that are maximally independent. It is implemented in scikit-learn using the Fast ICA algorithm. Typically, ICA is not used for reducing dimensionality but for separating superimposed signals. Since the ICA model does not include a noise term, for the model to be correct, whitening must be applied. This can be done internally using the whiten argument or manually using one of the PCA variants.

Faster Regional Convolutional Neural Network (FasterR-CNN)
type: NamedIndividual
type: Mining Algorithm
inherits: Artificial Neural Network (ANN)
inherits: Convolutional Neural Network (CNN)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
References: Girshick, Ross. "Fast R-CNN." Proceedings of the IEEE International Conference on Computer Vision. 2015
Acronym: FasterR-CNN

Genetic-Wavelet Neural Network Reconstruction Algorithm (G-WNNRA)
type: NamedIndividual
type: Mining Algorithm
inherits: Artificial Neural Network (ANN)
is transformation of: Classification
References: Zhao, Yun, Yong He, and Xing Xu. "A novel algorithm for damage recognition on pest-infested oilseed rape leaves." Computers and electronics in agriculture 89 (2012): 41-50.
Acronym: G-WNNRA

Genetic Algorithm (GA)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: GA
comment: Genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection.

Generative Adversarial Networks (GAN)
type: NamedIndividual
type: Mining Algorithm

Generative Adversarial Networks (GANs)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
References: I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengiom "Generative adversarial nets", Advances in neural information processing systems (2014), pp. 2672-2680
Acronym: GANs
comment: GANs (Goodfellow et al., 2014) consist of two separate neural networks: a generator G that takes a random noise vector z and generates synthetic data G(z); a discriminator D that takes an input x or G(z) and output a probability Dxor D(Gz) to indicate whether the input derived from the synthetic distribution of G(z) or from the true data distribution.

Gradient Boosting Decision Tree (GBDT)
type: NamedIndividual
type: Mining Algorithm
inherits: Decision Tree (DT)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: GBDT
comment: Gradient boosting Decision Tree is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. It builds the model in a stage-wise fashion like other boosting methods do, and it generalizes them by allowing optimization of an arbitrary differentiable loss function.

Gradient Boosting Regression Tree (GBRT)
type: NamedIndividual
type: Mining Algorithm
Acronym: GBRT
comment: Gradient Boosted Regression Trees (GBRT) or shorter Gradient Boosting is a flexible non-parametric statistical learning technique for classification and regression.

Gini Impurity (GI)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
Acronym: GI
comment: Gini Impurity is a measurement of the likelihood of an incorrect classification of a new instance of a random variable, if that new instance were randomly classified according to the distribution of class labels from the data set.

Gray level co-occurrence matrix (GLCM)
type: NamedIndividual
type: Mining Algorithm
Acronym: GLCM
comment: Grey Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR).

Gaussian Mixture Model (GMM)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Cluster
is transformation of: Clustering
Acronym: GMM
comment: Gaussian mixture models are a probabilistic model for representing normally distributed subpopulations within an overall population. Mixture models in general don't require knowing which subpopulation a data point belongs to, allowing the model to learn the subpopulations automatically. Since subpopulation assignment is not known, this constitutes a form of unsupervised learning.
alternative label: Gaussian Mixture (Gaussian)

Genetic Neural Network (GNN)
type: NamedIndividual
type: Mining Algorithm
inherits: Artificial Neural Network (ANN)
is transformation of: Classification
is transformation of: Regression
Acronym: GNN
comment: A genetic neural network (GNN) method is provided for efficient determination of quantitative structure property relationships. See the references given below for a description of the GNN and its application.

Generalized Predictive Control
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: GPC

Gaussian Process Regression (GPR)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Regression
is transformation of: Regressor
Acronym: GPR

General Regression Neural Network (GRNN)
type: NamedIndividual
type: Mining Algorithm
inherits: Artificial Neural Network (ANN)
is transformation of: Regression
is transformation of: Regressor
References: Specht, D. F. (2002-08-06). "A general regression neural network". IEEE Transactions on Neural Networks. 2 (6): 568–576. doi:10.1109/72.97934
Acronym: GRNN
comment: Generalized regression neural network (GRNN) is a variation to radial basis neural networks. GRNN was suggested by D.F. Specht in 1991.

Geologically Weighted Regression (GWR)
type: NamedIndividual
type: Mining Algorithm
Acronym: GWR

Hierarchical Clusterer (HC)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
defined in: Weka
is transformation of: Cluster
is transformation of: Clustering
Acronym: HC
comment: Hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters.

Hidden Markov Model (HMM)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: HMM
comment: Hidden Markov models are generative models, in which the joint distribution of observations and hidden states, or equivalently both the prior distribution of hidden states (the transition probabilities) and conditional distribution of observations given states (the emission probabilities), is modeled.

Histogram of Gradients (HOG)
type: NamedIndividual
type: Mining Algorithm
Acronym: HOG

Histograms of Oriented Optical Flow (HOOF)
type: NamedIndividual
type: Mining Algorithm
References: R. Chaudhry, A. Ravichandran, G. Hager, R. Vidal, "Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions", IEEE Int. Conf. Comput. Vis. (2009), pp. 1932-1939
Acronym: HOOF

ID3 Algorithm (ID3)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Regression
Acronym: ID3
comment: ID3 algorithm is a simple decision tree learning algorithm developed by Ross Quinlan. ID3 is the precursor to the C4.5 algorithm, and is typically used in the machine learning and natural language processing domains.

Information Gain (IG)
type: NamedIndividual
type: Mining Algorithm
defined in: Weka
is transformation of: Data Selection
Acronym: IG
comment: Information Gain, like Gini Impurity, is a metric used to train Decision Trees. Specifically, these metrics measure the quality of a split. Information Gain is calculated for a split by subtracting the weighted entropies of each branch from the original entropy. When training a Decision Tree using these metrics, the best split is chosen by maximizing Information Gain.

Indicator Kriging (IK)
type: NamedIndividual
type: Mining Algorithm
Acronym: IK
comment: Indicator Kriging (IK) is a non-parametric geostatistical method for estimating the probability that the value of an attribute is not greater than a specific threshold, , at a given location U.
comment: P. Goovaerts, R. Webster, J.-P. Dubois, "Assessing the risk of soil contamination in the Swiss Jura using indicator geostatistics", Environ. Ecol. Stat., 4 (1997), pp. 49-64

Incremental Principal Components Analysis (IPCA)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
inherits: Principal Component Analysis (PCA)
is transformation of: Data Selection
Acronym: IPCA
comment: Incremental principal components analysis (IPCA). Linear dimensionality reduction using Singular Value Decomposition of the data, keeping only the most significant singular vectors to project the data to a lower dimensional space. The input data is centered but not scaled for each feature before applying the SVD. Depending on the size of the input data, this algorithm can be much more memory efficient than a PCA, and allows sparse input. This algorithm has constant memory complexity, on the order of batch_size * n_features, enabling use of np.memmap files without loading the entire file into memory. For sparse matrices, the input is converted to dense in batches (in order to be able to subtract the mean) which avoids storing the entire dense matrix at any one time. The computational overhead of each SVD is O(batch_size * n_features ** 2), but only 2 * batch_size samples remain in memory at a time. There will be n_samples / batch_size SVD computations to get the principal components, versus 1 large SVD of complexity O(n_samples * n_features ** 2) for PCA.

Improved Particle Swarm Optimization (IPSO)
type: NamedIndividual
type: Mining Algorithm
Acronym: IPSO
comment: Improved Particle Swarm Optimization (IPSO) algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems.

Iterative Self-Organizing Data Analysis (ISODATA)
type: NamedIndividual
type: Mining Algorithm
Acronym: ISODATA

J48
type: NamedIndividual
type: Mining Algorithm
Acronym: J48

Kernel-based Extreme Learning Machine (KELM)
type: NamedIndividual
type: Mining Algorithm
References: Huang GB, Zhu QY, Siew CK. "Extreme learning machine: theory and applications". Neurocomputing 2006;70(1-3):489-501.
Acronym: KELM

Kernel Fisher Discriminant Analysis (KFDA)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
inherits: Discriminant Analysis (DA)
is transformation of: Data Selection
Acronym: KFDA
comment: Kernel Fisher Discriminant Analysis (KFDA), also known as generalized discriminant analysis and kernel discriminant analysis, is a kernelized version of linear discriminant analysis (LDA). It is named after Ronald Fisher. Using the kernel trick, LDA is implicitly performed in a new feature space, which allows non-linear mappings to be learned.

K-Means (KMeans)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
defined in: Scikit-learn
defined in: Weka
is transformation of: Cluster
is transformation of: Clustering
Acronym: KMeans
comment: KMeans algorithm clusters data by trying to separate samples in n groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares (see below). This algorithm requires the number of clusters to be specified. It scales well to large number of samples and has been used across a large range of application areas in many different fields.
definition: sklearn.cluster.KMeans

K-Nearest Neighbours (KNN)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
defined in: Scikit-learn
defined in: Weka
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: KNN
comment: K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases by a majority vote of its k neighbors. The case being assigned to the class is most common amongst its K nearest neighbors measured by a distance function. These distance functions can be Euclidean, Manhattan, Minkowski and Hamming distance. First three functions are used for continuous function and fourth one (Hamming) for categorical variables. If K = 1, then the case is simply assigned to the class of its nearest neighbor. At times, choosing the value for K can be a challenge while performing KNN modeling.
definition: Classifier: classifiers.lazy.IBk

Kernel Principal Component Analysis (KPCA)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
defined in: Scikit-learn
inherits: Principal Component Analysis (PCA)
is transformation of: Data Selection
Acronym: KPCA
comment: KernelPCA (KPCA) is an extension of PCA which achieves non-linear dimensionality reduction through the use of kernels (see Pairwise metrics, Affinities and Kernels). It has many applications including denoising, compression and structured prediction (kernel dependency estimation). KernelPCA supports both transform and inverse_transform.

Kernel Ridge Regression (KRR)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: KRR
comment: Kernel Ridge Regression (KRR) combines ridge regression (linear least squares with l2-norm regularization) with the kernel trick. It thus learns a linear function in the space induced by the respective kernel and the data. For non-linear kernels, this corresponds to a non-linear function in the original space. The form of the model learned by KRR is identical to support vector regression (SVR). However, different loss functions are used: KRR uses squared error loss while support vector regression uses epsilon-insensitive loss, both combined with l2 regularization. In contrast to SVR, fitting a KRR model can be done in closed-form and is typically faster for medium-sized datasets. On the other hand, the learned model is non-sparse and thus slower than SVR, which learns a sparse model for epsilon > 0, at prediction-time.

Last Angle Regression (LAR)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
is transformation of: Regression
is transformation of: Regressor
References: Efron, Bradley; Hastie, Trevor; Johnstone, Iain; Tibshirani, Robert (2004). "Least Angle Regression". Annals of Statistics. 32 (2): pp. 407–499. arXiv:math/0406456. doi:10.1214/009053604000000067.
Acronym: LARS
comment: Least-angle regression (LARS) is an algorithm for fitting linear regression models to high-dimensional data, developed by Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani.

Least Absolute Shrinkage and Selection Operator (LASSO)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Classification
is transformation of: Classifier
Acronym: LASSO
comment: Least Absolute Shrinkage and Selection Operator (LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the statistical model it produces. It was originally introduced in geophysics literature in 1986, and later independently rediscovered and popularized in 1996 by Robert Tibshirani, who coined the term and provided further insights into the observed performance. LASSO was originally formulated for linear regression models and this simple case reveals a substantial amount about the behavior of the estimator, including its relationship to ridge regression and best subset selection and the connections between lasso coefficient estimates and so-called soft thresholding. It also reveals that (like standard linear regression) the coefficient estimates do not need to be unique if covariates are collinear.

Linear Discriminant Analysis (LDA)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
defined in: Scikit-learn
is transformation of: Classification
is transformation of: Classifier
is transformation of: Data Selection
Acronym: LDA
comment: Linear Discriminant analysis (DA) is a multivariate technique used to separate two or more groups of observations (individuals) based on variables measured on each experimental unit (sample) and find the contribution of each variable in separating the groups.

Linear-Quadratic Regulator (LQR)
type: NamedIndividual
type: Mining Algorithm
defined in: Matlab
Acronym: LQR
comment: Linear quadratic regulator (LQR) is a well-known design technique that provides practical feedback gains.

Linear Regression (LR)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
defined in: Scikit-learn
is transformation of: Regression
is transformation of: Regressor
Acronym: LR
comment: Linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression.

Least Squares Generative Adversarial Networks (LSGAN)
type: NamedIndividual
type: Mining Algorithm
Acronym: LSGAN

Linear spectral mixture analysis (LSMA)
type: NamedIndividual
type: Mining Algorithm
Acronym: LSMA
comment: Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials present in an image pixel.

Least Squares Support Vector Machine (LSSVM)
type: NamedIndividual
type: Mining Algorithm
inherits: Support Vector Machine (SVM)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: LSSVM
comment: Least-squares support-vector machines (LS-SVM) are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis. In this version one finds the solution by solving a set of linear equations instead of a convex quadratic programming (QP) problem for classical SVMs. Least-squares SVM classifiers were proposed by Suykens and Vandewalle. LS-SVMs are a class of kernel-based learning methods.

Long Short-term Memory (LSTM)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: LSTM
comment: Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can not only process single data points (such as images), but also entire sequences of data (such as speech or video).

M5Tree (M5Tree)
type: NamedIndividual
type: Mining Algorithm
References: J.R. Quinlan, "Learning with continuous classes", Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence, Hobart, Australia, 16–18 November, World Scientific, Singapore (1992), pp. 343-348
Acronym: M5Tree
comment: M5Tree is an expansion of regression trees in the sense that they relate leaves with multivariate linear models. M5Tree is a strategy for managing continuous class problems that give a structural presentation of the data and a piecewise linear fit of the class. M5Tree has a conventional decision tree structure but utilize linear function at the leaves rather than discrete class names.

Multivariate Adaptive Regression Splines (MARS)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Regression
is transformation of: Regressor
References: Friedman, J. H. (1991). "Multivariate Adaptive Regression Splines". The Annals of Statistics. 19 (1): 1–67. CiteSeerX 10.1.1.382.970. doi:10.1214/aos/1176347963
Acronym: MARS
comment: Multivariate Adaptive Regression Splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models nonlinearities and interactions between variables.

Mahalanobis Distance (MD)
type: NamedIndividual
type: Mining Algorithm
Acronym: MD
comment: Mahalanobis distance (MD) is the distance between two points in multivariate space. In a regular Euclidean space, variables (e.g. x, y, z) are represented by axes drawn at right angles to each other; The distance between any two points can be measured with a ruler.

Maximum Entropy (ME)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
Acronym: ME
comment: Maximum Entropy states that the probability distribution which best represents the current state of knowledge is the one with largest entropy, in the context of precisely stated prior data (such as a proposition that expresses testable information).

Modified Genetic Algorithm (MGA)
type: NamedIndividual
type: Mining Algorithm
inherits: Genetic Algorithm (GA)
is transformation of: Regression
Acronym: MGA
comment: This algorithm follows the same principals and utilizes the same sigmoidal models for estimating YP0 and YPN as described by Solie et al. (2012)

Machine Learning (ML)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Association
is transformation of: Association Rule
is transformation of: Classification
is transformation of: Classifier
is transformation of: Cluster
is transformation of: Clustering
is transformation of: Regression
is transformation of: Regressor
Acronym: ML

Mule Lightbar (MLB)
type: NamedIndividual
type: Mining Algorithm
Acronym: MLB

Maximum Likelihood Classification (MLC)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
References: J.N. Nilsson, "Learning Machines: Foundations of Trainable Pattern-Classifying Systems", McGraw-Hill Series in Systems Science, McGraw-Hill Book Company, New York (1965)
Acronym: MLC
comment: Maximum likelihood classification (MLC) is a method for determining a known class of distributions as the maximum for a given statistic. The root of MLC can be traced back to electrical engineering [5]. An assumption of normality is made for the training samples.

Multi-layer Perceptron (MLP)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
defined in: Scikit-learn
inherits: Artificial Neural Network (ANN)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: MLP
comment: A multilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN). The term MLP is used ambiguously, sometimes loosely to any feedforward ANN, sometimes strictly to refer to networks composed of multiple layers of perceptrons (with threshold activation);
definition: sklearn.neural_network.MLPClassifier

Multinomial Logistic Regression (MLR)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
inherits: Linear Regression (LR)
is transformation of: Regression
is transformation of: Regressor
Acronym: MLR
comment: Multiple linear regression (MLR), also known simply as multiple regression, is a statistical technique that uses several explanatory variables to predict the outcome of a response variable. The goal of multiple linear regression (MLR) is to model the linear relationship between the explanatory (independent) variables and response (dependent) variable.

Model Predictive Control (MPC)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
References: Arnold, Michele, and Göran Andersson. "Model predictive control of energy storage including uncertain forecasts." Power Systems Computation Conference (PSCC), Stockholm, Sweden. Vol. 23. 2011.
Acronym: MPC

Modified Partial Least Squares (MPLS)
type: NamedIndividual
type: Mining Algorithm
inherits: Partial Least Squares (PLS)
is transformation of: Classification
is transformation of: Classifier
Acronym: MPLS
comment: MPLS is an improvement that have been made to the traditional partial least squares (PLS) regression. The MPLS procedure copes more effectively with non-analyte interference in multicomponent determinations and provides an estimate of the interference spectrum. The interference spectrum can be used to partially or quantitatively correct the measured spectrum before determination of analyte concentrations. Hence the accuracy of analysis is improved.

Markov Random Fields (MRF)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
Acronym: MRF
comment: Markov Random Field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. In other words, a random field is said to be a Markov random field if it satisfies Markov properties. A Markov network or MRF is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed and acyclic, whereas Markov networks are undirected and may be cyclic. Thus, a Markov network can represent certain dependencies that a Bayesian network cannot; on the other hand, it can't represent certain dependencies that a Bayesian network can. The underlying graph of a Markov random field may be finite or infinite.

Multi-resolution Segmentation (MRS)
type: NamedIndividual
type: Mining Algorithm
applied to: Satellite Imagery
applied to: Spectral Image
is transformation of: Classification
is transformation of: Classifier
References: M. Baatz, A. Schape, "Multiresolution Segmentation: an optimization approach for high quality multi-scale image segmentation", Angewandte Geographische Informationsverarbeitung XII. Beitrage zum AGIT-Symposium Salzburg 2000, Karlsruhe, Herbert Wichmann Verlag (2000), pp. 12-23
Acronym: MRS
comment: Multiresolution Segmentation (MRS) is the process of dividing up your images into segments based on its spectral properties.

Mean Shift (MS)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Classifier
is transformation of: Clustering
Acronym: MS
comment: Mean shift is a hill climbing algorithm which involves shifting this kernel iteratively to a higher density region until convergence. Every shift is defined by a mean shift vector. At every iteration the kernel is shifted to the centroid or the mean of the points within it.
comment: clusterers.MeanShift

Mixture Tuned Matched Filtering (MTMF)
type: NamedIndividual
type: Mining Algorithm
Acronym: MTMF

Multi-threshold segmentation (MTS)
type: NamedIndividual
type: Mining Algorithm
applied to: Satellite Imagery
applied to: Spectral Image
is transformation of: Classification
is transformation of: Classifier
References: A.P. Carleer, O. Debeir, E. Wolff, "Assessment of very high spatial resolution satellite image segmentations", Photogramm. Eng. Rem. Sens., 71 (11) (2005), pp. 1285-1294
Acronym: MTS
comment: Multiple thresholding Segmentation is used to distinct objects, subdivided from an image by multiple thresholds, and each thresholds should be appropriate for the corresponding segmentation

Naive Bayes (NB)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: NB
comment: Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naiveâ€ assumption of conditional independence between every pair of features given the value of the class variable. Naive Bayes models can be used to tackle large scale classification problems for which the full training set might not fit in memory. To handle this case, modified methods expose a partial_fit method that can be used incrementally as done with other classifiers as demonstrated in Out-of-core classification of text documents. All naive Bayes classifiers support sample weighting. + MultinomialNB, + BernoulliNB, + GaussianNB + CategoricalNB

Normalized Classification Entropy (NCE)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
Acronym: NCE
comment: Normalized classification entropy (NCE) is used when deciding how many clusters are most appropriate for creating management zones, which can help the user obtain management zones simply and quickly (BEZDEK, 1981). By minimizing the value for NCE and FPI, the optimum number of clusters can be found.

Non-Negative Matrix Factorization (NMF)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Data Selection
Acronym: NMF
comment: Non-Negative Matrix Factorization (NMF) finds two non-negative matrices (W, H) whose product approximates the non- negative matrix X. This factorization can be used for example for dimensionality reduction, source separation or topic extraction.

Nonlinear Model Predictive Control (NMPC)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
Acronym: NMPC
comment: Nonlinear Model Predictive Control (NMPC) is a variant of model predictive control (MPC) that is characterized by the use of nonlinear system models in the prediction. As in linear MPC, NMPC requires the iterative solution of optimal control problems on a finite prediction horizon.

Object-based Image Analysis (OBIA)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
Acronym: OBIA
comment: Object-based image analysis (OBIA) is one of several approaches developed to overcome the limitations of the pixel-based approaches. It incorporates spectral, textural and contextual information to identify thematic classes in an image. The first step in OBIA is to segment the image into homogeneous objects. The term object here stands for a contiguous cluster of pixels. Segmentation is based on pre-defined parameters like compactness, shape, and scale, derived from real-world knowledge of the features that one wants to identify (Mason et al. 1988). In crop mapping, for instance, this will require understanding of size and shape of farm fields in the area of interest. Over- and under-segmentation are obvious threats to these approaches, and need to be addressed, and not all semantically meaningful entities may be identified. In a second step, each object (segment) is classified on the basis of one or more statistical properties of the contained pixels. This means that all pixels within a segment are assigned to one class, eliminating the within-field spectral variability and mixed pixels problems associated with pixel-based approaches. Several studies have confirmed the superiority of OBIA over pixel-based classifications, especially in heterogeneous agricultural landscapes and urban areas (Blaschke, 2010; Myint, 2006; Myint et al., 2011; Peña-Barragán et al., 2011).

Ordinary Kriging (OK)
type: NamedIndividual
type: Mining Algorithm
Acronym: OK
comment: Ordinary kriging (OK) is a commonly-used linear method of spatial prediction to provide estimates of variables at unvisited sites.

On-Line Analytical Processing (OLAP)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: OLAP
comment: Online analytical processing (OLAP) is an approach to answer multi-dimensional analytical (MDA) queries swiftly in computing. OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications emerging.

Ordinary Least Squares (OLS)
type: NamedIndividual
type: Mining Algorithm
defined in: Apache Mahout
is transformation of: Regression
is transformation of: Regressor
Acronym: OLS
comment: Ordinary Least Squares (OLS) is a type of linear least squares method for estimating the unknown parameters in a linear regression model. OLS chooses the parameters of a linear function of a set of explanatory variables by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values of the variable being observed) in the given dataset and those predicted by the linear function of the independent variable.

Optimum-Path Forest (OPF)
type: NamedIndividual
type: Mining Algorithm
Acronym: OPF
comment: Optimum-Path Forest (OPF) classifier was introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification. In this paper, we present the Efficient OPF (EOPF), which is an enhanced and faster version of the traditional OPF, and validate it for the automatic recognition of white matter and gray matter in magnetic resonance images of the human brain.

Ordering Points To Identify the Clustering Structure (OPTICS)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Cluster
is transformation of: Clustering
Acronym: OPTICS
comment: OPTICS (Ordering Points To Identify the Clustering Structure), closely related to DBSCAN, finds core sample of high density and expands clusters from them [R2c55e37003fe-1]. Unlike DBSCAN, keeps cluster hierarchy for a variable neighborhood radius. Better suited for usage on large datasets than the current sklearn implementation of DBSCAN.
definition: clusterers.OPTICS

Partitioning Around Mediods (PAM)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Cluster
is transformation of: Clustering
References: Kaufman, L., Rousseeuw, P.J., 1990. "Finding Groups in Data: An Introduction to Cluster Analysis". Wiley, New York.
Acronym: PAM
comment: PAM uses a greedy search which may not find the optimum solution, but it is faster than exhaustive search.

Principal Component Analysis (PCA)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
defined in: Scikit-learn
is transformation of: Data Selection
Acronym: PCA
comment: Principal component analysis (PCA). Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space. The input data is centered but not scaled for each feature before applying the SVD.

Fast Principal Component Analysis (FastPCA)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
inherits: Principal Component Analysis (PCA)
is transformation of: Data Selection
Acronym: FastPCA

Principal Component Regression (PCR)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Regression
is transformation of: Regressor
Acronym: PCR
comment: Principal component regression (PCR) is a regression analysis technique that is based on principal component analysis (PCA). In PCR, instead of regressing the dependent variable on the explanatory variables directly, the principal components of the explanatory variables are used as regressors.

Pyramid of histograms of orientation gradients (PHOG)
type: NamedIndividual
type: Mining Algorithm
Acronym: PHOG

Partial Least Squares (PLS)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: PLS
comment: Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the X and Y data are projected to new spaces, the PLS family of methods are known as bilinear factor models. Partial least squares discriminant analysis (PLS-DA) is a variant used when the Y is categorical. PLS is used to find the fundamental relations between two matrices (X and Y), i.e. a latent variable approach to modeling the covariance structures in these two spaces. A PLS model will try to find the multidimensional direction in the X space that explains the maximum multidimensional variance direction in the Y space. PLS regression is particularly suited when the matrix of predictors has more variables than observations, and when there is multicollinearity among X values. By contrast, standard regression will fail in these cases (unless it is regularized). Partial least squares was introduced by the Swedish statistician Herman O. A. Wold, who then developed it with his son, Svante Wold. An alternative term for PLS (and more correct according to Svante Wold[1]) is projection to latent structures, but the term partial least squares is still dominant in many areas. Although the original applications were in the social sciences, PLS regression is today most widely used in chemometrics and related areas. It is also used in bioinformatics, sensometrics, neuroscience, and anthropology.

Partial Least Squares Discriminant Analysis (PLSDA)
type: NamedIndividual
type: Mining Algorithm
inherits: Partial Least Squares (PLS)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: PLSDA
comment: Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the X and Y data are projected to new spaces, the PLS family of methods are known as bilinear factor models. Partial least squares discriminant analysis (PLS-DA) is a variant used when the Y is categorical.

Partial Least Squares Regression (PLSR)
type: NamedIndividual
type: Mining Algorithm
inherits: Partial Least Squares (PLS)
is transformation of: Regression
is transformation of: Regressor
References: Wold, S; Sjostrom, M.; Eriksson, L. (2001). "PLS-regression: a basic tool of chemometrics". Chemometrics and Intelligent Laboratory Systems.
Acronym: PLSR
comment: Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. Because both the X and Y data are projected to new spaces, the PLS family of methods are known as bilinear factor models. Partial least squares discriminant analysis (PLS-DA) is a variant used when the Y is categorical.

Particle Swarm Optimization (PSO)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Regression
is transformation of: Regressor
References: J. Kennedy, R. Eberhart, Particle swarm optimization, Proc IEEE International Conference on Neural Networks, Perth, Australia (1995), pp. 1942-1948
Acronym: PSO
comment: Particle Swarm Optimization (PSO) is an evolutionary computation technique through individual improvement plus population cooperation and competition, hybrid particle swarm optimization algorithm is proposed by incorporating chaos.

PageRank Algorithm (PageRabk)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Cluster
is transformation of: Clustering
Acronym: PageRank
comment: PageRank is commonly used by search engines like Google. It is a link analysis algorithm that determines the relative importance of an object linked within a network of objects. Link analysis is a type of network analysis that explores the associations among objects. Google search uses this algorithm by understanding the backlinks between web pages.

Quadratic Discriminant Analysis (QDA)
type: NamedIndividual
type: Mining Algorithm
inherits: Discriminant Analysis (DA)
is transformation of: Classification
is transformation of: Classifier
Acronym: QDA
comment: Quadratic discriminant analysis QDA is a variant of LDA in which an individual covariance matrix is estimated for every class of observations. QDA is particularly useful if there is prior knowledge that individual classes exhibit distinct covariances.

Quantile Regression Forests (QRF)
type: NamedIndividual
type: Mining Algorithm
Acronym: QRF

Regional Convolutional Neural Network (R-CNN)
type: NamedIndividual
type: Mining Algorithm
inherits: Artificial Neural Network (ANN)
inherits: Convolutional Neural Network (CNN)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
References: Girshick, R., J. Donahue, T. Darrell, and J. Malik. "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation." CVPR '14 Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Pages 580-587. 2014
Acronym: R-CNN
comment: Region Convolutional Neural Networks (R-CNN) is a deep learning approach for Object detection, it combines rectangular region proposals with convolutional neural network features. R-CNN is a two-stage detection algorithm: - The first stage identifies a subset of regions in an image that might contain an object. - The second stage classifies the object in each region.

Radial Basis Function (RBF)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Regression
is transformation of: Regressor
Acronym: RBF
comment: Radial basis functions are means to approximate multivariable (also called multivariate) functions by linear combinations of terms based on a single univariate function (the radial basis function). This is radialised so that in can be used in more than one dimension. They are usually applied to approximate functions or data (Powell 1981,Cheney 1966,Davis 1975) which are only known at a finite number of points (or too difficult to evaluate otherwise), so that then evaluations of the approximating function can take place often and efficiently.

Random Forest (RF)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: RF
comment: A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree.

Rule Generation (RG)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
References: Phadikar, Santanu, Jaya Sil, and Asit Kumar Das. "Rice diseases classification using feature selection and rule generation techniques." Computers and electronics in agriculture 90 (2013): 76-85.
Acronym: RG

Regression kriging (RK)
type: NamedIndividual
type: Mining Algorithm
Acronym: RK
comment: Regression kriging (RK) is another method of spatial prediction that uses secondary variable(s) to improve the predictions of the primary variable at unsampled locations.

Reinforcement Learning (RL)
type: NamedIndividual
type: Mining Algorithm
inherits: Machine Learning (ML)
is transformation of: Classification
is transformation of: Clustering
is transformation of: Regression
Acronym: RL
comment: Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Recurrent neural network (RNN)
type: NamedIndividual
type: Mining Algorithm
defined in: Keras
defined in: TensorFlow
inherits: Artificial Neural Network (ANN)
is transformation of: Classification
is transformation of: Classifier
References: Stéphane Mallat. "Understanding deep convolutional networks". arXiv preprint arXiv:1601.04920, 2016.
Acronym: RNN
comment: A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. This makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Receiver Operating Characteristic (ROC)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
Acronym: ROC

Ridge regression (RR)
type: NamedIndividual
type: Mining Algorithm
References: Rao, C. R., and Venkat N. Gudivada. "Computational analysis and understanding of natural languages: Principles, methods and applications". Elsevier, 2018.
Acronym: RR
comment: Ridge regression (RR) is a regularization technique that penalizes the L2-norm of the coefficients in linear regression.

Deep Residual Network (ResNet)
type: NamedIndividual
type: Mining Algorithm
defined in: TensorFlow
inherits: Artificial Neural Network (ANN)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
Acronym: ResNet
comment: A residual neural network (ResNet) is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections, or shortcuts to jump over some layers. Typical ResNet models are implemented with double- or triple- layer skips that contain nonlinearities (ReLU) and batch normalization in between.

Spectral Analytics (SA)
type: NamedIndividual
type: Mining Algorithm
related to: Crop Imagery
related to: Spectral Image
Acronym: SA
comment: Spectral Analytics (SA) is approaches that estimate the strength of different frequency components (the power spectrum) of a time-domain signal in sprectral images.

Self-Attention Convolutional Neural Network (SACNN)
type: NamedIndividual
type: Mining Algorithm
inherits: Artificial Neural Network (ANN)
inherits: Convolutional Neural Network (CNN)
is transformation of: Classification
is transformation of: Classifier
References: Zeng, Weihui, and Miao Li. "Crop leaf disease recognition based on Self-Attention convolutional neural network." Computers and Electronics in Agriculture 172 (2020): 105341.
Acronym: SACNN

Spectral Angle Mapper (SAM)
type: NamedIndividual
type: Mining Algorithm
References: Kruse, Fred A., A. B. Lefkoff, J. W. Boardman, K. B. Heidebrecht, A. T. Shapiro, P. J. Barloon, and A. F. H. Goetz. "The spectral image processing system (SIPS) - interactive visualization and analysis of imaging spectrometer data." Remote sensing of environment 44, no. 2-3 (1993): 145-163.
Acronym: SAM
comment: Spectral Angle Mapper Classification (SAM) is an automated method for directly comparing image spectra to a known spectra (usually determined in a lab or in the field with a spectrometer) or an endmember. This method treats both (the questioned and known) spectra as vectors and calculates the spectral angle between them. This method is insensitive to illumination since the SAM algorithm uses only the vector direction and not the vector length. The result of the SAM classification is an image showing the best match at each pixel. This method is typically used as a first cut for determining the mineralogy and works well in areas of homogeneous regions. The USGS maintains a large spectral library, mostly composed of mineral and soil types, which image spectra can be directly compared.

Stepwise Discriminant Analysis (SDA)
type: NamedIndividual
type: Mining Algorithm
inherits: Discriminant Analysis (DA)
is transformation of: Classification
is transformation of: Classifier
Acronym: SDA

Sequential Forward Floating Selection (SFFS)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Data Selection
References: Ferri, F. J., Pudil P., Hatef, M., Kittler, J. (1994). "Comparative study of techniques for large-scale feature selection." Pattern Recognition in Practice IV : 403-413.
References: Pudil, P., Novovicova, J., & Kittler, J. (1994). "Floating search methods in feature selection." Pattern recognition letters 15.11 (1994): 1119-1125.
Acronym: SFFS
comment: Sequential Forward Floating Selection (SFFS) applies a number of backward steps at each forward step and the evaluates the resulting subsets. The new subsets are compared to the previous ones and selected if better.

Stochastic Gradient Boosting (SGB)
type: NamedIndividual
type: Mining Algorithm
Acronym: SGB
comment: Stochastic Gradient Boosting (SGB) is an ensemble learning algorithm combined with boosting and decision tree and makes a prediction by weighting the ensemble members of all trees.

Stochastic Gradient Descent (SGD)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Classification
is transformation of: Classifier
Acronym: SGD
comment: Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated from a randomly selected subset of the data). Especially in high-dimensional optimization problems this reduces the computational burden, achieving faster iterations in trade for a lower convergence rate.
definition: sklearn.linear_model.SGDClassifier

Stochastic Gradient Descent Regression (SGDR)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
is transformation of: Regression
is transformation of: Regressor
Acronym: SGDR
comment: Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to fitting linear classifiers and regressors under convex loss functions such as (linear) Support Vector Machines and Logistic Regression. Even though SGD has been around in the machine learning community for a long time, it has received a considerable amount of attention just recently in the context of large-scale learning. SGD has been successfully applied to large-scale and sparse machine learning problems often encountered in text classification and natural language processing. Given that the data is sparse, the classifiers in this module easily scale to problems with more than 10^5 training examples and more than 10^5 features.

Scale Invariant Feature Transform (SIFT)
type: NamedIndividual
type: Mining Algorithm
related to: Imagery
References: Lowe, David G. (1999). "Object recognition from local scale-invariant features". Proceedings of the International Conference on Computer Vision. 2. pp. 1150–1157.
Acronym: SIFT
comment: Scale-invariant feature transform (SIFT) is a feature detection algorithm in computer vision to detect and describe local features in images. It was published by David Lowe in 1999. Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving.

Supervised Kohonen Network (SKN)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
References: Melssen, Willem, Ron Wehrens, and Lutgarde Buydens. "Supervised Kohonen networks for classification problems." Chemometrics and Intelligent Laboratory Systems 83.2 (2006): 99-113.
Acronym: SKN

Simple Linear Iterative Clustering (SLIC)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
References: Achanta, Radhakrishna, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk. "SLIC superpixels compared to state-of-the-art superpixel methods." IEEE transactions on pattern analysis and machine intelligence 34, no. 11 (2012): 2274-2282.
Acronym: SLIC
comment: Simple Linear Iterative Clustering (SLIC) algorithm is increasingly applied to different kinds of image processing because of its excellent perceptually meaningful characteristics.

Stepwise Multiple Linear Regression (SMLR)
type: NamedIndividual
type: Mining Algorithm
inherits: Linear Regression (LR)
is transformation of: Regression
is transformation of: Regressor
Acronym: SMLR
comment: Stepwise Multiple Linear Regressions (SMLR) is a way of cleverly reducing the number of wavelengths in order to find a least-squares solution, based on the meaningful assumption that repeated and useless information may be present in some wavelengths.

Successive Projections Algorithm (SPA)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
References: Araujo, Mario Cesar Ugulino, Teresa Cristina Bezerra Saldanha, Roberto Kawakami Harrop Galvao, Takashi Yoneyama, Henrique Caldas Chame, and Valeria Visani. "The successive projections algorithm for variable selection in spectroscopic multicomponent analysis". Chemometrics and Intelligent Laboratory Systems 57(2), 65–73 (2001)
Acronym: SPA
comment: Successive Projection Algorithm (SPA) is a fast algorithm to tackle separable nonnegative matrix factorization (NMF). Given a nonnegative data matrix X, SPA identifies an index set K such that there exists a nonnegative matrix H with X≈X(:,K)H. SPA has been successfully used as a pure-pixel search algorithm in hyperspectral unmixing and for anchor word selection in document classification. Moreover, SPA is provably robust in low-noise settings. The main drawbacks of SPA are that it is not robust to outliers and does not take the data fitting term into account when selecting the indices in K.

Spectral Regression Discriminant Analysis (SRDA)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
inherits: Discriminant Analysis (DA)
is transformation of: Data Selection
Acronym: SRDA
comment: Sparse Principal Components Analysis (SparsePCA) Finds the set of sparse components that can optimally reconstruct the data. The amount of sparseness is controllable by the coefficient of the L1 penalty, given by the parameter alpha.

Speeded-Up Robust Features (SURF)
type: NamedIndividual
type: Mining Algorithm
related to: Imagery
References: Bay, H., Ess, A., Tuytelaars, T., & Van Gool, L. (2008). "Speeded-up robust features (SURF)". Computer vision and image understanding, 110(3), 346-359.
Acronym: SURF
comment: Speeded up robust features (SURF) is a patented local feature detector and descriptor, is used in Computer Vision (CV). It can be used for tasks such as object recognition, image registration, classification, or 3D reconstruction. It is partly inspired by the scale-invariant feature transform (SIFT) descriptor. The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT.

Singular Value Decomposition (SVD)
type: NamedIndividual
type: Mining Algorithm
Acronym: SVD
comment: Singular value decomposition (SVD) is a factorization of a real or complex matrix. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any {\displaystyle m\times n}m\times n matrix. It is related to the polar decomposition.

Support Vector Machine (SVM)
type: NamedIndividual
type: Mining Algorithm
defined in: Machine Learning PYthon (mlpy)
defined in: Scikit-learn
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
is transformation of: Regressor
example: # Dataset: http://mlpy.sourceforge.net/docs/3.5/_downloads/spiral.data >>> import numpy as np >>> import matplotlib.pyplot as plt >>> import mlpy >>> f = np.loadtxt("spiral.data") >>> x, y = f[:, :2], f[:, 2] >>> svm = mlpy.LibSvm(svm_type='c_svc', kernel_type='rbf', gamma=100) >>> svm.learn(x, y) >>> xmin, xmax = x[:,0].min()-0.1, x[:,0].max()+0.1 >>> ymin, ymax = x[:,1].min()-0.1, x[:,1].max()+0.1 >>> xx, yy = np.meshgrid(np.arange(xmin, xmax, 0.01), np.arange(ymin, ymax, 0.01)) >>> xnew = np.c_[xx.ravel(), yy.ravel()] >>> ynew = svm.pred(xnew).reshape(xx.shape) >>> fig = plt.figure(1) >>> plt.set_cmap(plt.cm.Paired) >>> plt.pcolormesh(xx, yy, ynew) >>> plt.scatter(x[:,0], x[:,1], c=y) >>> plt.show()
example: >>> from sklearn import svm >>> X = [[0, 0], [1, 1]] >>> y = [0, 1] >>> clf = svm.SVC() >>> clf.fit(X, y) >>> clf.predict([[2., 2.]])
References: Cortes, Corinna, and Vladimir Vapnik. "Support vector machine." Machine learning 20, no. 3 (1995): 273-297.
Acronym: SVM
comment: Support Vector Machine (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. In the SVM algorithm, we plot each data item as a point in n-dimensional space (where n is number of features you have) with the value of each feature being the value of a particular coordinate. Then, we perform classification by finding the hyper-plane that differentiates the two classes very well (look at the below snapshot).
definition: sklearn.svm.SVC

Support Vector Machine-Feature Selection (SVM-FS)
type: NamedIndividual
type: Mining Algorithm
inherits: Support Vector Machine (SVM)
is transformation of: Data Selection
Acronym: SVM-FS

Support Vector Regression (SVR)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
inherits: Support Vector Machine (SVM)
is transformation of: Regression
is transformation of: Regressor
References: Drucker, Harris, Chris JC Burges, Linda Kaufman, Alex Smola, and Vladimir Vapnik. "Support vector regression machines." Advances in neural information processing systems 9 (1997): 155-161.
Acronym: SVR
comment: Support Vector Regression (SVR) is built based on the concept of Support Vector Machine (SVM). It is one among the popular Machine Learning models that can be used in classification problems or assigning classes when the data is not linearly separable.

Stepwise (SW)
type: NamedIndividual
type: Mining Algorithm
Acronym: SW

Simplex Volume Maximization (SiVM)
type: NamedIndividual
type: Mining Algorithm
References: Alfeld, Matthias, et al. "Simplex Volume Maximization (SiVM): A matrix factorization algorithm with non-negative constrains and low computing demands for the interpretation of full spectral X-ray fluorescence imaging data." Microchemical Journal 132 (2017): 179-184.
Acronym: SiVM

Sparse Principal Components Analysis (SparsePCA)
type: NamedIndividual
type: Mining Algorithm
defined in: Scikit-learn
inherits: Principal Component Analysis (PCA)
is transformation of: Data Selection
References: Jenatton, Rodolphe, Guillaume Obozinski, and Francis Bach. "Structured sparse principal component analysis." In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 366-373. JMLR Workshop and Conference Proceedings, 2010.
Acronym: SparsePCA
comment: Sparse Principal Components Analysis (SparsePCA) Finds the set of sparse components that can optimally reconstruct the data. The amount of sparseness is controllable by the coefficient of the L1 penalty, given by the parameter alpha.

Univariate linear regression (ULR)
type: NamedIndividual
type: Mining Algorithm
Acronym: ULR

Unfold Principal Component Analysis (UPCA)
type: NamedIndividual
type: Mining Algorithm
inherits: Principal Component Analysis (PCA)
is transformation of: Data Selection
Acronym: UPCA

Undefine (Undefine)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Association
is transformation of: Association Rule
is transformation of: Classification
is transformation of: Classifier
is transformation of: Cluster
is transformation of: Clustering
is transformation of: Data Selection
is transformation of: Regression
Acronym: Undefine
comment: Undefine

Vegetation Indices (VI)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Regression
Acronym: VI
comment: Vegetation Index (VI) is used to identify, detect from spectral images.

Universal Background Subtraction Algorithm for Video Sequences (ViBe)
type: NamedIndividual
type: Mining Algorithm
References: Barnich, O., & Van Droogenbroeck, M. (2010). "ViBe: A universal background subtraction algorithm for video sequences". IEEE Transactions on Image processing, 20(6), 1709-1724.
Acronym: ViBe
comment: A universal background subtraction algorithm for video sequences (ViBe) is a fast, simple, efficient and with optimal sample attenuation based on background modeling. ViBe is a background subtraction algorithm which has been presented at the IEEE ICASSP 2009 conference and was refined in later publications. More precisely, it is a software module for extracting background information from moving images. It has been developed by Oliver Barnich and Marc Van Droogenbroeck of the Montefiore Institute, University of Liège, Belgium.

Wavelength (WL)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Regression
Acronym: WL

Wavelet Neural Network (WNN)
type: NamedIndividual
type: Mining Algorithm
inherits: Artificial Neural Network (ANN)
is transformation of: Regression
Acronym: WNN
comment: wavelet neural network (WNN)

XGBoost
type: NamedIndividual
type: Mining Algorithm
inherits: Decision Tree (DT)
is transformation of: Classification
is transformation of: Classifier
is transformation of: Regression
References: Friedman J, Hastie T, Tibshirani R, et al. (2000). "Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)." The annals of statistics, 28(2), 337–407.
Acronym: XGBoost
comment: XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. In prediction problems involving unstructured data (images, text, etc.) artificial neural networks tend to outperform all other algorithms or frameworks. However, when it comes to small-to-medium structured/tabular data, decision tree based algorithms are considered best-in-class right now. Please see the chart below for the evolution of tree-based algorithms over the years.

XY-fusion network (XYF)
type: NamedIndividual
type: Mining Algorithm
is transformation of: Classification
is transformation of: Classifier
References: Melssen, Willem, Ron Wehrens, and Lutgarde Buydens. "Supervised Kohonen networks for classification problems." Chemometrics and Intelligent Laboratory Systems 83.2 (2006): 99-113.
Acronym: XYF

Conditional GAN (cGAN)
type: NamedIndividual
type: Mining Algorithm

AllenNLP
type: NamedIndividual
type: Toolkit
Acronym: AllenNLP
comment: AllenNLP is a powerful tool for prototyping with good text processing capabilities. This tool is less effective for production if compared to SpaCy but it is largely used in research. Additionally, it has PyTorch, a very popular deep learning framework that enables customizing models more flexibly than SpaCy. It automates some of the tasks which are essential for almost every deep learning model. It provides a lot of modules like Seq2VecEncoder, Seq2SeqEncoder.

Alpine Climate
type: NamedIndividual
type: Climate Type
comment: Alpine climate is the typical weather (climate) for the regions above the tree line. This climate is also referred to as a mountain climate or highland climate.
label: Highland climate
label: Mountain climate

Alternaria Blight
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
comment: Alternaria blight, caused by the fungus Alternaria cucumerina, is a common disease of most cucurbits in warm, rainy weather.

Alternaria Leaf Blight
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Wheat
development: The fungus survives as conidia on seed or as mycelia within seed. sporulation on lower leaves provides inoculum that can be dispersed by wind, leading to secondary spread of the disease. Seed-borne inoculum often results in spike infections late in the crop cycle. High humidity or irrigation, as well as warmer temperatures (20 to 25C) favor infection and disease development.
distribution: Bread wheat and durum wheat, as well as several related grasses, are the primary hosts. The disease is common in the eastern and central areas of the Asian Subcontinent.
important: Alternaria leaf blight can be very severe if environmental conditions are favorable for disease development; major losses can result when susceptible cultivars are grown.
symptoms: Small, chlorotic, oval- or elliptical-shaped lesions appear and, as they enlarge, these lesions become irregular in shape. The chlorotic borders of the lesions may become diffuse and turn light to dark brown in color (24). Lesions are difficult to distinguish from those caused by Helminthosporium spp. Infection usually starts on the lower leaves, but symptoms can be found on all plant parts.
comment: The fungus Alternaria cucumerina causes Alternaria leaf blight. This disease is most common melon, but can also affect cucumber, pumpkin and squash. Alternaria leaf blight does not commonly infect fruit. It can reduce yield and quality through reduced plant vigor and sunscald of exposed fruit.

Alternaria Leaf Spot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Turnip
causes: Fungal
management: Plant only pathogen-free seed; rotate crops; applications of appropriate fungicides control disease when present
symptoms: Small dark spots on leaves which turn brown to gray; lesions may be round or angular and may possess a purple-black margin; lesions may form concentric rings, become brittle and crack in center; dark brown elongated lesions may develop on stems and petioles
comment: Alternaria leaf spot in the garden is a real problem for growers of brassicas, but it also makes life miserable for tomato and potato growers, causing plaque-like spots on leaves and fruits. Treating Alternaria can be difficult, so many gardeners do what they can to prevent this fungus from getting a toe-hold in their plots.

Alternaria Solani
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Africa Eggplant
affects: Potato
affects: Tomato
life cycle: The life cycle starts with the fungus overwintering in crop residues or wild members of the family Solanaceae, such as black nightshade. In the spring, conidia are produced. Multicellular conidia are splashed by water or by wind onto an uninfected plant. The conidia infect the plant by entering through small wounds, stomata, or direct penetration. Infections usually start on older leaves close to the ground. The fungus takes time to grow and eventually forms a lesion. From this lesion, more conidia are created and released. These conidia infect other plants or other parts of the same plant within the same growing season. Every part of the plant can be infected and form lesions. This is especially important when fruit or tubers are infected as they can be used to spread the disease.
comment: Alternaria solani is a fungal pathogen that produces a disease in tomato and potato plants called early blight. The pathogen produces distinctive "bullseye" patterned leaf spots and can also cause stem lesions and fruit rot on tomato and tuber blight on potato. Despite the name "early," foliar symptoms usually occur on older leaves.

Alternaria Triticina
type: NamedIndividual
type: Fungus

Alteryx Analytics
type: NamedIndividual
type: Toolkit

Altrenaria Leaf Spot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Cotton
life cycle: A. brassicae and A. brassicicola produce asexual conidia via conidiophores. At optimum temperatures (20-30 C), the average time of sporulation is 13 hours. Conidia are dispersed via water and wind after sporulation. Once they come into contact with a leaf surface under optimal environmental conditions, germination can begin. Germination of conidia occurs most efficiently when temperatures are higher, with germination time increasing as temperature decreases. Germination occurs most quickly at temperatures between 21 and 28 C. The presence of moisture as water or a high relative humidity (at least 95%) is required for germination.[6] After germination, the pathogen begins to infect the host via penetration of the leaf surface using an appressorium and infection peg. Leaf cells are infected, and the symptomatic lesions begin to appear, with conidia-producing conidiophores being produced from mature lesions. Further dispersal of conidia by water or wind occurs from these new conidiophores. This dispersal can be local from one leaf to an adjacent leaf, or it can be long distance from plant to plant. 'The spores can be blown in the wind for distances up to 1.1 miles.'A. brassicae and A. brassicicola survive in the form of microsclerotia and chlamydospores which appear after infected leaves have partially decayed. Both of these survival structures develop best at low temperature (3 C) and they have been found to be resistant to desiccation and freezing. Conidiophores eventually develop from the microsclerotia and chlamydospores, allowing for the production of conidia again and the cycle to repeat.
management: A. brassicae and A. brassicicola can be chemically controlled by applying fungicides as foliar sprays. These fungicides include iprodione and chlorothalonil. It is recommended to practice crop rotation with non-cruciferous crops This creates periods of time when no active pathogen is present in the area due to lack of a host. If there are no pathogens present, they cannot produce microsclerotia and chlamydospores that would eventually be input into the surrounding area via plant debris, reducing chances of future infection. It is also recommended to irrigate in the morning when leaves can dry quickly, and to orient rows according to the wind. Morning irrigation promotes shorter periods of leaf wetness, disrupting the pathogens’ germination and infection. Orientation of rows disrupts dispersal of conidia via wind by reducing contact. Application of straw mulch can reduce disease incidence by acting as a barrier against soil-borne inoculum, disrupting dispersal of conidia from conidiophores developed from microsclerotia and chlamydospores. Maintaining control of cruciferous weeds is another important management practice, as this removes potential hosts for the pathogen that can aid in the spread of the disease.
comment: Alternaria leaf spot or Alternaria leaf blight are a group of fungal diseases in plants, that have a variety of hosts. The diseases infects common garden plants, such as cabbage, and are caused by several closely related species of fungi. Some of these fungal species target specific plants, while others have been known to target plant families. One commercially relevant plant genus that can be affected by Alternaria Leaf Spot is Brassica, as the cosmetic issues caused by symptomatic lesions can lead to rejection of crops by distributors and buyers. When certain crops such as cauliflower and broccoli are infected, the heads deteriorate and there is a complete loss of marketability. Secondary soft-rotting organisms can infect stored cabbage that has been affected by Alternaria Leaf Spot by entering through symptomatic lesions. Alternaria Leaf Spot diseases that affect Brassica species are caused by the pathogens Alternaria brassicae and Alternaria brassicicola.

American Samoa
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:AS
agricultural land: 49 sq. km
area: 199 km²
climate: tropical marine, moderated by southeast trade winds; annual rainfall averages about 3 m; rainy season (November to April), dry season (May to October); little seasonal temperature variation
density: 326/km²
latitude: -170.70145
longitude: -14.3083
population: 68,200 (2007)
comment: American Samoa /səˈmoʊə/ is an unincorporated territory of the United States located in the South Pacific Ocean, southeast of Samoa. Its location is centered on 14°18′S 170°42′W﻿ / ﻿14.3°S 170.7°W﻿ / -14.3; -170.7. It is east of the International Date Line, while Samoa is west of the Line. The total land area is 199 square kilometers, slightly more than Washington, D.C. American Samoa is the southernmost territory of the United States and one of two U.S. territories south of the Equator, along with the uninhabited Jarvis Island. Tuna products are the main exports, and the main trading partner is the rest of the United States.

Ammonia, anhydrous
type: NamedIndividual
type: Chemical Fertilizer
Acronym: NH3
comment: Ammonia is a compound of nitrogen and hydrogen with the formula NH3. A stable binary ... as ammonia liquor (usually 28% ammonia in water) or as pressurized or refrigerated anhydrous liquid ammonia transported in tank cars or cylinders.

Ammonium nitrate
type: NamedIndividual
type: Chemical Fertilizer
Acronym: NH4NO3
comment: Ammonium nitrate is a chemical compound with the chemical formula NH4NO3. It is a white crystalline solid consisting of ions of ammonium and nitrate. It is highly soluble in water and hygroscopic as a solid, although it does not form hydrates. It is predominantly used in agriculture as a high-nitrogen fertilizer.
definition: https://en.wikipedia.org/wiki/Ammonium_nitrate

Ammonium sulphate
type: NamedIndividual
type: Chemical Fertilizer
Acronym: (NH4)2SO4
comment: Ammonium sulfate (NH4)2SO4, is an inorganic salt with a number of commercial uses. The most common use is as a soil fertilizer. It contains 21% nitrogen and 24% sulfur.
definition: https://en.wikipedia.org/wiki/Ammonium_sulfate

Analytic Solver
type: NamedIndividual
type: Toolkit

Andorra
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:AD
agricultural land: 203 sq. km
area: 468 km²
climate: temperate; snowy, cold winters and warm, dry summers
density: 154/km²
latitude: 1.597563
longitude: 42.541817
population: 71,822 (2007)
comment: Andorra, officially the Principality of Andorra, is a sovereign landlocked microstate on the Iberian Peninsula, in the eastern Pyrenees, bordered by France to the north and Spain to the south. Believed to have been created by Charlemagne, Andorra was ruled by the count of Urgell until 988, when it was transferred to the Roman Catholic Diocese of Urgell. The present principality was formed by a charter in 1278. It is known as a principality as it is a diarchy headed by two princes: the Bishop of Urgell in Catalonia, Spain, and the President of the French Republic.

Angola
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:AO
agricultural land: 583,900 sq. km
area: 1,246,700 km²
climate: semiarid in south and along coast to Luanda; north has cool, dry season (May to October) and hot, rainy season (November to April)
density: 13/km²
latitude: 17.880669
longitude: -11.207334
population: 16,941,000 (2007)
comment: Angola, officially the Republic of Angola, is a country located on the west coast of Southern Africa. It is the second-largest lusophone country in both total area and population, and it is the seventh-largest country in Africa, bordered by Namibia to the south, the Democratic Republic of the Congo to the north, Zambia to the east, and the Atlantic Ocean to the west. Angola has an exclave province, the province of Cabinda that borders the Republic of the Congo and the Democratic Republic of the Congo. The capital and largest city is Luanda.

Anguilla
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:AI
area: 102 km²
climate: tropical; moderated by northeast trade winds
density: 132/km²
latitude: -63.183456
longitude: 18.389801
population: 13,477 (2006)
comment: Anguilla is a British overseas territory in the Caribbean. It is one of the most northerly of the Leeward Islands in the Lesser Antilles, lying east of Puerto Rico and the Virgin Islands and directly north of Saint Martin. The territory consists of the main island of Anguilla, approximately 16 miles long by 3 miles wide at its widest point, together with a number of much smaller islands and cays with no permanent population. The territory's capital is The Valley. The total land area of the territory is 35 square miles, with a population of approximately 18,090 .

Angular Leaf spot
type: NamedIndividual
type: Crop Disease
affects: Cashew
comment: Angular leaf spot is a bacterial disease caused by Pseudomonas syringae pv. lachrymans. Although angular leaf spot can infect any of the cucurbit crops, it is less common on cucumber due to the availability of resistant varieties.

Antarctica
type: NamedIndividual
type: Continent
type: Country
has country: Heard Island and McDonald Islands
ISO code: ISO 3166-1:AQ
area: 14,000,000 km²
climate: severe low temperatures vary with latitude, elevation, and distance from the ocean; East Antarctica is colder than West Antarctica because of its higher elevation; Antarctic Peninsula has the most moderate climate; higher temperatures occur in January along the coast and average slightly below freezing
latitude: 0.0
longitude: -76.760544
comment: Antarctica is Earth's southernmost continent. It contains the geographic South Pole and is situated in the Antarctic region of the Southern Hemisphere, almost entirely south of the Antarctic Circle, and is surrounded by the Southern Ocean. At 14,200,000 square kilometres, it is the fifth-largest continent and nearly twice the size of Australia. At 0.00008 people per square kilometre, it is by far the least densely populated continent. About 98% of Antarctica is covered by ice that averages 1.9 km in thickness, which extends to all but the northernmost reaches of the Antarctic Peninsula.

Anthracnose
type: NamedIndividual
type: Crop Disease
affects: Coffee
affects: Cotton
affects: Oak
affects: Walnut
comment: Anthracnose is a fungal disease that tends to attack plants in the spring when the weather is cool and wet, primarily on leaves and twigs. The fungi overwinter in dead twigs and fallen leaves. Cool, rainy weather creates perfect conditions for the spores to spread.

Antigua and Barbuda
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:AG
agricultural land: 90 sq. km
area: 280 km²
climate: tropical maritime; little seasonal temperature variation
density: 184/km²
latitude: -62.005745
longitude: 17.328304
population: 84,522 (2008)
comment: Antigua and Barbuda is a sovereign island country in the West Indies in the Americas, lying between the Caribbean Sea and the Atlantic Ocean. It consists of two major islands, Antigua and Barbuda separated by 63 km (39 mi), and smaller islands (including Great Bird, Green, Guiana, Long, Maiden, Prickly Pear, York Islands, Redonda). The permanent population number is about 97,120 (2019 est.), with 97% being resident on Antigua. The capital and largest port and city is St. John's on Antigua, with Codrington being the largest town on Barbuda. Lying near each other, Antigua and Barbuda are in the middle of the Leeward Islands, part of the Lesser Antilles, roughly at 17°N of the equator.

Ants
type: NamedIndividual
type: Crop Disease
type: Crop Pest
management: Management Strategies: To prevent ants from entering the foliage crowns of fruit trees, where they may aggravate aphid problems, use a 4-inch wide strip of polyester fiber matting to snugly surround the trunk, covered by a 4-inch wide strip of household plastic wrap. Smear this with a sticky substance such as Ant-Bar. The barrier will stretch as the tree grows, but once it splits, replace it. Organic Strategies Indoor nests: Strategies 1, 2, 3, and 4 are strictly organic approaches. For an organic approach to Strategy 5, consult the Organic Materials Review Institute (OMRI™) for appropriate diatomaceous earth products. Outdoor nests: Strategies 1, 2, and 3 are strictly organic approaches.
comment: Ants are attracted to the sticky honeydew secreted by aphids, leafhoppers, mealybugs, whiteflies and scale insects. While not problematic in and of itself, the presence of ants in your garden may indicate and/or exacerbate aphid problems. See Aphids, Hoppers, Mealybugs, Scales and Whiteflies.

Apache Mahout
type: NamedIndividual
type: Toolkit
has algorithm: Canopy Clustering (CC)
has algorithm: Ordinary Least Squares (OLS)
comment: Apache Mahout is a distributed linear algebra framework and mathematically expressive Scala DSL designed to let mathematicians, statisticians, and data scientists quickly implement their own algorithms.

Aphids
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Corn
affects: Lemon
affects: Maize
affects: Rye
affects: Wheat
distribution: Species commonly found on cereals throughout the world include: Rhopalosiphum padi (bird cherry-oat aphid) Schizaphis graminum (greenbug) R. maidis (corn leaf aphid) Metopoliphium dirhodum (rose grass aphid) Sitobion avenae (English grain aphid) Diuraphis noxis (Russian wheat aphid)
important: Aphids are important and widespread pests on cereal crops. When feeding in sufficient numbers, they can cause significant damage. In addition, the species listed above may act as vectors of barley yellow dwarf virus.
life cycle: The life cycle of aphids involve winged (alates), wingless (apterous), sexual, and asexual forms. When feeding on cereals, the females of most aphid species reproduce asexually (without being fertilized), giving rise to nymphs rather than eggs.
management: Aphid numbers tend to naturally decline as leaves harden off but can be a problem on young trees or varieties which continually produce flushes of new growth; pesticides are not generally recommended due to resistance and trees can withstand a high degree of leaf curling.
symptoms: Aphids are nearly transparent, soft-bodied sucking insects. When present in sufficient numbers, aphids can cause yellowing and premature death of leaves. They exude drops of sugary liquid known as "honeydew", which may cause tiny scorch marks on the foliage and tends to encourage the development of sooty molds. The feeding of Schizaphis graminin is especially damaging, resulting in the development of necrotic areas sometimes accompanied by purpling and rolling of the infested leaves. The feeding of Diuraphis noxis produces long white stripes on the leaves, leaf rolling, postrate growth habit, and sterile heads.
comment: Aphids are small sap-sucking insects and members of the superfamily Aphidoidea. Common names include greenfly and blackfly, although individuals within a species can vary widely in colour. The group includes the fluffy white woolly aphids.

Apple Anthracnose
type: NamedIndividual
type: Crop Disease
affects: Apple
comment: Apple anthracnose is a fungal disease that significantly impacts apple production in the maritime Pacific Northwest and in regions with similar climates. The fungus produces cankers on trees and a post-harvest fruit rot known as “Bull’s-eye rot”. The cankers serve as the source of inoculum for inciting the fruit rot phase of the disease and can limit long-term orchard productivity. Species within the fungal genus Neofabraea are the causal agents responsible for production of apple tree cankers and Bull’s-eye fruit rot. The geographical distribution of Neofabraea species varies significantly with the environment, with tree cankers (apple anthracnose canker) west of the Cascade Mountains attributed to Neofabraea malicorticis, whereas tree cankers (perennial canker) east of the Cascade Mountains are attributed to Neofabraea perennans. Other potential disease-causing species are Neofabraea alba and Cryptosporiopsis kienholzii.

Apple Gray Spot
type: NamedIndividual
type: Crop Disease

Apple replant disease
type: NamedIndividual
type: Crop Disease
affects: Apple
affects: Fruit
controls: Plant nematode-free trees. If the nursery site where trees were propagated had significant replant disease pressure young trees likely carry inocula in the roots which may serve to infest a grower’s site. Crop rotation. Many other crops can host the pathogens in this complex, thus crop rotation is not a guaranteed strategy to lower potential disease pressure. However, a five year plus rotation to a non-woody crop can help reduce pressure. For small-scale, soil in the planting hole can be replaced with good quality soil from a non-orchard site. In order to effectively replace the soil, the planting hole should be 7 feet (length) X 7 feet (width) X 2 ½ feet (depth) (Benson 1976); (Smith 1994).
life cycle: The disease is caused by a pathogen complex that consists of fungi, oomycetes (fungi-like organisms) and nematodes. Individual sites may have one or multiple organisms causing the syndrome (Figure 1). In Washington State, the disease is caused by multiple species within several genera of fungi: Rhizoctonia, Ilyonectria; oomycetes: Phytophthora, Pythium and in some sites lesion nematode Pratylenchus penetrans (Mazzola 1997, 1998). Contaminated soil, irrigation water, or planting stock can all be sources of inocula. The fungal and oomycete pathogens produce overwintering structures like sclerotia (Rhizoctonia), chlamydospores (Ilyonectria), and oospores (Phytophthora and Pythium) that can survive in dead or dormant roots and soil. Lesion nematodes survive in the soil and plant residues as eggs and as multiple generations of juveniles and adults. (Agrios 2005). Once the new trees are planted or during the spring when dormant roots become active, the pathogen propagules germinate and parasite nematode eggs hatch in response to exudates from the roots. The fungi and oomycete mycelia grow from the propagules and infect the fine roots. While fungi such as Rhizoctonia cause extensive root rot, Pythium spp. cause a more insidious level of damage by stripping root hairs resulting in diminished capacity of the plant to take up water and nutrients. Lesion nematode can directly penetrate roots at a variety of stages (II stage juvenile, II stage juvenile, III stage juvenile, IV stage juvenile, and adults). Lesion nematode feeds from outside and inside the root. Feeding of the nematode kills root cells and when the nematodes move through the root they damage and disrupt root tissue inhibiting water and nutrient movement into the plant. Feeding damage also provides access channels for fungi, bacteria and other organisms. Oomycete pathogens in the complex can be dispersed by free water during irrigation as they have motile spores called zoospores. Apple replant disease cycle: Pathog…
symptoms: The disease symptoms are visible shortly after planting new trees. Uneven growth over the orchard block, stunting, and shortened internodes can signify replant disease in the orchard. When a tree is uprooted, discolored roots, root tip necrosis, and reduced root biomass can be seen. Young trees may die within the first year. Many will survive but overall fruit production and quality are reduced.
Acronym: ARD
comment: Replant disease is a widespread problem in intensive tree fruit and nut growing areas. It is characterized by reduced productivity in fields repeatedly planted to the same or closely related tree fruit or nut crops. It can also occur in tree fruit and nut nurseries.

Apple Rot
type: NamedIndividual
type: Crop Disease
affects: Apple
comment: Apple rot infections produce slightly sunken, circular brown or black spots that may be covered by a red halo.

Apple Scab
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Apple
comment: Apple scab is a common disease of plants in the rose family (Rosaceae) that is caused by the ascomycete fungus Venturia inaequalis.

April
type: NamedIndividual
type: Month Of Year

Arable
type: NamedIndividual
type: Topography

Areolate Mildew
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
management: Use of resistant varieties is the best control strategy for Areolate Mildew. Incorporating crop residue and crop rotation will help reduce inoculum for next season. Fungicides (e.g. azoxystrobin and pyraclostrobin) were effective in managing this disease in Georgia.
symptoms: Small lesions appear on leaves in the lower canopy late in the growing season. The lesions, 3-4 mm (~0.15 in) wide and restricted by a major leaf vein, are slightly chlorotic on the upper leaf surface with a white mildew growth on the lower surface. Lesions may become necrotic and resemble bacterial blight. In severe cases, premature defoliation will occur.
comment: Areolate mildew has been confirmed on samples collected from Halifax County, North Carolina. This is likely not the only field given recent wet weather conditions. Symptoms of this disease including white mildew covering parts of or entire leaves.

Argentina
type: NamedIndividual
type: Country
belongs to: South America
has climate: Temperate Climate
ISO code: ISO 3166-1:AR
agricultural land: 1.48 million sq. km
area: 2,780,403 km²
climate: mostly temperate; arid in southeast; subantarctic in southwest
latitude: -63.58481
longitude: -38.421295
population: 40,482,000
comment: Argentina, officially the Argentine Republic, is a country located mostly in the southern half of South America. Sharing the bulk of the Southern Cone with Chile to the west, the country is also bordered by Bolivia and Paraguay to the north, Brazil to the northeast, Uruguay and the South Atlantic Ocean to the east, and the Drake Passage to the south. With a mainland area of 2,780,400 km2, Argentina is the eighth-largest country in the world, the fourth largest in the Americas, the second largest in South America after Brazil, and the largest Spanish-speaking nation by area. The sovereign state is subdivided into twenty-three provinces and one autonomous city, Buenos Aires, which is the federal capital of the nation as decided by Congress. The provinces and the capital have their own constitutions, but exist under a federal system. Argentina claims sovereignty over part of Antarctica, the Falkland Islands, and South Georgia and the South Sandwich Islands.

Arid Climate
type: NamedIndividual
type: Climate Type
comment: A region is arid when it is characterized by a severe lack of available water, to the extent of hindering or preventing the growth and development of plant and animal life. Environments subject to arid climates tend to lack vegetation and are called xeric or desertic.
label: Dry climate

Armenia
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:AM
agricultural land: 17,106 sq. km
area: 29,800 km²
climate: highland continental, hot summers, cold winters
density: 101/km²
latitude: 45.039906
longitude: 40.066177
population: 3,231,900 (07/2008)
comment: Armenia, officially the Republic of Armenia, is a landlocked country located in the Armenian Highlands of Western Asia. It is a part of the Caucasus region; and is bordered by Turkey to the west, Georgia to the north, the Lachin corridor under a Russian peacekeeping force, and Azerbaijan to the east, and Iran and the Azerbaijani exclave of Nakhchivan to the south.

Armillaria root rot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Apricot
caused by: Fungal
management: Once a tree is infected there is no treatment and it should be removed, fumigants do not control fungi in soil adequately; do not plant apricot in newly cleared forest or on the site of old orchards with a history of Armillaria.
symptoms: If tree is infected after it has reached 5 years of age then typical symptoms include poor terminal growth and small leaves; around midsummer the whole tree suddenly collapses; in orchards trees usually die in a circular pattern; infected trees often have a fan-shaped white fungal mat growing between the bark and wood of the crown.
References: https://plantvillage.psu.edu/topics/apricot/infos
comment: Fungus survives in dead roots; symptoms similar to Phytophthora root rot.

Armyworms
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Barley
affects: Rye
affects: Wheat
development: Insect can go through 3-5 generations a year
distribution: Larvae are generally omnivorous in attacking grasses. Species of these insects are found in most cereal-growing areas of the world.
important: Cutworms and armyworms sporadically cause severe damage; when they do, they can devastate large areas.
life cycle: Adult cutworms and army worms are moths, and the females lay eggs on leaves and leaf sheaths near the ground. These eggs hatch within a few days and initially the larvae feed close to where they hatch. The larvae are found in cracks in the soil or under rocks during the day, feeding at night or early in the morning. In damp weather, they may feed all day.
management: Organic methods of controlling armyworms include biological control by natural enemies which parasitize the larvae and the application of Bacillus thuringiensis; there are chemicals available for commercial control but many that are available for the home garden do not provide adequate control of the larvae
symptoms: The primary symptom is defoliation of the plant. Larvae feed on leaves, chewing from the edges to the midrib, or on the heads of cereal plants. Heavy infestations can be very destructive; larvae may climb the plant and sever the neck just below the head. Some species may be found feeding at the soil surface, others underground feeding on roots, and still others feeding inside the stem.
alternative label: Cutworms
alternative label: Stalk Borers

Aruba
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:AW
agricultural land: 20 sq. km
area: 193 km²
climate: tropical marine; little seasonal temperature variation
density: 581/km²
latitude: -69.964
longitude: 12.518355
population: 104,494 (2008)
comment: Aruba is an island and a constituent country of the Kingdom of the Netherlands in the mid-south of the Caribbean Sea, about 29 kilometres north of the Venezuelan peninsula of Paraguaná and 80 kilometres northwest of Curaçao. It measures 32 kilometres long from its northwestern to its southeastern end and 10 kilometres across at its widest point. Together with Bonaire and Curaçao, Aruba forms a group referred to as the ABC islands. Collectively, these and the other three Dutch substantial islands in the Caribbean are often called the Dutch Caribbean, of which Aruba has about ​1⁄3 of the population.

Asia
type: NamedIndividual
type: Continent
has country: Armenia
has country: Azerbaijan
has country: Bangladesh
has country: Bhutan
has country: Brunei Darussalam
has country: Cambodia
has country: China
has country: Cyprus
has country: Georgia
has country: India
has country: Indonesia
has country: Japan
has country: Kazakhstan
has country: Korea
has country: Kyrgyzstan
has country: Laos
has country: Malaysia
has country: Maldives
has country: Mongolia
has country: Myanmar
has country: Nepal
has country: Northern Mariana Islands
has country: Philippines
has country: Russian Federation
has country: Singapore
has country: South Korea
has country: Sri Lanka
has country: Taiwan
has country: Tajikistan
has country: Thailand
has country: Timor-Leste
has country: Turkmenistan
has country: Uzbekistan
has country: Vietnam

Asiatic Rice Borer
type: NamedIndividual
type: Crop Pest
has disease: Rice

Asparagus
type: NamedIndividual
type: Crop
harvesting: Allow asparagus ferns to remain until the Spring before cutting or mowing them back to the ground to allow new spears to grow. The new spears are ready to harvest when they reach 17 to 22 cm (7-9 in) in length. The spears can simply be snapped by hand close to the ground. The spears can be harvested every 2 to 4 days for a period of 4 to 6 weeks in the second year (third year if grown from seed). Avoid over-harvesting as this will decrease the yield the following year. The harvest can be extended in the third year (fourth for seed grown plants) to 6-8 weeks.
propagation: Growing asparagus from seed is more time consuming and labor intensive than growing from divided crowns. If growing from seed, be aware that you will be unable to harvest the spears for longer than if grown from crowns (spears can generally be harvested in the third year after planting). Asparagus seeds should be sown indoors 12 to 14 weeks before planting outside. This should be timed so that the outdoor planting date is after all danger of frost for your area. Seeds should be sown in mix, planting 0.6 cm (0.25 in) deep. The seeds should be kept moist, but not wet and will germinate in approximately 3 weeks. Seedlings should be fully hardened off prior to planting outdoors, using a cold frame where possible. The asparagus nursery bed should be prepared in advance by digging deeply to break up the soil and incorporating plenty of organic matter such as well-rotted manure and by blending a complete fertilizer into the soil just prior to transplanting. Plant the seedlings 25 to 38 cm (10-25 in) apart in a trench approximately 10 cm (4 in) deep. The trench should be gradually filled in as the seedlings grow. Keep the soil moist by providing about 2.5 cm (1 in) of water weekly. Once the plants are established, irrigation is generally only required in very dry areas due to the deep root system that the plant produces. It is important to control weeds as not only will it prevent any competition with the asparagus plant but it will also ensure that the young spears can be seen and harvested at the correct time. The asparagus plants should be transplanted to their final planting site in the second year following the directions for planting crowns (below). When purchasing asparagus crowns, choose disease-free, 1 year old crowns from a reputable grower. All-male hybrids tend to be produce more spears than females and are a good choice for growing at home. Crowns should be planted once the soil has warmed to 10°C (50°F). Dig a trench 12 to 15 cm (5-6 in) deep and add super pho…
requirements: Asparagus grows best in regions with hot days and cool nights and requires 90–150 days of cold temperature to break dormancy. It will grow in most soils as long as they are well-draining and will grow optimally at a pH between 6.5 and 7.0. Asparagus should be planted in a sunny location in the home garden and it is best to avoid low lying areas as a late frost will cause damage to newly emerged spears.
uses: Asparagus spears can be eaten raw or cooked. They are low in calories and very low in sodium. Asparagus is a particularly good source of vitamin B6, calcium, magnesium , zinc, vitamin A, vitamin C, vitamin E, vitamin K, thiamin, riboflavin, rutin, niacin, folic acid, iron, phosphorus, potassium, copper, manganese, selenium, chromium, dietary fiber, and protein.
References: https://plantvillage.psu.edu/topics/asparagus/infos
comment: Asparagus, Asparagus officinalis, is an herbaceous perennial plant in the family Asparagaceae which is grown for its young shoots, or spears, which are eaten as a vegetable. The asparagus plant is tall with scale like leaves emerging from the underground stem (rhizome) and has stout stems and feathery foliage. The flowers are bell shaped and occur alone or in pairs. They are green-white to yellow in color. After flowering, a round red berry is formed with 1 to 6 black seeds. Asparagus can live for 20 or more years and can attain a height between 100–150 cm (39.4–59.1 in). Asparagus originates from Europe, northern Africa, and western Asia.

Atractomorpha Sinensis
type: NamedIndividual
type: Crop Pest

August
type: NamedIndividual
type: Month Of Year

Aulacophora Indica
type: NamedIndividual
type: Crop Pest
comment: Aulacophora Indica (Gmelin)

Australia
type: NamedIndividual
type: Country
belongs to: Oceania
has climate: Humid Subtropical Climate
has climate: Temperate Climate
has climate: Tropical Rainforest Climate
ISO code: ISO 3166-1:AU
agricultural land: 4.1 million sq. km
area: 7,686,850 km²
climate: generally arid to semiarid; temperate in south and east; tropical in north
density: 2.833/km²
latitude: 135.03845
longitude: -26.99425
population: 21,585,178 (2009)
comment: Australia, officially the Commonwealth of Australia, is a sovereign country comprising the mainland of the Australian continent, the island of Tasmania, and numerous smaller islands. It is the largest country in Oceania and the world's sixth-largest country by total area. Its population of nearly 26 million is highly urbanised and heavily concentrated on the eastern seaboard. Australia's capital is Canberra, and its largest city is Sydney. The country's other major metropolitan areas are Melbourne, Brisbane, Perth, and Adelaide.

Australian Plague Locusts
type: NamedIndividual
type: Crop Pest
alternative label: Chortoicetes Terminifera

Austria
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Humid Continental Climate
has climate: IceCap Climate
has climate: Marine Climate
has climate: Polar Climate
has climate: Boreal climate
ISO code: ISO 3166-1:AT
agricultural land: 28,690 sq. km
area: 83,872 km²
climate: temperate; continental, cloudy; cold winters with frequent rain and some snow in lowlands and snow in mountains; moderate summers with occasional showers
density: 99/km²
latitude: 13.345768
longitude: 47.696507
population: 8,316,487 (2007)
comment: Austria, officially the Republic of Austria, is a landlocked East Alpine country in the southern part of Central Europe. It is composed of nine federated states, one of which is Vienna, Austria's capital and largest city. It is bordered by Germany to the northwest, the Czech Republic to the north, Slovakia to the northeast, Hungary to the east, Slovenia and Italy to the south, and Switzerland and Liechtenstein to the west. Austria occupies an area of 83,879 km2 and has a population of nearly 9 million people. While German is the country's official language, many Austrians communicate informally in a variety of Bavarian dialects.

AutoML
type: NamedIndividual
type: Pretrained Model
label: AutoML, an approach that automates the design of machine learning models. AutoML can design small neural networks that perform on par with neural networks designed by human experts, these results were constrained to small academic datasets like CIFAR-10, and Penn Treebank. Abstract from authors: In Learning Transferable Architectures for Scalable Image Recognition, we apply AutoML to the ImageNet image classification and COCO object detection dataset -- two of the most respected large scale academic datasets in computer vision. These two datasets prove a great challenge for us because they are orders of magnitude larger than CIFAR-10 and Penn Treebank datasets. For instance, naively applying AutoML directly to ImageNet would require many months of training our method. To be able to apply our method to ImageNet we have altered the AutoML approach to be more tractable to large-scale datasets: We redesigned the search space so that AutoML could find the best layer which can then be stacked many times in a flexible manner to create a final network. We performed architecture search on CIFAR-10 and transferred the best learned architecture to ImageNet image classification and COCO object detection. With this method, AutoML was able to find the best layers that work well on CIFAR-10 but work well on ImageNet classification and COCO object detection. These two layers are combined to form a novel architecture, which we called “NASNet”.

Autumn
type: NamedIndividual
type: Season
alternative label: Fall

Avalanche
type: NamedIndividual
type: Planetary Phenomena
comment: An avalanche (also called a snowslide) is an event that occurs when a cohesive slab of snow lying upon a weaker layer of snow fractures and slides down a steep slope. Avalanches are typically triggered in a starting zone from a mechanical failure in the snowpack (slab avalanche) when the forces of the snow exceed its strength but sometimes only with gradual widening (loose snow avalanche). After initiation, avalanches usually accelerate rapidly and grow in mass and volume as they entrain more snow. If the avalanche moves fast enough, some of the snow may mix with the air forming a powder snow avalanche, which is a type of gravity current.

Average Quality
type: NamedIndividual
type: Quality

Azerbaijan
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:AZ
agricultural land: 47,687 sq. km
area: 86,600 km²
climate: dry, semiarid steppe
density: 97/km²
latitude: 47.68988
longitude: 40.15134
population: 8,699,000 (2008)
comment: Azerbaijan, officially the Republic of Azerbaijan, is a country in the Caucasus region of Eurasia. Located at the crossroads of Eastern Europe and Western Asia, it is bounded by the Caspian Sea to the east, the Russian republic of Dagestan to the north, Georgia to the northwest, Armenia and Turkey to the west, and Iran to the south.

Bacillus Subtilis
type: NamedIndividual
type: Bacterial
comment: Bacillus subtilis, known also as the hay bacillus or grass bacillus, is a Gram-positive, catalase-positive bacterium, found in soil and the gastrointestinal tract of ruminants and humans. As a member of the genus Bacillus, B. subtilis is rod-shaped, and can form a tough, protective endospore, allowing it to tolerate extreme environmental conditions. B. subtilis has historically been classified as an obligate aerobe, though evidence exists that it is a facultative anaerobe. B. subtilis is considered the best studied Gram-positive bacterium and a model organism to study bacterial chromosome replication and cell differentiation. It is one of the bacterial champions in secreted enzyme production and used on an industrial scale by biotechnology companies.

Bacterial Black Chaff
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Cereal
affects: Wheat
development: The bacterium can be seed borne and persists on crop residues in the soil, tolerating warm as well as freezing temperatures. Free moisture is required for infection and spread of the broken epidermal tissue. The disease is spread by splashing rain, plant contact, and insects.
distribution: These diseases occur worldwide on all small grain cereals and many grasses.
important: Black chaff rarely causes significant damage, even though symptoms often may be extensive.
symptoms: Black chaff and bacterial stripe are both caused by the same organism; the site and extent of the symptoms depends on the strain of the bacterium, the affected cultivar and environmental conditions. "Bacterial black chaff" occurs primarily on the glumes. Initial symptoms are narrow chlorotic lesions or stripes that have a water-soaked appearance; droplets of sticky yellowish exudate may appear with extended periods of rain or dew. The exudate dries to form crusty droplets or a translucent film on the surface of affected tissues. The film may crack and give a scaly appearance. If infection occurs early in the crop cycle, the spike may be infected, resulting in sterility; when the disease is severe, entire leaves or spikes may be killed.
comment: Bacterial Black chaff of wheat is caused by the bacterium Xanthomonas campestris pathovar (pv.) translucens. It enters plants through stomata and wounds. During wet weather or humid weather the bacterium multiplies rapidly and produces a cream to yellow bacterial ooze in the form of slime or viscous droplets on infected plant parts. This ooze appears light colored and scale-like when dry. The black chaff bacterium survives on and in seed. It is suspected to survive on crop residue and in the soil, but this is not well documented. During the growing season, the bacterium is capable of living on the plant surface as an epiphyte without causing symptoms. Over short distances, it is spread by splashing water, plant-to-plant contact, and insects.It is spread over long distances by contaminated seed, which is the primary source of inoculum.
alternative label: Bacterial Stripe

Bacterial Blight
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Barley
affects: Cassava
affects: Coffee
affects: Cotton
affects: Rice
affects: Rye
affects: Soybean
caused by: Xanthomonas oryzae
development: Occurs wherever barley is grown
management: Use only certified, disease-free seed; treat seeds with a fungicide prior to planting to prevent diseases which allow bacteria to enter easily; practice crop rotation to reduce disease build-up in soil.
symptoms: Water soaked spots on foliage; shriveling dead leaves; glossy yellow or brown streaks; plant appears stunted, slow plant growth.
comment: Bacterial blight creates water-soaked lesions usually start at leaf margins, a few cm from the tip and spreading towards the leaf base. Length and width of the affected areas increase while colour changes from yellowish to light brown due to drying

Bacterial Canker
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Cherry
management: If the disease is introduced to an area, all infected trees should be removed and destroyed; in areas where disease is endemic, windbreaks can help to reduce disease severity; cultural control of the disease should focus on controlling leaf miner populations, utilizing wind breaks and applications of copper sprays.
symptoms: Raised lesions on leaves, often at leaf margin or tip; lesions may also be present on twigs and fruits; young lesions are usually surrounded by yellow halo; depressed brown craters formed from collapse of lesions.
comment: Bacterial canker infections occur during fall, winter and early spring (during cool, wet weather) and are spread by rain or water, and pruning tools.

Bacterial Leaf Blight
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Rice
comment: Bacterial leaf blight, also called bacterial blight, deadly bacterial disease that is among the most destructive afflictions of cultivated rice (Oryza sativa and O. glaberrima). In severe epidemics, crop loss may be as high as 75 percent, and millions of hectares of rice are infected annually. The disease was first observed in 1884–85 in Kyushu, Japan, and the causal agent, the bacterium Xanthomonas oryzae pathovar oryzae (also referred to as Xoo), was identified in 1911, at that time having been named Bacillus oryzae. Thriving in warm, humid environments, bacterial blight has been observed in rice-growing regions of Asia, the western coast of Africa, Australia, Latin America, and the Caribbean. Although not commonly found in the United States, a bacterial strain related to Xoo has been listed as an agricultural select agent by the U.S. Department of Agriculture, a designation that places it under strict regulations. Bacterial blight first becomes evident as water-soaked streaks that spread from the leaf tips and margins, becoming larger and eventually releasing a milky ooze that dries into yellow droplets. Characteristic grayish white lesions then appear on the leaves, signaling the late stages of infection, when leaves dry out and die. In seedlings, the leaves dry out and wilt, a syndrome known as kresek. Infected seedlings usually are killed by bacterial blight within two to three weeks of being infected; adult plants may survive, though rice yield and quality are diminished. Since rice paddies are flooded throughout most of the growing season, Xoo may easily spread among crops; bacteria travel through the water from infected plants to the roots and leaves of neighbouring rice plants. Wind and water may also help spread Xoo bacteria to other crops and rice paddies. Various mechanisms of disease, including quorum sensing and biofilm formation, have been observed in rice bacterial blight and Xoo. In addition to rice, Xoo may infect other plants, such as rice cut-gr…

Bacterial Leaf Scorch
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
life cycle: Infected leafhoppers and spittlebugs feed on the succulent, terminal shoots of susceptible host trees, transmitting the bacteria. Xylem vessels become clogged with bacterium as it travels within, multiplying and infecting other parts of the tree. There are no viable control options for the insect vectors. The cold-sensitive bacteria overwinter in protected areas within the xylem of the tree, and their populations begin to climb again as the next growing season progresses.
management: Management Strategies 1. Maintain plant vigor. There is no cure for the disease. Keeping susceptible trees healthy and thriving can help them resist infection and survive longer once they are infected. 2. Practice good sanitation. Branches that have died due to bacterial leaf scorch should be routinely removed. Infected trees that are in a severe state of decline should also be removed. Disinfect pruning tools with a 10% bleach solution between pruning cuts. 3. Plant resistant species. In areas where bacterial leaf scorch has occurred, avoid planting highly susceptible trees. 4. Antibiotic injections. Oxytetracycline root flare injections applied in spring can reduce bacterium levels and delay symptoms by a couple of weeks. They are expensive, need to be reapplied each year, and possible damage resulting from long-term use is unknown. A certified arborist should be contacted if you are considering injections.
symptoms: The first noticeable symptom is premature browning of leaves in mid-summer. Symptoms worsen throughout late summer and fall. Leaf margins turn brown, beginning with the older leaves and moving outward, spreading to leaves toward the branch tip. In most, but not all infected trees, browned, dead areas of the leaf are separated from green tissue by a narrow yellow border. The browned leaves may drop from the tree. Infected trees leaf-out normally the following year, with leaves on a few more branches turning prematurely brown in late summer. Symptoms become progressively worse over a period of 3 to 8 years, until the entire tree turns brown prematurely. The lack of green, chlorophyll producing leaves year after year leads to twig, branch, and limb death due to continual defoliation. Bacterial leaf scorch can easily be mistaken for oak wilt or Dutch elm disease, except for the following: The cycle of bacterial leaf scorch repeats and becomes worse over a long period of time. Oak wilt and Dutch elm disease are both capable of killing susceptible trees within a matter of months. There is no streaking of the sapwood with bacterial leaf scorch. In bacterial leaf scorch, the leaf browning develops from the leaf edges and works toward the mid-vein, whereas browning tends to happen in a more overall, uniform manner with oak wilt and Dutch elm disease. Bacterial leaf scorch can also be mistaken for drought and heat stress. However, damage by bacterial leaf scorch begins in old leaves and spreads to the branch tips, with browning around the leaf edges. Damage due to environmental stresses tends to cause overall browning to the canopy and to individual leaves. Trees tend to react to environmental stress soon after damaging conditions occur whereas bacterial leaf scorch is unique in its timing. Leaf browning is generally not noticed until mid-summer and intensifies through late summer and fall. The only way to confirm the diagnosis of bacterial leaf scorch is through laboratory a…
Acronym: BLS
comment: Bacterial leaf scorch (BLS) is a systemic disease caused by the bacterium Xylella fastidiosa, which invades the xylem (water and nutrient conducting tissues) of susceptible trees. It is most commonly seen in pin, red, shingle, bur, and white oaks, but can also affect elm, oak, sycamore, mulberry, sweetgum, sugar maple, and red maple. Xylem-feeding leafhoppers and spittlebugs spread the bacterium from tree to tree. Transmission between trees through root grafts has also been reported. There is no cure for this disease; it is chronic and potentially fatal.

Bacterial Leaf Spot
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Lettuce
comment: Bacterial leaf spot can occur on both leaf and head lettuce varieties. As with most bacterial diseases, the pathogen, Xanthomonas campestris pv. vitians, is highly dependent on wet, cool conditions for infection and disease development. Symptoms develop only if rain or sprinkler irrigation is present.

Bacterial Leaf Streak
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Cereal
affects: Wheat
development: Disease spread through infected seed and splashing water
distribution: These diseases occur worldwide on all small grain cereals and many grasses.
management: Avoid planting seed from infected fields; avoid overhead irrigation; plant less susceptible cultivars
symptoms: Sudden appearance of water-soaked, light brown, elongated lesions on upper leaves; lesions quickly dry out and turn into necrotic streaks on the leaves; black stripes occur on glumes and purple black lesions appear on rachis and peduncle if infection is in the head
comment: Bacterial leaf streak (BLS), also known as black chaff, is a common bacterial disease of wheat. The disease is caused by the bacterial species Xanthomonas translucens pv. undulosa. The pathogen is found globally, but is a primary problem in the US in the lower mid-south and can reduce yields by up to 40 percent.[6] BLS is primarily seed-borne (the disease is transmitted by seed) and survives in and on the seed, but may also survive in crop residue in the soil in the off-season.

Bacterial Spike Blight
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Cereal
affects: Wheat
development: The bacteria persists in organic material in the soil. It attacks wheat when it comes in contact with the plant apex within the leaf whorl, and this transmission is often facilitated by the nematode A. tritici.
distribution: Wheat is the only cultivated host, though some wild grasses are susceptible to attack. The disease is frequently reported in the Asian Subcontinent.
important: Bacterial spike blight is not economically important.
symptoms: A yellow exudate on the spikes is indicative of bacterial spike blight. When dry, the exudate is white. Often the spikes and necks will emerge as a distorted, sticky mass (53). The early leaves may also be wrinkled or twisted. This bacteria is associated with the nematode Anguina tritici in some regions.
label: Yellow Ear Rot

Bacterial Spot
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Mango
affects: Pepper
affects: Tomato
caused by: Xanthomonas campestris
management: Provide windbreaks for plants; prune out infected twigs; protective sprays of copper during wet weather help to protect plants from the disease
symptoms: Angular, water-soaked spots on leaves which coalesce and turn black; black cankerous lesions on stems which crack and exude a gummy substance; irregular black lesions on fruits which extend into the flesh and exude gum; fruits dropping from plant
comment: Bacterial spot is caused by four species of Xanthomonas and occurs worldwide wherever tomatoes are grown. Bacterial spot causes leaf and fruit spots, which leads to defoliation, sun-scalded fruit, and yield loss. Due to diversity within the bacterial spot pathogens, the disease can occur at different temperatures and is a threat to tomato production worldwide. Disease development is favored by temperatures of 75 to 86 ℉ and high precipitation. In North Carolina, it is more prevalent in seasons with high precipitation and less prevalent during dry years.

Bacterial Wetwood
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
life cycle: Organisms, most commonly bacteria, infect the tree deep in the tissue and start a process of fermentation. The resulting pressure (up to 60 psi) forces the bacterial ooze out cracks, branch stubs and pruning cuts. This ooze discolors bark, kills the cambium near the cut preventing proper callusing of wounds and kills grass and other plants on which it drips. Bacterial wetwood is noticed externally when it exudes slime and leaves a stain on the bark. Wilting of foliage is common in young trees; old trees are more likely to develop a general decline in vigor or branch dieback in the upper crown.
management: Management Strategies 1. There is no cure or preventive treatment to avoid infection and development of bacterial wetwood. The following strategies may help. 2. Fertilization. Fertilize stressed trees to stimulate vigorous growth and lessen the severity of the disease but refrain from over fertilizing healthy vigorous trees as this may increase their susceptibility to the disorder. 3. Pruning. Remove any dead and weak branches. Promptly remove any loose or diseased bark around the area and make a clean cut arround wounds to facilitate healing. It is advided to disinfect tools with 70% rubbing alcohol before pruning a tree. Inserting a drainage tube to drain the area is not recommended. Insertion of a drainage tube can spread the disease and result in more harm than good.
symptoms: Symptoms vary with geographical location suggesting that the environment influences disease development. Trees in the western United States show more variability in internal disease development with much less bleeding and symptom expression than trees in the East and Midwest. Wetwood appears internally in the trunk and large limbs as a dark brown-black water-soaked area when the area is sectioned. The first external sign of wetwood is usually bubbling and seepage from wounded tissue in V-shaped branch crotches, wounds made by removal of branches, injection holes and trunk cracks. Insects are often attracted to the ooze on which they feed but there is not any evidence that these insects cause damage or transmit the bacteria but it is believed that some wood boring insects such as beetles may transmit the disease.
comment: Bacterial wetwood, also known as slime flux, is caused by an infection of one or more of several bacteria. It results in a water-soaked, oozing or bleeding condition of wood, which occurs in the trunk, branches and roots of shade and ornamental trees. It is normally not serious in most trees but is a chronic disease, which causes concern and can contribute to a general decline in tree vigor over time. Trees most susceptible to the disease include: elms, apple, crabapple, London plane, redbud, aspen, dogwood, magnolia, Russian olive, beech, fir, maple, sour gum, birches, hemlock, mountain ash, sycamore, boxelder, hickory, mulberry, sweet gum, butternut, horse chestnut, oaks, tulip tree, cottonwood, linden, pines, black locust, poplar, willow and walnut.

Bacterial Wilt
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Cucumber
life cycle: The bacteria overwinter in the digestive system of the cucumber beetle. In the spring, bacterial wilt is spread from plant to plant through both the striped and spotted cucumber beetles that feed on cucumbers and other relatives of this family. The bacteria are released through the insect excrement and move into host plants through the stomates and wounds, most likely the ones made when the insects feed. Insects ingest more bacteria as they feed on infected plants, and the cycle is repeated.
management: Management Strategies 1. Protect plants with netting. Prevent cucumber beetles from feeding and infecting plants by covering them with netting or porous fabric. 2. Remove and destroy plant material when symptoms of wilting are first noticed. There are no cures for the disease. Beetles spread the bacterium from infected plants to healthy plants. 3. Grow susceptible crops on rotation every third year. Since beetles overwinter in the soil and carry the bacterium, the cycle can be disrupted by only planting the host in an area every third year. 4. Avoid planting cucurbits next to corn. Spotted cucumber larvae also feed on corn; avoiding close plantings of these two crops may help control the beetles on cucurbits. 5. Grow varieties that tolerate bacterial wilt like butternut or acorn squash and Saladin or County Fair 83 cucumbers. No muskmelon varieties are known to be tolerant to bacterial wilt. 6. Dust plants with insecticide in the spring before the cucumber beetles have a chance to lay eggs (April-June). Apply pyrethrin or carbaryl (Sevin). Try an insecticide-bait combination such as Adios that has cucurbitacin, the beetle attractant, and a small amount of carbaryl (Sevin).
symptoms: Infected plants initially show wilting and drying of individual leaves. As the leaves wilt and shrivel, stems may dry out suddenly. Later, wilting spreads to entire branches and vines. Wilting will occur during the middle of the day during periods with high water stress. The vine may recover at night. Eventually, however, the entire vine will wither, collapse, and die. In partially resistant plants, symptoms appear as dwarfing, excessive blooms, and branching. A good diagnostic test for this wilt is to cut a wilted stem near the base. Touch a knife blade to the cut and draw away from the cut. White to clear strings of the bacterial ooze will be strung out from the cut made on the infected plant to the knife blade. This diagnostic test is not 100% reliable. Further investigation may be necessary.
comment: Cucumber bacterial wilt is caused by the bacterium, Erwinia tracheiphila, and is characterized initially by wilting and drying of individual leaves, especially those exhibiting cucumber beetle injury. Cucumbers and muskmelons are more susceptible than pumpkins or squash; the disease is rarely a problem on watermelons.

Bahamas
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:BS
agricultural land: 150 sq. km
area: 13,878 km²
climate: tropical marine; moderated by warm waters of Gulf Stream
density: 23.27/km²
latitude: -76.605415
longitude: 24.074047
population: 330,549 (2007)
comment: The Bahamas, known officially as the Commonwealth of The Bahamas, is a country within the Lucayan Archipelago of the West Indies in the Atlantic. It takes up 97% of the Lucayan Archipelago's land area and is home to 88% of the archipelago's population. The archipelagic state consists of more than 700 islands, cays, and islets in the Atlantic Ocean, and is located north of Cuba and northwest of the island of Hispaniola and the Turks and Caicos Islands, southeast of the US state of Florida, and east of the Florida Keys. The capital is Nassau on the island of New Providence. The Royal Bahamas Defence Force describes The Bahamas' territory as encompassing 470,000 km2 of ocean space.

Bahrain
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:BH
agricultural land: 83.6 sq. km
area: 665 km²
climate: arid; mild, pleasant winters; very hot, humid summers
density: 1454/km²
latitude: 50.60727
longitude: 25.912418
population: 1,046,814 1 (2007)
comment: Bahrain, officially the Kingdom of Bahrain, is a country in the Persian Gulf. The island nation comprises a small archipelago made up of 51 natural islands and an additional 33 artificial islands, centered around Bahrain Island which make up around 83 percent of the country's landmass. The country is situated between the Qatari peninsula and the north eastern coast of Saudi Arabia to which it is connected by the 25-kilometre King Fahd Causeway. According to the 2010 census, Bahrain's 2020 population is estimated at 1,701,575 people mid-year according to UN data. Half of people counted are non-nationals willing to report their status. At 780 square kilometres in size, it is the third-smallest nation in Asia after the Maldives and Singapore. The capital and largest city is Manama.

Bakanae disease
type: NamedIndividual
type: Crop Disease
affects: Rice
comment: Bakanae or bakanae disease from the Japanese for "foolish seedling", is a disease that infects the rice plant.

Banana Sigotka
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Banana
comment: BananaSigotka is a leaf-spot disease of banana plants caused by the ascomycete fungus Mycosphaerella fijiensis (Morelet). Also known as black leaf streak.

Banana Speckle
type: NamedIndividual
type: Crop Disease
affects: Banana
comment: Banana speckle is a common general name given to several species of fungi that are capable of reflecting diffused circular or irregular blotches on banana leaves that come in many colors such as grey, brown, and black.

Bangladesh
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:BD
agricultural land: 91,280 sq. km
area: 147,570 km²
climate: tropical; mild winter (October to March); hot, humid summer (March to June); humid, warm rainy monsoon (June to October)
density: 1045/km²
latitude: 90.351
longitude: 23.6086
population: 150,448,340 (2007)
comment: Bangladesh, officially the People's Republic of Bangladesh, is a country in South Asia. It is the eighth-most populous country in the world, with a population exceeding 163 million people, in an area of 148,460 square kilometres, making it one of the most densely populated countries in the world. Bangladesh shares land borders with India to the west, north, and east, Myanmar to the southeast, and the Bay of Bengal to the south. It is narrowly separated from Nepal and Bhutan by the Siliguri Corridor, and from China by Sikkim, in the north, respectively. Dhaka, the capital and largest city, is the nation's economic, political, and cultural hub. Chittagong, the largest seaport, is the second-largest city.

Barbados
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:BB
agricultural land: 150 sq. km
area: 431 km²
climate: tropical; rainy season (June to October)
latitude: -59.53465
longitude: 13.18355
population: 279,000 (2006)
comment: Barbados is an island country in the Lesser Antilles of the West Indies, in the Caribbean region of North America. It is 34 kilometres in length and up to 23 km in width, covering an area of 432 km2 . It is in the western area of the North Atlantic and 100 km east of the Windward Islands and the Caribbean Sea; therein, Barbados is the most easterly of the Caribbean Islands. Barbados is east of the Windwards, part of the Lesser Antilles, roughly at 13°N of the equator. It is about 168 km east of both the countries of Saint Lucia and Saint Vincent and the Grenadines and 180 km south-east of Martinique and 400 km north-east of Trinidad and Tobago. Barbados is outside the principal Atlantic hurricane belt. Its capital and largest city is Bridgetown.

Bare land
type: NamedIndividual
type: LandUse

Barley Stripe
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Barley
caused by: Pyrenophora graminea
development: Occurs wherever winter barley is grown.
management: Use only certified seed.
symptoms: Small yellow spots on seedling leaves; yellow to tan stripes along leaf blade before heading; red margins on stripes; death of diseased tissue; heads not emerging; plants stunted.
comment: Barley stripe is a seed-borne disease that initially infects seedlings. · Once established it grows systematically throughout the plant.

BYD
type: NamedIndividual
type: Crop Disease
type: Viral Disease
affects: Barley
affects: Cereal
affects: Rye
affects: Wheat
development: Temperatures of approximately 20C are favorable for disease development and symptoms appear approximately 14 days after infection.
distribution: The term barley yellow dwarf virus (BYDV) includes several related viruses that are all aphid transmitted. Over 20 species of aphids may act as vectors. BYD is probably the most widely distributed viral disease of cereals in the world. It attacks not only wheat, but also barley, triticale, oats, and many other grass species.
important: Infections occurring early in the crop cycel can result in yield losses of more than 20%, and much larger losses have been recorded.
management: Grow resistant or tolerant varieties; avoid planting crop very early or very late when aphid populations are high.
symptoms: The symptoms of barley yellow dwarf (BYD) vary with the affected crop cultivar, the age of the plant at the time of infection, the strain of the virus, and environmental conditions. Symptoms often are masked by or confused with other problems. Affected plants show a yellowing (54) or reddening (on oats and some wheats) of leaves, stunting, an upright posture of thickened stiff leaves (55), reduced root growth, delayed (or no) heading, and a reduction in yield. The heads of affected plants tend to remain erect and become black and discolored during ripening due to colonization by saprophytic fungi.
comment: Barley yellow dwarf virus (BYDV), a member of the Luteoviruses, is a group of five closely related virus strains. Strains of BYDV differ serologically and in virulence, host range and vector specificity. Virus particles are spherical. BYDV is transmitted by more than 20 aphid species. The most important are the oat bird-cherry aphid, the corn leaf aphid, the English grain aphid and the green bug. Aphids acquire BYDV by feeding on infected plants and transmit the virus in subsequent feedings.
label: Barley Yellow Dwarf

Barley yellow dwarf virus
type: NamedIndividual
type: Virus

Bacterial Leaf Blight
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Barley
affects: Cereal
affects: Wheat
development: The pathogens survives on crop debris, as well as various grass hosts. It is disseminated by splashing rain or by insects, and can be seed borne.
distribution: The disease can affect all small grain cereal crops; distribution is worldwide.
important: Basal glume rot usually is not economically important, but is frequently reported in humid cereal-growing areas.
symptoms: The leaves, culms, and spikes of wheat and triticale can be infected. Infections begin as small, dark green, water-soaked lesions that turn dark brown to blackish in color. On the spikelets, lesions generally start at the base of the glume and may eventually extend over the entire glume. Diseased glumes have a translucent appearance when held toward the light. Dark brown to black discoloration occurs with age. The disease may spread to the rachis, and lesions may also develop on the kernels. Under wet or humid conditions, a whitish gray bacterial ooze may be present. Stem infections result in dark discoloration of the stem; leaf infections result in small, irregular, water-soaked lesions. Symptoms can be confused with those of other bacterial diseases, genetic melanism (false black chaff), septoria nodorum blotch (glume blotch), and frost damage.
comment: Basal Glume Rot, also known as spikelet rot and basal glume blotch, affects glumes on spikes of wheat, triticale, barley, rye and some non- economic grasses.
label: Basal Glume Rot

Basal Stem Rot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
development: Fatal disease of aloes; disease emergence favors cold, damp conditions.
management: Pieces of plant may be saved by taking cuttings above rotted portion.
symptoms: Base of plant turning reddish brown to black and rotting.
comment: Basal stem rot (BSR) caused by Ganoderma boninense which is the most threatening disease of oil palm
label: Basal Stem Rot (BSR)

Beach
type: NamedIndividual
type: Topography

Beet Necrotic Yellow Vein Virus
type: NamedIndividual
type: Virus
causes: Rhizomania

Beetle
type: NamedIndividual
type: Crop Pest
comment: Beetles are a group of insects that form the order Coleoptera, in the superorder Endopterygota. Their front pair of wings are hardened into wing-cases, elytra, distinguishing them from most other insects. The Coleoptera, with about 400,000 species, is the largest of all orders, constituting almost 40% of described insects and 25% of all known animal life-forms; new species are discovered frequently.

Belarus
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:BY
agricultural land: 88,750 sq. km
area: 207,600 km²
climate: cold winters, cool and moist summers; transitional between continental and maritime
density: 49/km²
latitude: 27.986708
longitude: 53.71888
population: 9,689,800 (2008)
comment: Belarus, officially the Republic of Belarus, is a landlocked country in Eastern Europe. It is bordered by Russia to the east and northeast, Ukraine to the south, Poland to the west, and Lithuania and Latvia to the northwest. Covering an area of 207,600 square kilometres and with a population of 9.4 million, Belarus is the thirteenth-largest and the twentieth-most populous country in Europe. The country is administratively divided into seven regions, and is one of the world's most urbanized, with over 40% of its total land area forested. Minsk is the country's capital and largest city.

Belgium
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Marine Climate
ISO code: ISO 3166-1:BE
agricultural land: 13,370 sq. km
area: 30,528 km²
climate: temperate; mild winters, cool summers; rainy, humid, cloudy
density: 344.32/km²
latitude: 4.476773
longitude: 50.50117
population: 10,666,866 (2008)
comment: Belgium, officially the Kingdom of Belgium, is a country in Western Europe. It is bordered by the Netherlands to the north, Germany to the east, Luxembourg to the southeast, France to the southwest, and the North Sea to the northwest. It covers an area of 30,689 km2 and has a population of more than 11.5 million, making it the 22nd most densely populated country in the world and the 6th most densely populated country in Europe, with a density of 376 per square kilometre . The capital and largest city is Brussels; other major cities are Antwerp, Ghent, Charleroi, Liège, Bruges, Namur, and Leuven.

Belize
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:BZ
agricultural land: 1,570 sq. km
area: 22,966 km²
climate: tropical; very hot and humid; rainy season (May to November); dry season (February to May)
density: 13/km²
latitude: -88.346466
longitude: 17.192928
population: 320,000 (2008)
comment: Belize is a Caribbean country located on the northeastern coast of Central America. Belize is bordered on the north by Mexico, on the east by the Caribbean Sea, and on the south and west by Guatemala. It has an area of 22,970 square kilometres and a population of 419,199 . Its mainland is about 290 km long and 110 km wide. It has the lowest population and population density in Central America. The country's population growth rate of 1.87% per year is the second highest in the region and one of the highest in the Western Hemisphere.

Below Or Zero Temperature
type: NamedIndividual
type: Temperature

Below Room Temperature
type: NamedIndividual
type: Temperature

Bemisia Tabaci
type: NamedIndividual
type: Crop Pest
comment: Bemisia Tabaci (Gennadius)

Benin
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:BJ
agricultural land: 34,300 sq. km
area: 112,622 km²
climate: tropical; hot, humid in south; semiarid in north
density: 75/km²
latitude: 2.313138
longitude: 9.322047
population: 8,439,000 (2005)
comment: Benin, officially the Republic of Benin, is a country in West Africa. It is bordered by Togo to the west, Nigeria to the east, Burkina Faso to the north-west, and Niger to the north-east. The majority of its population lives on the small southern coastline of the Bight of Benin, part of the Gulf of Guinea in the northernmost tropical portion of the Atlantic Ocean. The capital of Benin is Porto-Novo, but the seat of government is in Cotonou, the country's largest city and economic capital. Benin covers an area of 114,763 square kilometres and its population in 2018 was estimated to be approximately 11.49 million. Benin is a tropical nation, highly dependent on agriculture, and is a large exporter of cotton and palm oil. Substantial employment and income arise from subsistence farming.

Bermuda
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:BM
agricultural land: 7.4 sq. km
area: 53.3 km²
climate: subtropical; mild, humid; gales, strong winds common in winter
density: 1,239/km²
latitude: -64.77402
longitude: 32.320236
population: 66,163 (2007)
comment: Bermuda is a British Overseas Territory in the North Atlantic Ocean. It is approximately 1,035 km east-southeast of Cape Hatteras, North Carolina ; 1,236 km south of Cape Sable Island, Nova Scotia; 1,759 km northeast of Cuba, and 1,538 km due north of the British Virgin Islands. Though it is typically referred to in the singular, Bermuda consists of 181 islands; the largest of these islands is known as Main Island. The capital city of Bermuda is Hamilton. Bermuda is internally self-governing, with its own constitution and cabinet of ministers selected from the elected Members of the lower house of a Parliament that enacts local laws. As the national government, the Government of the United Kingdom is ultimately responsible for ensuring good governance within British Overseas Territories, and retains responsibility for defence and foreign relations. As of July 2018, it has a population of 71,176, making it the most populous of the British overseas territories. Bermuda's largest industries are offshore insurance, reinsurance, and tourism. Bermuda had one of the world's highest GDP per capita for most of the 20th century.

Bhutan
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:BT
agricultural land: 5,200 sq. km
area: 47,000 km²
climate: varies; tropical in southern plains; cool winters and hot summers in central valleys; severe winters and cool summers in Himalayas
density: 14/km²
latitude: 90.44246
longitude: 27.515705
population: 672,425 (2005)
comment: Bhutan, officially known as the Kingdom of Bhutan, is a landlocked country in the Eastern Himalayas. It is bordered by China to the north and India to the south. Nepal and Bangladesh are located in proximity to Bhutan but do not share a land border. The country has a population of over 754,000 and a territory of 38,394 square kilometers which ranks 133rd in terms of land area. Bhutan is a constitutional monarchy with Mahayana Buddhism as the state religion.

Bipolarisoryza fungus
type: NamedIndividual
type: Fungus

Bitter Gourd
type: NamedIndividual
type: Crop

Black Chaff
type: NamedIndividual
type: Crop Disease
affects: Wheat
management: The most effective management strategy for black chaff is use of certified, pathogen-free seed. In seed increase and certification programs, plants should be raised at least a ¼ mile away from potential sources of inoculum. These include irrigated commercial production fields and fields with wheat stubble. Controlling volunteer cereals and grassy weeds can help to reduce primary inoculum. Irrigation management is critical in creating an environment that is less favorable to disease development and spread. Irrigation should be managed in such a way as to allow the plant canopy to dry completely between irrigations. Although highly resistant wheat cultivars are currently not available, those known to be highly susceptible to black chaff should be avoided.
symptoms: Black chaff derives its name from the darkened glumes of infected plants. This symptom is similar to that caused by genetic melanism (darkening of tissue) and glume blotch incited by Septoria nodorum. Black chaff can be distinguished from other diseases by the appearance of cream to yellow bacterial ooze in the form of slime or viscous droplets produced on infected plant parts during wet or humid weather. Bands of necrotic and healthy tissue on awns ("barber's pole") are indicative of black chaff. A dark brown to purple discoloration may appear on the stem below the head and above the flag leaf. On leaves, the disease is known bacterial streak or bacterial stripe. Symptoms start as small water-soaked spots or streaks that turn brown after a few days. Lesions are irregularly shaped and elongate and may extend the length of the leaf blade. In wheat, a diffuse lime-green halo may surround lesions. Leaf symptoms give plants an overall orange cast.
comment: Black chaff of wheat is caused by the bacterium Xanthomonas campestris pathovar (pv.) translucens. It enters plants through stomata and wounds. During wet weather or humid weather the bacterium multiplies rapidly and produces a cream to yellow bacterial ooze in the form of slime or viscous droplets on infected plant parts. This ooze appears light colored and scale-like when dry. The black chaff bacterium survives on and in seed. It is suspected to survive on crop residue and in the soil, but this is not well documented. During the growing season, the bacterium is capable of living on the plant surface as an epiphyte without causing symptoms. Over short distances, it is spread by splashing water, plant-to-plant contact, and insects.It is spread over long distances by contaminated seed, which is the primary source of inoculum.

Black Measles
type: NamedIndividual
type: Crop Disease

Black Mould
type: NamedIndividual
type: Crop Disease
comment: Black mold, or Stachybotrys chartarum, surfaces as blackish-green spots or blemishes.

Black Pepper
type: NamedIndividual
type: Crop
has disease: Pepper lace bug
propagation: Black pepper can be propagated from dry seeds or, as is most common for commercial production, from cuttings or stolons of established plants. Cuttings are usually taken from the secondary runners of the plant and should possess one or two leaves. Cuttings are then rooted in a seedbed before transplanting when the plant has 4-7 new leaves. In cultivation, a trellis is used to support the plant. The trellis should be at least 4 m (13 ft) high. Black pepper should be planted using an 8 × 8 m (26 × 26 ft) spacing (i.e. 8 m between individual plants and 8 m between rows).
requirements: Black pepper is a tropical plant and does not tolerate frost. Plants will not tolerate temperatures below 12˚C (53.6˚F). The plants will grow best in wet, tropical climates at ambient temperatures between 25 and 30˚C (77-86˚F) in a deep, well-draining soil with a good capacity to retain water. The soil should be rich in organic matter with a pH between 5.5 and 6.0. The plants require an annual rainfall of approximately 2000 mm and will require additional irrigation in drier areas.
uses: Black pepper plants are used to produce black, white and green pepper. Black pepper is the product of drying the fruit to produce the familiar black peppercorns. White pepper is produced by soaking the fruits in water for approximately a week to recover the seed from the decomposed fruit. Green pepper is produced by drying the unripe fruit in a way that retains the green colour. Green peppercorns are often conserved by pickling.
References: https://plantvillage.psu.edu/topics/black-pepper/infos
comment: Black pepper, Piper nigrum, is a climbing perennial plant in the family Piperaceae which is grown for its fruits. The fruits are used to produce black, white and green peppercorns which are commonly used as a spice in cooking. Black pepper may be vining or have bushy, wooden stems. The plant has simple, alternating leaves which are oval in shape and produces clusters, or spikes, of 50 to 150 flowers. The fruits develop on the flower spike and are small spherical fruits which are green and ripen to red. Each stem can produce 20–30 spikes. Black pepper can grow to be 10 m (33 ft) in height but under cultivation it is usually restricted to 3-4 m (10–13 ft). It is a perennial plant which can live for over 30 years, with a commercial lifespan of 12–20 years. Black pepper may also be referred to as pepper or Madagascar pepper and is native to South and Southeast Asia.

Black Point
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Barley
affects: Rye
affects: Wheat
comment: Black point (blackpoint, kernel smudge or just smudge) is often considered a fungal disease that affects wheat, barley and rye. It is thought to be caused by various species of Alternaria, Fusarium, and Helminthosporium, and possibly other fungal genera.

Black Root Rot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Citrus
affects: Lemon
affects: Limes
affects: Orange
management: Keep glasshouses well lit and warm during winter to encourage vigorous root growth; use good quality potting soil which provides good aeration.
symptoms: Small brown-black lesions on roots which may coalesce and turn entire root black; root cortex may slough off to reveal the vascular tissue below; leaves of plant may be chlorotic.
comment: Serious disease of glasshouse grown citrus trees; pathogen usually drops to non-damaging levels after tree is transplanted to the field.

Black Rot
type: NamedIndividual
type: Crop Disease

Black Sigatoka
type: NamedIndividual
type: Crop Disease
comment: Black sigatoka is a leaf-spot disease of banana plants caused by the ascomycete fungus Mycosphaerella fijiensis (Morelet). Also known as black leaf streak, it was discovered in 1963 and named for its similarities with yellow Sigatoka, which is caused by Mycosphaerella musicola (Mulder).

Black Spot
type: NamedIndividual
type: Crop Disease
affects: Citrus
comment: The citrus black spot also abbreviated as CBS is available at the time when the plant is sensitive and the atmosphere is favorable for disease. The symptoms of the citrus leaf and fruits are small, round, and under dangerous spots with gray squares. The color of black spot lesion is a dark brown having spot diameter of 0.12 to 0.4 in.

Black Stem Rust
type: NamedIndividual
type: Crop Disease
affects: Wheat
comment: Black stem rusts appear on the leaves, leaf sheaths, stems and glumes. These develop into darker pustules, particularly on the leaf sheaths and stems. Black stem rust is spread by means of wind borne spores. There is an overwintering stage which re-infects crops via an intermediate host, the shrub barberry. In the UK, barberry is not believed to be an important factor in disease spread. Epidemics usually start from wind borne spores from continental Europe, and for this reason the disease tends to be confined to the South West of England.

Black Twig Borer
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Coffee
management: Prune out infested twigs and stems and destroy; flagging branches should be pruned back a few inches from the beginning of symptomatic areas; adequate fertilizer and irrigation to ensure vigorous plants can speed recovery from pruning injury
symptoms: Wilting and yellowing of foliage, often at end of twigs and branches (termed "flagging"); o pin sized hole can often be found on the underside of the flagging stems or twigs where the insect has entered the plant; twigs and stems are hollowed out and can be seen by cutting open the affected tissue; the adult beetle is small and black, approx. 2 mm in length and is rarely seen; eggs and pupae are creamy white in color
comment: Damage caused by the beetles promotes secondary infestation by bacteria and other fungi; adult beetles overwinter in the plant

Tea netted blister blight
type: NamedIndividual
type: Crop Disease
affects: Tea
comment: The blister blight disease is one such serious foliar tea disease caused by the obligate biotrophic fungus Exobasidium vexans. E. vexans, belonging to the phylum basidiomycete primarily infects the young succulent harvestable tea leaves and results in ~40% yield crop loss.

Blister Spot
type: NamedIndividual
type: Crop Disease
affects: Coffee
biological control: The bacteria overwinter in apple buds, leaf scars and diseased fruit left on the orchard floor from the previous season. Although infected apple buds appear healthy, the bacteria multiply during the spring and are rain splashed to leaves and other plant surfaces throughout the orchard. The bacteria survive and multiply on leaf surfaces including weeds in the orchard, without causing any disease symptoms. Warm, humid or wet conditions during the spring and early summer favour a build up of bacterial populations and subsequent infections of leaf veins and fruit. During late spring or early summer, a brief shower is all that is required to distribute the bacteria onto the developing fruit where they infect through the fruit pores or lenticels. Young Mutsu fruit are most susceptible beginning two weeks after petal fall lasting for about six weeks (late July). After this point the lenticels on fruit are no longer susceptible to infection by the bacterium.
symptoms: Initially symptoms appear as small water-soaked raised blisters associated with the lenticils (fruit pores) on the fruit surface. The first spots are often detected near the calyx end of fruit growing towards the outside of the tree facing the sun. Symptoms often do not appear until mid to late July after which time the bacteria can no longer infect the fruit. The lesion eventually appears as a brown blister with a purplish black margin expanding to no more than 4-5 mm in diameter on the fruit surface. Lesions rarely penetrate the flesh to more than 1-2 mm deep. The lesions do not develop into fruit decay, but several to more than 100 blister spots can occur on a single fruit reducing the fresh market quality significantly.
comment: Blister spot is an economic disease of Mutsu (Crispin) apple fruit in Eastern North America and is caused by the bacterium Pseudomonas syringae pv. papulans. It is seldom of economic importance on other apple cultivars although minor outbreaks of blister spot have occurred on Fuji and RedCort. Blister spot, caused by the bacterium Psuedomonas syringae pv. papulans, is probably present in most apple orchards. Many cultivars including Golden Delicious, Red Delicious, Jonagold and Cortland are susceptible to this disease but Mutsu (Crispin) is very susceptible. Other susceptible cultivars usually become infected only when planted adjacent to an infected block of the very susceptible Mutsu cultivar. Infections occur especially if frequent rains occur during the mid to late July when the fruit is at a susceptible growth stage.

Blizzard
type: NamedIndividual
type: Weather Phenomena
comment: A blizzard is a severe snowstorm characterized by strong sustained winds of at least 56 km/h (35 mph) and lasting for a prolonged period of time—typically three hours or more. A ground blizzard is a weather condition where snow is not falling but loose snow on the ground is lifted and blown by strong winds. Blizzards can have an immense size and usually stretch to hundreds or thousands of kilometres.

Blue Mold
type: NamedIndividual
type: Crop Disease
affects: Apple
biological control: Orchard sanitation to remove decayed fruit and organic debris on the orchard floor helps reduce inoculum levels of Penicillium spp. in the orchard. Good harvest and handling practices designed to minimize fruit punctures and bruises are critical to minimize blue mold infections. However, because postharvest environments create more favorable conditions for infections and decay development, mainly due to high humidity levels, postharvest sanitation practices have a greater impact on blue mold control. It is important to clean and sanitize rooms (air, walls, floors) and bins at least once a season before using them to store a new crop. Sanitation of the packing-line from the dump-tank all-the-way to the sorters needs to be done regularly during the packing season. Several sanitizers, i.e. chlorine and chlorine dioxide, hydrogen peroxide, organic aids, electrolyzed water and ozone, are available and all have different efficacies and uses (Bernat et al., 2018; Feliziani et al., 2016). It is recommended to consult extension specialists or consultants in your area and the product labels to optimize sanitation. Recent cold or hot fogging technologies have shown good levels of efficacy in sanitizing larger facilities (entire rooms, lines, stacked bins) more efficiently.
chemical control: Most current preharvest fungicides applied days before harvest may not provide the highest level of efficacy as Penicillium infections typically occur after harvest. Therefore, postharvest fungicide applications remain the most effective chemical way to control blue mold. Currently, there are four single-site postharvest fungicides labeled for postharvest application (Table 2). Thiabendazole (Mertect) has been largely used in the PNW since the 1970s until early 2000 when two new postharvest fungicides, fludioxonil (Scholar) and pyrimethanil (Penbotec), were registered for postharvest decay management. In 2016, difenoconazole was labeled for postharvest application in pome fruit and other commodities. With current absence of resistant strains these four fungicides have a high level of efficacy against blue mold, with fludioxonil shown to have the highest efficacy. For decades, postharvest fungicides have been applied as a drench at harvest or as a spray on the packing-line. However, in recent years, thermonebulization (also called fog or aerosol or dry application) has become common in the PNW pome fruit industry. One of the advantages of fog application is that it reduces the spread of spores between bins compared to when non-sanitized bins are used for drenching. Although fog application still requires more optimization for other diseases, it has been shown to have a better efficacy against blue mold compared to drenching. Formulations to apply thiabendazole, pyrimethanil , and fludioxonil through TNB exist already, whereas difenoconazole’s dry formulation is pending. Besides these four single-site fungicides, captan, a multi-site fungicide, is also registered for postharvest application. Its efficacy is fair and may help for short-term storage and for managing fungicide resistance development. Postharvest fungicides should be applied soon after harvest because the more time that elapses after harvest, the higher the risk for decay to develop in storage.
life cycle: Penicillium spp. are typical postharvest pathogens, and although some inoculum can be found in orchard soils and on organic debris on the orchard floor, fruit are seldom infected by Penicillium while on the tree. Penicillium survive easily as airborne spores in storage room air, walls, floors and bins. When bins are immersed in dump-tanks, large amounts of spores can be released into the water and carried out in flume water infecting additional fruit on the packing line. Infections may spread through extended storage, especially for packers who pre-size and store wet fruit (Amiri and Bompeix, 2005; Sanderson and Spotts, 1995). Infection by P. expansum may start immediately after harvest on fresh wounds caused during picking and handling (Amiri and Bompeix, 2005). These lesions will develop slowly in cold storage and may produce large amounts of spores that are air-disseminated by continuous fan movements during storage to infect additional fruit, air, walls and bins. Infections by Penicillium occurring through lenticels are rare but possible, especially as the fruit over-mature and lenticels may breakdown. On rare occasions, remaining infected parts of flowers can serve as a source of inoculum of Penicillium and other pathogens to infect seeds inside the fruit that may cause “core rot” later in storage. Although certain cultivars such as Honeycrisp and Gala may seem more susceptible to blue mold infections, all current commercial apple and pear cultivars are susceptible to blue mold, especially through wounds.
management: Penicillium expansum is considered to have a medium to high risk for fungicide resistance development. Because the fungus sporulates profusely, there is a risk that many spores will be exposed to the applied fungicide which increases the risk of selecting for resistance. Resistance to thiabendazole has been reported in the PNW (Li and Xiao 2008) and elsewhere. In Washington State, frequencies vary between packers based on the usage frequency. Resistance to pyrimethanil (Penbotec) has been found to range between 0 and 50% in Washington (Caiazzo et al., 2014, Amiri and Pandit, 2019) whereas 5% to 10% of the population showed tolerance to fludioxonil (Scholar) (Amiri and Pandit 2019). Given the known risk and the limited number of postharvest fungicides available, it is critical that these materials are used appropriately to limit the development of fungicide resistance. Knowing what type and level of resistance in each warehouse is the first step in developing an effective spray program. WSU researchers (Contact Dr. Amiri) can help with conducting a risk assessment. The second step is to implement sound annual cleaning and sanitation to remove or reduce the residual inoculum from previous seasons. It is critical to apply a different FRAC group for postharvest treatments each season. Although resistance to thiabendazole has been documented, 50% of PNW packers can still use this fungicide if they follow good sanitation practices. It is recommended not to apply thiabendazole (FRAC 1) postharvest if Topsin-M (FRAC1) was used preharvest. The two most effective fungicides pyrimethanil (FRAC 9) and fludioxonil (FRAC 12) can be alternated on a yearly basis. In some cultivars, known to be susceptible to bull’s eye rot (BER), is recommended to apply FDL with thiabendazole to improve control of BER. Academy (DIF + fludioxonil) has shown high efficacy levels against blue mold after seven months of cold storage (Ali and Amiri 2018; Jurick et al. 2018). In Washington State, Academy…
symptoms: Blue mold originates primarily from wounds, such as punctures, splits, bruises and limb frictions, infected by Penicillium spp. At early infection stages, blue mold symptoms include light tan to dark brown circular lesions with very sharp margin between diseased and healthy tissues (Fig 1A-C). The decayed tissue is medium-soft and watery. Decayed tissue can be readily separated from the healthy tissue, leaving a “bowl-like” cavity (Fig. 1D). Green spore masses may appear on the decayed area, starting at the infection site. As the decayed area ages, green spores may turn blue giving the common name of “blue mold” to the disease. Decayed fruit has an earthy, musty odor. Blue-green spore masses (Fig. 1G) on the lesion and the associated musty odor are diagnostic of blue mold. When decay has fully developed and the lesion softens, green-blue spore masses may be less apparent and blue mold can be misdiagnosed as Mucor rot. However, Mucor rot is characterized by a sweet odor and usually softens much quicker than blue mold (Table 1). Blue mold can also originate from infections through the stem bowl (Fig. 1E), where potential openings may have been caused by stem removal or through cracks and splits that are common on cultivars such as Gala and Honeycrisp. Calyx-end blue mold (Fig. 1F,I) may occur as a result of fruit drenching or in some cases due to core rot spreading to the surface of the fruit.
comment: Blue mold is the most important postharvest disease of apples and pears worldwide and in the Pacific Northwest (Amiri and Ali, 2016; Rosenberger, 1990). Some strains of the blue mold causal fungus, Penicillium expansum, secrete patulin, a mycotoxin with mutagenic, neurotoxic, and gastrointestinal effects (Ramalingam et al., 2019). Therefore, blue mold is an economic concern not only to the fresh-fruit industry, but also to the fruit-processing industry since Penicillium contamination can result in patulin concentrations higher than the 50 µg/kg (ppb) limit in apple and pear processed products.

Blueberry
type: NamedIndividual
type: Crop
type: Fruit Tree
description: Blueberry is a crown forming, woody, perennial shrub in the family Ericaceae grown for its fruits, or berries, of the same name. The blueberry plant possesses oval or elliptical leaves which grow alternately on the stems. The stems, or twigs, are yellow/green in color and turn reddish in winter. Flowers are bell shaped and can be either white or pink, occurring in clusters of 8 to 10 flowers. Berries are blue to black in color and 0.6-1.3 cm (0.25-0.5 in) in size. Lowbush blueberry plants (including Vaccinium angustifolium) are smaller in stature, reaching heights of 35 to 60 cm (13.8-23.6 in) and with proper management can produce fruit for between 40 and 50 years. Lowbush blueberries are also sometimes referred to as Maine, New Hampshire or wild blueberry and are native to eastern/central Canada and northeastern United States. Highbush blueberries (including Vaccinium corymbosum) are generally 2-3 m (6.6-9.8 ft) in height but can reach up to 5 m (16.4 ft) and may also be referred to as huckleberry or swamp blueberry. Highbush blueberry accounts for most of the commercial production of blueberry fruit and is also native only to North America.
propagation: Lowbush blueberry grows best in well-drained, acidic soils with a pH between 4.5 and 4.8 and should be planted in early Spring as purchased plants or as “plugsâ€ (a small cutting, including root, from an established bush). The newly planted blueberry should be covered in a couple of inches of mulch to conserve moisture in the soil. Any flowers produced by the plants should be removed for at least one year after planting to promote vigor and new growth. Plants should be pruned by cutting close to the ground and a pruned plant will not produce any fruit in the first year, but in the second year subsequent to pruning their should be a good yield. Growth can be stimulated by field-burning, as these plants are very fire-tolerant. Highbush blueberry can be propagated from seeds, cuttings from an established plant or bare root and should be planted in late Fall. Blueberry plants grow best in well drained, highly acidic soils with a pH between 4.5 and 5.8. The plants have a shallow root system and should be planted in a hole at least twice as large as the existing root system and mulched with a 7-10 cm (3-4 in) layer of organic mulch. The first year of blossom should be removed to encourage new growth and it is also recommended to do the same in the second year. Plants should be pruned annually, particularly after the first two years, to promote plant vigour and remove diseased or broken canes. Irrigation is recommended due to the shallow root system and greatly promotes the fruit yield this practice is highly recommended if growing commercially.
uses: Blueberries can be eaten fresh or can be dried or baked for further processing. Frozen or pureed berries are commonly used to make jams and preserves and baked goods. Lowbush blueberry is commonly used to make wine.
References: https://plantvillage.psu.edu/topics/blueberry/infos
comment: Blueberry is a crown forming, woody, perennial shrub in the family Ericaceae grown for its fruits, or berries, of the same name. The blueberry plant possesses oval or elliptical leaves which grow alternately on the stems. The stems, or twigs, are yellow/green in color and turn reddish in winter. Flowers are bell shaped and can be either white or pink, occurring in clusters of 8 to 10 flowers. Berries are blue to black in color and 0.6-1.3 cm (0.25-0.5 in) in size. Lowbush blueberry plants (including Vaccinium angustifolium) are smaller in stature, reaching heights of 35 to 60 cm (13.8-23.6 in) and with proper management can produce fruit for between 40 and 50 years. Lowbush blueberries are also sometimes referred to as Maine, New Hampshire or wild blueberry and are native to eastern/central Canada and northeastern United States. Highbush blueberries (including Vaccinium corymbosum) are generally 2-3 m (6.6-9.8 ft) in height but can reach up to 5 m (16.4 ft) and may also be referred to as huckleberry or swamp blueberry. Highbush blueberry accounts for most of the commercial production of blueberry fruit and is also native only to North America.

Blumeria Graminis
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
comment: Blumeria graminis (commonly called barley powdery mildew or corn mildew) is a fungus that causes powdery mildew on grasses, including cereals. It is the only species in the genus Blumeria. It has also been called Erysiphe graminis and (by its anamorph) Oidium monilioides or Oidium tritici.

Bolivia
type: NamedIndividual
type: Country
belongs to: South America
ISO code: ISO 3166-1:BO
agricultural land: 370,550 sq. km
area: 1,098,581 km²
climate: varies with altitude; humid and tropical to cold and semiarid
density: 8.4/km²
latitude: -63.54943
longitude: -16.28835
population: 9,119,152 (2007)
comment: Bolivia /bəˈlɪviə/, officially the Plurinational State of Bolivia, is a landlocked country located in western-central South America. The constitutional capital is Sucre, while the seat of government and executive capital is La Paz. The largest city and principal industrial center is Santa Cruz de la Sierra, located on the Llanos Orientales, a mostly flat region in the east of the country.

Boll Rot
type: NamedIndividual
type: Crop Disease
affects: Cotton

Booting stage
type: NamedIndividual
type: Growth Stage
related to: Wheat
comment: Stage 4: Booting, Development of harvestable vegetative plant parts, bolting For sorghum plants, the booting stage is when the sorghum panicle, also known as the head, is in the flag leaf sheath and can be seen as a bulge or swelling. Leaf collars of all leaves are visible, and the panicle is pushed up through the flag leaf collar by the upper stalk, known as the peduncle. The length of the peduncle can be affected by stress at this time and is influenced by hybrid genetics. With most grain sorghum hybrids, the boot stage occurs approximately 50 to 60 days after emergence. Moisture and heat stress during the boot stage and for the next 14 days will significantly lower yield. The plant progresses from the boot stage to the heading stage in three to five days.
comment: Stage 4: Booting, Development of harvestable vegetative plant parts, bolting 41 Early boot stage: flag leaf sheath extending 43 Mid boot stage: flag leaf sheath just visibly swollen 45 Late boot stage: flag leaf sheath swollen 47 Flag leaf sheath opening 49 First awns visible (in awned forms only) Growth Stages of Sorghum: Seeding, Emergence, Three-leaf, Five-leaf, Growing point differentation, Flag leaf visible, Booting, Flowering, Soft Dough, Hard Dough, Physiological maturity

Bosnia and Herzegovina
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:BA
agricultural land: 21,510 sq. km
area: 51,197 km²
climate: hot summers and cold winters; areas of high elevation have short, cool summers and long, severe winters; mild, rainy winters along coast
density: 76/km²
latitude: 17.67233
longitude: 43.919456
population: 3,981,239 (2007)
comment: Bosnia and Herzegovina, abbreviated BiH or B&H, sometimes called Bosnia–Herzegovina and often known informally as Bosnia, is a country in South and Southeast Europe, located within the Balkans. Sarajevo is the capital and largest city.

Botrytis Cinerea
type: NamedIndividual
type: Fungus
affects: Grape
affects: Grapefruit
affects: Grapevines
comment: Botrytis cinerea is a necrotrophic fungus that affects many plant species, although its most notable hosts may be wine grapes. In viticulture, it is commonly known as "botrytis bunch rot"; in horticulture, it is usually called "grey mould" or "gray mold".

Botswana
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:BW
agricultural land: 258,610 sq. km
area: 600,370 km²
climate: semiarid; warm winters and hot summers
density: 3.0/km²
latitude: 24.680159
longitude: -22.344028
population: 1,639,833 (2006)
comment: Botswana, officially the Republic of Botswana, is a landlocked country in Southern Africa. Botswana is topographically flat, with up to 70 percent of its territory being the Kalahari Desert. It is bordered by South Africa to the south and southeast, Namibia to the west and north, and Zimbabwe to the northeast. Its border with Zambia to the north near Kazungula is poorly defined due to being in the midst of the Zambezi River. This border with Zambia is, at most, a few hundred metres long.

Bouvet Island
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:BV
area: 49 km²
climate: antarctic
latitude: 3.413194
longitude: -54.4232
comment: Bouvet Island is an uninhabited subantarctic volcanic island located in the South Atlantic Ocean at 54°25′S 3°22′E﻿ / ﻿54.417°S 3.367°E﻿ / -54.417; 3.367Coordinates: 54°25′S 3°22′E﻿ / ﻿54.417°S 3.367°E﻿ / -54.417; 3.367. Politically, it is a dependency of Norway and, because it lies north of the region covered by the Antarctic Treaty System, it is not part of that system. Geographically, it is situated at the southern end of the Mid-Atlantic Ridge and is the most remote island in the world, lying as it does 1,700 kilometres north of the Princess Astrid Coast of Queen Maud Land, Antarctica, 1,900 kilometres east of the South Sandwich Islands, 1,600 kilometres south of Gough Island, and 2,600 kilometres south-southwest of the coast of South Africa.

Brazil
type: NamedIndividual
type: Country
belongs to: South America
has climate: Tropical Savanna Climate
ISO code: ISO 3166-1:BR
agricultural land: 2.75 million sq. km
area: 8,514,877 km²
climate: mostly tropical, but temperate in south
density: 22/km²
latitude: -51.412292
longitude: -14.242914
population: 196,342,592 (2008)
comment: Brazil, officially the Federative Republic of Brazil, is the largest country in both South America and Latin America. At 8.5 million square kilometers and with over 211 million people, Brazil is the world's fifth-largest country by area and the sixth most populous. Its capital is Brasília, and its most populous city is São Paulo. The federation is composed of the union of the 26 states and the Federal District. It is the largest country to have Portuguese as an official language and the only one in the Americas; it is also one of the most multicultural and ethnically diverse nations, due to over a century of mass immigration from around the world; as well as the most populous Roman Catholic-majority country.

Brown Coloured Larva Of Looper Moth
type: NamedIndividual
type: Crop Pest
alternative label: Geometridae

Brown Eye Spot
type: NamedIndividual
type: Crop Disease
comment: Black point (blackpoint, kernel smudge or just smudge) is often considered a fungal disease that affects wheat, barley and rye. It is thought to be caused by various species of Alternaria, Fusarium, and Helminthosporium, and possibly other fungal genera. The fungus forms after the seeds have set but while they are still green and it is potentiated by high humidity. Infected areas are brown to black in color, and as the disease spreads the kernels may become shriveled. Occasionally the infected areas have a reddish tinge.

Brown Garden Snail
type: NamedIndividual
type: Crop Pest
alternative label: Cornu Aspersum

Brown Leaf Spot
type: NamedIndividual
type: Crop Disease
comment: Brown leaf spot is a very common disease of potato that is found in most potato-growing areas. It is often confused with early blight (caused by A. solani) and the two pathogens are closely related.

Brown Plant Hopper
type: NamedIndividual
type: Crop Pest

Brown spot
type: NamedIndividual
type: Crop Disease
affects: Corn
affects: Soybean
caused by: Marssonina Coronaria
comment: Brown spot has been historically largely ignored as one of the most common and most damaging rice diseases.

Brown Stink Bug
type: NamedIndividual
type: Crop Pest
comment: The brown marmorated stink bug (Halyomorpha halys) is an insect in the family Pentatomidae, native to China, Japan, Korea and other Asian regions.

Brunei Darussalam
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:BN
agricultural land: 114 sq. km
area: 5,765 km²
climate: tropical; hot, humid, rainy
density: 66/km²
latitude: 114.71545
longitude: 4.525134
population: 381,371 (2008)
comment: Brunei, officially the Nation of Brunei, the Abode of Peace, is a country located on the north coast of the island of Borneo in Southeast Asia. Apart from its coastline with the South China Sea, the country is completely surrounded by the Malaysian state of Sarawak. It is separated into two parts by the Sarawak district of Limbang. Brunei is the only sovereign state completely on the island of Borneo; the remainder of the island's territory is divided between the nations of Malaysia and Indonesia. Brunei's population was 428,963 in 2018. The government is an absolute monarchy under the Sultan, which implements a combination of English common law and sharia law, as well as direct general Islamic practices.

Bulgaria
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Continental Climate
has climate: Marine Climate
has climate: Dry summer climate
ISO code: ISO 3166-1:BG
agricultural land: 50,880 sq. km
area: 110,910 km²
climate: temperate; cold, damp winters; hot, dry summers
density: 68.9/km²
latitude: 25.481543
longitude: 42.72523
population: 7,640,238 (2008)
comment: Bulgaria, officially the Republic of Bulgaria, is a country in Southeast Europe. It is bordered by Romania to the north, Serbia and North Macedonia to the west, Greece and Turkey to the south, and the Black Sea to the east. The capital and largest city is Sofia; other major cities are Plovdiv, Varna and Burgas. With a territory of 110,994 square kilometres, Bulgaria is Europe's sixteenth-largest country.

Bunchy Top Disease
type: NamedIndividual
type: Crop Disease
affects: Banana
symptoms: Bunchy top is named after one of the most characteristic symptoms of an advanced infection, when the leaves become progressively dwarfed, upright and bunched at the top of the plant, with wavy and chlorotic margins that tend to turn necrotic. Cabbage top27 and curly top28 (for abaca) have also been used to term the disease. Initial symptoms may be difficult to detect for a novice due to their inconspicuous nature. The first symptoms are dark green streaks on the lower portion of the leaf's midrib and later on the secondary veins. Removing the waxy white coating on the midrib makes it easier to see the streaking. The streaks consist of dot-dash patterns, the so-called 'Morse code', the most diagnostic symptom of bunchy top. As infection progresses, streak symptoms become more evident on the leaf blade. Dark-green hook-like extensions of the veins can also be seen in the narrow, light-green zone between the midrib and the lamina. The short hooks point down along the midrib toward the petiole. These hooks are best observed from the underside of the leaf by holding the leaf to the light. The symptoms are most severe and distinctive when the infection arises from the virus moving from the parent plant to the suckers. These plants are typically stunted (under 1 m) and rarely produce fruit. When aphids carrying the virus (also called viruliferous aphids) feed on a healthy plant, the symptoms are usually milder and only appear in tissues formed after the infection. The first symptoms usually appear at the earliest in the second leaf to emerge after a plant has become infected. The leaf will look normal to untrained eyes but on closer inspection it will have the dots and dashes on the midrib, petiole and blade. Each new infected leaf is narrower and shorter and also stands more upright than the previous one. The margin also curls up. If the plant flowers, the veins of the bracts of the inflorescence may show discontinuous streaks that resemble the 'Morse code' symptoms on pe…
comment: Bunchy top is a viral disease caused by the Banana bunchy top virus (BBTV). The disease, often called BBTD for banana bunchy top disease, gets its name from the bunchy appearance of infected plants. By that time, however, the virus has most likely been spread to other plants by the banana aphid, Pentalonia nigronervosa. Infected plants cannot recover and will serve as a source of viral particles unless they are destroyed. The virus is also spread through infected planting material.

Burkina Faso
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:BF
agricultural land: 117,650 sq. km
area: 274,000 km²
climate: tropical; warm, dry winters; hot, wet summers
density: 48/km²
latitude: -1.55676
longitude: 12.24185
population: 13,228,000 (2005)
comment: Burkina Faso is a landlocked country in West Africa that covers an area of around 274,200 square kilometres and is bordered by Mali to the northwest, Niger to the northeast, Benin to the southeast, Togo and Ghana to the south, and the Ivory Coast to the southwest. The July 2019 population estimate by the United Nations was 20,321,378. Previously called Republic of Upper Volta, it was renamed "Burkina Faso" on 4 August 1984 by President Thomas Sankara. Its citizens are known as Burkinabé or Burkinabè, and its capital is Ouagadougou. Due to French colonialism, the country's official language of government and business is French. However, only 15% of the population actually speaks French on a regular basis. There are 59 native languages spoken in Burkina, with the most common language, Moore, spoken by roughly 50% of Burkinabé.

Burundi
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:BI
agricultural land: 22,200 sq. km
area: 27,830 km²
climate: equatorial; high plateau with considerable altitude variation (772 m to 2,670 m above sea level); average annual temperature varies with altitude from 23 to 17 degrees centigrade but is generally moderate as the average altitude is about 1,700 m; average annual rainfall is about 150 cm; two wet seasons (February to May and September to November), and two dry seasons (June to August and December to January)
density: 271/km²
latitude: 29.920395
longitude: -3.387918
population: 3,589,434 (1978)
comment: Burundi, officially the Republic of Burundi, is a landlocked country in the Great Rift Valley where the African Great Lakes region and East Africa converge. It is bordered by Rwanda to the north, Tanzania to the east and southeast, and the Democratic Republic of the Congo to the west; Lake Tanganyika lies along its southwestern border. The capital cities are Gitega and Bujumbura.

Caffe
type: NamedIndividual
type: Toolkit
comment: Caffe is another popular deep learning framework geared towards the image processing field. It was developed by Yangqing Jia during his Ph.D at the University of Claifornia, Berkeley. And yes, it’s open source as well! First, a caveat – Caffe’s support for recurrent networks and language modeling is not as great as the above three frameworks. But where Caffe stands out is it’s speed of processing and learning from images. That is easily it’s primary USP.

Calcium ammonium nitrate
type: NamedIndividual
type: Chemical Fertilizer

Callitettix Versicolor
type: NamedIndividual
type: Crop Pest

Cambodia
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:KH
agricultural land: 56,550 sq. km
area: 181,035 km²
climate: tropical; rainy, monsoon season (May to November); dry season (December to April); little seasonal temperature variation
density: 74/km²
latitude: 104.98386
longitude: 11.98483
population: 13,388,910 (2008)
comment: Cambodia, officially the Kingdom of Cambodia, is a country located in the southern portion of the Indochinese peninsula in Southeast Asia. It is 181,035 square kilometres in area, bordered by Thailand to the northwest, Laos to the northeast, Vietnam to the east and the Gulf of Thailand to the southwest.

Cameroon
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:CM
agricultural land: 96,000 sq. km
area: 475,442 km²
climate: varies with terrain, from tropical along coast to semiarid and hot in north
density: 37/km²
latitude: 12.343439
longitude: 7.365302
population: 17,795,000 (2005)
comment: Cameroon, officially the Republic of Cameroon, is a country in Central Africa. It is bordered by Nigeria to the west and north; Chad to the northeast; the Central African Republic to the east; and Equatorial Guinea, Gabon and the Republic of the Congo to the south. Cameroon's coastline lies on the Bight of Biafra, part of the Gulf of Guinea and the Atlantic Ocean. Although Cameroon is not an ECOWAS member state, it is geographically and historically in West Africa; the Southern Cameroons, which now form her Nord-Ouest and Sud-Ouest Regions, have particularly strong links to West African history. The country is sometimes identified as West African and other times as Central African due to its strategic position at the crossroads between West and Central Africa. Cameroon is home to over 250 native languages spoken by nearly 25 million people.

Canada
type: NamedIndividual
type: Country
belongs to: North America
has climate: Continental Climate
has climate: Temperate Climate
ISO code: ISO 3166-1:CA
agricultural land: 625,970 sq. km
area: 9,984,670 km²
climate: varies from temperate in south to subarctic and arctic in north
density: 3.2/km²
latitude: -96.81145
longitude: 62.393604
population: 33,548,000 (2009)
comment: Canada is a country in the northern part of North America. Its ten provinces and three territories extend from the Atlantic to the Pacific and northward into the Arctic Ocean, covering 9.98 million square kilometres, making it the world's second-largest country by total area. Its southern and western border with the United States, stretching 8,891 kilometres, is the world's longest bi-national land border. Canada's capital is Ottawa, and its three largest metropolitan areas are Toronto, Montreal, and Vancouver.

Canker
type: NamedIndividual
type: Crop Disease
affects: Citrus
comment: Canker, plant disease, caused by numerous species of fungi and bacteria, that occurs primarily on woody species. ... Symptoms include round-to-irregular sunken, swollen, flattened, cracked, discoloured, or dead areas on the stems (canes), twigs, limbs, or trunk.

Cape Verde
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:CV
agricultural land: 750 sq. km
area: 4,033 km²
climate: temperate; warm, dry summer; precipitation meager and erratic
density: 126/km²
latitude: -24.014095
longitude: 15.999696
population: 426,998 (2008)
comment: Cape Verde or Cabo Verde, officially the Republic of Cabo Verde, is an archipelago and island country in the central Atlantic Ocean, consisting of ten volcanic islands with a combined land area of about 4,033 square kilometres . These islands lie between 600 to 850 kilometres west of Cap-Vert situated at the westernmost point of continental Africa. The Cape Verde islands form part of the Macaronesia ecoregion, along with the Azores, the Canary Islands, Madeira, and the Savage Isles.

Captan
type: NamedIndividual
type: Pesticides
comment: Captan is a general use pesticide (GUP) that belongs to the phthalimide class of fungicides. It is a white solid, although commercial samples appear yellow or brownish.

Carbofuran
type: NamedIndividual
type: Insecticide
comment: Carbofuran is one of the most toxic carbamate pesticides. It is marketed under the trade names Furadan, by FMC Corporation and Curaterr 10 GR, by Bayer, among several others. It is used to control insects in a wide variety of field crops, including potatoes, corn and soybeans. It is a systemic insecticide, which means that the plant absorbs it through the roots, and from there the plant distributes it throughout its organs where insecticidal concentrations are attained. Carbofuran also has contact activity against pests.

Carnation
type: NamedIndividual
type: Flower
comment: Dianthus caryophyllus, commonly known as the carnation or clove pink, is a species of Dianthus. It is probably native to the Mediterranean region but its exact range is unknown due to extensive cultivation for the last 2,000 years.

Carrot
type: NamedIndividual
type: Crop
type: Vegetable
description: Carrot, Daucus carota, is an edible, biennial herb in the family Apiaceae grown for its edible root. The carrot plant produces a rosette of 8-12 leaves above ground and a fleshy conical taproot below ground. The plant produces small (2 mm) flowers which are white, red or purple in color. The root can grow to between 5 and 50 cm (2.0-20 in) long and reach 5 cm (2.0 in) in diameter. The foliage of the plant can reach a height of 150 cm (59.1 in) when in flower. The carrot plant can be annual or biennial and may also be referred to as wild carrot. The plant is believed to have originated in Europe or the Western Mediterranean.
harvesting: Carrots are generally ready to harvest after around 2-3 months when the roots have reached 1.3 cm (0.5 in) in diameter. Allowing the carrots to stay in the ground for at least one frost makes them sweeter but care should be taken to harvest before the ground freezes or to cover the plants to prevent freezing. Carrots are harvested by gently digging around the plant to expose the top of the root and gently, but firmly pulling the root from the soil by grasping the top of the carrot just above the root. Carrot tops should be twisted off and the roots washed prior to refrigeration in airtight bags. Carrots may also be stored in moist sand to keep them fresh prior to use.
maintenance: Carrots benefit from a plentiful moisture supply and should be provided with 2.5 cm (1 in) of water each week. Mulching around the plants helps to conserve moisture and reduce weeds. Any weeds growing around the plants should be carefully removed. The plants should be fertilized 5-6 weeks after the seeds are sown.
propagation: Carrot is usually direct seeded and should be planted 3-5 weeks before the least frost date in your area. Soil should be prepared prior to planting by removing rocks and breaking up any hard lumps of soil down to a depth of at least 30 cm (12 in). It is also beneficial to work some compost into the soil prior to planting. Avoid using fresh manure as it can cause forking of the roots. Sow seeds 6 mm (0.25 in) deep, leaving 5 cm (2 in) between seeds and approximately 30 cm (1 ft) apart. When seedlings reach 2.5 cm (1 in) in height, thin them to a final spacing of 7.5 cm (3 in) between plants by snipping with scissors - this avoids damaging plant roots.
requirements: arrots are cool-season crops which can be planted in early Spring and left in the ground all summer for harvest in the fall. Carrots grow best in a well-draining, loose, sandy soil which is free of large rocks and has a pH between 5.5 and 7.0. They require full sun for optimum development but will tolerate some very light shade. The optimum temperature for their growth is between 16 and 24oC (61-74oF). Carrot seedlings are very sensitive to extremes of temperature, very hot weather may kill the plant whereas temperatures below 10oC (50oF) tend to cause the development of longer, paler roots. The plants also require plenty moisture and organic matter. Carrots do very well in raised beds and can also be grown in containers.
uses: Carrot roots are eaten as a vegetable and can be consumed fresh or cooked. Carrot juice is consumed as a beverage. The leaves of the plant can be used as feed for animals.
References: https://plantvillage.psu.edu/topics/carrot/infos
comment: Carrot, Daucus carota, is an edible, biennial herb in the family Apiaceae grown for its edible root. The carrot plant produces a rosette of 8-12 leaves above ground and a fleshy conical taproot below ground. The plant produces small (2 mm) flowers which are white, red or purple in color. The root can grow to between 5 and 50 cm (2.0-20 in) long and reach 5 cm (2.0 in) in diameter. The foliage of the plant can reach a height of 150 cm (59.1 in) when in flower. The carrot plant can be annual or biennial and may also be referred to as wild carrot. The plant is believed to have originated in Europe or the Western Mediterranean.

Cassava Bacterial Blight
type: NamedIndividual
type: Crop Disease
affects: Cassava
comment: Bacterial blight of cassava is caused by Xanthomonas axonopodis pv. manihotis. Originally discovered in Brazil in 1912, the disease has followed cultivation of cassava across the world.[1] Among diseases which afflict cassava worldwide, bacterial blight causes the largest losses in terms of yield.

Cassava Brown Streak Virus Disease
type: NamedIndividual
type: Crop Disease
affects: Cassava
comment: Cassava brown streak virus disease (CBSD) is a damaging disease of cassava plants, and is especially troublesome in East Africa. It was first identified in 1936 in Tanzania, and has spread to other coastal areas of East Africa, from Kenya to Mozambique. Recently, it was found that two distinct viruses are responsible for the disease: cassava brown streak virus (CBSV) and Ugandan cassava brown streak virus (UCBSV). Both have (+)ss RNA genomes, belong to the genus Ipomovirus in the family Potyviridae, and produce generally similar symptoms in infected plants. Root rot renders the cassava tuber inedible, resulting in severe loss of economic value; therefore, current research focuses on achieving cultivars that do not develop the necrotic rot. This disease is considered to be the biggest threat to food security in coastal East Africa and around the eastern lakes

Cassava Green Mite
type: NamedIndividual
type: Crop Disease
affects: Cassava
comment: Cassava green mite, M. tanajoa, is of Neotropical origin but was accidentally introduced to Africa in 1971 (Nyiira, 1972). By 1985, the pest had spread throughout the cassava belt of Africa (Yaninek and Heren, 1988). M. tanajoa affects the important annual crop cassava (Manihot esculenta) and can cause a reduction of about 50% in leaf weight, and up to 80% tuber yield loss (Shukla, 1976; Gutierrez et al., 1988; Pallangyo et al., 2004). M. tanajoa is mainly dispersed by human activity, whereby infested plant materials and contaminated media are transported over long distances. Natural dispersion by wind and water may also spread the cassava green mite. In areas where both the pest and host plant are exotic, there is no evidence that indigenous natural enemies are significant factors in limiting the mite population growth rates. M. tanajoa can feed and reproduce on other plant species (Moraes et al., 1995) and is reported as a quarantine pest (Delalibera et al., 1992; EPPO, 2009).

Cassava Green Spider Mite
type: NamedIndividual
type: Crop Disease
affects: Cassava
biological control: Several predator species are reported to effectively reduce mite populations. Introduction of Amblyseius limonicus and A. idaeus reduced green spider mite infestation by 50%. The predatory mites Typhlodromalus aripo and T. manihoti have been established in several countries in Africa, successfully controlling populations of the cassava green spider mite. Parasitic fungi of the genus Neozygites also showed good results in several countries, causing mortalities in cassava green spider mites. Sprays containing neem oil compounds can also show satisfying results.
chemical control: Alway consider an integrated approach with preventive measures together with biological treatments if available. Chemical control of Mononychellus tanajoa is not recommended as it can result in the development of resistance and secondary disease outbreaks. Only the acaricide abamectin was found effective for the control of the pest.
symptoms: Symptoms vary from a few chlorotic spots to complete chlorosis. These symptoms are somehow similar to African cassava mosaic disease, and should not be confused. Heavily attacked leaves are stunted and become deformed. Severe attacks cause the terminal leaves to die and drop, and the shoot tip looks like a "candle stick". Green spider mites are major pests in dry season. Severe mite attack results in 20-80 % loss in tuber yield. These mites usually feed from the underside of young leaves, on green stems and auxiliary buds of cassava. They insert their piercing and sucking mouthparts into individual cells and extract the content, among other the green chlorophyll. On leaves, the feeding activity appear to the naked eye as minute yellowish spots on the lamina. Heavily infection leads to mottled leaves with poor growth that can subsequently die and shed. The attack of the terminal shoots results in a characteristic 'candle stick' symptom, referring to the necrotic aspect and shedding of shoot tips. Cassava plants aged 2-9 months are the most vulnerable to infestation. Severe mite attack can result in 20-80 % loss in tuber yield. Moreover, the quality of the cassava stems is also compromised, often resulting in a shortage of planting materials for perpetuation of the crop.
comment: This mite is green in colour at a young age turning yellowish as adult. Adult females attain a size of 0.8 mm. They appear as yellowish green specks to the naked eye. They occur on the lower surface of young leaves, green stems and auxiliary buds of cassava. Damage initially appears as yellowish "pinpricks" on the surface of young leaves.

Cassava Mosaic Disease
type: NamedIndividual
type: Crop Disease
affects: Cassava
distribution: Previously, CMBs were thought to show geographic structuring with ACMV limited to West and Central African countries towards the west of the Rift Valley and in South Africa, EACMV to the eastern part of the Rift Valley in coastal Kenya, Tanzania, Malawi, Zimbabwe and Madagascar, and ICMV to India and Sri Lanka (33). However, subsequent reports have shown that most of the seven CMBs reported from sub-Saharan Africa are widespread across the sub-continent (6,8,15,56), whereas ICMV and SLCMV appear to have remained confined to cassava-growing regions of India and Sri Lanka (56,70). Interestingly, there is no report to date of CMD from South America, the center of origin of cassava. This suggests that CMBs could be ‘native’ to Africa where they perpetuated in indigenous hosts, and that cassava became an ‘accidental’ host after its introduction into the continent. This type of ‘new encounter phenomenon’ (14), where a pathogen that co-evolved with indigenous plant species makes a host ‘jump’ to an introduced plant species where it now becomes an important pathogen, has been reported for several other crops that have been introduced to Africa (72).
comment: All cassava mosaic begomoviruses (CMBs) viruses belong to the genus Begomovirus (family Geminiviridae) and are thus collectively called cassava mosaic geminiviruses (CMGs) or cassava mosaic begomoviruses (CMBs). Cassava is vegetatively propagated, hence CMD is spread primarily via movement of cuttings from disease-affected cassava. Consequently, the spread of CMD and CMGs into new localities typically mirrors patterns of cassava cuttings exchange among farmers. Once infected cuttings are planted, the virus establishes readily and can be transmitted within and between fields via feeding behaviour of the whitefly (Bemisia tabaci) vector of CMGs. Yield loss due to CMD ranges from 12 to 82% depending on cassava variety and infection type (Owor et al., 2004) culminating in >30 million tonnes of fresh cassava roots loss annually (Legg et al., 2006).
comment: Cassava is vulnerable to a broad range of diseases caused by viruses (Table 1). Among them, cassava mosaic disease (CMD) is the most severe and widespread, limiting production of the crop in sub-Saharan Africa. CMD produces a variety of foliar symptoms that include mosaic, mottling, misshapen and twisted leaflets, and an overall reduction in size of leaves and plants (Figs. 2 and 3). CMD-affected cassava plants produce few or no tubers (Fig. 1C and 1D) depending on the severity of the disease and the age of the plant at the time of infection.

Caterpillars
type: NamedIndividual
type: Crop Disease
type: Crop Pest
comment: Caterpillars are the larvae of moths and butterflies, and like their winged metamorphoses, they come in all shapes and sizes. While all species share a common appetite for plant foliage, the good news is that the methods for controlling them are basically the same. Before deciding upon a control method, take the time to identify which caterpillars might eventually become the gorgeous butterflies and lovely nocturnal moths we usually welcome to our gardens. These larvae should be protected, and fortunately, most are colorful and harmless. Caterpillars inflict damage by eating the foliage and stems of just about any plant in your flower or vegetable garden, as well as your fruit and shade trees. Furthermore, they are voracious eaters, and can defoliate a plant in a short period of time. Telltale symptoms of a caterpillar infestation are holes in leaves and chewed leaf edges, as well as leaves that are rolled up or fastened with silk. If you are able to catch the infestation early on, the best strategy for controlling caterpillars is to handpick them, then crush them or drop them in a pail of soapy water. Make sure you also remove rolled or folded leaves that shelter the caterpillars. (Make sure you wear gloves before handling saddleback caterpillars, as their sting is severe.) To prevent inadvertently killing desirable butterflies- and moths-to-be, use a good field guide such as Peterson First Guide to Caterpillars and pick only those caterpillars which become drab adults. Cleanup and disposal of garden debris throughout the growing season, followed by a thorough end-of-season cleanup, will help reduce the size of future generations of caterpillars. Furthermore, many insects--such as fireflies, ground beetles, soldier beetles, stink bugs, and tachinid flies-- prey upon or parasitize caterpillars, and will gladly assist you in your quest to keep caterpillar numbers under control.

Cave
type: NamedIndividual
type: Topography
comment: Cave is a natural underground chamber in a hillside or cliff.

Cayman Islands
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:KY
agricultural land: 27 sq. km
area: 260 km²
climate: tropical marine; warm, rainy summers (May to October) and cool, relatively dry winters (November to April)
density: 139.5/km²
latitude: -80.580025
longitude: 19.512363
population: 70,000 (2006)
comment: The Cayman Islands, is a self-governing British Overseas Territory in the western Caribbean Sea. The 264-square-kilometre territory comprises the three islands of Grand Cayman, Cayman Brac and Little Cayman, which are located to the south of Cuba and northeast of Honduras, between Jamaica and Mexico's Yucatán Peninsula. The capital city is George Town on Grand Cayman, which is the most populous of the three islands.

Cedar Apple Rust
type: NamedIndividual
type: Crop Disease
affects: Apple
comment: Cedar apple rust is a member of the family Pucciniaceae, a group of fungi that contains many species that usually require two or more hosts to complete the life cycle. Members of this family are known as rusts because the color of many is orange or reddish at some point in the life cycle. Many rusts are disease agents of crop plants including black stem rust of wheat, soybean rust and white pine blister rust. From three to five different spore types are produce by rusts, but one is always a basidiospore. These spores are produced on structures called basidia place the family is in the Basidiomycota, a large group that includes many common mushrooms. Large infestations of this rust can reduce yield on apples, blemish the fruit, and lead to weakening and death of redcedar.

Centipedes
type: NamedIndividual
type: Crop Disease
type: Crop Pest
life cycle: Centipede adults overwinter in secluded, moist places. In spring and summer females lay eggs in damp soil. Some species are born complete and others add segments and legs as they grow. They mature in 2—3 years and live up to six years.
management: Management Strategies 1. Control Outdoors. Millipedes and centipedes are considered beneficial outdoors; therefore, encourage their presence. To help prevent them from moving indoors: remove organic matter in contact with the house foundation, minimize watering near building foundations, clean up piles of rocks, leaves, compost and general refuse near the building, and make sure doors and windows fit snugly (caulk crevices). Pesticides can be used to treat the foundation and an adjacent three-foot ground strip with dust or aerosol spray labeled for these organisms. Follow label directions precisely. 2. Control Indoors. Hand pick or vacuum to control individuals. Reduce moisture and remove hiding places, or place sticky traps against walls near entryways. You can also use a pesticides if populations are high or persistent. A contact or aerosol sprays containing pyrethrins or carbaryl can be used. Treat baseboards, cracks and crevices. Follow label directions precisely.
comment: Centipedes, commonly called "hundred-leggers", have elongated, flattened bodies with fewer than 25 pairs of long legs, one pair per segment. The legs project laterally from the body. The head is flattened and projects two long antennae. Just behind the head are the jaws that contain venom glands used to paralyze their prey. Centipedes are predators, feeding on insects and spiders. They move rapidly and never coil up their bodies. They are yellow to brown in color and just over an inch in length. Centipede populations do not increase in great numbers. They occasionally enter homes where they prefer dark, moist areas. The house centipede (Scutigera) does enter homes and may reproduce indoors. Centipedes are considered beneficial in the garden and home because they control insect populations

Central African Republic
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:CF
agricultural land: 50,800 sq. km
area: 622,984 km²
climate: tropical; hot, dry winters; mild to hot, wet summers
density: 6.77/km²
latitude: 20.94176
longitude: 6.614041
population: 4,216,666 (2007)
comment: The Central African Republic is a landlocked country in Central Africa. It is bordered by Chad to the north, Sudan to the northeast, South Sudan to the southeast, the Democratic Republic of the Congo to the south, the Republic of the Congo to the southwest and Cameroon to the west. The CAR covers a land area of about 620,000 square kilometres and had an estimated population of around 4.7 million as of 2018. As of 2021, the CAR is the scene of a civil war, ongoing since 2012.

Cercospora Leaf Spot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Coffee
causes: Fungus
development: Disease can be spread by wind, water-splash and through human movement through fields, particularly when plants are wet
management: Ensure crop is adequately fertilized as nutrient deficient plants are more susceptible to the disease; remove all crop debris from filed after pruning to prevent build up of inoculum; good plant spacing and pruning to open up the canopy promotes good air circulation around foliage and protects against disease; if disease does occur then it can be controlled with the use of copper fungicides where available
symptoms: Brown spots on foliage which enlarge and develop gray-white center and a red-brown margin; lesions may also be surrounded by a yellow halo or may have a burned appearance if lesions are very numerous; infected leaves may drop from plant prematurely; lesions on green berries are brown and sunken and may have a purplish halo; infected red berries may have large black sunken areas
comment: Cercospora leaf spot is an infectious disease that affects smooth, panicle, oakleaf and bigleaf types of hydrangea in both landscapes and nurseries. This disease is caused by the fungus Cercospora hydrangea and is perhaps the most common disease seen on this ornamental during the months of July through October. The disease rarely kills the plant, but if it is severe, it can reduce overall plant vigor by repeated defoliation. For bigleaf-type hydrangeas in the landscape, Cercospora leaf spot tends to be less severe under shady conditions, but in nursery environments under shady conditions, frequent overhead irrigation can intensify disease activity and subsequent defoliation and loss of vigor.

Cereal
type: NamedIndividual
type: Crop
has disease: Bacterial Black Chaff
has disease: Bacterial Leaf Streak
has disease: BYD
has disease: Bacterial Leaf Blight
has disease: Soilborne Wheat Mosaic
related to: Rice
related to: Wheat
comment: A cereal is any plant cultivated (grown) for the edible components of its grain composed of the endosperm, germ, and bran.

Cereal Leaf Beetle
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Wheat
distribution: Cereal leaf beetles can be a problem on fall-sown cereals. Wheats with hairy leaves are affected less.
important: Significant yield losses can occur in winter wheat and fall-sown spring wheat. Yield losses of from 14% to more than 25% have occurred with natural infestations.
life cycle: The insect produces one generation per year. Adults begin their feeding activity in the spring. They lay yellow eggs, either singly or in small chains, covering them with a sticky film that the soil and the adults emerge in summer. Adults overwinter underneath plant debris on the soil surface, in leaf sheaths and ears of standing maize, or under the bark of trees.
symptoms: Adult beetles are 4-5 mm long, have a black head, light brown thorax, and a shiny blue-green wing cover with parallel lines of small dots (65). Larvae are a dull to bright yellow color, but soon take on the appearance of a slimy, globular, black mass due to the mound of fecal material they produce and accumulate on their backs (66; 67). The most prominent symptom of cereal leaf beetle infestations is the distinct, longitudinal stripes on leaves (68); these stripes are produced by the feeding of adult beetles and of larvae.
comment: The cereal leaf beetle (Oulema melanopus) is a significant crop pest, described by Carl Linnaeus in 1758. The cereal leaf beetle can be a problematic pest in cereal. Cereal leaf beetles (CLB) overwinter and emerge in the spring, where they can move into winter and then spring wheat. Larvae and adults can do significant damage to crops. Biological control has been particularly effective in reducing cereal leaf beetle populations.

Ceroplastes Ceriferus
type: NamedIndividual
type: Crop Pest
comment: Ceroplastes Ceriferus (Anderson)

Cervicular tree
type: NamedIndividual
type: Symptom

Ceutorhynchus Asper Roelofs
type: NamedIndividual
type: Crop Pest
comment: Ceutorhynchus Asper Roelofs

Chad
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:TD
agricultural land: 499,320 sq. km
area: 1,284,000 km²
climate: tropical in south, desert in north
density: 7.9/km²
latitude: 18.738068
longitude: 15.445718
population: 10,780,600 (2007)
comment: Chad, officially known as the Republic of Chad, is a landlocked country in north-central Africa. It is bordered by Libya to the north, Sudan to the east, the Central African Republic to the south, Cameroon to the south-west, Nigeria to the southwest, and Niger to the west.

Chalk Soil
type: NamedIndividual
type: Soil Type
comment: Chalk soil - can be either light or heavy but always highly alkaline due to the calcium carbonate or lime within its structure. As these soils are alkaline they will not support the growth of ericaceous plants that require acidic soils to grow. If a chalky soil shows signs of visible white lumps then they can’t be acidified and gardeners should be resigned to only choose plants that prefer an alkaline soil.

Chauliops Fallax Scott
type: NamedIndividual
type: Crop Pest
comment: Chauliops Fallax Scott

Chemical Injury
type: NamedIndividual
type: Crop Injure

Chestnut Blight
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Chestnut
life cycle: The success of this disease must be attributed, as least in part, to its ability to spread in so many different ways. Vectors include insects, birds, other animals, wind, and rain. Ascospores or conidia enter fresh wounds, probably caused by insects, usually in bark surrounding branches and twigs. Buff to orange mycelial fans then spread into living bark and cambium tissue. As cell death occurs, the fungus spreads into living tissue. Thus, as branches and twigs are girdled, the canker moves toward living tissue–ultimately to the main trunk of the tree. It does not, however, invade the crown. The environmental conditions necessary to the survival of the chestnut blight pathogen seem virtually unlimited. No condition in which a chestnut can live discourages or suppresses the pathogen. It can live anywhere chestnuts live and can reproduce at any time of the year.
management: Management Strategies 1 There is no cure for chestnut blight. Once the pathogen is present in an area, and it is already present in most areas, any American chestnut is at extreme risk; therefore, do not plant them. Other species should only be planted in preferred growing conditions; stressed sites will place them at risk. 2. Choose resistant species. Although the search for a blight resistant American chestnut has been ongoing for nearly a century, no blight resistant American chestnuts are currently on the market. Specimens sold as "blight free" have not been exposed to the disease (i.e., blight-free does not mean blight resistant). Nut growers should avoid the American chestnut. Choose hybrids of the Chinese chestnut (Castanea mollissima) instead. Recommended cultivars include: ‘Eaton’, ‘Peach’, ‘Gideon’, ‘Sleeping Giant’, ‘Qing’, ‘Au (Autumn) Homestead’; ‘Willamette’ is no longer recommended. Check with the University of Missouri Center for Agroforestry for information on growing, pruning and care of the Chinese hybrids.
symptoms: Obvious symptoms on the American chestnut include flagging (the yellowing and dying of individual branches) and dead leaves that hang onto branches over the first winter. On young trees and on juvenile branches of mature trees, cankers appear as yellowish to reddish areas in the bark around branch crotches; these cankers can girdle and kill a branch within a few weeks. On mature trees, cankers are initially imperceptible unless the bark is stripped off. Later, as the canker expands, the bark swells and cracks, eventually falling off. Yellowish brown fruiting bodies may be visible on the surface of the bark around the canker or in the cracks in the bark. In American chestnuts, the trunk is eventually girdled which can occur within a few weeks for young trees and within a few years for mature trees. Few last longer than four years. Suckers usually shoot up from the surrounding roots of American chestnuts providing a constant supply of susceptible tissue for the pathogen’s continued existence; this suckering does not occur in other species. Live oaks decline slowly. Foliage at the crown is chlorotic and sparse with dead branches. Cankers are difficult to discern. Severe dieback can occur on post oaks and scarlet oaks, but smaller cankers or superficial cankers are more common. Cankers on post oak are more pronounced than on live oaks and the bark will usually slough off, while on scarlet oaks the cankers appear swollen and remain covered with bark. Swelling can also indicate cankers on white oak. Infected Chinese and Japanese chestnuts are usually stressed trees. Chinese chestnuts display twig or branch dieback or targetlike cankers, and bark will eventually slough off. Buff to orangish flat, mycelial fans in the diseased bark are characteristic of chestnut blight although positive diagnosis of the disease requires a laboratory test to culture the fungal pathogen.
comment: Chestnut blight canker on main stem of American chestnut (Castanea dentata); note dead branch in center of canker, discolored bark, and orange bumps (stroma). L. Haugen, USFS, Bugwood.org. Unlike other blights, chestnut blight is not just associated with shoot dieback; it can kill twigs and branches of any size. It is actually a canker disease caused by a fungus, Cryphonectria parasitica, formerly known as Endothia parasitica. The significance of chestnut blight is tied to the species it decimated- the American chestnut, Castanea dentata. In 1900 the American chestnut was not only the dominant species of eastern hardwood forests in the U.S., it was the most important tree commercially, because of its beautiful, rot resistant lumber, because of the quantities of edible nuts it produced, and because of the rapidity with which it could repopulate clear-cut areas of the forest. Chestnut blight was first identified in the United States in New York City in 1904, but it probably entered the country earlier, in the late 1800s on imported Asian chestnuts. By 1950, 99.9% of American chestnuts were reduced to either dead stumps or stumps with adventitious sprouts arising from the roots surrounding them. Many of these stumps persist today and are still sending up diseased sprouts that are as susceptible to chestnut canker as the parent plant. Other species that are highly susceptible to chestnut blight are the Allegheny chestnut (C. pumila), the bush chinkapin chestnut (C. alnifolia) and the European chestnut (C. sativa). The last species, Castanea sativa, is not native to the U.S. and is restricted here. Some oaks are host to the disease; these include live, post, scarlet and white oaks. It is also occasionally found on shagbark hickory, red maple and staghorn sumac. Chinese and Japanese chestnuts have shown some resistance to chestnut blight; for these Cryphonectria parasitica is a stress pathogen only. No other species of chestnut, however, has the characteristics necessary …

Chile
type: NamedIndividual
type: Country
belongs to: South America
has climate: Humid Subtropical Climate
has climate: Dry summer climate
has climate: Tundra Climate
ISO code: ISO 3166-1:CL
agricultural land: 157,890 sq. km
area: 756,950 km²
climate: temperate; desert in north; Mediterranean in central region; cool and damp in south
density: 22/km²
latitude: -70.642
longitude: -33.46912
population: 16,763,470 (2008)
comment: Chile, officially the Republic of Chile, is a country in western South America. It occupies a long, narrow strip of land between the Andes to the east and the Pacific Ocean to the west. Chile covers an area of 756,096 square kilometres and has a population of 17.5 million as of 2017. The capital and largest city is Santiago and the national language is Spanish.

Chilo Suppressalis
type: NamedIndividual
type: Crop Pest
comment: Chilo Suppressalis (Walker)

China
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:CN
agricultural land: 5.19 million sq. km
area: 9,671,018 km²
climate: extremely diverse; tropical in south to subarctic in north
density: 140/km²
latitude: 104.1954
longitude: 35.86166
population: 1,321,851,888 (2007)
comment: China, officially the People's Republic of China, is a country in East Asia. It is the world's most populous country, with a population of around 1.4 billion. Covering approximately 9.6 million square kilometers, it is the world's third or fourth-largest country by area. The country is officially divided into 23 provinces, five autonomous regions, four direct-controlled municipalities, and the special administrative regions of Hong Kong and Macau.

Chlofluwersuron 500g/l
type: NamedIndividual
type: Insecticide

Chlorofluazuran
type: NamedIndividual
type: Insecticide
comment: Chlorfluazuron is a benzoylurea insecticide, an organochlorine insecticide, an organofluorine insecticide and a dichlorobenzene. Chlorfluazuron is an insect growth regulator that inhibits chitin synthesis and provides good control of various pest insects, especially Lepidoptera, at a low.

Chlorosis
type: NamedIndividual
type: Crop Disease
affects: Citrus
comment: Chlorosis is a yellowing of leaf tissue due to a lack of chlorophyll. Possible causes of chlorosis include poor drainage, damaged roots, compacted roots, high alkalinity, and nutrient deficiencies in the plant.

Chlorothalonil (Dacornil)
type: NamedIndividual
type: Pesticides
comment: Chlorothalonil (2,4,5,6-tetrachloroisophthalonitrile) is an organic compound mainly used as a broad spectrum, nonsystemic fungicide, with other uses as a wood protectant, pesticide, acaricide, and to control mold, mildew, bacteria, algae.

Christmas Island
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:CX
area: 135 km²
climate: tropical with a wet season (December to April) and dry season; heat and humidity moderated by trade winds
density: 11.06/km²
latitude: 105.629265
longitude: -10.495819
population: 1,493 (2006)
comment: Christmas Island, officially known as the Territory of Christmas Island, is an Australian external territory comprising the island of the same name. It is located in the Indian Ocean, around 350 kilometres south of Java and Sumatra and around 1,550 km north-west of the closest point on the Australian mainland. It lies 2,600 km northwest of Perth and 1,327 km south of Singapore. It has an area of 135 square kilometres .

Chromatomyia Horticola
type: NamedIndividual
type: Crop Pest
comment: Chromatomyia Horticola (Goureau)

Chrysocus Chinensis
type: NamedIndividual
type: Crop Pest

Cicadas
type: NamedIndividual
type: Crop Disease
type: Crop Pest
life cycle: In some species of cicada, the males remain in one location and call to attract females. Sometimes several males aggregate and call in chorus. In other species, the males move from place to place, usually with quieter calls while searching for females. The Tettigarctidae differ from other cicadas in producing vibrations in the substrate rather than audible sounds. After mating, the female cuts slits into the bark of a twig where she deposits her eggs. When the eggs hatch, the newly hatched nymphs drop to the ground and burrow. Cicadas live underground as nymphs for most of their lives at depths down to about 2.5 m (8 ft). Nymphs have strong front legs for digging and excavating chambers in close proximity to roots where they feed on xylem sap. In the process, their bodies and interior of the burrow become coated in anal fluids. In wet habitats, larger species construct mud towers above ground to aerate their burrows. In the final nymphal instar, they construct an exit tunnel to the surface and emerge. They then moult (shed their skins) on a nearby plant for the last time, and emerge as adults. The exuviae or abandoned exoskeletons remain, still clinging to the bark of the tree.
comment: Cicadas are sizable, noisy insects with stout bodies, wide blunt heads, and large transparent wings. They emerge from the soil in mid to late summer, and spend much of their time thereafter in trees, where they molt and issue their sometimes startlingly loud mating calls. Some species' populations fluctuate cyclically. None cause serious damage to more mature trees in your yard and garden though small trees, especially young orchard trees, may suffer significant damage.

Cicadella Viridis
type: NamedIndividual
type: Crop Pest

Cifuna Locuples
type: NamedIndividual
type: Crop Pest

Citrus Greening Disease
type: NamedIndividual
type: Crop Disease
affects: Citrus
affects: Limes
affects: Orange
comment: Citrus greening disease is a disease of citrus caused by a vector-transmitted pathogen. The causative agents are motile bacteria, Candidatus Liberibacter spp.

Citrus Leaf Miner
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Citrus
affects: Lemon
affects: Orange
management: Insecticide application are rarely warranted in mature orchards as yields are unaffected; young trees should be treated with appropriate insecticides to prevent retarded growth; cultural control methods include removal of water sprouts from trees and refraining from pruning live branches more than once a year to encourage uniform growth flushes which are short in duration.
symptoms: Thin, winding trails on leaves; heavy infestation can result in curled and distorted leaves; adult leafminer is a tiny moth which lays its eggs in the leaf; larvae hatch and feed on leaf interior.
comment: Leaf miners attack flushes of young growth and are unable to enter leaves once they harden.

Clay
type: NamedIndividual
type: Soil Texture
definition: Clay >= 40 Silt <= 40 Sand <= 45

Clay Loam
type: NamedIndividual
type: Soil Texture
definition: Clay >= 27 and Clay <= 40 Sand >= 20 and Sand <= 45 Silt >= 15 and Silt <= 52

Clayey Soils
type: NamedIndividual
type: Soil Type
comment: Clay soil are heavy soils that benefit from high nutrients. Clay soils remain wet and cold in winter and dry out in summer. These soils are made of over 25 percent clay, and because of the spaces found between clay particles, clay soils hold a high amount of water. Because these soils drain slowly and take longer to warm up in summer, combined with drying out and cracking in summer, they can often test gardeners.

Cletus Punctiger
type: NamedIndividual
type: Crop Pest
comment: Cletus Punctiger (Dallas)

Climate Change
type: NamedIndividual
type: Planetary Phenomena
comment: Climate change is the rising average temperature of Earth's climate system, also called global warming. It is driving changes in rainfall patterns, extreme weather, arrival of seasons, and more.

Clove
type: NamedIndividual
type: Crop
type: Forest Tree
comment: Cloves are the aromatic flower buds of a tree in the family Myrtaceae, Syzygium aromaticum. They are native to the Maluku Islands (or Moluccas) in Indonesia, and are commonly used as a spice. Cloves are available throughout the year owing to different harvest seasons in different countries.

Cnaphalocrocis Medinalis
type: NamedIndividual
type: Crop Pest

Coat
type: NamedIndividual
type: Topography

Cocos Islands
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:CC
area: 14 km²
climate: tropical with high humidity, moderated by the southeast trade winds for about nine months of the year
latitude: 96.87096
longitude: -12.164165
population: 628 (2004)
comment: The Territory of Cocos Islands is an Australian external territory in the Indian Ocean, comprising a small archipelago approximately midway between Australia and Sri Lanka . It is part of Southeast Asia and is in the Southern Hemisphere. The territory's dual name reflects that the islands have historically been known as either the Cocos Islands or the Keeling Islands.

Coffee Berry Borer
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Coffee
management: Removal of dropped berries and debris on plantation floor can help reduce sources of new infections; remove any berries remaining on plants after harvest; insecticide application is only effective if applied when the female beetle is still in the entry tunnel and has not yet penetrated deep into the berry
symptoms: Fruit dropping from plants; small holes may be evident on red cherries; when the insect is feeding, debris is pushed out of the hole and forms a brown or grey deposit on top of the hole; adult beetle can be found by cutting open the berry; adult is a tiny black beetle approx. 1.5-2.5 mm in length; larvae are white grubs with brown heads
Acronym: CBB
comment: Female beetle lays clusters of eggs inside the berries; insect undergoes up to 5 generations per year

Coffee Leaf Rust
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Coffee
symptoms: The symptoms of coffee rust include small, yellowish, oily spots on the upper leaf surface that expand into larger round spots that turn bright orange to red and finally brown with a yellow border. The rust pustules are powdery and orange-yellow on the underleaf surface. Later the pustules turn black. Rusted leaves drop so that affected trees are virtually denuded; such trees have significantly lower coffee yields and usually die within a few years.
Acronym: CLR
comment: Coffee rust, also called coffee leaf rust, devastating foliar disease of coffee plants caused by the fungus Hemileia vastatrix. Long known in coffee-growing areas of Africa, the Near East, India, Asia, and Australasia, coffee rust was discovered in 1970 to be widespread in Brazil, the first known infected area in the Western Hemisphere.

Colaphellus Bowvingi
type: NamedIndividual
type: Crop Pest

Colombia
type: NamedIndividual
type: Country
belongs to: South America
ISO code: ISO 3166-1:CO
agricultural land: 437,856 sq. km
area: 1,141,748 km²
climate: tropical along coast and eastern plains; cooler in highlands
density: 40/km²
latitude: -74.29897
longitude: 4.579904
population: 44,760,630 (2009)
comment: Colombia, officially the Republic of Colombia, is a country in South America with territories in North America. Colombia is bounded on the north by the Caribbean Sea, the northwest by Panama, the south by Ecuador and Peru, the east by Venezuela, the southeast by Brazil, and the west by the Pacific Ocean. It comprises 32 departments and the Capital District of Bogotá, the country's largest city.

Colposcelis Signata
type: NamedIndividual
type: Crop Pest

Common root rot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Barley
causes: Fungi
development: Generally occurs wherever barley is grown but is more common in water stressed plants.
management: No chemical treatments for this disease; plant crop in late fall to avoid warm soils which favor emergence of disease; do not fertilize crop excessively; use irrigation to reduce water stress.
symptoms: Brown lesions on leaves nearest soil extending to stem; resembles drought; death of lower leaves; rotting roots.
comment: Common root rot is a disease of wheat caused by one or more fungi. Cochliobolus sativus, Fusarium culmorum and F. graminearum are the most common pathogens responsible for common root rot.

Common Rust
type: NamedIndividual
type: Crop Disease
management: The use of resistant hybrids is the primary management strategy for the control of common rust. Timely planting of corn early during the growing season may help to avoid high inoculum levels or environmental conditions that would promote disease development.
symptoms: Symptoms of common rust often appear after silking. Small, round to elongate brown pustules form on both leaf surfaces and other above ground parts of the plant. As the pustules mature they become brown to black. If disease is severe, the leaves may yellow and die early.
comment: Common rust is a fungal disease that affects roses, snapdragons, bluegrass and woody perennials. Common rust is caused by the fungus Puccinia sorghi. Late occurring infections have limited impact on yield. The fungus overwinters on plants in southern states and airborne spores are wind-blown to northern states during the growing season. Disease development is favored by cool, moist weather (60 – 70◦ F).

Common Smut
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Corn
affects: Maize
causes: Fungus
development: Fungus overwinters on crop debris or in the soil and can survive for several years; fungus usually enters the plant through wounds; application of nitrogen fertilizer increases incidence of disease, while application of phosphorous fertilizer decreases infection.
management: Although many practices may be recommended for the control of common smut, the only method that is completely effective is to grow resistant corn hybrids.
symptoms: Tumor-like galls on plant tissues which are initially green-white or silvery white in color; interior of galls darken and turn into masses of powdery dark brown or black spores (with the exception of galls on leaves which remain greenish in color); galls may reach up to 15 cm in diameter and are common on ears, tassels, shoots or midrib of leaves; galls on leaves remain small and do not burst open.
comment: Common smut occurs in almost every field of corn in Nebraska and is not normally a cause for concern except when at high incidence when it can pose a threat to yield or compromises or stalk integrity. The causal agent of common smut is the fungus Ustilago maydis. Common smut can be confused with another smut disease of corn that occurs less commonly in Nebraska, called head smut. For more information on head smut, consult the PDC article dedicated to that disease.

Comoros
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:KM
agricultural land: 1,550 sq. km
area: 2,235 km²
climate: tropical marine; rainy season (November to May)
density: 275/km²
latitude: 43.877007
longitude: -11.888101
population: 798,000 (2005)
comment: The Comoros, officially the Union of the Comoros, is an island country in the Indian Ocean, at the northern end of the Mozambique Channel off the eastern coast of Africa. It shares maritime borders with Madagascar and Mayotte to the southeast, Tanzania to the northwest, Mozambique to the west, and the Seychelles to the northeast. Its capital and largest city is Moroni. The religion of the majority of the population, and the official state religion, is Sunni Islam. As a member of the Arab League, it is the only country in the Arab world which is entirely in the Southern Hemisphere. It is also a member state of the African Union, the Organisation internationale de la Francophonie, the Organisation of Islamic Cooperation, and the Indian Ocean Commission. The country has three official languages: Comorian, French and Arabic.

Complete Bunt
type: NamedIndividual
type: Crop Disease
affects: Wheat
management: Control of common bunt includes using clean seed, seed treatments chemicals and resistant cultivars. Historically, seed treatment with organomercury fungicides reduced common bunt to manageable levels. Systemic seed treatment fungicides include carboxin, difenoconazole, triadimenol and others and are highly effective. However, in Australia and Greece, strains of T. laevis have developed resistance to polychlorobenzene fungicides.
symptoms: Plants with common bunt may be moderately stunted but infected plants cannot be easily recognized until near maturity and even then it is seldom conspicuous. After initial infection, the entire kernel is converted into a sorus consisting of a dark brown to black mass of teliospores covered by a modified periderm, which is thin and papery. The sorus is light to dark brown and is called a bunt ball. The bunt balls resemble wheat kernels but tend to be more spherical. The bunted heads are slender, bluish-green and may stay greener longer than healthy heads. The bunt balls change to a dull gray-brown at maturity, at which they become conspicuous. The fragile covering of the bunt balls are ruptured at harvest, producing clouds of spores. The spores have a fishy odor. Intact sori can also be found among harvested grain.
comment: Common bunt, also known as stinking smut and covered smut, is a disease of both spring and winter wheats. It is caused by two very closely related fungi, Tilletia tritici (syn. Tilletia caries) and T. laevis (syn. T. foetida).

Congo
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:CG
agricultural land: 105,600 sq. km
area: 342,000 km²
climate: tropical; rainy season (March to June); dry season (June to October); persistent high temperatures and humidity; particularly enervating climate astride the Equator
density: 12/km²
latitude: 14.927424
longitude: -0.662615
population: 3,999,000 (2005)
comment: The Republic of the Congo, also known as Congo-Brazzaville, the Congo Republic or simply either Congo or the Congo is a country located in the western coast of Central Africa. To the west lies Gabon; Cameroon to its northwest and the Central African Republic to its northeast; the Democratic Republic of the Congo to the southeast and the Angolan exclave of Cabinda to its south; and the Atlantic Ocean to its southwest. The official language is French.

Continental Climate
type: NamedIndividual
type: Climate Type
comment: Continental climates often have a significant annual variation in temperature (hot summers and cold winters).

Continental Shelve
type: NamedIndividual
type: Topography
comment: A continental shelf is a portion of a continent that is submerged under an area of relatively shallow water known as a shelf sea. Much of these shelves has been exposed during glacial periods and interglacial periods. The shelf surrounding an island is known as an insular shelf.

Controlling Bamboo
type: NamedIndividual
type: Crop Disease
comment: Bamboos are divided into clumpers or runners, which can have a major impact on how well behaved they will be in your garden.

Cook Islands
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:CK
area: 240 km²
climate: tropical oceanic; moderated by trade winds; a dry season from April to November and a more humid season from December to March
density: 76/km²
latitude: -159.86343
longitude: -21.2081
population: 19,569 (2006)
comment: The Cook Islands are a self-governing island country in the South Pacific Ocean in free association with New Zealand. It comprises 15 islands whose total land area is 240 square kilometres . The Cook Islands' Exclusive Economic Zone covers 1,960,027 square kilometres of ocean.

Copper phytotoxicity
type: NamedIndividual
type: Crop Disease
affects: Soybean

Coral Reef
type: NamedIndividual
type: Topography
comment: A coral reef is an underwater ecosystem characterized by reef-building corals. Reefs are formed of colonies of coral polyps held together by calcium carbonate. Most coral reefs are built from stony corals, whose polyps cluster in groups.

Cordana leaf spot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Banana
distribution: Cordana leaf spot caused by N. musae is found in banana plantations all over the tropics. Cordana leaf spot caused by N. johnstonii was reported in Australia (Queensland, New South Wales, Norfolk Island, Lord Howe Island), Indonesia (Papua province), Malaysia (Cameron Highlands), Philippines and Tonga. The observations from Australia indicate that N. johnstonii is better adapted to cooler climates. The species was found in herbarium samples of Cordana leaf spot collected in New South Wales starting in the 1930s, as well as on Lord Howe Island off the coast of New South Wales, and Norfolk Island (between Australia and New Zealand), whereas N. musae was found to be the cause of most Cordana leaf spot in Queensland and was also identified in the Northern Territory. The distribution of the two species overlaps in the southern part of Queensland, close to the border with New South Wales
symptoms: The most characteristic symptoms of the disease are on the leaf. They are large, pale brown, oval to fusiform necrotic lesions with pale grey concentric ring patterns, with a dark brown border surrounded by a bright yellow halo separating the lesion from the healthy leaf tissue6 . Often, lesions coalesce into large necrotic patches. The leaves ultimately turn brown and dry out. The leaf spots caused by N. musae are larger and oval to elliptical in shape, while those caused by N. johnstonii are generally smaller and become more fusiform with age5 . Invasion often occurs at leaf margin in plants weakened by senescence, adverse environmental conditions, nutritional deficiencies, wounds or infections cused by other pathogens. Symptoms are often seen around lesions caused by other pathogens. When the infection is associated with other diseases, e.g. with black leaf streak, the lesions are enlarged and become necrotic. This occurs especially under humid conditions.
comment: Copper (Cu) is a widespread soil contaminant that is known to be highly toxic to soil biota. Limited information is available on the response of wild endemic species to Cu in the literature, which hinders ecological risk assessments and revegetation.
comment: Cordana leaf spot is a disease of banana that, even though it is common worlwide, has generally little impact on production. It is caused by two Neocordana fungi that are often found as secondary invaders of leaf lesions caused by other fungi.

Cordial Leaf Spot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Banana
distribution: Cordana leaf spot caused by N. musae is found in banana plantations all over the tropics. Cordana leaf spot caused by N. johnstonii was reported in Australia (Queensland, New South Wales, Norfolk Island, Lord Howe Island), Indonesia (Papua province), Malaysia (Cameron Highlands), Philippines and Tonga. The observations from Australia indicate that N. johnstonii is better adapted to cooler climates. The species was found in herbarium samples of Cordana leaf spot collected in New South Wales starting in the 1930s, as well as on Lord Howe Island off the coast of New South Wales, and Norfolk Island (between Australia and New Zealand), whereas N. musae was found to be the cause of most Cordana leaf spot in Queensland and was also identified in the Northern Territory.
symptoms: The most characteristic symptoms of the disease are on the leaf. They are large, pale brown, oval to fusiform necrotic lesions with pale grey concentric ring patterns, with a dark brown border surrounded by a bright yellow halo separating the lesion from the healthy leaf tissue6 . Often, lesions coalesce into large necrotic patches. The leaves ultimately turn brown and dry out. The leaf spots caused by N. musae are larger and oval to elliptical in shape, while those caused by N. johnstonii are generally smaller and become more fusiform with age5 . Invasion often occurs at leaf margin in plants weakened by senescence, adverse environmental conditions, nutritional deficiencies, wounds or infections cused by other pathogens. Symptoms are often seen around lesions caused by other pathogens. When the infection is associated with other diseases, e.g. with black leaf streak, the lesions are enlarged and become necrotic. This occurs especially under humid conditions.
comment: Cordana leaf spot is a disease of banana that, even though it is common worlwide, has generally little impact on production. It is caused by two Neocordana fungi that are often found as secondary invaders of leaf lesions caused by other fungi.
comment: Cordial Leaf Spot

Corn Earworm
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Corn
affects: Maize
causes: Insect
development: Adult insect is a pale green to tan, medium sized moth; can be a very damaging pests ofcorn; insect overwinters as pupae in the soil.
management: Corn earworms are most problematic on sweet corn varieties and treatment should be applied at egg hatch; monitor plants for eggs and young larvae and also natural enemies that could be damaged by chemicals; Bacillus thuringiensis or Entrust SC may be applied to control insects on organically grown plants; appropriate chemical treatment may be required for control in commercial plantations.
symptoms: Feeding damage to leaves, tassel and leaf whorls; preferred feeding site is the ear and insect produces extensive excrement at the tip of the ear; younger larvae feed on silks, severing them from the plant; young caterpillars are cream-white in color with a black head and black hairs; older larvae may be yellow-green to almost black in color with fine white lines along their body and black spots at the base of hairs; eggs are laid singly on both upper and lower leaf surfaces and are initially creamy white but develop a brown-red ring after 24 hours and darken prior to hatching.
comment: Corn earworms are moderately hairy larvae that vary from yellow, to green, to red to brownish black. They may be found feeding in the ear tips following silking.

Corynebacterium tritici
type: NamedIndividual
type: Bacterial

Corynespora cassiicola
type: NamedIndividual
type: Fungus
comment: Corynespora cassiicola is a species of fungus well known as a plant pathogen. It is a sac fungus in the family Corynesporascaceae. It is the type species of the genus Corynespora.

Corythucha Ciliata
type: NamedIndividual
type: Crop Pest
comment: Corythucha Ciliata (Say)

Corythucha Marmorata
type: NamedIndividual
type: Crop Pest
comment: Corythucha Marmorata (Uhler)

Costa Rica
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:CR
agricultural land: 18,800 sq. km
area: 51,100 km²
climate: tropical and subtropical; dry season (December to April); rainy season (May to November); cooler in highlands
density: 85/km²
latitude: -84.82532
longitude: 8.357946
population: 4,133,884 (2007)
comment: Costa Rica, officially the Republic of Costa Rica, is a country in Central America, bordered by Nicaragua to the north, the Caribbean Sea to the northeast, Panama to the southeast, the Pacific Ocean to the southwest, and Ecuador to the south of Cocos Island. It has a population of around 5 million in a land area of 51,060 square kilometers . An estimated 333,980 people live in the capital and largest city, San José, with around 2 million people in the surrounding metropolitan area.

Cote dIvoire
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:CI
agricultural land: 205,000 sq. km
area: 322,460 km²
climate: tropical along coast, semiarid in far north; three seasons - warm and dry (November to March), hot and dry (March to May), hot and wet (June to October)
density: 42/km²
population: 18,373,060 (2008)
comment: Ivory Coast, also known as Côte d'Ivoire, officially the Republic of Côte d'Ivoire, is a country located on the south coast of West Africa. Ivory Coast's political capital is Yamoussoukro in the centre of the country, while its economic capital and largest city is the port city of Abidjan. It borders Guinea to the northwest, Liberia to the west, Mali to the northwest, Burkina Faso to the northeast, Ghana to the east, and the Gulf of Guinea to the south. The official language of the republic is French, with local indigenous languages also being widely used that include Baoulé, Dioula, Dan, Anyin, and Cebaara Senufo. In total, there are around 78 different languages spoken in Ivory Coast. The country has large populations of adherents of Christianity, Islam and various indigenous religions.
label: Ivory Coast

Covered smut
type: NamedIndividual
type: Crop Disease
affects: Barley
causes: Fungus
development: Smut masses burst during harvest and further transmit disease; crushed spore masses have an odor similar to rotting fish.
management: Use only certified smut-free seed; treat seeds with hot water prior to planting to kill fungi; treat seeds with contact fungicide; grow resistant varieties.
symptoms: Stunted growth; late emergence of heads; kernels replaced with grey fungal masses.
comment: Covered smut is a disease affecting seed heads and generally remains undetected until head emergence. Infected heads may emerge later than healthy ones and the ears often break through the sheath below the flag leaf. Infected heads are filled with dark brown to black sooty spores instead of grains.

Cow Pat Pit
type: NamedIndividual
type: Organic Mature

Crabgrass
type: NamedIndividual
type: Crop Disease
type: Weeds
life cycle: Crabgrass is a warm season annual grass that overwinters as seeds and germinates when soil temperatures in mid spring consistently remain at 50 to 55 degrees for a number of days in a row. Seeds can continue to germinate throughout the summer if soil temperatures and moisture conditions are adequate. It grows during the heat of summer, sets seeds and is killed by the first frost.
management: Management Strategies 1. Practice good cultural techniques to keep lawns healthy and free of drought, nutritional or injury-induced stress. Cool season grasses fertilized in the fall and mid to late spring will go into mid-summer dormancy later and will compete better with the crabgrass. 2. Set the lawnmower at 3 inches high. Taller grass will shade the soil and decrease the germination rate of crabgrass seeds. 3. Pull crabgrass plants before they set seed. One plant can produce 150,000 seeds. 4. Overseed existing cool season lawns in the fall. A thick turf in the spring will shade the lawn, retard seed germination and better compete with any germinating weed plants. 5. Apply corn gluten meal herbicide about two to three weeks before crabgrass seeds begin to germinate. In Missouri, this would be between mid March to mid April or about the time when forsythia first begins to bloom. Corn gluten will inhibit the germination of turf grass seeds and should not be used within six weeks of reseeding a lawn. Corn gluten is only about 65% effective compared to synthetic products but is an organic control. 6. Apply a pre-emergent crabgrass herbicide about two weeks before crabgrass seeds begin to germinate. In mid-Missouri, this would be between mid March to mid April or about the time when forsythia first begins to bloom. Applying a pre-emergent herbicide after the crabgrass has begun to germinate will not provide good control but may inhibit seedlings at the 2-leaf stage or seeds that are slower to germinate. Products that combine pre-emergent herbicides and fertilizer are not recommended. Read and follow label directions carefully. 7. Apply a post-emergent crabgrass herbicide if a pre-emergent herbicide was not used or if it was ineffective and control is warranted in early summer. Read and follow label directions carefully. 8. Do nothing in late summer. Crabgrass is an annual grass that will be killed by frost. Applying a post-emergent herbicide in late summer may not be …
symptoms: There are three crabgrass species commonly found as weeds in lawns - hairy crabgrass (Digitaria sanguinalis), southern crabgrass (Digitaria ciliaris) and smooth crabgrass (Digitaria ischaemum). All three species are pale green, have a prostrate or ascending growth habit, and have flat 5-10 mm wide blades. The seed heads have 2-6 branches with 2 rows of seeds along each branch. Crabgrass may be found in a wide variety of landscape settings but grows best in full sun.
comment: Crabgrass is an annual grassy lawn weed that spreads along the ground creating a dense mat that smothers desirable grasses.

Crickets
type: NamedIndividual
type: Crop Disease
type: Crop Pest
comment: These nocturnal insects may do more than chirp at night: they can also become home and garden pests during late summer and early fall. In the garden, they may feed on the tender seedlings of your late season crops, as well as on almost any part of any garden plant when they are numerous enough. At this point they may also enter homes for shelter or food, particularly when vegetation becomes dry or scarce or as cold weather approaches. Once inside, they may feed on clothing and fabrics spotted or stained by spilled food or perspiration, leaving frayed holes in their wake. Pesticidal soaps such as Safers, and insecticidal dusts such as diatomaceous earth and boric acid are effective in controlling crickets. Exclude crickets from homes with tightly-fitting doors, windows and screens. Since crickets are attracted to light, consider installing yellow, non-attractive light bulbs near potential access areas.

Croatia
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:HR
agricultural land: 13,261 sq. km
area: 56,542 km²
climate: Mediterranean and continental; continental climate predominant with hot summers and cold winters; mild winters, dry summers along coast
density: 81/km²
latitude: 16.468918
longitude: 44.466328
population: 4,453,500 (2008)
comment: Croatia, officially the Republic of Croatia, is a country at the crossroads of Central and Southeast Europe on the Adriatic Sea. It borders Slovenia to the northwest, Hungary to the northeast, Serbia to the east, Bosnia and Herzegovina and Montenegro to the southeast, and shares a maritime border with Italy to the west and southwest. Its capital and largest city, Zagreb, forms one of the country's primary subdivisions, with twenty counties. Croatia has 56,594 square kilometres and a population of 4.07 million.

Crop Flowering
type: NamedIndividual
type: Cropping
comment: The action of producing flowers of crop.

Cropland
type: NamedIndividual
type: Farm

Crown and Root Rot
type: NamedIndividual
type: Crop Disease
affects: Wheat
management: Cultural practices influence environmental stress which, in turn, greatly affects the incidence and severity of root and crown rot. Plant winter wheat at the recommended date for your geographic area. Early planting often depletes soil moisture reserves which promotes a higher incidence of root and crown rot. Always plant varieties adapted to your growing area and conditions. When planting place seed into a firm, mellow seedbed and cover with sufficient soil to prevent rapid drying. Use good quality seed with a high germination and treat the seed with a seed treatment fungicide before planting. Crop rotation can lower the amount of inoculum and hence the incidence and severity of crown and root rots caused by B. sorokiniana and Fusarium spp.
symptoms: Symptoms of root and crown rot of winter wheat may first appear on the roots and sub-crown internodes in late fall as small brown to black lesions on the primary and secondary roots. These lesions vary in length from less than 1/8 inch to over 1 inch. Diseased crowns are brown in contrast to the white color of healthy crowns. Wheat crowns rot to a greater or lesser degree depending on the stage and severity of disease development. Plants with severe crown damage generally do not survive. Those with moderate root and crown damage tiller sparsely. Individual fields affected by root and crown rot contain scattered pockets of dead and dying plants. Affected areas often follow terrace ridges or occur on exposed slopes. Fields with loose seedbeds often show a higher incidence of root and crown rot.
comment: Root and crown rot of winter wheat is caused by the infection of roots and crowns by Bipolaris sorokiniana and several species of Fusarium. The mycelium of B. sorokiniana is a deep olive-brown. Conidiophores are single or clustered, simple, erect and with septations. Conidia are olive-brown and ovate to oblong with tapered ends, are three to 10-septate and slightly curved. The teleomorph is Cociobolus sativus, an ascomycete. Fusarium graminearum and F. culmorum are the most common Fusarium spp. causing root and crown rots in winter wheat. Other species include F. avenaceum and F. poe. Fusarium spp. survive in the soil in host debris as perithecia (sexual fruiting structures) chlamydospores (asexual survival structures), or as mycelium. They also can be seedborne.F. graminearum also causes Fusarium head blight or scab.

Crown Galls
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Blueberry
affects: Oak
life cycle: Crown gall forming bacteria inhabit the soil and survive for many years. The bacterium can initially be brought in on the roots of infected plants. It then spreads by soil and water movement or contaminated pruning tools. Bacteria enter the plant through wounds possibly caused by chewing insects, cultivation damage, or from grafting and pruning tools. The crown gall bacterium has been known to survive more than two years in the soil in the absence of susceptible plants. It can live for several years in decomposing galls buried in the soil. Crown gall is likely to be more serious in limed soil than in acid soils so soil pH could be important in limiting the disease.
management: Management Strategies 1. Prune out infected material. Crown gall cannot be eliminated from a shrub even though the infected plant may live for many years. To improve the appearance of the plant, prune out and destroy infected stems below the galled area. Sterilize the pruning shears after each cut with a mixture of one part bleach to nine parts water. 2. Destroy infected plants. Destroy the infected plant. The bacterium will remain in the soil so it is important to plant a resistant plant species. If the same species needs to be planted in the area, remove and replace the soil or consider soil sterilization. 3. Sterilize the soil. Soils known to be infected with crown gall bacteria can be sterilized using chemicals, heat, or antibiotics. This is not practical for most home gardeners. A biological control has been introduced using a bacterium, Agrobacterium radiobactor strain 84. This bacterium was discovered to be antagonistic to crown gall bacterium. It is available for use as a preplant treatment by dipping nursery stock in a suspension of the live bacteria in water. 4. Exclude the problem. When purchasing forsythia and euonymus plants, inspect them carefully for signs of galls. Do not purchase plants that show gall-forming symptoms. 5. Replant with a more resistant plant species. The following plants recommended by the University of Illinois show greater resistance to bacterial crown gall: barberry, hornbeam, true cedars, ginkgo, golden raintree, tulip tree, mahonia, spruce, linden, boxwood, catalpa, beech, holly, larch, magnolia, black gum, pine, Douglas fir, bald cypress, hemlock, birch, firethorn, redbud, smoke tree, sweet gum, deutzia, serviceberry, yellowwood, yew, and zelkova.
symptoms: Galls are most commonly found near ground level on the roots and lower branches of the plants. As the galls enlarge, they become woody and hard. The outer layer turns brown and corky. The plant may be weakened and stunted with some branch or tip dieback. Symptoms may not develop immediately after infection. Galls grow most rapidly during the warm months of the year.
comment: Crown gall is a plant disease caused by the soil-inhabiting bacterium, Agrobacterium tumefaciens. The bacterium causes abnormal growths or galls on roots, twigs, and branches of euonymus and other shrubs primarily in the rose family. The bacterium stimulates the rapid growth of plant cells that results in the galls. In addition to being unsightly, the galls weaken and stunt the growth of the plant. Although galls can disrupt the flow of water and nutrients up the roots and branches, they usually do not cause total plant death. The disease can spread to other susceptible plants through contaminated soil and tools. Most chemical treatments are not effective. The galls on forsythia, viburnum, highbush blueberry, American elm, hickory, maple, oak, and privet are believed to be caused by a fungus, Phomopsis sp. Since its cultural controls are the same as for bacterial crown gall, both are treated together herein. Chemical treatments, however, will differ. Chemical treatments for both are of very limited value to the home gardener. Only those relating to bacterial crown gall have been addressed.

Cuba
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:CU
agricultural land: 65,700 sq. km
area: 110,861 km²
climate: tropical; moderated by trade winds; dry season (November to April); rainy season (May to October)
density: 102/km²
latitude: -79.5446
longitude: 21.555931
population: 11,423,952 (2008)
comment: Cuba, officially the Republic of Cuba, is a country comprising the island of Cuba, as well as Isla de la Juventud and several minor archipelagos. Cuba is located in the northern Caribbean where the Caribbean Sea, Gulf of Mexico and Atlantic Ocean meet. It is east of the Yucatán Peninsula, south of both the U.S. state of Florida and the Bahamas, west of Hispaniola, and north of both Jamaica and the Cayman Islands. Havana is the largest city and capital; other major cities include Santiago de Cuba and Camagüey. The official area of the Republic of Cuba is 109,884 km2 . The main island of Cuba is the largest island in Cuba and in the Caribbean, with an area of 104,556 km2 . Cuba is the second-most populous country in the Caribbean after Haiti, with over 11 million inhabitants.

Cucumber Mosaic
type: NamedIndividual
type: Crop Disease
type: Viral Disease
affects: Cucumber
caused by: Cucumber mosaic virus
chemical control: There are no chemical controls available to control virues. The use of insecticides to reduce aphid transmission is not practical.
controls: Buy certified, virus-free seeds and plants. Consider using CMV-resistant varieties of lettuce, spinach, cucurbits (e.g., cucumber, melon and squash) and other vegetables where available. Seed catalogs often contain information on CMV resistance that can be useful for variety selection. Remove weed hosts whenever possible around your garden and mulch vegetable and ornamental gardens to inhibit weed growth. Consider using floating row covers where possible to prevent aphids from reaching susceptible plants. DO NOT use insecticides to control aphids because such treatments are unlikely to act fast enough to prevent aphids from transmitting CMV, and may actually stimulate aphids to move and feed more widely, thus leading to increased spread of the virus.
distribution: Cucumber mosaic is caused by Cucumber mosaic virus (CMV) which can overwinter in susceptible biennial or perennial weeds, as well as in perennial agricultural crops (e.g., alfalfa) and perennial herbaceous and woody ornamentals. Seeds and even pollen from certain host plants can carry the virus, and thus the virus can be spread via these plant parts. More commonly, CMV is spread by aphids [see the University of Wisconsin Garden Facts XHT1043 (“Aphids”) for details on these insect pests] which can pick up the virus from infected plants and transmit it to healthy plants as they feed. Over 80 species of aphids can potentially transmit CMV. The severity of cucumber mosaic oftentimes depends on the size and activity of aphid populations in an area, as well as on the number infected plants in an area serving as reservoirs for the virus.
symptoms: CMV infects 1200 species in over 100 plant families and can cause significant economic losses in many vegetable and horticultural crops. CMV causes a systemic infection in most host plants, but may remain symptomless in some crops like alfalfa. Symptoms of cucumber mosaic can vary greatly depending on the crop infected and the age of the plant when infection occurs. Plants infected early in the season are severely stunted and leaves are malformed, and fruit are unmarketable because of pronounced rugosity (roughness) on the fruit surface, as shown on the infected zucchini plant and fruit. Infection of vining crops, such as muskmelon, show severely stunted growing tips, and although fruit may not show symptoms they are of poor quality. If the yellow squash variety grown lacks the precocious gene, color breaking will occur on the fruit, causing the fruit to show green blotchy patterns, but these symptoms are absent in yellow squash varieties with the precocious gene. Color breaking on fruit of varieties without the precocious gene will also occur with Watermelon mosaic potyvirus (WMV) infection; however this protection does not hold true for Papaya ringspot potyvirus or Zucchini yellow mosaic potyvirus, where both foliage and fruit of yellow squash are severely affected. Pumpkin is another cucurbit, that when infected at any early stage, will express severe foliar mosaic and the fruit will show a mosaic pattern and would be unmarketable There is no known cure for cucumber mosaic. Infected plants should be removed and destroyed to eliminate the plants as potential reservoirs for the virus (which can subsequently be spread to other nearby healthy plants). Infected plants can be burned (where allowed by local ordinance), deep buried or hot composted. Killing infected plants with herbicides can also be an effective management strategy.
comment: Cucumber mosaic is a viral disease of worldwide distribution that affects over 1200 plant species. Hosts include a wide range of fruits, vegetables, herbaceous and woody ornamentals, and weeds. The disease has perhaps its biggest impact in vegetable production where it can cause significant losses in yield and vegetable quality. What does cucumber mosaic look like? Symptoms of cucumber mosaic can vary widely depending on host species, host variety, and time of infection. Typical symptoms include stunting of entire plants, mosaic or mottling (i.e., blotchy white, yellow, and light green areas) and ring spots (i.e., ring-like areas of discolored tissue) on leaves and fruits, and a variety of growth distortions such as cupping, puckering and strapping (i.e., elongation and thinning) of leaves as well as warts on fruits. In extreme situations, parts of an affected plant or even an entire plant may die from the disease.

Cucumber mosaic virus
type: NamedIndividual
type: Virus
causes: Cucumber Mosaic
comment: Cucumber mosaic virus (CMV) is one of the most common plant viruses, causing yellow mottling, distorted leaves and stunted growth in a wide range of garden plants, not just cucumbers.

Cyclonic Storms
type: NamedIndividual
type: Planetary Phenomena
comment: A cyclonic storm, also known as tropical cyclone, is a rapidly rotating storm system characterized by a low-pressure center, a closed low-level atmospheric circulation, strong winds, and a spiral arrangement of thunderstorms that produce heavy rain or squalls.

Cydia pomonella
type: NamedIndividual
type: Crop Pest

Cylindrocladium Leaf Spot
type: NamedIndividual
type: Crop Disease
controls: Irrigation management and moisture control (keep leaves dry; irrigation timing and application method; reduce overhead irrigation; use solid-cover greenhouses); increased plant spacing; reduce relative humidity; sanitation practices to reduce inoculum (examine crop every few days); segregate severely diseased plants; minimize all plant wounding and plant stressors
symptoms: Leaf spots generally grayish brown, dark brown or nearly black; lesions often gray in center and may have a brownish black edge; young leaf spots are brown, circular (0.5 mm diameter) and sometimes surrounded by a chlorotic zone about 1 mm wide; old leaf spots are circular to elliptical, up to 3 x 5 mm; rachis and petioles may be infected; as disease develops, lesions coalesce with large bands of necrotic leaf tissue appearing
comment: Calonectria leaf spot (Cylindrocladium leaf spot)

Cyprus
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:CY
agricultural land: 1,185 sq. km
area: 9,251 km²
climate: temperate; Mediterranean with hot, dry summers and cool winters
density: 85/km²
latitude: 33.4355
longitude: 35.132523
population: 801,600 (2009)
comment: Cyprus, officially called the Republic of Cyprus, is an island nation in the eastern Mediterranean Sea. It is the third largest and third most populous island in the Mediterranean, and is located south of Turkey; west of Syria; northwest of Lebanon, Israel and Palestine; north of Egypt; and southeast of Greece. Nicosia is the country's capital and largest city.

Cytospora Canker
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
life cycle: The fungus overwinters in cankered bark. Spores from fruiting bodies formed in cankers are spread by rain, wind, insects, birds, and man. Infection occurs through wounds. The fungus grows and kills the bark, then expands until the entire branch is killed from the branch being girdled.
management: Management Strategies 1. Prune out diseased branches. Prune out all infected branches and burn or remove them from the site. Prune in late winter when it is sunny and the trees are dry. Dip pruning tools into a solution of 1–part bleach and 9–parts water between cuts to reduce spreading the disease. After pruning, oil blades to prevent rust. 2. Avoid injuries to bark and stems. Wounds can provide entry points for the pathogen, especially during wet periods. 3.Improve plant health. Water during dry periods and aerate the soil around the tree to relieve soil compaction and facilitate water and nutrient penetration. Fertilize to maintain vigor. 4. Maintain good air circulation. Chances of infection are greater on plants that are crowded together. Space plants out when planting to accommodate mature growth. 5. Fungicidal sprays are seldom effective and are not recommended. Once the tree is infected, the disease is difficult to control. Bordeaux mixture or other copper-containing fungicides may help slow the spread of the disease. The best time to apply fungicides has not been established. Consider using fungicidal sprays only if the tree is very valuable and you are willing to pay for regular sprayings. 6. Remove the infected tree and plant a different species or variety. Blue and Norway spruce are most susceptible to Cytospora canker.
symptoms: Dying of lower branches is usually the first symptom. The needles turn brown and after a few months drop off, and white or light blue patches of resin become obvious on the dead bark of larger branches. As the disease progresses over several years, higher branches also die. Dead areas called cankers are formed. The infected inner bark tissue and cambium are brown in contrast to the normal light color of healthy tissue, but the wood beneath the infected bark is not discolored. As cankers enlarge and girdle the stem, parts above the diseased area lose color, make little growth, wilt or wither, and die back from the tips. Infected branches often start growth later in the spring. The resin exuded from cankered branches is visible on dead bark after infected needles are cast. This is the most obvious symptom on infected branches.
comment: Cytospora canker is caused by a fungus, Leucostoma kunzei (formerly known as Cytospora kunzei). In its perfect stage, it is known as Valsa kunzei. It is a destructive disease of many of the spruces, especially Colorado blue and Norway spruces, from the Midwest to New England. The disease is more common on trees over 15 years old and trees under stress and of low vigor. Trees with shallow roots, weakened by mechanical injury, insect, or weather stresses, and trees growing on unfavorable sites are most susceptible. The disease destroys the symmetry of spruce trees and in time may kill them. Infections on Colorado blue spruces are usually confined to branches. Branches and trunks are damaged on black, Engelmann, Norway, red, and white spruces. Cytospora cankers have also been found on Douglas fir, hemlock, larch, and balsam fir.

Czech
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:CZ
agricultural land: 42,290 sq. km
area: 78,866 km²
climate: temperate; cool summers; cold, cloudy, humid winters
density: 132/km²
latitude: 15.06768
longitude: 49.71175
population: 10,424,926 (2008)
comment: The Czech Republic, also known by its short-form name, Czechia, is a landlocked country in Central Europe. It is bordered by Austria to the south, Germany to the west, Poland to the northeast, and Slovakia to the east. The Czech Republic has a hilly landscape that covers an area of 78,866 square kilometers with a mostly temperate continental climate and oceanic climate. It is a unitary parliamentary republic.

The Democratic Republic of Congo
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:CD
agricultural land: 257,550 sq. km
area: 2,344,858 km²
climate: tropical; hot and humid in equatorial river basin; cooler and drier in southern highlands; cooler and wetter in eastern highlands; north of Equator - wet season (April to October), dry season (December to February); south of Equator - wet season (November to March), dry season (April to October)
density: 25/km²
latitude: 25.620117
longitude: -3.030812
population: 62,600,000 (2007)
comment: The Democratic Republic of the Congo, also known as Congo-Kinshasa, DR Congo, the DROC, or simply either Congo or the Congo, and historically Zaire, is a country in Central Africa. It is, by area, the largest country in sub-Saharan Africa, the second-largest in all of Africa, and the 11th-largest in the world. With a population of around 107 million, the Democratic Republic of the Congo is the most-populous officially Francophone country in the world, as well as the 3rd-most populous country in Africa and the 14th-most populous country in the world. It is a member of the United Nations, Non-Aligned Movement, African Union, and COMESA. Since 2015, the Eastern DR Congo has been the scene of an ongoing military conflict in Kivu.

DUALEM-2
type: NamedIndividual
type: Sensor
related to: Electrical Conductivity
comment: The DUALEM-2, a commercially available type of EMI sensor, is an instrument that combines two geometries, called "horizontal co-planar" (HCP) and "perpendicular co-planar" (PRP). The DUALEM-2 sensor provides simultaneous ECa readings to soil depths of exploration (DOE) of 1 m (HCP) and 3.2 m (PRP).

Dataiku DSS
type: NamedIndividual
type: Toolkit

Dataset AES-CD9214
type: NamedIndividual
type: Dataset
related to: Leaf Disease
state: Number of Images for each disease : Rice sheath blight: 3559 Rice blast : 2741 Rice flax spot: 795 Cucumber powdery mildew: 763 Cucumber downy mildew: 780 Cucumber target spot: 576
comment: AES-CD9214 is a challenging dataset, which is collected in a natural environment. The images in the dataset have different backgrounds, various noises, and environmental factors such as different resolutions, angles, and illumination. The number of examples in the AES-CD9214 dataset is unbalanced, which is challenging to the recognition methods. The disease name and number of corresponding images in our introduce AES-CD9214 dataset. Number of Images for each disease : Rice sheath blight: 3559 Rice blast : 2741 Rice flax spot: 795 Cucumber powdery mildew: 763 Cucumber downy mildew: 780 Cucumber target spot: 576

Dataset CIFAR-10
type: NamedIndividual
type: Dataset
Acronym: CIFAR-10
comment: The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes. The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. There are 6,000 images of each class.

ImageNet
type: NamedIndividual
type: Dataset
References: Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. "Imagenet: A large-scale hierarchical image database." In 2009 IEEE conference on computer vision and pattern recognition, pp. 248-255. Ieee, 2009.
comment: ImageNet is a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total.

Dataset MK-D2
type: NamedIndividual
type: Dataset
related to: Dataset MalayaKew (MK) Leaf
comment: The MK-D2 is a subset of the Malaya Kew leaf dataset, including 44 types of plant leaves. Number of pictures in each type of the MK-D2 training set is 788, and that of the test set is 200.

Dataset MalayaKew (MK) Leaf
type: NamedIndividual
type: Dataset
related to: Crop Imagery
related to: Leaf
state: There are two(2) folders associated with the dataset and a ReadMe file: + D1 dataset - It consists of segmented leaf images with size 256 * 256 pixels. - Number of training and testing images is 2288 and 528 respectively. + Dataset Groundtruth - It consists of cropped image patches of leaf with size 256 * 256 pixels. - Number of training and testing images is 34672 and 8800 respectively.
comment: MalayaKew (MK) Leaf dataset was collected at the Royal Botanic Gardens, Kew, England. It consists of scan-like images of leaves from 44 species classes. This dataset is very challenging as leaves from different species classes have very similar appearance. There are two(2) folders associated with the dataset and a ReadMe file: + D1 dataset - It consists of segmented leaf images with size 256 * 256 pixels. - Number of training and testing images is 2288 and 528 respectively. + Dataset Groundtruth - It consists of cropped image patches of leaf with size 256 * 256 pixels. - Number of training and testing images is 34672 and 8800 respectively.

National Bureau of Agricultural Insect Resources (NBAIR) dataset
type: NamedIndividual
type: Dataset
comment: The National Bureau of Agricultural Insect Resources (NBAIR) dataset used as first insect dataset that consists of 40 classes of field crop insect images.

Dataset NSIS
type: NamedIndividual
type: Dataset
related to: Soil
state: NSIS dataset with 13,000 geo-referenced soil profiles
comment: NSIS dataset with 13,000 geo-referenced soil profiles

Dataset PlantVillage
type: NamedIndividual
type: Dataset
related to: Crop Imagery
related to: Leaf
state: PlantVillage have 54,306 images, with 26 diseases for 14 crop plants.
References: Hughes, D., Salathe, Marcel, 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics, pp. 1-13.
comment: PlantVillage have 54,306 images, with 26 diseases for 14 crop plants.

Xie1 insect dataset
type: NamedIndividual
type: Dataset
description: {CifunaLocuples; EurydemaDominulus; TettigellaViridis; ColaphellusBowvingi; ColposcelisSignata; PierisRapae; MarucaTestulalis; EurydemaGebleri; AtractomorphaSinensis; ErthesinaFullo; SympiezomiasVelatus; ChromatomyiaHorticola; SogatellaFurcifera; EysacorisGuttiger; CletusPunctiger; DolerusTritici; CnaphalocrocisMedinalis; PentfaleusMajor; LaodelphaxStriatellua; SitobionAvenae; ChiloSuppressalis; AeliaSibirica; MythimnaSeparta; NephotettixBipunctatus}
References: C. Xie, J. Zhang, R. Li, J. Li, P. Hong, J. Xia, P. Chen, "Automatic classification for field crop insects via multiple-task sparse representation and multiple-kernel learning", Comput. Electron. Agric., 119 (2015), pp. 123-132
comment: Xie1 insect dataset contains 24 different types of insects from fieldbased insect collection, including about 60 images per species.

Xie2 insect dataset
type: NamedIndividual
type: Dataset
description: DolycorisBaccarum; LycormaDelicatula; EurydemaDominulus; PierisRapae; HalyomorphaHalys; SpilosomaObliqua; GraphosomaRubrolineata; LuperomorphaSuturalisChen; LeptocorisaAcuta; SesamiaInferens; CicadellaViridis; CallitettixVersicolor; ScotinopharaLurida; CletusPunctiger; NezaraViridula; DicladispaArmigera; RiptortusPedestris; MarucaTestulalisGryer; ChauliopsFallaxScott; ChiloSuppressalis; StolliaVentralis; NilaparvataLugens; DiostrombusPolitusUhler; PhyllotretaStriolata; AulacophoraIndica; LaodelphaxStriatellus; CeroplastesCeriferus; CorythuchaMarmorata; DryocosmusKuriphilusYasumatsu; PorthesiaTaiwanaShiraki; ChromatomyiaHorticola; IscadiaInexacta; PlutellaXylostella; EmpoascaFlavescens; DolerusTriticiChu; SpodopteraLitura; CorythuchaCiliata; BemisiaTabaci; CeutorhynchusAsperRoelofs; StrongyloidesVariegatus;
References: C. Xie, R. Wang, J. Zhang, P. Chen, W. Dong, R. Li, T. Chen, H. Chen, "Multi-level learning features for automatic classification of field crop pests", Comput. Electron. Agric., 152 (2018), pp. 233-241
comment: Xie2 insect dataset contains 24 different types of insects from fieldbased insect collection, including about ... images per species.

Daytime Heating
type: NamedIndividual
type: Planetary Phenomena
comment: Daytime heating occurs it increases the air's capacity to evaporate water vapor. The sun heats the ground which in turn heats the air above the ground.

Decay Fungi
type: NamedIndividual
type: Crop Disease
type: Crop Injure
comment: Decay fungi produce spores, which act like seeds of higher plants in that they are capable of starting a new growth of fungus in an area where conditions are favorable.

December
type: NamedIndividual
type: Month Of Year

DeepSpeed
type: NamedIndividual
type: Toolkit
Acronym: DeepSpeed
comment: DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU: Extreme scale: Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. Extremely memory efficient: With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of arts, democratizing multi-billion-parameter model training such that many deep learning scientists can explore bigger and better models. Extremely long sequence length: Sparse attention of DeepSpeed powers an order-of-magnitude longer input sequence and obtains up to 6x faster execution comparing with dense transformers. Extremely communication efficient: 3D parallelism improves communication efficiency allows users to train multi-billion-parameter models 2–7x faster on clusters with limited network bandwidth. 1-bit Adam/1-bit LAMB reduce communication volume by up to 5x while achieving similar convergence efficiency to Adam/LAMB, allowing for scaling to different types of GPU clusters and networks. Early adopters of DeepSpeed have already produced a language model (LM) with over 17B parameters called Turing-NLG, establishing a new SOTA in the LM category. DeepSpeed is an important part of Microsoft’s new AI at Scale initiative to enable next-generation AI capabilities at scale
comment: https://www.deepspeed.ai/

Deeplearning4j
type: NamedIndividual
type: Toolkit
comment: Deeplearning4j is implemented in Java and is hence more efficient as compared to Python. It uses the tensor library called ND4J which provides an ability to work with n-dimensional arrays (also called tensors). This framework also supports both CPUs and GPUs. Deeplearning4j treats the task of loading data and training algorithms as separate processes. This separation of functions provides a whole lot of flexibility. And who wouldn’t like that, especially in deep learning?! Deeplearning4j works with different data types as well: - Images - csv - plain text, etc. The kind of deep learning models you can build using Deeplearning4j are: - Convolutional Neural Networks (CNNs) - Recurrent Neural Networks (RNNs) - Long Short-Term Memory (LSTM) and many other architectures.

Deightoniella Leaf Spot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Banana
comment: Banana Deightoniella leaf & fruit spot, banana leaf blotch

Delta
type: NamedIndividual
type: Topography

Denmark
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Temperate Climate
ISO code: ISO 3166-1:DK
agricultural land: 26,900 sq. km
area: 43,098.31 km²
climate: temperate; humid and overcast; mild, windy winters and cool summers
density: 127.8/km²
latitude: 11.6344
longitude: 56.155106
population: 5,505,995 (2008)
comment: Denmark, officially the Kingdom of Denmark, is a Nordic country in Northern Europe. Denmark proper, which is the southernmost of the Scandinavian countries, consists of a peninsula, Jutland, and an archipelago of 443 named islands, with the largest being Zealand, Funen and the North Jutlandic Island. The islands are characterised by flat, arable land and sandy coasts, low elevation and a temperate climate. Denmark lies southwest of Sweden and south of Norway, and is bordered to the south by Germany. The Kingdom of Denmark is constitutionally a unitary state comprising Denmark proper and the two autonomous territories in the North Atlantic Ocean: the Faroe Islands and Greenland. Denmark has a total area of 42,943 km2 as of 2020, and the total area including Greenland and the Faroe Islands is 2,210,579 km2 . Denmark proper has a population of 5.83 million .

Desert Climate
type: NamedIndividual
type: Climate Type
comment: The desert climate (in the Köppen climate classification BWh and BWk), is a climate in which there is an excess of evaporation over precipitation. The typically bald, rocky, or sandy surfaces in desert climates hold little moisture and evaporate the little rainfall they receive. Covering 14.2% of earth's land area, hot deserts are the most common type of climate on earth after polar climate.

DAP
type: NamedIndividual
type: Chemical Fertilizer
label: Diammonium phosphate

Diaphania Pyloalis Larva
type: NamedIndividual
type: Crop Pest

Dicladispa Armigera
type: NamedIndividual
type: Crop Pest
comment: DicladispaArmigera (Olivier)

Diostrombus Politus Uhler
type: NamedIndividual
type: Crop Pest
comment: Diostrombus Politus Uhler

Diplodia Leaf Streak
type: NamedIndividual
type: Crop Disease
comment: Diplodia Leaf Streak lesions on corn leaf. Diplodia leaf lesions are mostly oval to elongated and contain black pycnidia. Leaf lesions are brown, often with yellow margins. Lesions are mostly oval to elongated and contain black pycnidia (fungal fruiting structures)

Djibouti
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:DJ
agricultural land: 17,020 sq. km
area: 23,200 km²
climate: desert; torrid, dry
density: 22/km²
latitude: 42.59521
longitude: 11.808375
population: 496,374 (07/2007)
comment: Djibouti, officially the Republic of Djibouti, is a country located in the Horn of Africa. It is bordered by Somaliland in the south, Ethiopia in the southwest, Eritrea in the north, and the Red Sea and the Gulf of Aden in the east. Across the Gulf of Aden is Yemen. The country has a total area of 23,200 km2 . The Republic of Djibouti is predominantly inhabited by two ethnic groups, the Somali and the Afar people, with the former comprising the majority of the population.

Dodder
type: NamedIndividual
type: Crop Disease
type: Weeds
life cycle: Dodder seeds germinate in soil and can live on their own for 5 to 10 days until they are about a foot tall. If they have not found a suitable host by this time the seedlings will die. Seedlings that find a suitable host twine around the plant and insert haustoria (modified adventitious roots) into the tender stem. The haustoria penetrate and tap the plant's vascular system for water, minerals and nutrients. Plants are weakly photosynthetic, but most produce very little food on their own. They rely upon their host plant for survival. As the vine taps the host plant its connection to the soil is severed. Small, white, bell-shaped flowers form in late summer and early fall and can produce copious amounts of seed. Plants are annual and are killed by frost. Plants regrow from seed each year.
management: Management Strategies 1. Remove the plants. As soon as the thin vining stems of dodder are observed they should be gathered and removed before seed is set. Removal of the host plant is usually recommended. Complete eradication from the host plant is usually not possible. 2. Scout for and remove new seedlings as they appear. Monitor areas previously infested with dodder closely and pull young seedling before they attach themselves to new plants and certainly before they set seed. Seeds can survive in the soil for more than 20 years. 3. Treat with an herbicide. Dodder and the host plant can be killed by treating with glyphosate, a non-selective herbicide. As a follow-up measure to kill germinating seeds, treat soil with a preemergent herbicide such as Dacthal before seeds germinate in spring. Organic Strategies Strategies 1 and 2 are strictly organic approaches.
comment: Dodder is an annual seed-bearing parasitic vine in the dodder family (formerly placed in the morning-glory family). Its thin, thread-like, yellow or orange stems grow rapidly entwining and covering their host plants. Cuscate is the most common genus and is found throughout the US and Canada. Of the 50 species that occur, most are found in tropical and warm-temperate areas but some species also occur in cooler areas including St. Louis where they can grow from seed each year and infest herbaceous and small woody plants. Since seeds can be difficult to separate from some agricultural crops dodder has been spread widely through agriculture.

Dolerus Tritici
type: NamedIndividual
type: Crop Pest

Dolerus Tritici Chu
type: NamedIndividual
type: Crop Pest
comment: Dolerus Tritici Chu

Dolycoris Baccarum
type: NamedIndividual
type: Crop Pest

Dominica
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:DM
agricultural land: 260 sq. km
area: 754 km²
climate: tropical; moderated by northeast trade winds; heavy rainfall
density: 105/km²
latitude: -61.36413
longitude: 15.416749
population: 72,514 (2008)
comment: Dominica, officially the Commonwealth of Dominica, is an island country in the Caribbean. The capital, Roseau, is located on the western side of the island. It is geographically situated as part of the Windward Islands chain in the Lesser Antilles archipelago in the Caribbean Sea. The island is located near Guadeloupe to the northwest and Martinique to the south-southeast. Its area is 750 km2, and the highest point is Morne Diablotins, at 1,447 m in elevation. The population was 71,293 at the 2011 census.

Dominican
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:DO
agricultural land: 24,470 sq. km
area: 48,730 km²
climate: tropical maritime; little seasonal temperature variation; seasonal variation in rainfall
density: 201/km²
latitude: -69.89763
longitude: 18.482592
population: 9,904,000 (2008)
comment: The Dominican Republic is a country located on the island of Hispaniola in the Greater Antilles archipelago of the Caribbean region. It occupies the eastern five-eighths of the island, which it shares with Haiti, making Hispaniola one of only two Caribbean islands, along with Saint Martin, that are shared by two sovereign states. The Dominican Republic is the second-largest nation in the Antilles by area at 48,671 square kilometers, and third-largest by population, with approximately 10.8 million people, of whom approximately 3.3 million live in the metropolitan area of Santo Domingo, the capital city. The official language of the country is Spanish.

Downy mildew
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Barley
biological control: Downy mildews are caused by microscopic, fungus-like (Oomycete) organisms. Despite a similar name and certain similarities in symptoms, they are unrelated to the powdery mildews. The disease is spread by spores produced on the underside of infected leaves. These spores are splashed by rain, and are also sometimes carried for long distances on the wind. Extended periods of leaf wetness are required for infection, so severe outbreaks of downy mildew are only likely to occur outdoors during wet summers. Where plants such as lettuces are grown in glasshouses, the disease may be present throughout the year and can cause major problems in spring and autumn, as it is very difficult to control humidity and leaf wetness at these times. Many downy mildews produce their spores at night, and extended periods of high humidity or leaf wetness overnight can lead to severe outbreaks. The airborne spores remain viable for just a short time, but most downy mildews also produce a second spore type (a resting spore known as an oospore) within the affected plant tissues. These resting spores are much more resilient. They are released into the soil as the diseased material rots down, and are likely to survive for an extended period (up to five years). If the host plant is grown again in contaminated soil while the resting spores are still viable, they may infect it through the roots to cause a systemic infection (one that spreads throughout the plant).
causes: Fungus
chemical control: There are no fungicides available to amateur gardeners for use against downy mildews.
development: Occurs wherever winter barley is grown, usually after excessive rainfall.
management: Plant crop in well-draining soils; control weeds in field which can act as reservoirs for the disease; sow seed only from disease free plants.
symptoms: Unlike with powdery mildews, plants affected by downy mildew are not so easy to recognise. Look out for the following: Discoloured blotches on the upper leaf surface. These may be pale green, yellow, purple or brown, depending on the plant affected. The blotches sometimes have straight edges (e.g. on lettuce) if they are bordered by the leaf veins A mould-like growth on the underside of the leaf, corresponding to the blotch on the upper surface. This growth may be white, grey or purple, depending on the species of downy mildew On some plants (e.g. pea, pansy) the growth is easy to see with the naked eye. On others (e.g. foxglove, rose) it can be difficult to see, even with a hand lens Severely affected leaves may shrivel and turn brown (e.g. tobacco plant), or turn yellow and fall prematurely (e.g. Impatiens, rose) Occasionally, other plant parts can be affected, e.g. cauliflower curds, Impatiens flower buds, pea pods Severely affected plants are often stunted and lack vigour. In some cases (e.g. columbine, Impatiens, tobacco plant) the plant may die
comment: Downy mildews can spoil the appearance of ornamental plants and affect the yield and quality of edible crops. Control depends on cultural techniques as no effective fungicides are available to amateur gardeners.

Drainage To Ground water
type: NamedIndividual
type: Soil Phenomena
comment: Groundwater recharge or deep drainage or deep percolation is a hydrologic process, where water moves downward from surface water to groundwater. Recharge is the primary method through which water enters an aquifer.
label: Groundwater recharge

Drechslera Tritici-repentis
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
comment: Pyrenophora tritici-repentis (telomorph) and Drechslera tritici-repentis (anamorph) is a necrotrophic plant pathogen of fungal origin, phylum Ascomycota. The pathogen causes a disease originally named yellow spot but now commonly called tan spot, yellow leaf spot, yellow leaf blotch or helminthosporiosis. At least eight races of the pathogen are known to occur based on their virulence on a wheat differential set.

Arid Climate
type: NamedIndividual
type: Climate Type
label: Dry climate

Dry Humidity
type: NamedIndividual
type: Humidity

Dryocosmus Kuriphilus Yasumatsu
type: NamedIndividual
type: Crop Pest
comment: Dryocosmus Kuriphilus Yasumatsu

Dune
type: NamedIndividual
type: Topography

Dust Storm
type: NamedIndividual
type: Planetary Phenomena
comment: A dust storm, also called sandstorm, is a meteorological phenomenon common in arid and semi-arid regions. Dust storms arise when a gust front or other strong wind blows loose sand and dirt from a dry surface. Fine particles are transported by saltation and suspension, a process that moves soil from one place and deposits it in another.

Dutch Elm Disease
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
life cycle: The fungus is primarily dependent on insectvectored transmission from tree to tree over long distances. There are 2 vectors for the fungus in North America, the native American elm bark beetle and the less prevalent European elm bark beetle. Both of these beetles feed and breed under the bark of living or recently dead elm trees or logs. They carry the spores of the fungus from infected trees and innoculate healthy trees as they feed. Both species of elm bark beetles are effective carriers. The cycle of infection by the fungus is tied to the life cycle of the vectors. The beetles breed in recently dead elm wood or weakened living trees. If the fungus is present in breeding sites, emerging beetles will carry spores to healthy elms and introduce the fungus in feeding sites on young twigs. The beetles can fly up to 1/4 mile in search of feeding or breeding sites, but they may be blown many miles by winds. The disease may also spread by root grafts from a diseased elm tree to a healthy elm tree provided that the root systems overlap.
management: Management Strategies 1. There is no cure for the disease. If your elm tree has leaves yellowing or wilting on one or more branches, cut off several small branches and look for brown streaking in the sapwood. If brown streaking is evident, a laboratory test is suggested for positive identification. This disease may be confused with other canker and wilt pathogens of elm. 2. Maintain plant health. Provide adequate amounts of water and fertilizer. Mild cases of Dutch elm disease may respond to pruning of diseased wood if less than 5% of the tree is infected. Control the insect vectors. Systemic fungicides injected into the tree at 1–3 year intervals have proven beneficial in providing protection. An arborist should be consulted to discuss costs and potential benefits. 3. Remove diseased trees. Severe cases require complete removal of the tree. Wood should not be saved for firewood. It should be chipped to destroy the beetle’s breeding place. Nearby American elms may also be infected through root grafts. Destroying root grafts by mechanical severing can slow the spread of the disease, if done before the fungus has moved into healthy elm trees. This may not be practical in a home setting. 4. Replace diseased trees with varieties resistant to Dutch elm disease. The true Chinese elm, Ulmus parviflora, a tree with multi-colored bark, is one choice. A related tree with a vase shape similar to American elm is the Japanese zelkova, Zelkova serrata. Both are resistant to Dutch elm disease but not immune.
symptoms: Symptoms develop rapidly during a period of 4–6 weeks after leaves reach full size. The first visible symptoms are yellowing of foliage followed by wilting and browning, a condition called flagging. Usually a single branch is affected first; wilted branches die rapidly and leaves brown, curl, and often drop prematurely. The symptoms spread to nearby branches and then to one whole part of the tree. The entire tree finally wilts and dies. This progression of symptoms may develop in one season or may take several years. Positive diagnosis of the disease requires a laboratory test to culture the fungal pathogen.
comment: Dutch elm disease is a wilt disease caused by the fungus, Ceratocystis ulmi. It was described in Ohio in 1930. By the 1980's, it could be found in most of the U.S. It is a serious and fatal disease of American elms. Even after years of study, there is no effective cure for the disease.

Ear Cockle
type: NamedIndividual
type: Crop Disease
affects: Wheat
comment: Ear-cockle is caused by the nematode. Its occurrence has been reported from all major wheat growing regions of the country.

Early Blight
type: NamedIndividual
type: Crop Disease

Early Scorch
type: NamedIndividual
type: Crop Disease

Earwig
type: NamedIndividual
type: Crop Pest
alternative label: Dermaptera

Ecuador
type: NamedIndividual
type: Country
belongs to: South America
ISO code: ISO 3166-1:EC
agricultural land: 73,460.7 sq. km
area: 256,370 km²
climate: tropical along coast, becoming cooler inland at higher elevations; tropical in Amazonian jungle lowlands
density: 53.8/km²
latitude: -78.7297
longitude: -0.184799
population: 13,922,000 (2008)
comment: Ecuador, officially the Republic of Ecuador, is a country in northwestern South America, bordered by Colombia on the north, Peru on the east and south, and the Pacific Ocean on the west. Ecuador also includes the Galápagos Islands in the Pacific, about 1,000 kilometres west of the mainland. The capital is Quito.

Egypt
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:EG
agricultural land: 36,650 sq. km
area: 1,002,450 km²
climate: desert; hot, dry summers with moderate winters
density: 74/km²
latitude: 30.794851
longitude: 26.696367
population: 75,500,662 (2008)
comment: Egypt, officially the Arab Republic of Egypt, is a transcontinental country spanning the northeast corner of Africa and southwest corner of Asia by a land bridge formed by the Sinai Peninsula. Egypt is a Mediterranean country bordered by the Gaza Strip and Israel to the northeast, the Gulf of Aqaba and the Red Sea to the east, Sudan to the south, and Libya to the west. Across the Gulf of Aqaba lies Jordan, across the Red Sea lies Saudi Arabia, and across the Mediterranean lie Greece, Turkey and Cyprus, although none share a land border with Egypt.

El Salvador
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:SV
agricultural land: 15,320 sq. km
area: 21,040 km²
climate: tropical; rainy season (May to October); dry season (November to April); tropical on coast; temperate in uplands
density: 318.7/km²
latitude: -88.910416
longitude: 13.796873
population: 6,948,073 (2008)
comment: El Salvador, officially the Republic of El Salvador, is a country in Central America. It is bordered on the northeast by Honduras, on the northwest by Guatemala, and on the south by the Pacific Ocean. El Salvador's capital and largest city is San Salvador. As of 2018, the country had a population of approximately 6.42 million, making it the smallest and second-least populated country in Central America.

Emergence
type: NamedIndividual
type: Growth Stage
comment: The plant is visible when the first leaf, known as the coleoptile leaf, breaks through the soil surface. The coleoptile leaf is shorter than the later-emerging leaves and has a rounded leaf tip. Emergence time can range from 3 to 14 days, depending largely on soil temperature, moisture, seeding depth and seedling vigor. Growth Stages of Sorghum: Seeding, Emergence, Three-leaf, Five-leaf, Growing point differentation, Flag leaf visible, Booting, Flowering, Soft Dough, Hard Dough, Physiological maturity

Empoasca Flavescens
type: NamedIndividual
type: Crop Pest
comment: Empoasca Flavescens (Fabricius)

Equatorial Guinea
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:GQ
agricultural land: 3,040 sq. km
area: 28,051 km²
climate: tropical; always hot, humid
density: 18/km²
latitude: 8.469045
longitude: 1.163017
population: 504,000 (2005)
comment: Equatorial Guinea, officially the Republic of Equatorial Guinea, is a country located on the west coast of Central Africa, with an area of 28,000 square kilometres . Formerly the colony of Spanish Guinea, its post-independence name evokes its location near both the Equator and the Gulf of Guinea. As of 2020, the country had a population of 1,454,789.

Eritrea
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:ER
agricultural land: 75,920 sq. km
area: 117,600 km²
climate: hot, dry desert strip along Red Sea coast; cooler and wetter in the central highlands (up to 61 cm of rainfall annually, heaviest June to September); semiarid in western hills and lowlands
density: 37/km²
latitude: 39.793705
longitude: 15.181324
population: 4,401,009 (2005)
comment: Eritrea, officially the State of Eritrea, is a country in Eastern Africa, with its capital at Asmara. It is bordered by Ethiopia in the south, Sudan in the west, and Djibouti in the southeast. The northeastern and eastern parts of Eritrea have an extensive coastline along the Red Sea. The nation has a total area of approximately 117,600 km2, and includes the Dahlak Archipelago and several of the Hanish Islands.

Erthesina Fullo
type: NamedIndividual
type: Crop Pest

Erysiphe Graminis
type: NamedIndividual
type: Fungus

Estonia
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:EE
agricultural land: 9,450 sq. km
area: 45,228 km²
climate: maritime; wet, moderate winters, cool summers
density: 29/km²
latitude: 24.987158
longitude: 58.665665
population: 1,340,602 (2007)
comment: Estonia, officially the Republic of Estonia, is a country in Northern Europe. It is bordered to the north by the Gulf of Finland across from Finland, to the west by the Baltic Sea across from Sweden, to the south by Latvia, and to the east by Lake Peipus and Russia. The territory of Estonia consists of the mainland and of 2,222 islands on eastern coast of the Baltic Sea, covering a total area of 45,227 km2, and is influenced by a humid continental climate. Tallinn, the capital of Estonia, and Tartu are the largest cities and urban areas in the country. Other notable cities include Narva, Pärnu, Kohtla-Järve and Viljandi. The official language of the country, Estonian, is the second-most-spoken Finnic language.

Estuary
type: NamedIndividual
type: Topography

Eteofenprox
type: NamedIndividual
type: Insecticide
comment: Etofenprox is a pyrethroid derivative which is used as an insecticide. Mitsui Chemicals Agro Inc. is the main manufacturer of the chemical. It is also used as an ingredient in flea medication for cats and dogs.

Ethiopia
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:ET
agricultural land: 356,830 sq. km
area: 1,104,300 km²
climate: tropical monsoon with wide topographic-induced variation
density: 70/km²
latitude: 40.493057
longitude: 9.146986
population: 85,254,090 (2008)
comment: Ethiopia, officially the Federal Democratic Republic of Ethiopia, is a landlocked country in the Horn of Africa. It shares borders with Eritrea to the north, Djibouti and Somaliland to the northeast, Somalia to the east, Kenya to the south, South Sudan to the west and Sudan to the northwest. Ethiopia has a total area of 1,100,000 square kilometres and over 109 million inhabitants and is the 13th-most populous country in the world and the 2nd-most populous in Africa. The capital and largest city, Addis Ababa, lies several kilometres west of the East African Rift that splits the country into the African and Somali tectonic plates.

Europe
type: NamedIndividual
type: Continent
has country: Albania
has country: Andorra
has country: Austria
has country: Belarus
has country: Belgium
has country: Bosnia and Herzegovina
has country: Bulgaria
has country: Croatia
has country: Czech
has country: Denmark
has country: Estonia
has country: Faroe Islands
has country: Finland
has country: France
has country: Germany
has country: Gibraltar
has country: Greece
has country: Guernsey
has country: Hungary
has country: Iceland
has country: Ireland
has country: Isle of Man
has country: Italy
has country: Jersey
has country: Latvia
has country: Liechtenstein
has country: Lithuania
has country: Luxembourg
has country: Macedonia
has country: Madagascar
has country: Malta
has country: Marshall Islands
has country: Netherlands
has country: Norway
has country: Poland
has country: Portugal
has country: Romania
has country: San Marino
has country: Serbia
has country: Slovakia
has country: Slovenia
has country: Spain
has country: Svalbard and Jan Mayen
has country: Sweden
has country: Switzerland
has country: Ukraine
has country: United Kingdom
has country: Holy See

Eurydema Dominulus
type: NamedIndividual
type: Crop Pest

Eurydema Gebleri
type: NamedIndividual
type: Crop Pest

Euschistus Servus
type: NamedIndividual
type: Crop Disease
type: Crop Pest
comment: Euschistus servus, the brown stink bug, is a species of stink bug in the family Pentatomidae.
alternative label: E. servus

Excellent Quality
type: NamedIndividual
type: Quality

Exobasidium vexans
type: NamedIndividual
type: Crop Disease
affects: Tea
comment: Exobasidium vexans is a plant pathogen affecting tea. Blister blight caused by Exobasidium vexans is a devastating leaf disease in tea (Camellia sinensis) in almost all tea growing regions in Asia. This disease causes serious crop losses under inclement weather conditions besides affecting quality of made tea. Although tea cultivars show varying degrees of resistance/susceptibility to blister blight, a cultivar showing total resistance to blister blight has not yet been identified.

Expanding
type: NamedIndividual
type: Growth Stage

Extreme Frost
type: NamedIndividual
type: Temperature

Extreme Heat
type: NamedIndividual
type: Temperature

Eyespot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Sugarcane
affects: Wheat
development: Primary infection occurs from conidia or mycelia produced on crop debris on or near the soil surface; contact with the developing coleoptile or basal areas of young culms is required. The fungus is limited to the basal areas of the plant. Disease development is favored by cool, damp weather, and by high humidity at the soil level.
distribution: Wheat, triticale, rye, oats, and other related grasses can be affected by the disease, with wheat being the most susceptible; winter wheat and fall-sown spring wheat are more frequently damaged. Eyespot occurs in cool, moist climates where fall-sown cereals predominate.
important: Eyespot may kill individual tillers or even whole plants. More commonly, yield losses result from reduced kernel size and number, and from lodging.
symptoms: The most obvious symptoms of this disease are the eye-shaped, elliptical lesions produced on the internodes of the lower stem (39). The lesions are bordered by dark brown to greenish brown rings, have straw-colored centers, and frequently develop on the leaf sheath at soil level. These lesions may coalesce and lose their distinct "eye-spot" appearance. When disease development is severe, the stem or culm may break near the ground or through the lesion where the stem is weakened (40). Symptoms do not appear on the roots.
comment: Eyespot is an important fungal disease of wheat caused by the necrotrophic fungus Tapesia yallundae (syn: Pseudocercosporella herpotrichoides; W-type [anamorph]; Oculimacula yallundae) and Tapesia acuformis (syn: Pseudocercosporella herpotrichoides; R-type [anamorph]; Oculimacula acuformis). It is also called Strawbreaker. Eyespot is more severe where wheat is grown continuously and when the weather is cool and moist. Treating crops against eyespot with fungicide costs millions to farmers and is complicated by the pathogen becoming resistant to the more commonly used fungicides.
label: Strawbreaker

Eysacoris Guttiger
type: NamedIndividual
type: Crop Pest

Falkland Islands
type: NamedIndividual
type: Country
belongs to: South America
ISO code: ISO 3166-1:FK
area: 12,173 km²
climate: cold marine; strong westerly winds, cloudy, humid; rain occurs on more than half of days in year; average annual rainfall is 24 inches in Stanley; occasional snow all year, except in January and February, but typically does not accumulate
density: 0.25/km²
latitude: -59.523613
longitude: -51.796253
population: 3,060 (2005)
comment: The Falkland Islands is an archipelago in the South Atlantic Ocean on the Patagonian Shelf. The principal islands are about 300 miles east of South America's southern Patagonian coast and about 752 miles from the northern tip of the Antarctic Peninsula, at a latitude of about 52°S. The archipelago, with an area of 4,700 square miles, comprises East Falkland, West Falkland, and 776 smaller islands. As a British overseas territory, the Falklands have internal self-governance, and the United Kingdom takes responsibility for their defence and foreign affairs. The Falkland Islands' capital is Stanley on East Falkland.

Faroe Islands
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:FO
agricultural land: 30 sq. km
area: 1,399 km²
climate: mild winters, cool summers; usually overcast; foggy, windy
density: 34/km²
latitude: -6.969929
longitude: 61.89784
population: 48,500 (8/2007)
comment: The Faroe Islands or just Faroes are a North Atlantic archipelago located 320 kilometres north-northwest of Scotland, and about halfway between Norway and Iceland. Like Greenland, it is an autonomous territory within the Kingdom of Denmark. The islands have a total area of about 1,400 square kilometres with a population of 52,703 as of September 2020.

February
type: NamedIndividual
type: Month Of Year

Fertilizers n.e.c.
type: NamedIndividual
type: Chemical Fertilizer

Fibrous root
type: NamedIndividual
type: Root
comment: A fibrous root system is the opposite of a taproot system. It is usually formed by thin, moderately branching roots growing from the stem. A fibrous root system is universal in monocotyledonous plants and ferns. The fibrous root systems look like a mat made out of roots when the tree has reached full maturity. Most trees begin life with a taproot, but after one to a few years change to a wide-spreading fibrous root system with mainly horizontal surface roots and only a few vertical, deep anchoring roots. A typical mature tree 30–50 m tall has a root system that extends horizontally in all directions as far as the tree is tall or more, but well over 95% of the roots are in the top 50 cm depth of soil. A few plants with fibrous root systems: - Coconut palm - Grass - Rosemary Fibrous roots grow fairly close to the surface of the ground. Leaves with parallel venation have fibrous roots. Forages have a fibrous root system, which helps combat erosion by anchoring the plants to the top layer of the soil, and covering the entirety of the field, as it is a non-row crop. In a fibrous root system, the roots grow downwards into the soil, and also branch off sideways throughout the soil. This forms a mass of fine roots, with no distinct tap root, because the embryonic root dies back while the plant is still young and growing.

Fiji
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:FJ
agricultural land: 4,276 sq. km
area: 18,274 km²
climate: tropical marine; only slight seasonal temperature variation
density: 46/km²
latitude: -179.06557
longitude: -16.578043
population: 837,271 (9/2007)
comment: Fiji, officially the Republic of Fiji, is an island country in Melanesia, part of Oceania in the South Pacific Ocean. It lies about 1,100 nautical miles northeast of New Zealand. Fiji consists of an archipelago of more than 330 islands—of which about 110 are permanently inhabited—and more than 500 islets, amounting to a total land area of about 18,300 square kilometres . The most outlying island group is Ono-i-Lau. About 87% of the total population of 883,483 live on the two major islands, Viti Levu and Vanua Levu. About three-quarters of Fijians live on Viti Levu's coasts: either in the capital city of Suva; or in smaller urban centres such as Nadi—where tourism is the major local industry; or in Lautoka, where the sugar-cane industry is dominant. The interior of Viti Levu is sparsely inhabited because of its terrain.

Finland
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Continental Climate
has climate: Marine Climate
has climate: Temperate Climate
ISO code: ISO 3166-1:FI
agricultural land: 22,860 sq. km
area: 338,145 km²
climate: cold temperate; potentially subarctic but comparatively mild because of moderating influence of the North Atlantic Current, Baltic Sea, and more than 60,000 lakes
density: 16/km²
latitude: 25.377699
longitude: 64.88375
population: 5,327,490 (2008)
comment: Finland, officially the Republic of Finland, is a Nordic country located in Northern Europe. It shares land borders with Sweden to the west, Russia to the east, Norway to the north, and is defined by the Gulf of Bothnia to the west, and the Gulf of Finland of the Baltic Sea across Estonia to the south. Finland covers an area of 338,455 square kilometres, with a population of 5.5 million. Helsinki is the country's capital and largest city. Finnish, the native language of the Finns, is among the only few Finnic languages in the world. The climate varies relative to latitude, from the southern humid continental climate to the northern boreal climate. Finland is primarily a boreal forest biome, with more than 180,000 recorded lakes.

Fiord
type: NamedIndividual
type: Topography

Fire Blight
type: NamedIndividual
type: Crop Disease
affects: Apple
biological control: Plant on resistant rootstock. Resistant rootstocks (e.g. Geneva series for apples) do not make the scion less susceptible, but will help prevent tree death from rootstock blight. Sanitation. In winter, cut out old blight cankers as thoroughly as possible. Ideally, cut blight before you prune for tree structure so that the blighted cuttings can be removed from the orchard. Compared to cuts made in summer, winter removal cuts can be made closer to the visible canker edge. In winter the pathogen is confined to the cankered area. Cut at the next “horticulturally sensible” site below the canker. You do not need to sterilize tools when you are cutting on fully dormant trees. Late dormant copper applications may also provide orchard sanitation, reducing inocula levels going into spring (Elkins et al. 2015). During the summer, cut out blight when you see it. Make summer cuts AT LEAST 12-18” below the edge of the visible canker. Cut more aggressively in young, vigorous trees or susceptible varieties. Removing a strike can greatly reduce further damage to the tree if cut early. Manage the orchard environment. In addition to warm temperatures moisture is required to create infections. As little as two to three hours of wetting is sufficient to trigger infections. Manage weeds/cover crops to limit relative humidity. Do not irrigate during bloom. Blossom removal in young blocks. Blossom removal in young blocks and removal of late blooms limits the numbers of flowers and thus reduces potential points of infection. Keep vigor of the tree moderate. Moderating vigor will not prevent infection, but it can reduce damage to the tree.
chemical control: There is a risk of fire blight infection any time there are flowers on the tree, the weather is warm, and wetting occurs. Watch for and protect secondary blossoms during the three weeks after petal fall, which is the most common time of fire blight infection. Most sprays only protect the blooms that are open. Protect new blooms as they open. In warm weather, follow-up sprays are needed every few days.
life cycle: Erwinia amylovora overwinters within diseased plant tissue (e.g. cankers). In 20 to 50% of cankers active cells survive the winter (van der Zwet and Beer 1991) and when humidity is high in the spring the pathogen oozes out of these cankers. This ooze is attractive to bees, flies and other insects who transfer the blight pathogen to flowers. Pathogen cells can also be moved from old cankers to flowers by splashed and wind-blown rain. Pathogen cells multiply quickly on nutrient-rich floral stigmas when temperatures are warm (70-80 F is optimal for the pathogen) (Ogawa and English 1991). Bacterial colonies can then be washed down the style into the floral cup by water (usually from rain or heavy dew) where they can invade flowers through the nectaries. Once initial blossoms are infested, insects and rain can move the pathogen to additional flowers (Pattemore et al. 2014, Johnson et al. 1993). If the pathogen is successful in infecting the developing fruit-let, the disease spreads into the cambium (just between the bark and the wood) of the tree, killing young host tissues as it progresses (Momol et al. 1998) creating characteristic strikes and cankers. Pathogen cells migrate inside the tree well ahead of visible symptoms where they can accumulate in other susceptible tissue such as one-year-old shoot tips and susceptible rootstocks causing infections distant from the original infection point. Erwinia amylovora can also infect susceptible one and two-year-old tissue directly through wounds (e.g. insect feeding and hail) causing shoot blight infections.
management: Conventional Management Prebloom: Fixed-copper sanitation, but only if fire blight was in the orchard last year. Early bloom: Apply biologicals (Blossom Protect) during early bloom. If fire blight was in the orchard last year, apply two applications of the biological. Re-apply biologicals a second time if lime sulfur was applied. Lime sulfur applied during early bloom is also antimicrobial and reduces blight pressure. Early bloom to petal fall: Watch the model. After a period of warm weather, best results are obtained when antibiotics are applied within the 24-hour window before flower wetting during a high infection risk period. Products used must contact the interior of the flowers in sufficient water and approved wetting agent to completely cover the interior. Repeated antibiotic sprays may be necessary during extended high or extreme risk periods. One pound of any 17% oxytetracycline product per 100 gallons gives a 200-ppm solution. Kasugamycin is another effective antibiotic. Some trials have shown that a full rate of Kasugamycin and a full rate of oxytetracycline provides excellent control. Antibiotics plus Actigard (2oz) has increased control in WA and OR trials by 10%. Applications of less than 100 gal/A can be effective on small trees if flower interiors are well covered, but do not drop the ppm below 200 (oxytetracycline). Application by ground equipment on each row is highly recommended (aircraft is NOT recommended). Many fire blight bacteria in the Pacific Northwest are resistant to streptomycin, another registered antibiotic. Petal fall to two weeks after. Continue protective programs one to two weeks post petal fall. Warm conditions during late bloom increase fire blight risk for late blooms still present. Organic Management Prebloom: Fixed copper sanitation if fire blight was in the orchard last year. Early bloom: Lime sulfur plus oil (apples only). One to two applications of biologicals (Blossom Protect). Reapply biological after lime sulfur, which i…
symptoms: Overwintering cankers can appear black, grey or violet. Older cankers may have dry sunken tissue (Fig. 1). If the bark is cut from the edge of an active canker, reddish flecking can be seen in the wood near the canker margin. (Teviotdale 2011). Blossom symptoms become apparent one to two weeks after infection (Fig. 2). The floral receptacle, ovary, and peduncles become water soaked and dull, grayish green in appearance. Later tissues shrivel and turn brown to black. During periods of high humidity, small droplets of bacterial ooze form on water-soaked and discolored tissues. Ooze droplets start creamy white, becoming amber tinted as they age (Johnson 2000). Shoot symptoms. Tips of shoots may wilt rapidly to form a “shepherd’s crook” (Fig. 3). Leaves on diseased shoots often show blackening along the midrib and veins before becoming fully necrotic, and cling firmly to the host after death (a key diagnostic feature.) Numerous diseased shoots give a tree a burnt, blighted appearance, hence the disease name. Rootstocks infections usually develop near the graft union as a result of internal movement of the pathogen through the tree or from infection of root suckers. The bark of infected rootstocks may show water-soaking, purplish to black discoloration, cracking, or signs of bacterial ooze. Red-brown streaking may be apparent in cambium just under the bark. Symptoms of rootstock blight can be confused with Phytophthora collar rot. Malling 26 and 9 rootstocks are highly susceptible to fire blight (Johnson 2000).
comment: Fire blight is an important disease effecting pear and apple. Infections commonly occur during bloom or on late blooms during the three weeks following petal fall. Increased acreage of highly susceptible apple varieties on highly susceptible rootstocks has increased the danger that infected blocks will suffer significant damage.

Fireblight
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Apple
affects: Pear
life cycle: Fireblight bacteria overwinter in cankers larger than one inch in diameter and in mummified fruits. In spring, the cankers produce a white or amber ooze which contains the bacteria. The ooze dries and is carried to new sites by insects, wind, rain, or pruning equipment where it causes new infections. Hail damage is often followed by a rapid spread of the disease. When terminal buds set and tissue hardens, the disease stops progressing.
management: Management Strategies 1.Prune out diseased wood. In late summer or winter, prune out all diseased wood at least 10 to 15 inches below the infection and destroy. Disinfect tools between cuts by dipping in a 10% chlorine bleach solution. Oil tools after use. Avoid pruning in spring when bacterial ooze is prevalent. Also, clean up and dispose of any dead twigs and mummified fruit on the ground. These may harbor the bacteria. Here is a good video by Nathan Brandt of MU Extension on fireblight and how to prune it out. Fireblight Video. 2. Minimize succulent vegetative growth. Ensure good drainage and avoid heavy fertilizing in spring that leads to an abundance of succulent growth. If plant vigor is lacking, fertilize lightly in the fall. Avoid excessive pruning which can also cause lush growth, but do remove suckers at the base of the tree and watersprouts which arise from the trunk or branches. 3. Remove cankers. If cankers are present on large branches and trunks, remove them in winter. Include 1 inch of healthy bark on each side and 3–4 inches on the ends. Disinfect all tools between use. 4. Eliminate old pear, apple, or quince trees in the area that may harbor the disease. 5. Replace diseased plants with resistant varieties. Many resistant varieties are now available. Ask at your nursery or garden center for fireblight-resistant varieties. 6. Chemical sprays are not very effective. streptomycin or Bordeaux sprayed during bloom, then every 3–5 days when the daily high is 65 degrees F and humidity is about 65% can minimize infection. Most homeowners would be better served by replacing a problem tree than going to the expense and bother of spraying.
symptoms: Symptoms begin as dark green, water-soaked spots on tissue where it is penetrated by bacteria. Common entry points are through wounds, blossoms, and natural openings such as stomatas and nectaries. As the disease progresses, leaves and twigs take on a black shriveled appearance as if scorched by fire. A characteristic shepherd's crook often develops as tender growth rapidly wilts and dies. As the twigs die back, dead sunken areas on the stem called cankers develop. These can girdle the stem causing even more dieback. Young trees can be killed in one season, but it generally takes several years of continuous dieback for death to occur.
comment: Fireblight is caused by the bacterium, Erwinia amylovora. It is a serious disease that affects new leaves, fruit, flowers, and stems of over 75 species of trees and shrubs in the rose family including: apple, crabapple, hawthorn, pear, pyracantha, cotoneaster, spirea, flowering quince, and mountain-ash. Temperature, humidity, insect vectors, and wounding can all affect the severity of infection. If not controlled, it can cause flower and fruit blight, twig and branch dieback, or even kill the plant.

Five-leaf Stage
type: NamedIndividual
type: Growth Stage
comment: The collar of the fifth fully expanded leaf is visible, and the growing point is at or just below the soil surface. This stage occurs approximately 20 to 25 days after emergence, when the plant is 7 to 9 inches tall. Farmers should apply most post-emergent herbicides at or before this time. Side-dress fertilizer applications are best made at this stage or within the following 10 to 15 days.

Flag-leaf Stage
type: NamedIndividual
type: Growth Stage
comment: The last leaf to emerge prior to heading is called the flag leaf, which is shorter than the preceding leaves. The sorghum plant is in the flag leaf stage when the leaf tip is visible in the whorl. The last two or three leaves will fully expand during this period. Typically, the plant progresses from the flag leaf tip being visible to the boot stage in five to seven days.

Flag Smut
type: NamedIndividual
type: Crop Disease
affects: Wheat
comment: Flag smut causes whitish or yellowish to dark grey longitudinal stripes between veins of infected plants. The stripes may appear as blisters between veins. These later may rupture exposing spores of this fungus making the stripes look very black. Infected plants are stunted and have fewer tillers. The leaves on infected plants become distorted/twisted and may be split longitudinally along the blisters.

Flea Beetles
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Bean
affects: Corn
affects: Tomato
life cycle: Adults overwinter in the soil and plant debris, emerging in spring to feed. They lay eggs in the soil and die by early July. The legless white or gray larvae hatch in a week and eat plant roots for 2–3 weeks. The larvae pupate in the soil and mature into adults in 2–3 weeks. There may be more than one generation a year.
management: Management Strategies 1. Transplant large sturdy seedlings. Large transplants will be most able to tolerate damage. Monitor plants closely so that pests may be controlled before damage is severe. When planting seeds directly into the garden, sow extra seeds to allow for loss. Growing susceptible plants interspersed with less susceptible plants may help. 2. Use row covers on susceptible plants, especially seedlings and eggplant. 3. Avoid flea beetles.Plant susceptible crops as late as possible to avoid flea beetles, which are most active in early spring. 4. Till the soil in fall or early spring. This helps to remove the weeds and crop debris that provide a habitat for overwintering adults. During the growing season, cultivate soil to disrupt larval habitat and control weeds to prevent flea beetles from jumping in from adjacent weedy areas. 5. Use white sticky traps to monitor and trap flea beetles. 6. Use insecticides. Effective products include insecticidal soaps, pyrethrins, sabadilla, Neem (when registered for food crops), diatomaceous earth, or permethrin (Eight). Planting trap crops such as radish and then spraying those plants can help reduce the crop’s exposure to insecticide. Predatory nematodes may be used as a soil drench to control larvae. Organic Strategies Strategies 1, 2, 3, 4, and 5 are strictly organic approaches. For an organic approach to Strategy 6, consult the Organic Materials Review Institute (OMRI™) for appropriate insecticidal soap, pyrethrin, and Neem products.
symptoms: Since flea beetles are so small and active, it is easy to miss them. Watch for a sudden burst of jumping black spots among the leaves. Disturbing the foliage of the affected plant or of nearby plants may cause them to jump again. Commonly found in newly planted vegetable gardens, flea beetles are most harmful to seedlings. Larger plants are usually able to withstand damage. A light infestation of flea beetles is characterized by tiny round pits or holes in leaves, creating a shot hole pattern. Heavier infestations may cause bleached or pitted areas, ragged holes, or even total loss of leaves resulting in wilted or stunted plants. Although the larvae feed on roots of the same plants, they usually do not cause injury. Larval tunneling may sometimes cause scarring on potato tubers.
comment: Small round holes in leaves and insects that jump like fleas are two signs that a plant is infested with flea beetles. These shiny oval beetles may be black, brown, bronze, or striped and are only 1/10 inch long. They quickly leap out of sight when disturbed. There are several species of flea beetle and while some feed on only a few closely related plants, others feed on a wide variety of crop plants. Many vegetable crops are affected including tomatoes, potatoes, cole crops (cabbage family), turnips, radishes, corn, beans, and beets. Flea beetles are especially attracted to eggplants and plants may be almost totally defoliated. Flea beetles may also be vectors for transmission of disease such as bacterial wilt in corn.

Flood
type: NamedIndividual
type: Weather Phenomena
comment: A flood is an overflow of water that submerges land that is usually dry. In the sense of "flowing water", the word may also be applied to the inflow of the tide. Floods are an area of study of the discipline hydrology and are of significant concern in agriculture, civil engineering and public health.

Fonio
type: NamedIndividual
type: Cereal
type: Crop
description: White fonio, Digitaria exilis, is an annual grass in the family Poaceae grown for its grain which is commonly used to make porridge. Fonio is an erect, tillering grass with delicate stems and alternate leaves. The leaves are blade like or linear and tapering and can reach up to 15 cm (6 in) in length. The plant produces spikelets, where the grain is produced, on branched, spike-like panicles. A single inflorescence possesses 2-5 racemes with spikelets arranged in pairs or in threes or fours, giving it a lacy appearance. White fonio can reach a height of 30-80 cm (11.8-31.5 in) depending on variety and is an annual plant, harvested after one growing season. White fonio may also be referred to fonio, hungry rice or hungry millet and originates from West Africa.
harvesting: Fonio is harvested using traditional methods, The plants are cut using a knife or sickle and gathered into sheaths for drying. Processing the grain is a laborious process that requires beating the dried plants to extract the grain and then dehulling the grain using a mortar.
maintenance: Fonio requires little maintenance after planting and is highly adapted to drought conditions.
propagation: Fonio is direct seeded by broadcasting in the field and germinates and grows rapidly, out-competing weeds and negating the need for weeding the field. The soil is roughly loosened prior to planting and seed is generally broadcast at a rate of 10 to 30 kg of seed/ha. The seeds are then lightly dug into the soil or covered using a hoe and allowed to germinate.
requirements: Fonio is a tropical plant and requires a dry season to grow properly. The plant grows optimally at temperatures between 25 and 30oC (77-86oF) and in areas where the average rainfall is between 900 and 1000 mm. Fonio can be grown on a range of soils and it is generally grown in soils that are considered too nutrient poor for other crops. The plants will grow best in light, sandy soils and it is often planted in rotation with rice, sorghum or millet.
uses: The fonio grain is consumed after cooking, either as a porridge or couscous. The grain may also be ground and mixed with other cereals. Fonio grain may be used to brew beers and the straw can be used as animal fodder.
References: https://plantvillage.psu.edu/topics/fonio/infos
comment: Fonio is the term for two cultivated grasses in the genus Digitaria that are notable crops in parts of West Africa. They are millets with small grains.

Forest
type: NamedIndividual
type: LandUse
type: Topography

France
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Humid Continental Climate
has climate: Humid Subtropical Climate
has climate: Marine Climate
has climate: Dry summer climate
ISO code: ISO 3166-1:FR
agricultural land: 290,900 sq. km
area: 674,843 km²
climate: metropolitan France: generally cool winters and mild summers, but mild winters and hot summers along the Mediterranean; occasional strong, cold, dry, north-to-northwesterly wind known as mistral
density: 115/km²
latitude: 2.209119
longitude: 46.2267
population: 65,073,482 (1/2009)
comment: France, officially the French Republic, is a country primarily located in Western Europe, consisting of metropolitan France and several overseas regions and territories. The metropolitan area of France extends from the Rhine to the Atlantic Ocean and from the Mediterranean Sea to the English Channel and the North Sea. The overseas territories include French Guiana in South America and several islands in the Atlantic, Pacific and Indian Oceans. France borders Belgium, Luxembourg and Germany to the northeast, Switzerland, Monaco and Italy to the east, Andorra and Spain to the south, as well as the Netherlands, Suriname and Brazil in the Americas. The country's eighteen integral regions span a combined area of 643,801 km2 and a total population of 67.4 million . France is a unitary semi-presidential republic with its capital in Paris, the country's largest city and main cultural and commercial centre. Other major urban areas include Lyon, Marseille, Toulouse, Bordeaux, Lille and Nice. France, including its overseas territories, has the most time zones of any country, with a total of twelve.

Fresh Breeze
type: NamedIndividual
type: Wind Type

Fresh Gale
type: NamedIndividual
type: Wind Type

Frog Eye Leaf Spot
type: NamedIndividual
type: Crop Disease
controls: Fungicide applications can be very effective controlling Frogeye Leaf Spot. Products applied to soybean in reproductive stages R3 (beginning pod) to R5 (beginning seed) are most effective. Group 11 Quinone Outside Inhibitor (QoI) fungicides (formerly referred to as “strobilurin”) were historically the most effective and widely used for management of Frogeye Leaf Spot. Since 2010, resistance to this class of fungicides has been documented in numerous other states. In 2019, soybean leaf samples with FLS were collected from fields in 10 Nebraska counties. QoI fungicide resistance was confirmed in samples from all 10 of the Nebraska counties sampled in this small survey (Fig. 1). It is unknown how widespread fungicide resistance is in the frogeye leaf spot pathogen in Nebraska yet. Soybean fields with a history of Frogeye Leaf Spot should be monitored carefully for disease development. If a fungicide application is necessary to control Frogeye Leaf Spot, avoid spraying products that only contain Group 11 QoI active ingredients. Products with two or more effective active ingredients with mixed modes of action (from multiple FRAC fungicide classes) will be more likely to provide effective control of the disease.
pathogen: Frogeye leaf spot is caused by the fungus Cercospora sojina. This disease is becoming more common in Nebraska. In some years it may hasten maturity by causing premature defoliation. Yield loss estimates due to frogeye leaf spot have been reported as high as 30% nationally with extensive leaf blighting, but for Nebraska, I would estimate less than 20% in highly susceptible varieties. The disease is most severe when soybean is grown continuously in the same field, particularly in fields where tillage is reduced since this is a residue-borne disease. The primary source for this disease is infested residue, infected seed, and airborne spores.
symptoms: Infection can occur at any stage of soybean development, but most often occurs after flowering and is typically in the upper canopy. Initial symptoms are small, dark spots on the leaves. Spots eventually enlarge to a diameter of about ¼ inch and the centers of the lesions become gray to brown and have a reddish-purple margin. Individual leaf spots can coalesce to create irregular patterns of blighting on the leaf. In addition, stems and pods can also be affected. Stem infections appear later in the season and will be long narrow dark lesions with flattened centers. Pod lesions will be circular to elongate, slightly sunken and reddish-brown. As with the foliar lesions, the centers of these lesions will become gray to brown as they mature. Severe pod infection can result in infected and discolored seed. Seed symptoms will appear as gray and brown areas on the seed and which can be blotches to specks on the seed coat. Infected seed can have cracked and flaking seed coats.
comment: Frogeye Leaf Spot is more severe in continuously cropped soybean fields. Reduced tillage systems will tend to have more as the pathogen overwinters in the residue.

Fruit Development stage
type: NamedIndividual
type: Growth Stage
related to: Wheat
comment: Stage 7: Development of fruit 71 Watery ripe: first grains have reached half their final size 73 Early milk 75 Medium milk: grain content milky, grains reached final size, still green 77 Late milk
alternative label: Stage 7: Development of fruit

Fungal Blast
type: NamedIndividual
type: Crop Disease
type: Fungal Disease

F. graminearum
type: NamedIndividual
type: Fungus
label: Fusarium graminearum
label: Gibberella zeae

Fusarium Head Blight
type: NamedIndividual
type: Crop Disease
affects: Barley
affects: Corn
affects: Oats
affects: Wheat
causes: Fungus
development: Since the pathogen infects kernel, the disease causes high yield loss, low test weights and low seed germination. Another major problem is pathogen produces mycotoxin, deoxynivalenol (DON) which is an vomitoxin.
management: Grow available resistant varieties. If the disease is severe, spray suitable fungicide.
symptoms: Initial symptoms show bleaching of some of the florets in the spike. Under favorable conditions, premature blight or bleaching of whole spike may occur. As the disease progress head turns tan to brown discoloration. Also, we can see pink or orange color mold appears at the base of the florets. The kernels become shriveled, white, and chalky.
comment: Fusarium head blight (FHB), also known as scab, is a fungal disease of small grain cereals including wheat, barley, oats, rye, corn, triticale, canary seed and some forage grasses.

Fusarium Root Rot
type: NamedIndividual
type: Crop Disease
type: Viral Disease
affects: Corn
management: Genetic Resistance There are no hybrids in Nebraska resistant to Fusarium root rot. Chemical/Biological Control Seed treated with fungicide may protect seedlings from root rot for a short time. Cultural Practices Rotating crops may help reduce the severity of Fusarium root rot. Minimizing Stress on the Plants Anything that stresses the plant such as herbicide injury, foliar diseases, hail damage or drought may make it more susceptible to root rot. Injury to the roots is not necessary for infection but is helpful to the pathogen. As the plants mature and root mass increases the roots become more susceptible to root rot, sometimes moving up the plant into the crown and into the stalk, causing crown and stalk rot.
pathogen: Fusarium root rot of corn is caused by numerous species, most commonly F. oxysporum Schlectend.:Fr and F. solani (Mart.) Sacc. However, F. verticilliodes e J. Sheld. (sexual stage: G. moniliformis) , F. subglutinans (sexual stage: G. subglutinans) (Wollenweb. & Reinking) P. E. Nelson, T. A. Tousson & Marassas, F. acuminatum Ellis & Verh., F. equiseti (Corda) Sacc. also cause root rot root rot in younger plants.
symptoms: Because root rots typically occur as complexes symptoms vary and it is difficult to determine if Fusarium is a primary or secondary pathogen or a saprophyte that colonized after another root rot pathogen became established. Symptoms generally include dark brown to black, discolored, decaying or completely rotted roots.
comment: Fusarium root rot is caused by the fungus Fusarium solani f. sp. phaseoli. The Fusarium root rot fungus occurs in most cultivated soils of the region where dry beans have been grown previously; and most plants grown in these soils will become infected to some degree before they mature.

Fusarium Stalk Rot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
management: Genetic Resistance There are no hybrids in Nebraska resistant to stalk rot, many hybrids are tolerant to other stresses that may predispose plants to stalk rot. Chemical/Biological Control There are no fungicides currently labeled for use in Nebraska specifically for managing Fusarium stalk rot. However, fungicide applications may be beneficial in lessening stalk rot severity if leaf diseases reach economic threshold levels due to the stress they place on the plant. Plant Density Plant populations should not exceed 28,000 to 32,000 plants per acre in fields known to have a history of stalk rot. Minimize stress on plants: Common stresses that lead to stalk rot include; high nitrogen, low potassium fertility, high moisture in the mid to late season after a dry early season, moisture stress early in the season and during grain fill, high leaf disease incidence. Physical damage that creates wounds allowing the pathogen to enter such as insect damage or hail storms may also predispose corn plants to stalk rot.
pathogen: Fusarium stalk rot of corn, the most common corn stalk rot in Nebraska, is caused by several species, Fusarium verticilliodes e J. Sheld. (sexual stage: G. moniliformis Sawada) Ito in Ito & Kimura, formerly Fusarium moniliforme, F. proliferatum (T. Matsushima) Nirenberg (sexual stage: G. proliferatum), F. subglutinans (sexual stage: G. subglutinans). The pathogens are seed borne with up to 75% of all seed planted being infected with F. verticilliodes. The importance of seed infection to subsequent development of root rot and stalk rot is not clear. The pathogen also overwinters as mycelium in debris on or in the soil, where it can infect the plant directly through the roots causing root rot and lower stalk rot. Two types of conidia are produced, macroconidia and microconidia, that can be splash dispersed onto leaves and washed down the leaf into the sheath and infect at the nodes. European corn borer has been shown to be an important vector moving F. verticilliodes from plant to plant (adult stage) and creating wounds necessary to allow the fungus to enter the plant (laval stage). Fusarium root and stalk rots typically occur in complexes with other root and stalk rots such as Gibberella, Diplodia, and Colletotrichum.
symptoms: The leaves turn from a healthy green color to a dull green and the lower stalk becomes yellowed. Premature plant death may occur and lodging due to disintegration of the internal stalk pith tissue; when squeezed between the thumb and index finger at the lower nodes,the stalk often collapses. Mycelium is often seen at the nodes. When split open the stalks may reveal a pink discoloration often confused with the red color of Gibberella stalk rot.
comment: Fusarium stalk rot can cause shredded pith that may be a whitish-pink to salmon color. Fusarium stalk rot is among the most common stalk rots in the Midwest. Fusarium stalk rot can cause brown streaks on the lower internodes. Fusarium stalk rot symptoms.

Fusarium Wilt
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Cotton
affects: Tomato
life cycle: Fusarium fungi survive in the soil or associated with plant debris for up to ten years. Disease development is favored by warm soil temperatures, and symptoms are most prevalent when temperatures range from 80–90 degrees F. The fungi enter the plants through their roots and are then spread throughout the plant by the plant's water-conducting vessels.
management: Management Strategies 1. Plant resistant varieties. These varieties are labeled VF and include cultivars such as ‘Spring Giant’, ‘Burpee VF’, ‘Supersonic’, ‘Celebrity’, ‘Manalucie’, ‘Better Boy’, and ‘Small Fry’. 2. Remove infected plants from the garden. Removal of infected plants will help limit the disease's spread. Soil sterilization or fumigation will eliminate wilt fungi from the soil but are impractical for home gardeners. Soil replacement should be considered. 3. Avoid over-application of high nitrogen fertilizers. High soil nitrogen levels accompanied by low potassium levels can increase susceptibility to the fungus. Use a soil test to determine potassium levels and other nutrient deficiencies. 4. Avoid activity in wet plantings. Movement of wet soil from place to place via shoes or tools will spread the disease. 5. Sanitize stakes and tomato cages at the end of the season. Avoid using soil-encrusted tools and supports season after season. A thorough cleaning with water will reduce most risk of transmitting the disease. 6. For four years, do not plant solanaceous plants in the area where infection occurred. Tomato, potato, pepper, and eggplant are all susceptible to the disease and may allow its survival year after year in the same planting area.
symptoms: Fusarium often causes yellowing on one side of the plant or leaf. Yellowing begins with the older, bottom leaves, followed by wilting, browning, and defoliation. Growth is typically stunted, and little or no fruit develops. Brown, vascular tissue can be found when the infected stem is cut at its base. Infected plants often die before maturing.
comment: If your tomato plants yellow and wilt on one side of the plant or one side of a leaf, they may have Fusarium wilt. Fusarium wilt on tomatoes is caused by Fusarium oxysporumsp. lycopersici. It is a soilborn fungus. The organism is specific for tomato and is very longlived in all regions of the United States. The disease develops more quickly in soils that are high in nitrogen and low in potassium. In addition, plants grown in sandy soils tend to contract this disease more often.

GOCI
type: NamedIndividual
type: Sensor
comment: Geostationary Ocean Color Imager (GOCI)

Gabon
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:GA
agricultural land: 51,600 sq. km
area: 267,745 km²
climate: tropical; always hot, humid
density: 5.4/km²
latitude: 11.598909
longitude: -0.828097
population: 1,454,867 (2005)
comment: Gabon, officially the Gabonese Republic, is a country on the west coast of Central Africa. Located on the equator, Gabon is bordered by Equatorial Guinea to the northwest, Cameroon to the north, the Republic of the Congo on the east and south, and the Gulf of Guinea to the west. It has an area of nearly 270,000 square kilometres and its population is estimated at 2.1 million people. Its capital and largest city is Libreville. The official language is French.

Gall Midge
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Mango
comment: Gall midge, (family Cecidomyiidae, or Itonididae), any minute, delicate insect (order Diptera) characterized by beaded, somewhat hairy antennae and few veins in the short-haired wings. The brightly coloured larvae live in leaves and flowers, usually causing the formation of tissue swellings (galls). A few live in galls produced by other dipterans. Pupation takes place in the gall or in the soil; the winter is passed in an immature stage. The Hessian fly (Mayetiola destructor) is the most serious pest within the family. In Europe and North America the chrysanthemum midge (Diarthronomyia hypogaea) makes small galls in the leaves. The rose midge (Dasyneura rhodophaga) infests the young buds and shoots of roses and is a serious pest in greenhouses but rarely outside. Some other serious pests are the wheat midge, sorghum midge, rice midge, clover midge, and pear midge. Tobacco fumigation and dust on soil are used to control these insects.

Gambia
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:GM
agricultural land: 6,150 sq. km
area: 10,380 km²
climate: tropical; hot, rainy season (June to November); cooler, dry season (November to May)
density: 153.5/km²
latitude: -15.311436
longitude: 13.445411
population: 1,700,000 (2007)
comment: The Gambia, officially the Republic of The Gambia, is a country in West Africa. It is the smallest country within mainland Africa, and is surrounded by Senegal, except for its western coast on the Atlantic Ocean. The Gambia is situated on both sides of the lower reaches of the Gambia River, the nation's namesake, which flows through the centre of The Gambia and empties into the Atlantic Ocean. It has an area of 10,689 square kilometres with a population of 1,857,181 as of the April 2013 census. Banjul is the Gambian capital and the country's largest metropolitan area. The largest cities are Serekunda and Brikama.

GenSim
type: NamedIndividual
type: Toolkit
References: Rehurek, Radim, and Petr Sojka. "Software framework for topic modelling with large corpora." In Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks. 2010.
Acronym: GenSim
comment: Gensim is a highly specialized Python library that largely deals with topic modeling tasks using algorithms like Latent Dirichlet Allocation (LDA). It’s also excellent at recognizing text similarities, indexing texts, and navigating different documents. This library is fast, scalable, and good at handling large volumes of data. Here are some tutorials to get started.

Gentle Breeze
type: NamedIndividual
type: Wind Type

Georgia
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:GE
agricultural land: 24,690 sq. km
area: 69,700 km²
climate: warm and pleasant; Mediterranean-like on Black Sea coast
density: 68/km²
latitude: -83.222786
longitude: 32.678276
population: 4,730,8413 (2008)
comment: Georgia is a country located at the intersection of Eastern Europe and Western Asia. It is a part of the Caucasus region, bounded to the west by the Black Sea, to the north and east by Russia, to the south by Turkey and Armenia, and to the southeast by Azerbaijan. Georgia covers a territory of 69,700 square kilometres, and its approximate population is about 3.716 million. Georgia is a unitary parliamentary republic, with the government elected through a representative democracy. Tbilisi is the country's capital and largest city.

Germany
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Temperate Climate
ISO code: ISO 3166-1:DE
agricultural land: 167,190 sq. km
area: 357,021 km²
climate: temperate and marine; cool, cloudy, wet winters and summers; occasional warm mountain (foehn) wind
density: 230/km²
latitude: 10.454239
longitude: 51.163475
population: 82,060,000 (12/2008)
comment: Germany, officially the Federal Republic of Germany, is a country at the intersection of Central and Western Europe. It is situated between the Baltic and North seas to the north, and the Alps to the south; covering an area of 357,022 square kilometres, with a population of over 83 million within its 16 constituent states. It borders Denmark to the north, Poland and the Czech Republic to the east, Austria and Switzerland to the south, and France, Luxembourg, Belgium, and the Netherlands to the west. Germany is the second-most populous country in Europe after Russia, as well as the most populous member state of the European Union. Its capital and largest city is Berlin, and its financial centre is Frankfurt; the largest urban area is the Ruhr.

Ghana
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:GH
agricultural land: 159,000 sq. km
area: 238,535 km²
climate: tropical; warm and comparatively dry along southeast coast; hot and humid in southwest; hot and dry in north
density: 93/km²
latitude: -1.031819
longitude: 7.955012
population: 23,000,000 (2008)
comment: Ghana, officially the Republic of Ghana which was formerly called Gold Coast, is a country along the Gulf of Guinea and the Atlantic Ocean, in the subregion of West Africa. Spanning a land mass of 238,535 km2, Ghana is bordered by the Ivory Coast in the west, Burkina Faso in the north, Togo in the east, the Gulf of Guinea and the Atlantic Ocean in the south. Ghana means "Warrior King" in the Soninke language.

Gibberella Zeae
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
comment: Gibberella zeae, also known by the name of its anamorph Fusarium graminearum, is a fungal plant pathogen which causes fusarium head blight, a devastating disease on wheat and barley. The pathogen is responsible for billions of dollars in economic losses worldwide each year. Infection causes shifts in the amino acid composition of wheat, resulting in shriveled kernels and contaminating the remaining grain with mycotoxins, mainly deoxynivalenol, which inhibits protein biosynthesis; and zearalenone, an estrogenic mycotoxin. These toxins cause vomiting, liver damage, and reproductive defects in livestock, and are harmful to humans through contaminated food. Despite great efforts to find resistance genes against F. graminearum, no completely resistant variety is currently available. Research on the biology of F. graminearum is directed towards gaining insight into more details about the infection process and reveal weak spots in the life cycle of this pathogen to develop fungicides that can protect wheat from scab infection.

Gibraltar
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:GI
area: 6.8 km²
climate: Mediterranean with mild winters and warm summers
density: 4,290/km²
latitude: -5.353033
longitude: 36.133022
population: 28,875 (1/2008)
comment: Gibraltar is a British Overseas Territory located at the southern tip of the Iberian Peninsula. It has an area of 6.7 km2 and is bordered to the north by Spain. The landscape is dominated by the Rock of Gibraltar at the foot of which is a densely populated town area, home to over 32,000 people, primarily Gibraltarians.

Glume Blightrdfs:label
type: NamedIndividual
type: Crop Disease
affects: Rice

Good Quality
type: NamedIndividual
type: Quality

Grain Filling
type: NamedIndividual
type: Growth Stage

Grapefruit
type: NamedIndividual
type: Citrus
type: Crop
type: Fruit Tree
comment: The grapefruit is a subtropical citrus tree known for its relatively large sour to semisweet, somewhat bitter fruit. The interior flesh is segmented and varies in color from pale yellow to dark pink.

Grapevine Leafroll
type: NamedIndividual
type: Crop Disease
type: Viral Disease
affects: Grape
comment: Grapevine leafroll disease (GLD) is one of the most important grapevine viral diseases affecting grapevines worldwide. The impact on vine health, crop yield, and quality is difficult to assess due to a high number of variables, but significant economic losses are consistently reported over the lifespan of a vineyard if intervention strategies are not implemented.

Graphosoma Rubrolineata
type: NamedIndividual
type: Crop Pest

Gray Leaf Spot
type: NamedIndividual
type: Crop Disease
affects: Corn
management: Genetic Resistance Hybrid tolerance/resistance is the best option for management of GLS. Tolerant hybrids yield well under moderate to high disease pressure. Cultural Practices Tillage can reduce the risk of GLS when the residue is incorporated into the soil and decomposition results. However, when other GLS management measures are employed; e.g., crop rotation, the use of tolerant hybrids, and the application of fungicides, the benefits from reduced tillage (e.g., increased water retention, reduced erosion) outweigh the negative impacts of GLS. Rotation to crops other than corn for at least one year in low risk fields (minimum two years in high risk fields) may minimize the severity of GLS. Chemical/Biological Control Under high disease pressure, timely fungicide applications can greatly minimize impact on yield. These applications are costly and usually only practical when disease pressure is high.
pathogen: Cercospora zea-maydis Tehon & E. Y. Daniels. (Teleomorh: Mycospharella). Fungal structures: hyphae and conidia. Occurs throughout the Corn Belt from mid-Atlantic states to Nebraska and Colorado. Two populations distinguishable by growth rate on laboratory media, geographic distribution, production of the toxin cercosporin, and molecular analysis have been described: one population occurs only in the eastern Corn Belt and mid-Atlantic states, while the other is found throughout the USA. They do not differ in virulence.
symptoms: All corn hybrids and inbreds are susceptible to Gray Leaf Spot (GLS). In minimum tillage fields with a history of GLS, symptoms can occur on the lower leaves prior to tasseling. Immature GLS lesions can be easily confused with lesions caused by other pathogens; e.g., eyespot (Kabatiella), anthracnose (Colletrichum). Mature lesions on leaves are rectangular in shape and restricted by leaf veins; reverse lighting reveals a yellow halo on most hybrids. Mature lesions are diagnostic and easily distinguishable from other diseases. As lesions expand they coalesce resulting in a blighting of large portions of the leaf. When the source of primary innoculum is within the field, symptoms originate on the lower leaves and progress up the plant as infection cycles occur. When severe, the entire plant may be blighted. Stalk infections have been reported and can result in lodging if severe. Lesions on sheath or husk tissues may be be oval to ellipsoidal in shape and are not usually diagnostic. Severely affected fields can appear to be experiencing early dry-down. The time from infection to the production of more innoculum can be 14-21 days on a susceptible hybrid and 21-28 days on a tolerant hybrid.

Gray Mold
type: NamedIndividual
type: Crop Disease
affects: Apple
biological control: Since most Botrytis infections occur in the orchard, sanitation practices include removing decayed or mummified fruit and organic debris from the orchard floor to reduce inoculum loads preharvest. This will reduce the number of infected fruits and hence limit gray mold development in storage. Moreover, good harvest and handling aimed to minimize punctures and bruises on the fruit will further limit the infections that may occur through wounds. Certain cultivars that tend to split such as Gala and Honeycrisp need to be harvested at the right maturity to minimize splits at the stem bowl, and a good fungicide coverage should be achieved to ensure enough fungicide is available to prevent infections through splits. Clipping the stems of certain cultivars such as Honeycrisp will minimize injuries and reduce infections.
chemical control: Because Botrytis survives and infects fruit in the orchard, applying effective fungicides in a timely manner is key to reducing infections before harvest. Recent research has shown good efficacy in reducing gray mold with four preharvest sprays a season applied at: (1) at bloom or at petal fall to reduce the number of spores on flowers and fruit, (2) at fruitlet stage (1.5-2 inch diameter), (3) mid-July followed by (4) a final spray 7 days before harvest. It is highly recommended to rotate fungicides from different FRAC groups (Table 2) to avoid fungicide resistance. The most effective preharvest fungicides include those from FRAC 1, 7, 9 11, and 17 (pear). Other multi-site fungicides, such as Ziram and Captan, are good rotation or tank-mix partners to improve efficacy and reduce fungicide resistance. At harvest, there are four single-site postharvest fungicides labeled for postharvest application (Table 2). Thiabendazole (e.g. Mertect) has been largely used in the PNW since the 1970s until early 2000s when two new postharvest fungicides, fludioxonil (e.g. Scholar) and pyrimethanil (e.g. Penbotec) were registered for postharvest decay management. In 2016, difenoconazole (eg. Academy difenconazole + fludioxonil) was registered for postharvest application in pome fruit and other commodities. In orchards with no resistant populations, the four labeled fungicides have a high level of efficacy against gray mold, with pyrimethanil being the most effective due better systemic activity. For decades, postharvest fungicides have been applied as a drench a harvest or as a spray on the packing-line. However, in recent years, thermonebulization (also called fog or aerosol or dry application) has become common in the PNW pome fruit industry. Although fog application may be more effective in reducing the spread of Penicillium and Mucor spores better compared to drenching, it still requires some optimization for preharvest pathogens like Botrytis. Formulations to apply thiabendazol…
life cycle: Inoculum of Botrytis can survive on organic matter in the orchard or in mummies left on trees or fallen to the ground. Under certain unfavorable conditions, such as very low or very high temperatures, Botrytis may produce sclerotia that will allow it to survive until conditions improve. When temperatures become moderate mycelia grow from the sclerotia and sporulate. The resulting fungal spores are disseminated through wind, water splash, or infected materials entering the orchard. Although Botrytis, like many other pathogens can easily infect fruit through splits, wounds or punctures occurring at harvest, gray mold infections generally start earlier in the orchard. The fungus can infect flowers at bloom and fruits during the growing season. Infections remain latent (dormant) until fruit have been in storage for several months, and then develop symptoms. Spores of Botrytis are common on the stems and in the calyx bowl and commonly cause infection in these areas. (Fig. 1A,B,F). Secondary infections of fruit through fruit-to-fruit contact (nesting) during storage is common,(Fig. 1I) especially when fruit were not fully covered with the fungicide applied at harvest and when under high relative humidity. Eventually latent infections may be carried in packed fruit and symptoms may develop during transit or at grocery stores.
management: Botrytis cinerea is considered a high-risk pathogen for fungicide resistance development. The plastic genome and the profuse sporulation of the fungus increase the risk that many spores will be exposed to the applied fungicide, which increases the risk of selecting for resistance. Therefore, the first important step in limiting fungicide resistance is to implement sound annual preharvest sanitations (mummies and leaf litter removal, weed management) to remove or reduce the residual inoculum from previous seasons and limit exposure to fungicides. Extended storage periods, for up to 12 months, can also increase the risks of fungicide resistance development because of the continuous exposure of the fungus to the fungicide. Resistance to both active ingredients of Pristine (pyraclostrobin and boscalid), the most frequently used preharvest fungicide, has been reported in Washington (Kim and Xiao, 2010) and confirmed in recent surveys (Amiri, unpublished data). For postharvest fungicides, resistance to TBZ has been reported first in 2010 in the PNW (Zhao et al., 2010) and recent surveys found about 20% of the overall statewide population was resistant to TBZ as well as to Topsin-M (Amiri, unpublished data). Resistance to PYR (Penbotec) has also been found in recent surveys to range between 0 and 40% in Washington (Amiri et al., 2019) whereas some Botrytis populations showed tolerance to FDL (Scholar) (Amiri, unpublished data). Given the known risks and the limited number of fungicides available preharvest and postharvest, it is critical that these materials are used appropriately to limit the development of fungicide resistance development. Growers should rotate fungicides from different FRAC groups (1, 3, 7, 11, 19) used to control powdery mildew in the early growing season and other pathogens later in the season. It is highly recommended not to apply TBZ (FRAC 1) postharvest if Topsin-M (FRAC 1) has been applied preharvest. At the time of harvest, it is critical to appl…
symptoms: Symptoms of gray mold include a decayed area with a light brown to dark brown appearance across which the color remains similar. The decayed fruit is spongy, and diseased tissue does not separate from the healthy tissue as dies blue mold(Table 1). The internal decayed flesh usually has irregular margins and appears light brown to brown at the margin (Fig. 1C). Under high relative humidity conditions and as storage extends, fluffy white to gray mycelium and grayish spore masses may appear (Fig 1G,H). During early infection gray mold-infected fruit does not have a distinct odor, but as the decay advances, rotten fruit may have a “cedar-like” smell and the entire decayed fruit may appear “baked” and eventually may turn softer compared to earlier stages. Early symptoms of gray mold can be confused with those of speck rot on apple and Phacidiopycnis rot on pear. Speck rot is usually firmer than gray mold and the fruit flesh at the margin of speck or Phacidiopycnis rot appears translucent and water-soaked, whereas internal decayed flesh of gray mold usually appears brown.
comment: Gray mold is a common postharvest disease on apples and pears wherever the fruits are grown. This disease can cause significant losses in both apples and pears during storage with losses as high as 20-50%. Recent surveys in the Pacific Northwest showed gray mold to be the second most important postharvest decay after blue mold. Gray mold is mainly caused by Botrytis cinerea Pers., (sexual form Botryotinia fuckeliana (de Bary) Whetzel) although other B. mali has also been reported. Botrytis is a ﬁlamentous necro-trophic (can survive on dead tissue) fungus that can infect more than 200 plant species. This wide host-range is due to a very plastic genome and diverse species with varying virulence and pathogenicity. Botrytis cinerea is thermo-tolerant able to grow at temperatures as low as -2ºC (28ºF) and germinate at 0ºC (32ºF) (Coley-Smith et al., 1980; Rosenberger and Xiao, 2014). Its growth significantly slows down at temperatures above 32ºC (90ºF). Wet conditions during the growing season can be very conducive for Botrytis infections (Sommer 1985).

Greasy spot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Citrus
comment: The fungus caused by greasy spot is caused by the fungus Mycosphaerella citri. Whether you grow citrus trees for a fresh fruit market or processing plant or just for your own use, you need to be able to control greasy spot fungus. If you allow the fungus to simply live, you will end up with a ruined fruit crop.

Greece
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Dry summer climate
ISO code: ISO 3166-1:GR
agricultural land: 81,520 sq. km
area: 131,990 km²
climate: temperate; mild, wet winters; hot, dry summers
density: 84/km²
latitude: 24.508326
longitude: 38.275276
population: 11,216,708 (2008)
comment: Greece, also known as Hellas, and officially the Hellenic Republic, is a country located in Southeast Europe. Its population is approximately 10.7 million as of 2018; Athens is its largest and capital city, followed by Thessaloniki. Situated on the southern tip of the Balkans, Greece is located at the crossroads of Europe, Asia, and Africa. It shares land borders with Albania to the northwest, North Macedonia and Bulgaria to the north, and Turkey to the northeast. The Aegean Sea lies to the east of the mainland, the Ionian Sea to the west, the Cretan Sea and the Mediterranean Sea to the south. Greece has the longest coastline on the Mediterranean Basin and the 11th longest coastline in the world at 13,676 km in length, featuring many islands, of which 227 are inhabited. Eighty percent of Greece is mountainous, with Mount Olympus being the highest peak at 2,918 metres . The country consists of nine traditional geographic regions: Macedonia, Central Greece, the Peloponnese, Thessaly, Epirus, the Aegean Islands, Thrace, Crete, and the Ionian Islands.

Greenhouse Slug
type: NamedIndividual
type: Crop Pest
alternative label: Milax Gagates

Greenland
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:GL
agricultural land: 2,359 sq. km
area: 2,166,086 km²
climate: arctic to subarctic; cool summers, cold winters
density: 0.027/km²
latitude: -42.28789
longitude: 71.70235
population: 57,564 (2007)
comment: Greenland is the world's largest island, located between the Arctic and Atlantic oceans, east of the Canadian Arctic Archipelago. Greenland is an autonomous territory within the Kingdom of Denmark. Though physiographically a part of the continent of North America, Greenland has been politically and culturally associated with Europe for more than a millennium. The majority of its residents are Inuit, whose ancestors migrated from Alaska through Northern Canada, gradually settling across the island by the 13th century.

Grenada
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:GD
agricultural land: 110 sq. km
area: 344 km²
climate: tropical; tempered by northeast trade winds
density: 259.5/km²
latitude: -61.58821
longitude: 12.260794
population: 110,000 (2005)
comment: Grenada is a country in the West Indies in the Caribbean Sea at the southern end of the Grenadines island chain. Grenada consists of the island of Grenada itself, two smaller islands, Carriacou and Petite Martinique, and several small islands which lie to the north of the main island and are a part of the Grenadines. It is located northwest of Trinidad and Tobago, northeast of Venezuela and southwest of Saint Vincent and the Grenadines. Its size is 348.5 square kilometres, and it had an estimated population of 112,523 in July 2020. Its capital is St. George's. Grenada is also known as the "Island of Spice" due to its production of nutmeg and mace crops.

Grey Spot
type: NamedIndividual
type: Crop Disease

Grubs
type: NamedIndividual
type: Crop Disease
type: Crop Pest
comment: Grubs are the larvae of beetles. They are between 3/4" and 1 1/2" in length, white to grayish with brown heads, and commonly curl into a "C" shape when disturbed. They feed underground on roots (particularly those of turf grasses) for one year or more, depending on the species. Damage caused by grubs begins in early fall, when the larvae begin to hatch from eggs laid in July and August. The larvae feed on roots until the ground freezes in late fall, then burrow more deeply underground to stay warm. In spring, they return to the surface and resume feeding on roots. Damaged sections of lawn appear burned and can be lifted with ease; upon doing so the culprits will be exposed. From your lawn, grubs may move into your vegetable garden and proceed to eat roots and bases of plants, especially in early summer. They can cripple or ruin plants without being detected. Vulnerable crops include young apple trees, blackberries, corn, onions, potatoes and strawberries. Other symptoms of infestation include the presence of moles, shrews, chipmunks, skunks, raccoons, and birds, all of whom relish grubs and will dig up your lawn or garden in search of a good snack. Large numbers of adult beetles being attracted to lights at night may also signal a problem. Use of parasitic nematodes is a promising method of controlling grubs. The nematodes inject a killing bacteria into grubs but leave earthworms and other beneficial soil life unharmed. Up to four applications may be required. Milky spore disease or Bacillus popilliae is an effective long term control. While it is expensive and takes 3 to 5 years to be completely effective, an application can last 10 years or more when properly applied. Specific recommendations for lawns. Grub problems can be prevented or reduced by keeping your lawn healthy with proper watering and fertilizing. Keep in mind that adult beetles lay their eggs in July and August and prefer moist, lush lawns; keep the lawn on the dry side during this period and mow it …

Gryllotalpa
type: NamedIndividual
type: Crop Pest

Guadeloupe
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:GP
area: 1,628 km²
climate: subtropical tempered by trade winds; moderately high humidity
density: 249/km²
latitude: -62.07638
longitude: 16.980835
population: 405,500 (1/2008)
comment: Guadeloupe is an archipelago and overseas department and region of France in the Caribbean. It consists of six inhabited islands—Basse-Terre, Grande-Terre, Marie-Galante, La Désirade, and the two inhabited Îles des Saintes—as well as many uninhabited islands and outcroppings. It is south of Antigua and Barbuda and Montserrat, and north of Dominica. The region's capital city is Basse-Terre, located on the southern west coast of Basse-Terre Island; however, the most populous city is Les Abymes and the main center of business is neighbouring Pointe-à-Pitre, both located on Grande-Terre Island.

Guatemala
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:GT
agricultural land: 43,950 sq. km
area: 108,890 km²
climate: tropical; hot, humid in lowlands; cooler in highlands
density: 134.6/km²
latitude: -90.232346
longitude: 15.77626
population: 13,000,001 (2008)
comment: Guatemala, officially the Republic of Guatemala, is a country in Central America bordered by Mexico to the north and west, Belize and the Caribbean to the northeast, Honduras to the east, El Salvador to the southeast and the Pacific Ocean to the south. With an estimated population of around 17.2 million, it is the most populous country in Central America and has the 11th largest national population in the Americas. Guatemala is a representative democracy; its capital and largest city is Nueva Guatemala de la Asunción, also known as Guatemala City.

Guernsey
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:GG
area: 78 km²
climate: temperate with mild winters and cool summers; about 50% of days are overcast
density: 836.3/km²
latitude: -2.42319
longitude: 49.575687
population: 65,573 (2007)
comment: Guernsey is an island in the English Channel off the coast of Normandy that is part of the Bailiwick of Guernsey, a British Crown Dependency.

Guinea
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:GN
area: 245,857 km²
climate: generally hot and humid; monsoonal-type rainy season (June to November) with southwesterly winds; dry season (December to May) with northeasterly harmattan winds
density: 38/km²
latitude: -11.363663
longitude: 9.934886
population: 10,211,437 (2005)
comment: Guinea, officially the Republic of Guinea, is a coastal country in West Africa. Formerly known as French Guinea, the modern country is sometimes referred to as Guinea-Conakry to distinguish it from other countries with "Guinea" in the name and the eponymous region, such as Guinea-Bissau and Equatorial Guinea. Guinea has a population of 12.4 million and an area of 245,857 square kilometres .

Guinea-Bissau
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:GW
agricultural land: 16,300 sq. km
area: 36,544 km²
climate: tropical; generally hot and humid; monsoonal-type rainy season (June to November) with southwesterly winds; dry season (December to May) with northeasterly harmattan winds
density: 44/km²
latitude: -15.177028
longitude: 11.770379
population: 1,586,000 (2005)
comment: Guinea-Bissau, officially the Republic of Guinea-Bissau, is a country in West Africa that covers 36,125 square kilometres with an estimated population of 1,874,303. It borders Senegal to the north and Guinea to the south-east.

Gummosis
type: NamedIndividual
type: Crop Disease
type: Crop Stress
affects: Citrus
affects: Fruit Tree
affects: Orange
life cycle: Cytospora canker is caused by one of two different fungi. The fungus overwinters on dead wood or in sunken lesions. Curly orange fungal chains will release spores in the spring which are distributed by winds and rain splash. Once the spores land on a host tree, they enter through wounds made from insect boring, mechanical injuries, or winter injury. Symptoms are more prevalent during warm (70–85 degrees F), wet spring weather as the moisture facilitates entry into wounds. Trees just coming out of dormancy are most susceptible to the pathogen.
symptoms: On trees infected with Cytospora canker, new shoots or leaves may turn yellow or wilt. Sunken lesions develop on the bark. These lesions enlarge, and gummy, amber-colored sap oozes from the bark. Curly orange threads (fungal chains) may grow out of the bark as the disease progresses. Leaves may brown and drop. The disease kills the wood underneath the cankers, often causing whole branches to die. Infected wood and the defoliation that may occur weakens the tree, but if the disease infects the trunk, the whole tree may die.
comment: Gummosis is a nonspecific condition where sap leaks from a wound in the tree. It usually occurs when the tree has a perennial or bacterial canker, or is attacked by the peach tree borer.
comment: Gummosis is the oozing of sap from wounds or cankers on fruit trees. Gummosis can result from environmental stress, mechanical injury, or disease and insect infestation. Cytospora canker or Valsa canker, the fungal cause of gummosis, affects stone fruit trees like apricot, cherry, peach, and plum. Cytospora infection is distinguishable from insect damage and mechanical injuries because sawdust or pieces of bark are not mixed in the sap, as it would be with insect or mechanical damage. Cytospora canker is also known as perennial canker.

Gummosis Disease
type: NamedIndividual
type: Crop Disease
comment: Gummosis is the oozing of sap from wounds or cankers on fruit trees. Gummosis can result from environmental stress, mechanical injury, or disease and insect infestation. Cytospora canker or Valsa canker, the fungal cause of gummosis, affects stone fruit trees like apricot, cherry, peach, and plum.

Guyana
type: NamedIndividual
type: Country
belongs to: South America
ISO code: ISO 3166-1:GY
agricultural land: 16,770 sq. km
area: 214,999 km²
climate: tropical; hot, humid, moderated by northeast trade winds; two rainy seasons (May to August, November to January)
density: 3.5/km²
latitude: -58.93826
longitude: 4.866323
population: 858,8631 (3/2008)
comment: Guyana, officially the Co‑operative Republic of Guyana, is a country on the northern mainland of South America and the capital city is Georgetown. Guyana is bordered by the Atlantic Ocean to the north, Brazil to the south and southwest, Venezuela to the west, and Suriname to the east. With 215,000 square kilometres, Guyana is the third-smallest sovereign state by area in mainland South America after Uruguay and Suriname; it is also the second-least populous sovereign state in South America after Suriname.

Gypsy Moth Larva
type: NamedIndividual
type: Crop Pest

Hailstorm
type: NamedIndividual
type: Planetary Phenomena
comment: Hail is a form of solid precipitation. It is distinct from ice pellets, though the two are often confused. It consists of balls or irregular lumps of ice, each of which is called a hailstone. Ice pellets fall generally in cold weather while hail growth is greatly inhibited during cold surface temperatures.

Haiti
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:HT
agricultural land: 17,700 sq. km
area: 27,751 km²
climate: tropical; semiarid where mountains in east cut off trade winds
density: 335/km²
latitude: -73.045975
longitude: 19.054426
population: 8,706,497 (2007)
comment: Haiti, officially the Republic of Haiti and formerly known as Hayti, is a country located on the island of Hispaniola in the Greater Antilles archipelago of the Caribbean Sea, to the east of Cuba and Jamaica and south of The Bahamas and the Turks and Caicos Islands. It occupies the western three-eighths of the island which it shares with the Dominican Republic. To its south-west lies the small island of Navassa Island, which is claimed by Haiti but is disputed as a United States territory under federal administration. Haiti is 27,750 square kilometers in size, the third largest country in the Caribbean by area, and has an estimated population of 11.4 million, making it the most populous country in the Caribbean.

Halyomorpha Halys
type: NamedIndividual
type: Crop Pest

Hard Dough
type: NamedIndividual
type: Growth Stage
comment: At the hard dough stage, grain has reached 75 percent of its final dry weight, and nutrient uptake is almost complete. The grain can no longer be crushed between the thumb and index finger. The seed coat is no longer green and has turned its final color, which can be white, tan, bronze or red. The total moisture content of the plant is now decreasing daily, and water stress during this time tends to promote lodging. The hard dough stage typically lasts 10 to 14 days.

Heading
type: NamedIndividual
type: Growth Stage
comment: Heading
comment: Heading date is characterized together by the vegetative growth phase i.e., the time period from germination to panicle initiation and the reproductive phase, meaning the time period from panicle initiation to heading.

Healthy
type: NamedIndividual
type: Crop Problem
type: Health
type: State
comment: Healthy mentions a state which plants have firm leaves, well-formed flowers and fruit, and well-developed root systems. Unhealthy plants show damage to leaves, stems, roots, flowers or fruit. A pest is an animal that damages plants. Disease is caused by bacteria, fungi, and viruses.

Healthy Trees
type: NamedIndividual
type: State
is state of: Crop Disease
is state of: Crop Disease by Tier4

Heard Island and McDonald Islands
type: NamedIndividual
type: Country
belongs to: Antarctica
ISO code: ISO 3166-1:HM
area: 65,000 km²
latitude: 73.22784
longitude: -53.05071
comment: The Territory of Heard Island and McDonald Islands is an Australian external territory comprising a volcanic group of mostly barren Antarctic islands, about two-thirds of the way from Madagascar to Antarctica. The group's overall size is 372 km2 in area and it has 101.9 km of coastline. Discovered in the mid-19th century, the islands have been an Australian territory since 1947 and contain the country's two active volcanoes. The summit of one, Mawson Peak, is higher than any mountain on the Australian mainland. The islands lie on the Kerguelen Plateau in the Indian Ocean.

Heat
type: NamedIndividual
type: Temperature

Heathland
type: NamedIndividual
type: Topography

Heavy Precipitation
type: NamedIndividual
type: Precipitation

Omiodes indicata
type: NamedIndividual
type: Crop Disease
affects: Soybean
comment: Omiodes indicata, the bean-leaf webworm moth or soybean leaf folder, is a species of moth of the family Crambidae.

Helimenthosporium Spp
type: NamedIndividual
type: Fungus

Helminthosporium Leaf Blotch
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Wheat
development: Primary infections tend to be on the lower leaves, beginning as chlorotic flecks or spots. These infection sites enlarge, turn dark brown, and often coalesce. When the disease is severe, affected leaves or leaf sheaths may die prematurely.
distribution: Spot blotch affects wheat, triticale, barley, and most grasses. It is found worldwide, but is especially prevalent in more humid and higher rainfall areas.
important: If infection occurs early in the crop cycle and conditions remain favorable for development, complete defoliation is possible; major reductions in yield and severely shrivelled kernels will then results.
symptoms: Lesions caused by this disease are elongated to oval in shape and are generally a dark brown color. As lesions mature, the centers often turn a light brown to tan color, surrounded by an irregular dark brown ring (21 on leaf; 22 on spike).
comment: This disease is caused by a group of fungi (Bipolaris, Drechslera, and Exserohilum spp. (previously classified as Helminthosporium fungi) that is active over a wide range of temperatures. At any given time of the year, at least one species within this fungal group can be isolated.
label: Spot Blotch

Helminthosporium Turcicum
type: NamedIndividual
type: Crop Disease

Barley Midge
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Barley
affects: Cereal
affects: Rye
affects: Wheat
distribution: The Hessian fly is mainly a pest of wheat, but it may attack barley, rye, and other grasses. This pest has been reported in most wheat-growing areas of the world.
important: This is one of the most destructive insect pests on cereals. Widespread outbreaks have occurred and, in some locations (such as North Africa and the USA), the past recurs annually.
life cycle: Adult flies emerge in the spring from pupae that have overwintered in straw or stubble. The minute, oblong eggs are reddish in color and are laid in rows on the upper sides of leaves. The eggs hatch within one week; the white, legless larvae settle behind the leaf sheaths and suck the sap of the plant. They develop into translucent, pale green, slug-like maggots. The reddish brown pupae, commonly called "flag seed" because of their resemblance to the seed of the flax plant, are oval shaped, flattened, taper to a point, and are 3-5 mm long. They are found behind leaf sheaths, usually at a node.
symptoms: Severe infestations of Hessian flies result in stunting of the plants, thin stands, lodging, and reduced yield. Injury is caused entirely by the larvae, which suck juices from plant tissues (70). If infestation occurs during jointing, infested stems often will break prior to maturity. The Hessian fly is 3-4 mm long, has a black head and thorax, and a pinkish or yellow-brown abdomen.
comment: The Hessian fly or barley midge, Mayetiola destructor, is a species of fly that is a significant pest of cereal crops including wheat, barley and rye.
label: Hessian Fly
label: Mayetiola Destructor

High Air Pollution
type: NamedIndividual
type: Air Pollution

High Solar Radiation
type: NamedIndividual
type: Solar Radiation

Highland
type: NamedIndividual
type: Topography

Highland Climate
type: NamedIndividual
type: Climate Type
comment: Alpine climate is the typical weather (climate) for the regions above the tree line. This climate is also referred to as a mountain climate or highland climate.

Highly Infested
type: NamedIndividual
type: Crop Injure

Honduras
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:HN
agricultural land: 32,200 sq. km
area: 112,492 km²
climate: subtropical in lowlands, temperate in mountains
density: 64/km²
latitude: -86.2531
longitude: 15.203019
population: 7,483,763 (9/2007)
comment: Honduras, officially the Republic of Honduras, is a country in Central America. The republic of Honduras is bordered to the west by Guatemala, to the southwest by El Salvador, to the southeast by Nicaragua, to the south by the Pacific Ocean at the Gulf of Fonseca, and to the north by the Gulf of Honduras, a large inlet of the Caribbean Sea.

Hornworms
type: NamedIndividual
type: Crop Disease
type: Crop Pest
life cycle: Hornworms overwinter in the soil as hard-shelled, brown pupae. Large adult moths, known as sphinx or hummingbird moths, emerge in May or June and deposit spherical green eggs on the undersides of leaves of host plants. The larvae hatch a week later and feed on foliage and fruit for three to four weeks until reaching full development. Pupation occurs in the soil and adults emerge 2 to 4 weeks later to lay second generation eggs. In Missouri there may be one or two generations depending on location.
management: Management Strategies 1. Handpick caterpillars. Handpicking is usually all that is needed to control these pests in a home garden. Larvae are most easily located in early morning, often on the exterior of the plant. Leave any caterpillars with small white cocoons on their backs; they are being parasitized by a braconid wasp, which will soon produce more wasps to control them. 2. Biological control. Bacterial insecticides containing Bacillus thuringiensis kurstaki (Btk) are effective when larvae are smaller. It may take several days for the caterpillars to die, but feeding generally stops shortly after treatment. 3. Cultivation. Disking or rototilling after harvest destroys pupae in soil, reducing overwintering numbers. 4. Chemical control. An application of carbaryl (Sevin) or permethrin will effectively control hornworms if handpicking is impractical. However, chemical control will also reduce the numbers of beneficial insects. Organic Strategies Strategies 1 and 3 are strictly organic approaches. For an organic approach to Strategy 2, consult the Organic Materials Review Institute (OMRI™) for appropriate Bacillus thuringiensis (Bt) products.
symptoms: Larvae of both species consume entire leaves and small stems and may chew large pieces from green fruit. Hornworm damage is obvious when the infestation is moderate to heavy because of the large amount of defoliation. Search for the large caterpillars. Large, black droppings on the leaves or ground beneath the plant will usually indicate the presence of hornworms.
comment: Tobacco hornworm (Lepidoptera) on tomato (Lycopersicon); note reddish colored spike or "horn" at tail end that gives the hornworm its name The tomato hornworm, Manduca quinquemaculata, and the tobacco hornworm, M. sexta, are common pests of tomato, tobacco, eggplant, pepper, and potato throughout most of the United States. The hornworms are large (up to 4 inches long), bright green caterpillars with diagonal white stripes and a prominent horn at the rear. The two species have slightly different markings. The tomato hornworm has 8 diagonal white stripes on each side; the horn is straight and black. The tobacco hornworm has 7 diagonal white stripes; its horn is curved and red.

Huanglongbing
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Citrus
affects: Lemon
affects: Limes
affects: Orange
management: (i) Cultural control Once a tree becomes infected with HLB, it cannot be cured. Control is therefore reliant on preventing the disease occurring in the first place and this is achieved through strict quarantining to prevent the introduction of citrus psyllids to areas which are currently free of the pest. Areas which are subject to quarantine have restrictions placed on the movement of citrus plants, fruit, equipment and items made from citrus. Infected trees should be removed as quickly as possible from plantations and destroyed. Identification of infected trees should be achieved through several surveys to ensure that infected trees which are not yet showing symptoms are identified. In Florida, the recommendation is to scout groves at least 4 times a year for disease symptoms. (ii) Control of citrus psyllids Citrus psyllid populations can be controlled through the application of chemical sprays. Insecticides have proved very effective at controlling T. eryreae in South Africa where systemic insecticides are applied to the tree at the base of the trunk. In areas of the USA, Citrus health management areas (CHMAs) have been created to encourage neighbouring growers to work together to prevent the disease. Control strategies which have been implemented by the program include scouting, mapping and large-scale spraying to control citrus psyllids.
symptoms: Yellowing of one limb or one area of canopy; yellowing of leaf veins; blotchy mottling on leaf blades; twig and limb dieback; fruits dropping prematurely; small upwardly pointing leaves; small, misshapen fruit; fruit very bitter.
transmission: Huanglongbing can be transmitted by citrus psyllids or by grafting. The Asian citrus psyllid, Diaphorina citri is responsible for the spread of the disease in Asia and Oceania, Brazil and North America whereas the African citrus psyllid, Trioza erytreae is the main vector in Africa and Madagascar. Both psyllid species are present the Indian Ocean islands of Reunion and Mauritius, Citrus psyllids Citrus psyllids are tiny (3-4 mm) sap-sucking insects that excrete a sticky, sugary substance called honeydew. Both the Asian and African citrus psyllids are mottled brown in color but the Asian citrus psyllid possesses a brown head and the African species has a black head. Adult citrus psyllids will jump and/or fly for a short distance when they are disturbed. They are usually found on the undersides of leaves, often in high numbers. When a psyllid feeds on an infected plant, it acquires the disease after 15 to 30 minutes and feeding and is able to transmit the disease to new hosts after a period of 21 days. In order to transmit the disease successfully, the psyllids need only feed on a new host for a period of 15 minutes in order for successful transmission to occur. It is hypothesized that the bacterium multiplies within the body of the psyllid prior to transmission but this theory requires validation through experiments. Grafting Although the primary method of spread of the Huanglongbing bacterium is via the movement of citrus psyllids, the disease can also be transmitted through grafting practices. The ability of Huanglongbing to be transmitted by grafting was first demonstrated by Lin Kongxiang through experimental work which was published in 1956. The disease in not transmitted at high rates through grafting as as not all buds on infected trees contain the bacterium.
comment: Huanglongbing (Citrus greening, Yellow dragon disease) was first reported from Southern China in 1919 by American botanist Otto August Reinking who described a "yellow shoot" disease of citrus while evaluating diseases of economic plants in Southern China. A subsequent field survey conducted between 1941 and 1955 on citrus plants in the provinces of Guangdong, Fujian and Jiangx by Chinese plant pathologist Lin Kongxiang (Kung Hsiang) determined that that the disease likely originated in Chaozhou county in Guangdong as early as the 1870s. Lin adopted the name the local farmers had given to the disease of "huang long bing", which translates to "yellow dragon disease", a reference to the yellow coloration of new shoots on the infected trees. By 1936 Huanglongbing was considered a serious disease of citrus in China and it subsequently spread across Southeast Asia reaching Indonesia in 1948 and Taiwan in 1950 before spreading to the Philippines, Thailand and Malaysia in the 1950s, 60s and 70s respectively. The disease has been known by various names in different countries - "greening" in South Africa, "mottle leaf" in the Philippines, "dieback" in India and "vein phloem degeneration" in Indonesia - but in 1995 the disease was officially named Huanglongbing by the International Organization of Citrus Virologists (IOCV) and this name is now widely used to describe the disease in Africa, America and Asia. Biology and ecology The organism that causes Huanglongbing is a Gram-negative bacterium that is limited to the plant phloem - the plant system responsible for the delivery of sugars from the leaves to the growing parts of the plant. The bacteria involved have so far not been isolated and cultured but the disease is believed to be caused by bacteria belonging to the genus Candidatus Liberibacter. It is believed that there are at least two different forms of the disease, an African heat-sensitive form, L. africanus which survives in cool areas with temperatures below 30-32 C…

Humid Continental Climate
type: NamedIndividual
type: Climate Type
comment: A humid continental climate is a climatic region defined by Russo-German climatologist Wladimir Köppen in 1900, typified by four distinct seasons and large seasonal temperature differences, with warm to hot (and often humid) summers and cold (sometimes severely cold in the northern areas) winters. Precipitation is usually distributed throughout the year. The definition of this climate regarding temperature is as follows: the mean temperature of the coldest month must be below - 3 oC (26.6 oF) [2] and there must be at least four months whose mean temperatures are at or above 10 oC (50 oF). In addition, the location in question must not be semi-arid or arid.

Humid Subtropical Climate
type: NamedIndividual
type: Climate Type
comment: A humid subtropical climate is a zone of climate characterized by hot and humid summers, and cold to mild winters. These climates normally lie on the southeast side of all continents, generally between latitudes 25o and 35o and are located poleward from adjacent tropical climates.

Hungary
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:HU
agricultural land: 53,370 sq. km
area: 93,030 km²
climate: temperate; cold, cloudy, humid winters; warm summers
density: 109/km²
latitude: 19.505783
longitude: 47.16116
population: 10,034,000 (2/2008)
comment: Hungary is a country in Central Europe. Spanning 93,030 square kilometres in the Carpathian Basin, it borders Slovakia to the north, Ukraine to the northeast, Romania to the east and southeast, Serbia to the south, Croatia and Slovenia to the southwest, and Austria to the west. With about 10 million inhabitants, Hungary is a medium-sized member state of the European Union. The official language is Hungarian, which is the most widely spoken Uralic language in the world, and among the few non-Indo-European languages to be widely spoken in Europe. Hungary's capital and largest city is Budapest; other major urban areas include Debrecen, Szeged, Miskolc, Pécs, and Győr.

Hurricane
type: NamedIndividual
type: Wind Type

Hydrangea
type: NamedIndividual
type: Flower

Hypoxylon Dieback
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
life cycle: The fungus enters the tree through wounds on the branches or trunk and grows in the sapwood where it kills this conductive tissue. There is evidence that the pathogen may invade young trees but not cause a problem until the tree is under stress. Infected branches progressively die back as the fungus moves downward. Dead, sunken patches develop on larger trunks where the advancing fungus kills the conducting sapwood. Trees may die in one to two years or sooner if the early stages of the disease go unrecognized.
management: Management Strategies 1. There is no effective control for the disease. If over 15% of the crown is infected, cut the tree to ground level and burn or have a hauler remove it from the site. Since the fungus can remain active in dead wood, do not chip the wood and use for mulch. Also, any wood burned as fuel should be used quickly. 2. If 15% or less of the crown is infected, prune out all infected branches and cankers. Dispose of as noted above. Also improve the health of the tree by watering and fertilizing.
symptoms: Yellowing and wilting leaves may signal the onset of the disease, but these symptoms are merely general indicators that the plant is under stress and are not necessarily caused by the fungus. The most recognizable sign of a problem is the sloughing of patches of bark on the branches or trunk which exposes a silvery or cushiony layer of fungus. Prior to the bark sloughing, sunken or depressed areas appear in infected areas where the fungus has killed the cambium. Dead or dying limbs in the tree may also be the result of hypoxylon canker.
comment: Hypoxylon canker is caused by an opportunistic fungi, Biscogniauxia (formerly Hypoxylon) atropunctatum. Hypoxylon is unable to cause disease in healthy trees but is quick to colonize weakened or dying bark and wood. Oaks in the black oak group (pin, red, black, scarlet, and willow oak) are more susceptible than those in the white oak group (burr, chestnut, swamp white, and white oak). The disease is also found as a saprophyte (living on dead wood) on basswood, beech, hickory, hornbeam, maple, and sycamore.

IceCap Climate
type: NamedIndividual
type: Climate Type
comment: An ice cap climate is a polar climate where no mean monthly temperature exceeds 0 oC (32 oF). The climate covers areas in or near the high latitudes (65o latitude) to polar regions (70-90o north and south latitude), such as Antarctica and Greenland, that have vast deserts of snow and ice. In the coldest months, most ice cap climates have mean temperatures between - 30 and - 55 oC (-22 to -67 oF). Ice cap climates are normally covered by a permanent layer of ice and have no vegetation.

Iceland
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:IS
agricultural land: 15,910 sq. km
area: 103,000 km²
climate: temperate; moderated by North Atlantic Current; mild, windy winters; damp, cool summers
density: 3.1/km²
latitude: -19.02117
longitude: 64.92526
population: 319,7561 (1/2008)
comment: Iceland is a Nordic island country in the North Atlantic Ocean, with a population of 356,991 and an area of 103,000 km2, making it the most sparsely populated country in Europe. The capital and largest city is Reykjavík. Reykjavík and the surrounding areas in the southwest of the country are home to over two-thirds of the population. Iceland is volcanically and geologically active. The interior consists of a plateau characterised by sand and lava fields, mountains, and glaciers, and many glacial rivers flow to the sea through the lowlands. Iceland is warmed by the Gulf Stream and has a temperate climate, despite a high latitude just outside the Arctic Circle. Its high latitude and marine influence keep summers chilly, with most of its islands having a polar climate.

Immature
type: NamedIndividual
type: State
is state of: Maturity

India
type: NamedIndividual
type: Country
belongs to: Asia
has climate: Arid Climate
has climate: Highland Climate
has climate: Humid Subtropical Climate
has climate: Tropical Wet Climate
ISO code: ISO 3166-1:IN
agricultural land: 1.8 million sq. km
area: 3,287,240 km²
climate: varies from tropical monsoon in south to temperate in north
density: 349/km²
latitude: 82.39713
longitude: 21.125673
population: 1,147,995,904 (2008)
comment: India, officially the Republic of India, is a country in South Asia. It is the second-most populous country, the seventh-largest country by land area, and the most populous democracy in the world. Bounded by the Indian Ocean on the south, the Arabian Sea on the southwest, and the Bay of Bengal on the southeast, it shares land borders with Pakistan to the west; China, Nepal, and Bhutan to the north; and Bangladesh and Myanmar to the east. In the Indian Ocean, India is in the vicinity of Sri Lanka and the Maldives; its Andaman and Nicobar Islands share a maritime border with Thailand and Indonesia.

Indonesia
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:ID
agricultural land: 545,000 sq. km
area: 1,919,440 km²
climate: tropical; hot, humid; more moderate in highlands
density: 134/km²
latitude: 118.01557
longitude: -2.55022
population: 237,512,352 (2008)
comment: Indonesia, officially the Republic of Indonesia, is a country in Southeast Asia and Oceania between the Indian and Pacific oceans. It consists of more than seventeen thousand islands, including Sumatra, Java, Sulawesi, and parts of Borneo and New Guinea. Indonesia is the world's largest island country and the 14th-largest country by land area, at 1,904,569 square kilometres . With more than 270 million people, Indonesia is the world's fourth-most populous country and the most populous Muslim-majority country. Java, the world's most populous island, is home to more than half of the country's population.

Inflorescence Emergence stage
type: NamedIndividual
type: Growth Stage
related to: Wheat
comment: Stage 5: Inflorescence emergence, heading 51 Beginning of heading: tip of inflorescence emerged from sheath, first spikelet just visible 52 20% of inflorescence emerged 53 30% of inflorescence emerged 54 40% of inflorescence emerged 55 Middle of heading: half of inflorescence emerged 56 60% of inflorescence emerged 57 70% of inflorescence emerged 58 80% of inflorescence emerged 59 End of heading: inflorescence fully emerged
label: Stage 5: Inflorescence emergence, heading

Inland Water
type: NamedIndividual
type: Topography

Insect
type: NamedIndividual
type: Crop Problem
comment: Insects have a chitinous exoskeleton, a three-part body (head, thorax and abdomen), three pairs of jointed legs, compound eyes and one pair of antennae. Insects are the most diverse group of animals; they include more than a million described species and represent more than half of all known living organisms.

Iran
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:IR
agricultural land: 489,570 sq. km
area: 1,648,195 km²
climate: mostly arid or semiarid, subtropical along Caspian coast
density: 42/km²
latitude: 53.68236
longitude: 32.420654
population: 70,472,846 (2007)
comment: Iran, also called Persia and officially the Islamic Republic of Iran, is a country in Western Asia. It is bordered to the northwest by Armenia and Azerbaijan, to the north by the Caspian Sea, to the northeast by Turkmenistan, to the east by Afghanistan, to the southeast by Pakistan, to the south by the Persian Gulf and the Gulf of Oman, and to the west by Turkey and Iraq. Iran covers an area of 1,648,195 km2, with a population of 83 million. It is the second largest country in the Middle East, and its capital and largest city is Tehran.

Iraq
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:IQ
agricultural land: 82,100 sq. km
area: 438,317 km²
climate: mostly desert; mild to cool winters with dry, hot, cloudless summers; northern mountainous regions along Iranian and Turkish borders experience cold winters with occasionally heavy snows that melt in early spring, sometimes causing extensive flooding in central and southern Iraq
density: 66/km²
latitude: 43.685898
longitude: 33.228443
population: 29,267,0004 (2007)
comment: Iraq, officially the Republic of Iraq, is a country in Western Asia, bordered by Turkey to the north, Iran to the east, Kuwait to the southeast, Saudi Arabia to the south, Jordan to the southwest and Syria to the west. The capital and largest city is Baghdad. Iraq is home to diverse ethnic groups including Arabs, Kurds, Turkmens, Assyrians, Yazidis, Shabakis, Armenians, Mandaeans, Circassians, Sabians and Kawliya. Around 99% of the country's 38 million citizens are Muslims, with small minorities of Christians, Yarsans, Yezidis and Mandeans also present. The official languages of Iraq are Arabic and Kurdish.

Ireland
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Marine Climate
ISO code: ISO 3166-1:IE
agricultural land: 45,550 sq. km
area: 70,273 km²
climate: temperate maritime; modified by North Atlantic Current; mild winters, cool summers; consistently humid; overcast about half the time
density: 60.3/km²
latitude: -8.307638
longitude: 53.40103
population: 4,422,100 (2008)
temperature: - 5oC (23oF) - 26oC (79oF)
comment: Ireland, also known as the Republic of Ireland, is a country in north-western Europe occupying 26 of 32 counties of the island of Ireland. The capital and largest city is Dublin, which is located on the eastern side of the island. Around 40% of the country's population of 4.9 million people resides in the Greater Dublin Area. The sovereign state shares its only land border with Northern Ireland which is part of the United Kingdom. It is otherwise surrounded by the Atlantic Ocean, with the Celtic Sea to the south, St George's Channel to the south-east, and the Irish Sea to the east. It is a unitary, parliamentary republic. The legislature, the Oireachtas, consists of a lower house, Dáil Éireann, an upper house, Seanad Éireann, and an elected President who serves as the largely ceremonial head of state, but with some important powers and duties. The head of government is the Taoiseach, who is elected by the Dáil and appointed by the President; the Taoiseach in turn appoints other government ministers.

Iris borer
type: NamedIndividual
type: Crop Disease
type: Crop Pest
life cycle: Adult moths emerge from the soil in August and September, mate, and deposit eggs on dead iris leaves. The crevices of dried and crinkled leaves or rolled leaf areas make a good place to lay their eggs. The base of leaves and other plant debris is the wintering place of the eggs. Eggs are laid in groups of 3–5 or more; a single female moth may deposit 1000 eggs before dying. Moths are typical millers— dark gray to brown, with a wingspan of about 2 inches. There is only one generation per year. The eggs overwinter in the plant material and hatch in April or early May as the new iris leaves are expanding. The small, young larvae crawl up onto the new iris leaves and make tiny pinpoint holes as they enter. Once the larvae enter the foliage, they act as leafminers, tunneling to the base of the leaves throughout the spring. By early to mid-July the larvae reach the soil area and tunnel into the rhizomes. In the rhizome, the larvae grow to be 1 to 2 inches in length. In late July to early August, the iris borer larvae move from the rhizomes into the soil to pupate. The pupa is a non-feeding transition stage between the larva and the adult moth. Pupae are dark brown to black and are usually found in the top 2 inches of soil. After two to three weeks in the pupa stage, the iris borer moth emerges. The adult brown moths emerge in late August and September to mate, lay eggs, and repeat the cycle.
management: Management Strategies 1. Sanitation. The main key to iris borer prevention is removal and destruction of the previous year’s dead foliage before April 1. This sanitation practice will reduce the problem by destroying overwintering eggs in the residues. This is best done in spring. Peel off the dead foliage down to the rhizome. 2. Inspect bed. If signs of borers are apparent, dig the rhizomes after blooming is completed. Inspect them carefully, cut out damaged ones, and replant. During the spring, inspect the leaves for signs of borers larvae chewing and pinch down the leaves to kill any larvae that may be inside. 3. Precaution. An insecticide containing pyrethrum spread or sprayed around the base of plants in the spring may help in destroying the newly hatched larvae before they have a chance to enter leaves. 4. Insecticides. If the problem is severe, insecticides are available to help combat borers. When iris leaves are 3–4 inches long, begin spraying at two-week intervals, using azadirachtin (Bio-Neem, Margosan-O), endosulfan (Thiodan), or methyl nonyl ketone. These are available under various trade names at nurseries and garden centers. Always follow label directions and read the entire label before using. 5. Replanting. When resetting your iris beds, it is important to trim away damaged or rotted areas on the rhizomes. To eliminate soft rots caused by bacteria, cut off the damaged parts of the rhizomes and dip the undamaged portions in a solution of 1–part household chlorine bleach and 9 parts of water. Organic Strategies Strategies 1, 2, and 3 are strictly organic approaches. For an organic approach to Strategy 4, consult the Organic Materials Review Institute (OMRI™) for appropriate spinosad products.
symptoms: Damage is typified by dark streaks, water-soaked areas, and ragged edges on young leaves in May and June, caused by feeding of the young larvae. As the larvae feed, they move down into the rhizome where they continue feeding. Soft rot can set in, causing leaves to yellow and fall over and rhizomes may become soft and foul-smelling. Cutting open the rhizome will reveal the borer, a 1- to 2- inch, fat-bodied, pink larva with a brown head.
comment: The iris borer causes more damage to iris plants than any other insect. Feeding of the borer larva opens the rhizome to attack by soft rot that can quickly turn rhizomes into foul-smelling mush. A healthy bed of iris can be destroyed quickly.

Iscadia Inexacta
type: NamedIndividual
type: Crop Pest
comment: Iscadia Inexacta (Walker)

Island
type: NamedIndividual
type: Topography

Isle of Man
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:IM
agricultural land: 426 sq. km
area: 572 km²
climate: temperate; cool summers and mild winters; overcast about a third of the time
density: 131.2/km²
latitude: -4.57084
longitude: 54.232185
population: 80,058
comment: The Isle of Man, also known as Mann, is a self-governing British Crown dependency in the Irish Sea between Great Britain and Ireland. The head of state, Queen Elizabeth II, holds the title Lord of Mann and is represented by a lieutenant governor. The United Kingdom is responsible for the isle's military defence.

Israel
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:IL
agricultural land: 5,205 sq. km
area: 22,072 km²
climate: temperate; hot and dry in southern and eastern desert areas
density: 324/km²
latitude: 34.979008
longitude: 31.392042
population: 7,282,0002 (2008)
comment: Israel, officially known as the State of Israel, is a country in Western Asia, located on the southeastern shore of the Mediterranean Sea and the northern shore of the Red Sea. It has land borders with Lebanon to the north, Syria to the northeast, Jordan on the east, the Palestinian territories of the West Bank and the Gaza Strip to the east and west, respectively, and Egypt to the southwest. Israel's economic and technological center is Tel Aviv, while its seat of government and proclaimed capital is Jerusalem, although international recognition of the state's sovereignty over Jerusalem is limited.

Italy
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Humid Continental Climate
has climate: Humid Subtropical Climate
ISO code: ISO 3166-1:IT
agricultural land: 139,326 sq. km
area: 301,338 km²
climate: predominantly Mediterranean; Alpine in far north; hot, dry in south
density: 197.6/km²
latitude: 12.5735
longitude: 41.292667
population: 59,829,710 (1/2008)
comment: Italy, officially the Italian Republic, is a country consisting of a continental part, delimited by the Alps, a peninsula and several islands surrounding it. Italy is located in Southern Europe, and is also considered part of Western Europe. A unitary parliamentary republic with Rome as its capital, the country covers a total area of 301,340 km2 and shares land borders with France, Switzerland, Austria, Slovenia, and the enclaved microstates of Vatican City and San Marino. Italy has a territorial enclave in Switzerland and a maritime exclave in Tunisian waters . With around 60 million inhabitants, Italy is the third-most populous member state of the European Union.

Jamaica
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:JM
agricultural land: 4,490 sq. km
area: 11,100 km²
climate: tropical; hot, humid; temperate interior
density: 252/km²
latitude: -77.273476
longitude: 18.115265
population: 2,804,332 (2008)
comment: Jamaica is an island country situated in the Caribbean Sea. Spanning 10,990 square kilometres in area, it is the third-largest island of the Greater Antilles and the Caribbean . Jamaica lies about 145 kilometres south of Cuba, and 191 kilometres west of Hispaniola ; the British Overseas Territory of the Cayman Islands lies some 215 kilometres to the north-west.

January
type: NamedIndividual
type: Month Of Year

Japan
type: NamedIndividual
type: Country
belongs to: Asia
has climate: Humid Continental Climate
has climate: Humid Subtropical Climate
ISO code: ISO 3166-1:JP
agricultural land: 45,610 sq. km
area: 377,873 km²
climate: varies from tropical in south to cool temperate in north
density: 337/km²
latitude: 139.77
longitude: 35.67
population: 127,433,494 (2007)
comment: Japan is an island country in East Asia, located in the northwest Pacific Ocean. It is bordered on the west by the Sea of Japan, and extends from the Sea of Okhotsk in the north toward the East China Sea and Taiwan in the south. Part of the Ring of Fire, Japan spans an archipelago of 6852 islands covering 377,975 square kilometers ; the five main islands are Hokkaido, Honshu, Shikoku, Kyushu, and Okinawa. Tokyo is Japan's capital and largest city; other major cities include Yokohama, Osaka, Nagoya, Sapporo, Fukuoka, Kobe, and Kyoto.

Jersey
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:JE
area: 116 km²
climate: temperate; mild winters and cool summers
density: 7,801/km²
latitude: -2.141056
longitude: 49.217445
population: 90,8001 (12/2007)
comment: Jersey, officially the Bailiwick of Jersey, is an island and British Crown Dependency near the coast of Normandy, France. It is the second-closest of the Channel Islands to France, after Alderney. Jersey was part of the Duchy of Normandy, whose dukes went on to become kings of England from 1066. After Normandy was lost by the kings of England in the 13th century, and the ducal title surrendered to France, Jersey and the other Channel Islands remained attached to the English Crown.

Jointing
type: NamedIndividual
type: Growth Stage
comment: Jointing is the point in the plant's life cycle when the growing point moves above the soil surface. each tiller is moving up the stem.

Jordan
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:JO
agricultural land: 10,026 sq. km
area: 89,341 km²
climate: mostly arid desert; rainy season in west (November to April)
density: 64/km²
latitude: 37.13059
longitude: 31.276773
population: 6,198,677 (2008)
comment: Jordan, officially the Hashemite Kingdom of Jordan, is an Arab country in the Levant region of Western Asia, on the East Bank of the Jordan River. Jordan is bordered by Saudi Arabia, Iraq, Syria, Israel and Palestine . The Dead Sea is located along its western borders and the country has a 26-kilometre coastline on the Red Sea in its extreme south-west. Jordan is strategically located at the crossroads of Asia, Africa and Europe. The capital, Amman, is Jordan's most populous city as well as the country's economic, political and cultural centre.

July
type: NamedIndividual
type: Month Of Year

June
type: NamedIndividual
type: Month Of Year

KNIME
type: NamedIndividual
type: Toolkit
comment: KNIME, the Konstanz Information Miner, is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept. A graphical user interface and use of JDBC allows assembly of nodes blending different data sources, including preprocessing (ETL: Extraction, Transformation, Loading), for modeling, data analysis and visualization without, or with only minimal, programming.

Karrst
type: NamedIndividual
type: Topography

Katydids
type: NamedIndividual
type: Crop Disease
type: Crop Pest
comment: Katydids are members of the grasshopper family, and can be distinguished by their long "horns," bright green color, and by the male's loud, shrill call which sounds like "Katy did" and thus has earned them their onomatopoeic name. They do not pose any particular problem for the home gardener, but do feed on shrub and tree foliage.

Kazakhstan
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:KZ
agricultural land: 2.09 million sq. km
area: 2,724,900 km²
climate: continental, cold winters and hot summers, arid and semiarid
density: 5.4/km²
latitude: 66.90226
longitude: 48.18407
population: 15,217,711 (1/2006)
comment: Kazakhstan, officially the Republic of Kazakhstan, is a transcontinental country mainly located in Central Asia with a smaller portion west of the Ural River in Eastern Europe. It covers a land area of 2,724,900 square kilometres, and shares land borders with Russia in the north, China in the east, and Kyrgyzstan, Uzbekistan, and Turkmenistan in the south while also adjoining a large part of the Caspian Sea in the southwest. Kazakhstan does not border Mongolia, although they are only 37 kilometers apart, separated by a short portion of the border between Russia and China.

Kenya
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:KE
agricultural land: 274,500 sq. km
area: 580,367 km²
climate: varies from tropical along coast to arid in interior
density: 59/km²
latitude: 37.90397
longitude: 0.413841
population: 37,953,8401 (2008)
comment: Kenya, officially the Republic of Kenya, is a country in Eastern Africa. At 580,367 square kilometres, Kenya is the world's 48th largest country by total area. With a population of more than 47.6 million people in the 2019 census, Kenya is the 29th most populous country. Kenya's capital and largest city is Nairobi, while its oldest city and first capital is the coastal city of Mombasa. Kisumu City is the third largest city and also an inland port on Lake Victoria. Other important urban centres include Nakuru and Eldoret. As of 2020, Kenya is the third largest economy in sub-Saharan Africa after Nigeria and South Africa. Kenya is bordered by South Sudan to the northwest, Ethiopia to the north, Somalia to the east, Uganda to the west, Tanzania to the south, and the Indian Ocean to the southeast.

Keras
type: NamedIndividual
type: Toolkit
has algorithm: Artificial Neural Network (ANN)
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Deep Convolutional Neural Network (DCNN)
has algorithm: Recurrent neural network (RNN)
has algorithm: Stochastic Gradient Descent (SGD)
comment: Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano. Keras is a pretty solid framework to start your deep learning journey. If you are familiar with Python and are not doing some high-level research or developing some special kind of neural network, Keras is for you. The focus is more on achieving results rather than getting bogged down by the model intricacies. So if you are given a project related to, say image classification or sequence models, start with Keras. You will be able to get a working model very quickly. Keras is also integrated in TensorFlow and hence you can also build your model using tf.keras.

Kiribati
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:KI
agricultural land: 340 sq. km
area: 726 km²
climate: tropical; marine, hot and humid, moderated by trade winds
density: 137/km²
latitude: -170.34291
longitude: -3.356279
population: 105,432 (2005)
comment: Kiribati, officially the Republic of Kiribati, is an independent island nation in the central Pacific Ocean. The permanent population is over 119,000, more than half of whom live on Tarawa atoll. The state comprises 32 atolls and one raised coral island, Banaba. They have a total land area of 811 square kilometres and are dispersed over 3.5 million km2 .

Kochia
type: NamedIndividual
type: Weeds

Korea
type: NamedIndividual
type: Country
belongs to: Asia
has climate: Continental Climate
has climate: Humid Continental Climate
has climate: Marine Climate
ISO code: ISO 3166-1:KP
agricultural land: 25,550 sq. km
area: 120,540 km²
climate: temperate with rainfall concentrated in summer
density: 190/km²
latitude: 127.76692
longitude: 35.907757
population: 23,790,000 (2007)
comment: North Korea, officially the Democratic People's Republic of Korea, is a country in East Asia constituting the northern part of the Korean Peninsula. The country is bordered to the north by China and by Russia along the Amnok and Tumen rivers, and to the south by South Korea, with the heavily fortified Korean Demilitarized Zone separating the two. North Korea, like its southern counterpart, claims to be the legitimate government of the entire peninsula and adjacent islands. Pyongyang is the country's capital and largest city.
label: North Korea

Kuwait
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:KW
agricultural land: 1,520 sq. km
area: 17,818 km²
climate: dry desert; intensely hot summers; short, cool winters
density: 131/km²
latitude: 47.49453
longitude: 29.310284
population: 3,399,637 (2007)
comment: Kuwait, officially the State of Kuwait, is a country in Western Asia. It is situated in the northern edge of Eastern Arabia at the tip of the Persian Gulf, bordering Iraq to the north and Saudi Arabia to the south. As of 2016, Kuwait has a population of 4.5 million people: 1.3 million are Kuwaitis and 3.2 million are expatriates. Expatriates account for approximately 70% of the population.

Kyrgyzstan
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:KG
agricultural land: 106,085 sq. km
area: 199,900 km²
climate: dry continental to polar in high Tien Shan Mountains; subtropical in southwest (Fergana Valley); temperate in northern foothill zone
density: 26/km²
latitude: 74.7799
longitude: 41.20554
population: 5,356,869 (2008)
comment: Kyrgyzstan, officially the Kyrgyz Republic, also known as Kirghizia, is a landlocked country in Central Asia. It is bordered by Kazakhstan, Uzbekistan, Tajikistan, and China. Its capital and largest city is Bishkek.

LIONoso
type: NamedIndividual
type: Toolkit
comment: LIONsolver is an integrated software for data mining, business intelligence, analytics, and modeling Learning and Intelligent OptimizatioN and reactive business intelligence approach. A non-profit version is available as LIONoso.

Lace bugs
type: NamedIndividual
type: Crop Disease
life cycle: Female lace bugs lay groups of eggs on the underside of leaves most often along the midrib. These eggs are partially inserted into the leaf tissue and are pale and almost completely hidden inside the leaf. Many species secrete a brownish substance that hardens over the eggs and secures them to the leaf. Dark spiny nymphs hatch from the eggs and go through 5 to 6 nymphal instars before reaching adulthood. A complete life cycle takes only 30 to 40 days; several generations may occur each year. Lace bugs are more prevalent in late summer and fall. In late summer, both adults and nymphs will be feeding at the same time. Some species overwinter as eggs in the leaf or bark crevices or similar protected areas while other species overwinter in the adult stage.
management: Management Strategies 1. Live with the insects but maintain plant vigor. In many cases, the damage occurs late enough in the season or is minor enough not to require action. Keeping plants healthy by watering during periods of drought will limit any damage caused by the insects. 2. Select planting areas carefully. Plant susceptible species away from sidewalks and lawns as dropping leaves, twigs, and lace bug excrement can be bad enough to be considered unpleasant. 3. Spray with water. Nymphs can be dislodged by forceful sprays of water and are vulnerable to control by contact with insecticidal soaps. 4. Sanitation. Prompt removal of leaves and twigs under plants can remove overwintering pests and limit the chances of reinfestation. 5. Control. Natural enemies are usually effective in maintaining populations at a low level and bringing outbreaks under control. Chemical controls are usually only used on shade and ornamental trees. Control of the azalea lace bug on evergreen azaleas is often required as the loss of leaves on these plants can have a more serious impact on plant health. 6. Use insecticides. Pesticides registered for use include acephate (Orthene), bendiocarb (Turcam, Closure), capsaicin, carbaryl (Sevin), disulfoton (Di-syston), malathion, permethrin, pyrethrins, and horticultural oils. Spray as soon as eggs hatch on leaves in early spring. Some control is possible with a dormant spray applied just before growth in spring. Organic Strategies Strategies 1, 2, 4, and the use of natural enemies in Strategy 5 are strictly organic approaches. For an organic approach to Strategy 6, consult the Organic Materials Review Institute (OMRI™) for appropriate pyrethrin products.
symptoms: If heavily infested, the leaves may turn yellow and fall from trees and shrubs. The lace bug feeding mechanism is one that pierces and sucks the plant juices from leaves. This leaves tiny chlorotic flecks on the upper leaf surface. The underside of the leaves will have adults and spiny, dark-colored nymphs. The underside of leaves will look dirty with dark-brown spots and stains. When disturbed, lace bugs exhibit a peculiar bouncing movement.
comment: Lace bugs are tiny insects that grow no more than one-eighth inch long. Small, clear cells cover their wings and thorax, giving them their lacy appearance. They feed by sucking the sap from the foliage of trees and shrubs, leaving them looking mottled, stippled and discolored. Dealing with lace bugs can be annoying but the good news is that with effective lace bug treatment, you can rid them of the garden.

Lagoon
type: NamedIndividual
type: Topography

Lake
type: NamedIndividual
type: Topography

Laodelphax Striatellua
type: NamedIndividual
type: Crop Disease
comment: Laodelphax striatellus is a species of true bug belonging to the family Delphacidae. It is native to Eurasia and Northern Africa.

Laodelphax Striatellus
type: NamedIndividual
type: Crop Pest
comment: Laodelphax Striatellus (Fallen)

Laos
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:LA
agricultural land: 23,780 sq. km
area: 236,800 km²
climate: tropical monsoon; rainy season (May to November); dry season (December to April)
density: 25/km²
latitude: 103.895035
longitude: 18.20521
population: 6,521,998 (2007)
comment: Laos, officially the Lao People's Democratic Republic, is a socialist state and the only landlocked country in Southeast Asia. At the heart of the Indochinese Peninsula, Laos is bordered by Myanmar and China to the northwest, Vietnam to the east, Cambodia to the southeast and Thailand to the west and southwest.

Larva Of Diamondback Moth
type: NamedIndividual
type: Crop Pest
alternative label: Plutella Xylostella

Larva Of Green Fruit Worm
type: NamedIndividual
type: Crop Pest
alternative label: Lithophane Antennata

Larva Of Orange-Striped Oakworm
type: NamedIndividual
type: Crop Pest
alternative label: Anisota Senatoria

Laspeyresia Pomonella
type: NamedIndividual
type: Crop Pest

Latitude
type: NamedIndividual
type: Location
comment: Latitude is an angle (defined below) which ranges from 0° at the Equator to 90° (North or South) at the poles. Lines of constant latitude, or parallels, run east–west as circles parallel to the equator. Latitude is used together with longitude to specify the precise location of features on the surface of the Earth.

Latvia
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:LV
agricultural land: 18,160 sq. km
area: 64,589 km²
climate: maritime; wet, moderate winters
density: 36/km²
latitude: 24.605814
longitude: 56.880215
population: 2,261,100 (1/2009)
comment: Latvia, officially known as the Republic of Latvia,, is a country in the Baltic region of Northern Europe. It is one of the Baltic states; and is bordered by Estonia to the north, Lithuania to the south, Russia to the east, Belarus to the southeast, and shares a maritime border with Sweden to the west. Latvia covers an area of 64,589 km2, with a population of 1.9 million. The country has a temperate seasonal climate. Its capital and largest city is Riga. Latvians belong to the ethno-linguistic group of the Balts; and speak Latvian, one of the only two surviving Baltic languages. Russians are a prominent minority in the country, at some 24% of the population.

Laurel wilt
type: NamedIndividual
type: Crop Disease
comment: Laurel wilt (Lw) disease is an exotic and lethal disease that can kill laurel family trees very fast.

Leaf Blight
type: NamedIndividual
type: Crop Disease
comment: Leaf Blight refers to a specific symptom affecting plants in response to infection by a pathogenic organism.

Leaf Blotch
type: NamedIndividual
type: Crop Disease

Leaf Brown spot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Rice
caused by: Bipolarisoryza fungus
comment: Typical classical brown spot symptoms are observed at tillering stage and beyond. Small foliar lesions are found and the shapes of the lesions vary from circular to oval with light brown colour to gray at the center and reddish brown colour at margin

Leaf Curl
type: NamedIndividual
type: Crop Disease
affects: Cotton

Leaf Development stage
type: NamedIndividual
type: Growth Stage
related to: Wheat
comment: Stage 1: Leaf development 10 First leaf through coleoptile 11 First leaf unfolded 12 2 leaves unfolded 13 3 leaves unfolded 1 . Stages continuous till . . . 19 9 or more leaves unfolded
label: Stage 1: Leaf development
alternative label: Leaf growth stage (LGS)

Leaf Disease
type: NamedIndividual
type: Crop Disease

Leaf Miner
type: NamedIndividual
type: Crop Disease
affects: Coffee
comment: Leaf miner is any one of numerous species of insects in which the larval stage lives in, and eats, the leaf tissue of plants. The vast majority of leaf-mining insects are moths (Lepidoptera), sawflies (Symphyta, the mother clade of wasps), and flies (Diptera), though some beetles also exhibit this behavior.

Leaf Mold
type: NamedIndividual
type: Crop Disease
comment: Leafmould is formed from decaying leaves and produces an invaluable soil conditioner. The best quality leafmould is produced from the leaves of oak, beech or hornbeam.

Leaf Rust
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Wheat
development: Primary infections usually are light and develop from wind-borne urediospores that may have travelled long distances. The disease can develop rapidly when free moisture is available and temperatures are near 20C. Successive generations of urediospores can be produced every 10-14 days if conditions are favorable. As plants mature or when environmental conditions are not favorable, masses of black teliospores may become evident (2).
distribution: Leaf rust can affect wheat, triticale and many other related grasses. The disease is found wherever temperate cereals are grown. The alternate hosts are Thalictrum, Isopryum, Anemonella, and Anchusa spp.
important: Severe early infections can cause significant yield losses, mainly by reducing the number of kernels per spike, test weights, and kernel quality.
symptoms: The postules are circular or slightly elliptical, smaller than those of stem rust, usually do not coalesce, and contain masses of orange to orange-brown urediospores. Infection sites primarily are found on the upper surfaces of leaves and leaf sheaths (1), and occasionally on the neck and awns.
comment: Puccinia triticina is an obligate parasite on wheat. The uredinia and telia occur on Triticum; pycnia and aecia are rare occurring on meadow rue (Thalictrum spp.). Unlike those of stem rust, uredinia are with out conspicuously torn epidermal tissues at their margins. They are round to ovoid, orange-red, and erumpent. Urediniospores are subgloboid, red-brown with thick echinulated walls; teliospores are two-celled and round at the apex.

Leaf Scald
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Barley
development: Disease is transmitted by water splash and infected seeds.
management: Use disease free seeds. Grow available resistant varieties. Follow crop rotation. Remove and destroy the infected crop residue. Keep filed free from weeds and other crop plants.
symptoms: Appearance of dark, pale or bluish gray lesions on leaves. As the disease progress, these spots enlarge into oval lesions with bluish gray centers and dark brown margins. The spots may join together and appear like rapid scalding.
comment: Leaf scald is a vascular disease caused by the bacterium Xanthomonas albilineans, characterized by creamy or grayish streaking and later withering of the leaves. Eyespot, characterized by yellowish oval lesions on leaves and stems, is a disease caused by the fungus Helminthosporium sacchari.

Leaf Scorch
type: NamedIndividual
type: Crop Disease
comment: Leaf scorch is a non-infectious, physiological condition caused by unfavorable environmental situations. It is not caused by fungus, bacteria, or virus. The problem may appear on almost any plant if weather conditions are favorable, such as high temperatures, dry winds, and low soil moisture. When large amounts of water evaporate from leaf surfaces, the plant roots are unable to furnish enough water to compensate for the transpirational loss. Leaf tissue dies as a result. Young trees or those that are already in stress due to insect infestations, diseases, or other factors are more susceptible than those growing vigorously and in good condition. Plants that are prone to leaf scorch include Japanese maple, Norway maple, sugar maple, beech, ash, oak, linden, birch, alpine currant, horse chestnut, white pine, rhododendron, viburnum, and flowering dogwood.

Leaf Spot
type: NamedIndividual
type: Crop Disease
affects: Peanut

Leafhopper
type: NamedIndividual
type: Crop Pest

Leaves infestations
type: NamedIndividual
type: Symptom

Lebanon
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:LB
agricultural land: 6,380 sq. km
area: 10,452 km²
climate: Mediterranean; mild to cool, wet winters with hot, dry summers; Lebanon mountains experience heavy winter snows
density: 401/km²
latitude: 35.87673
longitude: 33.873154
population: 4,099,000 (2007)
comment: Lebanon, officially known as the Lebanese Republic, is a country in the Levant region of Western Asia, and the transcontinental region of the Middle East. It is bordered by Syria to the north and east and Israel to the south, while Cyprus lies west across the Mediterranean Sea. Lebanon's location at the crossroads of the Mediterranean Basin and the Arabian hinterland has contributed to its rich history and shaped a cultural identity of religious and ethnic diversity. At just 10,452 km2, it is one of the smallest recognized sovereign states in the Asian continent. The official language, Arabic, is the most common language spoken by the citizens of Lebanon.

Lentil
type: NamedIndividual
type: Crop
comment: The lentil is an edible legume. It is an annual plant known for its lens-shaped seeds. It is about 40 cm (16 in) tall, and the seeds grow in pods, usually with two seeds in each. As a food crop, producing 58% combined of the world total.

Citrus leprosis
type: NamedIndividual
type: Crop Disease
comment: Citrus leprosis (CL) is an economically important viral disease affecting citrus crops. This emerging disease is widely distributed in South and Central America, from Argentina to Mexico. The disease is associated with up to three different non-systemic viruses, which cause similar symptoms in the citrus hosts and are transmitted by the same vector, mites of the genus Brevipalpus; although they have vastly different genomes. Citrus leprosis virus nuclear type (CiLV-N) is found in the nuclei and cytoplasm of infected cells, while Citrus leprosis virus cytoplasmic type (CiLV-C) is found in the endoplasmic reticulum.
label: Leprosis

Leptocorisa Acuta
type: NamedIndividual
type: Crop Pest

Lesotho
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:LS
agricultural land: 23,120 sq. km
area: 30,355 km²
climate: temperate; cool to cold, dry winters; hot, wet summers
density: 59/km²
latitude: 28.247414
longitude: -29.62051
population: 1,795,0001 (2005)
comment: Lesotho, officially the Kingdom of Lesotho, is an enclaved country within the border of South Africa. It is by far the largest of the world's three independent states completely surrounded by the territory of another country, with Vatican City and San Marino being the other two. Additionally, it is the only such state outside the Italian peninsula, and the only one that is not a microstate. Lesotho is just over 30,000 km2 and has a population of around 2 million. Its capital and largest city is Maseru. The official language is Sesotho.

Lesser Grain Borer
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Barley
affects: Cereal
affects: Corn
affects: Rice
affects: Wheat
comment: Lesser grain borers mainly attack wheat, corn, rice and millet. Both the larvae and adults are primary pests. They bore irregularly shaped holes into whole, undamaged kernels and the larvae, immature stages, may develop inside the grain. Larval and adult feeding in and on grain kernels may leave only dust and thin brown shells. A sweet, musty odor is often associated with infestations of this insect.

Liberia
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:LR
agricultural land: 26,300 sq. km
area: 111,369 km²
climate: tropical; hot, humid; dry winters with hot days and cool to cold nights; wet, cloudy summers with frequent heavy showers
density: 29/km²
latitude: -9.428598
longitude: 6.452424
population: 3,489,072 (2008)
comment: Liberia, officially the Republic of Liberia, is a country on the West African coast. It is bordered by Sierra Leone to its northwest, Guinea to its north, Côte d'Ivoire to its east, and the Atlantic Ocean to its south-southwest. It has a population of around 5 million and covers an area of 111,369 square kilometers . English is the official language, but over 20 indigenous languages are spoken, representing the numerous ethnic groups who make up more than 95% of the population. The country's capital and largest city is Monrovia.

Libya
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:LY
agricultural land: 155,850 sq. km
area: 1,759,541 km²
climate: Mediterranean along coast; dry, extreme desert interior
density: 3.2/km²
latitude: 17.26882
longitude: 26.338524
population: 6,173,579 (2008)
comment: Libya, officially the State of Libya, is a country in the Maghreb region in North Africa bordered by the Mediterranean Sea to the north, Egypt to the east, Sudan to the southeast, Chad to the south, Niger to the southwest, Algeria to the west and Tunisia to the northwest. The sovereign state is made of three historical regions: Tripolitania, Fezzan and Cyrenaica. With an area of almost 700,000 square miles, Libya is the fourth largest country in Africa and is the 16th largest country in the world. Libya has the 10th-largest proven oil reserves of any country in the world. The largest city and capital, Tripoli, is located in western Libya and contains over three million of Libya's seven million people.

Liechtenstein
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:LI
agricultural land: 65 sq. km
area: 160.4 km²
climate: continental; cold, cloudy winters with frequent snow or rain; cool to moderately warm, cloudy, humid summers
density: 221/km²
latitude: 9.553797
longitude: 47.159496
population: 35,322 (2007)
comment: Liechtenstein, officially the Principality of Liechtenstein, is a German-speaking microstate situated in the Alps and in the southwest of Central Europe. The principality is a semi-constitutional monarchy headed by the Prince of Liechtenstein; the Prince's extensive powers are equivalent to those of a President in a semi-presidential system.

Light Air
type: NamedIndividual
type: Wind Type

Light Breeze
type: NamedIndividual
type: Wind Type

Light Precipitation
type: NamedIndividual
type: Precipitation

Light Wind
type: NamedIndividual
type: Wind Type

Lily
type: NamedIndividual
type: Flower

Lithuania
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:LT
agricultural land: 28,059 sq. km
area: 65,200 km²
climate: transitional, between maritime and continental; wet, moderate winters and summers
density: 52/km²
latitude: 23.898157
longitude: 55.173496
population: 3,350,400 (2009)
comment: Lithuania, officially the Republic of Lithuania, is a country in the Baltic region of Europe. It is one of the Baltic states, and lies on the eastern shore of the Baltic Sea, to the southeast of Sweden and the east of Denmark, with Latvia to the north, Belarus to the east and south, Poland to the south, and Kaliningrad Oblast of Russia to the southwest. Lithuania covers an area of 65,300 km2, with a population of 2.7 million. Its capital and largest city is Vilnius; other major cities are Kaunas and Klaipėda. Lithuanians belong to the ethno-linguistic group of the Balts, and speak Lithuanian; one of only two living Baltic languages.

Lixa Pequena
type: NamedIndividual
type: Crop Disease
comment: Peach leaf curl is a plant disease characterized by distortion and coloration of leaves and is caused by the fungus Taphrina deformans, which infects peach, nectarine, and almond trees. T. deformans is found in the United States, Europe, Asia, Africa, Australia, and New Zealand. Peach leaf curl reduces the amount of leaves and fruit produced by pe.

Loam
type: NamedIndividual
type: Soil Texture
definition: Clay >= 7 and Clay <= 27 Sand >= 23 and Sand <= 52 Silt >= 28 and Silt <= 50

Loamy Sand
type: NamedIndividual
type: Soil Texture
definition: Clay <= 15 Sand >= 70 and Sand <= 86 Silt<= 30

Loamy Soils
type: NamedIndividual
type: Soil Type
comment: Loam soil - are a mixture of sand, silt and clay that are combined to avoid the negative effects of each type. These soils are fertile, easy to work with and provide good drainage. Depending on their predominant composition they can be either sandy or clay loam. As the soils are a perfect balance of soil particles, they are considered to be a gardeners best friend, but still benefit from topping up with additional organic matter.

Locust
type: NamedIndividual
type: Crop Pest

Locusta Migratoria
type: NamedIndividual
type: Crop Pest

Longitude
type: NamedIndividual
type: Location
comment: Longitude is a geographic coordinate that specifies the east–west position of a point on the Earth's surface, or the surface of a celestial body. It is an angular measurement, usually expressed in degrees and denoted by the Greek letter lambda. Meridians (lines running from pole to pole) connect points with the same longitude.

Loose Smut
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Barley
affects: Rye
affects: Wheat
controls: Control of loose smut requires a chemical seed treatment with a systemic fungicide which is able to penetrate into the seed. Carboxin and triazoles have been shown to be highly effective. Accordingly, products containing azoles such as triadimenol, bitertanol, tebuconazole, prothioconazole, triticonazole and fluquinconazole effectively control the disease.
controls: In organic farming systems hot-water treatments (49-52 oC for 12 minutes) are used for control of loose smut of cereals.
development: Wind blown teliospores that land on the flowers of wheat plants can germinate and infect the developing embryo of the kernel. The mycelium of the loose smut fungus remains dormant in the embryonic tissues of the kernel until the kernel begins to germinate. The mycelium then develops along with the growing point of the plant, and at flowering time replaces the floral parts of the spike with masses of black spores. Infection and disease development are favored by cool, humid conditions, which prolong the flowering period of the host plant.
distribution: The disease can occur wherever wheat is grown.
important: Yield losses depend on the number of spikes affected by the disease; incidence is usually less than one percent and rarely exceeds thirty percent of the spikes in any given location.
symptoms: The entire inflorescence, except the rachis, is replaced by masses of smut spores. These black teliospores often are blown away by the wind, leaving only the bare rachis and remnants of other floral structures.
comment: It is a disease that can destroy a large proportion of a barley crop. Loose smut replaces grain heads with smut, or masses of spores which infect the open flowers of healthy plants and grow into the seed, without showing any symptoms. Seeds appear healthy and only when they reach maturity the following season is it clear that they were infected. Systemic fungicides are the major control method for loose smut.
label: Carbón volador, Carbón desnudo del trigo @sp
label: Charbon nu du blé @fr
label: Weizenflugbrand @ge

Low Air Pollution
type: NamedIndividual
type: Air Pollution

Low Solar Radiation
type: NamedIndividual
type: Solar Radiation

Low land
type: NamedIndividual
type: Topography

Luperomorpha Suturalis Chen
type: NamedIndividual
type: Crop Pest

Luxembourg
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:LU
agricultural land: 1,310 sq. km
area: 2,586.4 km²
climate: modified continental with mild winters, cool summers
density: 186/km²
latitude: 6.133299
longitude: 49.81549
population: 480,222 (2007)
comment: Luxembourg, officially the Grand Duchy of Luxembourg, is a landlocked country in Western Europe. It is bordered by Belgium to the west and north, Germany to the east, and France to the south. Its capital, Luxembourg City, is one of the four official capitals of the European Union and the seat of the Court of Justice of the European Union, the highest judicial authority in the EU. Its culture, people, and languages are highly intertwined with its neighbours, making it a mixture of French and German cultures. It has three official languages: French, German, and the national language of Luxembourgish.

Lycorma Delicatula
type: NamedIndividual
type: Crop Pest

Macadamia
type: NamedIndividual
type: Crop
harvesting: Grafted trees will begin to produce a crop 3 years after planting but it is not until the 7th year that the crop will be of any economic significance. Trees will give maximum yields 15 to 20 years after planting. Nuts should be collected when they fall from the tree and this should be done every 1-2 weeks during the nut producing periods. In commercial plantations, nuts can be collected mechanically or by hand.
maintenance: During the first four years after planting, animal manure and a thick layer of organic mulch can be applied around the trees year round. after this period, the amount of manure added should be decreased to prevent the trees being provided with too much nitrogen and being deficient in potassium. In the first 4 years following planting, vegetative growth should be stimulated through pruning. Once the leading shoot has reached a height of approximately 80 cm (31.5 in) it should be clipped to stimulate branching. Established trees are usually pruned in the Spring when old, unproductive and diseased branches are removed from the canopy. Any nuts remaining on the tree should also be removed. Mulches are best applied after nut harvest to allow the material to decompose before the next crop. Avoid piling mulch up against the trunks to prevent disease.
propagation: When planting numerous macadamia trees in a plantation, consideration must be given to drainage, irrigation, row alignment and wind protection. Planting distances tend to be site specific and are dependent on factors such as the macadamia variety, soil and rainfall. In general, macadamia trees are planted 4 to 8 m (13-26 ft) apart within a row, allowing 7 to 11 m (23-36 ft) between rows. Young trees are planted in Spring and Fall by digging holes which are 45 cm deep and 60 cm in diameter. Once the tree is in position, the hole is backfilled with soil. it is recommended that some compost and rock phosphate be added to the backfill at time of planting. The soil around the tree should then be gently tamped and the tree watered. Staking will help to protect the young trees from wind damage. Grafted trees usually begin to produce fruit 3 years after planting.
requirements: Macadamia trees originate from the sub-tropical rainforests of Australia and therefore tend to grow best in areas of high humidity and high rainfall. They will grow optimally at average annual temperatures between 20 and 25°C (68–77°F) in a deep, well draining loam and sandy loam with a pH between 5.0 and 5.5. Trees should not be planted in heavy clays. It is recommended that macadamia only be planted in frost free areas, young trees will be killed by temperatures of -6°C (21°F) although older, established trees can tolerate some light frost.
uses: The kernels of the macadamia nut are mainly consumed as a snack food. The kernels from M. integrifolia and M. tetraphylla can be eaten raw from the shell while the remaining Macadamia species contain toxins which must be removed prior to consumption. Macadamia may also be used in baked goods and confectionery.
comment: Macadamia trees belong to the genus Macadamia which contains four species of tree or shrub in the family Proteaceae which are grown for their edible seeds (nuts). Cultivated macadamia is a derivative of the subtropical species' Macadamia integrifoia and Macadamia tetraphylla and their hybrids. Macadamia plants naturally have multiple stems but are trained to a central leader system (single stem) in plantations. The leaves of the trees are oval or oblong and are arranged in whorls of 3 or 4 depending on variety. The tree produces creamy white or pink-red flowers on racemes of 100–300 flowers and a rounded fleshy fruit up to 27 mm (1 in) in diameter. The flesh covers a single spherical or elliptical seed (nut) with a white or gray kernel depending on the variety of tree. Macadamia can reach a height of 20 m (65.6 ft) and have a commercial lifespan of 40–60 years. Macadamia may also be referred to as Queensland nut or Australian nut and originates from Australia.

Macedonia
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:ME
ISO code: ISO 3166-1:MK
agricultural land: 11,180 sq. km
area: 13,812 km²
area: 25,713 km²
climate: Mediterranean climate, hot dry summers and autumns and relatively cold winters with heavy snowfalls inland
climate: warm, dry summers and autumns; relatively cold winters with heavy snowfall
density: 50/km²
density: 81/km²
latitude: 21.743027
longitude: 41.613594
population: 2,061,315 (2008)
population: 678,177 (2008)
comment: North Macedonia, officially the Republic of North Macedonia, is a country in Southeast Europe. It gained independence in 1991 as one of the successor states of Yugoslavia. North Macedonia is a landlocked country bordering with Kosovo to the northwest, Serbia to the north, Bulgaria to the east, Greece to the south, and Albania to the west. It constitutes approximately the northern third of the larger geographical region of Macedonia. Skopje, the capital and largest city, is home to a quarter of the country's 2.06 million population. The majority of the residents are ethnic Macedonians, a South Slavic people. Albanians form a significant minority at around 25%, followed by Turks, Romani, Serbs, Bosniaks, and Aromanians .
label: Montenegro

Madagascar
type: NamedIndividual
type: Country
belongs to: Africa
belongs to: Europe
ISO code: ISO 3166-1:MC
ISO code: ISO 3166-1:MG
agricultural land: 1.14 million sq. km
agricultural land: 413,950 sq. km
area: 1.95 km²
area: 587,041 km²
climate: Mediterranean with mild, wet winters and hot, dry summers
climate: tropical along coast, temperate inland, arid in south
density: 16,818/km²
density: 33/km²
latitude: 46.83758
latitude: 7.424291
longitude: -18.777191
longitude: 43.738346
population: 20,042,551 (2008)
population: 32,796 (2008)
comment: Madagascar, officially the Republic of Madagascar, and previously known as the Malagasy Republic, is an island country in the Indian Ocean, approximately 400 kilometres off the coast of East Africa. At 592,800 square kilometres Madagascar is the world's second-largest island country. The nation comprises the island of Madagascar and numerous smaller peripheral islands. Following the prehistoric breakup of the supercontinent Gondwana, Madagascar split from the Indian subcontinent around 88 million years ago, allowing native plants and animals to evolve in relative isolation. Consequently, Madagascar is a biodiversity hotspot; over 90% of its wildlife is found nowhere else on Earth. The island's diverse ecosystems and unique wildlife are threatened by the encroachment of the rapidly growing human population and other environmental threats.
alternative label: Monaco

Magnaporthe oryzae
type: NamedIndividual
type: Fungus
causes: Rice blast
comment: Magnaporthe oryzae or rice blast fungus, causes rice blast, the most important fungal rice disease in the world. Taxonomic research indicated that M. oryzae is distinct from M. grisea, a species that is morphologically indistinguishable from M. oryzae, a species that affects crabgrass (Digitaria). M. oryzae, infects millet and other grasses but primarily rice, the most economically important host. Leaf blast may result in significant economic loss; however, it is the panicle blast symptom that has the greater economic impact.

Malawi
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:MW
agricultural land: 55,800 sq. km
area: 118,484 km²
climate: sub-tropical; rainy season (November to May); dry season (May to November)
density: 118/km²
latitude: 34.295383
longitude: -13.24627
population: 13,931,831 (2008)
comment: Malawi, officially the Republic of Malawi, is a landlocked country in southeastern Africa that was formerly known as Nyasaland. It is bordered by Zambia to the west, Tanzania to the north and northeast, and Mozambique to the east, south and southwest. Malawi spans over 118,484 km2 and has an estimated population of 19,431,566 . Lake Malawi, also known as Lake Nyasa, takes up about a third of Malawi's area. Malawi's capital is Lilongwe. Its second-largest is Blantyre, its third-largest is Mzuzu and its fourth-largest is its former capital, Zomba. The name Malawi comes from the Maravi, an old name for the Chewa people who inhabit the area. The country is nicknamed "The Warm Heart of Africa" because of the friendliness of its people.

Malaysia
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:MY
agricultural land: 78,700 sq. km
area: 329,847 km²
climate: tropical; annual southwest (April to October) and northeast (October to February) monsoons
density: 5 84/km²
latitude: 101.968185
longitude: 3.994242
population: 27,730,000 (9/2008)
comment: Malaysia is a country in Southeast Asia. The federal constitutional monarchy consists of thirteen states and three federal territories, separated by the South China Sea into two regions, Peninsular Malaysia and Borneo's East Malaysia. Peninsular Malaysia shares a land and maritime border with Thailand and maritime borders with Singapore, Vietnam, and Indonesia. East Malaysia shares land and maritime borders with Brunei and Indonesia and a maritime border with the Philippines and Vietnam. Kuala Lumpur is the national capital and largest city while Putrajaya is the seat of the federal government. With a population of over 32 million, Malaysia is the world's 43rd-most populous country. The southernmost point of continental Eurasia is in Tanjung Piai. In the tropics, Malaysia is one of 17 megadiverse countries, home to a number of endemic species.

Maldives
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:MV
agricultural land: 70 sq. km
area: 298 km²
climate: tropical; hot, humid; dry, northeast monsoon (November to March); rainy, southwest monsoon (June to August)
density: 1,105/km²
latitude: 73.19929
longitude: 3.202413
population: 350,000 (2006)
comment: Maldives, officially the Republic of Maldives, is a small archipelagic state in South Asia, situated in the Arabian Sea of the Indian Ocean. It lies southwest of Sri Lanka and India, about 700 kilometres from the Asian continent's mainland. The chain of 26 atolls stretches from Ihavandhippolhu Atoll in the north to Addu Atoll in the south . Comprising a territory spanning roughly 298 square kilometres, Maldives is one of the world's most geographically dispersed sovereign states as well as the smallest Asian country by land area and, with around 557,426 inhabitants, the 2nd least populous country in Asia. Malé is the capital and the most populated city, traditionally called the "King's Island" where the ancient royal dynasties ruled for its central location.

Mali
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:ML
agricultural land: 416,210 sq. km
area: 1,240,192 km²
climate: subtropical to arid; hot and dry (February to June); rainy, humid, and mild (June to November); cool and dry (November to February)
density: 11/km²
latitude: -3.998823
longitude: 17.580778
population: 11,995,402 (2007)
comment: Mali, officially the Republic of Mali, is a landlocked country in West Africa. Mali is the eighth-largest country in Africa, with an area of just over 1,240,000 square kilometres . The population of Mali is 19.1 million. 67% of its population was estimated to be under the age of 25 in 2017. Its capital is Bamako. The sovereign state of Mali consists of eight regions and its borders on the north reach deep into the middle of the Sahara Desert. The country's southern part is in the Sudanian savanna, where the majority of inhabitants live, and both the Niger and Senegal rivers pass through. The country's economy centres on agriculture and mining. One of Mali's most prominent natural resources is gold, and the country is the third largest producer of gold on the African continent. It also exports salt.

Malta
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:MT
agricultural land: 103 sq. km
area: 316 km²
climate: Mediterranean; mild, rainy winters; hot, dry summers
density: 1,282/km²
latitude: 14.384604
longitude: 35.94615
population: 419,285 (2007)
comment: Malta, officially known as the Republic of Malta and formerly Melita, is a Southern European island country consisting of an archipelago in the Mediterranean Sea. It lies 80 km south of Italy, 284 km east of Tunisia, and 333 km north of Libya. With a population of about 515,000 over an area of 316 km2, Malta is the world's tenth smallest country in area and fourth most densely populated sovereign country. Its capital is Valletta, which is the smallest national capital in the European Union by area at 0.61 km2 . The official and national language is Maltese, which is descended from Sicilian Arabic that developed during the Emirate of Sicily, while English serves as the second official language. Italian and Sicilian also previously served as official and cultural languages on the island for centuries, with Italian being an official language in Malta until 1934 and a majority of the current Maltese population being at least conversational in the Italian language.

Mancozeb
type: NamedIndividual
type: Pesticides
comment: Mancozeb is a dithiocarbamate non-systemic agricultural fungicide with multi-site, protective action on contact. It is a combination of two other dithiocarbamates: maneb and zineb.[1] The mixture controls many fungal diseases in a wide range of field crops, fruits, nuts, vegetables, and ornamentals. It is marketed as Penncozeb, Trimanoc, Vondozeb, Dithane, Manzeb, Nemispot, and Manzane.

Mandarin orange
type: NamedIndividual
type: Crop
comment: The mandarin orange , also known as the mandarin or mandarine, is a small citrus tree fruit. Treated as a distinct species of orange, it is usually eaten plain or in fruit salads. Tangerines are a group of orange-coloured citrus fruit consisting of hybrids of mandarin orange with some pomelo contribution.

Maneb
type: NamedIndividual
type: Pesticides
comment: Maneb is a fungicide and a polymeric complex of manganese with the ethylene bis(dithiocarbamate) anionic ligand.

Mango
type: NamedIndividual
type: Crop
type: Fruit Tree
has disease: Algal spot
has disease: Anthracnose
has disease: Bacterial Spot
has disease: Powdery Mildew
description: Mango, Mangifera indica, is an evergreen tree in the family Anacardiaceae grown for its edible fruit. The mango tree is erect and branching with a thick trunk and broad, rounded canopy. The leaves of the tree are are shiny and dark green. They are either elliptical or lanceolate with long petioles and a leathery texture. The tree produces dense clusters of flowers with cream-pink petals on branched panicles. The mango fruit is roughly oval in shape, with uneven sides. The fruit is a drupe, with an outer flesh surrounding a stone. The flesh is soft and bright yellow-orange in color. The skin of the fruit is yellow-green to red. Mango trees can grow to a height of 45 m (148 ft) and can live for in excess of 100 years.
harvesting: Mango fruits are usually ready for harvest 4-5 months after flowering. Fruits that are ready for harvesting will snap easily from the tree. If the fruit does not dislodge with a slight pull then it is not fully mature and should be left to ripen fully. Fruits can be harvested by hand or, in commercial plantations, with the aid of special fruit picking devices. Mango fruit is delicate and easily bruised and must be handled carefully. Mango sap is very caustic and additional care must be taken during harvest to prevent sap coming into contact with the skin of the fruit or it will result in dark blemishes.
maintenance: Mango trees are easy to maintain once established. They are tolerant of drought but will perform best if irrigated during dry spells. Mango planted in the home garden does not generally require regular fertilization. if leaves are beginning to look pale or yellow, a balanced fertilizer may be applied once or twice every year. When grown commercially, mango trees require regular pruning to open up the canopy. Pruning keep the canopy at a manageable size and also promotes good air circulation around the leaves and fruit, reducing the incidence of disease.
propagation: Seeds Mango seeds are commonly used to produce rootstock or in the production of new cultivars. Seeds can be polyembryonic or monoembryonic. Polyembryonic seeds can produce between 3 and 10 seedlings from one seed, the majority of which will exhibit the characteristics of the parent plant. Monoembryonic seeds produce only one seedling from each seed and it usually does not breed true to type. Monoembryonic mango varieties are usually vegetatively propagated by grafting onto polyembryonic rootstocks. Seeds are collected from fully ripe fruit before chilling. The kernel is then removed from the endocarp and is planted within 24 hours of collection due to a rapid degeneration in seed viability. Mango seeds should be planted to a depth of 2 cm (0.8 in) and should be positioned on their sides to promote a straight growth habit. Monoembryonic seeds should be planted to a depth of 1-2 cm (0.4-0.8 in) in 1 to 5 gallon pots containing a well-draining potting soil. Polyembryonic seeds are usually planted in beds to allow space for the multiple seedlings. It is common to plant above a root barrier to facilitate the removal of seedlings from the bed when the time comes to pot them. Generally only 3-4 of the most vigorous seedlings are selected and potted up. Potted seedlings are usually raised under 50 to 80% shade and hardened to the sun prior to plating in the field. Vegetative propagation Desirable mango varieties can be vegetatively propagated by budding and grafting to ensure that the new tree shares the genetic characteristics of the parent. Twigs for propagation are best collected from healthy, mature trees when the trees are beginning a growth flush but when most of the terminals are still dormant. The twigs for propagation quickly lose their viability so it is important to keep them moist and cool after cutting and make the grafts immediately. The twigs should be of the same girth as the rootstock wood onto which it will be grafted and at least the thickness of a penci…
requirements: Mango trees grow best in tropical or subtropical climates where there is no danger of frost and especially in areas where the rainfall over the four summer months (June to September) totals 75 to 250 cm (30 to 100 in) and is followed by 8 months of dry weather. The trees grow optimally at temperatures of 24–27°C (75.2–80.6°F) with a relatively cool dry season and where heat is highest during flowering and fruiting. Mangos will tolerate almost any soil as long as it is well draining. Optimal growth will be achieved in rich, deep, well drained, loams. However, very rich soils will promote vegetative growth at the expense of flower and fruit production and should be avoided. The trees also grow well in sand, gravel and limestone soils. The optimum soil pH is between 5.5 and 7.5. Trees should be positioned in full sunlight for optimal fruit production.
uses: Mango is commonly eaten as a fresh fruit. The fruit may also be processed to produce mango pickles or chutneys.
comment: Mango, Mangifera indica, is an evergreen tree in the family Anacardiaceae grown for its edible fruit. The mango tree is erect and branching with a thick trunk and broad, rounded canopy. The leaves of the tree are are shiny and dark green. They are either elliptical or lanceolate with long petioles and a leathery texture. The tree produces dense clusters of flowers with cream-pink petals on branched panicles. The mango fruit is roughly oval in shape, with uneven sides. The fruit is a drupe, with an outer flesh surrounding a stone. The flesh is soft and bright yellow-orange in color. The skin of the fruit is yellow-green to red. Mango trees can grow to a height of 45 m (148 ft) and can live for in excess of 100 years.

Manioc
type: NamedIndividual
type: Crop
comment: See Cassava

March
type: NamedIndividual
type: Month Of Year

Marine Climate
type: NamedIndividual
type: Climate Type
comment: An oceanic climate, also known as a marine climate or temperate oceanic climate, is the Köppen classification of climate typical of west coasts in higher middle latitudes of continents, and generally features cool summers (relative to their latitude) and cool but not cold winters, with a relatively narrow annual temperature range and few extremes of temperature. Oceanic climates are defined as having a monthly mean temperature below 22 oC (72 oF) in the warmest month, and above 0 oC (32 oF) (or - 3 oC (27 oF)) in the coldest month. This climate type is often caused by the onshore flow from the cool, high latitude oceans that are found west of their location.
label: Oceanic Climate

Marshall Islands
type: NamedIndividual
type: Country
belongs to: Europe
belongs to: Oceania
ISO code: ISO 3166-1:MD
ISO code: ISO 3166-1:MH
agricultural land: 130 sq. km
agricultural land: 24,590 sq. km
area: 181 km²
area: 33,846 km²
climate: moderate winters, warm summers
climate: tropical; hot and humid; wet season May to November; islands border typhoon belt
density: 121,9/km²
density: 326/km²
latitude: 159.8964
latitude: 28.389887
longitude: 46.979073
longitude: 9.61502
population: 4,128,047 (2008)
population: 61,963 (2005)
comment: The Marshall Islands, officially the Republic of the Marshall Islands, is an island country and an associated state of the United States near the Equator in the Pacific Ocean, slightly west of the International Date Line. Geographically, the country is part of the larger island group of Micronesia. The country's population of 58,413 people is spread out over 29 coral atolls, comprising 1,156 individual islands and islets. The capital and largest city is Majuro. It has the largest portion of its territory made of water of any sovereign state, at 97.87%.

Marssonina Coronaria
type: NamedIndividual
type: Pathogen
causes: Brown spot

Martinique
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:MQ
area: 1,128 km²
density: 356/km²
latitude: -61.022816
longitude: 14.63554
population: 402,000 (1/2008)
comment: Martinique is an island and an Overseas department/region and single territorial collectivity of France, and therefore an integral part of the French Republic, located in the Lesser Antilles of the West Indies in the eastern Caribbean Sea, with a land area of 1,128 square kilometres and a population of 376,480 inhabitants as of January 2016. One of the Windward Islands, it is directly north of Saint Lucia, northwest of Barbados and south of Dominica. Martinique is also an Outermost Region of European Union as a special territory of the European Union and the currency in use is the euro. Virtually the entire population speaks both French and Martinican Creole.

Maruca Testulalis
type: NamedIndividual
type: Crop Pest

Maruca Testulalis Gryer
type: NamedIndividual
type: Crop Pest
comment: Maruca Testulalis Gryer

Matlab
type: NamedIndividual
type: Toolkit
comment: MATLAB combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook.

Mature
type: NamedIndividual
type: State
is state of: Maturity

Mauritania
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:MR
agricultural land: 397,110 sq. km
area: 1,030,700 km²
climate: desert; constantly hot, dry, dusty
density: 3.0/km²
latitude: -10.947097
longitude: 21.00681
population: 3,069,000 (2005)
comment: Mauritania, officially the Islamic Republic of Mauritania, is a country in Northwest Africa. It is the eleventh largest sovereign state in Africa and is bordered by the Atlantic Ocean to the west, Western Sahara to the north and northwest, Algeria to the northeast, Mali to the east and southeast, and Senegal to the southwest.

Mauritius
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:MU
agricultural land: 890 sq. km
area: 2,040 km²
climate: tropical, modified by southeast trade winds; warm, dry winter (May to November); hot, wet, humid summer (November to May)
density: 616/km²
latitude: 60.006447
longitude: -15.422485
population: 1,264,866 (2007)
comment: Mauritius, officially the Republic of Mauritius, is an island nation in the Indian Ocean about 2,000 kilometres (1,200 mi) off the southeast coast of the African continent, east of Madagascar. It includes the main island (also called Mauritius), as well as Rodrigues, Agaléga and St. Brandon. The islands of Mauritius and Rodrigues, along with nearby Réunion (a French overseas department), are part of the Mascarene Islands. The capital and largest city, Port Louis, is located on Mauritius, where most of the population is concentrated. The country spans 2,040 square kilometres (790 sq mi) and has an Exclusive Economic Zone covering 2.3 million square kilometres.

May
type: NamedIndividual
type: Month Of Year

Mealybugs
type: NamedIndividual
type: Crop Disease
type: Crop Pest
comment: Male mealybugs resemble tiny flies. Females are wingless and covered with a powdery wax. When masses of them feed on stems, leaves and fruits, they resemble gobs of moldy cotton. They are related to aphids, psyllids and phylloxera, and cause similar problems: they damage plants by sucking their juices and spreading diseases, and the honeydew they secrete invites sooty fungus, thereby hampering photosynthesis. Ants spread mealybugs from plant to plant, and furthermore protect them from many natural enemies. Out-of-doors, mealybugs constitute a greater problem in the South, since most species are tropical or greenhouse insects. They primarily affect fruit trees. Favored hosts of the citrus mealybug include avocado, citrus, and potato; the comstock mealybug can infest apple, grape, peach, and pear crops; and avocado, citrus banana, and plum are vulnerable to the longtailed mealybug. Commercially available beneficial insects are the best control for mealybugs. A certain type of lady beetle known as "mealybug destroyer" lives up to its name by controlling citrus mealybugs; chalcid wasps are effective on infested grape vines and those trees damaged by longtailed mealybugs. Alternately, control mealybugs mechanically by knocking mealybugs off of trees with a good, strong spray of water or soapy water; citrus mealybugs can also be controlled with a spray of kerosene emulsion. Controlling mealybugs on house plants can be challenging. They can be hand picked or removed with a cotton swab and rubbing alcohol but several treatments may be necessary to effect control. Horticultural oils and insecticidal soaps can also be used.

Dry summer climate
type: NamedIndividual
type: Climate Type
comment: A Mediterranean climate /ËŒmÉ›dÉªtÉ™ËˆreÉªniÉ™n/ or dry summer climate is characterized by dry summers and mild, wet winters. The climate receives its name from the Mediterranean Basin, where this climate type is most common. Mediterranean climate zones are typically located along the western sides of continents, between roughly 30 and 45 degrees north and south of the equator. The main cause of Mediterranean, or dry summer climate, is the subtropical ridge which extends northwards during the summer and migrates south during the winter due to increasing north-south temperature differences.
label: Mediterranean Climate

Medium Air Pollution
type: NamedIndividual
type: Air Pollution

Melanose
type: NamedIndividual
type: Crop Disease
affects: Citrus
life cycle: Dispersal. Rain or overhead irrigation water spreads the anamorph spores over short distances to susceptible tissues in the citrus canopy. The ascospores are dispersed by wind over longer distances. The more dead wood that exists in a canopy, the more ascospores will be produced. Most fruit infections probably start other infections caused by conidia. Infection. Fruits are susceptible to infection from about 3–5 months after petal drop, depending on the area. Approximately 8–24 hours of continuous moisture on leaf or fruit surfaces is required for infection to occur, depending on air temperature (shorter periods at higher air temperature). Therefore, periods of extended rainfall at warm locations are most likely to initiate rapid and severe melanose disease development
symptoms: Infections occur on leaves that are not fully expanded. Darkly colored, raised, corky pustules appear after leaf infection. The pustules may be surrounded by yellowed leaf tissue or yellow halos. The yellow color may later turn green. Severely infected shoot apices may become distorted or die back. Fully expanded, mature leaves resist infection. On fruits infection appears as darkly colored and usually raised pustules of various sizes. The pustules may coalesce and produce a cracked appearance, known as mudcake melanose, or may spread with flowing water on the fruit surface to form tear-stain symptoms. Pustules may be larger in size when the pathogen infects very young fruits.
comment: Melanose disease can affect young leaves and fruits of certain citrus species or varieties when the tissues grow and expand during extended periods of rainy or humid weather conditions. The symptoms of this widely distributed fungal disease vary from small spots or scab-like lesions to patterns of damage referred to as tear-drop, mudcake, and star melanose.

Mesothermal Climate
type: NamedIndividual
type: Climate Type

Methyl bromide
type: NamedIndividual
type: Pesticides
comment: Bromomethane, commonly known as methyl bromide, is an organobromine compound with formula CH3Br. This colorless, odorless, nonflammable gas is produced both industrially and biologically. It has a tetrahedral shape and it is a recognized ozone-depleting chemical. It was used extensively as a pesticide until being phased out by most countries in the early 2000s.
alternative label: Bromomethane

Adjusted Mutual Information (AMI)
type: NamedIndividual
type: Evaluation Metric
defined in: Scikit-learn
is evaluation metric of: Clustering
is transformation of: Evaluation
Acronym: AMI
comment: Adjusted Mutual Information between two clusterings. Adjusted Mutual Information (AMI) is an adjustment of the Mutual Information (MI) score to account for chance. It accounts for the fact that the MI is generally higher for two clusterings with a larger number of clusters, regardless of whether there is actually more information shared. For two clusterings and , the AMI is given as: AMI(U, V) = [MI(U, V) - E(MI(U, V))] / [avg(H(U), H(V)) - E(MI(U, V))] This metric is independent of the absolute values of the labels: a permutation of the class or cluster label values won’t change the score value in any way. This metric is furthermore symmetric: switching label_true with label_pred will return the same score value. This can be useful to measure the agreement of two independent label assignments strategies on the same dataset when the real ground truth is not known.

Adjusted Rand Index (ARI)
type: NamedIndividual
type: Evaluation Metric
defined in: Scikit-learn
is evaluation metric of: Clustering
is transformation of: Evaluation
Acronym: ARI
comment: Rand index adjusted for chance. The Rand Index computes a similarity measure between two clusterings by considering all pairs of samples and counting pairs that are assigned in the same or different clusters in the predicted and true clusterings. The raw RI score is then “adjusted for chanceâ€ into the ARI score using the following scheme: ARI = (RI - Expected_RI) / (max(RI) - Expected_RI) The adjusted Rand index is thus ensured to have a value close to 0.0 for random labeling independently of the number of clusters and samples and exactly 1.0 when the clusterings are identical (up to a permutation). ARI is a symmetric measure: adjusted_rand_score(a, b) == adjusted_rand_score(b, a)

Area Under Curve (AUC)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: AUC
comment: Area Under Curve(AUC) is one of the most widely used metrics for evaluation. It is used for binary classification problem. AUC of a classifier is equal to the probability that the classifier will rank a randomly chosen positive example higher than a randomly chosen negative example. Before defining AUC, let us understand two basic terms : + True Positive Rate (Sensitivity) : True Positive Rate is defined as TP/ (FN+TP). True Positive Rate corresponds to the proportion of positive data points that are correctly considered as positive, with respect to all positive data points. Image for post + True Negative Rate (Specificity) : True Negative Rate is defined as TN / (FP+TN). False Positive Rate corresponds to the proportion of negative data points that are correctly considered as negative, with respect to all negative data points. Image for post + False Positive Rate : False Positive Rate is defined as FP / (FP+TN). False Positive Rate corresponds to the proportion of negative data points that are mistakenly considered as positive, with respect to all negative data points.

Accuracy (ACC)
type: NamedIndividual
type: Evaluation Metric
defined in: Machine Learning PYthon (mlpy)
defined in: Scikit-learn
is evaluation metric of: Association Rule
is evaluation metric of: Classification
is evaluation metric of: Clustering
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: ACC
comment: Accuracy refers to the closeness of a measured value to a standard or known value.

Correct Classification Rate (CCR)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: CCR
comment: Correct Classification Rate (CCR) is the sum of the number on the diagonal divided by the sample size in the test data.

Confusion Matrix
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: CM
comment: Confusion Matrix as the name suggests gives us a matrix as output and describes the complete performance of the model. Lets assume we have a binary classification problem. We have some samples belonging to two classes : YES or NO. Also, we have our own classifier which predicts a class for a given input sample.

False Discovery Rate (FDR)
type: NamedIndividual
type: Evaluation Metric
Acronym: FDR
comment: False Discovery Rate (FDR) is the number of people who do not have the disease but are identified as having the disease (all FPs), divided by the total number of people who are identified as having the disease (includes all FPs and TPs).
definition: FDR = FP / (FP + TP)

False Positive Rate (FPR)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: FPR
comment: False Positive Rate (FPR) is the number of people who do not have the disease but are identified as having the disease (all FPs), divided by the total number of people who do not have the disease (includes all FPs and TNs).
definition: FPR (False Positive Rate) = # False Positives / # negatives = FP / (FP+TN)

F-score (F)
type: NamedIndividual
type: Evaluation Metric
defined in: Scikit-learn
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: Fscore
comment: F-score evaluation metric combines both precision and recall as a measure of effectiveness of classification. It is calculated in terms of ratio of weighted importance on either recall or precision as determined by β coefficient.
definition: F= 2 x (Recall * Precision) / ( Recall + Precision )

Gini Coefficient (Gscore)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: Gscore
comment: Gini coefficient measures the inequality among values of a frequency distribution (for example, levels of income). A Gini coefficient of zero expresses perfect equality, where all values are the same (for example, where everyone has the same income).

Homogeneity Score (Hscore)
type: NamedIndividual
type: Evaluation Metric
defined in: Scikit-learn
is evaluation metric of: Clustering
is transformation of: Evaluation
Acronym: Hscore
comment: Homogeneity metric of a cluster labeling given a ground truth. A clustering result satisfies homogeneity if all of its clusters contain only data points which are members of a single class. This metric is independent of the absolute values of the labels: a permutation of the class or cluster label values won’t change the score value in any way. This metric is not symmetric: switching label_true with label_pred will return the completeness_score which will be different in general.

Jaccard Similarity Coefficient (Jscore)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Classification
is transformation of: Evaluation
Source: sklearn.metrics.jaccard_score
Acronym: JaccardScore
comment: Jaccard index, or Jaccard similarity coefficient, defined as the size of the intersection divided by the size of the union of two label sets, is used to compare set of predicted labels for a sample to the corresponding set of labels in y_true.

Kappa Coefficients (Kappa)
type: NamedIndividual
type: Evaluation Metric
defined in: Scikit-learn
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: Kscore
comment: Two novice raters will be selected to read the excerpts and themes in order to determine inter-rater reliability. Raters will be provided a list of excerpts and themes. They will be instructed to read each excerpt and each theme prior to analysis. Once they have read over the materials, they are instructed to read each excerpt, one by one, and rate whether they believe each of the themes are either present or absent. The raters were instructed to indicate which themes were present or absent in each of the excerpts. The rater could determine that none, one, some, or all of the themes were present in the excerpts. Each theme could be present in more than one excerpt and each excerpt could contain more than one theme. A Kappa coefficient will be used to verify the presence of the themes that were presented. The Kappa coefficient is a statistical measure of inter-rater reliability or agreement that is used to assess qualitative documents and determine agreement between two raters. The equation used to calculate kappa is: Κ = PR(e), where Pr(a) is the observed agreement among the raters and Pr(e) is the hypothetical probability of the raters indicating a chance agreement

Lin’s Concordance Correlation Coefficient (LCCC)
type: NamedIndividual
type: Evaluation Metric
References: Lin, Lawrence I-Kuei. March, 1989. 'A Concordance Correlation Coefficient to Evaluate Reproducibility.' Biometrics 45, 255-268.
Acronym: LCCC
comment: Lin’s concordance correlation coefficient (CCC) is the concordance between a new test or measurement (Y) and a gold standard test or measurement (X). This statistic quantifies the agreement between these two measures of the same variable (e.g. chemical concentration). Like a correlation, CCC ranges from -1 to 1, with perfect agreement at 1. It cannot exceed the absolute value of ρ, Pearson’s correlation coefficient between Y and X. It can be legitimately calculated on as few as ten observations.

Logarithmic Loss
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: LogLoss
comment: Logarithmic Loss or Log Loss, works by penalising the false classifications. It works well for multi-class classification. When working with Log Loss, the classifier must assign probability to each class for all the samples.

Mean Absolute Bias Error (MABE)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: MABE
comment: The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of prediction accuracy of a forecasting method in statistics.

Mean Absolute Deviation (MAD)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: MAD
comment: The mean absolute deviation of a dataset is the average distance between each data point and the mean. It gives us an idea about the variability in a dataset.

Mean Absolute Error (MAE)
type: NamedIndividual
type: Evaluation Metric
defined in: Scikit-learn
is evaluation metric of: Regression
is transformation of: Evaluation
example: from sklearn.metrics import mean_absolute_error y_true = [3, -0.5, 2, 7] y_pred = [2.5, 0.0, 2, 8] mean_absolute_error(y_true, y_pred)
Source: sklearn.metrics.mean_absolute_error
Acronym: MAE
comment: Mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement. MAE is calculated as:

Mean Absolute Percentage Error (MAPE)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: MAPE
comment: The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of prediction accuracy of a forecasting method in statistics, for example in trend estimation, also used as a loss function for regression problems in machine learning.

Model Efficiency (ME)
type: NamedIndividual
type: Evaluation Metric
Acronym: ME

Mean Absolute Error (MAE)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: MEA
comment: Mean Absolute Error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement.

Mean Relative Error (MRE)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: MRE
comment: Mean of Relative Error (MRE) is often used, it is defined as the absolute value of the ratio of the error to the actual observed value.

Mean Squared Error (MSE)
type: NamedIndividual
type: Evaluation Metric
defined in: Machine Learning PYthon (mlpy)
defined in: Scikit-learn
is evaluation metric of: Regression
is transformation of: Evaluation
example: from sklearn.metrics import mean_squared_error y_true = [3, -0.5, 2, 7] y_pred = [2.5, 0.0, 2, 8] mean_squared_error(y_true, y_pred)
Source: sklearn.metrics.mean_squared_error
Acronym: MSE
comment: The MSE is a measure of the quality of an estimator. As it is derived from the square of Euclidean distance, it is always a positive value with the error decreasing as the error approaches zero.

Mean Squared Logarithmic Error (MSLE)
type: NamedIndividual
type: Evaluation Metric
is transformation of: Evaluation
Acronym: MSLE
comment: Mean squared logarithmic error is, as the name suggests, a variation of the Mean Squared Error.

Negative Predictive Value (NPV)
type: NamedIndividual
type: Evaluation Metric
defined in: Machine Learning PYthon (mlpy)
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: NPV
comment: Negative Predictive Value (NPV), computed as: NPV = TN / PN Total Samples (TS) Predicted Positives (PP) Predicted Negatives (PN) Actual Positives (AP) True Positives (TP) False Negatives (FN) Actual Negatives (AN) False Positives (FP) True Negatives (TN)

Nash-Sutcliffe efficiency (NSE)
type: NamedIndividual
type: Evaluation Metric
Acronym: NSE
comment: The Nash–Sutcliffe efficiency is calculated as one minus the ratio of the error variance of the modeled time-series divided by the variance of the observed time-series. In the situation of a perfect model with an estimation error variance equal to zero, the resulting Nash–Sutcliffe Efficiency equals 1 (NSE = 1). Conversely, a model that produces an estimation error variance equal to the variance of the observed time series results in a Nash–Sutcliffe Efficiency of 0.0 (NSE = 0). In reality, NSE = 0 indicates that the model has the same predictive skill as the mean of the time-series in terms of the sum of the squared error. In the case of a modeled time series with an estimation error variance that is significantly larger than the variance of the observations, the NSE becomes negative. An efficiency less than zero (NSE < 0) occurs when the observed mean is a better predictor than the model. Values of the NSE nearer to 1, suggest a model with more predictive skill.

Overall Success Rate (OSR)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: OSR

Percentage of Bias (PBIAS)
type: NamedIndividual
type: Evaluation Metric
References: P.O. Yapo, H.V. Gupta, S. Sorooshian, "Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data", J. Hydrol., 181 (1996), pp. 23-48
Acronym: PBIAS

Pearson Correlation Coefficient (PCC)
type: NamedIndividual
type: Evaluation Metric
Acronym: PCC
comment: Pearson correlation coefficient (PCC), also referred to as Pearson's r, the Pearson product-moment correlation coefficient (PPMCC), or the bivariate correlation, is a measure of linear correlation between two sets of data. It is the covariance of two variables, divided by the product of their standard deviations; thus it is essentially a normalised measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationship or correlation. As a simple example, one would expect the age and height of a sample of teenagers from a high school to have a Pearson correlation coefficient significantly greater than 0, but less than 1 (as 1 would represent an unrealistically perfect correlation).

Positive Predictive Value (PPV)
type: NamedIndividual
type: Evaluation Metric
defined in: Machine Learning PYthon (mlpy)
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: PPV
comment: Positive Predictive Value (PPV) computed as: PPV = TP / PP Total Samples (TS) Predicted Positives (PP) Predicted Negatives (PN) Actual Positives (AP) True Positives (TP) False Negatives (FN) Actual Negatives (AN) False Positives (FP) True Negatives (TN)

Precision (P)
type: NamedIndividual
type: Evaluation Metric
defined in: Scikit-learn
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: Prec
comment: Precision can be measured as of the total actual positive cases, how many positives were predicted correctly. It can be represented as: Precision = TP / (TP + FP)

R2 score (R2)
type: NamedIndividual
type: Evaluation Metric
defined in: Scikit-learn
is evaluation metric of: Regression
is transformation of: Evaluation
Source: sklearn.metrics.r2_score
Acronym: R2score
comment: R^2 (coefficient of determination) regression score function.

Relative Error (RE)
type: NamedIndividual
type: Evaluation Metric
Acronym: RE

Root Mean Square Deviation (RMSD)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: RMSD
comment: The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed. The RMSD represents the square root of the second sample moment of the differences between predicted values and observed values or the quadratic mean of these differences.

Root Mean Squared Error (RMSE)
type: NamedIndividual
type: Evaluation Metric
defined in: Scikit-learn
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: RMSE
comment: RMSE is a measure of the differences between values predicted by a model or an estimator and the values actually observed. It is the standard deviation of the residuals. Residuals are a measure of how far from the regression line data points are.

Root Mean Square Error of Cross Validation (RMSECV)
type: NamedIndividual
type: Evaluation Metric
Acronym: RMSECV

Root Mean Square Error of Prediction (RMSEP)
type: NamedIndividual
type: Evaluation Metric
Acronym: RMSEP
comment: Root Mean Square Error of Prediction (RMSEP) is a commonly used measure for the predictive capacity of a model.

Root Mean Square Error for Validation (RMSEV)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: RMSEV

Root Mean Squared Log Error (RMSLE)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: RMSLE
comment: Root Mean Squared Logarithmic Error (RMSLE) is the techniques to find out the difference between the values predicted by your machine learning model and the actual values.

Relative Overall Accuracy (ROA)
type: NamedIndividual
type: Evaluation Metric
is transformation of: Evaluation
Acronym: ROA
comment: Relative Overall Accuracy

Ratio of Performance to Deviation (RPD)
type: NamedIndividual
type: Evaluation Metric
is transformation of: Evaluation
Acronym: RPD

Relative Root Mean Square Error (RRMSE)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: RRMSE
comment: Relative Root Mean Square Error (RRMSE) is calculated by dividing the RMSE by the mean observed data.

Root Relative Square Error (RRSE)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: RRSE
comment: The Root Relative Squared Ưrror (RRSE) is relative to what it would have been if a simple predictor had been used. This simple predictor is just the average of the actual values. Thus, the relative squared error takes the total squared error and normalizes it by dividing by the total squared error of the simple predictor. By taking the square root of the relative squared error one reduces the error to the same dimensions as the quantity being predicted.

Ratio of RMSE to the Standard Deviation of Observations (RSR)
type: NamedIndividual
type: Evaluation Metric
Acronym: RSR

Recall (R)
type: NamedIndividual
type: Evaluation Metric
defined in: Scikit-learn
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: R
comment: Recall is described as the measured of how many of the positive predictions were correct. It can be represented as: Recall = TP / (TP + FN)

Standard Error (SE)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: SE
comment: Standard Error is a measure of precision for a statistic (slope, intercept or custom calculations). Standard error is an estimate of amount of sampling error as we typically don’t know the population parameters and that we are using a sample. The standard error of an estimator reflects how it varies under repeated sampling (ie repeated training set). Standard Error can be seen as the standard deviation of the error distribution. It determines the spread of the x's around the mean. From two data set from the same population, we can get for the slope 0,5 or - 0.1 for instance. Standard Error permits to say how close is a coefficient to 0.
definition: SD = SD / sqrt(N) SD : Standard Deviation N : Samle Size

Standard Error Of Prediction (SEP)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Classification
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: SEP
comment: Standard Error of Prediction (SEP) measures the precision of the prediction (i.e. the difference between repeated measurements). The SEP squared is approximately equal to the RMSEP squared minus the Bias squared.

Silhouette score (Sscore)
type: NamedIndividual
type: Evaluation Metric
defined in: Scikit-learn
is evaluation metric of: Clustering
is transformation of: Evaluation
Acronym: Sscore
comment: The Silhouette Coefficient is calculated using the mean intra-cluster distance (a) and the mean nearest-cluster distance (b) for each sample. The Silhouette Coefficient for a sample is (b - a) / max(a, b). To clarify, b is the distance between a sample and the nearest cluster that the sample is not a part of. Note that Silhouette Coefficient is only defined if number of labels is 2 <= n_labels <= n_samples - 1. This function returns the mean Silhouette Coefficient over all samples. To obtain the values for each sample, use silhouette_samples. The best value is 1 and the worst value is -1. Values near 0 indicate overlapping clusters. Negative values generally indicate that a sample has been assigned to the wrong cluster, as a different cluster is more similar.

True Negative Rate (TNR)
type: NamedIndividual
type: Evaluation Metric
Acronym: TNR
comment: True Negative Rate (TNR), also known as specificity, is the proportion of people who do not have the disease who are identified as not having the disease.
definition: TNR = TN / (TN + FP)

True Positive Rate (TPR)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Classification
is transformation of: Evaluation
Acronym: TPR
comment: True Positive Rate (TPR), also known as sensitivity, is the proportion of people who have the disease who are identified as having the disease.
definition: TPR (True Positive Rate) = # True positives / # positives = Recall = TP / (TP+FN)

Watanabe-Akaike Information Criterion (WAIC)
type: NamedIndividual
type: Evaluation Metric
is evaluation metric of: Regression
is transformation of: Evaluation
Acronym: WAIC
comment: Widely Applicable Information Criterion (WAIC), also known as Watanabe–Akaike information criterion, is the generalized version of the Akaike information criterion (AIC) onto singular statistical models. Widely applicable Bayesian information criterion (WBIC) is the generalized version of Bayesian information criterion (BIC) onto singular statistical models.

Mexico
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:MX
agricultural land: 1.03 million sq. km
area: 1,972,550 km²
climate: varies from tropical to desert
density: 55/km²
latitude: -102.54343
longitude: 23.624811
population: 106,682,500 (2008)
comment: Mexico, officially the United Mexican States, is a country in the southern portion of North America. It is bordered to the north by the United States; to the south and west by the Pacific Ocean; to the southeast by Guatemala, Belize, and the Caribbean Sea; and to the east by the Gulf of Mexico. Mexico covers 1,972,550 square kilometers, making it the world's 13th-largest country by area; with approximately 126,014,024 inhabitants, it is 10th-most-populous country and has the most Spanish-speakers. Mexico is organized as a federation comprising 31 states and Mexico City, its capital and largest metropolis. Other major urban areas include Guadalajara, Monterrey, Puebla, Toluca, Tijuana, Ciudad Juárez, and León.

Micronesia
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:FM
agricultural land: 954 sq. km
area: 702 km²
climate: tropical; heavy year-round rainfall, especially in the eastern islands; located on southern edge of the typhoon belt with occasionally severe damage
density: 154/km²
latitude: 150.54385
longitude: 7.641036
population: 107,862 (2007)
comment: The Federated States of Micronesia or simply Micronesia, is an island country in Oceania associated with the United States. It consists of four states – from west to east, Yap, Chuuk, Pohnpei and Kosrae – that are spread across the western Pacific Ocean. Together, the states comprise around 607 islands that cover a longitudinal distance of almost 2,700 km just north of the equator. They lie northeast of New Guinea, south of Guam and the Marianas, west of Nauru and the Marshall Islands, east of Palau and the Philippines, about 2,900 km north of eastern Australia, 3,400 km southeast of Japan, and some 4,000 km southwest of the main islands of Hawaii.

Microthermal Climate
type: NamedIndividual
type: Climate Type

Middle East
type: NamedIndividual
type: Continent
has country: Afghanistan
has country: Bahrain
has country: Egypt
has country: Iran
has country: Iraq
has country: Israel
has country: Jordan
has country: Kuwait
has country: Lebanon
has country: Oman
has country: Pakistan
has country: Palestinian Territory
has country: Qatar
has country: Saudi Arabia
has country: Syria
has country: Turkey
has country: United Arab Emirates
has country: Yemen

Mildly-infected Trees
type: NamedIndividual
type: State
is state of: Crop Disease
is state of: Crop Disease by Tier4

Milky
type: NamedIndividual
type: Growth Stage
comment: The milk stage is the first part of maturing, and this is when the kernel begins forming. The kernel is fully formed during the dough stage, and the plant takes nutrients from the stalk and leaves and transfers them to the kernel.

Millipedes
type: NamedIndividual
type: Crop Disease
type: Crop Pest
life cycle: Millipede adults overwinter in the soil. In spring the female lays 20-300 eggs in the soil. The first stage larvae have only three pairs of legs on three body segments. The larvae go through 7—10 development stages and become adults in 2—5 years. Adults live for several years.
management: Management Strategies 1. Control Outdoors. Millipedes and centipedes are considered beneficial outdoors; therefore, encourage their presence. To help prevent them from moving indoors: remove organic matter in contact with the house foundation, minimize watering near building foundations, clean up piles of rocks, leaves, compost and general refuse near the building, and make sure doors and windows fit snugly (caulk crevices). Pesticides can be used to treat the foundation and an adjacent three-foot ground strip with dust or aerosol spray labeled for these organisms. Follow label directions precisely. 2. Control Indoors. Hand pick or vacuum to control individuals. Reduce moisture and remove hiding places, or place sticky traps against walls near entryways. You can also use a pesticides if populations are high or persistent. A contact or aerosol sprays containing pyrethrins or carbaryl can be used. Treat baseboards, cracks and crevices. Follow label directions precisely.
comment: Millipedes, commonly called "thousand-leggers", have elongated, cylindrical bodies with 25+ pairs of short legs, two pairs per body segment. The legs are held beneath the body and appear to ripple in waves as they move. The head is rounded and projects two short antennae. The body color ranges from light brown to black. Millipedes move slowly and coil up when disturbed or dead. They range in length from a half inch to six inches. They are not venomous but possess glands that produce an irritating, foul-smelling fluid. They feed on decaying vegetable matter. Millipedes migrate in great numbers when it is too hot or too cold, too wet or too dry. They may migrate indoors, but they do not reproduce indoors. They are considered beneficial in the garden because they enrich the soil by breaking down decaying vegetation.

Mineral Soils
type: NamedIndividual
type: Soil Type

Mistletoe
type: NamedIndividual
type: Crop Disease
type: Weeds
life cycle: Plants in the genus Phoradendron can have either broad-leaves like the familiar mistletoe sold at Christmas time or can have scale-like leaves. Both have short internodes and dense balls of foliage on branches and trunks and usually occur at the top of trees where light intensity is good. The fruits are eaten by birds and the seeds, which have a sticky coating are excreted and attach to young stems where the seeds germinate and penetrate the stem. They infest a wide range of flowering trees and shrubs as well as some conifers, most notably junipers. Mistletoes in the genus Arcenithoblum are referred to as dwarf mistletoes. The sticky seeds are ejected forcibly from fruits where they spread to nearby stems or plants. Seeds also stick to birds and other animals, which then transport them to other locations.
management: Management Strategies 1. Prune out infected branches. If practicable, prune out branches that are infected or cut back shoots to the point of origination. Shoots may regrow and need to be recut every couple of years. 2. Treat with an herbicide. In some situations treatment of mistletoes with an herbicide when the host plant is dormant has reportedly been useful. Organic Strategies Strategy 1 is a strictly organic approach.
comment: Mistletoes are photosynthetic, seed-bearing plants that are parasitic on the stems of several trees and shrubs from which they tap water and minerals. They occur in the genera Phoradendron, Viscum, and Arceuthobium. They can reduced plant vigor and result in dieback or even death. In North America about 230 species occur in the genus Phoradendron growing in forests below a crescent from New York south to Arkansas and west up to Oregon. Occurrence in Missouri is rare. Viscum album was introduced into California from Europe in 1900. The arceuthobiums are referred to as the dwarf mistletoes and are a serious pest in Canadian forests extending through western North America south to Mexico and Central America.

Mites
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Cassava
affects: Strawberry
comment: Mites are small arthropods belonging to the class Arachnida and the subclass Acari. Minute red, black or brown mites are closely related to spiders, hence their common label of "spider mites." They attack just about anything in your flower or vegetable garden, including fruit crops. They especially like cucurbits, beans and tomatoes. In addition, plants on the inside of your house often fall victim to the indiscriminate mite. Mites feed on plants by sucking out plant juices, leaving leaves stippled, yellow and dry, or with pale yellow spots or blotches. They also suck chlorophyll out of leaves, causing small white dots to appear. Finally, they inject toxins into the leaves, discoloring and distorting them. If you notice any of the above symptoms, or fine webbing covering leaves, shoots and flowers, your plant is probably infested with mites. Since they are almost invisible to the naked eye, you can confirm your suspicion through examination of the undersides of the leaves with a magnifying glass. Tap a few leaves or a small branch tip against a sheet of white paper and look for the tiny culprits crawling on the paper. Since mites thrive in hot, dry conditions, light infestations can usually be controlled simply with water, sprayed forcefully on the affected plant every other day in the early morning, beginning as soon as the first stippling of leaves is detected. Make sure you spray the undersides of the leaves, and wash plants at least three times. If mites persist or infestation is heavy, spray with insecticidal soap every 3 to 5 days for two weeks; for fruit trees, a mixture of insecticidal soap and a light horticultural oil is effective. Severely infested plants or infested portions may require destruction and disposal, since its hard to rein in a mite infestation once it's well underway.

Machine Learning PYthon (mlpy)
type: NamedIndividual
type: Toolkit
has algorithm: Hierarchical Clusterer (HC)
has algorithm: Kernel Fisher Discriminant Analysis (KFDA)
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Kernel Principal Component Analysis (KPCA)
has algorithm: Last Angle Regression (LAR)
has algorithm: Linear Regression (LR)
has algorithm: Multi-layer Perceptron (MLP)
has algorithm: Principal Component Analysis (PCA)
has algorithm: Fast Principal Component Analysis (FastPCA)
has algorithm: Spectral Regression Discriminant Analysis (SRDA)
has algorithm: Accuracy (ACC)
has algorithm: Mean Squared Error (MSE)
has algorithm: Negative Predictive Value (NPV)
has algorithm: Positive Predictive Value (PPV)
comment: Machine Learning PYthon (mlpy) is a high-performance Python library for predictive modeling.
label: Mlpy

Moderate Breeze
type: NamedIndividual
type: Wind Type

Moderate Gale
type: NamedIndividual
type: Wind Type

Moderate Precipitation
type: NamedIndividual
type: Precipitation

Moderate Solar Radiation
type: NamedIndividual
type: Solar Radiation

Moderately-infected Trees
type: NamedIndividual
type: State
is state of: Crop Disease
is state of: Crop Disease by Tier4

Moist Humidity
type: NamedIndividual
type: Humidity

Moldova
type: NamedIndividual
type: Country
comment: Moldova, officially the Republic of Moldova, is a landlocked country in Eastern Europe. It is bordered by Romania to the west and Ukraine to the north, east, and south. The capital city is Chișinău.

Monaco
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1: MD
area: 33,846 km2 (13,068 sq mi)
density: 86.2/km2 (223.3/sq mi)
population: 2,640,400
comment: Monaco, officially the Principality of Monaco, is a sovereign city-state and microstate on the French Riviera close to the Italian region of Liguria, in Western Europe. Bordered by France to the north, east and west, it is lapped by the Mediterranean Sea to the south. The principality is home to 38,682 residents, of whom 9,486 are Monégasque nationals; it is widely recognised for being one of the most expensive and wealthiest places in the world. The official language is French, although Monégasque, Italian and English are spoken and understood by a sizeable group.

MoneyLearn
type: NamedIndividual
type: Toolkit
Acronym: MoneyLearn
comment: MoneyLearn is an easy-to-use, NLP tool that helps in obtaining valuable insights from the text data. The tool enables in performing text analysis such as sentiment analysis, topic classification, or keyword extraction, etc. The tool is used to train text analysis models to deliver accurate insights and once it is done then you can easily connect the models to your favorite apps like Excel. Google sheets through MonkeyLearn’s APIs that are available in all major programming languages.

Mongolia
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:MN
agricultural land: 5,120 sq. km
area: 1,564,116 km²
climate: desert; continental (large daily and seasonal temperature ranges)
density: 1.7/km²
latitude: 103.83698
longitude: 46.860943
population: 2,951,786 (2007)
comment: Mongolia is a landlocked country in East Asia. Its area is roughly equivalent with the historical territory of Outer Mongolia, which is sometimes used to refer to the current state. It is situated between Russia to the north and China to the south, where it neighbours the Inner Mongolia Autonomous Region. Mongolia does not share a border with Kazakhstan, although only 37 kilometres separate them.

Montenegro
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:ME
area: 13,812 km2
density: 45/km2 (116.5/sq mi)
population: 622,359
comment: Montenegro is a country in southeast Europe on the Adriatic coast of the Balkans. It borders Bosnia and Herzegovina to the north, Serbia to the northeast, Kosovo to the east, Albania to the southeast, the Adriatic Sea and Croatia to the west. The largest and capital city Podgorica covers 10.4% of Montenegro's territory and is home to 29.9% of the country's population, while Cetinje has the status of old royal capital. Major group in the country are Montenegrins. Serbs form a significant minority at 28.7%, followed by Bosniaks, Albanians, Croats and Roma people.

Montserrat
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:MS
area: 102 km²
latitude: -62.194504
longitude: 16.744167
population: 4,488 (2005)
comment: Montserrat is a British Overseas Territory in the Caribbean. The island is in the Leeward Islands, which is part of the chain known as the Lesser Antilles, in the West Indies. Montserrat measures approximately 16 km in length and 11 km in width, with approximately 40 km of coastline. Montserrat is nicknamed "The Emerald Isle of the Caribbean" both for its resemblance to coastal Ireland and for the Irish ancestry of many of its inhabitants. Montserrat is the only non-fully sovereign full member of the Caribbean Community and the Organisation of Eastern Caribbean States.

Morocco
type: NamedIndividual
type: Country
belongs to: Africa
has climate: Dry summer climate
ISO code: ISO 3166-1:MA
agricultural land: 301,038 sq. km
area: 446,550 km²
climate: Mediterranean, becoming more extreme in the interior
density: 70/km²
latitude: -7.080164
longitude: 31.795067
population: 31,352,000 (2008)
comment: Morocco, officially the Kingdom of Morocco, is a country located in the Maghreb region of North Africa. It overlooks the Mediterranean Sea to the north and the Atlantic Ocean to the west, and has land borders with Algeria to the east and the disputed territory of Western Sahara to the south. Morocco also claims the exclaves of Ceuta, Melilla and Peñón de Vélez de la Gomera and several small Spanish-controlled islands off its coast. The capital is Rabat and the largest city is Casablanca. Morocco spans an area of 710,850 km2 and has a population of over 37 million.

Mosaic
type: NamedIndividual
type: Crop Disease
type: Viral Disease
affects: Garlic
caused by: Mosaic Virus
management: Plant virus-free cloves that were produced from meristem tip culture in virus-free conditions
symptoms: Mosaic patterns on leaves; chlorotic mottling or streaks on leaves; stunted plant growth and reduced bulb size
comment: Transmitted by aphids; infections can be latent and produce no symptoms; infection in garlic are often found alongside other viruses such as onion yellow dwarf

Mosaic of Citrus
type: NamedIndividual
type: Crop Disease
affects: Citrus
comment: Citrus yellow mosaic virus is a destructive disease of citrus in India. Symptoms of the virus include chlorotic spots on older leaves, and an overall decline in fruit production. The disease is spread by the citrus mealybug, dodder, as well as through the use of infected budwood.

Mosaic Virus
type: NamedIndividual
type: Virus
has disease: Cereal
has disease: Wheat
label: Soil-borne wheat mosaic virus

Mosses
type: NamedIndividual
type: Crop Disease
type: Weeds
life cycle: Moss prefers to grow and become established in the winter with growth peaking in early, wet springs. If grass is established in the fall, moss will fail to compete for space in which to grow. This presents the best strategy for avoiding moss development.
management: Management Strategies 1. Cultural practices to inhibit moss growth and stimulate growth of desirable healthy lawn grass. A. Test soil for pH and nutrient content. If deficient in nutrients, follow recommendations of soil test. Fertilize with nitrogen just before the growth cycle. B. Improve drainage. In moist areas with poor drainage, add soil or contour the area to move water away from low lying areas. C. Increase light penetration. Trim lower branches of shade trees and thin to improve light penetration to the soil surface and improve air circulation. A ground cover may be used in lieu of grass if shade is too intense. D. Loosen compacted soils. Compacted soil should be tilled or core aerated to reduce compaction. Loosening the top 2 to 4 inches of soil will help. Cultivation breaks up the algae crust or moss mats. 2. Manual removal. Remove as much of the moss as possible by raking or dethatching. 3. Treatment.Treat the mossy areas with iron compounds; liquid ferric sulfate is the most effective. Fertilizers with iron compounds also work if very fine, near-dust formulations are used. Moss killers containing zinc sulfate will kill the moss but can also damage grass. Buy only products which state "for controlling moss in lawns" as some are for removal of moss in sidewalks and roofs and can be harmful to other plants. Lime and copper sulfate are ineffective in killing moss. 4. Turf selection. In shaded, well-drained soil, plant fine fescues. For shaded, moist areas, plant rough bluegrass. Neither grass, however, will survive extremely heavy shade or soil saturated for long periods. 5. Watering. Keep grass in good condition in the summer by watering deeply to establish deep roots. If an automatic watering system is used, water plentifully once or twice a week (1 inch per watering). Organic Strategies Strategies 1.B, 1.C, 1.D, 2, 4, and 5 are strictly organic approaches. Strategy 1.A could be considered an organic approach if an organic fertilizer is used.
symptoms: Moss in the lawn is quite visible and can be annoying. Large areas void of grass may be covered by moss. These small primitive plants have reduced leaves and reproduce by spores, not seeds.
comment: Mosses are small, primitive plants that have reduced leaves and a mass of fine, thread-like stems. A mild, wet winter combined with turfgrass which is thin and weak offers an opportunity for moss and/or algae growth. Mosses are not parasitic on turfgrass. They produce their own food, grow where grass will not, and form a soft mat of growth, which makes a barrier, preventing grass from growing. Shady conditions and infertile and/or compacted, poorly drained soil encourage moss growth.

Mostly Cloudy
type: NamedIndividual
type: Cloud Cover

Mozambique
type: NamedIndividual
type: Country
belongs to: Africa
has climate: Alpine Climate
ISO code: ISO 3166-1:MZ
agricultural land: 494,000 sq. km
area: 801,590 km²
climate: tropical to subtropical
density: 25/km²
latitude: 35.530895
longitude: -18.670284
population: 21,397,000 (2007)
comment: Mozambique, officially the Republic of Mozambique, is a country located in Southeastern Africa bordered by the Indian Ocean to the east, Tanzania to the north, Malawi and Zambia to the northwest, Zimbabwe to the west, and Eswatini and South Africa to the southwest. The sovereign state is separated from the Comoros, Mayotte and Madagascar by the Mozambique Channel to the east. The capital and largest city of Mozambique is Maputo .

Mucks
type: NamedIndividual
type: Soil Type

Mucor Rot
type: NamedIndividual
type: Crop Disease
affects: Apple
controls: Because Mucor is a type of fungi with completely different (biology, physiology, etiology) from the other fungi known to decay pome fruit, none of the current pre or postharvest fungicides are effective against Mucor rot. Therefore, good orchard and harvest management practices are key to managing this disease. Orchard sanitation includes removal of fallen mummified fruit, and treatment or removal of leaf litter and other debris from the orchard floor to help reduce inoculum levels in the soil in the upcoming season. Flail mowing after harvest can spread inoculum and should be avoided. Prior to the start of the new storage season, it is important to clean and sanitize rooms (air, walls, floors) and bins at least once a season prior to storing a new crop. Sanitation of the packing-line from dump-tank all-the-way to the sorters needs to be done regularly during the packing season. Several sanitizers, i.e. chlorine and chlorine dioxide, hydrogen peroxide, organic aids, electrolyzed water and ozone, are available and all have different efficacies and uses (Bernat et al., 2018; Feliziani et al., 2016). Consult extension specialists or consultants in your area and the product labels to optimize sanitation. Recent cold or hot fogging technologies have shown good levels of efficacy in sanitizing larger facilities (entire rooms, lines, stack of bins) more efficiently. During harvest, fruit bins should be positioned on the orchard floor to minimize contamination of the undersides of bins by infested soil or organic debris. Fruits that fall to the ground during harvest should not be put back in fruit bins. Undersides of bins should be brushed clean before stacking. Fruits harvested during dry weather are less susceptible to Mucor rot than those harvested in wet weather. On the packing line, thorough rinsing of fruit can help remove spores and lower decay rates, but recycled rinse water can also spread spores to healthy fruit. Because humidity tends to be slightly higher in roo…
life cycle: Mucor piriformis is a soilborne pathogen that survives as sporangiophores mainly in the top layer of the orchard soil, especially in organic matter such as fallen fruit and debris. Sporangiospore populations peak in the fall or winter depending on moisture, temperature and nutrient availability in the soil. Sporangia may survive on bins if not sanitized after each use. Infections occur primarily through contact of bins with infested soils, but can also be transmitted through spores spread by other vectors such as birds, rodents and insects, particularly vinegar flies and nitidulid beetles. Fruits are most susceptible to infections during the final month before harvest, and late-harvested or over-mature fruits are more susceptible to Mucor and other decays. Infections mainly occur during harvest if fruit are harvest into non-cleaned bins, during drenching from bin to bin or in the dump- tank and flume water during packing. The pathogen is cold-tolerant as germination can occur between -1ºC (30ºF) and 24℃, with 20℃ being optimal but not at temperatures above 26ºC (78ºF). Mucor rot progresses rapidly and completely decays the fruit in less than two months in cold storage.
symptoms: Mucor rot typically originates at puncture wounds in the cuticle, or at the stem or calyx end of the fruit. At early stages of infection, long, coarse white or gray mycelium with pin-shaped dark spore heads may appear at wound sites, splits, or fruit lenticels. Once infected, fruit will rapidly decay and liquefy even in cold storage or controlled atmosphere (CA), with complete decay in less than two months at 0℃ (32 ºF). The decayed tissue is generally light brown to golden in color and is very soft and juicy in texture. It can be readily separated from any healthy tissue and the skin can be very easily peeled from the infected flesh. When the fruit is ruptured, abundant sporangia-containing juice is released, and can spread to cause secondary infections. At early infection stages, Mucor rot symptoms can be sometimes confused with those of blue mold but Mucor has a distinct sweet odor.
comment: Mucor rot is a common problem on pome fruits (apples and pears) in the Pacific Northwest and elsewhere, as well as on other commercial crops including stone fruits (cherry, peach, nectarine, prune, plum), raspberries, and citrus fruits. Although not as important as blue and gray mold the disease can be a challenge because none of the current postharvest fungicides are effective against Mucor. Good bin sanitation, harvest management and water-sanitation practices are critical to manage the disease. Mucor rot is caused primarily by Mucor piriformis A. Fisch. 1982. Other Mucor species that may cause Mucor rot in pome fruits include M. mucedo Fresen., M. racemosus Fresen., and M. strictus Hagem. However. Only M. piriformis has been found to cause significant losses on many cultivars of apple and pear in cold storage.

Myanmar
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:MM
area: 676,578 km²
density: 75/km²
latitude: 95.95622
longitude: 21.913965
population: 55,390,000 (2006)
comment: Myanmar or Burma, officially the Republic of the Union of Myanmar, is a country in Southeast Asia. Myanmar is bordered by Bangladesh and India to its northwest, China to its northeast, Laos and Thailand to its east and southeast, and the Andaman Sea and the Bay of Bengal to its south and southwest. Myanmar is the largest country in Mainland Southeast Asia and the 10th largest in Asia by area. As of 2017, the population was about 54 million. Its capital city is Naypyidaw, and its largest city is Yangon .

Myrothecium Leaf Spot
type: NamedIndividual
type: Crop Disease
comment: Myrothecium leaf spot causes leaf spots, concentric rings may develop in the lesions. Raised black sporodochia develop on diseased tissue. In high humidity sporodochia are encircled by a tuft white growth.

Mythimna Separta
type: NamedIndividual
type: Crop Pest

NK compounds
type: NamedIndividual
type: Chemical Fertilizer

Natural Language Toolkit (NLTK)
type: NamedIndividual
type: Toolkit
Acronym: NLTK
comment: Natural Language Toolkit or NLTK is one of the most popular platforms to build Python programmes. It offers a suite of open source Python modules, tutorials and data sets to support the research and development of NLP. More than 50 corpora and lexical resources are recipients of interfaces from NLTK. These include: A suite of text processing libraries for classification Tokenisation Stemming Tagging Parsing Semantic reasoning Wrappers for industrial-strength NLP libraries WordNet It is suitable for all kinds of programmers– students, educators, engineers, researchers, and industry professionals. NLTK can be accessed in Python version 3.6 and above and is available for Windows, Linux, Mac OS X and Linux.

NPK fertilizers
type: NamedIndividual
type: Chemical Fertilizer

NP compounds
type: NamedIndividual
type: Chemical Fertilizer

Namibia
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:NA
agricultural land: 388,090 sq. km
area: 825,418 km²
climate: desert; hot, dry; rainfall sparse and erratic
density: 2.5/km²
latitude: 18.486164
longitude: -22.965662
population: 1,820,916 (2005)
comment: Namibia, officially the Republic of Namibia, is a country in Southern Africa. Its western border is the Atlantic Ocean; it shares land borders with Zambia and Angola to the north, Botswana to the east and South Africa to the south and east. Although it does not border Zimbabwe, less than 200 metres of the Zambezi River separates the two countries. Namibia gained independence from South Africa on 21 March 1990, following the Namibian War of Independence. Its capital and largest city is Windhoek. Namibia is a member state of the United Nations, the Southern African Development Community, the African Union and the Commonwealth of Nations.

Nauru
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:NR
area: 21 km²
climate: tropical with a monsoonal pattern; rainy season (November to February)
density: 649/km²
latitude: 166.92213
longitude: -0.528309
population: 13,770 (2008)
comment: Nauru, officially the Republic of Nauru and formerly known as Pleasant Island, is an island country and microstate in Oceania, in the Central Pacific. Its nearest neighbour is Banaba Island in Kiribati, 300 km to the east. It further lies northwest of Tuvalu, 1,300 km northeast of the Solomon Islands, east-northeast of Papua New Guinea, southeast of the Federated States of Micronesia and south of the Marshall Islands. With only a 21 km2 area, Nauru is the third-smallest country in the world behind Vatican City and Monaco, making it the smallest republic. Additionally, its population of 10,670 is the world's second smallest, after Vatican City.

Nectria Canker
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
life cycle: Nectria galligena overwinters in the callus tissue growing slowly while its host is dormant. During moist periods, creamy white cushion-like fruiting structures will develop. These are followed by a second type of reproductive structure, which is red to reddish orange, pin-head sized and lemon-shaped, in autumn through spring. During rain or other moist weather, spores are released and dispersed by wind or water infecting susceptible plants through natural openings such as leaf scars or through wounds from improper pruning, sunscald, storm damage, frost cracks or other mechanical damage. As the fungus grows, it kills bark, cambium, and the outermost sapwood. The life cycle of the Nectria dieback fungus is similar to that of Nectria canker. Creamy to coral pink to pink-orange or light purplish red spore-producing structures develop in spring or early summer. These will age to tan, brown, or nearly black. Orange-red fruiting structures, which mature to dark reddish brown and may persist through winter, are produced in summer and autumn. Both structures release spores that are dispersed by water and can invade susceptible tissue producing cankers and dieback.
management: Management Strategies 1. Proper selection. Choose trees and shrubs that are well adapted to the climate of the area to minimize infection due to freeze damage and other environmental stresses. 2. Maintain plant vigor. Keep plants healthy and growing vigorously by using good cultural techniques. These include choosing the appropriate planting site, watering during dry periods, using mulch around the base of the tree or shrub and fertilizing and pruning properly. Pruning is best done in late winter. Avoid pruning in spring when higher moisture can increase risk of infection or in late summer and autumn, which can delay the plant’s natural cold hardiness response. Minimize any wounding due to root pruning, transplanting or lawnmowers to reduce infection sites. 3. Prune. Prune out branch cankers during dry periods when conditions are unfavorable for infection. Disinfect pruning tools in a 1-part bleach to 9-parts water solution between each cut.
symptoms: Although it is most common in spring and fall, the Nectria fungus can infect plants throughout the year as long as there is sufficient moisture and the temperature is above freezing. Plants that are stressed by cold, drought, mechanical injuries or other disease are especially susceptible. Infections may be worse in autumn and winter when the host plant is dormant and wound recovery is weaker than in the growing season. The first symptom of a Nectria infection is a depressed discolored area of bark near wounds or at the base of dead twigs or branches. These cankers are usually not noticed until other symptoms appear. The first easily visible signs of Nectria canker are small creamy white or red to reddish orange fruiting structures and the development of callus tissue. This callus tissue is produced as the host plant attempts to isolate the fungus. If the callus does not isolate the infection, the fungus will continue to grow into healthy wood and the plant will respond by growing another ridge of callus tissue. This alternation of fungal growth and callus ridge, which may occur for many years, results in a rounded or elongated target-like shape. The bark of older ridges may decay and weather away exposing the ridges of wood underneath. This disease grows slowly and larger stems are rarely girdled, although multiple lesions may grow together and kill a branch or the entire plant. Plants that are stressed are most severely affected by the disease. This fungus may also affect apple fruit causing it to rot during storage. The first obvious sign of Nectria dieback may not occur until spring when the plant begins to grow. Affected twigs, branches, or even entire plants will not produce leaves or may wilt suddenly. Larger branches or small trees may be girdled and killed. The fungus produces reproductive structures that vary in color from creamy, coral pink, pink-orange, light purplish red or orange-red and that darken as they mature.
comment: Worldwide, Nectria fungi cause several common canker and dieback diseases, especially in hardwood trees. Nectria canker, which is caused by the fungus, Nectria galligena, may occur on over 60 species of trees and shrubs including apple, ash, birch, dogwood, elm, sweet gum, holly, maple, pear and walnut. A similar disease infects members of the magnolia family. Nectria canker is usually not a fatal disease, but it can cause considerable damage as the cankered area is weakened and susceptible to breakage. It may also adversely affect the appearance of the affected plant. This disease is important commercially as it reduces the quality and quantity of forest products. Another member of the Nectria genus, Nectria cinnabarina, causes the disease Nectria dieback. Also known as coral spot Nectria canker or Nectria canker, this disease occurs on many plant species, including apple, ash, barberry, birch, boxwood, crabapple, elm, hickory, honey locust, linden, maple, pear, rose and Japanese zelkova. Nectria cinnabarina usually grows as a saprophyte on dead wood, but if a plant is wounded or otherwise stressed, the fungus becomes an opportunistic weak parasite, producing cankers and causing dieback of twigs and branches. Maples are especially affected by this disease as are recently wounded or severely pruned trees and shrubs, urban ornamentals and new transplants of other species.

Neglectible Solar Radiation
type: NamedIndividual
type: Solar Radiation

Nematodes
type: NamedIndividual
type: Crop Disease
affects: Corn
diagnosis: Because disease caused by nematodes can't be diagnosed based solely on plant symptoms, it is necessary to collect samples and submit them to a laboratory for nematode analysis. Most nematologists agree that it is important to estimate the population densities of nematodes both in the soil and inside root tissue when conducting a nematode analysis. So, some labs may require collection of separate root samples in addition to soil samples when conducting nematode analysis. Contact your local laboratory, to confirm that they conduct nematode analyses and for specific instructions on sample submission and associated fees. The UNL Plant and Pest Diagnostic Clinic conducts nematode analysis.
management: Nematode management in corn can be difficult because of the chronic nature of the problem. Crop rotation may or may not be effective as determined by the feeding tendencies of the nematode species in the field. Some soil applied nematicides are labeled for use on corn, but until recently, the narrow profit margin of the crop has limited their use except under severe nematode pressure. As of 2007, at least two agrochemical companies are testing seed treatment nematicides and expect to release them for use on corn soon.
pathogen: Plant parasitic nematodes are microscopic worms that cause some of the world's most severe crop diseases and yield loss and every plant has some nematodes that can parasitize it. For corn, there are at least a dozen different genera of plant parasitic nematodes that have been identified in Nebraska corn fields. And, each genus can have multiple species that can feed on corn.
symptoms: There are many types of symptoms caused by nematodes of corn, although diagnoses cannot be based on symptoms because most are general in nature and may mimic those caused by other common problems, such as insect injury, soil compaction, nutrient deficiency, and herbicide injury, among other things. Some of the most common types of plant symptoms may range from subtle to severe and can be manifested as root injury, such as necrosis and malformation and stunting, chlorosis, and wilting of the aboveground plant parts. Nematodes may be distributed over an entire field, but the most concentrated population densities are in patches that are seemingly randomly distributed in the field. This distribution can cause the development of ‘hot spots' in a field (photo). In the most severe cases, plants at the center of these hot spots can die due to the severe injury or at least sustain substantial root injury.
comment: Multiple plant-parasitic nematodes feed on corn roots including Pratylenchus, Longidorus, Xiphinema, and Helicotylenchus spp., among others. Although prevalent, they rarely cause economic damage. Symptoms of nematode damage can occur any time during the season and include poor or uneven stands, chlorosis, stunting, and small or poorly filled ears. Often, symptoms occur in patches where nematode numbers are highest. Because these symptoms are not unique, damage is often attributed to causes such as weather, soil conditions, or fertility. Root symptoms depend on the type of nematode involved and can include stunting, root swelling and malformation, and discolored lesions.

Nepal
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:NP
agricultural land: 42,590 sq. km
area: 147,181 km²
climate: varies from cool summers and severe winters in north to subtropical summers and mild winters in south
density: 184/km²
latitude: 84.12781
longitude: 28.395052
population: 29,519,114 (2008)
comment: Nepal, officially the Federal Democratic Republic of Nepal, is a sovereign country in South Asia. It is mainly in the Himalayas, but also includes parts of the Indo-Gangetic Plain. It is the 49th largest country by population and 93rd largest country by area. It is landlocked and borders Tibet in the north and India in the south, east and west, while Bangladesh is located only 27 km from its southeastern tip and Bhutan is separated from it by the Indian state of Sikkim. Nepal has a diverse geography, including fertile plains, subalpine forested hills, and eight of the world's ten tallest mountains, including Mount Everest, the highest point on Earth. Kathmandu is the capital and the largest city. Nepal is a multiethnic country, with Nepali as the official language.

Nephotettix Bipunctatus
type: NamedIndividual
type: Crop Pest

Net Blotch
type: NamedIndividual
type: Crop Disease
affects: Barley
causes: Fungus
development: High humidity promotes spread of the disease.
management: Rotate barley with resistant crops; grow resistant varieties; remove and crop residue from soil surface; destroy volunteer barley plants.
symptoms: Dark green water soaked spots; narrow brown blotches with netted appearance, surrounding tissue yellow; stripes running the length of leaf.

Netherlands
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Marine Climate
ISO code: ISO 3166-1:NL
agricultural land: 18,948 sq. km
area: 41,526 km²
climate: temperate; marine; cool summers and mild winters
density: 396/km²
latitude: 5.262636
longitude: 52.163074
population: 16,440,113 (2008)
comment: The Netherlands, informally Holland, is a country primarily located in Western Europe and partly in the Caribbean. It is the largest of four constituent countries of the Kingdom of the Netherlands. In Europe, the Netherlands consists of twelve provinces, bordering Germany to the east, Belgium to the south, and the North Sea to the northwest, with maritime borders in the North Sea with those countries and the United Kingdom. In the Caribbean, it consists of three special municipalities: the islands of Bonaire, Sint Eustatius and Saba. The country's official language is Dutch, with West Frisian as a secondary official language in the province of Friesland, and English and Papiamento as secondary official languages in the Caribbean Netherlands. Dutch Low Saxon and Limburgish are recognised regional languages, while Sinte Romani and Yiddish are recognised non-territorial languages.

Netherlands Antilles
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:AN
area: 800 km²
climate: tropical; ameliorated by northeast trade winds
density: 229/km²
latitude: -66.05044
longitude: 15.021722
population: 183,000 (2005)
comment: The Netherlands Antilles was a constituent country of the Kingdom of the Netherlands. The country consisted of several island territories located in the Caribbean Sea. The islands were also informally known as the Dutch Antilles. The country came into being in 1954 as the autonomous successor of the Dutch colony of Curaçao and Dependencies and was dissolved in 2010. The Dutch colony of Surinam, although it was relatively close by on the continent of South America, did not become part of the Netherlands Antilles but became a separate autonomous country in 1954. All the island territories that belonged to the Netherlands Antilles remain part of the kingdom today, although the legal status of each differs. As a group they are still commonly called the Dutch Caribbean, regardless of their legal status. People from this former territory continue to be called Antilleans in the Netherlands.

Neural Designer
type: NamedIndividual
type: Toolkit

New Caledonia
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:NC
agricultural land: 2,510 sq. km
area: 18,575 km²
climate: tropical; modified by southeast trade winds; hot, humid
density: 13/km²
latitude: 167.81303
longitude: -21.132204
population: 244,410 (1/2008)
comment: New Caledonia is a special collectivity of France in the southwest Pacific Ocean, south of Vanuatu, about 1,210 km east of Australia and 17,000 km from Metropolitan France. The archipelago, part of the Melanesia subregion, includes the main island of Grande Terre, the Loyalty Islands, the Chesterfield Islands, the Belep archipelago, the Isle of Pines, and a few remote islets. The Chesterfield Islands are in the Coral Sea. French people, especially locals, call Grande Terre Le Caillou .

New Zealand
type: NamedIndividual
type: Country
belongs to: Oceania
has climate: Marine Climate
ISO code: ISO 3166-1:NZ
agricultural land: 113,710 sq. km
area: 268,680 km²
climate: temperate with sharp regional contrasts
density: 15/km²
latitude: 172.4634
longitude: -41.2121
population: 4,280,0005 (9/2008)
comment: New Zealand is an island country in the southwestern Pacific Ocean. It consists of two main landmasses—the North Island and the South Island —and more than 700 smaller islands, covering a total area of 268,021 square kilometres . New Zealand is about 2,000 kilometres east of Australia across the Tasman Sea and 1,000 kilometres south of the islands of New Caledonia, Fiji, and Tonga. The country's varied topography and sharp mountain peaks, including the Southern Alps, owe much to tectonic uplift and volcanic eruptions. New Zealand's capital city is Wellington, and its most populous city is Auckland.

Nezara Viridula
type: NamedIndividual
type: Crop Pest
comment: NezaraViridula (Linnaeus)

Nicaragua
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:NI
agricultural land: 51,460 sq. km
area: 129,494 km²
climate: tropical in lowlands, cooler in highlands
density: 42/km²
latitude: -85.14119
longitude: 12.866726
population: 5,603,000 (2006)
comment: Nicaragua, officially the Republic of Nicaragua, is the largest country in the Central American isthmus, bordered by Honduras to the northwest, the Caribbean to the east, Costa Rica to the south, and the Pacific Ocean to the southwest. Managua is the country's capital and largest city and is also the third-largest city in Central America, behind Tegucigalpa and Guatemala City. The multi-ethnic population of six million includes people of indigenous, European, African, and Asian heritage. The main language is Spanish. Indigenous tribes on the Mosquito Coast speak their own languages and English.

Niger
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:NE
agricultural land: 437,820 sq. km
area: 1,267,000 km²
climate: desert; mostly hot, dry, dusty; tropical in extreme south
latitude: 8.080946
longitude: 17.611
population: 13,272,679 (2008)
comment: Niger or the Niger, officially the Republic of the Niger, is a landlocked country in West Africa named after the Niger River. Niger is bordered by Libya to the northeast, Chad to the east, Nigeria to the south, Benin and Burkina Faso to the southwest, Mali to the west, and Algeria to the northwest. Niger covers a land area of almost 1,270,000 km2, making it the largest country in West Africa. Over 80% of its land area lies in the Sahara Desert. The country's predominantly Muslim population of about 22 million live mostly in clusters in the far south and west of the country. The capital and largest city is Niamey, located in Niger's southwest corner.

Nigeria
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:NG
agricultural land: 762,000 sq. km
area: 923,768 km²
climate: varies; equatorial in south, tropical in center, arid in north
density: 145/km²
latitude: 8.674252
longitude: 9.084575
population: 148,000,000 (2007)
comment: Nigeria, officially the Federal Republic of Nigeria, is a country in West Africa bordering Niger in the north, Chad in the northeast, Cameroon in the east, and Benin in the west. Its southern coast is on the Gulf of Guinea in the Atlantic Ocean. It is a federal republic comprising 36 states and the Federal Capital Territory, where the capital, Abuja, is located. Lagos is the most populous city in the country and the African continent, as well as one of the largest metropolitan areas in the world.

Nighttime Cooling
type: NamedIndividual
type: Planetary Phenomena
comment: Night cooling refers to the operation of natural ventilation at night in order to purge excess heat and cool the building fabric.

NilaparvataLugens
type: NamedIndividual
type: Crop Pest
comment: Nilaparvata Lugens
comment: Nilaparvata Lugens (Stal)

Nimblewill
type: NamedIndividual
type: Crop Disease
type: Weeds
life cycle: Nimblewill is a warm-season perennial grass, which flowers from August to October. It turns brown at the first frost and is very slow to green up in the spring. It spreads by seeds and spreading stems.
management: Management Strategies 1. Maintenance. Maintain a dense, healthy lawn through proper turf selection, establishment, and maintenance. Improve soil drainage, reduce shade and increase air movement. 2. Chemical control. Presently only one herbicide available to homeowners, Tenacity (active ingredient mesotrione), provides selective control of nimblewill in cool-season lawns such as Kentucky bluegrass, tall fescue, fine fescues and perennial ryegrass. Application rates can vary so follow label directions. It is not approved for use on warm-season grasses such as zoysia grass. Several applications are needed for good control and a non-ionic surfactant should be added to the mix. Nimblewill will become bleached and white as it dies. A cheaper control method is to apply a non-selective, post-emergent herbicide such as glyphosate (Roundup) to patches of nimblewill and then replant the areas. Apply the herbicide when the nimblewill is actively growing during the summer. Applying in late autumn to early spring when it is dormant will not provide control. Glyphosate, being a non-selective herbicide, will kill desirable lawn grasses as well. 3. Watering. Improve drainage as nimblewill thrives in poorly drained moist soil. Organic Strategies Strategies 1 and 4 are strictly organic approaches.
symptoms: Nimblewill has thin flat leaves with four veins on the upper surface; its leaves are usually less than 2 inches long and are blue-green. Leaves are rolled in the bud with a very short membranous ligule and only a few hairs near the ligule. The hard thin stems begin growing upright but later often lie flat on the ground near the base and become upright farther up the stem. It can be confused with creeping bentgrass which is finer in texture with narrow green leaves, a large, pointed ligule and no hairs around the ligule. Another look-a-like is Bermuda grass, which can be distinguished by its fringed ligule of fringed hairs.
comment: Nimblewill, Muhlenbergia schreberi, is a warm-season perennial grass, which forms dense mats one or more feet in diameter in sun or shade. Patches look fuzzy, somewhat like a scouring pad, with a dull, blue-green tinge making them quite noticeable against the greener lawn grasses. Its stoloniferous growth pattern makes it resemble creeping bentgrass and Bermuda grass. Because it is a warm-season grass, patches of nimblewill appear as brown, dead spots early in the year and can be confused with actual dead spots. During the growing season its blue-green color and "fuzzy" faded appearance helps to differentiate it from other grasses. It is weakly attached to its roots and pulls up easily. It grows well in either sun or shade and often invades shady areas of turf where other more desirable grasses find it difficult to grow.

Nitrogenous
type: NamedIndividual
type: Chemical Fertilizer
comment: A nitrogenous base, or nitrogen-containing base, is an organic molecule with a nitrogen atom that has the chemical properties of a base. The main biological function of a nitrogenous base is to bond nucleic acids together. A nitrogenous base owes its basic properties to the lone pair of electrons of a nitrogen atom. The nitrogenous fertilizer industry includes the production of synthetic ammonia, nitric acid, ammonium nitrate, and urea. Synthetic ammonia and nitric acid are used primarily as intermediates in the production of ammonium nitrate and urea fertilizers.

Nitrogenous Fertilizers
type: NamedIndividual
type: Chemical Fertilizer

Niue
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:NU
area: 269 km²
climate: tropical; modified by southeast trade winds
latitude: -169.86308
longitude: -19.051632
population: 1,679 (2007)
comment: Niue is an island country in the South Pacific Ocean, 2,400 kilometres northeast of New Zealand. Niue's land area is about 261 square kilometres and its population, predominantly Polynesian, was about 1,600 in 2016. Niue is located in a triangle between Tonga, Samoa, and the Cook Islands. It is 604 kilometers northeast of Tonga. The island is commonly referred to as "The Rock", which comes from the traditional name "Rock of Polynesia". Niue is one of the world's largest coral islands. The terrain of the island has two noticeable levels. The higher level is made up of a limestone cliff running along the coast, with a plateau in the centre of the island reaching approximately 60 metres above sea level. The lower level is a coastal terrace approximately 0.5 km wide and about 25–27 metres high, which slopes down and meets the sea in small cliffs. A coral reef surrounds the island, with the only major break in the reef being in the central western coast, close to the capital, Alofi.

No Cloud Cover
type: NamedIndividual
type: Cloud Cover

No Precipitation
type: NamedIndividual
type: Precipitation

No Wind
type: NamedIndividual
type: Wind Type

Non Acidic Soils
type: NamedIndividual
type: Soil Type

None
type: NamedIndividual
type: State

Norfolk Island
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:NF
area: 34.6 km²
climate: subtropical; mild, little seasonal temperature variation
density: 61.1/km²
latitude: 167.95622
longitude: -29.064198
population: 2,128 (2008)
comment: Norfolk Island is an external territory of Australia located in the Pacific Ocean between New Zealand and New Caledonia, 1,412 kilometres directly east of Australia's Evans Head and about 900 kilometres from Lord Howe Island. Together with the neighbouring Phillip Island and Nepean Island, the three islands collectively form the Territory of Norfolk Island. At the 2016 census, it had 1748 inhabitants living on a total area of about 35 km2 . Its capital is Kingston.

North America
type: NamedIndividual
type: Continent
has country: Anguilla
has country: Antigua and Barbuda
has country: Aruba
has country: Bahamas
has country: Barbados
has country: Belize
has country: Bermuda
has country: Canada
has country: Cayman Islands
has country: Costa Rica
has country: Cuba
has country: Dominica
has country: Dominican
has country: El Salvador
has country: Greenland
has country: Grenada
has country: Guadeloupe
has country: Guatemala
has country: Haiti
has country: Honduras
has country: Jamaica
has country: Martinique
has country: Mexico
has country: Montserrat
has country: Netherlands Antilles
has country: Nicaragua
has country: Panama
has country: Puerto Rico
has country: Saint Kitts and Nevis
has country: Saint Lucia
has country: Saint Pierre and Miquelon
has country: Saint Vincent and the Grenadines
has country: Trinidad and Tobago
has country: United States

Northern Corn Leaf Blight
type: NamedIndividual
type: Crop Disease
comment: Northern corn leaf blight (NCLB), caused by the fungus Exserohilum turcicum, is an increasingly important disease in the U.S. Corn Belt. The disease has appeared annually in Indiana, and has increased in prevalence since the mid- to late 2000s. NCLB can cause yield loss if it develops before or during the tasseling and silking phases of corn development. Hybrid susceptibility, cropping practices, and weather strongly influence disease development.

Northern Leaf Blight
type: NamedIndividual
type: Crop Disease
comment: Northern corn leaf blight (NCLB) or Turcicum leaf blight (TLB) is a foliar disease of corn (maize) caused by Exserohilum turcicum, the anamorph of the ascomycete Setosphaeria turcica. With its characteristic cigar-shaped lesions, this disease can cause significant yield loss in susceptible corn hybrids.

Northern Mariana Islands
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:MP
agricultural land: 30 sq. km
area: 477 km²
climate: tropical marine; moderated by northeast trade winds, little seasonal temperature variation; dry season December to June, rainy season July to October
density: 168/km²
latitude: 145.48364
longitude: 17.325079
population: 86,616 (2007)
comment: The Northern Mariana Islands, officially the Commonwealth of the Northern Mariana Islands, is an unincorporated territory and commonwealth of the United States consisting of 14 islands in the northwestern Pacific Ocean. The CNMI includes the 14 northernmost islands in the Mariana Archipelago; the southernmost island, Guam, is a separate U.S. territory. The CNMI and Guam are the westernmost territories of the United States.

Norway
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:NO
agricultural land: 9,980 sq. km
area: 385,252 km²
climate: temperate along coast, modified by North Atlantic Current; colder interior with increased precipitation and colder summers; rainy year-round on west coast
density: 12/km²
latitude: 17.79853
longitude: 64.57658
population: 4,805,437 (1/2009)
comment: Norway, officially the Kingdom of Norway, is a Nordic country in Northern Europe whose mainland territory comprises the western and northernmost portion of the Scandinavian Peninsula. The remote Arctic island of Jan Mayen and the archipelago of Svalbard also form part of Norway. Bouvet Island, located in the Subantarctic, is a dependency of Norway; it also lays claims to the Antarctic territories of Peter I Island and Queen Maud Land.

November
type: NamedIndividual
type: Month Of Year

Nucleotide
type: NamedIndividual
type: Biomolecule
comment: Nucleotides are organic molecules consisting of a nucleoside and a phosphate. They serve as monomeric units of the nucleic acid polymers deoxyribonucleic acid (DNA) and ribonucleic acid (RNA), both of which are essential biomolecules within all life-forms on Earth. Nucleotides are obtained in the diet and are also synthesized from common nutrients by the liver.

Nutrient Deficiency
type: NamedIndividual
type: Plant Disorder
related to: Nutrient

Nutrient Stress
type: NamedIndividual
type: Crop Stress
related to: Nutrient
comment: Nutrient stress in crops results into symptoms that may be detected by the use of hyperspectral or measured by multispectral imaging. Both heavy metal contamination and deficiencies in nutrients soils can be assessed with the technology.

Ozone (O3)
type: NamedIndividual
type: Gas
Acronym: O3
comment: Ozone, or trioxygen, is an inorganic molecule with the chemical formula O3.

Oak Wilt
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Oak
life cycle: The fungus spreads through the water-conducting vessels of the sapwood. The tree’s response to the presence of the fungus results in the disruption of sap flow, and the affected areas wilt. Oak wilt can spread to healthy trees through natural grafts with roots of adjacent oaks of the same species up to 50 feet apart. Root grafts join together the vascular systems of the trees, forming a network through which the disease can spread. The disease can also spread by sap-feeding beetles that transmit spores of the oak wilt fungus from infected trees to healthy ones.
management: Management Strategies 1. There is no cure for the disease. If oak wilt is suspected, a laboratory test is needed to make a positive diagnosis. Contact an arborist or an extension office on sampling procedures and fees associated with the laboratory testing. Samples of freshly wilted stems (not dead) about 1/2 to 1 inch in diameter and 6–10 inches in length are needed for the laboratory test. 2. Sever root grafts. Destroying root grafts with chemicals or by mechanical means can slow the spread of the disease from diseased to healthy oak trees. Since there is a delay between infection and the appearance of symptoms, destroying root grafts is a gamble. Root grafts do not occur between the black oak and white oak groups. 3. Improve plant vigor. Your best guard against getting oak wilt is to keep your oak trees, especially oaks in the black oak group, healthy. If your oak trees do not appear in the best of health, have an experienced arborist evaluate their health and recommend a course of action. Mild cases in white oaks may respond to pruning of diseased wood plus fertilizing and watering to increase plant vigor. 4. Avoid pruning or wounding the tree between mid-March and late June. During this time of year, insects carrying the disease are attracted by the sap which flows freely from wounds. The safest time to prune oaks is during winter before mid-March. 5. Plant white oaks rather than the more susceptible black oaks. If you do plant black oaks, be certain they are more than 50 feet apart to eliminate future disease spread via root grafts. Limit black oak use where oak wilt is prevalent.
symptoms: The first symptoms include a dull-green appearance of wilted leaves. Later, wilted leaves curl and turn tan or bronze, beginning at the outer portions of the leaves. The base of the leaf and the main vein will remain green for some time. Defoliation may be delayed for weeks. Peeled bark or a cut branch from an infected tree may show a brown or black discoloration in the outer annual sapwood ring. Positive diagnosis of oak wilt requires laboratory culturing and identification.
comment: Oak wilt is a systematic disease caused by a fungus, Ceratocystis fagacearum. The fungus invades the water-conducting tissues of oak trees. The black oak group (red, black, scarlet, and pin oaks) is more susceptible than the white oak group (white, bur, chinkapin, and swamp oaks). Oak wilt ranges from Minnesota east to Pennsylvania, south to South Carolina and Tennessee, west to central Texas, and north through Kansas and Nebraska. Infection through wounds is especially critical between April 1 and July 1 and during later periods of summer rains. There is no cure for oak wilt, so control consists of measures to prevent the disease from spreading.

Oase
type: NamedIndividual
type: Topography

Oceania
type: NamedIndividual
type: Continent
has country: American Samoa
has country: Australia
has country: Christmas Island
has country: Cocos Islands
has country: Cook Islands
has country: Fiji
has country: Kiribati
has country: Marshall Islands
has country: Micronesia
has country: Nauru
has country: New Caledonia
has country: New Zealand
has country: Niue
has country: Norfolk Island
has country: Palau
has country: Papua New Guinea
has country: Pitcairn
has country: Samoa
has country: Solomon Islands
has country: Tokelau
has country: Tonga
has country: Tuvalu
has country: Vanuatu
has country: Wallis and Futuna

Oceanic Climate
type: NamedIndividual
type: Climate Type

October
type: NamedIndividual
type: Month Of Year

Oleander
type: NamedIndividual
type: Crop
type: Flower

Oleocellosis
type: NamedIndividual
type: Crop Disease
type: Crop Injure
affects: Citrus
comment: Oleocellosis (sometimes known as ‘oil spotting or burn’) occurs following mechanical injury to the fruit. It is caused by the rupturing of oil glands releasing toxic oil during harvest and/or transport from the field to the packing house. This oil kills the nearby cells of the flavedo and can also cause spotting on the surface of adjacent fruit. It is more common when fruit is turgid and the weather is cold and/or wet at harvest. Oleocellosis predisposes fruit to storage rots, chilling injury and burning from ozone (when used to control diseases that develop during storage). The oil from the ruptured cells inhibits degreening and discolours the rind. Careful harvesting, transport and use of GA helps reduce the problem.

Oman
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:OM
agricultural land: 17,705 sq. km
area: 309,500 km²
climate: dry desert; hot, humid along coast; hot, dry interior; strong southwest summer monsoon (May to September) in far south
density: 8.3/km²
latitude: 55.91864
longitude: 21.516863
population: 2,577,000 (2006)
comment: Oman, officially the Sultanate of Oman, is a country on the southeastern coast of the Arabian Peninsula in Western Asia and the oldest independent state in the Arab world. Located in a strategically important position at the mouth of the Persian Gulf, the country shares land borders with the United Arab Emirates to the northwest, Saudi Arabia to the west, and Yemen to the southwest, and shares marine borders with Iran and Pakistan. The coast is formed by the Arabian Sea on the southeast and the Gulf of Oman on the northeast. The Madha and Musandam exclaves are surrounded by the UAE on their land borders, with the Strait of Hormuz and the Gulf of Oman forming Musandam's coastal boundaries.

OpenNLP
type: NamedIndividual
type: Toolkit
Acronym: OpenNLP
comment: Apache OpenNLP - Data Analysis and Sentiment Analysis Accessibility is essential when you need a tool for long-term use, which is challenging in the realm of Natural Language Processing open-source tools. Because while being powered with the right features, it could be too complex to use. Apache OpenNLP is an open-source library for those who prefer practicality and accessibility. Like Stanford CoreNLP, it uses Java NLP libraries with Python decorators. While NLTK and Stanford CoreNLP are state-of-the-art libraries with tons of additions, OpenNLP is a simple yet useful tool. Besides, you can configure OpenNLP in the way you need and get rid of unnecessary features. Apache OpenLP is the right choice for: Named Entity Recognition Sentence Detection POS tagging Tokenization You can use OpenNLP for all sorts of text data analysis and sentiment analysis operations. It is also perfect in preparing text corpora for generators and conversational interfaces.

Opium Poppy
type: NamedIndividual
type: Flower
comment: Papaver somniferum, commonly known as the opium poppy or breadseed poppy, is a species of flowering plant in the family Papaveraceae. It is the species of plant from which both opium and poppy seeds are derived and is also a valuable ornamental plant, grown in gardens. Its native range is probably the eastern Mediterranean, but is now obscured by ancient introductions and cultivation, being naturalized across much of Europe and Asia.

Optimum Humidity
type: NamedIndividual
type: Humidity

Oracle Data Mining
type: NamedIndividual
type: Toolkit

Orange rust
type: NamedIndividual
type: Crop Disease
affects: Sugarcane
comment: Orange rust does not usually kill plants, but it can significantly reduce vegetative growth and yield. The disease is caused by the fungus Gymnoconia nitens. The orange spores are spread by wind and infect leaves of healthy plants during long periods of leaf wetness provided by rain or dew. Orange rust is favored by relatively low temperatures (50-70 degrees Fahrenheit) and wet conditions. The fungus is systemic and overwinters in the crown and roots of infected plants, leading to the production of new infected canes every year. Without management, the number of infected plants in a field may increase from year to year until most plants are infected.

Orchid
type: NamedIndividual
type: Flower

Organic Soils
type: NamedIndividual
type: Soil Type

Oriental fruit fly
type: NamedIndividual
type: Crop Pest

Ornamental
type: NamedIndividual
type: Crop
comment: Ornamental
comment: Ornamental plants are plants that are grown for decorative purposes in gardens and landscape design projects, as houseplants, cut flowers and specimen display. The cultivation of ornamental plants is called floriculture, which forms a major branch of horticulture.

Overcast
type: NamedIndividual
type: Cloud Cover

Phased Array type L-band Synthetic Aperture Radar (PALSAR)
type: NamedIndividual
type: Satellite

PK compounds
type: NamedIndividual
type: Chemical Fertilizer

PROBA-V
type: NamedIndividual
type: Satellite
comment: PROBA-V, or PROBA-Vegetation (the V standing for vegetation and not the Roman numeral for 5), is a satellite in the European Space Agency's PROBA series.

Chlorops oryzae
type: NamedIndividual
type: Crop Pest
controls: Outbreaks of C. oryzae occurred throughout Japan in 1935-1942 and 1949-1955. The acreage of land under susceptible rice varieties and the amount of fertilizer applied appeared to be the main factors affecting the occurrence of C. oryzae. Ecological data and control methods have since been published by a number of authors in Japan. The development of resistant varieties, the cultural manipulation of crops and their environment, and chemical control have all been evaluated as methods of controlling C. oryzae.
symptoms: Newly hatched larvae of C. oryzae burrow into the stems of rice plants and move to the growing points where they feed on developing leaves. Leaves injured by the maggots exhibit punctures or discoloration. The first-generation larvae of the trivoltine type feed on three to six developing leaves. The last one or two injured leaves have large, characteristic scars arranged in two rows. These scars are produced by third-instar larvae. The larvae of the second generation in the trivoltine type and the first generation in the bivoltine type feed on developing leaves and young panicles. The injured leaves have small punctures caused by the first- to second-instar larvae. The injured ears have white glumes and punctured kernels and do not bend at the ripening stage.
label: Paddy Stem Maggot
label: Rice stem maggot

Pakistan
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:PK
agricultural land: 265,500 sq. km
area: 803,940 km²
climate: mostly hot, dry desert; temperate in northwest; arctic in north
density: 206/km²
latitude: 69.35975
longitude: 30.395847
population: 172,800,000 (2008)
comment: Pakistan, officially the Islamic Republic of Pakistan, is a country in South Asia. It is the world's fifth-most populous country with a population exceeding 212.2 million, and has the world's second-largest Muslim population. Pakistan is the 33rd-largest country by area, spanning 881,913 square kilometres . It has a 1,046-kilometre coastline along the Arabian Sea and Gulf of Oman in the south and is bordered by India to the east, Afghanistan to the west, Iran to the southwest, and China to the northeast. It is separated narrowly from Tajikistan by Afghanistan's Wakhan Corridor in the northwest, and also shares a maritime border with Oman.

Palau
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:PW
agricultural land: 50 sq. km
area: 459 km²
climate: tropical; hot and humid; wet season May to November
density: 43/km²
latitude: 130.1017
longitude: 4.525059
population: 20,842 (2007)
comment: Palau, officially the Republic of Palau and historically Belau, Palaos or Pelew, is an island country located in the western Pacific Ocean. The country contains approximately 340 islands, and together with parts of the Federated States of Micronesia, forms the western chain of the Caroline Islands. Its area is 466 square kilometers . The most populous island is Koror. The capital Ngerulmud is located on the nearby island of Babeldaob, in Melekeok State. Palau shares maritime boundaries with international waters to the north, Micronesia to the east, Indonesia to the south, and the Philippines to the west.

Palestinian Territory
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:PS
latitude: 35.233154
longitude: 31.952162
comment: The term "Palestinian territories" has been used for many years to describe the territories occupied by Israel since 1967, namely the West Bank and the Gaza Strip. More recently, the official United Nations terminology has been used, occupied Palestinian territory increasingly replacing other terms since 1999. The European Union also has adopted this usage. The term Occupied Palestinian Territory was used by the UN and other international organizations between October 1999 and December 2012 to refer to areas controlled by the Palestinian National Authority. The EU had utilized a parallel term Palestinian Authority territories occasionally during the same period. The International Court of Justice referred to the West Bank, including East Jerusalem, as "the Occupied Palestinian Territory" and this term was used as the legal definition by the ICJ in the ruling in July 2004.

Panama
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:PA
agricultural land: 22,670 sq. km
area: 75,517 km²
climate: tropical maritime; hot, humid, cloudy; prolonged rainy season (May to January), short dry season (January to May)
density: 43/km²
latitude: -80.11278
longitude: 8.421168
population: 3,309,679 (2008)
comment: Panama, officially the Republic of Panama, is a transcontinental country in Central America and South America, bordered by Costa Rica to the west, Colombia to the southeast, the Caribbean Sea to the north, and the Pacific Ocean to the south. The capital and largest city is Panama City, whose metropolitan area is home to nearly half the country's 4 million people.

Papua New Guinea
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:PG
agricultural land: 11,900 sq. km
area: 462,840 km²
climate: tropical; northwest monsoon (December to March), southeast monsoon (May to October); slight seasonal temperature variation
density: 13/km²
latitude: 150.16052
longitude: -6.267873
population: 6,300,000 (2007)
comment: Papua New Guinea, officially the Independent State of Papua New Guinea, is a country in Oceania that comprises the eastern half of the island of New Guinea and its offshore islands in Melanesia . Its capital, located along its southeastern coast, is Port Moresby. It is the world's third largest island country with 462,840 km2 .

Paraguay
type: NamedIndividual
type: Country
belongs to: South America
ISO code: ISO 3166-1:PY
agricultural land: 209,900 sq. km
area: 406,752 km²
climate: subtropical to temperate; substantial rainfall in the eastern portions, becoming semiarid in the far west
density: 15/km²
latitude: -58.453217
longitude: -23.45139
population: 6,158,000 (2005)
comment: Paraguay, officially the Republic of Paraguay, is a country in South America. It is bordered by Argentina to the south and southwest, Brazil to the east and northeast, and Bolivia to the northwest. Although one of only two landlocked countries in South America, the country has coasts, beaches and ports on the Paraguay and Paraná rivers that give exit to the Atlantic Ocean through the Paraná-Paraguay Waterway.

Parasa Lepida
type: NamedIndividual
type: Crop Pest

Karnal Bunt
type: NamedIndividual
type: Crop Disease
affects: Wheat
causes: The fungus, Tilletia indica, which can be introduced in contaminated seed. The fungus may become established as persistent spores in soil. It is not currently known to be in Pacific Northwest soils. If it or the disease were detected in locally produced wheat, the industry could suffer serious economic damage because the fungus is strictly regulated in international and domestic seed and grain trades.
controls: Controls have not been determined for this region. Pathogen-free seed continues to be the most effective way to prevent introducing the karnal bunt pathogen into Pacific Northwest wheat.
symptoms: Karnal bunt is at best difficult to detect and identify in the field. A microscope and biochemical tests are required to accurately identify the pathogen. The most conspicuous symptom is the presence of small numbers of harvested seeds containing tissue that is gray at first but later turns black. Discoloration often is limited to the embryo end only but may extend over the entire kernel.
comment: Karnal bunt (also known as partial bunt) is a fungal disease of wheat, durum wheat, and triticale. The smut fungus Tilletia indica, a basidiomycete, invades the kernels and obtains nutrients from the endosperm, leaving behind waste products with a disagreeable odor that makes bunted kernels too unpalatable for use in flour or pasta. While Karnal bunt generally does not lead to devastating crop losses, it has the potential to dramatically decrease yield and poses additional economic concerns through quarantines which limit the export of suspected infectious wheat products from certain areas, including the U.S. Several chemical control methods exist for Karnal bunt of wheat, but much work remains to be done in identifying resistant host varieties.
label: Partial Bunt

Partially Mature
type: NamedIndividual
type: State
is state of: Maturity

Partly Cloudy
type: NamedIndividual
type: Cloud Cover

Pattern
type: NamedIndividual
type: Toolkit
Acronym: Pattern
comment: Pattern is a web mining module for Python. It has tools for: - Data Mining: web services (Google, Twitter, Wikipedia), web crawler, HTML DOM parser - Natural Language Processing: part-of-speech taggers, n-gram search, sentiment analysis, WordNet - Machine Learning: vector space model, clustering, classification (KNN, SVM, Perceptron) - Network Analysis: graph centrality and visualization. It is well documented, thoroughly tested with 350+ unit tests and comes bundled with 50+ examples. The source code is licensed under BSD. Pattern, multi-purpose, open-source library, can be used for several different tasks — network analysis, text processing, machine learning, data mining and NLP. In the Pattern library, the parse method takes care of functions for tokenising and POS tagging. Pattern is very popular among students for its simple and straightforward syntax. In addition, it is easy to understand and comes to the use of web developers who need to work with text data.

Peat Soil
type: NamedIndividual
type: Soil Type
comment: Peat soil - are high in organic matter and retain a large amount of moisture. This type of soil is very rarely found in a garden and often imported into a garden to provide an optimum soil base for planting.

Pentfaleus Major
type: NamedIndividual
type: Crop Pest

Peony Blotch
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
life cycle: The late-season blotch phase provides the overwintering inoculum for the next year's infection, especially in older varieties. Fungicides work only to prevent infection; once the disease takes hold, there is no treatment.
management: Management Strategies 1. Remove debris. Be sure to remove all foliage to ground level each fall and destroy. Infected debris should not be added to the compost pile. 2. Apply fungicides. When the shoots are 2–4 inches high and the weather is cool, overcast, and damp, spray weekly with a fungicide until the flowers open. Reapply following rain or overhead watering. Mancozeb is currently registered for controlling peony blotch. 3. Resistant varieties. Replant with newer resistant varieties that are vigorous and thick-stemmed. 4. Watering and air circulation. Provide good air circulation by proper spacing of plants and foliage thinning to reduce humidity and promote leaf drying. Water early in the day, so that leaves dry thoroughly by night; drip irrigation is best. Organic Strategies Strategies 1, 3 and 4 are strictly organic approaches.
symptoms: Small, circular, red or purplish spots first appear superficially on the upper surface of young leaves. Later in the season they coalesce into large, glossy, irregular, dark purple blotches while the underside of the leaves becomes a dull chestnut brown. Short reddish-brown streaks appear on the young stems and petioles, and eventually the whole plant may be affected with purplish or brownish red spots.
comment: Peony blotch on peony (Paeonia), also called red spot or measles, this disease affects all aboveground parts of the peony. Caused by the fungal pathogen, Cladosporium paeoniae, this disease occurs each spring just before bloom. It is mostly found on older varieties and is not immediately lethal, though repeated bouts for several years will affect plant vitality. It does not cause early leaf drop or stem dieback but causes the plant to be unsightly and lose its attractiveness as spots coalesce to form blighted areas.

Pepper lace bug
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Black Pepper
definition: Insect is a damaging pest of black pepper in Malaysia and Indonesia
management: Small populations of the insect can be hand picked from plants and destroyed; insecticide application may be required to control high populations
symptoms: Brown or lack discoloration on inflorescences inflorescence and young berries wilting and turning gray; high infestations can cause inflorescence to collapse; lace bugs are sucking insects, the adult is gray-black in color and has distinctive horn-like protrusions on its shoulders; nymphs are light brown in color with a row af dark bristles on each side
References: https://plantvillage.psu.edu/topics/black-pepper/infos

Perennial Broadleaf Weeds
type: NamedIndividual
type: Crop Disease
type: Weeds
management: Management Strategies 1. Hand dig or spot treat. If only a few weeds are present, hand dig the weeds with a forked tool to cut the root off well below ground level. You can also spot treat using glyphosate (Roundup, Kleenup) or the chemicals listed below. Since glyphosate (Roundup, Kleenup) is NOT selective, it will also kill any grass you get it on so apply very carefully. This method works best with weeds such as thistle or large plantain plants. The herbicide can also be brushed on individual weeds, avoiding the grass. NOTE: 2,4-D products listed below used according to label directions will not damage grass caught in the overspray. 2. Use chemical herbicides. When problem areas are too large for spot treating, hose-end products are the easiest for homeowners to use. Garden centers and hardware stores carry many different brands that contain various combinations of 2,4- D, MCPA, MCPP, and dicamba, weed killers that control broadleaf weeds. First establish the identity of the weeds, then check product labels to determine which herbicide best controls the weeds you have. Then, be sure and follow label directions. Some weeds may require more than one application. Perennial weeds are best killed when they are actively growing, which is usually in spring and fall. Herbicides applied in the summer are usually less effective. 3. Shade out the weeds. Areas of weeds can also be killed by covering the affected areas with black plastic, felt paper, boards, or cardboard until the weeds are dead. This will kill all plants that are shaded, including both desirable and undesirable perennial grasses. This may take several weeks. The killed areas will need to be tilled and reseeded or sodded at a later date.
comment: Some of the most common perennial broadleaf weeds include bindweed, chicory, creeping bellflower, dandelion, ground ivy, mouseear chickweed, plantain, thistle, violets, and white clover. They differ from annual weeds in that they continue to grow year after year and once established, they are not affected by preemergent herbicides.

Perennial Grassy Weeds
type: NamedIndividual
type: Crop Disease
type: Weeds
management: Management Strategies 1. Dig or pull plants. This method can be effective for controlling invading, perennial grassy weeds in beds and other non-lawn spaces. Constant vigilance is required because remaining root fragments and other plant parts will usually sprout, leading to renewed infestation. This technique cannot be recommended for use in turf except on a very limited basis. The usual practices of maintaining healthy, vigorous lawns comprised of cool-season grasses will not exclude invasion by nimblewill, Bermudagrass, or zoysiagrass from infested adjoining plots. 2. Use herbicides. Perennial grassy weeds can usually be controlled only by the application of non-selective, general purpose herbicides, e.g., glyphosate (Roundup, Kleenup) or glyfosinate. Small patches of these weeds in turf may be controlled by spot spraying. Of course, all vegetation will be killed, so the spotted turf must be repaired by proper soil preparation and overseeding with desirable species. Heavily infested, larger areas will probably require broadcast spraying of entire areas as a first step in lawn renovation. Whenever possible, this exercise should be carried out in late summer or early fall. Carefully directed sprays of non-selective herbicides have proven useful for controlling heavy infestation of perennial, grassy weeds in beds or other relatively open spaces. Desirable plants must be protected by adequate shielding to prevent contact with the herbicides during application. Although somewhat tedious, these materials may also be applied by carefully painting them on the weedy plants' surfaces. 3. Use all herbicides according to label instructions. Equipment must be cleaned and should not be used for applications other than herbicides.
comment: The principal perennial grassy weeds in lawns include Bermudagrass (Cynodon dactylon), nimblewill (Muhlenbergia schreberi), quackgrass (Elymus repens), wild fescue (Festuca pratensis), and in certain cases, zoysiagrass (Zoysia spp.). Each of these grasses, except wild fescue, is characterized by a vigorous, invasive growth habit under usual horticultural practices. They are especially troublesome in turf, because, unlike their common broadleaf counterparts, selective herbicides for their control are unknown.

Peru
type: NamedIndividual
type: Country
belongs to: South America
ISO code: ISO 3166-1:PE
agricultural land: 215,000 sq. km
area: 1,285,220 km²
climate: varies from tropical in east to dry desert in west; temperate to frigid in Andes
density: 22/km²
latitude: -75.002365
longitude: -9.181352
population: 29,180,900 (2008)
comment: Peru, officially the Republic of Peru, is a country in western South America. It is bordered in the north by Ecuador and Colombia, in the east by Brazil, in the southeast by Bolivia, in the south by Chile, and in the south and west by the Pacific Ocean. Peru is a megadiverse country with habitats ranging from the arid plains of the Pacific coastal region in the west to the peaks of the Andes mountains extending from the north to the southeast of the country to the tropical Amazon Basin rainforest in the east with the Amazon river. At 1.28 million km2, Peru is the 19th largest country in the world, and the third largest in South America.

Pest Incidence
type: NamedIndividual
type: Crop Disease
type: Crop Pest

Pestalotiopsis theae
type: NamedIndividual
type: Crop Disease
comment: Pestalotiopsis theae is known to be a causal fungus for tea gray blight disease.

Phacidiopycnis Rot
type: NamedIndividual
type: Crop Disease
biological control: Since both Phacidiopycnis species are associated with cankers and dieback, preharvest sanitation practices are key in managing Phacidiopycnis rots. On pear, good pruning and removal of pruned material is recommended to reduce the inoculum load available in the upcoming season. Inoculum of Phacidiopycnis washingtonensis comes mainly from crabapple pollinizers, therefore, a good pruning along with a fungicide treatment of the pollinizers is key in managing the decay preharvest.
chemical control: Preharvest fungicide applications may prevent infections by Phacidiopycnis species. Among the current preharvest fungicides, Topsin-M has been found to be more effective compared to Pristine. Ziram applied within two weeks before harvest provides some control of stem-end and calyx-end rots. Because these two pathogens infect fruit through the stem- and calyx-ends, it is important to ensure good spray coverage of these potential entries to minimize such infections. Recent research has shown that current postharvest fungicides are the most effective in managing both Phacidiopycnis species with fludioxonil (Scholar) being the most effective (Table 2). Difenoconazole (Academy) has been labeled for postharvest application on pome fruit in 2016 and has shown a strong efficacy, especially against P. pyri. Formulations to apply thiabendazole, pyrimethanil and fludioxonil through thermonebulization (fog) exist already. In Washington State, Academy can be applied via drench at harvest if packers can follow specific fungicide waste management practices (https://agr.wa.gov/wastepesticide). A formulation for a dry application of Academy is under development. Phacidiopycnis species have a medium risk for fungicide resistance development, therefore, rotation of fungicides from different FRAC groups in the orchards and warehouse is important to avoid selection for populations that are resistant to current fungicides.
symptoms: Phacidiopycnis species cause infections via three different pathways: stem-end, calyx-end, or through wounds causing respectively stem-end rot, calyx-end rot and wound rot, respectively . Decayed area is spongy. In the early stage of symptom development, the decayed area appears water-soaked. Color of the decayed area varies with age. As the disease progresses, the advanced decayed area turns brown and then black, but the margin of decayed area continues to have a water-soaked appearance. Under high relative humidity, the fungus forms white mycelium. Pycnidia (pimple shaped fruiting bodies) of the fungus often form on the decayed area at advanced stages, starting from the infection sites. In the early stage of symptom development, Phacidiopycnis and speck rots can be misdiagnosed as gray mold or Sphaeropsis rot. The fruit flesh at the margin of Phacidiopycnis rots appears translucent and water-soaked, whereas internal decayed flesh of gray mold usually appears brown. Speck rot is slightly firmer than gray mold and forms brown to black specks around lenticels, especially on red apple cultivars.
comment: Phacidiopycnis rots are emerging postharvest diseases of apple and pear in the U.S. Pacific Northwest. These diseases have previously been reported in Europe and India and most recently in Chile. In Washington State and Oregon, Phacidiopycnis rot is common on pears, whereas speck rot is more common on apples in Washington State. Recent surveys have confirmed that these rots continue to be an emerging threat in the PNW and will require future management attention. Two Phacidiopycnis species are known to cause Phacidiopycnis rot on apple and pear. Phacidiopycnis rot of pear is caused by is the discomycete Potebniamyces pyri (Berk. & Br.) Dennis, anamorph (asexual) Phacidiopycnis pyri (Fuckel) Weindlmayr. The fungus produces macroconidia and microconidia with the later considered spermatia (male spores) in the fungus life cycle. Conidia germinate either by developing germ tubes, when enough nutrients are available, or by budding to produce secondary conidia when nutrient levels are low. On apples, Phacidiopycnis rot, more commonly known as speck rot, is caused by Phacidiopycnis washingtonensis (C.L. Xiao & J.D. Rogers) which was first reported in 2005 in the PNW. This species has since been reported on apples in Europe and Chile. Phacidiopycnis washingtonensis grows slightly faster than P. pyri and both species can infect the other fruit but are more common on their respective hosts, apple and pear.

Phaeosphaeria Leaf Spot
type: NamedIndividual
type: Crop Disease
comment: Phaeosphaeria leaf spot (PLS) is considered one of the major diseases that threaten the stability of maize production in tropical and subtropical African regions. The objective of the present study was to investigate the use of hyperspectral data in detecting the early stage of PLS in tropical maize.

Phaeosphaeria nodorum
type: NamedIndividual
type: Fungus
comment: Phaeosphaeria nodorum (synonym and correct taxonomic name: Parastagonospora nodorum) is a major fungal pathogen of wheat (Triticum aestivum), causing the disease Septoria nodorum blotch, and a member of the Dothideomycetes, a large fungal taxon that includes many important plant pathogens affecting all major crop plant families.
alternative label: P. nodorum

Philippines
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:PH
agricultural land: 121,000 sq. km
area: 300,000 km²
climate: tropical marine; northeast monsoon (November to April); southwest monsoon (May to October)
density: 295/km²
latitude: 121.766556
longitude: 12.867031
population: 90,500,000 (2008)
comment: The Philippines, officially the Republic of the Philippines, is an archipelagic country in Southeast Asia. Situated in the western Pacific Ocean, it consists of about 7,641 islands that are broadly categorized under three main geographical divisions from north to south: Luzon, Visayas, and Mindanao. The capital city of the Philippines is Manila and the most populous city is Quezon City, both within the urban area of Metro Manila. Bounded by the South China Sea to the west, the Philippine Sea to the east, and the Celebes Sea to the southwest, the Philippines shares maritime borders with Taiwan to the north, Japan to the northeast, Palau to the east, Indonesia to the south, Malaysia and Brunei to the southwest, Vietnam to the west, and China to the northwest.

Phoma Lingam
type: NamedIndividual
type: Crop Disease

Phosphate Rock
type: NamedIndividual
type: Chemical Fertilizer

Phosphatic
type: NamedIndividual
type: Chemical Fertilizer
comment: In chemistry, a phosphate is an anion, salt, functional group or ester derived from a phosphoric acid. It most commonly means orthophosphate, a derivative of orthophosphoric acid H3PO4.

Phosphatic Fertilizers
type: NamedIndividual
type: Chemical Fertilizer

Phyllotreta Striolata
type: NamedIndividual
type: Crop Pest
comment: Phyllotreta Striolata (Fabricius)

Physoderma Brown Spot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Corn
management: Although disease resistant germplasm is available in areas where the disease is a more consistent and serious problem, this disease is considered of minor importance in the Midwest and seed companies do not promote resistance to it in their hybrids in this region. There are some fungicides labeled for management of Physoderma brown spot, but this disease is rarely a threat to yield and shouldn't normally require treatment. Accurate identification of this disease is important, though, to avoid unnecessary treatment due to a disease misdiagnoses.
pathogen: Physoderma brown spot is caused by the chytridiomycete fungus, Physoderma maydis (syn. P. zeae-maydis), which is closely related to the oomycete or water mold fungi, such as the downy mildews. This disease is normally an infrequent, minor disease in corn that we observe during most years to a low degree in Nebraska. However, during some summers when weather conditions are more conducive for disease development, the disease can develop and greater incidence and cause alarm because its symptoms are similar to some other more serious diseases.
symptoms: The symptoms of Physoderma brown spot may be confused with some other diseases. Lesions are small and round to oblong, yellowish to brown in color, and can develop on the leaf blade, stalk, sheath, and husks. On the leaf blade, these young lesions can resemble those caused by rusts, such as early southern rust. However, closer inspection of these lesions under higher magnification reveals that they are not rust pustules. And, a more obvious difference is that Physoderma brown spot lesions frequently develop in distinct bands across the leaf, particularly at the base of the leaf. These lesions appear different in the midrib than on the remainder of the leaf blade. On the leaf midrib these lesions tend to be darker in color and sometimes larger, so their difference in appearance in this area from the surrounding leaf blade is a clue to the identity of this disease. As the disease progresses, the lesions expand in size, coalesce with neighboring lesions into larger lesions and darken in color ranging in color from chocolate to reddish brown or purple.
comment: Physoderma brown spot is caused by the fungus Physoderma maydis. Symptoms of Physoderma brown spot usually appear on mid-canopy leaves. Leaf lesions are numerous, very small (approximately ¼ inch in diameter), round to oval, yellowish to brown in color, and usually occur in broad bands across the leaf. Alternating bands of infected and noninfected tissues are common. Dark purplish to black oval spots also occur on the midrib of the leaf. Symptoms also may occur on the stalk, leaf sheath, and husks. Physoderma brown spot is more prevalent in wet growing seasons. Infection requires a combination of light, free water, and warm temperatures (75-85°F). Leaf infection occurs in the whorl when water is present for an extended time. Hybrids vary in their susceptibility to the disease, and crop rotation and tillage reduce survival of inoculum. Foliar fungicides labeled for Physoderma brown spot are available.

Phytophthora Root Rot
type: NamedIndividual
type: Crop Disease
affects: Apple
life cycle: Root rot-causing Phytophthora species can survive in the soil for years, as long as moist conditions persist. It can spread through splashing rain, irrigation water, and runoff water. Disease fungi can spread through contaminated soil and garden equipment as well. Rot is more likely to spread in early spring and late fall during cool, rainy weather. But symptoms are more likely during stress periods of low rainfall. Flooded and saturated soil conditions for 6–8 hours are especially conducive to the spread of root rots. Wounds are not required for infection.
management: A tree demonstrating phytophthora symptoms can sometimes be saved by removing the soil from around the base of the tree all the way down to the top of the main roots, cutting away any bark that’s oozy or dark and leaving the root system exposed. This allows the root system to dry completely, slowing the spread of phytophthora. In most other cases, phytophthora management is limited to prevention. Trees and shrubs, like azaleas, should be planted in well-draining areas, so their roots remain dry for most of the day. Phytophthora only needs four hours of standing water to germinate, making it difficult to combat in areas with poor drainage. Some gardeners get around this by planting their landscaping on mounds 8 to 10 inches (20-25 cm.) tall and being extra careful to plant crowns at the same depth they were at the nursery (often indicated by a dark line on the trunk). Well-established trees are less frequently affected than younger trees because they have widely spread roots that provide lots of avenues for water and nutrition. If a few sections are damaged, they are prepared to compensate. In smaller plants, such as shrubs or vegetables, all bets are off– they can be lost to phytophthora before you even realize the disease has taken hold.
symptoms: Leaves will appear drought stressed, sometimes turning dull green, yellow, red, or purple as they wilt. Infected trees may survive a few years before the disease kills the whole plant. The bark around the soil-line may appear darkened. Cutting away some bark should reveal red-brown discoloration in the wood underneath it. Disease symptoms are distinguishable from Armillaria root rot because mycelial mats do not develop in tissues infected with Phytophthora root rot.
comment: Phytophthora root rot is the most common cause of root and stem base decay of a wide range of trees and shrubs. Many ornamental trees and shrubs are susceptible to Phytophthora root rot and can develop root and crown rot, particularly if the soil around the base of the plant remains wet for long periods of time. Typical symptoms of a root disease are apparent on infected plants. The leaves will appear droughtstressed and may die quickly as the weather warms in late spring or early summer. Trees that are especially susceptible include oak and dogwood.

Phytophthora Rot
type: NamedIndividual
type: Crop Disease
affects: Apple
biological control: Limit periods of soil saturation. Free water allows pathogen zoospores to move through the soil thus enabling disease transmission over longer distances. Monitor soil moisture and limit the amount of time a soil stays saturated. Irrigate to field capacity when soil moisture and tree monitoring indicate the need, and apply appropriate volumes without flooding soils. When over-head cooling is not properly cycled, soils may remain wet or saturated during the cooling season of July and August greatly increasing the risk of infections. Set your cooling system so that cycles appropriately cool fruit through evaporation. At this level, little cooling water should reach the ground. Choose well drained soils. Whenever possible plant fruit trees in well drained soils. Maintain and improve soil structure. Soils with high organic matter, aggregate stability, water infiltration, and low compaction are less likely to stay at overly wet levels for long periods. Adding organic matter in the form of compost, trimmings, grass clippings, and other material will build organic matter over time Monitor irrigation water. Irrigation water can be a source for Phytophthora infections. Fruit infections (sprinkler rot) of pear and apple was widespread in the Wenatchee River Valley of Washington when overhead sprinklers were common (1992–1999). Dr. Grove and colleagues found 749 isolates (at least nine distinct taxa) of Phytophthora spp. in irrigation canals in eastern Washington (Yamak et al. 2002). Monitoring of Phytophthora in irrigation water is generally done by suspending fruit in the canal (or water from the irrigation system) and checking for visible fruit rots. Choose resistant rootstock. Among dwarfing-apple rootstocks, Geneva series rootstocks are most resistant. M-9, M-2, and M-4 are moderately susceptible. M-7 and MM-111 are moderately susceptible; M-26 and MM-106 are susceptible; and MM-104 is highly susceptible (Table1). Among stone fruits, plums are relatively resistant, whereas…
chemical control: Chemical Controls Note that only select fungicide active ingredients are effective on oomycetes such as Phytophthora since they are not true fungi, but rather water molds. Always read and check the label before using a chemical control. Root dips. Some studies have shown that soaking dormant trees before planting in a fungicide solution can result in increased root and shoot growth when plants are grown under conducive conditions and the pathogen is present (Jeffers 1992). Treatment of the root zone. Metalaxyl (MetaStar2E) at 2 gal/acre or 6 fl oz/1000 sq ft. Treat the entire root zone; do not concentrate near the base of the tree. Group 4 fungicide. 48-hr reentry (Bielenin and Jones 1988; Türkölmez and Derviş 2017). Mefenoxam (RidomilGoldSL) at 2 quarts/acre in sufficient water to move it into the root zone. Treat the entire root zone; do not concentrate near the base of the tree. Will not revitalize trees with moderate to severe symptoms. Group 4 fungicide. 48-hr reentry (Adaskaveg 2011). Trunk and root-zone sprays. Phosphorus acid, Mono-and di-potassium salts (Agri-Fos, Alude, Fosphite, Oxiphos, Phostrol, Rampart). Do not combine with a copper-spray program for control ofother diseases. Rampart can also be trunk injected. Group 33 fungicide (Flett 1996). Fosetyl AL (Aliette WDG) for bearing and non-bearing apples. For cherry non-bearing trees only. Do not control with a copper spray program. Group 33 fungicide (Utkhede and Smith 1991).
life cycle: Several species of soil borne Phytophthora oomycetes (fungal-like organisms) cause root and crown rots. Many of these species are common in most orchard soil. Others can be introduced to an orchard site on contaminated soil, irrigation water or planting stock. Phytophthora spp. persist in the soil as either long strands of mycelium in infected wood or as thick-walled spores called oospores (Wilcox 1992). These oospores are extremely resilient and can remain viable in the soil for long periods of time. When soils are wet oospores germinate forming thin threads of fungal hyphae which can infect plant roots. When soils are saturated another type of spore called a zoospore develop within sporangia. Zoospores are swimming spores. They can move through the soil water to find new roots to attack. However, the soil must be flooded in order for them to swim through the soil and as such zoospores will only develop when the soil is saturated. Spores can also move with water across the surface of the soil in irrigation or run-off, thus transmitting the pathogen over large parts of a block quickly. The longer the soil stays wet the higher the risk of infection
symptoms: Trees impacted by crown and root rot may appear to have a slow general decline that from a distance can be confused with winter injury, nutrient deficiency, fire blight, or wet feet (where saturated soils reduce the oxygen level to the point where roots die) (Figures1–4). Susceptible varieties may decline more quickly. When Phytophthora rots are suspected, inspect the base of the tree and excavate soil from around the trunk and root system to examine tissues for crown and root rot symptoms. Upon peeling back the outer bark of the trunk, the normally green cambium will appear orange or brown. Roots may be a dark brown or orange and may slough off from the primary root. After cutting away the outer bark, a sharp line of demarcation is commonly apparent between the healthy and diseased (orange-brown) tissue. In comparison, roots killed by “wet feet” or drowning generally display an overall brown color and may have a decaying or fermenting smell. Crown rot (Figure 2) is used to describe the disease when the pathogen affects the lower section of the trunk at the soil line, whereas root rot primarily impacts the tree root system. Collar rot may appear as a dark brown, grey, or purplish depressed canker on the bark of the lower part of the scion which can be confused with fire blight (Figure 4). Trees may have both root and crown rot symptoms.
comment: Phytophthora rots can girdle the scion (collar rot), damage the rootstock just below the soil surface (crown rot), and cause necrosis and death of fine roots (root rot). While generally considered a larger problem in rain-fed growing regions with heavy soils, Phytophthora problems occur in Washington, especially where irrigation water carries the pathogen or where irrigation and overhead cooling practices create wet soil conditions for extended periods. Multiple species of Phytophthora have been implicated in crop damage with P. cactorum and P. syringae two of the more significant species. P. cambivora and P. citricola have also been isolated in Washington from symptomatic trees.

Phytotoxicity
type: NamedIndividual
type: Crop Disease
affects: Cotton
affects: Soybean
comment: Phytotoxicity is a toxic effect by a compound on plant growth. Such damage may be caused by a wide variety of compounds, including trace metals, salinity, pesticides, phytotoxins or allelochemicals.

PierisRapae
type: NamedIndividual
type: Crop Pest
See Also: Pieris Rapae

Pieris rapae Linnaeus
type: NamedIndividual
type: Crop Pest

Pillbugs
type: NamedIndividual
type: Crop Pest
life cycle: Eggs are laid by females and are carried in a pouch beneath the body. Young sowbugs and pillbugs, much like the adults, hatch from the eggs. The young remain in the pouch up to 2 months after hatching. In about a year they grow to maturity and breed mainly in the spring. They may live up to 3 years and have up to 3 broods.
management: Management Strategies 1. Limit entry indoors. The best control to limit indoor entry is to reduce moisture near the foundation. Mulch, leaves and other plant debris needs to be cleared because this material attracts sowbugs and pillbugs. Don’t allow moisture to accumulate neat the foundation. Make sure that you have diverted water away with working gutters, downspouts and splash blocks. Drainage problems can be helped with drain tile and soil should be graded so that rainfall can drain away from the foundation. 2. Seal entry locations. Cracks or openings should be caulked or sealed in some way. Expansion joints and gaps outside and along the basement floor should be sealed to reduce entry of sowbugs and pillbugs. 3. Limiting moisture can prevent invasion. Vacuum cleaning and sweeping can clean up the pests. Insecticides can be used when all else fails. Applying insecticides such as Sevin, and Permethrin (Spectracide Bug Stop) can be accomplished with a compressed air or hose end sprayer. 4. Repot plants in infested soil. If found in pots indoors or out, simply tip the plant out of pot, scrape off the soil and replant in fresh soil. 5. If found outdoors. Sowbugs and pillbugs general do little or no harm to plants. In large numbers they may feed on plants at ground level or on small roots, but the only real threat they present is that the feeding locations can provide openings for bacteria or fungi which could result in rot setting in.
comment: Pillbugs or roly-polies (Armadillidium vulgare), sometimes called "woodlice" are outdoor creatures that are often mistaken to be insects. They range in size from ¼ to ½ inch long and are dark to slate grey with seven pairs of legs. Sowbugs have a pair of tail like appendages but pillbugs do not. Pillbugs can roll into a ball when disturbed. This behavior has given them the common name of "roly-polies" Pillbugs live their lives in moist environments. Common places for them to live would be under mulch, compost, stones, flowerpots and other places on damp ground. Some places the creatures may explore would be damp basements and first floor levels and garages. Points of entry may be door thresholds and cracks. They do not bite people or damage structures. However they can damage plants in a greenhouse in high numbers. Sowbugs and pillbugs are scavengers and their main source of food is decaying organic matter. They do not survive indoors for more than a day or two without moist conditions. They are actually crustaceans that have adapted to living their life on land. They are closely related to lobsters, shrimp and crayfish and have gills.

Pine Wilt Disease
type: NamedIndividual
type: Crop Disease

Pitcairn
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:PN
area: 47 km²
density: 1/km²
latitude: -79.77675
longitude: 40.40559
population: 48 (2007)
comment: The Pitcairn Islands, officially the Pitcairn, Henderson, Ducie and Oeno Islands, is a group of four volcanic islands in the southern Pacific Ocean that form the sole British Overseas Territory in the Pacific Ocean. The four islands — Pitcairn proper, Henderson, Ducie, and Oeno — are scattered across several hundred kilometres of ocean and have a combined land area of about 18 square miles . Henderson Island accounts for 86% of the land area, but only Pitcairn Island is inhabited. The islands nearest to the Pitcairn Islands are Mangareva to the west and Easter Island to the east.

Plain
type: NamedIndividual
type: Topography

PlanetScope
type: NamedIndividual
type: Satellite

Plutella Xylostella
type: NamedIndividual
type: Crop Pest
comment: Plutella Xylostella (Linnaeus)

Poison Ivy
type: NamedIndividual
type: Crop Disease
type: Weeds
management: Management Strategies 1. Pull small plants. If only a few small plants are found, pull and dispose of the plants using rubber gloves and arm protection. Avoid touching the leaves. Dispose of the plants in a plastic bag so they will not be touched by others. All parts of the plant are toxic. NEVER BURN THE PLANTS. The fumes are as toxic as touching the plant and worse if inhaled. 2. Use herbicides. For small areas, poison ivy killers available in aerosol cans may be a good choice. They are effective and convenient but are too expensive for treating large areas. Another option is to use glyphosate (Roundup, Kleenup). Spray the leaves of actively growing plants taking care to protect nearby desirable plants from spray drift which can damage or kill them. Leaves can also be selectively "painted" with the solution using a brush or wiped on plants using a disposable cotton rag. Use rubber gloves approved for use with garden chemicals and avoid contact of the herbicide with skin and eyes. A combination of 2,4-D and Banvel is also effective, but use 2,4-D with care as nearby plants may be sensitive to the drift. To kill the roots of large plants, cut the stem at soil level. Then paint the cut with undiluted 2,4-D. Mound soil over the treated stub. Amino triazole (Amitrol-T, Amino Triazole 90, and Weedazol) is also highly effective and safe to use. Available as a liquid or wettable powder, it should be applied during periods of rapid plant growth. Thorough coverage of the foliage is essential. 3.Use biological controls. Sheep and goats will eat poison ivy. Tether them in areas where you need poison ivy cleared. Not all municipalities or neighbors may welcome this control measure. Organic Strategies Strategies 1 and 3 are strictly organic approaches.
comment: Poison ivy, Toxicodendron radicans, is usually a vine twining on trees and occasionally an upright bush if there is no support. Each leaf is made up of three leaflets more or less notched at the edges. Two of the leaflets form a pair on opposite sides of the leafstalk, while the third leaflet stands by itself at the tip of the leafstalk. Small greenish flowers grow in bunches attached to the main stem close to the point where each leaf joins it. Later in the season, clusters of poisonous, berry-like drupes form. They are a dirty yellowish-white, waxy berry similar to mistletoe. The leaves are red in early spring becoming shiny green in summer and then turning red or orange in autumn.

Poland
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Marine Climate
has climate: Temperate Climate
has condition: Continental Climate
ISO code: ISO 3166-1:PL
agricultural land: 147,790 sq. km
area: 312,679 km²
climate: temperate with cold, cloudy, moderately severe winters with frequent precipitation; mild summers with frequent showers and thundershowers
density: 122/km²
latitude: 19.145136
longitude: 51.919437
population: 38,116,000 (12/2007)
comment: Poland, officially the Republic of Poland, is a country located in Central Europe. It is divided into 16 administrative provinces, covering an area of 312,696 square kilometres, and has a largely temperate seasonal climate. With a population of nearly 38.5 million people, Poland is the fifth most populous member state of the European Union. Poland's capital and largest metropolis is Warsaw. Other major cities include Kraków, Łódź, Wrocław, Poznań, Gdańsk, and Szczecin.

Polar Climate
type: NamedIndividual
type: Climate Type
comment: The polar climate regions are characterized by a lack of warm summers. Every month in a polar climate has an average temperature of less than 10 oC (50 oF). Regions with polar climate cover more than 20% of the Earth's area. Most of these regions are far from the equator, and in this case, winter days are extremely short and summer days are extremely long ( could last for the entirety of each season or longer). A polar climate consists of cool summers and very cold winters, which results in treeless tundra, glaciers, or a permanent or semi-permanent layer of ice.

PolyAnalyst
type: NamedIndividual
type: Toolkit
comment: PolyAnalyst is an integrated data mining tool that allows the user to address all issues involved in a data analysis project: data import or linking to an external database, transformation, analysis, visualization, results reporting, and model application to the bulk of the data. The system assists in clustering, classifying, segmenting, predicting and explaining data, as well as finding association rules. PolyAnalyst offers a broad selection of data visualization tools including histograms, two-dimensional line and scatter plots, rule graphs, thermal charts and interactively rotating three-dimensional charts.

Pond
type: NamedIndividual
type: Topography

Poor Quality
type: NamedIndividual
type: Quality

Porthesia Taiwana Shiraki
type: NamedIndividual
type: Crop Pest
comment: Porthesia Taiwana Shiraki

Portugal
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Marine Climate
has climate: Dry summer climate
ISO code: ISO 3166-1:PT
agricultural land: 36,360 sq. km
area: 92,345 km²
climate: maritime temperate; cool and rainy in north, warmer and drier in south
density: 114/km²
latitude: -7.868482
longitude: 39.558636
population: 10,676,910 (2008)
comment: Portugal, officially the Portuguese Republic, is a country located on the Iberian Peninsula, in southwestern Europe. It is the westernmost sovereign state in mainland Europe, being bordered to the west and south by the Atlantic Ocean and to the north and east by Spain. Its territory also includes the Atlantic archipelagos of the Azores and Madeira, both autonomous regions with their own regional governments. The official and national language is Portuguese.

Portuguese Millipede
type: NamedIndividual
type: Crop Pest
alternative label: Ommatoiulus Moreleti

Potassic
type: NamedIndividual
type: Chemical Fertilizer
comment: Containing potassium

Potassic fertilizers
type: NamedIndividual
type: Chemical Fertilizer

Potassium Chloride
type: NamedIndividual
type: Chemical Fertilizer

Potassium Nitrate (KNO3)
type: NamedIndividual
type: Chemical Fertilizer
Acronym: KNO3
comment: Potassium Nitrate is a crystalline salt, KNO3; a strong oxidizer used especially in making gunpowder, as a fertilizer, and in medicine.

Potassium sulphate (K2SO4)
type: NamedIndividual
type: Chemical Fertilizer
Acronym: K2SO4
comment: Potassium sulfate (US) or potassium sulphate (UK), also called sulphate of potash (SOP), arcanite, or archaically potash of sulfur, is the inorganic compound with formula K2SO4, a white water-soluble solid. It is commonly used in fertilizers, providing both potassium and sulfur.
label: Sulphate of potash

Powdery Mildew
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Barley
affects: Cotton
affects: Grapevines
affects: Rye
affects: Soybean
affects: Wheat
development: The development of powdery mildew is favored by cool (15-22C), cloudy, and humid (75-100% relative humidity) conditions.
distribution: The fungus has a high degree of host specificity. Isolates infecting wheat do so exclusively; the same appears to be true for isolates infecting barley, oats, and rye. Further specialization exists in the form of races. Powdery mildew occurs worldwide in cool, humid, and semiarid areas where cereals are grown.
important: Powdery mildew can cause major yield losses if infection occurs early in the crop cycle and conditions remain favorable for development so that high infection levels are reached before heading.
management: Grow available resistant varieties. Follow crop rotation. Keep the field free from weeds and other unwanted plants. Remove and destroy the infected crop residue.
symptoms: On all hosts, the first visible symptoms of this disease are white to pale gray, fuzzy or powdery colonies of mycelia, and conidia on the upper surfaces of leaves and leaf sheaths (especially on lower leaves) , and sometimes on the spikes. Older fungal tissue is yellowish gray. This superficial fungal material can be rubbed off easily with the fingers. Host tissue beneath the fungal material becomes chlorotic or necrotic and, with severe infections, the leaves may die. Eventually, black spherical fruiting structures (cleistothecia) may develop in the mycelia, and can be seen without magnification.

Pressmud
type: NamedIndividual
type: Organic Mature

ALBERT
type: NamedIndividual
type: Pretrained Model
Acronym: ALBERT
comment: Google ALBERT is a deep-learning NLP model, an upgrade of BERT, which has advanced on 12 NLP tasks including the competitive SQuAD v2.0 and SAT-style comprehension RACE benchmark. The model has been released as an open-source implementation on the TensorFlow framework and includes many ready-to-use pertained language representation models. The model further uses 89% fewer parameters than the BERT model — only 12M parameters and with way less loss of accuracy while evaluating, with an average of 80.1% accuracy. The model uses two optimisations to reduce model size — factorisation of the embedding layer and parameter-sharing across the hidden layers of the network.

AlexNet
type: NamedIndividual
type: Pretrained Model
is transformation of: Crop Imagery
is transformation of: Imagery
trained on: ImageNet
References: Krizhevsky, A., Sutskever, I., Hinton, G.E. 2012. "Imagenet classification with deep convolutional neural networks". In: Advances in Neural Information Processing Systems, Eds. F. Pereira, C.J.C. Burges, L. Bottou, and K.Q. Weinberger (Curran Associates, Inc.), pp. 1097–1105.
References: Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012): 1097-1105.
Acronym: AlexNet
comment: AlexNet is the name of a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton. AlexNet competed in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012. The network achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up. The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units (GPUs) during training.

AlexNetOWTBn
type: NamedIndividual
type: Pretrained Model
is transformation of: Crop Imagery
is transformation of: Imagery
References: Krizhevsky, A. 2014. "One weird trick for parallelizing convolutional neural networks". arXiv:1404.5997.
Acronym: AlexNetOWTBn
comment: AlexNet is the name of a convolutional neural network (CNN) architecture, designed by Alex Krizhevsky in collaboration with Ilya Sutskever and Geoffrey Hinton. AlexNet competed in the ImageNet Large Scale Visual Recognition Challenge on September 30, 2012. The network achieved a top-5 error of 15.3%, more than 10.8 percentage points lower than that of the runner up. The original paper's primary result was that the depth of the model was essential for its high performance, which was computationally expensive, but made feasible due to the utilization of graphics processing units (GPUs) during training.

BART
type: NamedIndividual
type: Pretrained Model
comment: BART model was proposed in BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer on 29 Oct, 2019. According to the abstract, Bart uses a standard seq2seq/machine translation architecture with a bidirectional encoder (like BERT) and a left-to-right decoder (like GPT). The pretraining task involves randomly shuffling the order of the original sentences and a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.

BARThez
type: NamedIndividual
type: Pretrained Model
comment: BARThez model was proposed in BARThez: a Skilled Pretrained French Sequence-to-Sequence Model by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct, 2020. The abstract of the paper: Inductive transfer learning, enabled by self-supervised learning, have taken the entire Natural Language Processing (NLP) field by storm, with models such as BERT and BART setting new state of the art on countless natural language understanding tasks. While there are some notable exceptions, most of the available models and research have been conducted for the English language. In this work, we introduce BARThez, the first BART model for the French language (to the best of our knowledge). BARThez was pretrained on a very large monolingual French corpus from past research that we adapted to suit BART’s perturbation schemes. Unlike already existing BERT-based French language models such as CamemBERT and FlauBERT, BARThez is particularly well-suited for generative tasks, since not only its encoder but also its decoder is pretrained. In addition to discriminative tasks from the FLUE benchmark, we evaluate BARThez on a novel summarization dataset, OrangeSum, that we release with this paper. We also continue the pretraining of an already pretrained multilingual BART on BARThez’s corpus, and we show that the resulting model, which we call mBARTHez, provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT.

BERT
type: NamedIndividual
type: Pretrained Model
References: Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171-4186. 2019.
Acronym: BERT
comment: BERT (Bidirectional Encoder Representations from Transformers) applies Transformers, a popular attention model, to language modelling. This mechanism has an encoder to read the input text and a decoder that produces a prediction for the task. This model uses the technique of masking out some of the words in the input and then condition each word bidirectionally to predict the masked words. BERT also learns to model relationships between sentences, predicts if the sentences are connected or not.

BEiT
type: NamedIndividual
type: Pretrained Model
comment: BEiT model was proposed in BEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei. Inspired by BERT, BEiT is the first paper that makes self-supervised pre-training of Vision Transformers (ViTs) outperform supervised pre-training. Rather than pre-training the model to predict the class of an image (as done in the original ViT paper), BEiT models are pre-trained to predict visual tokens from the codebook of OpenAI’s DALL-E model given masked patches. The abstract from the paper is the following: We introduce a self-supervised vision representation model BEiT, which stands for Bidirectional Encoder representation from Image Transformers. Following BERT developed in the natural language processing area, we propose a masked image modeling task to pretrain vision Transformers. Specifically, each image has two views in our pre-training, i.e, image patches (such as 16x16 pixels), and visual tokens (i.e., discrete tokens). We first “tokenize” the original image into visual tokens. Then we randomly mask some image patches and fed them into the backbone Transformer. The pre-training objective is to recover the original visual tokens based on the corrupted image patches. After pre-training BEiT, we directly fine-tune the model parameters on downstream tasks by appending task layers upon the pretrained encoder. Experimental results on image classification and semantic segmentation show that our model achieves competitive results with previous pre-training methods. For example, base-size BEiT achieves 83.2% top-1 accuracy on ImageNet-1K, significantly outperforming from-scratch DeiT training (81.8%) with the same setup. Moreover, large-size BEiT obtains 86.3% only using ImageNet-1K, even outperforming ViT-L with supervised pre-training on ImageNet-22K (85.2%). Tips: BEiT models are regular Vision Transformers, but pre-trained in a self-supervised way rather than supervised. They outperform both the original model (ViT) as well as Data-efficient Image T…

BORT
type: NamedIndividual
type: Pretrained Model
comment: BORT model was proposed in Optimal Subarchitecture Extraction for BERT by Adrian de Wynter and Daniel J. Perry. It is an optimal subset of architectural parameters for the BERT, which the authors refer to as “Bort”. The abstract from the paper is the following: We extract an optimal subset of architectural parameters for the BERT architecture from Devlin et al. (2018) by applying recent breakthroughs in algorithms for neural architecture search. This optimal subset, which we refer to as “Bort”, is demonstrably smaller, having an effective (that is, not counting the embedding layer) size of 5.5% the original BERT-large architecture, and 16% of the net size. Bort is also able to be pretrained in 288 GPU hours, which is 1.2% of the time required to pretrain the highest-performing BERT parametric architectural variant, RoBERTa-large (Liu et al., 2019), and about 33% of that of the world-record, in GPU hours, required to train BERT-large on the same hardware. It is also 7.9x faster on a CPU, as well as being better performing than other compressed variants of the architecture, and some of the non-compressed variants: it obtains performance improvements of between 0.3% and 31%, absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks.

BPT
type: NamedIndividual
type: Pretrained Model
References: https://github.com/yzh119/BPT
Acronym: BPT
comment: BPT is Binary-Partitioning Transformer (BPT), which uses the transformer, or rather an enhanced version of it for text classification, machine translation, etc. However, using a transformer is still a costly process since it uses the self-attention mechanism. Self-attention just means that we are performing the attention operation on the sentence itself, as opposed to 2 different sentences (this is attention). Self-attention helps identify the relationship between the words in a single sentence. It is this self-attention mechanism that contributes to the cost of using a transformer. Bipartitioning, transformer The Binary-Partitioning Transformer (BPT) aims to improve the efficiency of the self-attention mechanism by treating the transformer as a graph neural network. Essentially, each node in this graph represents an input token. This is how the BP Transformer works: Step 1: Divide the sentence into 2 parts recursively until some stopping condition is reached. This is called a binary partitioning. So, for example, the sentence "I like going to New York" will have the following partitions: I like going; to New York I like; going; to New; York I; like; going; to; New; York Note: A sentence with n words will have 2*n – 1 partitions, and in the end, you have a complete binary tree. Step 2: Each partition is now a node in the Graph Neural Network. There can be 2 types of edges: The edge connecting a parent node to its children The edge connecting the leaf nodes with other nodes Step 3: Perform self-attention on each node of the graph with its neighboring nodes: Binary_partitioning, transformers The BPT achieves: SOTA performance on Chinese to English Machine Translation (BLEU score: 19.84) Accuracy of 92.12 for Sentiment Analysis on the IMDb dataset (combined with GloVE embedding) I appreciate this model in the sense that it made me revisit the concept of graphs and made me venture into looking up graph neural networks. I am admittedly late to the party, but I will surel…

BertGeneration
type: NamedIndividual
type: Pretrained Model
comment: BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using EncoderDecoderModel as proposed in Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. The abstract from the paper is the following: Unsupervised pretraining of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.

BertJapanese
type: NamedIndividual
type: Pretrained Model
comment: BertJapanese is a BERT models trained on Japanese text. There are models with two different tokenization methods: Tokenize with MeCab and WordPiece. This requires some extra dependencies, fugashi which is a wrapper around MeCab. Tokenize into characters.

Bertweet
type: NamedIndividual
type: Pretrained Model
comment: BERTweet model was proposed in BERTweet: A pre-trained language model for English Tweets by Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen. The abstract from the paper is the following: We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, having the same architecture as BERT-base (Devlin et al., 2019), is trained using the RoBERTa pre-training procedure (Liu et al., 2019). Experiments show that BERTweet outperforms strong baselines RoBERTa-base and XLM-R-base (Conneau et al., 2020), producing better performance results than the previous state-of-the-art models on three Tweet NLP tasks: Part-of-speech tagging, Named-entity recognition and text classification.

BigBird
type: NamedIndividual
type: Pretrained Model
comment: BigBird model was proposed in Big Bird: Transformers for Longer Sequences by Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon, Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it has been shown that applying sparse, global, and random attention approximates full attention, while being computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context, BigBird has shown improved performance on various long document NLP tasks, such as question answering and summarization, compared to BERT or RoBERTa. The abstract from the paper is the following: Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, BigBird drastically improves performance on various NLP tasks such as question answering and summ…

BigBirdPegasus
type: NamedIndividual
type: Pretrained Model
comment: BigBirdPegasus - The BigBird model was proposed in Big Bird: Transformers for Longer Sequences by Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon, Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. In addition to sparse attention, BigBird also applies global attention as well as random attention to the input sequence. Theoretically, it has been shown that applying sparse, global, and random attention approximates full attention, while being computationally much more efficient for longer sequences. As a consequence of the capability to handle longer context, BigBird has shown improved performance on various long document NLP tasks, such as question answering and summarization, compared to BERT or RoBERTa. The abstract from the paper is the following: Transformers-based models, such as BERT, have been one of the most successful deep learning models for NLP. Unfortunately, one of their core limitations is the quadratic dependency (mainly in terms of memory) on the sequence length due to their full attention mechanism. To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear. We show that BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model. Along the way, our theoretical analysis reveals some of the benefits of having O(1) global tokens (such as CLS), that attend to the entire sequence as part of the sparse attention mechanism. The proposed sparse attention can handle sequences of length up to 8x of what was previously possible using similar hardware. As a consequence of the capability to handle longer context, BigBird drastically improves performance on various NLP tasks such as questi…

Blenderbot
type: NamedIndividual
type: Pretrained Model
comment: Blenderbot - The Blender chatbot model was proposed in Recipes for building an open-domain chatbot Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston on 30 Apr 2020. The abstract of the paper is the following: Building open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, and displaying knowledge, empathy and personality appropriately, while maintaining a consistent persona. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.

ByT5
type: NamedIndividual
type: Pretrained Model
comment: ByT5 model was presented in ByT5: Towards a token-free future with pre-trained byte-to-byte models by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel. The abstract from the paper is the following: Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. Encoding text as a sequence of tokens requires a tokenizer, which is typically created as an independent artifact from the model. Token-free models that instead operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they minimize technical debt by removing complex and error-prone text preprocessing pipelines. Since byte or character sequences are longer than token sequences, past work on token-free models has often introduced new model architectures designed to amortize the cost of operating directly on raw text. In this paper, we show that a standard Transformer architecture can be used with minimal modifications to process byte sequences. We carefully characterize the trade-offs in terms of parameter count, training FLOPs, and inference speed, and show that byte-level models are competitive with their token-level counterparts. We also demonstrate that byte-level models are significantly more robust to noise and perform better on tasks that are sensitive to spelling and pronunciation. As part of our contribution, we release a new set of pre-trained byte-level Transformer models based on the T5 architecture, as well as all code and data used in our experiments.

CANINE
type: NamedIndividual
type: Pretrained Model
comment: CANINE model was proposed in CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting. It’s among the first papers that trains a Transformer without using an explicit tokenization step (such as Byte Pair Encoding (BPE), WordPiece or SentencePiece). Instead, the model is trained directly at a Unicode character-level. Training at a character-level inevitably comes with a longer sequence length, which CANINE solves with an efficient downsampling strategy, before applying a deep Transformer encoder. The abstract from the paper is the following: Pipelined NLP systems have largely been superseded by end-to-end neural modeling, yet nearly all commonly-used models still require an explicit tokenization step. While recent tokenization approaches based on data-derived subword lexicons are less brittle than manually engineered tokenizers, these techniques are not equally suited to all languages, and the use of any fixed vocabulary may limit a model’s ability to adapt. In this paper, we present CANINE, a neural encoder that operates directly on character sequences, without explicit tokenization or vocabulary, and a pre-training strategy that operates either directly on characters or optionally uses subwords as a soft inductive bias. To use its finer-grained input effectively and efficiently, CANINE combines downsampling, which reduces the input sequence length, with a deep transformer stack, which encodes context. CANINE outperforms a comparable mBERT model by 2.8 F1 on TyDi QA, a challenging multilingual benchmark, despite having 28% fewer model parameters.

CLIP
type: NamedIndividual
type: Pretrained Model
comment: CLIP model was proposed in Learning Transferable Visual Models From Natural Language Supervision by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever. CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3. The abstract from the paper is the following: State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training exampl…

CPM
type: NamedIndividual
type: Pretrained Model
comment: CPM model was proposed in CPM: A Large-scale Generative Chinese Pre-trained Language Model by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun. The abstract from the paper is the following: Pre-trained Language Models (PLMs) have proven to be beneficial for various downstream NLP tasks. Recently, GPT-3, with 175 billion parameters and 570GB training data, drew a lot of attention due to the capacity of few-shot (even zero-shot) learning. However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available. In this technical report, we release the Chinese Pre-trained Language Model (CPM) with generative pre-training on large-scale Chinese training data. To the best of our knowledge, CPM, with 2.6 billion parameters and 100GB Chinese training data, is the largest Chinese pre-trained language model, which could facilitate several downstream Chinese NLP tasks, such as conversation, essay generation, cloze test, and language understanding. Extensive experiments demonstrate that CPM achieves strong performance on many NLP tasks in the settings of few-shot (even zero-shot) learning. This model was contributed by canwenxu. The original implementation can be found here: https://github.com/TsinghuaAI/CPM-Generate Note: We only have a tokenizer here, since the model architecture is the same as GPT-2.

CTRL
type: NamedIndividual
type: Pretrained Model
comment: CTRL model was proposed in CTRL: A Conditional Transformer Language Model for Controllable Generation by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher. It’s a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.). The abstract from the paper is the following: Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model, trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data via model-based source attribution. Tips: CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences or links to generate coherent text. Refer to the original implementation for more information. CTRL is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left. CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as it can be observed in the run_generation.py example script. The PyTorch models can take the past as input, which is the previously computed key/value attention pairs. Using this past value prevents the model from re-computing pre-computed values in the context…

CaffeNet
type: NamedIndividual
type: Pretrained Model
related to: AlexNet
trained on: ImageNet
References: Jia, Yangqing, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. "Caffe: Convolutional architecture for fast feature embedding." In Proceedings of the 22nd ACM international conference on Multimedia, pp. 675-678. 2014.
Acronym: CaffeNet
comment: CaffeNet is a 1-GPU version of AlexNet

CamemBERT
type: NamedIndividual
type: Pretrained Model
comment: CamemBERT model was proposed in CamemBERT: a Tasty French Language Model by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah, and Benoît Sagot. It is based on Facebook’s RoBERTa model released in 2019. It is a model trained on 138GB of French text. The abstract from the paper is the following: Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models –in all languages except English– very limited. Aiming to address this issue for French, we release CamemBERT, a French version of the Bi-directional Encoders for Transformers (BERT). We measure the performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural language inference. CamemBERT improves the state of the art for most of the tasks considered. We release the pretrained model for CamemBERT hoping to foster research and downstream applications for French NLP.

CodeBERT
type: NamedIndividual
type: Pretrained Model
References: Feng, Zhangyin, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou et al. "Codebert: A pre-trained model for programming and natural languages." arXiv preprint arXiv:2002.08155 (2020).
References: https://github.com/microsoft/CodeBERT
Acronym: CodeBERT
comment: Microsoft’s CodeBERT, with ‘BERT’ suffix referring to Google’s BERT framework for NLP, has been built upon a bidirectional multi-layer neural architecture. By understanding the connection between natural language and programming language, the model can support tasks such as code search, code documentation generation etc. CodeBERT has also been evaluated on NL-PL tasks by fine-tuning model parameters and following that it achieved excellent performance on both natural language code search and code documentation generation. The model has further been trained on the large dataset from Github code repositories in six programming languages, including 2.1 million bimodal data points and 6.4 million unimodal codes.

ConvBERT
type: NamedIndividual
type: Pretrained Model
comment: ConvBERT model was proposed in ConvBERT: Improving BERT with Span-based Dynamic Convolution by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan. The abstract from the paper is the following: Pre-trained language models like BERT and its variants have recently achieved impressive performance in various natural language understanding tasks. However, BERT heavily relies on the global self-attention block and thus suffers large memory footprint and computation cost. Although all its attention heads query on the whole input sequence for generating the attention map from a global perspective, we observe some heads only need to learn local dependencies, which means the existence of computation redundancy. We therefore propose a novel span-based dynamic convolution to replace these self-attention heads to directly model local dependencies. The novel convolution heads, together with the rest self-attention heads, form a new mixed attention block that is more efficient at both global and local context learning. We equip BERT with this mixed attention design and build a ConvBERT model. Experiments have shown that ConvBERT significantly outperforms BERT and its variants in various downstream tasks, with lower training cost and fewer model parameters. Remarkably, ConvBERTbase model achieves 86.4 GLUE score, 0.7 higher than ELECTRAbase, while using less than 1/4 training cost. Code and pre-trained models will be released. ConvBERT training tips are similar to those of BERT.

DETR
type: NamedIndividual
type: Pretrained Model
comment: DETR model was proposed in End-to-End Object Detection with Transformers by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov and Sergey Zagoruyko. DETR consists of a convolutional backbone followed by an encoder-decoder Transformer which can be trained end-to-end for object detection. It greatly simplifies a lot of the complexity of models like Faster-R-CNN and Mask-R-CNN, which use things like region proposals, non-maximum suppression procedure and anchor generation. Moreover, DETR can also be naturally extended to perform panoptic segmentation, by simply adding a mask head on top of the decoder outputs. The abstract from the paper is the following: We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive baselines. This model was contributed by nielsr. The original code can be found here. The quickest way to get started with DETR is by che…

DPR
type: NamedIndividual
type: Pretrained Model
comment: Dense Passage Retrieval (DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. It was introduced in Dense Passage Retrieval for Open-Domain Question Answering by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. The abstract from the paper is the following: Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.

DeBERTa
type: NamedIndividual
type: Pretrained Model
comment: DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s BERT model released in 2018 and Facebook’s RoBERTa model released in 2019. It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in RoBERTa. The abstract from the paper is the following: Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa. This model was contributed by DeBERTa. This model TF 2.0 implementation was contributed by kamalkraj .

DeBERTa-v2
type: NamedIndividual
type: Pretrained Model
comment: DeBERTa-v2 - The DeBERTa model was proposed in DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google’s BERT model released in 2018 and Facebook’s RoBERTa model released in 2019. It builds on RoBERTa with disentangled attention and enhanced mask decoder training with half of the data used in RoBERTa. The abstract from the paper is the following: Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa. The following information is visible directly on the [original implementation repository](https://github.com/microsoft/DeBERTa). DeBERTa v2 is the second version of the DeBERTa model. It includes the 1.5B model used for the SuperGLUE single-model submission and achieving 89.9, versus human baseline 89.8. You can find mo…

DeiT
type: NamedIndividual
type: Pretrained Model
comment: DeiT model was proposed in Training data-efficient image transformers & distillation through attention by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou. The Vision Transformer (ViT) introduced in Dosovitskiy et al., 2020 has shown that one can match or even outperform existing convolutional neural networks using a Transformer encoder (BERT-like). However, the ViT models introduced in that paper required training on expensive infrastructure for multiple weeks, using external data. DeiT (data-efficient image transformers) are more efficiently trained transformers for image classification, requiring far less data and far less computing resources compared to the original ViT models. The abstract from the paper is the following: Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images using an expensive infrastructure, thereby limiting their adoption. In this work, we produce a competitive convolution-free transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external data. More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation token ensuring that the student learns from the teacher through attention. We show the interest of this token-based distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and models.

DenseNet
type: NamedIndividual
type: Pretrained Model
is transformation of: Crop Imagery
is transformation of: Field Imagery
is transformation of: Satellite Imagery
References: Huang, Gao, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. "Densely connected convolutional networks." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708. 2017.
References: Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. 2015. "Going deeper with convolutions". Proc. of the IEEE Conference on Computer Vision and Pattern Recognition.
Acronym: DenseNet
comment: A DenseNet is a type of convolutional neural network that utilises dense connections between layers, through Dense Blocks, where we connect all layers (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.

DialoGPT
type: NamedIndividual
type: Pretrained Model
comment: DialoGPT was proposed in DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan. It’s a GPT2 Model trained on 147M conversation-like exchanges extracted from Reddit. The abstract from the paper is the following: We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems. Tips: DialoGPT is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left. DialoGPT was trained with a causal language modeling (CLM) objective on conversational data and is therefore powerful at response generation in open-domain dialogue systems. DialoGPT enables the user to create a chat bot in just 10 lines of code as shown on DialoGPT’s model card. Training: In order to train or fine-tune DialoGPT, one can use causal language modeling training. To cite the official paper: We follow the OpenAI GPT-2 to model a multiturn dialogue session as a long text and frame the generation task as language modeling. We first concatenate all dialog turns within a dialogue session into a long text x_1,…, x_N (N is the sequence length), ended by the end-of-text token. For more information ple…

DistilBERT
type: NamedIndividual
type: Pretrained Model
References: Sanh, Victor, Lysandre Debut, Julien Chaumond, and Thomas Wolf. "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter." arXiv preprint arXiv:1910.01108 (2019).
References: https://huggingface.co/transformers/model_doc/distilbert.html
Acronym: DistilBERT
comment: The DistilBERT model was proposed in the blog post Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT, and the paper DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. DistilBERT is a small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than bert-base-uncased, runs 60% faster while preserving over 95% of BERT’s performances as measured on the GLUE language understanding benchmark.

ELECTRA
type: NamedIndividual
type: Pretrained Model
comment: ELECTRA model was proposed in the paper ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. ELECTRA is a new pretraining approach which trains two transformer models: the generator and the discriminator. The generator’s role is to replace tokens in a sequence, and is therefore trained as a masked language model. The discriminator, which is the model we’re interested in, tries to identify which tokens were replaced by the generator in the sequence. The abstract from the paper is the following: Masked language modeling (MLM) pretraining methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a more sample-efficient pretraining task called replaced token detection. Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments demonstrate this new pretraining task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out. As a result, the contextual representations learned by our approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained using 30x more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale, where it performs comparably to RoBERTa and XLNet while using less than 1…

ELMo
type: NamedIndividual
type: Pretrained Model
Acronym: ELMo
comment: ELMo, also known as Embeddings from Language Models is a deep contextualised word representation that models syntax and semantic of words as well as their linguistic contexts. The model, developed by Allen NLP, has been pre-trained on a huge text-corpus and learned functions from deep bi-directional models (biLM). ELMo can easily be added to the existing models, which drastically improves the functions across vast NLP problems, including answering questions, textual entailment and sentiment analysis.

ERNIE
type: NamedIndividual
type: Pretrained Model
Acronym: ERNIE
comment: ERNIE 1.0 (released in March 2019) has been a popular model for text classification, it was ERNIE 2.0 which became the talk of the town in the latter half of 2019. Developed by tech-giant Baidu, ERNIE outperformed Google XLNet and BERT on the GLUE benchmark for English. ERNIE stands for Enhanced Representation through kNowledge IntEgration, and ERNIE 2.0 is an upgraded version of ERNIE 1.0. ERNIE 1.0 was pathbreaking in its own way – it was one of the first models to leverage Knowledge Graphs. This incorporation further enhanced training the model for advanced tasks like Relation Classification and NamedEntityRecognition (NER).

BERT
type: NamedIndividual
type: Pretrained Model
References: https://paperswithcode.com/method/efficientnet
Acronym: EfficientNet
comment: EfficientNet is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a compound coefficient. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use times more computational resources, then we can simply increase the network depth by , width by , and image size by , where are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient to uniformly scales network width, depth, and resolution in a principled way. The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image. The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of MobileNetV2, in addition to squeeze-and-excitation blocks.
label: EfficientNet

FSMT
type: NamedIndividual
type: Pretrained Model
comment: FSMT (FairSeq MachineTranslation) models were introduced in Facebook FAIR’s WMT19 News Translation Task Submission by Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, Sergey Edunov. The abstract of the paper is the following: This paper describes Facebook FAIR’s submission to the WMT19 shared news translation task. We participate in two language pairs and four language directions, English <-> German and English <-> Russian. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling toolkit which rely on sampled back-translations. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our submissions are ranked first in all four directions of the human evaluation campaign. On En->De, our system significantly outperforms other systems as well as human translations. This system improves upon our WMT’18 submission by 4.5 BLEU points.

FlauBERT
type: NamedIndividual
type: Pretrained Model
comment: FlauBERT model was proposed in the paper FlauBERT: Unsupervised Language Model Pre-training for French by Hang Le et al. It’s a transformer model pretrained using a masked language modeling (MLM) objective (like BERT). The abstract from the paper is the following: Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts nowadays available, they provide an efficient way to pre-train continuous word representations that can be fine-tuned for a downstream task, along with their contextualization at the sentence level. This has been widely demonstrated for English using contextualized representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018; Radford et al., 2018; Devlin et al., 2019; Yang et al., 2019b). In this paper, we introduce and share FlauBERT, a model learned on a very large and heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the time they outperform other pretraining approaches. Different versions of FlauBERT as well as a unified evaluation protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research community for further reproducible experiments in French NLP.

Funnel-Transformer
type: NamedIndividual
type: Pretrained Model
comment: Funnel Transformer model was proposed in the paper Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing. It is a bidirectional transformer model, like BERT, but with a pooling operation after each block of layers, a bit like in traditional convolutional neural networks (CNN) in computer vision. The abstract from the paper is the following: With the success of language pretraining, it is highly desirable to develop more efficient architectures of good scalability that can exploit the abundant unlabeled data at a lower cost. To improve the efficiency, we examine the much-overlooked redundancy in maintaining a full-length token-level presentation, especially for tasks that only require a single-vector presentation of the sequence. With this intuition, we propose Funnel-Transformer which gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. More importantly, by re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, we further improve the model capacity. In addition, to perform token-level predictions as required by common pretraining objectives, Funnel-Transformer is able to recover a deep representation for each token from the reduced hidden sequence via a decoder. Empirically, with comparable or fewer FLOPs, Funnel-Transformer outperforms the standard Transformer on a wide variety of sequence-level prediction tasks, including text classification, language understanding, and reading comprehension. Tips: Since Funnel Transformer uses pooling, the sequence length of the hidden states changes after each block of layers. The base model therefore has a final sequence length that is a quarter of the original one. This model can be used directly for tasks that just require a sentence summary (like sequence classification or multiple choice). For other tasks, the full model is used; this full model has a decoder that upsamples the final hidden states …

GPT-2
type: NamedIndividual
type: Pretrained Model
Acronym: GPT2
comment: OpenAI’s GPT-2 or Generative Pre-Training version 2 is a state-of-the-art language model that can generate text like humans. It is unmatched when it comes to a model that is generalised yet capable of outperforming models trained on specific tasks.

GPT-3
type: NamedIndividual
type: Pretrained Model
inherits: GPT-2
Acronym: GPT3
comment: A large transformer-based language model that given a sequence of words within some text, predicts the next word. GPT-3 is one of the most controversial pre-trained models by OpenAI. This large scale transformer-based language model has been trained on 175 billion parameters, which is ten times more than any previous non-sparse language model available. The model has been trained to achieve strong performance on many NLP datasets, including tasks like translation, answering questions, as well as several tasks that require on-the-fly reasoning such as unscrambling words. With its recent advancements, it has been used even to write news articles and generate codes helping developers to build ML applications. GPT-3 is the largest model so far, and its impressive capabilities have positioned it to outrank other text prediction models. In June, this year, the company released its API for allowing the users to access the new AI models virtually.

GPTNeo
type: NamedIndividual
type: Pretrained Model
comment: GPTNeo model was released in the EleutherAI/gpt-neo repository by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy. It is a GPT2 like causal language model trained on the Pile dataset. The architecture is similar to GPT2 except that GPT Neo uses local attention in every other layer with a window size of 256 tokens.

GloVe
type: NamedIndividual
type: Pretrained Model
References: Pennington, Jeffrey, Richard Socher, and Christopher D. Manning. "Glove: Global vectors for word representation." In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp. 1532-1543. 2014.
References: https://nlp.stanford.edu/projects/glove/
Acronym: GloVe
comment: GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space.

GoogLeNet
type: NamedIndividual
type: Pretrained Model
is transformation of: Crop Imagery
is transformation of: Imagery
References: Szegedy, Christian, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. "Going deeper with convolutions." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9. 2015.
Acronym: GoogLeNet
comment: GoogLeNet is a convolutional neural network that is 22 layers deep. You can load a pretrained version of the network trained on either the ImageNet or Places365 data sets. The network trained on ImageNet classifies images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. The network trained on Places365 is similar to the network trained on ImageNet, but classifies images into 365 different place categories, such as field, park, runway, and lobby. These networks have learned different feature representations for a wide range of images. The pretrained networks both have an image input size of 224-by-224.
comment: GoogLeNet is a type of convolutional neural network based on the Inception architecture. It utilises Inception modules, which allow the network to choose between multiple convolutional filter sizes in each block. An Inception network stacks these modules on top of each other, with occasional max-pooling layers with stride 2 to halve the resolution of the grid.

Hubert
type: NamedIndividual
type: Pretrained Model
comment: Hubert was proposed in HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed. The abstract from the paper is the following: Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation. To deal with these three problems, we propose the Hidden-Unit BERT (HuBERT) approach for self-supervised speech representation learning, which utilizes an offline clustering step to provide aligned target labels for a BERT-like prediction loss. A key ingredient of our approach is applying the prediction loss over the masked regions only, which forces the model to learn a combined acoustic and language model over the continuous inputs. HuBERT relies primarily on the consistency of the unsupervised clustering step rather than the intrinsic quality of the assigned cluster labels. Starting with a simple k-means teacher of 100 clusters, and using two iterations of clustering, the HuBERT model either matches or improves upon the state-of-the-art wav2vec 2.0 performance on the Librispeech (960h) and Libri-light (60,000h) benchmarks with 10min, 1h, 10h, 100h, and 960h fine-tuning subsets. Using a 1B parameter model, HuBERT shows up to 19% and 13% relative WER reduction on the more challenging dev-other and test-other evaluation subsets.

I-BERT
type: NamedIndividual
type: Pretrained Model
comment: I-BERT model was proposed in I-BERT: Integer-only BERT Quantization by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney and Kurt Keutzer. It’s a quantized version of RoBERTa running inference up to four times faster. The abstract from the paper is the following: Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language Processing tasks. However, their memory footprint, inference latency, and power consumption are prohibitive for efficient inference at the edge, and even at the data center. While quantization can be a viable solution for this, previous work on quantizing Transformer based models use floating-point arithmetic during inference, which cannot efficiently utilize integer-only logical units such as the recent Turing Tensor Cores, or traditional integer-only ARM processors. In this work, we propose I-BERT, a novel quantization scheme for Transformer based models that quantizes the entire inference with integer-only arithmetic. Based on lightweight integer-only approximation methods for nonlinear operations, e.g., GELU, Softmax, and Layer Normalization, I-BERT performs an end-to-end integer-only BERT inference without any floating point calculation. We evaluate our approach on GLUE downstream tasks using RoBERTa-Base/Large. We show that for both cases, I-BERT achieves similar (and slightly higher) accuracy as compared to the full-precision baseline. Furthermore, our preliminary implementation of I-BERT shows a speedup of 2.4 - 4.0x for INT8 inference on a T4 GPU system as compared to FP32 inference. The framework has been developed in PyTorch and has been open-sourced.

BERT
type: NamedIndividual
type: Pretrained Model
Acronym: InceptionV3
comment: Inception-v3 is a convolutional neural network that is 48 layers deep. You can load a pretrained version of the network trained on more than a million images from the ImageNet database [1]. The pretrained network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 299-by-299.
label: InceptionV3

InceptionV4
type: NamedIndividual
type: Pretrained Model
is transformation of: Crop Imagery
is transformation of: Imagery
Acronym: InceptionV4
comment: Inception-v4 is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than Inception-v3.

LED
type: NamedIndividual
type: Pretrained Model
comment: LED model was proposed in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan. The abstract from the paper is the following: Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer’s attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA. We finally introduce the Longformer-Encoder-Decoder (LED), a Longformer variant for supporting long document generative sequence-to-sequence tasks, and demonstrate its effectiveness on the arXiv summarization dataset. Tips: LEDForConditionalGeneration is an extension of BartForConditionalGeneration exchanging the traditional self-attention layer with Longformer’s chunked self-attention layer. LEDTokenizer is an alias of BartTokenizer. LED works very well on long-range sequence-to-sequence tasks where the input_ids largely exceed a length of 1024 tokens. LED pads the input_ids to be a multiple of config.attention_window if required. Therefore a small speed-up is gained, when LEDTokenizer is used with the pad_to_multiple_of argument. LED makes use of global attention by means of the global_attention_mask (see LongformerModel). For summarization, it is advised to put global attention…

LUKE
type: NamedIndividual
type: Pretrained Model
comment: LUKE model was proposed in LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda and Yuji Matsumoto. It is based on RoBERTa and adds entity embeddings as well as an entity-aware self-attention mechanism, which helps improve performance on various downstream tasks involving reasoning about entities such as named entity recognition, extractive and cloze-style question answering, entity typing, and relation classification. The abstract from the paper is the following: Entity representations are useful in natural language tasks involving entities. In this paper, we propose new pretrained contextualized representations of words and entities based on the bidirectional transformer. The proposed model treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. Our model is trained using a new pretraining task based on the masked language model of BERT. The task involves predicting randomly masked words and entities in a large entity-annotated corpus retrieved from Wikipedia. We also propose an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the transformer, and considers the types of tokens (words or entities) when computing attention scores. The proposed model achieves impressive empirical performance on a wide range of entity-related tasks. In particular, it obtains state-of-the-art results on five well-known datasets: Open Entity (entity typing), TACRED (relation classification), CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), and SQuAD 1.1 (extractive question answering). Tips: This implementation is the same as RobertaModel with the addition of entity embeddings as well as an entity-aware self-attention mechanism, which improves performance on tasks involving reasoning about entities. LUKE treats entities as input tokens; therefore, it takes entity_…

LXMERT
type: NamedIndividual
type: Pretrained Model
comment: LXMERT model was proposed in LXMERT: Learning Cross-Modality Encoder Representations from Transformers by Hao Tan & Mohit Bansal. It is a series of bidirectional transformer encoders (one for the vision modality, one for the language modality, and then one to fuse both modalities) pretrained using a combination of masked language modeling, visual-language text alignment, ROI-feature regression, masked visual-attribute modeling, masked visual-object modeling, and visual-question answering objectives. The pretraining consists of multiple multi-modal datasets: MSCOCO, Visual-Genome + Visual-Genome Question Answering, VQA 2.0, and GQA. The abstract from the paper is the following: Vision-and-language reasoning requires an understanding of visual concepts, language semantics, and, most importantly, the alignment and relationships between these two modalities. We thus propose the LXMERT (Learning Cross-Modality Encoder Representations from Transformers) framework to learn these vision-and-language connections. In LXMERT, we build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative pretraining tasks: masked language modeling, masked object prediction (feature regression and label classification), cross-modality matching, and image question answering. These tasks help in learning both intra-modality and cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the state-of-the-art results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our pretrained cross-modality model by adapting it to a challenging visual-reasoning task, NLVR, and improve the previous best result by 22% absolute (54% to 76%). Las…

LayoutLM
type: NamedIndividual
type: Pretrained Model
comment: LayoutLM model was proposed in the paper LayoutLM: Pre-training of Text and Layout for Document Image Understanding by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. It’s a simple but effective pretraining method of text and layout for document image understanding and information extraction tasks, such as form understanding and receipt understanding. It obtains state-of-the-art results on several downstream tasks: form understanding: the FUNSD dataset (a collection of 199 annotated forms comprising more than 30,000 words). receipt understanding: the SROIE dataset (a collection of 626 receipts for training and 347 receipts for testing). document image classification: the RVL-CDIP dataset (a collection of 400,000 images belonging to one of 16 classes). The abstract from the paper is the following: Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the widespread use of pretraining models for NLP applications, they almost exclusively focus on text-level manipulation, while neglecting layout and style information that is vital for document image understanding. In this paper, we propose the LayoutLM to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents. Furthermore, we also leverage image features to incorporate words’ visual information into LayoutLM. To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for document-level pretraining. It achieves new state-of-the-art results in several downstream tasks, including form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification (from 93.07 to 94.42). Tips: In addition to input_ids, forward() also expects the input bbox, which are the bounding …

LayoutLMV2
type: NamedIndividual
type: Pretrained Model
comment: LayoutLMV2 model was proposed in LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. LayoutLMV2 improves LayoutLM to obtain state-of-the-art results across several document image understanding benchmarks: information extraction from scanned documents: the FUNSD dataset (a collection of 199 annotated forms comprising more than 30,000 words), the CORD dataset (a collection of 800 receipts for training, 100 for validation and 100 for testing), the SROIE dataset (a collection of 626 receipts for training and 347 receipts for testing) and the Kleister-NDA dataset (a collection of non-disclosure agreements from the EDGAR database, including 254 documents for training, 83 documents for validation, and 203 documents for testing). document image classification: the RVL-CDIP dataset (a collection of 400,000 images belonging to one of 16 classes). document visual question answering: the DocVQA dataset (a collection of 50,000 questions defined on 12,000+ document images). The abstract from the paper is the following: Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents. In this paper, we present LayoutLMv2 by pre-training text, layout and image in a multi-modal framework, where new model architectures and pre-training tasks are leveraged. Specifically, LayoutLMv2 not only uses the existing masked visual-language modeling task but also the new text-image alignment and text-image matching tasks in the pre-training stage, where cross-modality interaction is better learned. Meanwhile, it also integrates a spatial-aware self-attention mechanism into the Transformer architecture, so that the model can fully understand the relative positional rela…

LayoutXLM
type: NamedIndividual
type: Pretrained Model
comment: LayoutXLM was proposed in LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei. It’s a multilingual extension of the LayoutLMv2 model trained on 53 languages. The abstract from the paper is the following: Multimodal pre-training with text, layout, and image has achieved SOTA performance for visually-rich document understanding tasks recently, which demonstrates the great potential for joint learning across different modalities. In this paper, we present LayoutXLM, a multimodal pre-trained model for multilingual document understanding, which aims to bridge the language barriers for visually-rich document understanding. To accurately evaluate LayoutXLM, we also introduce a multilingual form understanding benchmark dataset named XFUN, which includes form understanding samples in 7 languages (Chinese, Japanese, Spanish, French, Italian, German, Portuguese), and key-value pairs are manually labeled for each language. Experiment results show that the LayoutXLM model has significantly outperformed the existing SOTA cross-lingual pre-trained models on the XFUN dataset.

LeNet
type: NamedIndividual
type: Pretrained Model
References: LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86, no. 11 (1998): 2278-2324.
Acronym: LeNet
comment: LeNet is a convolutional neural network structure proposed by Yann LeCun et al. in 1989. In general, LeNet refers to lenet-5 and is a simple convolutional neural network. Convolutional neural networks are a kind of feed-forward neural network whose artificial neurons can respond to a part of the surrounding cells in the coverage range and perform well in large-scale image processing.

Longformer
type: NamedIndividual
type: Pretrained Model
comment: Longformer model was presented in Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan. The abstract from the paper is the following: Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer. Longformer’s attention mechanism is a drop-in replacement for the standard self-attention and combines a local windowed attention with a task motivated global attention. Following prior work on long-sequence transformers, we evaluate Longformer on character-level language modeling and achieve state-of-the-art results on text8 and enwik8. In contrast to most prior work, we also pretrain Longformer and finetune it on a variety of downstream tasks. Our pretrained Longformer consistently outperforms RoBERTa on long document tasks and sets new state-of-the-art results on WikiHop and TriviaQA. Tips: Since the Longformer is based on RoBERTa, it doesn’t have token_type_ids. You don’t need to indicate which token belongs to which segment. Just separate your segments with the separation token tokenizer.sep_token (or ).

M2M100
type: NamedIndividual
type: Pretrained Model
comment: M2M100 model was proposed in Beyond English-Centric Multilingual Machine Translation by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin. The abstract from the paper is the following: Existing work in translation demonstrated the potential of massively multilingual machine translation by training a single model able to translate between any pair of languages. However, much of this work is English-Centric by training only on data which was translated from or to English. While this is supported by large sources of training data, it does not reflect translation needs worldwide. In this work, we create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Then, we explore how to effectively increase model capacity through a combination of dense scaling and language-specific sparse parameters to create high quality models. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT. We open-source our scripts so that others may reproduce the data, evaluation, and final M2M-100 model.

MBart
type: NamedIndividual
type: Pretrained Model
comment: MBart model was presented in Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer. According to the abstract, MBART is a sequence-to-sequence denoising auto-encoder pretrained on large-scale monolingual corpora in many languages using the BART objective. mBART is one of the first methods for pretraining a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text.

MBart-50
type: NamedIndividual
type: Pretrained Model
comment: MBart-50 is a MBart model, which was presented in Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer. According to the abstract, MBART is a sequence-to-sequence denoising auto-encoder pretrained on large-scale monolingual corpora in many languages using the BART objective. mBART is one of the first methods for pretraining a complete sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text.

MPNet
type: NamedIndividual
type: Pretrained Model
comment: MPNet model was proposed in MPNet: Masked and Permuted Pre-training for Language Understanding by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu. MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of masked language modeling and permuted language modeling for natural language understanding. The abstract from the paper is the following: BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models. Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g., BERT, XLNet, RoBERTa) under the same model setting.

MT5
type: NamedIndividual
type: Pretrained Model
comment: mT5 model was presented in mT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel. The abstract from the paper is the following: The recent “Text-to-Text Transfer Transformer” (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We describe the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. All of the code and model checkpoints

MarianMT
type: NamedIndividual
type: Pretrained Model
comment: MarianMT is a Translations model, but not identical to output in the test set linked to in each model card. The list of supported language pairs can be found here. Models were originally trained by Jörg Tiedemann using the Marian C++ library, which supports fast training and translation. All models are transformer encoder-decoders with 6 layers in each component. Each model’s performance is documented in a model card. The 80 opus models that require BPE preprocessing are not supported. The modeling code is the same as BartForConditionalGeneration with a few minor modifications: static (sinusoid) positional embeddings (MarianConfig.static_position_embeddings=True) no layernorm_embedding (MarianConfig.normalize_embedding=False) the model starts generating with pad_token_id (which has 0 as a token_embedding) as the prefix (Bart uses ), Code to bulk convert models can be found in convert_marian_to_pytorch.py. This model was contributed by sshleifer. Naming All model names use the following format: Helsinki-NLP/opus-mt-{src}-{tgt} The language codes used to name models are inconsistent. Two digit codes can usually be found here, three digit codes require googling “language code {code}”. Codes formatted like es_AR are usually code_{region}. That one is Spanish from Argentina. The models were converted in two stages. The first 1000 models use ISO-639-2 codes to identify languages, the second group use a combination of ISO-639-5 codes and ISO-639-2 codes. Examples Since Marian models are smaller than many other translation models available in the library, they can be useful for fine-tuning experiments and integration tests. Fine-tune on GPU Fine-tune on GPU with pytorch-lightning Multilingual Models All model names use the following format: Helsinki-NLP/opus-mt-{src}-{tgt}: If a model can output multiple languages, and you should specify a language code by prepending the desired output language to the src_text. You can see a models’s supported language codes in its model ca…

MegatronBERT
type: NamedIndividual
type: Pretrained Model
comment: MegatronBERT model was proposed in Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro. The abstract from the paper is the following: Recent work in language modeling demonstrates that training large transformer models advances the state of the art in Natural Language Processing applications. However, very large models can be quite difficult to train due to memory constraints. In this work, we present our techniques for training very large transformer models and implement a simple, efficient intra-layer model parallel approach that enables training transformer models with billions of parameters. Our approach does not require a new compiler or library changes, is orthogonal and complimentary to pipeline model parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We illustrate this approach by converging transformer based models up to 8.3 billion parameters using 512 GPUs. We sustain 15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline that sustains 39 TeraFLOPs, which is 30% of peak FLOPs. To demonstrate that large language models can further advance the state of the art (SOTA), we train an 8.3 billion parameter transformer language model similar to GPT-2 and a 3.9 billion parameter model similar to BERT. We show that careful attention to the placement of layer normalization in BERT-like models is critical to achieving increased performance as the model size grows. Using the GPT-2 model we achieve SOTA results on the WikiText103 (10.8 compared to SOTA perplexity of 15.8) and LAMBADA (66.5% compared to SOTA accuracy of 63.2%) datasets. Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy of 89.4%).

MegatronGPT2
type: NamedIndividual
type: Pretrained Model
comment: MegatronGPT2 model was proposed in Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro. The abstract from the paper is the following: Recent work in language modeling demonstrates that training large transformer models advances the state of the art in Natural Language Processing applications. However, very large models can be quite difficult to train due to memory constraints. In this work, we present our techniques for training very large transformer models and implement a simple, efficient intra-layer model parallel approach that enables training transformer models with billions of parameters. Our approach does not require a new compiler or library changes, is orthogonal and complimentary to pipeline model parallelism, and can be fully implemented with the insertion of a few communication operations in native PyTorch. We illustrate this approach by converging transformer based models up to 8.3 billion parameters using 512 GPUs. We sustain 15.1 PetaFLOPs across the entire application with 76% scaling efficiency when compared to a strong single GPU baseline that sustains 39 TeraFLOPs, which is 30% of peak FLOPs. To demonstrate that large language models can further advance the state of the art (SOTA), we train an 8.3 billion parameter transformer language model similar to GPT-2 and a 3.9 billion parameter model similar to BERT. We show that careful attention to the placement of layer normalization in BERT-like models is critical to achieving increased performance as the model size grows. Using the GPT-2 model we achieve SOTA results on the WikiText103 (10.8 compared to SOTA perplexity of 15.8) and LAMBADA (66.5% compared to SOTA accuracy of 63.2%) datasets. Our BERT model achieves SOTA results on the RACE dataset (90.9% compared to SOTA accuracy of 89.4%).

MobileBERT
type: NamedIndividual
type: Pretrained Model
comment: MobileBERT model was proposed in MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou. It’s a bidirectional transformer based on the BERT model, which is compressed and accelerated using several approaches. The abstract from the paper is the following: Natural Language Processing (NLP) has recently achieved great success by using huge pre-trained models with hundreds of millions of parameters. However, these models suffer from heavy model sizes and high latency such that they cannot be deployed to resource-limited mobile devices. In this paper, we propose MobileBERT for compressing and accelerating the popular BERT model. Like the original BERT, MobileBERT is task-agnostic, that is, it can be generically applied to various downstream NLP tasks via simple fine-tuning. Basically, MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks. To train MobileBERT, we first train a specially designed teacher model, an inverted-bottleneck incorporated BERT_LARGE model. Then, we conduct knowledge transfer from this teacher to MobileBERT. Empirical studies show that MobileBERT is 4.3x smaller and 5.5x faster than BERT_BASE while achieving competitive results on well-known benchmarks. On the natural language inference tasks of GLUE, MobileBERT achieves a GLUEscore o 77.7 (0.6 lower than BERT_BASE), and 62 ms latency on a Pixel 4 phone. On the SQuAD v1.1/v2.0 question answering task, MobileBERT achieves a dev F1 score of 90.0/79.2 (1.5/2.1 higher than BERT_BASE). Tips: MobileBERT is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than the left. MobileBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore efficient at predicting masked tokens and at NLU in general, …

MobileNet
type: NamedIndividual
type: Pretrained Model
Acronym: MobileNet
comment: MobileNetV2 is a convolutional neural network architecture that seeks to perform well on mobile devices. It is based on an inverted residual structure where the residual connections are between the bottleneck layers. The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity. As a whole, the architecture of MobileNetV2 contains the initial fully convolution layer with 32 filters, followed by 19 residual bottleneck layers.

NABoE
type: NamedIndividual
type: Pretrained Model
References: Yamada, Ikuya, and Hiroyuki Shindo. "Neural attentive bag-of-entities model for text classification." arXiv preprint arXiv:1909.01259 (2019).
Acronym: NABoE
comment: NABoE is Neural Attentive Bag-of-Entities Model for Text Classification (NABoE) Neural networks have always been the most popular models for NLP tasks and they outperform the more traditional models. Additionally, replacing entities with words while building the knowledge base from the corpus has improved model learning. This means that instead of building vocabulary from the words in a corpus, we build a bag of entities using Entity Linking. Though there has been research on this method of representing the corpus to the model, the NABoE model goes a step further by: + Using a Neural network to detect the entities + Using the attention mechanism to compute the weights on the detected entities (this decides the relevance of the entities for the document in question) Bag_Entitities, text_classification The Neural Attentive Bag of Entities model uses the Wikipedia corpus to detect the associated entities with a word. For example, the word "Apple" can refer to the fruit, the company, and other possible entities. Once all these entities are retrieved, the weight of each entity is calculated using the softmax-based attention function. This gives a smaller subset of entities which are relevant only to that particular document. In the end, the final representation of the word is given by its vectorized embedding combined with the vectorized embedding of the relevant entities associated with the word.

NASNet
type: NamedIndividual
type: Pretrained Model
Acronym: NASNet
comment: NASNet refers to Neural Architecture Search Network, a family of models that were designed automatically by learning the model architectures directly on the dataset of interest. Here we consider NASNet-A, the highest performance model that was found for the CIFAR-10 dataset, and then extended to ImageNet 2012 dataset, obtaining state of the art performance on CIFAR-10 and ImageNet 2012. Only the NASNet-A models, and their respective weights, which are suited for ImageNet 2012 are provided.

NASNetLarge
type: NamedIndividual
type: Pretrained Model
inherits: NASNet
Acronym: NASNetLarge
comment: NASNet-Large is a convolutional neural network that is trained on more than a million images from the ImageNet database. The network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 331-by-331.

NASNetMobile
type: NamedIndividual
type: Pretrained Model
inherits: NASNet
Acronym: NASNetMobile
comment: NASNet-Mobile is a convolutional neural network that is trained on more than a million images from the ImageNet database. The network can classify images into 1000 object categories, such as keyboard, mouse, pencil, and many animals. As a result, the network has learned rich feature representations for a wide range of images. The network has an image input size of 224-by-224.

OverFeat
type: NamedIndividual
type: Pretrained Model
Acronym: OverFeat
comment: OverFeat is an Integrated Recognition, Localization and Detection using Convolutional Networks

Pegasus
type: NamedIndividual
type: Pretrained Model
comment: Pegasus model was proposed in PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019. Pegasus’ pretraining task is intentionally similar to summarization: important sentences are removed/masked from an input document and are generated together as one output sequence from the remaining sentences, similar to an extractive summary. Pegasus achieves SOTA summarization performance on all 12 downstream tasks, as measured by ROUGE and human eval.

PhoBERT
type: NamedIndividual
type: Pretrained Model
comment: PhoBERT model was proposed in PhoBERT: Pre-trained language models for Vietnamese by Dat Quoc Nguyen, Anh Tuan Nguyen. The abstract from the paper is the following: We present PhoBERT with two versions, PhoBERT-base and PhoBERT-large, the first public large-scale monolingual language models pre-trained for Vietnamese. Experimental results show that PhoBERT consistently outperforms the recent best pre-trained multilingual model XLM-R (Conneau et al., 2020) and improves the state-of-the-art in multiple Vietnamese-specific NLP tasks including Part-of-speech tagging, Dependency parsing, Named-entity recognition and Natural language inference.

ProphetNet
type: NamedIndividual
type: Pretrained Model
comment: ProphetNet model was proposed in ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou on 13 Jan, 2020. ProphetNet is an encoder-decoder model and can predict n-future tokens for “ngram” language modeling instead of just the next token. The abstract from the paper is the following: In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.

RAG
type: NamedIndividual
type: Pretrained Model
comment: Retrieval-augmented generation (“RAG”) models combine the powers of pretrained dense retrieval (DPR) and sequence-to-sequence models. RAG models retrieve documents, pass them to a seq2seq model, then marginalize to generate outputs. The retriever and seq2seq modules are initialized from pretrained models, and fine-tuned jointly, allowing both retrieval and generation to adapt to downstream tasks. It is based on the paper Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela. The abstract from the paper is the following: Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance lags behind task-specific architectures. Additionally, providing provenance for their decisions and updating their world knowledge remain open research problems. Pre-trained models with a differentiable access mechanism to explicit nonparametric memory can overcome this issue, but have so far been only investigated for extractive downstream tasks. We explore a general-purpose fine-tuning recipe for retrieval-augmented generation (RAG) — models which combine pre-trained parametric and non-parametric memory for language generation. We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever. We compare two RAG formulations, one which conditions on the same retrieved passages across the whole generated sequence, the other can use different passages per token. We fine-tune and evaluate our models on a wide rang…

Reformer
type: NamedIndividual
type: Pretrained Model
comment: Reformer model was proposed in the paper Reformer: The Efficient Transformer by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya. The abstract from the paper is the following: Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences. We introduce two techniques to improve the efficiency of Transformers. For one, we replace dot-product attention by one that uses locality-sensitive hashing, changing its complexity from O(L^2) to O(Llog(L)), where L is the length of the sequence. Furthermore, we use reversible residual layers instead of the standard residuals, which allows storing activations only once in the training process instead of N times, where N is the number of layers. The resulting model, the Reformer, performs on par with Transformer models while being much more memory-efficient and much faster on long sequences.

RemBERT
type: NamedIndividual
type: Pretrained Model
comment: RemBERT model was proposed in Rethinking Embedding Coupling in Pre-trained Language Models by Hyung Won Chung, Thibault Févry, Henry Tsai, Melvin Johnson, Sebastian Ruder. The abstract from the paper is the following: We re-evaluate the standard practice of sharing weights between input and output embeddings in state-of-the-art pre-trained language models. We show that decoupled embeddings provide increased modeling flexibility, allowing us to significantly improve the efficiency of parameter allocation in the input embedding of multilingual models. By reallocating the input embedding parameters in the Transformer layers, we achieve dramatically better performance on standard natural language understanding tasks with the same number of parameters during fine-tuning. We also show that allocating additional capacity to the output embedding provides benefits to the model that persist through the fine-tuning stage even though the output embedding is discarded after pre-training. Our analysis shows that larger output embeddings prevent the model’s last layers from overspecializing to the pre-training task and encourage Transformer representations to be more general and more transferable to other tasks and languages. Harnessing these findings, we are able to train models that achieve strong performance on the XTREME benchmark without increasing the number of parameters at the fine-tuning stage.

ResNet
type: NamedIndividual
type: Pretrained Model
Acronym: ResNet
comment: A residual neural network (ResNet) is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex.

ResNet50
type: NamedIndividual
type: Pretrained Model
is transformation of: Crop Imagery
is transformation of: Field Imagery
is transformation of: Imagery
is transformation of: Satellite Imagery
Acronym: ResNet50
comment: ResNet-50 is a pretrained Deep Learning model for image classification of the Convolutional Neural Network(CNN, or ConvNet), which is a class of deep neural networks, most commonly applied to analyzing visual imagery. ResNet-50 is 50 layers deep and is trained on a million images of 1000 categories from the ImageNet database. Furthermore the model has over 23 million trainable parameters, which indicates a deep architecture that makes it better for image recognition. Using a pretrained model is a highly effective approach, compared if you need to build it from scratch, where you need to collect great amounts of data and train it yourself. Of course, there are other pretrained deep models to use such as AlexNet, GoogleNet or VGG19, but the ResNet-50 is noted for excellent generalization performance with fewer error rates on recognition tasks and is therefore a useful tool to know.

RetinaNet
type: NamedIndividual
type: Pretrained Model
Acronym: RetinaNet
comment: RetinaNet is a one-stage object detection model that utilizes a focal loss function to address class imbalance during training. Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples. RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks. The backbone is responsible for computing a convolutional feature map over an entire input image and is an off-the-self convolutional network. The first subnet performs convolutional object classification on the backbone's output; the second subnet performs convolutional bounding box regression. The two subnetworks feature a simple design that the authors propose specifically for one-stage, dense detection.

RetriBERT
type: NamedIndividual
type: Pretrained Model
comment: RetriBERT model was proposed in the blog post Explain Anything Like I’m Five: A Model for Open Domain Long Form Question Answering. RetriBERT is a small model that uses either a single or pair of BERT encoders with lower-dimension projection for dense semantic indexing of text.

RoBERTa
type: NamedIndividual
type: Pretrained Model
References: Liu, Yinhan, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. "Roberta: A robustly optimized bert pretraining approach." arXiv preprint arXiv:1907.11692 (2019).
Acronym: RoBERTa
comment: Facebook’s RoBERTa is an optimised method for pre-training a self-supervised NLP system built on BERT’s language masking strategy. The model has been trained to predict intentionally hidden sections of text within otherwise unannotated language examples. RoBERTa modifies key hyperparameters in the model BERT allowing it to improve on the masked language modelling objective leading to better downstream task performance. The researchers are also training RoBERTa on more data than BERT and for a more extended amount of time. Researchers used existing unannotated natural language processing data sets as well as public news articles to train the model.

RoFormer
type: NamedIndividual
type: Pretrained Model
comment: RoFormer model was proposed in RoFormer: Enhanced Transformer with Rotary Position Embedding by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu. The abstract from the paper is the following: Position encoding in transformer architecture provides supervision for dependency modeling between elements at different positions in the sequence. We investigate various methods to encode positional information in transformer-based language models and propose a novel implementation named Rotary Position Embedding(RoPE). The proposed RoPE encodes absolute positional information with rotation matrix and naturally incorporates explicit relative position dependency in self-attention formulation. Notably, RoPE comes with valuable properties such as flexibility of being expand to any sequence lengths, decaying inter-token dependency with increasing relative distances, and capability of equipping the linear self-attention with relative position encoding. As a result, the enhanced transformer with rotary position embedding, or RoFormer, achieves superior performance in tasks with long texts. We release the theoretical analysis along with some preliminary experiment results on Chinese data. The undergoing experiment for English benchmark will soon be updated.

SegNet
type: NamedIndividual
type: Pretrained Model
Acronym: SegNet
comment: SegNet neural network, developed by Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla, all from the University of Cambridge, is a convolutional neural network used for semantic pixel wise labeling. This problem is more commonly called semantic segmentation. Networks used for semantic segmentation typically take an RGB image as input data, and have a label which is an n channel image, where n is the number of labels involved. Each channel corresponds to a label, for example cars, road, etc, and each pixel in a certain channel will be 1 or 0 depending on whether that pixel belongs to the label corresponding to that channel. SegNet consists of layers called encoders and decoders. Each encoder applies convolution, batch normalization and a non linearity, then applies max pooling on the result, while storing the index of the value extracted from each window. Decoders are similar to the encoders, the difference is that they don’t have a non linearity, and they upsample their input, using indices stored from the encoding stage. After the final decoder, the output is fed to a softmax classifier which gives the final prediction. The prediction is going to be an n channel image so we have to write a separate function to convert it into an RGB image where we can look at the results qualitatively.

ShuffleNet
type: NamedIndividual
type: Pretrained Model
Acronym: ShuffleNet
comment: ShuffleNet is an Extremely Efficient Convolutional Neural Network for Mobile Devices It is introduced as an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13x actual speedup over AlexNet while maintaining comparable accuracy

Speech2Text
type: NamedIndividual
type: Pretrained Model
comment: Speech2Text model was proposed in fairseq S2T: Fast Speech-to-Text Modeling with fairseq by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino. It’s a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. Speech2Text has been fine-tuned on several datasets for ASR and ST: LibriSpeech, CoVoST 2, MuST-C.

Splinter
type: NamedIndividual
type: Pretrained Model
comment: Splinter model was proposed in Few-Shot Question Answering by Pretraining Span Selection by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy. Splinter is an encoder-only transformer (similar to BERT) pretrained using the recurring span selection task on a large corpus comprising Wikipedia and the Toronto Book Corpus. The abstract from the paper is the following: In several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order of 100,000 annotated questions and answers. We explore the more realistic few-shot setting, where only a few hundred training examples are available, and observe that standard models perform poorly, highlighting the discrepancy between current pretraining objectives and question answering. We propose a new pretraining scheme tailored for question answering: recurring span selection. Given a passage with multiple sets of recurring spans, we mask in each set all recurring spans but one, and ask the model to select the correct span in the passage for each masked span. Masked spans are replaced with a special token, viewed as a question representation, that is later used during fine-tuning to select the answer span. The resulting model obtains surprisingly good results on multiple benchmarks (e.g., 72.7 F1 on SQuAD with only 128 training examples), while maintaining competitive performance in the high-resource setting.

SqueezeBERT
type: NamedIndividual
type: Pretrained Model
comment: SqueezeBERT model was proposed in SqueezeBERT: What can computer vision teach NLP about efficient neural networks? by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, Kurt W. Keutzer. It’s a bidirectional transformer similar to the BERT model. The key difference between the BERT architecture and the SqueezeBERT architecture is that SqueezeBERT uses grouped convolutions instead of fully-connected layers for the Q, K, V and FFN layers. The abstract from the paper is the following: Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets, large computing systems, and better neural network models, natural language processing (NLP) technology has made significant strides in understanding, proofreading, and organizing these messages. Thus, there is a significant opportunity to deploy NLP in myriad applications to help web users, social networks, and businesses. In particular, we consider smartphones and other mobile devices as crucial platforms for deploying NLP models at scale. However, today’s highly-accurate NLP neural network models such as BERT and RoBERTa are extremely computationally expensive, with BERT-base taking 1.7 seconds to classify a text snippet on a Pixel 3 smartphone. In this work, we observe that methods such as grouped convolutions have yielded significant speedups for computer vision networks, but many of these techniques have not been adopted by NLP neural network designers. We demonstrate how to replace several operations in self-attention layers with grouped convolutions, and we use this technique in a novel network architecture called SqueezeBERT, which runs 4.3x faster than BERT-base on the Pixel 3 while achieving competitive accuracy on the GLUE test set. The SqueezeBERT code will be released.

T5
type: NamedIndividual
type: Pretrained Model
related to: PyTorch
References: Raffel, Colin, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer." Journal of Machine Learning Research 21 (2020): 1-67.
Acronym: T5
comment: T5 is a transformer model which aims to provide great flexibility and provide better semantic understanding through the training of multiple tasks at once. Transformer-based language model trained on multiple tasks including summarization, sentiment-analysis, q&a, translation etc. The implementation in this repo is an adaptation of the onnxt5 repo which makes the export and use of T5 with ONNX easier.
comment: T5 library serves primarily as code for reproducing the experiments in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. In the paper, we demonstrate how to achieve state-of-the-art results on multiple NLP tasks using a text-to-text transformer pre-trained on a large text corpus. The bulk of the code in this repository is used for loading, preprocessing, mixing, and evaluating datasets. It also provides a way to fine-tune the pre-trained models released alongside the publication. The t5 library can be used for future model development by providing useful modules for training and fine-tuning (potentially huge) models on mixtures of text-to-text tasks.
alternative label: T5: Text-To-Text Transfer Transformer

TAPAS
type: NamedIndividual
type: Pretrained Model
comment: TAPAS model was proposed in TAPAS: Weakly Supervised Table Parsing via Pre-training by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos. It’s a BERT-based model specifically designed (and pre-trained) for answering questions about tabular data. Compared to BERT, TAPAS uses relative position embeddings and has 7 token types that encode tabular structure. TAPAS is pre-trained on the masked language modeling (MLM) objective on a large dataset comprising millions of tables from English Wikipedia and corresponding texts. For question answering, TAPAS has 2 heads on top: a cell selection head and an aggregation head, for (optionally) performing aggregations (such as counting or summing) among selected cells. TAPAS has been fine-tuned on several datasets: SQA (Sequential Question Answering by Microsoft), WTQ (Wiki Table Questions by Stanford University) and WikiSQL (by Salesforce). It achieves state-of-the-art on both SQA and WTQ, while having comparable performance to SOTA on WikiSQL, with a much simpler architecture. The abstract from the paper is the following: Answering natural language questions over tables is usually seen as a semantic parsing task. To alleviate the collection cost of full logical forms, one popular approach focuses on weak supervision consisting of denotations instead of logical forms. However, training semantic parsers from weak supervision poses difficulties, and in addition, the generated logical forms are only used as an intermediate step prior to retrieving the denotation. In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms. TAPAS trains from weak supervision, and predicts the denotation by selecting table cells and optionally applying a corresponding aggregation operator to such selection. TAPAS extends BERT’s architecture to encode tables as input, initializes from an effective joint pre-training of text segments and tables crawl…

ToD-BERT
type: NamedIndividual
type: Pretrained Model
Acronym: ToD-BERT
comment: ToD-BERT is Pre-trained Natural Language Understanding for Task-Oriented Dialogues.

Transformer-XL
type: NamedIndividual
type: Pretrained Model
comment: Transformer-XL model was proposed in Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov. It’s a causal (uni-directional) transformer with relative positioning (sinusoïdal) embeddings which can reuse previously computed hidden-states to attend to longer context (memory). This model also uses adaptive softmax inputs and outputs (tied). The abstract from the paper is the following: Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens.

ULMFiT
type: NamedIndividual
type: Pretrained Model
Acronym: ULMFiT
comment: ULMFiT, also known as Universal Language Model Fine-tuning, is an effective transfer learning method which can be used to perform any sort of NLP tasks. The model performs significantly on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, the model has only been trained on 100 labelled examples to match the performance of model training from scratch on 100x more data. The creators have open-sourced their pre-trained models and code for developers to use.

VGG
type: NamedIndividual
type: Pretrained Model
related to: Artificial Neural Network (ANN)
related to: Convolutional Neural Network (CNN)
related to: Imagery
related to: Keras
References: Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
References: https://keras.io/api/applications/vgg/
Acronym: VGG
comment: VGG16 and VGG19 have been introduced in Very Deep Convolutional Networks for Large Scale Image Recognition, Karen Simonyan, Andrew Zisserman, 2014, https://arxiv.org/abs/1409.1556. The network used 3×3 convolutional layers stacked and alternated with max pooling, two 4096 fully-connected layers, followed by a softmax classifier. The 16 and 19 stand for the number of weight layers in the network (columns D and E):

VGG16
type: NamedIndividual
type: Pretrained Model
inherits: VGG
Acronym: VGG16
comment: VGG16 and VGG19 have been introduced in Very Deep Convolutional Networks for Large Scale Image Recognition, Karen Simonyan, Andrew Zisserman, 2014, https://arxiv.org/abs/1409.1556. The network used 3×3 convolutional layers stacked and alternated with max pooling, two 4096 fully-connected layers, followed by a softmax classifier. The 16 and 19 stand for the number of weight layers in the network (columns D and E):

VGG19
type: NamedIndividual
type: Pretrained Model
inherits: VGG
Acronym: VGG19
comment: VGG16 and VGG19 have been introduced in Very Deep Convolutional Networks for Large Scale Image Recognition, Karen Simonyan, Andrew Zisserman, 2014, https://arxiv.org/abs/1409.1556. The network used 3×3 convolutional layers stacked and alternated with max pooling, two 4096 fully-connected layers, followed by a softmax classifier. The 16 and 19 stand for the number of weight layers in the network (columns D and E):

Vision Transformer (ViT)
type: NamedIndividual
type: Pretrained Model
comment: Vision Transformer (ViT) model was proposed in An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. It’s the first paper that successfully trains a Transformer encoder on ImageNet, attaining very good results compared to familiar convolutional architectures. The abstract from the paper is the following: While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.

VisualBERT
type: NamedIndividual
type: Pretrained Model
comment: VisualBERT model was proposed in VisualBERT: A Simple and Performant Baseline for Vision and Language by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang. VisualBERT is a neural network trained on a variety of (image, text) pairs. The abstract from the paper is the following: We propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks. VisualBERT consists of a stack of Transformer layers that implicitly align elements of an input text and regions in an associated input image with self-attention. We further propose two visually-grounded language model objectives for pre-training VisualBERT on image caption data. Experiments on four vision-and-language tasks including VQA, VCR, NLVR2, and Flickr30K show that VisualBERT outperforms or rivals with state-of-the-art models while being significantly simpler. Further analysis demonstrates that VisualBERT can ground elements of language to image regions without any explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between verbs and image regions corresponding to their arguments. Tips: Most of the checkpoints provided work with the VisualBertForPreTraining configuration. Other checkpoints provided are the fine-tuned checkpoints for down-stream tasks - VQA (‘visualbert-vqa’), VCR (‘visualbert-vcr’), NLVR2 (‘visualbert-nlvr2’). Hence, if you are not working on these downstream tasks, it is recommended that you use the pretrained checkpoints. For the VCR task, the authors use a fine-tuned detector for generating visual embeddings, for all the checkpoints. We do not provide the detector and its weights as a part of the package, but it will be available in the research projects, and the states can be loaded directly into the detector provided. Usage: VisualBERT is a multi-modal vision and language model. It can be used for visual question answering, multiple choice, visual reasoning and region-to-phrase …

XLM
type: NamedIndividual
type: Pretrained Model
comment: XLM model was proposed in Cross-lingual Language Model Pretraining by Guillaume Lample, Alexis Conneau. It’s a transformer pretrained using one of the following objectives: a causal language modeling (CLM) objective (next token prediction), a masked language modeling (MLM) objective (BERT-like), or a Translation Language Modeling (TLM) object (extension of BERT’s MLM to multiple language inputs) The abstract from the paper is the following: Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT’16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT’16 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.

XLM-ProphetNet
type: NamedIndividual
type: Pretrained Model
comment: XLM-ProphetNet model was proposed in ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou on 13 Jan, 2020. XLM-ProphetNet is an encoder-decoder model and can predict n-future tokens for “ngram” language modeling instead of just the next token. Its architecture is identical to ProhpetNet, but the model was trained on the multi-lingual “wiki100” Wikipedia dump. The abstract from the paper is the following: In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.

XLM-RoBERTa
type: NamedIndividual
type: Pretrained Model
comment: XLM-RoBERTa model was proposed in Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook’s RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. The abstract from the paper is the following: This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make XLM-R code, data, and models publicly available.

XLNet
type: NamedIndividual
type: Pretrained Model
References: Yang, Zhilin, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. "XLNet: Generalized autoregressive pretraining for language understanding." arXiv preprint arXiv:1906.08237 (2019).
Acronym: XLNet
comment: XLNet is an unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context. Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking.

XLSR-Wav2Vec2
type: NamedIndividual
type: Pretrained Model
comment: XLSR-Wav2Vec2 model was proposed in Unsupervised Cross-Lingual Representation Learning For Speech Recognition by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli. The abstract from the paper is the following: This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing XLSR-53, a large model pretrained in 53 languages.

Xception
type: NamedIndividual
type: Pretrained Model
Acronym: Xception
comment: Xception Model is proposed by Francois Chollet. Xception is an extension of the inception Architecture which replaces the standard Inception modules with depthwise Separable Convolutions.

YOLOv2
type: NamedIndividual
type: Pretrained Model
related to: Keras
References: Redmon, Joseph, and Ali Farhadi. "YOLO9000: better, faster, stronger." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7263-7271. 2017.
Acronym: YOLOv2
comment: YOLO is an ultra popular object detection framework for deep learning applications. This repository contains implementations of YOLOv2 in Keras. While the developers have tested the framework on all sorts of object images – like kangaroo detection, self-driving car, red blood cell detection, etc., they have released the pretrained model for raccoon detection.

YOLOv3
type: NamedIndividual
type: Pretrained Model
inherits: YOLOv2
related to: Keras
Acronym: YOLOv3
comment: YOLO vesion 3 is an ultra popular object detection framework for deep learning applications. This repository contains implementations of YOLOv2 in Keras. While the developers have tested the framework on all sorts of object images – like kangaroo detection, self-driving car, red blood cell detection, etc., they have released the pretrained model for raccoon detection.

ZFNet
type: NamedIndividual
type: Pretrained Model
Acronym: ZFNet
comment: NASNet refers to Neural Architecture Search Network, a family of models that were designed automatically by learning the model architectures directly on the dataset of interest.

fastText
type: NamedIndividual
type: Pretrained Model
References: Bojanowski, Piotr, Edouard Grave, Armand Joulin, and Tomas Mikolov. "Enriching word vectors with subword information." Transactions of the Association for Computational Linguistics 5 (2017): 135-146.
References: https://github.com/facebookresearch/fastText
Acronym: fastText
comment: fastText is a library for efficient learning of word representations and sentence classification.

herBERT
type: NamedIndividual
type: Pretrained Model
comment: herBERT model was proposed in KLEJ: Comprehensive Benchmark for Polish Language Understanding by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, and Ireneusz Gawlik. It is a BERT-based Language Model trained on Polish Corpora using only MLM objective with dynamic masking of whole words. The abstract from the paper is the following: In recent years, a series of Transformer-based models unlocked major improvements in general natural language understanding (NLU) tasks. Such a fast pace of research would not be possible without general NLU benchmarks, which allow for a fair comparison of the proposed methods. However, such benchmarks are available only for a handful of languages. To alleviate this issue, we introduce a comprehensive multi-task benchmark for the Polish language understanding, accompanied by an online leaderboard. It consists of a diverse set of tasks, adopted from existing datasets for named entity recognition, question-answering, textual entailment, and others. We also introduce a new sentiment analysis task for the e-commerce domain, named Allegro Reviews (AR). To ensure a common evaluation scheme and promote models that generalize to different NLU tasks, the benchmark includes datasets from varying domains and applications. Additionally, we release HerBERT, a Transformer-based model trained specifically for the Polish language, which has the best average performance and obtains the best results for three out of nine tasks. Finally, we provide an extensive evaluation, including several standard baselines and recently proposed, multilingual Transformer-based models.

Propineb (Antrocol)
type: NamedIndividual
type: Pesticides
See Also: Propineb, a contact fungicide with broad spectrum activity against various diseases of rice, chilli, grapes, potato and other vegetables and fruits. Propineb is a polymeric zinc-containing dithiocarbamate. Due to the release of zinc, the application of Antracol results in greening effect on the crop and subsequent improvement in quality of produce.

Pseudomonas Putida
type: NamedIndividual
type: Virus
comment: Pseudomonas putida is a Gram-negative, rod-shaped, saprotrophic soil bacterium. Based on 16S rRNA analysis, P. putida was taxonomically confirmed to be a Pseudomonas species (sensu stricto) and placed, along with several other species, in the P. putida group, to which it lends its name.[1] However, a recent phylogenomic analysis [2] of 494 complete genomes from the entire Pseudomonas genus clearly showed that the genomes that were named as P. putida did not form a monophyletic clade, but were dispersed and formed a wider evolutionary group (the putida group) that included other species as well, such as Pseudomonas alkylphenolia, Pseudomonas monteilii, Pseudomonas cremoricolorata, Pseudomonas fulva, Pseudomonas parafulva, Pseudomonas entomophila, Pseudomonas mosselii, and Pseudomonas plecoglossicida. A variety of P. putida, called multiplasmid hydrocarbon-degrading Pseudomonas, is the first patented organism in the world. Because it is a living organism, the patent was disputed and brought before the United States Supreme Court in the historic court case Diamond v. Chakrabarty, which the inventor, Ananda Mohan Chakrabarty, won. It demonstrates a very diverse metabolism, including the ability to degrade organic solvents such as toluene.[3] This ability has been put to use in bioremediation, or the use of microorganisms to degrade environmental pollutants. Use of P. putida is preferable to some other Pseudomonas species capable of such degradation, as it is a safe species of bacteria, unlike P. aeruginosa, for example, which is an opportunistic human pathogen.

Pseudomonas atrofaciens
type: NamedIndividual
type: Bacterial
label: Pseudomonas syringae
label: Pseudomonas syringae pv. atrofaciens

Psyllids
type: NamedIndividual
type: Crop Disease
type: Crop Pest
comment: These insect pests, similar to aphids, can damage the tender growth of trees by sucking out the plant's juices. They also secrete honeydew that coats leaves and may encourage the growth of a sooty black mold. Symptoms of damage include yellowing and dropping leaves. To control these pests, spray with insecticidal soap every 3-4 days until the insects are gone. In fall, spray trees with a light horticultural oil and, in early spring, spray trees with a dormant oil before the leaves emerge. After the leaves emerge, spray insecticidal soap once a week for 3 weeks.

Puccinia Graminis Tritici
type: NamedIndividual
type: Fungus
causes: Black Rust

Puccinia Hordei
type: NamedIndividual
type: Crop Disease

Puccinia Recondita
type: NamedIndividual
type: Fungus

Puccinia Striiformis
type: NamedIndividual
type: Fungus

Puerto Rico
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:PR
agricultural land: 1,900 sq. km
area: 9,104 km²
climate: tropical marine, mild; little seasonal temperature variation
density: 438/km²
latitude: -66.58363
longitude: 18.198626
population: 3,994,259 (2007)
comment: Puerto Rico, officially the Commonwealth of Puerto Rico is a Caribbean island and unincorporated territory of the United States. It is located in the northeast Caribbean Sea, approximately 1,000 miles southeast of Miami, Florida.

PyCaret
type: NamedIndividual
type: Toolkit
comment: PyCaret is an open source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of notebook environment.

PyNLPl
type: NamedIndividual
type: Toolkit
Acronym: PyNLPl
comment: PyNLPl Python library for NLP contains modules for both standard and less common NLP tasks. Its use case ranges from basic functions like extracting n-grams and frequency lists to building simple language models. In addition, PyNLPl comes with an entire library for working with FoLiA XML. It works on Python 2.7 and Python 3. Find in-depth information on common functions, data types, experiments, formats, language models, search algorithms and more

PyTorch
type: NamedIndividual
type: Toolkit
Acronym: PyTorch
comment: PyTorch is An open source machine learning framework that accelerates the path from research prototyping to production deployment.

Pyrenophora graminea
type: NamedIndividual
type: Fungus
causes: Barley Stripe
comment: Pyrenophora graminea is the causal agent of barley stripe. Barley stripe is disease of barley that once caused significant crop yield losses in many areas of the world.

Pyrenophora Teres
type: NamedIndividual
type: Crop Disease
comment: Pyrenophora teres is a necrotrophic fungal pathogen of some plant species, the most significant of which are economically important agricultural crops such as barley. Toxins include aspergillomarasmine A and related compounds.

Qatar
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:QA
agricultural land: 660 sq. km
area: 11,437 km²
climate: arid; mild, pleasant winters; very hot, humid summers
density: 74/km²
latitude: 51.592407
longitude: 25.365366
population: 1,541,130 (10/2008)
comment: Qatar, officially the State of Qatar, is a country located in Western Asia, occupying the small Qatar Peninsula on the northeastern coast of the Arabian Peninsula. Its sole land border is with neighbouring Gulf Cooperation Council monarchy Saudi Arabia to the south, with the rest of its territory surrounded by the Persian Gulf. The Gulf of Bahrain, an inlet of the Persian Gulf, separates Qatar from nearby Bahrain.

Quinalphos
type: NamedIndividual
type: Insecticide
used by: Coffee
used by: Rice
used by: Wheat
comment: Quinalphos is an organothiophosphate chemical chiefly used as a pesticide. It is a reddish-brown liquid. The chemical formula is C12H15N2O3PS, and IUPAC name O,O-diethyl O-quinoxalin-2-yl phosphorothioate. Quinalphos is classified as a yellow label (highly toxic) pesticide in India, and is widely used in the following crops: wheat, rice, coffee, sugarcane, and cotton.

Radish
type: NamedIndividual
type: Crop

Radopholus similis
type: NamedIndividual
type: Crop Disease
affects: Banana
biological control: Paecilomyces lilacinus is a fungus that parasitizes eggs, juveniles and adults of R. similis. It can be applied as dip, soil drench or incorporated into the soil. Liquid and powder formulations containing P. lilacinus and Paecilomyces oxalicum are marketed commercially. Purified extracts of Penicillium oxalicum, Penicillium anatolicum and Aspergillus niger have shown high nematicidal activity. Strains of the bacterium Pseudomonas fluorescens have been found to inhibit the invasion of banana roots by R. similis.
chemical control: Nematicides are generally non-volatile organophosphates or carbamates. Their mode of action is mainly nematostatic, which means they do not kill nematodes, but act on the nematodes’ nervous system and interfere with their ability to hatch from eggs, move, penetrate the roots, feed and reproduce. Nematicides are applied as granules on the soil surface around the mat. Emulsifiable compounds are applied as liquid sprays or through irrigation systems. In order to avoid build-up of resistance of the nematodes to the chemicals, alternation with different compounds is recommended. It is important to always immediately irrigate after application and not apply nematicides when flooding occurs. The chemicals should be incorporated as quickly as possible. Nematicides are often applied routinely without regard to the severity of the problem, but it is better to strategically schedule application based on the population density and amount of damage in the crop. The optimum application time, dose and frequency are determined by nematicide efficiency, environmental conditions, as well as pathogenicity of local nematode strains and population dynamics. In most production areas, nematicide applications vary between 2 to 3 g of active ingredient per mat and 2-3 applications per year. Though nematicides are generally effective in controlling nematodes and are easy to use, they are expensive, highly toxic and may have a negative impact on the environment.
controls: Nematodes by themselves can only move about 1 meter per year. They may, however, be moved over longer distances in infested planting materials, on equipment, through irrigation water, etc. Once introduced, eradication of R. similis from the soil is virtually impossible and populations will build up more or less rapidly after planting. Yield losses may also be reduced through propping or guying of pseudostems to avoid toppling. Application of amendments (mulching, manure, agricultural wastes) or any other measures that improve soil fertility and root development may increase plant tolerance to nematodes. Adding of organic materials also improves the microbial activity and thus acts as a natural biological control. Improved drainage is also an important factor in reducing nematode damage in high-rainfall regions.
distribution: Radopholus similis was first described by Cobb. Although the first observations were made in Fiji, the nematode is found in almost all tropical and subtropical banana growing regions of the world, with some exceptions including Israel, the Canary Islands, Cape Verde Islands, Cyprus, Crete, Mauritius, Taiwan, the highlands of Eastern Africa, most of South Africa, Mozambique and parts of southern Mexico. The present geographical distribution is a reflection of historical movements of infested planting material and of the temperature preference of the pathogen, which ranges between 24 and 32°C. Optimum reproduction occurs at around 30°C. It does not reproduce below 16-17°C or above 33°C.
symptoms: Nematode infections are difficult to diagnose without looking at underground symptoms, but some above-ground symptoms can give some indication (although they can also be symptomatic of other stress factors): stunted plant growth and lack of vigour; reduction in the number and size of leaves; leaf yellowing; premature defoliation; increased susceptibility to wilt; reduced yield, small bunches; increased harvest-to-harvest time and plant toppling. Collecting root samples and looking at the underground symptoms provide better information. If a root is cleared of soil and then cut in half longitudinally, reddish-brown necrosis can be seen extending from the surface extending towards the centre, but not entering the stele. The corm may show reddish-brown necrosis. The feeding and tunneling of the nematodes produces characteristic reddish brown lesions throughout the cortex, but not in the stele. Three to four weeks after infection, when extensive cavities have formed, one or more deep cracks with raised margins appear on the root surface. Fungal and bacterial invasion of the lesions causes necrosis that penetrates the stele, resulting in root atrophy. Eventually, the root system may be reduced to a few short stubs. When the nematodes migrate into the rhizome cortex, they cause diffuse black lesions. The symptoms can be easily distinguished from those of fusarium wilt, because symptoms of the latter are confined to the vascular tissue in the stele and do not extend to the root surface. In general, there will be a reduced root system, i.e. a lower number of secondary and tertiary roots and root hairs. The destruction of root and corm tissues reduces water and mineral uptake. This results in a reduction of plant growth and development and may lead to severe reduction of bunch weight and a significant increase of the time period between two successive harvests. The destruction also results in a tendency for plants to uproot or topple, particularly during strong winds and hea…
comment: Radopholus similis, or burrowing nematode, is one of the most damaging and widespread nematodes attacking bananas, causing toppling or blackhead disease. It is considered to be the main nematode problem of intensive commercial bananas, especially Cavendish types. It is also commonly found in plantain and cooking bananas cultivated in the lowlands of central and eastern Africa, and the Caribbean. It is however generally absent in plantain roots in west Africa and central America.

Ragweed
type: NamedIndividual
type: Weeds

Ramularia Collo-cygni
type: NamedIndividual
type: Crop Disease

Rangeland
type: NamedIndividual
type: LandUse

Rapid Growth
type: NamedIndividual
type: Plant Phenomenon

Rapid Insight Veera
type: NamedIndividual
type: Toolkit
comment: Rapid Insight products enable users to turn their raw data into actionable information. The company's analytic software simplifies the extraction and analysis of data, enabling clients ranging from small businesses to Fortune 500 companies to fully utilize their information for data-driven decision making.

RapidMiner
type: NamedIndividual
type: Toolkit
comment: RapidMiner is a data science software platform developed by the company of the same name that provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics. It is used for business and commercial applications as well as for research, education, training, rapid prototyping, and application development and supports all steps of the machine learning process including data preparation, results visualization, model validation and optimization.

Red Flour Beetle
type: NamedIndividual
type: Crop Disease
affects: Cereal
affects: Wheat
comment: The red flour beetle (Tribolium castaneum) is a species of beetle in the family Tenebrionidae, the darkling beetles. It is a worldwide pest of stored products, particularly food grains, and a model organism for ethological and food safety research.

Red Rot
type: NamedIndividual
type: Crop Disease
affects: Sugarcane

Red Yellow Podzolic
type: NamedIndividual
type: Soil Type
comment: Sandy soil - are light, warm, dry and tend to be acidic and low in nutrients. Sandy soils are often known as light soils due to their high proportion of sand and little clay (clay weighs more than sand). These soils have quick water drainage and are easy to work with. They are quicker to warm up in spring than clay soils but tend to dry out in summer and suffer from low nutrients that are washed away by rain. The addition of organic matter can help give plants an additional boost of nutrients by improving the nutrient and water holding capacity of the soil.

Reproduction
type: NamedIndividual
type: Plant Phenomenon
comment: Reproduction is the production of new offspring in plants, which can be accomplished by sexual or asexual reproduction. Sexual reproduction produces offspring by the fusion of gametes, resulting in offspring genetically different from the parent or parents. Asexual reproduction produces new individuals without the fusion of gametes, genetically identical to the parent plants and each other, except when mutations occur.

Reunion
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:RE
area: 2,512 km²
density: 319/km²
latitude: 55.532063
longitude: -21.114532
population: 802,000 (1/2008)
comment: Réunion is an overseas department and region of the French Republic and an Indian Ocean island in East Africa, approximately 550 km east of the island of Madagascar and 175 km southwest of the island of Mauritius. As of January 2020, it had a population of 859,959.

Rhizobia
type: NamedIndividual
type: Bacterial
comment: Rhizobia are diazotrophic bacteria that fix nitrogen after becoming established inside the root nodules of legumes (Fabaceae). To express genes for nitrogen fixation, rhizobia require a plant host; they cannot independently fix nitrogen. In general, they are gram negative, motile, non-sporulating rods. Rhizobia are a "group of soil bacteria that infect the roots of legumes to form root nodules". Rhizobia are found in the soil and after infection, produce nodules in the legume where they fix nitrogen gas (N2) from the atmosphere turning it into a more readily useful form of nitrogen. From here, the nitrogen is exported from the nodules and used for growth in the legume. Once the legume dies, the nodule breaks down and releases the rhizobia back into the soil where they can live individually or reinfect a new legume host.

Rhizomania
type: NamedIndividual
type: Crop Disease
affects: Sugar beet
caused by: Beet Necrotic Yellow Vein Virus
biological control: Management of rhizomania can also be achieved by a combination of cultural practices that are aimed at interfering with the life cycle of the vector. Planting early into cool soils, avoiding over use of irrigation, crop rotation, and using resistant varieties will all help to reduce disease problems due to BNYVV.
chemical control: Soil fumigation and/or solarization where allowed or cost-effective can also be beneficial.
controls: Because of the zoosporic nature of the vector, very warm, water-saturated soils are required for effective dispersal and infection. Early infection results in much more severe stunting, and yield reductions. Infections occurring later in season may suffer loss of sucrose content, but often result in acceptable root yields.
symptoms: Foliar symptoms show various degrees of stunting, wilting, and yellowing and often may be confused with a nutritional deficiency. Root symptoms include a constricted taproot with a proliferation of small feeder roots, known as bearding, after early infection. They often exhibit a "wineglass" shape when infection occurs late. Severe infections will result in necrosis of vascular elements when roots are viewed in cross-section. The necrotic yellowing along leaf veins, for which the virus was named, is seldom seen in the field.
comment: Rhizomania disease is economically limiting wherever it occurs. These locations include growing areas in Japan, China, Europe, and the United States. The virus is transmitted by the soilborne plasmodiophorid-like fungus, Polymyxa betae, which is an obligate parasite infecting members of the Chenopodiaceae. The pathogen survives in long-lived survival structures of the vector called cystosori. The cystosori liberate zoospores that infect sugar beets, and if viruliferous, will transmit the virus into the roots.

Rhynchosporium Secalis
type: NamedIndividual
type: Crop Disease
affects: Barley
affects: Rye
comment: Rhynchosporium secalis is an ascomycete fungus that is the causal agent of barley and rye scald.

Rice blast
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Rice
caused by: Magnaporthe oryzae
caused by: PyriculariaGrisea
comment: Rice blast observed in both lowland and upland. Initially white to grayish green circular lesions or spots with dark green borders are found on the leaves. Older lesions on the leaves are elliptical or spindle-shaped with more or less pointed ends. A leaf infected by blast

Rice Gall Midge
type: NamedIndividual
type: Crop Pest

Rice Leaf Caterpillar
type: NamedIndividual
type: Crop Pest

Rice Leaf Folder
type: NamedIndividual
type: Crop Disease
affects: Rice
symptoms: The larvae spin threads that form bands across the leaf, contracting as they dry and tie the margins together, causing the leaf to roll. Inside the rolled leaves, the larvae scrape the surface tissues, creating whitish stripes. Rice fields appear scorched from the damage. Eggs, oval, flat on lower side, about 1 mm long and 0.5 mm wide, laid singly or in small groups on the underside of the leaves. Larvae, cream at first, becoming green when mature, about 16 mm long, covered in brown hairs, and with a light-brown head (Photo 1). Pupation is within a silken cocoon inside the folded leaf. Adults are golden-yellow, straw coloured or orange-brown (Photos 2&3) with two or three dark lines across the forewings (one may be incomplete). One or two of these lines coincide with similar lines in the hindwing. The wingspan is about 16 mm. The length of the life cycle varies over the range of the moth from 21 to more than 75 days. The moth is more active in the wetter times of the year. The adult is a strong flyer and in parts of Asia there are long distance migrations in the spring with return migrations in the autumn to over-winter as larvae or pupae.
Acronym: RLF
comment: Leaffolder caterpillars fold a rice leaf around themselves and attach the leaf margins together with silk strands. They feed inside the folded leaf creating longitudinal white and transparent streaks on the blade.

Rice Leaf Roller
type: NamedIndividual
type: Crop Pest

Rice Leafhopper
type: NamedIndividual
type: Crop Pest

Rice Stemfly
type: NamedIndividual
type: Crop Pest

Rice Water Weevil
type: NamedIndividual
type: Crop Pest

Rice Weevil
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Barley
affects: Cereal
affects: Corn
affects: Maize
affects: Oats
affects: Rice
affects: Wheat
comment: Rice weevil (Sitophilus oryzae) is a stored product pest which attacks seeds of several crops, including wheat, rice, and maize.

Rich Organic Soils
type: NamedIndividual
type: Soil Type

Ring Spot
type: NamedIndividual
type: Crop Disease
affects: Sugarcane

Ripening stage
type: NamedIndividual
type: Growth Stage
related to: Wheat
comment: Stage 8: Ripening 83 Early dough 85 Soft dough: grain content soft but dry. Fingernail impression not held 87 Hard dough: grain content solid. Fingernail impression held 89 Fully ripe: grain hard, difficult to divide with thumbnail
label: Stage 8: Ripening

Riptortus Pedestris
type: NamedIndividual
type: Crop Pest
comment: Riptortus Pedestris (Fabricius)

River
type: NamedIndividual
type: Topography

River Bank
type: NamedIndividual
type: Topography

Romania
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:RO
agricultural land: 139,820 sq. km
area: 238,391 km²
climate: temperate; cold, cloudy winters with frequent snow and fog; sunny summers with frequent showers and thunderstorms
density: 93/km²
latitude: 24.978283
longitude: 45.941868
population: 22,246,862 (2008)
comment: Romania is a country located at the crossroads of Central, Eastern, and Southeastern Europe. It shares land borders with Bulgaria to the south, Ukraine to the north, Hungary to the west, Serbia to the southwest, and Moldova to the east and has its opening to the Black Sea. It has a predominantly temperate-continental climate. With a total area of 238,397 square kilometres, Romania is the twelfth-largest country in Europe and the sixth-most populous member state of the European Union, having approximately 19.3 million inhabitants . Its capital and largest city is Bucharest. Other major urban areas include Cluj-Napoca, Timișoara, Iași, Constanța, Craiova, Brașov, and Galați.

Room Temperature
type: NamedIndividual
type: Temperature

Root Knot Nematode
type: NamedIndividual
type: Crop Disease
development: Root knot nematodes usually invade plants in the spring or early summer. Each root knot contains one or more females, which produce large egg masses within their saclike bodies. By midsummer the eggs are extruded and the nematodes overwinter as eggs. Usually there is one generation per year.
distribution: Root knot nematodes have a very wide host range, including all small grain cereals. Meloidogyne naasi appears to have specificity for cereals and grasses, and can be found wherever cereals are grown.
important: The damage caused by root knot nematodes depends on the number of egg masses in the soil. All cultivars of winter and spring wheat seem to be compatible hosts of the nematode. In factors as nutritional deficiencies, poor drainage, and soil-borne diseases can conceal the presence of nematodes.
symptoms: Infestations of root knot nematodes are characterized by the formation of small knots or galls near the tips of the roots. Above ground, infested plants are stunted and chlorotic. Excessive branching of affected roots sometimes occurs.
comment: Root-knot nematodes are plant-parasitic nematodes from the genus Meloidogyne. They exist in soil in areas with hot climates or short winters. About 2000 plants worldwide are susceptible to infection by root-knot nematodes and they cause approximately 5% of global crop loss. Root-knot nematode larvae infect plant roots, causing the development of root-knot galls that drain the plant's photosynthate and nutrients. Infection of young plants may be lethal, while infection of mature plants causes decreased yield.

Root Rot
type: NamedIndividual
type: Crop Disease
affects: Cotton

Rootworms
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Corn
comment: Rootworms are the larvae of beetles. They fall into two groups: clover rootworms and northern corn rootworms. The former species is found throughout the U.S. and southern Canada. The adult is light brown, oval, and 1/5" long. It lays its eggs at the base of a variety of host plants, including apple, beans, corn, grape, melon, potato, and strawberries. The whitish, plump larvae then burrow into the roots and chew the smaller ones. Adult beetles can cause minor damage to hosts by chewing long, curved or zigzag marks in leaves. Clover rootworms can be controlled by thorough fall cultivation, which should effectively destroy overwintering larvae.

Rosemary
type: NamedIndividual
type: Crop
type: Vegetable
harvesting: Rosemary can be harvested as soon as the plant is established in the the ground and has reached a suitable size. Branches are harvested by cutting the terminal growth (25–30 cm/9.8–11.8 in) before they become woody. Rosemary can be harvested several times in one season but it is important to allow the plant to recover and replace growth before the next harvest.
maintenance: Rosemary is very prone to root rot and should therefore be planted in a well-draining soil and watered sparingly. Allow the soil to dry before watering, generally watering once every 1–2 weeks is sufficient but watering should be adjusted to suit climatic conditions. Plants will benefit from the regular addition of a balanced fertilizer. A layer of mulch will protect the plants over winter.
propagation: Rosemary can be propagated from seed, cuttings, or by air layering. Seeds germinate slowly, emerging after 3–4 weeks, and have a low germination rate so vegetative propagation is preferred. Cuttings should be taken from healthy, vigorous plants by taking a clipping about 7.6 cm (3 in) in length from the end of a branch. The leaves should be removed from the lower half of the cutting before planting in light textured potting media to root. The cutting should be watered regularly and kept moist, but not wet while they root. The new plants will be ready for transplanting after approximately 8 weeks. after hardening, transplants should be planted 45 cm (18 in) apart allowing 1.2 m (4 ft) between rows.
requirements: Rosemary is suited to both temperate and subtropical areas with most varieties growing optimally at temperatures between 6 and 24°C (39–75°F) in a well-draining loamy soil with a pH between 6.0 and 7.0. Rosemary plants require at least 6 hours of sun every day, direct sunlight being best. Rosemary can survive mild winters but will not tolerate temperatures that are regularly below -3°C (26.6°F). Established rosemary is very drought resistant.
uses: Rosemary leaves can be used fresh or dried as a herb in cooking or in salads. The leaves and flowers can be used to extract rosemary oil which is used as a seasoning or as a scent in soaps and household products.
References: https://plantvillage.psu.edu/topics/rosemary/infos
comment: Rosemary, Rosmarinus officinalis, is an erect, bushy evergreen shrub in the family Lamiaceae grown for its leaves which are used as a herb. The rosemary plant is usually erect in growth habit and possess branched woody stems with tufts of leaves. The leaves are opposite and usually bladelike and glossy green. The plant produces clusters of 5–10 blue, purple or pink flowers on short lateral branches and small oval fruit. Rosemary plants can reach 2 m (6.6 ft) in height and can be productive for many years (up to 30 if conditions are favorable for its growth). Rosemary originates from the Mediterranean.

Russian Federation
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:RU
agricultural land: 2.15 million sq. km
area: 17,075,400 km²
climate: ranges from steppes in the south through humid continental in much of European Russia; subarctic in Siberia to tundra climate in the polar north; winters vary from cool along Black Sea coast to frigid in Siberia; summers vary from warm in the steppes to cool along Arctic coast
density: 8.3/km²
latitude: 105.318756
longitude: 61.52401
population: 142,008,838 (2008)
comment: Russia, or the Russian Federation, is a transcontinental country spanning Eastern Europe and Northern Asia. It is the largest country in the world; covering over 17,125,191 square kilometres, consisting of more than one-eighth of the Earth's inhabited land area, extending to eleven time zones, and has bordering sixteen sovereign nations. Russia has a population of 146.7 million; and is the ninth-most populous country, as well as the most populous country in Europe. Moscow, the capital, is the largest city in Europe, while Saint Petersburg is the second-largest city and the country's cultural centre. Russians are the largest Slavic and European nation; speaking Russian, the most spoken Slavic language, and the most spoken native language in Europe.

Rust
type: NamedIndividual
type: Crop Disease
affects: Soybean
comment: Rusts are plant diseases caused by pathogenic fungi of the order Pucciniales (previously known as Uredinales). An estimated 168 rust genera and approximately 7,000 species, more than half of which belong to the genus Puccinia, are currently accepted. Rust fungi are highly specialized plant pathogens with several unique features. Taken as a group, rust fungi are diverse and affect many kinds of plants. However, each species has a very narrow range of hosts and cannot be transmitted to non-host plants. In addition, most rust fungi cannot be grown easily in pure culture.

Rusty Grain Beetle
type: NamedIndividual
type: Crop Disease
affects: Cereal
affects: Wheat
comment: The rusty grain beetle is a cosmopolitan pest that is often found in stored grain in the northern United States and Canada.

Rwanda
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:RW
agricultural land: 19,200 sq. km
area: 26,798 km²
climate: temperate; two rainy seasons (February to April, November to January); mild in mountains with frost and snow possible
density: 343/km²
latitude: 29.876375
longitude: -1.94708
population: 10,186,063 (4/2008)
comment: Rwanda, officially the Republic of Rwanda, is a landlocked country in the Great Rift Valley, where the African Great Lakes region and East Africa converge. One of the smallest countries on the African mainland, its capital city is Kigali. Located a few degrees south of the Equator, Rwanda is bordered by Uganda, Tanzania, Burundi, and the Democratic Republic of the Congo. It is highly elevated, giving it the soubriquet "land of thousand hills", with its geography dominated by mountains in the west and savanna to the east, with numerous lakes throughout the country. The climate is temperate to subtropical, with two rainy seasons and two dry seasons each year. Rwanda has a population of over 12.6 million living on 26,338 km2 of land, and is the most densely populated mainland African country.

Saint Helena
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:SH
agricultural land: 110 sq. km
area: 420 km²
climate: Saint Helena - tropical; marine; mild, tempered by trade winds; Tristan da Cunha - temperate; marine, mild, tempered by trade winds (tends to be cooler than Saint Helena)
latitude: -10.029991
longitude: -24.127476
population: 4,255 (2/2008)
comment: Saint Helena is a British Overseas Territory located in the South Atlantic Ocean. It consists of a remote volcanic tropical island lying some 1,950 kilometres west of the coast of southwestern Africa, and 4,000 kilometres east of Rio de Janeiro on the South American coast. It is one of three constituent parts of the British Overseas Territories of Saint Helena, Ascension and Tristan da Cunha.

Saint Kitts and Nevis
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:KN
area: 261 km²
density: 164/km²
latitude: -62.706413
longitude: 17.25773
population: 42,696 (2005)
comment: Saint Kitts and Nevis, officially known as the Federation of Saint Christopher and Nevis, is an island country in the West Indies. Located in the Leeward Islands chain of the Lesser Antilles, it is the smallest sovereign state in the Western Hemisphere, in both area and population. The country is a Commonwealth realm, with Elizabeth II as Queen and head of state. It is the only federation in the Caribbean.

Saint Lucia
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:LC
area: 620 km²
density: 298/km²
latitude: -60.974174
longitude: 13.904011
population: 160,765 (2005)
comment: Saint Lucia is an island country in the West Indies in the eastern Caribbean Sea on the boundary with the Atlantic Ocean. The island was previously called Iyonola, the name given to the island by the native Arawaks and later, Hewanorra, the name given by the native Caribs, two separate Amerindian peoples. Part of the Lesser Antilles, it is located north/northeast of the island of Saint Vincent, northwest of Barbados and south of Martinique. It covers a land area of 617 km2 and reported a population of 165,595 in the 2010 census. Its capital is Castries.

Saint Pierre and Miquelon
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:PM
area: 242 km²
density: 25/km²
latitude: -56.289013
longitude: 46.949776
population: 6,125 (1/2006)
comment: Saint Pierre and Miquelon, officially the Territorial Collectivity of Saint-Pierre and Miquelon, is a self-governing territorial overseas collectivity of France in the North-western Atlantic Ocean near the Canadian province of Newfoundland and Labrador. It is the only remaining part of New France in North America to retain French sovereignty, appellate jurisdiction and mineral rights. Its residents are French citizens; the collectivity is a full member of the National Assembly and participates in senatorial and presidential elections. It covers 242 square kilometres of land and shores and has a population of 6,008 as of the March 2016 census.

Saint Vincent and the Grenadines
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:VC
area: 389 km²
density: 307/km²
latitude: -61.286568
longitude: 12.954002
population: 120,000 (2008)
comment: Saint Vincent and the Grenadines, also often known simply as Saint Vincent, is an island country in the Caribbean. It is located in the southeast Windward Islands of the Lesser Antilles, which lie in the West Indies at the southern end of the eastern border of the Caribbean Sea where the latter meets the Atlantic Ocean.

Salt Sieving
type: NamedIndividual
type: Soil Phenomena

Samoa
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:WS
agricultural land: 350 sq. km
area: 2,831 km²
climate: tropical; rainy season (November to April), dry season (May to October)
density: 65/km²
latitude: -172.10396
longitude: -13.74787
population: 188,540 (2008)
comment: Samoa, officially the Independent State of Samoa and until 1997 known as Western Samoa, is a Polynesian island country consisting of two main islands, two smaller, inhabited islands, and several smaller, uninhabited islands, including the Aleipata Islands . The capital city is Apia. The Lapita people discovered and settled the Samoan Islands around 3,500 years ago. They developed a Samoan language and Samoan cultural identity.

San Marino
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:SM
agricultural land: 10 sq. km
area: 61.2 km²
climate: Mediterranean; mild to cool winters; warm, sunny summers
density: 489/km²
latitude: 12.459873
longitude: 43.942947
population: 29,973 (2008)
comment: San Marino, officially the Republic of San Marino, also known as the Most Serene Republic of San Marino, is a microstate in Southern Europe completely enclosed by Italy.

Sand
type: NamedIndividual
type: Soil Texture
comment: Sand or sandy soil is formed by the smallest or fine particles of weathering rocks. This soil is known as the poorest type of soil for agriculture and growing plants as they have very low nutritional value and poor water holding capacity.
definition: Clay <= 10 Sand >= 86 Silt <= 14

Sandy Clay
type: NamedIndividual
type: Soil Texture
definition: Clay >= 35 and Clay <= 55 Sand >=45 and Sand <= 65 Silt <= 20

Sandy Clay Loam
type: NamedIndividual
type: Soil Texture
definition: Clay >= 20 and Clay <= 35 Sand >=45 and Sand <= 80 Silt <= 28

Sandy Loam
type: NamedIndividual
type: Soil Texture
definition: Clay <= 20 Sand >= 50 and Sand <= 70 Silt <= 50

Sandy Loamy
type: NamedIndividual
type: Soil Type

Sandy Soils
type: NamedIndividual
type: Soil Type
comment: Sandy soil - are light, warm, dry and tend to be acidic and low in nutrients. Sandy soils are often known as light soils due to their high proportion of sand and little clay (clay weighs more than sand). These soils have quick water drainage and are easy to work with. They are quicker to warm up in spring than clay soils but tend to dry out in summer and suffer from low nutrients that are washed away by rain. The addition of organic matter can help give plants an additional boost of nutrients by improving the nutrient and water holding capacity of the soil.

Sao Tome and Principe
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:ST
agricultural land: 487 sq. km
area: 964 km²
climate: tropical; hot, humid; one rainy season (October to May)
density: 171/km²
latitude: 6.968272
longitude: 0.843659
population: 157,000 (2005)
comment: São Tomé and Príncipe, officially the Democratic Republic of São Tomé and Príncipe, is an island country in the Gulf of Guinea, off the western equatorial coast of Central Africa. It consists of two archipelagos around the two main islands of São Tomé and Príncipe, about 140 km apart and about 250 and 225 km off the northwestern coast of Gabon.

Sarocladium oryzae
type: NamedIndividual
type: Pathogen
causes: Sheath rot
comment: Sarocladium oryzae (Sawada) is a plant pathogen causing the sheath rot disease of rice. In culture it produces 0.3–0.627 micrograms of helvolic acid and 0.9–4.8 micrograms of cerulenin per milliliter of culture medium.

Saudi Arabia
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:SA
agricultural land: 1.73 million sq. km
area: 2,149,690 km²
climate: harsh, dry desert with great temperature extremes
density: 11/km²
latitude: 45.08114
longitude: 24.126614
population: 27,601,038 (2007)
comment: Saudi Arabia, officially the Kingdom of Saudi Arabia, is a country in Western Asia constituting the vast majority of the Arabian Peninsula. With a land area of approximately 2,150,000 km2, Saudi Arabia is geographically the largest sovereign state in Western Asia, the second-largest in the Arab world, the fifth-largest in Asia, and the 12th-largest in the world. Saudi Arabia is bordered by Jordan and Iraq to the north, Kuwait to the northeast, Qatar, Bahrain, and the United Arab Emirates to the east, Oman to the southeast and Yemen to the south; it is separated from Egypt and Israel in the north-west by the Gulf of Aqaba. Saudi Arabia is the only country with both a Red Sea coast and a Persian Gulf coast, and most of its terrain consists of arid desert, lowland, steppe and mountains. As of October 2018, the Saudi economy was the largest in the Middle East and the 18th largest in the world. Saudi Arabia also has one of the world's youngest populations, with approximately 50 percent of its population of 34.2 million being under 25 years old.

Sawfly
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Cereal
affects: Wheat
distribution: Nearly all cultivated cereals and native grasses act as hosts, although wheat is preferred. Fall-sown cereals are more commonly attacked. Wheat lines having solid or partially solid stems are much less susceptible to attack. The wheat stem sawfly is a major problem in the Mediterranean Basin.
important: Sawfly can cause significant damage in some years, but infestations are usually discontinuous.
life cycle: Sawflies produce one generation per year. The larvae overwinter in the straw in the spring they pupate. Adult sawflies are small, fly-like wasps and appear from late spring to midsummer. The females deposit small white eggs in the upper nodes of stems just below the heads. Upon hatching, the legless white larvae bore into the stem and tunnel downward, feeding on the pith of the stem. When they have completed their feeding, they descend further and girdle the stem base.
symptoms: Damage by sawflies includes premature yellowing of the head and shrivelling of the grain. The larvae girdle the stem and, later in the crop cycle, lodging is common.
comment: Sawfly larvae are often confused with caterpillars but it is important to distinguish between them as controls that work for caterpillars may not be effective against sawfly larvae. Most commonly sawfly larvae are found feeding on foliage but some also bore or mine into stems, trunks and leaves. Following are the two most important categoies of sawflies, which are pests of plants.

Scab
type: NamedIndividual
type: Crop Disease
affects: Citrus
comment: In citrus leaf and fruits, the scab acne is a composite of fungal and organism tissue. These acnes are hardly raised and pink to light brown in color. The color of scab lesion is yellow-brown and dirty grey

Scikit-learn
type: NamedIndividual
type: Toolkit
has algorithm: Artificial Neural Network (ANN)
has algorithm: Decision Tree (DT)
has algorithm: Factor Analysis (FA)
has algorithm: Fast Algorithm for Independent Component Analysis (FastICA)
has algorithm: Gaussian Process Regression (GPR)
has algorithm: Incremental Principal Components Analysis (IPCA)
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Kernel Principal Component Analysis (KPCA)
has algorithm: Least Absolute Shrinkage and Selection Operator (LASSO)
has algorithm: Multi-layer Perceptron (MLP)
has algorithm: Principal Component Analysis (PCA)
has algorithm: Random Forest (RF)
has algorithm: Stochastic Gradient Descent (SGD)
has algorithm: Stochastic Gradient Descent Regression (SGDR)
has algorithm: Support Vector Machine (SVM)
has algorithm: Support Vector Regression (SVR)
has algorithm: Sparse Principal Components Analysis (SparsePCA)
has algorithm: Regression
References: Pedregosa et al., Scikit-learn: Machine Learning in Python, JMLR 12, pp. 2825-2830, 2011.
comment: Scikit-learn: Machine Learning in Python

Sclerotinia sclerotiorum
type: NamedIndividual
type: Fungus
comment: Sclerotinia sclerotiorum is a plant pathogenic fungus and can cause a disease called white mold if conditions are conducive. S. sclerotiorum can also be known as cottony rot, watery soft rot, stem rot, drop, crown rot and blossom blight. A key characteristic of this pathogen is its ability to produce black resting structures known as sclerotia and white fuzzy growths of mycelium on the plant it infects. These sclerotia give rise to a fruiting body in the spring that produces spores in a sac which is why fungi in this class are called sac fungi (Ascomycetes). This pathogen can occur on many continents and has a wide host range of plants. When S. sclerotiorum is onset in the field by favorable environmental conditions, losses can be great and control measures should be considered.
alternative label: S. sclerotiorum

Sclerotinia Stem Rot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
comment: Sclerotinia stem rot is the most economically significant canola disease in Canada. This disease, caused by the fungus Sclerotinia sclerotiorum, is heavily influenced by environmental conditions leading up to and during the flowering period of canola, which can make predicting outbreaks difficult. Control of this disease can be achieved with risk assessment tools, fungicides, and partial resistance available in some canola cultivars.

Scotinophara Lurida
type: NamedIndividual
type: Crop Pest
comment: Scotinophara Lurida (Burmeister)

Scotland
type: NamedIndividual
type: Country
belongs to: United Kingdom
ISO code: ISO 3166: GB-SCT
area: 77,933 km² (30,090 sq mi)
density: 67.5/km2 (174.8/sq mi)
population: 5,463,300

Seed Rot
type: NamedIndividual
type: Crop Disease
affects: Cotton
management: Seed treatments with fungicides provide protection against seed rot and seedling diseases, especially if seed is planted early or if environmental conditions are unfavorable to germination and seedling growth. Uneven stands caused by seed rot and seedling diseases can be partly compensated for by increasing seeding rates.
symptoms: Seed rot and seedling diseases often appear within a few days of planting. Whitish outgrowths of fungal mycelium emerge from cracks in the seed glumes or from the collar of the infected seedling's plumule. After a few days, the fungal mycelium resembles a halo that radiates from the infection point on the seed or seedling. Various algae colonize the fungal growth turning it green. In some cases the infected seed appears within a dark circular spot on the soil surface. This too may be the result of algae growth, but is most probably caused by secondary invasion of the seed and fungus by various aquatic bacteria. Early infection of germinating seeds, especially when temperatures are cool, will often result in seed rot or seedling mortality.
comment: Seed rot and seedling diseases often appear within a few days of planting. Whitish outgrowths of fungal mycelium emerge from cracks in the seed glumes or from the collar of the infected seedling's plumule. After a few days, the fungal mycelium resembles a halo that radiates from the infection point on the seed or seedling.

Seedling
type: NamedIndividual
type: Growth Stage
comment: Seedling is a stage of the young sporophyte developing out of a plant embryo from a seed. Seedling development starts with germination of the seed.

Seedling Disease Complex
type: NamedIndividual
type: Crop Disease

Semi-arid Climate
type: NamedIndividual
type: Climate Type
comment: A semi-arid climate or steppe climate is the climate of a region that receives precipitation below potential evapotranspiration, but not as low as a desert climate. There are different kinds of semi-arid climates, depending on variables such as temperature, and they give rise to different biomes.

Senegal
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:SN
agricultural land: 95,050 sq. km
area: 196,723 km²
climate: tropical; hot, humid; rainy season (May to November) has strong southeast winds; dry season (December to April) dominated by hot, dry, harmattan wind
density: 59/km²
latitude: -14.445561
longitude: 14.499454
population: 11,658,000 (2005)
comment: Senegal, officially the Republic of Senegal, is a country in West Africa. Senegal is bordered by Mauritania in the north, Mali to the east, Guinea to the southeast, and Guinea-Bissau to the southwest. Senegal nearly surrounds The Gambia, a country occupying a narrow sliver of land along the banks of the Gambia River, which separates Senegal's southern region of Casamance from the rest of the country. Senegal also shares a maritime border with Cape Verde. Senegal's economic and political capital is Dakar.

Senescence stage
type: NamedIndividual
type: Growth Stage
related to: Wheat
comment: Stage 9: Senescence 92 Over-ripe: grain very hard, cannot be dented by thumbnail 93 Grains loosening in day-time 97 Plant dead and collapsing 99 Harvested product
alternative label: Stage 9: Senescence

Sentinel-1
type: NamedIndividual
type: Satellite
comment: With the objectives of Land and Ocean monitoring, Sentinel-1 will be composed of two polar-orbiting satellites operating day and night, and will perform Radar imaging, enabling them to acquire imagery regardless of the weather. The first Sentinel-1 satellite was launched in April 2014.

Sentinel-2
type: NamedIndividual
type: Satellite
comment: The Copernicus Sentinel-2 mission comprises a constellation of two polar-orbiting satellites placed in the same sun-synchronous orbit, phased at 180° to each other. It aims at monitoring variability in land surface conditions, and its wide swath width (290 km) and high revisit time (10 days at the equator with one satellite, and 5 days with 2 satellites under cloud-free conditions which results in 2-3 days at mid-latitudes) will support monitoring of Earth's surface changes. The coverage limits are from between latitudes 56° south and 84° north.

Sentinel-3
type: NamedIndividual
type: Satellite
comment: The primary objective of Sentinel-3 is marine observation, and it will study sea-surface topography, sea and land surface temperature, ocean and land colour. Composed of three satellites, the mission's primary instrument is a radar altimeter, but the polar-orbiting satellites will carry multiple instruments, including optical imagers.

Sentinel-4
type: NamedIndividual
type: Satellite
comment: Sentinel-4 is dedicated to air quality monitoring. The Sentinel-4 UVN instrument is a spectrometer carried aboard Meteosat Third Generation satellites, operated by EUMETSAT. The mission aims to provide continuous monitoring of the composition of the Earth's atmosphere at high temporal and spatial resolution and the data will be used to support monitoring and forecasting over Europe.

Sentinel-5
type: NamedIndividual
type: Satellite
comment: Sentinel-5 is dedicated to air quality monitoring. The Sentinel-5 UVNS instrument is a spectrometer carried aboard the MetOp Second Generation satellites. The mission aims to provide continuous monitoring of the composition of the Earth's atmosphere. It provides wide-swath, global coverage data to monitor air quality around the world.

Sentinel-5P
type: NamedIndividual
type: Satellite
comment: A precursor satellite mission, Sentinel-5P aims to fill in the data gap and provide data continuity between the retirement of the Envisat satellite and NASA's Aura mission and the launch of Sentinel-5. The main objective of the Sentinel-5P mission is to perform atmospheric measurements, with high spatio-temporal resolution, relating to air quality, climate forcing, ozone and UV radiation. The satellite was successfully launched on 13 October 2017 from the Plesetsk cosmodrome in Russia.

September
type: NamedIndividual
type: Month Of Year

Septoria Brown Spot
type: NamedIndividual
type: Crop Disease
affects: Soybean
controls: Like most foliar diseases, Septoria brown spot is most common when conditions and leaves are wet and warm, ideally for extended periods of time. Because the pathogen survives and sporuates on soybean residue, minimum tillage and continuous soybeans may enhance this disease.
management: Septoria brown spot typically does not require management because it rarely causes significant losses. Soybean varieties are not available with resistance to this disease, but varieties can vary in their susceptibility. Rotation with non-legume crops and tillage may be beneficial, and foliar fungicides can provide some control under those rare conditions when an application may be warranted. - Crop rotation. This allows time for soybean straw with fruiting bodies to degrade. This disease is more severe in continuously cropped soybean fields. - Tillage. For fields with very high levels of brown spot, plow under soybean straw to promote rapid decay. - While brown spot can cause between 2 to 4 bu in yield loss, rarely is it economically feasible to manage this disease.
symptoms: Symptoms of brown spot appear first on the cotyledons and unifoliate leaves early in the growing season. Angular, red to brown spots that vary in size from tiny specks to quarter-inch diameter can be seen on the upper and lower leaf surfaces. Leaves with numerous spots rapidly turn yellow and fall to the ground. Defoliation proceeds from the bottom of the plant toward the top of the plant. Irregular brown lesions with indefinite borders may also develop on infected pods, stems and petioles.
comment: Septoria brown spot (also called brown spot) is common leaf disease of soybean across the Midwestern U.S. It's incidence can be high but it rarely develops to cause significant yield loss. Yield losses of five to eight percent may occur under severe conditions when much defoliation occurs.

Septoria Tritici Blotch
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Wheat
development: Initial infections tend to be on the lower leaves, progressing to the upper leaves and spikes if environmental conditions remain favorable. Cool temperatures (10-15C) and prolonged wet, cloudy weather favors the development of these diseases.
distribution: These are primarily diseases of wheat, but other cereals are somewhat susceptible. The diseases are limited to temperate wheat-growing areas where cool and moist conditions prevail.
important: Major losses can occur, through seed shrivelling and lower test weights, if these diseases reach severe levels prior to harvest.
symptoms: Initial infection sites tend to be irregular in shape, oval to elongated chlorotic spots or lesions. As these sites expand, the centers of the lesions become pale, straw colored, and slightly necrotic, often with numerous small black dots (pycnidia) (17). The lesions of septoria tritici blotch tend to be linear and restricted laterally (18), while those of septoria nodorum blotch (19) and septoria avenae blotch are more lens shaped. All above ground plant parts can be affected. Light infection produces only scattered lesions, but heavy infection can kill leaves, spikes (20 is S. nodorum), or even the entire plant. Identification of species in the field can be difficult, and microscopic examination is often necessary.

Septoria Leaf Blight
type: NamedIndividual
type: Crop Disease

Septoria Leaf Spot
type: NamedIndividual
type: Crop Disease

Leptosphaeria nodorum
type: NamedIndividual
type: Fungus
label: Seotiria Nodorum
label: Septoria Nodorum Blotch

Mycosphaerella graminicola
type: NamedIndividual
type: Fungus
label: Septoria Tritici
label: Septoria Tritici Blotch @fr

Serbia
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:RS
agricultural land: 50,610 sq. km
area: 88,361 km²
climate: in the north, continental climate (cold winters and hot, humid summers with well-distributed rainfall); in other parts, continental and Mediterranean climate (relatively cold winters with heavy snowfall and hot, dry summers and autumns)
density: 115/km²
population: 7,395,000 (2008)
comment: Belgrade is the capital and largest city of Serbia. It is located at the confluence of the Sava and Danube rivers and the crossroads of the Pannonian Plain and the Balkan Peninsula. Nearly 1.7 million people live within the administrative limits of the City of Belgrade, a quarter of the total population of Serbia.

Serious Damage
type: NamedIndividual
type: Health

SesamiaInferens
type: NamedIndividual
type: Crop Pest

Severely-infected Trees
type: NamedIndividual
type: State
is state of: Crop Disease
is state of: Crop Disease by Tier4

Seychelles
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:SC
agricultural land: 30 sq. km
area: 451 km²
climate: tropical marine; humid; cooler season during southeast monsoon (late May to September); warmer season during northwest monsoon (March to May)
density: 178/km²
latitude: 51.239353
longitude: -6.952612
population: 82, 247 (2008)
comment: Seychelles, officially the Republic of Seychelles, is an archipelagic island country in the Indian Ocean at the eastern edge of the Somali Sea. The country consists of 115 islands. Its capital and largest city, Victoria, lies 1,500 kilometres east of mainland Africa. Other nearby island countries and territories include the Comoros, Madagascar, Mauritius, and the French overseas regions of Mayotte and Réunion to the south; as well as Maldives and the Chagos Archipelago to the east. With a population of roughly 98,462, it has the smallest population of any sovereign African country.

Sheath rot
type: NamedIndividual
type: Crop Disease
affects: Rice
caused by: Sarocladium oryzae
comment: Rotting occurs on the leaf sheath that encloses the young panicles. The lesions start as oblong or some irregular spots with variation in colour from gray to light brown at centers, surrounded by distinct dark reddish brown margins are observed.

Sierra Leone
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:SL
agricultural land: 34,350 sq. km
area: 71,740 km²
climate: tropical; hot, humid; summer rainy season (May to December); winter dry season (December to April)
density: 83/km²
latitude: -11.795934
longitude: 8.464561
population: 6,294,774
comment: Sierra Leone, officially the Republic of Sierra Leone, informally Salone, is a country on the southwest coast of West Africa. It is bordered by Liberia to the southeast and Guinea to the northeast. Sierra Leone has a tropical climate with a diverse environment ranging from savanna to rainforests, a total area of 71,740 km2 and a population of 7,092,113 as of the 2015 census. The capital and largest city is Freetown. The country is divided into five administrative regions which are subdivided into sixteen districts. Sierra Leone is a constitutional republic with a unicameral parliament and a directly elected president. Sierra Leone is a Muslim majority country at approximately 78%, though with a politically influential Christian minority at about 21%. Muslims and Christians interact very well together and religious violence is extremely rare in the country. All the major Muslim and Christian holidays are officially national holidays in the country.

Silt
type: NamedIndividual
type: Soil Texture
comment: Silt is granular material of a size between sand and clay, whose mineral origin is quartz and feldspar. Silt may occur as a soil (often mixed with sand or clay) or as sediment mixed in suspension with water (also known as a suspended load) and soil in a body of water such as a river.
definition: Clay <= 12 Sand <= 20 Silt>= 88

Silty Clay
type: NamedIndividual
type: Soil Texture
definition: Clay >= 40 and Clay <= 60 Sand <= 20 Silt>= 40 and Silt <= 60

Silty Clay Loam
type: NamedIndividual
type: Soil Texture
definition: Clay >= 27 and Clay <= 40 Sand <= 20 Silt>= 40 and Silt <= 73

Silt Loam
type: NamedIndividual
type: Soil Texture
definition: Clay <= 27 Sand >= 20 and Sand <= 50 Silt>= 74 and Silt <= 88

Silty Soil
type: NamedIndividual
type: Soil Type
comment: Silt soil - are light and moisture retentive soils with a high fertility rating. As silt soils compromise of medium sized particles they are well drained and hold moisture well. As the particles are fine, they can be easily compacted and are prone to washing away with rain. By adding organic matter, the silt particles can be bound into more stable clumps.

Singapore
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:SG
agricultural land: 7.3 sq. km
area: 707.1 km²
climate: tropical; hot, humid, rainy; two distinct monsoon seasons - Northeastern monsoon (December to March) and Southwestern monsoon (June to September); inter-monsoon - frequent afternoon and early evening thunderstorms
density: 6,489/km²
latitude: 103.83268
longitude: 1.311341
population: 4,839,400 (2008)
comment: Singapore, officially the Republic of Singapore, is a sovereign island city-state in maritime Southeast Asia. It lies about one degree of latitude north of the equator, off the southern tip of the Malay Peninsula, bordering the Straits of Malacca to the west, the Riau Islands to the south, and the South China Sea to the east. The country's territory is composed of one main island, 63 satellite islands and islets, and one outlying islet, the combined area of which has increased by 25% since the country's independence as a result of extensive land reclamation projects. It has the second greatest population density in the world. The country has almost 5.7 million residents, 61% of whom are Singaporean citizens. There are four official languages of Singapore: English, Malay, Chinese and Tamil. English is the lingua franca. Multiracialism is enshrined in the constitution, and continues to shape national policies in education, housing, and politics.

Sinkhole
type: NamedIndividual
type: Planetary Phenomena
comment: A sinkhole, also known as a cenote, sink, sink-hole, swallet, swallow hole, or doline, is a depression or hole in the ground caused by some form of collapse of the surface layer.

Sisense
type: NamedIndividual
type: Toolkit
comment: Sisense is a business analytics software company with offices in New York City, San Francisco, Tel Aviv, London, Melbourne, Tokyo, and Scottsdale, Arizona.

Sitobion Avenae
type: NamedIndividual
type: Crop Pest

Slight Damage
type: NamedIndividual
type: Health

Slightly Infested
type: NamedIndividual
type: Crop Injure

Sloping Land
type: NamedIndividual
type: Topography

Slovakia
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:SK
agricultural land: 19,297 sq. km
area: 49,035 km²
climate: temperate; cool summers; cold, cloudy, humid winters
density: 111/km²
latitude: 19.699694
longitude: 48.672577
population: 5,379,455 (2001)
comment: Slovakia, officially the Slovak Republic, is a landlocked country in Central Europe. It is bordered by Poland to the north, Ukraine to the east, Hungary to the south, Austria to the west, and the Czech Republic to the northwest. Slovakia's mostly mountainous territory spans about 49,000 square kilometres, with a population of over 5.4 million. The capital and largest city is Bratislava, and the second-largest city is Košice.

Slovenia
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:SI
agricultural land: 4,585 sq. km
area: 20,273 km²
climate: Mediterranean climate on the coast, continental climate with mild to hot summers and cold winters in the plateaus and valleys to the east
density: 99.6/km²
latitude: 14.992997
longitude: 46.149204
population: 2,039,399 (2008)
comment: Slovenia, officially the Republic of Slovenia, is a country located in Central Europe at the crossroads of main European cultural and trade routes. It is bordered by Italy to the west, Austria to the north, Hungary to the northeast, Croatia to the southeast, and the Adriatic Sea to the southwest. Slovenia covers 20,271 square kilometers and has a population of 2.111 million. One of the successor states of the former Yugoslavia, Slovenia is now a parliamentary republic and member nation of the European Union, United Nations, and NATO. The capital and largest city is Ljubljana.

Small Brown Plant Hopper
type: NamedIndividual
type: Crop Pest

Snail
type: NamedIndividual
type: Crop Pest

Snow
type: NamedIndividual
type: Weather Phenomena
comment: Snow comprises individual ice crystals that grow while suspended in the atmosphere—usually within clouds—and then fall, accumulating on the ground where they undergo further changes. It consists of frozen crystalline water throughout its life cycle, starting when, under suitable conditions, the ice crystals form in the atmosphere, increase to millimeter size, precipitate and accumulate on surfaces, then metamorphose in place, and ultimately melt, slide or sublimate away.

Sodium Nitrate (NaNO3)
type: NamedIndividual
type: Chemical Fertilizer
comment: Sodium nitrate is a white deliquescent solid very soluble in water. It is a readily available source of the nitrate anion (NO3−), which is useful in several reactions carried out on industrial scales for the production of fertilizers, pyrotechnics and smoke bombs, glass and pottery enamels, food preservatives (esp. meats), and solid rocket propellant. It has been mined extensively for these purposes.
definition: https://en.wikipedia.org/wiki/Sodium_nitrate

Soft Dough
type: NamedIndividual
type: Growth Stage
comment: For Sorghum plants, the tree reaches the soft dough stage when the grain can be crushed between the thumb and index finger but no longer contains a milky liquid. At this stage, starch is rapidly accumulating, 50 percent of the grain's final weight has been achieved, and the whole plant moisture is approximately 65 to 68 percent. Stress at this time can significantly lower yield. By the end of the stage, the grain has colored, and the sorghum is ready to harvest as silage. The soft dough stage will last seven to 10 days or until the grain can no longer be crushed between the thumb and index finger.

Black scale
type: NamedIndividual
type: Crop Disease
affects: Citrus
affects: Lemon
affects: Orange
management: Organically acceptable methods of control include the application of horticultural oils and preservation of natural enemies.
symptoms: Leaves covered in sticky substance and may have growth of sooty mold; reduced tree vigor; leaves and/or fruit dropping from plants; presence of black, brown or gray flattened scales on leaves, twigs and/or branches.
comment: Insects can produce several overlapping generations per year.
label: Brown soft scale
label: Citricolla scale
label: Soft scales

Sogatella Furcifera
type: NamedIndividual
type: Crop Pest

Soil Cracking
type: NamedIndividual
type: Soil Phenomena

Soil Failure
type: NamedIndividual
type: Soil Phenomena

Soil Flow
type: NamedIndividual
type: Soil Phenomena

Soil Fragmentation
type: NamedIndividual
type: Soil Phenomena

Soil Hydrophobicity
type: NamedIndividual
type: Soil Phenomena

Soil Loam
type: NamedIndividual
type: Soil Type

Soil Moisture Hysteresis
type: NamedIndividual
type: Soil Phenomena

Soil Moisture Redistribution
type: NamedIndividual
type: Soil Phenomena

Soil Oxygen Deficiency
type: NamedIndividual
type: Soil Phenomena

Soil Rupture
type: NamedIndividual
type: Soil Phenomena

Soil Strain Hardening
type: NamedIndividual
type: Soil Phenomena

Soil Water Repellency
type: NamedIndividual
type: Soil Phenomena

Soilborne Wheat Mosaic
type: NamedIndividual
type: Crop Disease
type: Viral Disease
affects: Cereal
affects: Wheat
symptoms: Soil-borne wheat mosaic (SBWM) affects autumn-sown small grains. The first visual evidence of Soil-borne wheat mosaic virus (SBWMV) infection in a growing season usually occurs in the spring, after the crop begins to green up. However, in some years SBWM symptoms are expressed in the late autumn or early winter, especially in warmer climates. Irregular, chlorotic patches in the field suggest soil-borne viral infection, although similar symptoms may be caused by a number of other viruses or other biotic or abiotic factors. In dry environments, patches of symptomatic SBWMV-infected plants usually occur in low-lying, wet regions of the field that are conducive for infection by the swimming zoospores of its protozoan viral vector, Polymyxa graminis. In wetter or more humid climates, these patches may occur anywhere in the field. Upon closer inspection, SBWMV-infected plants are often stunted and the leaves may have a general chlorotic mosaic or irregular mottling and streaking. A few strains of the virus cause rosetting in highly susceptible wheat cultivars; leaves and tillers remain short, growth is bunchy or compact, and tillering is excessive. Perhaps the most diagnostic characteristic of SBWMV infection is that symptoms are not expressed on leaves that emerge after the average temperature rises above 20oC (68oF). Thus, SBWM chlorotic patches tend to disappear in late spring. SBWM is most often confused with a disease caused by another soil-borne virus, Wheat spindle streak mosaic virus (WSSMV), which can also induce stunting and chlorosis and is also temperature sensitive. A good diagnostic feature to distinguish between these two viruses is that WSSMV induces chlorotic streaks that are elongated and spindle-shaped and often have a dark green island in their center . However, accurate diagnosis of SBWM is complicated by the fact that symptom expression varies by host genotype and, as previously mentioned, similar symptoms may be caused by a range of biotic and abiot…
comment: Soil-borne wheat mosaic virus is a rod-shaped plant pathogen that can cause severe stunting and mosaic in susceptible wheat, barley and rye cultivars. The disease has often been misdiagnosed as a nutritional problem, but this has actually allowed in part for the fortuitous visual selection by breeding programs of resistant genotypes. Soil-borne wheat mosaic virus is part of the genus Furovirus.

Soil pH meter
type: NamedIndividual
type: Device
related to: Soil pH
comment: Soil pH meter is used to estimate soil pH

Solar Flare
type: NamedIndividual
type: Planetary Phenomena
comment: A solar flare is a sudden flash of increased brightness on the Sun, usually observed near its surface and in close proximity to a sunspot group. Powerful flares are often, but not always, accompanied by a coronal mass ejection. Even the most powerful flares are barely detectable in the total solar irradiance (the "solar constant"). Flares are closely associated with the ejection of plasmas and particles through the Sun's corona into outer space; flares also copiously emit radio waves. If the ejection is in the direction of the Earth, particles associated with this disturbance can penetrate into the upper atmosphere (the ionosphere) and cause bright auroras, and may even disrupt long range radio communication. It usually takes days for the solar plasma ejecta to reach Earth.

Solomon Islands
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:SB
agricultural land: 910 sq. km
area: 28,896 km²
climate: tropical monsoon; few extremes of temperature and weather
density: 17/km²
latitude: 162.17606
longitude: -8.685098
population: 552,438 (2005)
comment: The Solomon Islands are a sovereign state consisting of six major islands and over 900 smaller islands in Oceania, to the east of Papua New Guinea and northwest of Vanuatu. It has a land area of 28,400 square kilometres, and a population of 652,858. Its capital, Honiara, is located on the island of Guadalcanal. The country takes its name from the Solomon Islands archipelago, which is a collection of Melanesian islands that also includes the North Solomon Islands, but excludes outlying islands, such as the Santa Cruz Islands and Rennell and Bellona.

Somalia
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:SO
agricultural land: 441,290 sq. km
area: 637,661 km²
climate: principally desert; northeast monsoon (December to February), moderate temperatures in north and hot in south; southwest monsoon (May to October), torrid in the north and hot in the south, irregular rainfall, hot and humid periods (tangambili) between monsoons
density: 13/km²
latitude: 46.200832
longitude: 5.161575
population: 9,558,666 (2008)
comment: Somalia, officially the Federal Republic of Somalia, is a country in the Horn of Africa. It is bordered by Ethiopia to the west, Djibouti to the Northwest, the Gulf of Aden to the north, the Indian Ocean to the east, and Kenya to the southwest. Somalia has the longest coastline on Africa's mainland. Its terrain consists mainly of plateaus, plains, and highlands. Hot conditions prevail year-round, with periodic monsoon winds and irregular rainfall.

Sooty Mould
type: NamedIndividual
type: Crop Disease
type: Crop Injure
comment: Sooty mold is a collective term for different Ascomycete fungi, which includes many genera, commonly Cladosporium and Alternaria. It grows on plants and their fruit, but also environmental objects, like fences, garden furniture, stones, even cars. The mold benefits from either a sugary exudate produced by the plant or fruit, or if the plant is infested by honeydew-secreting insects or sap suckers. Sooty mold itself does little if any harm to the plant. Treatment is indicated when the mold is combined with insect infestation.

Sorghum
type: NamedIndividual
type: Cereal
type: Crop
has growth stage: Booting stage
has growth stage: Five-leaf Stage
has growth stage: Flag-leaf Stage
has growth stage: Flowering
has growth stage: Hard Dough
has growth stage: Maturity
has growth stage: Seeding
has growth stage: Soft Dough
has growth stage: Thee-Leaf Stage
comment: Sorghum is a genus of about 25 species of flowering plants in the grass family Poaceae. Some of these species have grown as cereals for human consumption and some in pastures for animals. One species, Sorghum bicolor, was originally domesticated in Africa and has since spread throughout the globe.

South America
type: NamedIndividual
type: Continent
has country: Argentina
has country: Bolivia
has country: Brazil
has country: Chile
has country: Colombia
has country: Ecuador
has country: Falkland Islands
has country: Guyana
has country: Paraguay
has country: Peru
has country: Suriname
has country: Uruguay
has country: Venezuela

South Africa
type: NamedIndividual
type: Country
belongs to: Africa
has climate: Temperate Climate
ISO code: ISO 3166-1:ZA
agricultural land: 963,740 sq. km
area: 1,221,037 km²
climate: mostly semiarid; subtropical along east coast; sunny days, cool nights
density: 39/km²
latitude: 27.229242
longitude: -34.552773
population: 47,900,000 (2008)
comment: South Africa, officially the Republic of South Africa, is the southernmost country in Africa. With over 59 million people, it is the world's 24th-most populous nation and covers an area of 1,221,037 square kilometres . South Africa has three capital cities: executive Pretoria, judicial Bloemfontein and legislative Cape Town. The largest city is Johannesburg. About 80% of South Africans are of Black African ancestry, divided among a variety of ethnic groups speaking different African languages. The remaining population consists of Africa's largest communities of European, Asian, and multiracial ancestry.

South Korea
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:KR
area: 100,032 km²
density: 493/km²
latitude: 128.48161
longitude: 35.859276
population: 49,044,790
comment: South Korea, officially the Republic of Korea, is a country in East Asia, constituting the southern part of the Korean Peninsula, and sharing a land border with North Korea. 25 million people, around half of the country's population of more than 51 million people, live in the Seoul Capital Area, the fifth-largest metropolitan area in the world.

South Sudan
type: NamedIndividual
type: Country
ISO code: ISO 3166-1: SS
area: 619,745 km2
density: 13.33/km2 (34.5/sq mi)
population: 12,778,250
comment: South Sudan, officially known as the Republic of South Sudan, is a landlocked country in East-Central Africa. It is bordered to the east by Ethiopia, to the north by Sudan, to the west by the Central African Republic, to the southwest by Democratic Republic of the Congo, to the south by Uganda and to the southeast by Kenya.

Southern Corn Leaf Blight
type: NamedIndividual
type: Crop Disease
comment: Southern corn leaf blight is a fungal disease of maize caused by the plant pathogen Bipolaris maydis (also known as Cochliobolus heterostrophus in its teleomorph state). The fungus is an Ascomycete and can use conidia or ascospores to infect. There are three races of B. maydis: Race O, Race C, and Race T; SCLB symptoms vary depending on the infectious pathogen's race. Race T is infectious to corn plants with the Texas male sterile cytoplasm (cms-T cytoplasm maize). For this reason, Race T is of particular interest. While SCLB thrives in warm, damp climates, the disease can be found in many of the world's maize-growing areas. Typical management practices include breeding for host resistance, cultural controls and fungicide use.

Southern Corn Rust
type: NamedIndividual
type: Crop Disease
comment: Southern rust is a disease caused by the fungal pathogen Puccinia polysora. ... Unlike other major foliar diseases of corn in North America, the rusts do not overwinter in the Corn Belt. Rusts develop first in southern corn fields, and then may spread into primary corn-growing states.

Southern Green Stink Bug
type: NamedIndividual
type: Crop Pest
comment: Nezara viridula, commonly known as the southern green stink bug (USA), southern green shield bug (UK) or green vegetable bug (Australia and New Zealand), is a plant-feeding stink bug. Believed to have originated in Ethiopia, it can now be found around the world. Because of its preference for certain species of legumes, such as beans and soybeans, it is an economically important pest on such crops.
alternative label: Nezara viridula

Southern Rust
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Corn
life cycle: At the start of the growing season, urediospores from infected corn residue are spread by wind and rain on to growing corn plants (Figure 1). Infection of these plants produces spores that serve as secondary inoculum and can be disseminated over hundreds of miles by wind. In the U.S., southern rust usually appears later in the growing season and is more prevalent in the southern states. In seasons with higher than average temperatures, southern rust can spread further up into the Corn Belt where it can impact corn yield. Puccinia polysora is not known to have an alternate host. Urediospores are the sole source of inoculum for both primary and secondary infection. Secondary infection can occur multiple times in fields where southern rust is present.
pathogen: Puccinia polysora Schwein. Fungal structures include, hyphae, urediniospores (produced within uredinia), and teliospores (produced within telia). As the season progresses, both urediniospores and teliospores can be produced in the same rust pustule. The asexually produced urediniospores are dispersed long distances and are responsible for the spread of rust from the deep south to the Northern Great Plains in years where epidemics occur. Teliospores are not important in disease development. Southern rust occurs commonly in the southeastern United States and periodically throughout the rest of the corn belt. This pathogen does not overwinter in Nebraska. Disease occurrence in Nebraska is dependent upon wind dispersal of urediniospores from southern states in early to mid July. Several different races of the pathogen exist.
symptoms: Early lesions on leaves are small and circular-to-oval, often with a prominent light green to yellow halo. As lesions mature, the fungus erupts through the leaf surface (epidermis. Light orange to cinnamon-red pustules (image) are the characteristic symptom on leaves; urediniospores that rub off on fingers are what impart the color to the lesion. Southern rust lesions are usually smaller than common rust lesions; common rust lesions are usually more elongate than circular. Unlike Common Rust of Corn, lesions of southern rust develop primarily on the upper leaf surfaces. Lesions develop on stalk, husk, and leaf sheath tissues as well. Under favorable weather conditions, leaves can be covered with lesions resulting in a leaf blight. Spores are wind-blown with new infections occurring every 7 days. As the season progresses, black teliospores are produced in a ring around the lesions.
comment: Southern rust is caused by the fungus Puccinia polysora. Although generally considered a tropical disease, southern rust can occur in important corn production areas of the United States and Canada. Symptoms are similar to common rust, but pustules are smaller and occur almost exclusively on the upper leaf surface. Pustules are usually circular or oval, very numerous, and densely scattered over the leaf surface. Spores are orange when they erupt from the pustule. As pustules age, they become chocolate brown to black, often forming dark circles around the original pustule.

Sowbugs
type: NamedIndividual
type: Crop Pest
label: Sowbugs (Porcellio laevis) and pillbugs or roly-polies (Armadillidium vulgare), sometimes called "woodlice" are outdoor creatures that are often mistaken to be insects. They range in size from ¼ to ½ inch long and are dark to slate grey with seven pairs of legs. Sowbugs have a pair of tail like appendages but pillbugs do not. Pillbugs can roll into a ball when disturbed. This behavior has given them the common name of "roly-polies" Sowbugs and pillbugs live their lives in moist environments. Common places for them to live would be under mulch, compost, stones, flowerpots and other places on damp ground. Some places the creatures may explore would be damp basements and first floor levels and garages. Points of entry may be door thresholds and cracks. They do not bite people or damage structures. However they can damage plants in a greenhouse in high numbers. Sowbugs and pillbugs are scavengers and their main source of food is decaying organic matter. They do not survive indoors for more than a day or two without moist conditions. They are actually crustaceans that have adapted to living their life on land. They are closely related to lobsters, shrimp and crayfish and have gills.

Soybean Mosaic
type: NamedIndividual
type: Crop Disease
affects: Soybean
caused by: Soybean Mosaic Virus
related to: Moisture
comment: Soybean mosaic virus (SMV) is a member of the plant virus genus Potyvirus (family Potyviridae). It infects mainly plants belonging to the family Fabaceae but has also been found infecting other economically important crops. SMV is the cause of soybean mosaic disease that occurs in all the soybean productions areas of the world. Soybean (Glycine max) is one of the most important sources of edible oil and proteins.

Soybean Mosaic Virus
type: NamedIndividual
type: Virus
causes: Soybean Mosaic
comment: Soybean mosaic virus (SMV) is a member of the plant virus genus Potyvirus (family Potyviridae). It infects mainly plants belonging to the family Fabaceae but has also been found infecting other economically important crops. SMV is the cause of soybean mosaic disease that occurs in all the soybean productions areas of the world. Soybean (Glycine max) is one of the most important sources of edible oil and proteins.

Soybean rust
type: NamedIndividual
type: Crop Disease
affects: Soybean
controls: Soybean rust can occur at all stages of soybean development, but is most common in or after the plants begin flowering and pods start developing in the middle of the growing season. Plants are most susceptible during reproductive stages. Extended periods of wet weather, moderate temperatures (59 degrees F to 85 degrees F), and high humidity (over 75 percent) favor disease development.
management: Scout the lower canopy of fields weekly, especially during wet weather after soybeans begin to flower. Soybean rust must be diagnosed at an early stage to be successfully managed. Cultural practices have had little effect on the disease. Soybean rust must be managed with the judicious use of fungicides applied properly and at the correct time. Soybean varieties that are resistant to soybean rust are in development.
symptoms: Symptoms begin on leaves in the lower plant canopy. Tan or reddish-brown lesions (spots) develop first on the underside of leaves. Small pustules (blisters) develop in the lesions, which break open and release masses of tan spores. The lesions and pustules, which can be seen with a 20X hand lens, may also appear on pods and stems. Early symptoms of soybean rust (before pustules develop) are difficult to distinguish from other common leaf diseases on soybean.
comment: Soybean rust is potentially one of the most significant diseases of soybean. It can spread quickly and cause leaf spots and defoliation of soybean plants.

Spain
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Marine Climate
has climate: Dry summer climate
has climate: Semi-arid Climate
ISO code: ISO 3166-1:ES
agricultural land: 275,342.5 sq. km
area: 504,030 km²
climate: temperate; clear, hot summers in interior, more moderate and cloudy along coast; cloudy, cold winters in interior, partly cloudy and cool along coast
density: 90/km²
latitude: -6.916349
longitude: 35.713898
population: 46,157,822 (2008)
comment: Spain, formally the Kingdom of Spain, is a country in Southwestern Europe with some pockets of territory across the Strait of Gibraltar and the Atlantic Ocean. Its continental European territory is situated on the Iberian Peninsula. Its territory also includes two archipelagos: the Canary Islands off the coast of North Africa, and the Balearic Islands in the Mediterranean Sea. The African exclaves of Ceuta, Melilla, and Peñón de Vélez de la Gomera make Spain the only European country to have a physical border with an African country . Several small islands in the Alboran Sea are also part of Spanish territory. The country's mainland is bordered to the south and east by the Mediterranean Sea; to the north and northeast by France, Andorra, and the Bay of Biscay; and to the west and northwest by Portugal and the Atlantic Ocean respectively.

Anthocyanin Content Index (ACI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Crop Stress
References: A.K. van den berg, T.D. Perkins, "Non-destructive estimation of anthocyanin content in autumn sugar maple leaves", Hortic. Sci., 40 (2005), pp. 685-686
References: Carter, Gregory A., William G. Cibula, and Richard L. Miller. "Narrow-band Reflectance Imagery Compared with ThermalImagery for Early Detection of Plant Stress." Journal of plant physiology 148.5 (1996): 515-522.
Acronym: ACI
comment: Anthocyanin Content Index (ACI) estimate Anths content from reflectance from sugar maple leaves.
definition: ACI = Green/NIR

Aerosol Free Vegetation Index (AFVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: AFVI
comment: Aerosol Free Vegetation Index (AFVI)

Adjust Green Degree Vegetation Index (AGVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: A. A. Gitelson, M. N. Merzlyak, and O. B. Chivkunova, "Optical properties and nondestructive estimation of anthocyanin content in plant leaves," Photochemistry and Photobiology, vol. 74, no. 1, pp. 38–45, 2001.
Acronym: AGVI
definition: AGVI = GVI − (1 + 0.018GVI) YVI − NSI / 2

Aphid-index (AI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Aphids
related to: Crop Pest
Acronym: AI
comment: Aphid-index (AI)
definition: AI = (R730 − R419)/(R730 + R419)−0.5R736

Anthocyanin Reflectance Index (ARI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Carotenoid
References: A.A. Gitelson, M.N. Merzlyak, O.B. Chivkunova, "Optical properties and nondestructive estimation of anthocyanin content in plant leaves", Photochemistry and Photobiology, 74 (1) (2001), pp. 38-45
Acronym: ARI
comment: Anthocyanin Reflectance Index (ARI) estimate Anths content from reflectance changes in the green region at leaf level
definition: ARI = (R550)^(-1) - (R700)^(-1)

Anthocyanin Reflectance Index 2 (ARI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Carotenoid
References: A.A. Gitelson, M.N. Merzlyak, O.B. Chivkunova, "Optical properties and nondestructive estimation of anthocyanin content in plant leaves", Photochemistry and Photobiology, 74 (1) (2001), pp. 38-45
Acronym: ARI2
comment: Anthocyanin Reflectance Index 2 (ARI2) estimate Anths content from reflectance changes in the green region at leaf level
definition: ARI2 = R800 x ARI

Atmospherically Resistant Vegetation Index (ARVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Kaufman YJ, Tanre D (1992), "Atmospherically resistant vegetation index (ARVI) for EOS-MODIS". IEEE Transactions on Geoscience and Remote Sensing 30: 261–270.
Acronym: ARVI
definition: ARVI = (NIR - (2 * Red) + Blue) / (NIR + (2 * Red) + Blue) NIR = [781:1399]

Atmospherically Resistant Vegetation Index 2 (ARVI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Kaufman, Yoram J., and Didier Tanre. "Atmospherically resistant vegetation index (ARVI) for EOS-MODIS." IEEE transactions on Geoscience and Remote Sensing 30.2 (1992): 261-270.
Acronym: ARVI2
definition: ARVI2 = −0.18 + 1.17((NIR−RED) / (NIR+RED))

Adjusted Soil Brightness Index (ASBI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Soil
Acronym: ASBI
definition: ASBI = 2.0 * YVI

Attenuated Total Reflection (ATR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Elmer, Perkin. "FT-IR Spectroscopy Attenuated Total Reflectance (ATR)." Technical note 27, no. 11 (2005).
Acronym: ATR
comment: Attenuated total reflection (ATR) is a sampling technique used in conjunction with infrared spectroscopy which enables samples to be examined directly in the solid or liquid state without further preparation.

Ajusted Transformed Soil-Ajusted VI (ATSAVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Baret, Fred, and Gerard Guyot. "Potentials and limits of vegetation indices for LAI and APAR assessment." Remote sensing of environment 35.2-3 (1991): 161-173.
Acronym: ATSAVI

Area of the absorption feature (Area)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Area
comment: Continuous removal features: The area of the absorption feature that is the product of DEP and WID
definition: Area = In the range of 550–750 nm

Band Depth (BD)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: O. Mutanga, A.K. Skidmore, "Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions", International Journal of Applied Earth Observation and Geoinformation, 5 (2) (2004), pp. 87-96
Acronym: BD

Band Depth Ratios (BDR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: O. Mutanga, A.K. Skidmore, "Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions", International Journal of Applied Earth Observation and Geoinformation, 5 (2) (2004), pp. 87-96
Acronym: BDR

Blue/Green Index (BGI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: BGI
definition: BGI = R400/R550

Band Depth Normalized to Band Area (BNA)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: O. Mutanga, A.K. Skidmore, "Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions", International Journal of Applied Earth Observation and Geoinformation, 5 (2) (2004), pp. 87-96
Acronym: BNA
comment: Band Depth Normalized to Band Area (BNA)

Blue Blue-Normalized Difference Vegetation Index (BNDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Yang, Chenghai, et al. "Airborne hyperspectral imagery and yield monitor data for mapping cotton yield variability." Precision Agriculture 5.5 (2004): 445-461.
Acronym: BNDVI

Blue/Red Index (BRI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: BRI
definition: BRI1=R400/R690

Blue-Wide Dynamic Range Vegetation Index (BWDRVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: BWDRVI

Band 1
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band1

Band 10
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band10

Band 11
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band11

Band 12
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band12

Band 2
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band2

Band 3
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band3

Band 4
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band4

Band 5
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band5

Band 6
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band6

Band 7
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band7

Band 8
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band8

Band 8a
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band8a

Band 9
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Band9

Band operation-Receiver Operating Characteristic curve (Band Math-ROC)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Xing Xu, Yun Zhao, "A Band Math-ROC operation for early differentiation between Sclerotinia sclerotiorum and Botrytis cinerea in oilseed rape", Computers and Electronics in Agriculture, Volume 118, October 2015, pages 116-123, Elsevier B.V.
Acronym: Band Math-ROC

Blue Band (B/Blue)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Bn
definition: Bn = B/(R + G + B)

Cellulose Absorption Index (CAI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
Acronym: CAI
comment: Cellulose absorption index (CAI) as a function of the amount of residue for mixed scenes of varying amounts of each crop residue and tree litter, shown for each of the three soils.
definition: CAI = 0.5 × (R2031 –R2211)–R2101

Chlorophyll Absorption Ratio Index (CARI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
References: Kim, M.S., Daughtry, C.S.T., Chappelle, E.W., McMurtrey, J.E. 1994. "The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation (APAR)". In Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing. France. Val d’Isere. pp. 299-306.
Acronym: CARI
definition: CARI = (|(a670 + R670 + b)|/(a2 + 1)1/2) — (R700/R670) a = (R700 - R550)/150, b = R550 - (a — 550)

Chlorophyll Absorption Ratio Index 2 (CARI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Kim, Moon Sung. The Use of Narrow Spectral Bands for Improving Remote Sensing Estimations of Fractionally Absorbed Photosynthetically Active Radiation. Diss. 1994.
Acronym: CARI2
definition: CARI2 = ((|(a⋅R670 + R670]+b)| / (a2+1)^0.5) (R700 / R670) a = (R700 - R550) / 150, b = R550 - a*R550

Canopy Chlorophyll Content Index (CCCI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Fitzgerald, G.; Rodriguez, D.; O’Leary, G. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI). Field Crops Res. 2010, 116, 318–324.
Acronym: CCCI
comment: Canopy Chlorophyll Content Index (CCCI) is a two-dimensional remote sensing index, derived from the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge (NDRE). The index employs a Near InfraRed (NIR) spectral band between 670 and 790 nm, which makes it perfect for Nitrogen content monitoring, as it can only be detected in 3 wavelengths: 670, 720, and 790 nm. A large number of studies have proven that CCCI is a robust canopy nutrition control measure that enhances the process of precise fertilizer application.
definition: CCCI = ( (NIR−rededge) / (NIR+rededge)) / ((NIR−Red) / (NIR+Red) )

Chlorophyll Index (CI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Biomass
related to: Chlorophyll
Acronym: CIgreen
definition: CI = NIR/Green - 1

ChlorophyII Index-Green (CIG)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: CIG
definition: CIG = NIR / (Green - 1)

Color Infrared (CIR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: CIR

Red Edge Chlorophyll Index (CIrededge)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: CIRE

Red edge chlorophyll index (CIRE1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
References: A A Gitelson, Y Gritz, M N Merzlyak, Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves, Journal of Plant Physiology, 160 (2003), pp. 271-282
Acronym: CIRE1
definition: CIRE1 = NIR / RE–1

Color Index for Vegetation Extraction (CIVE)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Kataoka, T., Kaneko, T., Okamoto, H., Hata, S., 2003. "Crop growth estimation system using machine vision." In: Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), vol. 2, pp. 1079–1083
Acronym: CIVE
definition: CIVE = 0.441 ∗ Rn − 0.811 ∗ Gn + 0.385 ∗ Bn + 18.78

ChlorophyII Index-Green (CIgreen)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Clgreen

Chlorophyll Index RedEdge (CIrededge)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: CIrededge

Chlorophyll Index RedEdge 710 (CIrededge710)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: CIrededge710
definition: CIrededge710 = R750/R710 −1

Carotenoid Reflectance Index (CRI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: CRI
definition: CRI = 1 /R505:515 - 1 / R535:565

Carotenoid Reflectance Index 2 (CRI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: CRI2

Carotenoid Reflectance Index (CRI550)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Carotenoid
References: Gitelson, Anatoly A., et al. "Non-destructive and remote sensing techniques for estimation of vegetation status." Proceedings of the 3rd European Conference on Precision Agriculture, pp. 205-210, Montpelier, France, 2001.
Acronym: CRI550
definition: CRI = 1/ R510 - 1 / R550

Carotenoid Reflectance Index (CRI700)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Gitelson, Anatoly A., et al. "Non-destructive and remote sensing techniques for estimation of vegetation status." Proceedings of the 3rd European Conference on Precision Agriculture, pp. 205-210, Montpelier, France, 2001.
Acronym: CRI700
comment: Carotenoid Reflectance Index (CRI700) is sufficient to estimate total Cars content in plant leaves
definition: CRI700 = 1/R510 - 1/R700

Carter Stress Index 1 (CSI1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
Acronym: CSI1
definition: CSI1 = R695 / R420

Carter Stress Index 2 (CSI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
Acronym: CSI2
definition: CSI2 = R695 / R760

Carter Index (CTR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: G.A. Carter, W.G. Cibula, T.R. Dell, "Spectral reflectance characteristics and digital imagery of a pine needle blight in the southeastern United States", Can. J. Forest Res., 26 (1996), pp. 402-407
Acronym: CTR
definition: CRT = R695/R760

Carter Index 2 (CTR2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: CTR-2
definition: CTR-2 = (R695/R760)

Chlorophyll Vegetation Index (CVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
References: Vincini, M., Frazzi, E., & D’Alessio, P. (2007). Comparison of narrow-band and broad-band vegetation indexes for canopy chlorophyll density estimation in sugar beet. In J. V. Staford (Ed.), Precision agriculture ‘07: Proceedings of the 6th European Conference on Precision Agriculture (pp. 189–196).
Acronym: CVI
definition: CVI = NIR x Red / Green − 1

Chlorophyll Index (ChI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Chl

Chl Stress Index 1 (Chl SI-1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: J.J. Read, L. Tarpley, J.M. Mckinion, K.R. Ready, "Narrow-band reflectance ratios for remote estimation of nitrogen status in cotton", J. Environ. Qual., 31 (2002), pp. 1442-1452
Acronym: Chl SI-1
definition: Chl SI-1 = (R415/R695)

Chl Stress Index 2 (Chl SI-2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: D. Zhao, K.R. Reddy, V.G. Kakani, J.J. Read, S. Koti, "Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton", Agron J., 97 (2005), pp. 89-98
Acronym: Chl SI-2
definition: ChlSI-2 = (R708/R915)

Chl Stress Index 3 (Chl SI-3)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: ChlSI-3 = (R551/R915)
References: D. Zhao, K.R. Reddy, V.G. Kakani, J.J. Read, S. Koti, "Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton", Agron J., 97 (2005), pp. 89-98
Acronym: ChlSI-3

Chlorophyll Index a (ChIa)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Chla
definition: Chla = (13.95 R665 - 6.80 R649) × V / (W×1000) where V is the volume of the extraction solution (ml) and W is the weight of the leaf sample (g).

Chlorophyll Index b (ChIb)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Chlb
definition: Chla = (24.96 R649 - 7.32 R665) × V / (W×1000) where V is the volume of the extraction solution (ml) and W is the weight of the leaf sample (g).

Double-peak Canopy Nitrogen Index (DCNI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Nitrogen (N)
References: Chen P.F., Haboudane D., Tremblay N., Wang J.H., Vigneault P., Li B.G. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat. Remote Sens. Environ. 2010;114:1987–1997.
Acronym: DCNI
definition: DCNI = ((R720 − R700)/(R700 − R670))/(R720 − R670 + 0.03)

Disease Ratio Index (DRI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: DRI
definition: DRI = R2022 / R529

Damage Sensitive Spectral Index (DSSI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: DSSI

Damage sensitive Spectral Index-2 (DSSI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: DSSI2
definition: DSSI2 = (R747 − R901 − R537 − R572)/(R747 − R901) + (R537-R572)
definition: M. Mirik, G.J. Michels, S.K. Mirik, N.C. Elliot, R. Bowling, "Hyperspectral spectrometry as a means to differentiate un-infested and infested winter wheat by Greenbug (Hemiptera: Aphididae)", J. Econ. Entomol., 61 (2006), pp. 319-346

Difference Vegetation Index (DVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Tucker, C. "Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sensing of Environment 8 (1979): 127–150.
Acronym: DVI
comment: Difference Vegetation Index (DVI) distinguishes between soil and vegetation, but it does not account for the difference between reflectance and radiance caused by atmospheric effects or shadows.
definition: DVI = NIR - RED

Disease-Water Stress Index (DWSI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water Stress
References: Apan, A.; Held, A.; Phinn, S.; Markley, J. Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery. Int. J. Remote Sens. 2004, 25, 489–498.
Acronym: DWSI
definition: DWSI = R1660/R550

Derivative within Blue Edge (Db)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Db
comment: Derivative features: Maximum value of 1st derivative within blue edge
definition: Blue edge covers 490–530 nm. Db = maximum value of 1st order derivatives within the blue edge of 35 bands

Depth of the spectral feature (Dep)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Dep
comment: Continuous removal features: The depth of the feature minimum relative to the hull
definition: In the range of 550–750 nm

Derivative within Red Edge (Dr)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Dr
comment: Derivative features: Maximum value of 1st derivative within red edge
definition: Red edge covers 670–737 nm. Dy = maximum value of 1st order derivatives within the red edge of 61 bands

Derivative within Yellow Edge (Dy)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Dy
comment: Derivative features: Maximum value of 1st derivative within yellow edge
definition: Yellow edge covers 550–582 nm. Dy = maximum value of 1st order derivatives within the yellow edge of 28 bands

Excess-Blue Index (EBI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: M.R. Golzarian, R.a. Frick, "Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis", Plant Meth., 7 (1) (2011), p. 28
Acronym: EBI
definition: EBI = (Bn − Gn) ∗ (Bn − Rn)

Excess-Green Index (EGI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: M.R. Golzarian, R.a. Frick, "Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis", Plant Meth., 7 (1) (2011), p. 28
Acronym: EGI
definition: EGI = (Gn − Rn) ∗ (Gn − Bn)

Enhanced Normalized Difference Vegetation Index (ENDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Strong, Conor J., Niall G. Burnside, and Dan Llewellyn. "The potential of small-Unmanned Aircraft Systems for the rapid detection of threatened unimproved grassland communities using an Enhanced Normalized Difference Vegetation Index." PloS one 12, no. 10 (2017): e0186193.
Acronym: ENDVI

Excess-Red Index (ERI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: M.R. Golzarian, R.a. Frick, "Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis", Plant Meth., 7 (1) (2011), p. 28
Acronym: ERI
definition: ERI = (Rn − Gn) ∗ (Rn − Bn)

Extended Spectral Angle Mapping (ESAM)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: ESAM
comment: Extended spectral angle mapping (ESAM)

Enhanced Vegetation Index (EVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Liu HQ, Huete AR (1995), "A feedback based modification of the NDVI to minimize canopy background and atmospheric noise". IEEE Transactions on Geoscience and Remote Sensing 33: 457–465.
Acronym: EVI
comment: Enhanced Vegetation Index (EVI) is an 'optimized' vegetation index designed to enhance the vegetation signal with improved sensitivity in high biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences.
definition: EVI = 2,5 * ((NIR - Red) / ((NIR) + (C1 * Red) - (C2 * Blue) + L))

Enhanced Vegetation Index 2 (EVI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112(10), 3833–3845.
Acronym: EVI2
comment: Enhanced Vegetation Index 2 (EVI2) improved sensitivity to high biomass while minimising soil and atmosphere infuence
definition: EVI2 = 2.5 * [ (NIR - R) / (NIR + 2.4*R + 1) ]

Excess Green (ExG)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: D.M. Woebbecke, G.E. Meyer, K. Von Bargen, D.A. Mortensen, "Plant species identification, size, and enumeration using machine vision techniques on near-binary images", Optics in Agriculture and Forestry, vol. 1836, SPIE (1992), pp. 208-219
Acronym: ExG
definition: ExG = 2 ∗ Gn − Rn − Bn

Excess Green Red (ExGR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: G.E. Meyer, J.A.C. Neto, "Verification of color vegetation indices for automated crop imaging applications", Comp. Electron. Agricult., 63 (2) (2008), pp. 282-293
Acronym: ExGR
definition: ExGR = ExG − ExR

Excess Red (ExR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: G.E. Meyer, T. Mehta, M.F. Kocher, D.A. Mortensen, A. Samal, "Textural imaging and discriminant analysis for distinguishing weeds for spot spraying", Trans. ASAE, 41 (4) (1998), pp. 1189-1197
Acronym: ExR
definition: ExR = 1.4 ∗ Rn − Gn

Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: FAPAR
comment: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR, sometimes also noted fAPAR or fPAR) is the fraction of the incoming solar radiation in the Photosynthetically Active Radiation spectral region that is absorbed by a photosynthetic organism, typically describing the light absorption across an integrated plant canopy. This biophysical variable is directly related to the primary productivity of photosynthesis and some models use it to estimate the assimilation of carbon dioxide in vegetation. FAPAR can be derived from space measurements in the solar spectral range and a number of state of the art algorithms have been proposed to estimate this important environmental variable. FAPAR can also be used as an indicator of the state and evolution of the vegetation cover; with this function, it advantageously replaces the Normalized Difference Vegetation Index (NDVI), provided it is itself properly estimated. Currently, there are some remote sensing products of FAPAR, such as AVHRR and MODIS.

Forest index (FI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: W. Ye, X. Li, X. Chen, G. Zhang, "A Spectral Index for Highlighting Forest Cover From Remotely Sensed Imagery", International Society for Optics and Photonics., Beijing (2014)
Acronym: FI

Greenness Above Bare Soil (GABS)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: GABS
definition: GABS = (GVI - 0.09178 SBI + 5.558959)

Green Atmospherically Resistant Vegetation Index (GARI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Gitelson, Anatoly A., Yoram J. Kaufman, and Mark N. Merzlyak. "Use of a green channel in remote sensing of global vegetation from EOS-MODIS." Remote sensing of Environment 58.3 (1996): 289-298.
Acronym: GARI
definition: GARI = [NIR−(Green−(Blue−Red))] / [NIR−(Green+(Blue−Red))]

Green Atmospherically Resistant Vegetation Index (GARVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: GARVI = (NIR - (B3 - B1 - B5)) / (NIR - (B3 + B1 - B5)) which, B1 is the reflectance of the BLUE band (432–453 nm), B3 is the reflectance of the GREEN band (542–578 nm), B5 is the reflectance of the vegetation red edge band at (697–712 nm) .
Acronym: GARVI

Green minus Blue (GB)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: D. Woebbecke, G.E. Meyer, K. Von Bargen, D.A. Mortensen, "Color indices for weed identification under various soil, residue, and lighting conditions", Trans. ASAE, 38 (1) (1995), pp. 259-269
Acronym: GB
definition: GB = Gn − Bn

Green Blue Normalised Difference Vegetation Index (GBNDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: GBNDVI
definition: GBNDVI = (NIR−G−B)/(NIR+G+B).

Green Chlorophyll Index (GCI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Gitelson, A., Y. Gritz, and M. Merzlyak. "Relationships Between Leaf Chlorophyll Content and Spectral Reflectance and Algorithms for Non-Destructive Chlorophyll Assessment in Higher Plant Leaves." Journal of Plant Physiology 160 (2003): 271-282.
Acronym: GCI
definition: GCI = (NIR / Green) - 1

Green Chlorophyll Vegetation Index (GCVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: GCVI = NIR/GREEN − 1
References: Gitelson, A.A., Vina, A., Arkebauer, T.J., Rundquist, D.C., Keydan, G., Leavitt, B., 2003. "Remote estimation of leaf area index and green leaf biomass in maize canopies.", Geophys. Res. Lett. 30 (5)
Acronym: GCVI

Green Difference Vegetation Index (GDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Sripada, R., et al. "Determining In-Season Nitrogen Requirements for Corn Using Aerial Color-Infrared Photography." Ph.D. dissertation, North Carolina State University, 2005.
Acronym: GDVI
comment: Green Difference Vegetation Index (GDVI) was originally designed with color-infrared photography to predict nitrogren requirements for corn.
definition: GDVI = NIR - Green

Global Environmental Monitoring Index (GEMI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Pinty, B., and M. Verstraete, "GEMI: a Non-Linear Index to Monitor Global Vegetation From Satellites". Vegetation 101 (1992): 15-20.
Acronym: GEMI
comment: Global Environmental Monitoring Index (GEMI) is a non-linear vegetation index, which is used for global environmental monitoring from satellite imagery and attempts to correct for atmospheric effects. It is similar to NDVI but is less sensitive to atmospheric effects. It is affected by bare soil; therefore, it is not recommended for use in areas of sparse or moderately dense vegetation. To compare observations under varying atmospheric and illumination conditions
definition: GEMI = eta(1 - 0.25 * eta) - (Red-0.125) / (1-Red) Where: eta = ( 2(NIR^2 - Red^2) + 1.5*NIR + 0.5*Red) / ( NIR + Red + 0.5)

Generalized soil adjusted vegetation index (GESAVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Gilabert MA, Gonzalez-Piqueras J, Garcia-Haro FJ, Melia J (2002), "A generalized soil-adjusted vegetation index". Remote Sensing of Environment 82: 303–310.
Acronym: GESAVI
definition: GESAVI = (NIR–aR–b)/(R+Z)

Greenness Index (GI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
Acronym: GI
definition: GI = ((2.0 x Green) - (Red + Blue)) / ((2.0 x Green) + Red + Blue)

Green Leaf Index (GLI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Louhaichi, M., M. Borman, and D. Johnson. "Spatially Located Platform and Aerial Photography for Documentation of Grazing Impacts on Wheat." Geocarto International 16, No. 1 (2001): 65-70.
Acronym: GLI
comment: Green Leaf Index (GLI) was originally designed for use with a digital RGB camera to measure wheat cover, where the red, green, and blue digital numbers (DNs) range from 0 to 255.
definition: GLI = ((Green-Red) + (Green-Blue)) / ((2 * Green) + Red + Blue)

Gitelson and Merzlyak (GM)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Chlorophyll
References: A.A. Gitelson, M.N. Merzlyak, "Remote estimation of chlorophyll content in higher plant leaves", Int. J. Remote Sens., 18 (1997), pp. 2691-2697
Acronym: GM
comment: Gitelson and Merzlyak (GM) is created to measure the chlorophyll content in vegetation leaves
definition: GM = R750/R700

Gitelson and Merzylak 1 (GM1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Gitelson, A. & Merzlyak, N. (1996b). Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology, 148, 494–500.
References: Gitelson, A., & Merzlyak, N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18(12), 2691–2697.
Acronym: GM1
comment: Gitelson and Merzylak 1 (GM1) and GM2 were created to measure the chlorophyll content in vegetation leaves
definition: GM1 = R750/R550

Gitelson and Merzylak 2 (GM2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Gitelson, A. & Merzlyak, N. (1996b). Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology, 148, 494–500.
References: Gitelson, A., & Merzlyak, N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18(12), 2691–2697.
Acronym: GM2
comment: Gitelson and Merzylak 2 (GM2) and GM1 were created to measure the chlorophyll content in vegetation leaves
definition: GM2 = R750/R700

Green Normalized Difference Vegetation Index (GNDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Biomass
related to: Chlorophyll
References: A.A. Gitelson, Y. Kaufman, M.N. Merzlyak, "Use of a green channel in remote sensing of global vegetation from EOS-MODIS", Remote sensing of Environment, 58 (3) (1996), pp. 289-298
Acronym: GNDVI
comment: Green Normalized Difference Vegetation Index (GNDVI) is similar to NDVI except that it measures the green spectrum from 540 to 570 nm instead of the red spectrum. This index is more sensitive to chlorophyll concentration than NDVI.
definition: GNDVI = (NIR - Green)/(NIR + Green)

Green Optimized Soil Adjusted Vegetation Index (GOSAVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Sripada, R., et al. "Determining In-Season Nitrogen Requirements for Corn Using Aerial Color-Infrared Photography." Ph.D. dissertation, North Carolina State University, 2005.
Acronym: GOSAVI
comment: Green Optimized Soil Adjusted Vegetation Index (GOSAVI) was originally designed with color-infrared photography to predict nitrogren requirements for corn. It is similar to OSAVI, but it substitutes the green band for red.
definition: GOSAVI = (NIR-Green) / (NIR+Green+0.16)

Green Re-normalized Difference Vegetation Index (GRDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: GRDVI

Green Re-normalized Difference Vegetation Index (GRNDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: GRNDVI

Green Ratio Vegetation Index (GRVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Sripada, R., et al. "Aerial Color Infrared Photography for Determining Early In-season Nitrogen Requirements in Corn." Agronomy Journal 98 (2006): 968-977.
Acronym: GRVI
comment: Green Ratio Vegetation Index (GRVI) is sensitive to photosynthetic rates in forest canopies, as green and red reflectances are strongly influenced by changes in leaf pigments.
definition: GRVI = NIR/Green

Green Soil Adjusted Vegetation Index (GSAVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Soil
related to: Vegetation
Acronym: GSAVI
definition: GSAVI = [(NIR−Green) / (NIR + Green+L) ] * (1+L)

Green Triangle Vegetation Index (GTVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: GTVI
definition: GTVI = (NDVI + 0.5) / (NDVI + 0.5) * (NDVI)^1/2 +0.5)

Green Vegetation Index (GVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Kauth, R., and G. Thomas. "The Tasselled Cap-A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen By Landsat" In Proceedings of the LARS 1976 Symposium of Machine Processing of Remotely-Sensed Data, West Lafayette, IN: Purdue University, pp. 4B41-4B51.
Acronym: GVI
comment: Green Vegetation Index (GVI) minimizes the effects of background soil while emphasizing green vegetation. It uses global coefficients that weigh the pixel values to generate new transformed bands. It is also known as the Landsat TM Tasseled Cap green vegetation index. Values range from -1 to 1. GVI was originally designed for use with Landsat TM, but it will also work with the corresponding bands of Landsat ETM+ and Landsat 8.
definition: GVI = (-0.2848 * TM1) + (-0.2435*TM2) + (-0.5436 * TM2) + (0.7234 * TM4) + (0.0840 * TM5)+ (0.1800 * TM7) or GVI = (-0.283MSS4 - 0.660MSS5 + 0.577MSS6 + 0.388MSS7) The NDVI can be noted as NDVI(MSS6) or NDVI(MSS7) according to the spectral band used (MSS6 or MSS7).

Green Band (G/Green)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Gn
definition: Gn = Green/(Red + Green + Blue)

Grassland Index (GrassI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Biomass
References: Bareth G, Bolten A, Hollberg J, Aasen H, Burkhart A, Schellberg J, 2015. "Feasibility study of using non-calibrated UAV-based RGB imagery for grassland monitoring: case study at the Rengen Long-term Grassland Experiment (RGE), Germany." Proc. DGPF annual conference’15, Cologne, Germany, 55–62.
Acronym: Grassl

Gray Light (Gray)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Gray
definition: Gray = 0.2898 ∗ Rn + 0.5870 ∗ Gn + 0.1140 ∗ Bn

Health-Index (HI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Biomass
Acronym: HI
comment: Health-Index (HI)
definition: HI = (R739 − R402)/(R739 + R402)−0.5R403

Infrared Percentage Vegetation Index (IPVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Crippen, R. "Calculating the Vegetation Index Faster." Remote Sensing of Environment 34 (1990): 71-73.
Acronym: IPVI
comment: Infrared Percentage Vegetation Index (IPVI) is functionally the same as NDVI, but it is computationally faster. Values range from 0 to 1.
definition: IPVI = NIR / (NIR + Red)

Leaf Area Index (LAI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Boegh, E., H. Soegaard, N. Broge, C. Hasager, N. Jensen, K. Schelde, and A. Thomsen. "Airborne Multi-spectral Data for Quantifying Leaf Area Index, Nitrogen Concentration and Photosynthetic Efficiency in Agriculture." Remote Sensing of Environment 81, no. 2-3 (2002): 179-193.
Acronym: LAI
comment: Leaf Area Index (LAI) is defined as the total one-sided leaf area per ground surfacearea. This index is used to estimate foliage cover and to forecast crop growth and yield. It is a measure of the canopy photosynthetic area. LAI is often usedas an indicator of growth stage and crop biomass. LAI measurements have also440been demonstrated to show correlations with canopy micro-climate and evap-otranspiration traits. .
definition: LAI = (3.618 * EVI - 0.118) Where EVI is the Enhanced Vegetation Index value.

Leaf Chlorophyll Content (LCC)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
References: Haboudane, D.; Miller, J.R.; Tremblay, N.; Zarco-Tejada, P.J.; Dextraze, L. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens. Environ. 2002, 81, 416–426.
Acronym: LCC
comment: Leaf chlorophyll content (LCC) is an important physiological indicator of the actual health status of individual plants. An accurate estimation of LCC can therefore provide valuable information for precision field management.
definition: LCC = Chla + Chlb

Leaf Chlorophyll Index (LCI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: LCI
definition: LCI = (R850−R710) / (R850 + R680)

Leaf Hopper Index (LHI)
type: NamedIndividual
type: Spectral Vegetation Indices
References: M. Prabhakar, Y.G. Prasad, M. Thirupathi, G. Sreedevi, B. Dharajothi, B. Venkateswarlu, "Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae)", Computers and Electronics in Agriculture, 79 (2011), pp. 189-198
Acronym: LHI

Leaf Nitrogen Concentration (LNC)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
predicts: Nitrogen (N)
Acronym: LNC
definition: LNC(%) = 0.14∗M1(fd496) + 0.14∗M2(fd499) + 0.11∗M3(fd689) + 0.29∗M4(fd797) + 0.32∗M5(fd882)

Land Surface Water Index (LSWI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: LSWI = (NIR − SWIR1)/(NIR + SWIR1)
References: Xiao, X., Boles, S., Liu, J., Zhuang, D., Liu, M., 2002. "Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data." Remote Sens. Environ. 82 (2), 335–348
Acronym: LSWI

Leaf Water Content Index (LWCI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
References: Hunt Jr, E. Raymond; Rock, Barrett N. "Detection of changes in leaf water content using Near- and Middle-Infrared reflectances", 1989
Acronym: LWCI
definition: LWCI = log(1-(NIR-MIDIR))-log(1-(NIR-MIDIR)).

Wavelength at Db (Lb)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Lb
comment: Derivative features: Lb is wavelength position at Db
definition: Lb is wavelength position at Db

LogR
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: LogR

Wavelength at Dr (Lr)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Lr
comment: Derivative features: Lr is wavelength position at Dr
definition: Lr is wavelength position at Dr

Wavelength at Dy (Ly)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Ly
comment: Derivative features: Ly is wavelength position at Dy
definition: Ly is wavelength position at Dy

Moisture Adjusted Vegetation Index (MAVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Zhu, Gaolong; Ju, Weimin; Chen, J. M.; Liu, Yibo (2015), "Formulas of several commonly used vegetation indices". PLOS ONE. Dataset. https://doi.org/10.1371/journal.pone.0102560.t001
Acronym: MAVI
definition: MAVI = (NIR–R)/(NIR+R+SWIR)

Modified Chlorophyll Absorption Ratio Index (MCARI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
References: C.S. Daughtry, C.L. Walthall, M.S. Kim, E.B. de Colstoun, J.E. McMurtrey, "Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance", Remote Sensing of Environment, 74 (2) (2000), pp. 229-239
Acronym: MCARI
definition: [(R700 - R670) - 0.2(R700 - R550)]/(R700/R670)

Modified Chlorophyll Absorption Ratio Index 1 (MCARI1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: MCARI1

Modified Chlorophyll Absorption Ratio Index 2 (MCARI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: C. Wu, Z. Niu, Q. Tang, W. Huang, "Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation", Agric. For. Meteorol., 148 (2008), pp. 1230-1241
Acronym: MCARI2
definition: MCARI2 = [(R750 - R705) - 0.2(R750 - R550)]/(R750/R705)

Moment Distance Index (MDI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: MDI

Maximum Difference Water Index (MDWI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
Acronym: MDWI

Modified Green Red Vegetation Index (MGRVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: MGRVI = (Green^2 - Red^2) / Green^2 + Red^2)
References: Bendig, J.; Yu, K.; Aasen, H.; Bolten, A.; Bennertz, S.; Broscheit, J.; Gnyp, M.L.; Bareth, G. "Combining uav-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley." Int. J. Appl. Earth Obs. Geoinf. 2015, 39, 79–87.
Acronym: MGRVI

Modified Green Soil Adjusted Vegetation Index (MGSAVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: MGSAVI

Modified Non-Linear Index (MNLI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Yang, Z., P. Willis, and R. Mueller. "Impact of Band-Ratio Enhanced AWIFS Image to Crop Classification Accuracy." Proceedings of the Pecora 17 Remote Sensing Symposium (2008), Denver, CO.
Acronym: MNLI
comment: Modified Non-Linear Index (MNLI) is an enhancement to the Non-Linear Index (NLI) that incorporates the Soil Adjusted Vegetation Index (SAVI) to account for the soil background. ENVI uses a canopy background adjustment factor (L) value of 0.5.
definition: MNLI = (NIR^2 - Red) * (1+L) / (NIR^2+Red+L)

Modified Red Simple Ratio Index (MRSRI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: MRSRI = (R750−R445)/(R705−R445)
Acronym: MRSRI

Modified Soil Adjusted Vegetation Index (MSAVI)
type: NamedIndividual
type: Spectral Vegetation Indices
inherits: Soil Adjusted Vegetation Index (SAVI)
is transformation of: Spectral Image
References: Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994), "A modified soil adjusted vegetation index". Remote Sensing of Environment 48: 119–126.
Acronym: MSAVI
definition: MSAVI = (2NIR+1–((2NIR+1)^2–8(NIR–Red))^0.5)/2

Modified Soil Adjusted Vegetation Index 1 (MSAVI1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: MSAVI1

Modified Soil Adjusted Vegetation Index 2 (MSAVI2)
type: NamedIndividual
type: Spectral Vegetation Indices
inherits: Soil Adjusted Vegetation Index (SAVI)
is transformation of: Spectral Image
References: Qi, J., A. Chehbouni, A. Huete, Y. Kerr, and S. Sorooshian. "A Modified Soil Adjusted Vegetation Index." Remote Sensing of Environment 48 (1994): 119-126.
Acronym: MSAVI2
comment: Modified Soil Adjusted Vegetation Index 2 (MSAVI2) is a simpler version of the MSAVI index proposed by Qi, et al (1994), which improves upon the Soil Adjusted Vegetation Index (SAVI). It reduces soil noise and increases the dynamic range of the vegetation signal. MSAVI2 is based on an inductive method that does not use a constant L value (as with SAVI) to highlight healthy vegetation.
definition: MSAVI2 = (2*NIR+1 - ((2*NIR+1)^2 -8(NIR-Red))^1/2 )/2

Moisture Stress Index (MSI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Welikhe P, Quansah JE, Fall S, Elhenney WMc (2017), "Estimation of Soil Moisture Percentage Using LANDSAT-based Moisture Stress Index". J Remote Sensing & GIS 6: 200. doi: 10.4172/2469-4134.1000200
Acronym: MSI
comment: Moisture Stress Index (MSI) is used for canopy stress analysis, productivity prediction and biophysical modeling.
definition: MSI = R1600 / R820

Mealybug Stress Index-1 (MSI-1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: MSI-1 = (R550 + R768 + R1454) − [R1454/(R550 + R768)]
References: Prabhakar, Mathyam, et al. "Hyperspectral indices for assessing damage by the solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton." Computers and Electronics in Agriculture 97 (2013): 61-70.
Acronym: MSI-3

Mealybug Stress Index-2 (MSI-2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: MSI-2 = (R550 + R768) − (R1454 + R674)/(R550 + R768) + (R1454 + R674)
References: Prabhakar, Mathyam, et al. "Hyperspectral indices for assessing damage by the solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton." Computers and Electronics in Agriculture 97 (2013): 61-70.
Acronym: MSI-2

Mealybug Stress Index-3 (MSI-3)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: MSI-3 = (R550 − R674)/(R550 + R674)
References: Prabhakar, Mathyam, et al. "Hyperspectral indices for assessing damage by the solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton." Computers and Electronics in Agriculture 97 (2013): 61-70.
Acronym: MSI-3

MERIS Terrestrial Chlorophyll Index (MTCI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Dash, Jadunandan, and P. J. Curran. "The MERIS terrestrial chlorophyll index." International Journal of Remote Sensing, (2004): 5403-5413.
Acronym: MTCI
definition: MTCI = (R754−R709) / (R709 −R681)

Modified Triangular Vegetation Index (MTVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: MTVI
comment: Modified Triangular Vegetation Index (MTVI)
definition: MTVI = 1.2[1.2(1.2(R761-R581) - 2.5(R651-R581)]

Modified Triangular Vegetation Index 1 (MTVI1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: MTVI1

Modified Triangular Vegetation Index (MTVI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Nitrogen (N)
References: Haboudane D., Miller J.R., Pattey E., Zarco-Tejada P.J., Strachan I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 2004;90:337–352.
Acronym: MTVI2
definition: MTVI2 = 1.5(1.2(R800 − R550) − 2.5(R670 − R550))/sqrt((2R800 + 1)^2 − (6R800 − 5sqrt(R670)) − 0.5)

Modified Triangular Vegetation Index 3 (MTVI3)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: MTVI3

Normalized Band Depth Index (NBDI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Biomass
References: O. Mutanga, A.K. Skidmore, "Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions", International Journal of Applied Earth Observation and Geoinformation, 5 (2) (2004), pp. 87-96
Acronym: NBDI

Narrow-Band Normalised Difference Vegetation Index (NBNDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: P.S. Thenkabail, R.B. Smith, E. De Pauw, "Hyperspectral vegetation indices and their relationships with agricultural crop characteristics", Remote Sensing of Environment, 71 (2) (2000), pp. 158-182
Acronym: NBNDVI
definition: NBNDVI = (R850 - R680)/(R850 + R680)

Normalized Burn Ratio (NBR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NBR
definition: NBR = (NIR - SWIR) / (NIR + SWIR)

Dry Bare-Soil Index (NBSI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Satellite Imagery
is transformation of: Spectral Image
related to: Soil
References: A. Rasul, H. Balzter, G. Ibrahim, H. Hameed, J. Wheeler, B. Adamu, et al., "Applying built-up and bare-soil indices from Landsat 8 to cities in dry climates", Land, 7 (2018), p. 81
Acronym: NBSI

Normalised Difference Index (ND705)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: ND705

Normalised Difference Built-Up Index (NDBI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Y. Zha, J. Gao, S. Ni, "Use of normalized difference built-up index in automatically mapping urban areas from TM imagery", Int. J. Remote Sens., 24 (2003), pp. 583-594
Acronym: NDBI

Normalised Difference Built-Up Index (NDBUI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Y. Zha, J. Gao, S. Ni, "Use of normalized difference built-up index in automatically mapping urban areas from TM imagery", Int. J. Remote Sens., 24 (2003), pp. 583-594
Acronym: NDBUI

Normalized Difference Chlorophyll Index (NDCI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: NDCI = (R762 − R527)/(R762 + R527)
References: A. Marshak, Y. Knyazikhin, A.B. Davis, W.J. Wiscombe, P., P., "Cloud-vegetation interaction: use of normalized difference cloud index for estimation of cloud optical thickness", Geophysical Research Letters, 27 (2000), pp. 1695-1698
Acronym: NDCI

Normalized Difference Greenness Vgetation Index (NDGI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Romero, M., Luo, Y., Su, B., & Fuentes, S. (2018). "Vineyard water status estimation using multispectral imagery from an UAV platform and machine learning algorithms for irrigation scheduling management." Computers and Electronics in Agriculture, 147, 109–117.
Acronym: NDGI

Normalised Difference Index (NDI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Crop Imagery
is transformation of: Spectral Image
References: Stajnko et al., D. Stajnko, M. Lakota, M. Hocevar, "Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging", Computers and Electronics in Agriculture, 42 (2004), pp. 31-34
Acronym: NDI
definition: NDI = (Green - Red) / (Green + Red)

Normalized Difference Infrared Index (NDII)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NDII

Normalized Difference Moisture Index-1 (NDMI-1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NDMI1
definition: NDMI1 = 860 –(R1640 –R2130) / 860 + (R1640 –R2130)

Normalized Difference Moisture Index-2 (NDMI-2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NDMI2
definition: NDMI2 = (R2200 –R1100)/(R2200 + R1100)

Normalized Difference Red-edge Index (NDRE)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Nitrogen (N)
Acronym: NDRE
comment: Normalized Difference Red Edge (NDRE) index which estimates leaf chlorophyll content was most useful in characterizing the leaf area dynamics/senescence patterns of contrasting genotypes.
definition: NDRE = (R790 - R720) / (R790 + R720)

Normalized Difference Red-edge Index 1 (NDRE1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NDRE1

Normalized Difference Red-edge Index 2 (NDRE2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: E.M. Barnes, T.R. Clarke, S.E. Richards, P.D. Colaizzi, J. Haberland, M. Kostrzewski, P. Waller, C. Choi, E. Riley, T. Thompson, R.J. Lascano, H. Li, M.S. Moran, "Coincident Detection of Crop Water Stress, Nitrogen Status, and Canopy Density Using Ground-Based Multispectral Data", In Proceedings of the Fifth International Conference on Precision Agriculture, Bloomington, MN, USA (Vol. 1619).
Acronym: NDRE2
definition: NDRE2 = (Band7 - Band5) / (Band7 + Band5) defined for Sentinel-2 bands

Normalized Difference Red-edge Index 3 (NDRE3)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: G. Navarro, I. Caballero, G. Silva, P.-C. Parra, Á. Vázquez, R. Caldeira, "Evaluation of forest fire on Madeira Island using sentinel-2A MSI imagery", Int. J. Appl. Earth Obs. Geoinf., 58 (2017), pp. 97-106
Acronym: NDRE3
Acronym: NDRE3 = (Band8A - Band7) / (Band8A + Band7) Definition for Sentinel-2 bands

Normalized Difference Red-edge Index (NDREI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NDREI
comment: Normalized Difference Red Edge (NDRE) index which estimates leaf chlorophyll content was most useful in characterizing the leaf area dynamics/senescence patterns of contrasting genotypes.

Normalized Difference Structural Index (NDSI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: L. Vescovo, G. Wohlfahrt, M. Balzarolo, S. Pilloni, M. Sottocornola, M. Rodeghiero, D. Gianelle, "New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass", Int. J. Remote Sens., 33 (7) (2012), pp. 2178-2195
Acronym: NDSI

Normalized Difference Tillage Index (NDTI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NDTI
definition: NDTI = (R1650 –R2215)/(R1650 + R2215)

Normalized Difference Vegetation Index (NDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Biomass
References: Rouse et al., J.W. Rouse, R.H. Haas, J.A. Schell, D.W. "Deering Monitoring vegetation systems in the Great Plains with ERTS", Proc. Third ERTS Symposium, 1 (1973), pp. 48-62
Acronym: NDVI
comment: Normalized Difference Vegetation Index (NDVI) is a simple graphical indicator that can be used to analyze remote sensing measurements, often from a space platform, assessing whether or not the target being observed contains live green vegetation.
definition: NDVI = (IR-R)/(IR+R) = (R800 − R680)/(R800 + R680)

Normalized Difference 750/705 Chl NDI (NDVI705)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NDVI705
definition: NDVI705 = (R750 −R705) / (R750 + R705)

Normalized Difference Water Index (NDWI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
References: Gao. "NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space." 1996.
Acronym: NDWI
comment: Normalized Difference Water Index (NDWI) is formulated by Gao combining reflectance of NIR and SWIR is more common and has wider range of application. It can be used for exploring water content at single leaf level as well as canopy/satellite level
definition: NDWI = (Green - NIR) / (Green + NIR) NDWI = (NIR - SWIR) / (NIR + SWIR)

Normalized Difference Yellowness Index (NDYI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Yield
Acronym: NDYI
definition: NDYI = (Red - Green) / (Red + Green)

Normalized Green Red Diference Index (NGRDI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NGRDI

Near-Infrared Index (NIR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NIR

Near-Infrared Spectroscopy (NIRS)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NIRS
comment: Near-infrared spectroscopy (NIRS) is a spectroscopic method that uses the near-infrared region of the electromagnetic spectrum (from 780 nm to 2500 nm).

Non-linear Vegetation Index (NLI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: N.S. Goel, W. Qi, "Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: A computer simulation", Remote Sensing Reviews, 10 (4) (1994), pp. 309-347
Acronym: NLI
comment: Non-linear Vegetation Index (NLI) assumes that the relationship between many vegetation indices and surface biophysical parameters is non-linear. It linearizes relationships with surface parameters that tend to be non-linear.
definition: NLI = (NIR^2 - Red)/(NIR^2 + Red)

Normalized Pigment Chlorophyll Index (NPCI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
related to: Nitrogen (N)
References: Aparicio, N., et al. "Effect of sensor view angle on the assessment of agronomic traits by ground level hyper-spectral reflectance measurements in durum wheat under contrasting Mediterranean conditions." International Journal of Remote Sensing 25.6 (2004): 1131-1152.
Acronym: NPCI
definition: NPCI = (R680 - R430) / (R680 + R430)

Normalized Pigment Chlorophyll Index 2 (NPCI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: J. Penuelas, J. Gamon, A. Fredeen, J. Merino, C. Field, "Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves", Rem. Sens. Environ., 48 (1994), pp. 135-146
Acronym: NPCI2
definition: NPCI2 = (R680 - R430) / (R680 + R430)

Normalized Phaeophytinization Index (NPQ)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NPQ
definition: NPQ = (R415 –R435)/(R415 + R435)

Nitrogen Reflectance Index (NRI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Nitrogen (N)
References: I. Filella, L. Serrano, J. Serra, J. Penuelas, "Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis", Crop Science, 35 (5) (1995), pp. 1400-1405
Acronym: NRI
definition: NRI = (R570 - R670)/(R570 + R670)

Nitrogen Stress Index 1 (NSI-1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: D. Zhao, K.R. Reddy, V.G. Kakani, J.J. Read, S. Koti, "Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton", Agron J., 97 (2005), pp. 89-98
Acronym: NSI-1
definition: NSI1 = (R415/R710)

Nitrogen Stress Index 2 (NSI-2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: D. Zhao, K.R. Reddy, V.G. Kakani, J.J. Read, S. Koti, "Selection of optimum reflectance ratios for estimating leaf nitrogen and chlorophyll concentrations of field-grown cotton", Agron J., 97 (2005), pp. 89-98
Acronym: NSI-2
definition: NSI2 = (R517/R413)

NIR Shoulder Region Index (NSRI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NSRI

Normalised Vegetation Index (NVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: NVI = (R777 − R747)/R673
Acronym: NVI

Water Stress and Canopy Temperature (NWI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
Acronym: NWI2
definition: Babar, M. A., Reynolds, M. P., Van Ginkel, M., Klatt, A. R., Raun, W. R., & Stone, M. L. (2006). Spectral refectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Science, 46(3), 1046–1057.
definition: NWI2 = (R970−R850) / (R970+R850)

Normalized Water Index -3 (NWI-3)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NWI-3
definition: NWI-3 = (R970 –R880)/(R970+R880)

N_1645_1715
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Phosphorus (P)
related to: Potassium (K)
References: Pimstein, A., Karnieli, A., Bansal, S. K., & Bonfild, D. J. (2011). Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy. Field Crops Research, 121, 125–135.
definition: N_1645_1715 = (R1645 - R1715)/(R1645 + R1715)

N_870_1450
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Phosphorus (P)
related to: Potassium (K)
References: Pimstein, A., Karnieli, A., Bansal, S. K., & Bonfild, D. J. (2011). Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy. Field Crops Research, 121, 125–135.
definition: N_870_1450 = (R870 - R1450)/(R870 + R1450)

Normalized G (NormR1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NormR1

Normalized NIR (NormR2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NormR2

Normalized R (NormR3)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NormR3

Optimized Soil Adjusted Vegetation Index (OSAVI)
type: NamedIndividual
type: Spectral Vegetation Indices
inherits: Soil Adjusted Vegetation Index (SAVI)
is transformation of: Spectral Image
References: Rondeaux G, Steven M, Baret F (1996), "Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment", 55: 95–107.
Acronym: OSAVI
definition: OSAVI = (NIR–Red)/(NIR+Red+Yellow)

Optimized Soil Adjusted Vegetation Index 2 (OSAVI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: C. Wu, Z. Niu, Q. Tang, W. Huang, Estimating chlorophyll content from hyperspectral vegetation indices: modeling and validation, Agric. For. Meteorol., 148 (2008), pp. 1230-1241
Acronym: OSAVI2
definition: OSAVI2 = (1+0.6)*(R750-R705) / (R750+R705-0.16)

Plant Biochemical Index (PBI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: PBI
definition: PBI = Band8 / Band 3 defined for Sentinel-2 bands

Powdery Mildew Index (PMI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Powdery Mildew
Acronym: PMI
comment: Powdery Mildew Index (PMI)
definition: PMI = (R400 – R735)/(R400 + R735)−0.5R403

Pan NDVI (PNDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: PNDVI

Plant Pigment Ratio (PPR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Pigment
References: Metternicht, G. (2003). Vegetation indices derived from high-resolution airborne videography for precision crop management. International Journal of Remote Sensing, 24(14), 2855–2877
Acronym: PPR
definition: PPR = (R550−R450) / (R550+R450)

Photochemical Reflectance Index (PRI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: J.A. Gamon, J. Penuelas, C.B. Field, "A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency", Remote Sensing of Environment, 41 (1) (1992), pp. 35-44
Acronym: PRI
definition: PRI = (R531 - R570)/(R531 + R570)

Pigment Specific Normalized Difference-a (PSNDa)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: PSNDa
definition: PSNDa = (R800 –R680)/(R800 + R680)

Pigment Specific Normalized Difference-b (PSNDb)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: PSNDb
definition: PSNDb = (R800 –R635)/(R800 + R635)

Pigment Specific Normalized Difference-c (PSNDc)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: PSNDc
definition: PSNDa = (R800 –R470)/(R800 + R470)

Plant Senescence Reflectance Index (PSRI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Crop Stress
References: M.N. Merzlyak, A.A. Gitelson, O.B. Chivkunova, V.Y. Rakitin, "Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening", Physiologia Plantarum, 106 (1) (1999), pp. 135-141
Acronym: PSRI
definition: PSRI = (R680 - R500)/R750

Pigments Specific Simple Ratio (PSSR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: PSSR = R800 / R635
References: A.A. Gitelson, N.M. Merzlyak, B.O. Chivkunova, "Optical properties and nondestructive estimation of anthocyanin content in plant leaves", Photochemistry and Photobiology, 74 (1) (2001), pp. 38-45
Acronym: PSSR

Pigment Specifc Simple Ratio (PSSRa)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Blackburn, G. A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing, 19(4), 657–675.
Acronym: PSSRa
definition: PSSRa = R800 / R680

Pigment Specifc Simple Ratio (PSSRb)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Blackburn, G. A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing, 19(4), 657–675.
Acronym: PSSRb

Pigment Specifc Simple Ratio (PSSRc)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: PSSRc
definition: PSSRc = (R800/R470)

Perpendicular Vegetation Index (PVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Richardson AJ, Wiegand CL (1977), "Distinguishing vegetation from soil background information". Photogrammetric Engineering and Remote Sensing 43: 1541–1552.
Acronym: PVI
definition: PVI = (NIR-aR-b)/(a^2+1)^1/2

Plant Water Index (PWI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
Acronym: PWI

P_1080_1460
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Phosphorus (P)
References: G. R. Mahajan, R. N. Sahoo, R. N. Pandey, V. K. Gupta, Dinesh Kumar,Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.),Precision Agriculture, Volume 15, pages 499–522 (2014) , Springer

P_670_1092
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Phosphorus (P)

P_670_1260
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Phosphorus (P)

Physiological Reflectance Index (PhRI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: J.A. Gamon, J. Penuelas, C.B. Field, "A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency", Remote Sensing of Environment, 41 (1) (1992), pp. 35-44
Acronym: PhRI
definition: PhRI = (R550 - R531)/(R550 + R531)

Ratio Analysis of Reflectance Spectra (RARS)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: E.W. Chappelle, M.S. Kim, M.M. Iii, "Ratio analysis of reflectance spectra (rars): an algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves", Remote Sens. Environ., 39 (1992), pp. 239-247
Acronym: RARS
comment: Ratio Analysis of Reflectance Spectra (RARS) estimates carotenoid pigment contents in foliage.
definition: RARS = R760/R500

Ratio Analysis of Refectance Spectral Chlorophyll-a (RARSa)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Chappelle, E. W., Kim, M. S., & McMurtrey, J. E. (1992). "Ration analysis of refectance spectra (RARS) - An algorithm for the remote estimation concentration of chlorophyll-a, chlorophyll-b, and carotenoid soybean leaves". Remote Sensing of Environment, 39(3), 239–247.
Acronym: RARSa
definition: RARSa = R675 / R700

Ratio Analysis of Refectance Spectral Chlorophyll-b (RARSb)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Chappelle, E. W., Kim, M. S., & McMurtrey, J. E. (1992). "Ration analysis of refectance spectra (RARS) - An algorithm for the remote estimation concentration of chlorophyll-a, chlorophyll-b, and carotenoid soybean leaves". Remote Sensing of Environment, 39(3), 239–247.
Acronym: RARSb
definition: RARSa = R675 / (R700 × R650)

Range Action Verification Index (RAVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: RAVI

Red Blue Index (RBI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: M.R. Golzarian, R.a. Frick, "Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis", Plant Meth., 7 (1) (2011), p. 28
Acronym: RBI
definition: RBI = (Rn − Bn)/(Rn + Bn)

Red-Blue NDVI (RBNDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: RBNDVI

Red-Edge Chlorophyll Index (RCI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
Acronym: CI
comment: Red-Edge Chlorophyll Index (RCI) is a narrow band in the vegetation reflectance spectrum between the transition of red to near infra-red. The total chlorophyll content is linearly correlated with the difference between the reciprocal reflectance of green/ red-edge bands and the NIR band.
definition: CI_rededge = (NIR/Red_edge) − 1

Ratio Drought Index (RDI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Drought
References: J.E. Pinder, K.W. McLeod, "Indications of Relative Drought Stress in Longleaf Pine From Thematic Mapper Data", 65 (1999), pp. 495-501
Acronym: RDI
definition: RDI = Band12 / Band8A defined for Sentinel-2 bands

Re-normalized Difference Vegetation Index (RNDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: RNDVI

RedEdge (RE)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: RE
definition: RE = RedEdge band

Red-Edge Difference Vegetation Index (REDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: REDVI

Red-Edge Infection Point (REIP)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Guyot, G., Baret, F., 1988. Utilisation de la haute résolution spectrale pour suivre l’état des 494 couverts végétaux. In: Proceedings of the 4th International colloquium on spectral signatures of 495 objects in remote sensing. ESA SP-287, Assois, France, pp. 279–286.
Acronym: REIP
definition: REIP = 700 + 40 × (NIR670 / 2 + NIR780 / 2 −NIR700) / (NIR740−NIR700)

Red-Edge Optimized SAVI (REOSAVI)
type: NamedIndividual
type: Spectral Vegetation Indices
inherits: Soil Adjusted Vegetation Index (SAVI)
is transformation of: Spectral Image
Acronym: SEOSAVI

Red-Edge Eosition (REP)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: REP
comment: The red-edge represents the abrupt reflectance change in 680-740 nm range of vegetation spectra, which is caused by the combined effects of strong chlorophyll absorption in the red wavelengths and high reflectance in the near-infrared (NIR) wavelengths due to the leaf internal scattering.
definition: REP = 700 + 40 x (Rre - R700) / (R740 - R700)

Red-Edge Position Determination (REPD)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: REPD

Red-Edge Renormalized Different Vegetation Index (RERDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: RERDVI

Red-Edge Renormalized Vegetation Index (RERVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: RERVI

Red-Edge Soil Adjusted Vegetation Index
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: RESAVI

Red Edge Triangular Vegetation Index (RETVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Chen, P., Tremblay, N., Wang, J., & Vigneault, P., (2009). "New spectral index for corn greenbiomass estimation." In Q. Tong & D. Li (Ed.), Proceedings of the 2nd International Conference on Earth Observation for Global Changes. Sichuan. China. May 25–29 2009. (pp. 507–514).
Acronym: RETVI
definition: RETVI = 100 (R750−R730) − 10(R750−R550) √(R700 / R670)

Red-Edge Vegetation Stress Index (REVSI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Crop Stress
References: Merton, R., Huntington, J., 1999. "Early simulation of the ARIES-1 satellite sensor for multi-temporal vegetation research derived from AVIRIS". In: Summaries of the Eight JPL Airborne Earth Science Workshop. Pasadena, CA: JPL, Publication. pp. 299-307.
Acronym: REVSI
definition: REVSI = [(R712 + R752)/2] - R732

Red-Edge Wide Dynamic Range Vegetation Index (REWDRVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: REWDRVI

Reflectance of Green band (RG)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Referred to RSR of Landsat-5 TM
Acronym: RG
comment: Within the range from 520 to 600 nm

Normalized Green-Red Difference Index (NGRDI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: NGRDI

Red Green Blue Vegetation Index (RGBVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: RGBVI = (Green^2 - Red*Blue) / Green^2 + Red*Blue)
References: Bendig, J.; Yu, K.; Aasen, H.; Bolten, A.; Bennertz, S.; Broscheit, J.; Gnyp, M.L.; Bareth, G. "Combining uav-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley." Int. J. Appl. Earth Obs. Geoinf. 2015, 39, 79–87.
Acronym: RGBVI

Red/Green Index (RGI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: RGI
definition: RGI = R690/R550

Red Green Ratio Index (RGR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: RGR
definition: RGR = Rred / Rgreen

Red Green Ratio Index (RGRI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: RGRI

Redness Index (RI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Escadafal, R. and Huete, A. R. (1991) Étude des propriétés spectrales des sols arides appliquée à l'amélioration des indices de végétation obtenus par télédétection. CR Acad. Sci. Paris 312 (2):1385-1391.
Acronym: RI
definition: RI = (Red - Green) / (Red + Green)

Reciprocal of Moisture Stress Index (RMSI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Crop Stress
related to: Soil Moisture
Acronym: RMSI

Re-normalized Difference Vegetation Index (RNDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: J. Roujean, F. Breon, "Estimating PAR absorbed by vegetation from bidirectional reflectance measurements", Remote Sensing of Environment, 51 (3) (1995), pp. 375-384
Acronym: RNDVI
comment: Sensitive to photosynthetically active radiation and biophysical parameters
definition: RNDVI = (RNIR - RR)/(RNIR + RR)0.5

Reflectance of near-infrared band (RNIR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Referred to RSR of Landsat-5 TM
Acronym: RNIR
comment: Within the range from 760 to 960 nm

Rot Index (ROTI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: ROTI
definition: ROTI = R1175/(R1982 + R691)

Reflectance of Red band (RR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Referred to RSR of Landsat-5 TM
Acronym: RR
comment: Within the range from 620 to 690 nm

Spectral RRI1
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: RRI1

Reduced Simple Ratio (RSR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Brown LJ, Chen JM, Leblanc SG, Cihlar J (2000), "A shortwave infrared correction to the simple ratio: an image and model analysis. Remote Sensing of Environment" 71: 16–25.
Acronym: RSR
definition: RSR = SR×(SWIRmax–SWIR)/(SWIRmax–SWIRmin)

Ratio Vegetation Index (RVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: RVI
comment: Simple Ratio (SR) or Ratio Vegetation Index (RVI) is simply the reflectance in the NIR band divided by the reflectance in the red band.
definition: RVI = SR = NIR/Red

Ratio Vegetation Structure Index (RVSI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Biomass
Acronym: RVSI
comment: Ratio Vegetation Structure Index (RVSI)
definition: RVSI = [(R712 + R752)/2]−R732

Red Band (R/Red)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Rn
definition: Rn = Red / (Red + Green + Blue)

Stem Area Index (SAI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: SAI

Spectral Angle Mapping (SAM)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J., et al. (1993). The spectral image-processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, 44(2–3), 145–163.
Acronym: SAM
comment: Spectral Angle Mapping (SAM) determines spectral similarity between two spectra by treating them as vectors in a space with dimensionality equal to the number of bands (nb) (Kruse et al. 1993). A simplified explanation of this can be given by considering a reference spectrum and a test spectrum from two-band data represented on a two dimensional plot as two points

Soil and Atmospherically Resistant Vegetation Index (SARVI)
type: NamedIndividual
type: Spectral Vegetation Indices
inherits: Soil Adjusted Vegetation Index (SAVI)
is transformation of: Spectral Image
related to: Soil
References: Baret, F.; Guyot, G.; Major, D, "TSAVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation", Proceedings of 12th Canadian Symposium on Remote Sensing and IGARSS, 1989
Acronym: SARVI
definition: SARVI = (1+L) [(800nm-(Red - y(Blue-Red))) / (800nm + -(Red - y(Blue-Red))+L)]

Soil Adjusted Vegetation Index (SAVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Biomass
related to: Soil
Acronym: SAVI
comment: Soil Adjusted Vegetation Index (SAVI) empirically derived NDVI products have been shown to be unstable, varying with soil colour, soil moisture, and saturation effects from high density vegetation. In an attempt to improve NDVI, Huete developed a vegetation index that accounted for the differential red and near-infrared extinction through the vegetation canopy. The index is a transformation technique that minimizes soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. The L-value is based on the amount of green vegetative cover. L is a default of 0.5, which means, generally, areas of moderate green vegetative cover. SAVI is sensitive to biophysical parameters
definition: SAVI = ((NIR - Red) / (NIR + Red + L)) x (1 + L)

Soil Brightness Index (SBI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Soil
References: Kauth, R., and G. Thomas. "The Tasselled Cap-A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen By Landsat" In Proceedings of the LARS 1976 Symposium of Machine Processing of Remotely-Sensed Data, West Lafayette, IN: Purdue University, pp. 4B41-4B51.
Acronym: SBI
definition: SBI = (0.332MSS4 + 0.603MSS5 + O.675MSS6 + 0.262MSS7) The NDVI can be noted as NDVI(MSS6) or NDVI(MSS7) according to the spectral band used (MSS6 or MSS7).

Simplified Canopy ChlorophyII Content Index
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: T. Raper, J. Varco, "Canopy-scale wavelength and vegetative index sensitivities to cotton growth parameters and nitrogen status", Precision Agric., 16 (1) (2015), pp. 62-76
Acronym: SCCCI
definition: SCCCI = NDREI/NDVI

Simple Difference (SD)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: SD
definition: SD=NIR−R.

Sum of 1st Derivative values within Blue edge (SDb)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: SDb
comment: Derivative features: Defined by sum of 1st order derivative values of 35 bands within the blue edge
definition: SDb = Sum of 1st order derivative values of 35 bands within the blue edge

Sum of 1st Derivative values within Red edge (SDr)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: SDr
comment: Derivative features: Defined by sum of 1st order derivative values of 61 bands within the red edge
definition: SDr = Sum of 1st order derivative values of 61 bands within the red edge

Sum of 1st Derivative values within Yellow edge (SDy)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: SDy
comment: Derivative features: Defined by sum of 1st order derivative values of 28 bands within the yellow edge
definition: SDy = Sum of 1st order derivative values of 28 bands within the yellow edge

Stress Index (SI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: SI = ρ693 / ρ759
References: Vidal, A., F. Pinglo, H. Durand, C. Devaux-Rox, and A. Maillet. 1994. “Evaluation of a Temporal Fire Risk Index in Mediterranean Forests from NOAA Thermal IR.” Remote Sensing of the Environment 49: 296–303.
Acronym: SI

Solar-Induced Chlorophyll Fluorescence (SIF)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Mohammed, G.H.; Colombo, R.; Middleton, E.M.; Rascher, U.; van der Tol, C.; Nedbal, L.; Goulas, Y.; Pérez-Priego, O.; Damm, A.; Meroni, M.; et al. "Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress". Remote sens. Environ. 2019, 231, 111177
Acronym: SIF

Structure Insensitive Pigment Index (SIPI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Pigment
Acronym: SIPI
comment: Structure Insensitive Pigment Index (SIPI) improved pigment index to avoid structural disturbances
definition: SIPI = (NIR - Blue) / (NIR - Red)

Spectral Polygon Vegetation Index (SPVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: SPVI

Simple Ratio (SR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
References: F. Baret, G. Guyot, "Potentials and limits of vegetation indices for LAI and APAR assessment", Remote Sensing of Environment, 35 (2-3) (1991), pp. 161-173
Acronym: SR
comment: Simple Ratio (SR) is a ratio of (1) the wavelength with highest reflectance for vegetation and (2) the wavelength of the deepest chlorophyll absorption. The simple equation is easy to understand and is effective over a wide range of conditions. As with the NDVI, it can saturate in dense vegetation when LAI becomes very high. Structural-related index minimising pigment infuence
definition: SR = NIR/Red

Simple Ratio 1 (SR1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: SR1

Spectral Reflectance Indices (SRI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Prasad, Bishwajit. Spectral reflectance indices in wheat breeding. VDM Verlag Dr. Müller, 2008.
Acronym: SRI
comment: Spectral reflectance indices (SRI) are one of the several indirect selection tools that have been reported to be associated with many physiological processes of different crop species.

Simple Ratio Pigment Index (SRPI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Penuelas J., Gamon J.A., Fredeen A.L., Merino J., Field C.B. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens. Environ. 1994;48:135–146.
Acronym: SRPI
definition: SRPI = R430/R680

Simple Ratio Vegetation Index (SRVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Zhao, D., Reddy, K. R., Kakani, V. G., Read, J. J., & Koti, S. (2007). "Canopy reflectance in cotton for growth assessment and lint yield prediction". European Journal of Agronomy, 26(3), 335–344.
Acronym: SRVI

Simple Ratio Water Index (SRWI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
References: Rallo, G., Minacapilli, M., Ciraolo, G., & Provenzano, G. (2014). "Detecting crop water status in mature olive groves using vegetation spectral measurements." Biosystems Engineering, 128, 52–68.
References: Zarco-Tejada, P. J., Rueda, C. A., & Ustin, S. L. (2003). "Water content estimation in vegetation with MODIS reflectance data and model inversion methods." Remote Sensing of Environment, 85(1), 109–124.
Acronym: SRWI
definition: SRWI = R858 / R1240 or SRWI = R680 / R1240

Shortwave Infrared (SWIR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: SWIR
comment: Shortwave Infrared (SWIR) is of the band combination meant to monitor drainage and soil patterns, stages of crop growth, and vegetation studies in general.

Shortwave Infrared Water Index (SWWI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
References: Y. Inoue, S. Morinaga, M. Shibayama, "Non-destructive estimation of water status of intact crop leaves based on spectral reflectance measurements", Japanese J. Crop Sci., 62 (1993), pp. 462-469
Acronym: SWWI
definition: SWWI = (R850 –R1650)/(R850 + R1650)

S_660_1080
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Sulphur (S)
References: G. R. Mahajan, R. N. Sahoo, R. N. Pandey, V. K. Gupta, Dinesh Kumar,Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.),Precision Agriculture, Volume 15, pages 499–522 (2014) , Springer
definition: S_660_1080 = (R1080 - R660) / (R1080 + R660)

S_660_1260
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: G. R. Mahajan, R. N. Sahoo, R. N. Pandey, V. K. Gupta, Dinesh Kumar,Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.),Precision Agriculture, Volume 15, pages 499–522 (2014) , Springer
definition: S_660_1260 = (R1260 - R660) / (R1260 + R660)

S_670_1090
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Sulphur (S)

Tea Anthracnose Normalized Index (TANI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Tea
References: Yuan, Lin, et al. "Detection of anthracnose in tea plants based on hyperspectral imaging." Computers and Electronics in Agriculture 167 (2019): 105039.
Acronym: TANI
comment: Tea Anthracnose Normalized Index (TANI)

Tea Anthracnose Ratio Index (TARI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Tea
References: Yuan, Lin, et al. "Detection of anthracnose in tea plants based on hyperspectral imaging." Computers and Electronics in Agriculture 167 (2019): 105039.
Acronym: TARI
comment: Tea Anthracnose Ratio Index (TARI)

Three-Band Water Index (TBWI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: TBWI
definition: TBWI = (R973 - R1720) / R1447

Transformed Chlorophyll Absorption and Reflectance Index (TCARI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
References: D. Haboudane, J.R. Miller, E. Pattery, P.J. Zarco-Tejad, I.B. Strachan, "Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture", Remote Sensing Environment, 90 (3) (2004), pp. 337-352
Acronym: TCARI
definition: TCARI = 3[(R700 - R670) - 0.2(R700 - R550)(R700/R670)]

Transformed Chlorophyll Absorption and Reflectance Index 2 (TCARI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: TCARI2

Triangular Chlorophyll Index (TCI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Hunt Jr, E. Raymond, et al. "Remote sensing leaf chlorophyll content using a visible band index." Agronomy journal 103.4 (2011): 1090-1099.
Acronym: TCI
definition: TCI = 1.2(R700−R550) −1.5(R670 − R550)⋅(R700 / R670)^(1/2)

Transformed Diference Vegetation Index (TDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Bannari, A., Asalhi, H., & Teillet, P. M. 2002. Transformed diference vegetation index (TDVI) for vegetation cover mapping. In IEEE International Geoscience and Remote Sensing Symposium, (Vol. 5, pp. 3053–3055).
Acronym: TDVI
comment: Transformed Diference Vegetation Index (TDVI) is a structural-related index minimising pig- ment infuence
definition: TDVI = 1.5 ∗[ (NIR − R) ∕ sqrt(NIR^2 + R + 0.5) ]

Triangular Greenness Index (TGI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Hunt, E., C. Daughtry, J. Eitel, and D. Long. "Remote Sensing Leaf Chlorophyll Content Using a Visible Band Index." Agronomy Journal 103, No. 4 (2011): 1090-1099.
Acronym: TGI
comment: Triangular Greenness Index (TGI) approximates the area of a triangle bounding a leaf reflectance spectrum, where the vertices are in the red, green, and blue wavelengths. The Lambda (L) terms represent the center wavelengths of the respective bands. The Rho (R) terms represent the pixel values of those bands. The original TGI equation (Hunt et al, 2011) used 670 nm, 550 nm, and 480 nm for the red, green, and blue wavelength centers, with a 10 nm band width. The following figure shows an ENVI spectral profile of a pixel that represents healthy, green vegetation. It shows the triangle bounding the vegetation spectrum.
definition: TGI = ((Lred-Lblue)(Rred-Rgreen) - (Lred-Lgreen)(Rred-Rblue)) / 2

Transformed Normalized Difference Vegetation Index (TNDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: TNDVI
comment: Transformed Normal Difference Vegetation index (TNDVI) is used to transform multi-spectral data into a single image band.
definition: TNDVI = Sqrt ((NIR-red/NIR+red) + 0.5).

Total Reflection (TR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: TR

Transformed soil adjusted vegetation index (TSAVI)
type: NamedIndividual
type: Spectral Vegetation Indices
inherits: Soil Adjusted Vegetation Index (SAVI)
is transformation of: Spectral Image
Acronym: TSAVI
definition: TSAVI = a(NIR-aR-b)/(R+a(NIR-b)+X(1+a^2))

Temperature Vegetation Dryness Index (TVDI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: TVDI

Triangular Vegetation Index (TVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: N.H. Broge, E. Leblanc, "Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density", Remote Sensing of Environment, 76 (2) (2001), pp. 156-172
Acronym: TVI
definition: TVI = 0.5[120(R750 - R550) - 200(R670 - R550)]

Ultraviolet (UV)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: UV
definition: UV : 350–399.9nm

Visible Atmospherically Resistant Index (VARI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Gitelson, A., et al. "Vegetation and Soil Lines in Visible Spectral Space: A Concept and Technique for Remote Estimation of Vegetation Fraction. International Journal of Remote Sensing 23 (2002): 2537−2562.
Acronym: VARI
comment: Visible Atmospherically Resistant Index (VARI) is based on the ARVI and is used to estimate the fraction of vegetation in a scene with low sensitivity to atmospheric effects.
definition: VARI = (Green-Red) / (Green + Red - Blue)

Vertex Component Analysis (VCA)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: VCA
comment: Vertex Component Analysis (VCA) is a linear unmixing method, aiming at estimating the number of reference endmembers, their spectral signatures and their abundance fractions.

Vegetation Condition Index (VCI)
type: NamedIndividual
type: Spectral Vegetation Indices
inherits: Normalized Difference Vegetation Index (NDVI)
is transformation of: Spectral Image
References: Liu, W.T., Kogan, F.N.,: Monitoring regional drought using the Vegetation Condition Index. In: International Journal of Remote Sensing, 1996, Vol. 17, 2761- 2782
Acronym: VCI
See Also: VCI = (NDVIj−NDVImin) / (NDVImax−NDVImin) * 100

Vegetation Fraction (VF)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: VF = (R550−R670) / (R550+R670)
References: Gitelson, A. A., R. Stark, U. Grits, D. Rundquist, Y. Kaufman, and D. Derry. 2002a. “Vegetation and Soil Lines in Visible Spectral Space: A Concept and Technique for Remote Estimation of Vegetation Fraction.” International Journal of Remote Sensing 23: 2537–2562.
Acronym: VF

Vegetation Index (VI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: VI
comment: A Vegetation Index (VI) is a spectral transformation of two or more bands designed to enhance the contribution of vegetation properties and allow reliable spatial and temporal inter-comparisons of terrestrial photosynthetic activity and canopy structural variations.

Green Vegetation Index (VIGreen)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: A.A. Gitelson, Y.J. Kaufman, R. Stark, D. Rundquist, "Novel algorithms for remote estimation of vegetation fraction", Rem. Sens. Environ., 80 (2002), pp. 76-87
Acronym: VIGreen
comment: Green Vegetation Index (VIGreen)
definition: VIGreen=R761-R651R761+R651

Visible Near Infrared Reflectance (VNIR)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Bilgili, A. Volkan, et al. "Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey." Journal of Arid Environments 74.2 (2010): 229-238.
Acronym: VNIR
comment: Visible Near Infrared Reflectance (VNIR) spectroscopy shows promise as a low-cost method that can be used to substitute or complement traditional soil characterization methods.

VNR
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Pearson, R.L., Miller, L.D., 1972. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado. In: Proc. 8th International Symposium on Remote Sensing of the Environment, vol. 2, pp. 1355–1379.
Acronym: VNR
definition: VNR = R / (NIR + R)

Vogelmann Red Edge Index (VOG1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
References: Vogelmann JE, Rock BN, Moss DM. 1993. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14:1563– 1575. http://dx.doi.org/10.1080/01431169308953986.
Acronym: VOG1
definition: VOG1 = R740 / R720

Vogelmann Red Edge Index (VOG2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
References: Vogelmann JE, Rock BN, Moss DM. 1993. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14:1563– 1575. http://dx.doi.org/10.1080/01431169308953986.
Acronym: VOG2
definition: VOG2 = (R734–R747) / (R715–R720)

Vogelmann Red Edge Index (VOG3)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
References: Vogelmann JE, Rock BN, Moss DM. 1993. Red edge spectral measurements from sugar maple leaves. International Journal of Remote Sensing 14:1563– 1575. http://dx.doi.org/10.1080/01431169308953986.
Acronym: VOG3
definition: VOG3 = (R734–R747) / (R715–R726)

Vegetation Red Edge (VRE)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: VRE

Vegetation Temperature Condition Index (VTCI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: VTCI
comment: Vegetation Temperature Condition Index
comment: Vegetation temperature condition index (VTCI)

Violet Light (VL)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: VL

Water Balance Index 1 (WABI1)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
References: Rapaport, T., Hochberg, U., Shoshany, M., Karnieli, A., & Rachmilevitch, S. (2015). "Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment". ISPRS Journal of Photogrammetry and Remote Sensing, 109, 88–97.
Acronym: WABI1
definition: WABI1 = (R1490−R531)/(R1490+R531)

Water Balance Index 2 (WABI2)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
References: Rapaport, T., Hochberg, U., Shoshany, M., Karnieli, A., & Rachmilevitch, S. (2015). "Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment". ISPRS Journal of Photogrammetry and Remote Sensing, 109, 88–97.
Acronym: WABI2
definition: WABI2 = (R1500−R538)/(R1500+R538)

Water Balance Index 3 (WABI3)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
References: Rapaport, T., Hochberg, U., Shoshany, M., Karnieli, A., & Rachmilevitch, S. (2015). "Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment". ISPRS Journal of Photogrammetry and Remote Sensing, 109, 88–97.
Acronym: WABI3
definition: WABI3 = (R1485−R550)/(R1485+R550)

Wavebands (WB)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: WB

Water Band Index (WBI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
References: Claudio HC, Cheng YF, Fuentes DA, Gamon JA, Luo HY, Oechel W, Qiu HL, Rahman AF, Sims DA (2006) Monitoring drought effects on vegetation water content and fluxes in chaparral with the 970 nm water band index. Remote Sens Environ 103:304–311
Acronym: WBI
definition: WBI = R900/R970

Wide Dynamic Range VI (WDRVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: A.A. Gitelson, "Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation", J. Plant Physiol., 161 (2004), pp. 165-173
Acronym: WDRVI
comment: Wide Dynamic Range VI (WDRVI) is similar to NDVI, but it uses a weighting coefficient (a) to reduce the disparity between the contributions of the near-infrared and red signals to the NDVI. The WDRVI is particularly effective in scenes that have moderate-to-high vegetation density when NDVI exceeds 0.6. NDVI tends to level off when vegetation fraction and leaf area index (LAI) increase, whereas the WDRVI is more sensitive to a wider range of vegetation. The weighting coefficient (alpha) can range from 0.1 to 0.2. ENVI uses a value of 0.2, as recommended by Henebry, Viña, and Gitelson (2004). To apply a different weighting coefficient value, use the Band Math tool. Estimate LAI, vegetation cover, biomass; Better than NDVI
definition: WDRVI = (alpha * NIR - Red) / (alpha * NIR + Red)

Weighted Difference Vegetation Index (WDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Clevers JPW (1989) The applications of a weighted infrared-red vegetation index for estimating leaf area index by correcting for soil moisture. Remote Sensing of Environment 29: 25–37.
Acronym: WDVI
definition: WDVI = NIR-aR

Water Index (WI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
References: R.A. Naidu, E.M. Perry, F.J. Pierce, T. Mekuria, "The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars", Comput. Electron. Agric., 66 (2009), pp. 38-45
Acronym: WI
definition: WI = R900/R970

Wavelength (WL)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: WL

Weighted Normalized Difference Vegetation Index (WNDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: WNDVI

Water Vapour (WV)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
Acronym: WV

Wavelength width at half DEP (Wid)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: Wid
comment: Continuous removal features: The full wavelength width at half DEP (nm)
definition: In the range of 550–750 nm

Yellowness Index (YI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Acronym: YI
comment: Yellowness Index (YI) is a number calculated from spectrophotometric data that describes the change in color of a test sample from clear or white to yellow. This test is most commonly used to evaluate color changes in a material caused by real or simulated outdoor exposure.
definition: YI = 100(1-Blue/Green) where B and G are respectively amber blue (B) and green (G) colorimeter readings.

Yellow rust index (YRI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Crop Disease
related to: Stripe Rust
References: Huang W., Guan Q., Luo J., Zhang J., Zhao J., Liang D., Huang L., Zhang D. New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014;7:2516–2524. doi: 10.1109/JSTARS.2013.2294961.
Acronym: YRI
comment: Yellow rust index (YRI)
definition: YRI = (R515 – R698)/(R515 + R698)−0.5R738

Yellow Vegetation Index (YVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Kauth, R., and G. Thomas. "The Tasselled Cap-A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen By Landsat" In Proceedings of the LARS 1976 Symposium of Machine Processing of Remotely-Sensed Data, West Lafayette, IN: Purdue University, pp. 4B41-4B51.
Acronym: YVI
definition: YVI = (-0.899MSS4 + 0.428MSS5 + 0.076MSS6 - 0.041MSS7) The NDVI can be noted as NDVI(MSS6) or NDVI(MSS7) according to the spectral band used (MSS6 or MSS7).

Zarco-Tejada & Miller (ZTM)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
References: Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., Noland, T. L., & Sampson, P. H. (2001). Scaling-up and model inversion methods with narrow-band optical indices for chlorophyll content estimation in closed forest canopies with hyper-spectral data. IEEE Transactions on Geoscience and Remote Sensing, 39(7), 1491–1507.
Acronym: ZTM
definition: ZTM = R750 / R710

Zygielbaum Water Stress Index (ZWSI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Water
Identifier: ZWSI = R720/R520
Acronym: ZWSI

Modified Chlorophyll Absorption Integral (MCAI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
related to: Chlorophyll
Acronym: MCAI

modified Normalized Difference index (mND)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: D.A. Sims, J.A. Gamon, "Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages", Rem. Sens. Environ., 81 (2002), pp. 337-354
Acronym: mND
definition: mND = (R750 - R705) / (R750 + R705 + 2R445)

Modified Normalized Difference Vegetation Index (MNDVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
References: Nemani R, Pierce L, Running S, Band L (1993), "Forest ecosystem processes at the watershed scale: sensitivity to remotely sensed leaf area index estimates". International Journal of Remote Sensing 14: 2519–2534.
Acronym: MNDVI
definition: MNDVI = NDVI×(SWIRmax–SWIR)/(SWIRmax–SWIRmin)

Modified Simple Ratio (MSR)
type: NamedIndividual
type: Spectral Vegetation Indices
inherits: Simple Ratio (SR)
is transformation of: Spectral Image
References: J.M. Chen, "Evaluation of vegetation indices and a modified simple ratio for boreal applications", Canadian Journal of Remote Sensing, 22 (3) (1996), pp. 229-242
Acronym: MSR
definition: MSR = (RNIR/RR - 1)/((RNIR/RR)^0.5 + 1)

mixed WDRVI (mWDRVI)
type: NamedIndividual
type: Spectral Vegetation Indices
is transformation of: Spectral Image
Identifier: mWDRVI = NDVI if NDVI ≤0.6 else WDRVI
Acronym: mWDRVI

Spider Mite
type: NamedIndividual
type: Crop Pest

Spider Mites
type: NamedIndividual
type: Crop Disease
type: Crop Pest
life cycle: Adult female spider mites overwinter under loose bark, in cracks in the soil, in leaf litter, and in other protected places. In the spring, adults emerge and begin laying eggs. Each female typically deposits 70 eggs and lives for only 30 days. Eggs hatch in 5–7 days. As many as 10 generations may occur each year. The young look similar to the adults, but newly hatched young have only six legs and do not possess the characteristic dark spots on the back. Reproduction of the two-spotted spider mite is favored by hot, dry conditions, so serious damage is likely to occur in mid-July to September. The spruce spider mite is most active durng cooler weather in Spring and Fall.
management: Management Strategies 1. Knock mites off plants with water. Spraying with a strong stream of water (particularly the undersides of leaves) will provide some control. Spray plants frequently to control future buildups. For severe infestations, affected plants or plant parts can be removed and destroyed. There are several natural predators that feed on spider mites. The use of chemical insecticides to control other garden pests can result in the death of these beneficial insects and a subsequent increase in the population of spider mites. 2. Use insecticidal soap. Insecticidal soaps specially formulated to kill insects and not damage plants are effective if used frequently until the problem is under control. 3. Use superior horticultural oil sprays. Highly refined oils sold as superior or horticultural oils are also very effective in controlling spider mites. The oil suffocates the mites. Unlike dormant oils, these oils are highly refined and under proper conditions, can be applied to plant foliage without damage. Follow label directions to avoid damage to some plants that may be sensitive. Superior oils are considered nontoxic and are less likely to kill beneficial insects. 4. Use chemical insecticides or miticides. A very safe product made from the seeds of a tropical tree is called Neem. It is commercially available under the name Margosan-O. Other chemical controls include malathion, bifenthrin, cyfluthrin, and kelthane. Be sure to follow all label directions when using pesticides. Many pesticides are very harmful to bees and fish when used improperly. Organic Strategies Strategy 1 is a strictly organic approach. For organic approaches to Strategies 2 and 4, consult the Organic Materials Review Institute (OMRI™) for appropriate insecticidal soap, Neem and miticide products.
symptoms: Spider mites are among the most ubiquitous of pests, attacking a wide variety of field, garden, greenhouse, nursery, and ornamental plants, as well as several weed species. Infestations of two-spotted spider mites result in the bleaching and stippling of leaves. Severe infestations may cause entire leaves to become bronzed, curled, and completely enveloped in sheets of webbing. Damage to the foliage may result in leaf drop and reduction in the overall vitality of the plant. When a leaf or branch is tapped over a white sheet of paper, the mites appear as small specks that resemble dust or pepper and may be seen to move.
comment: Spider mites are very common pests of outdoor plants. The mites suck juices from the plants, causing the plants to look dull and unhealthy. Mites also cause plants to lose vigor so that they may be unable to overcome a severe infestation, resulting in the plant's death. In Missouri, the most common outdoor spider mite is the two-spotted spider mite. The spruce spider mite infects conifers such as spruce, juniper, and arborvitae. They are less than 1/60th inch in size and difficult to see without a magnifying lens. They are not insects but rather a member of the spider family in which all members have eight legs.

Spilosoma Obliqua
type: NamedIndividual
type: Crop Pest

Spittlebugs
type: NamedIndividual
type: Crop Disease
type: Crop Pest
comment: Adult spittlebugs resemble robust leafhoppers (see Hoppers and Planthoppers) with many varied color patterns. They deposit eggs in rows between sheaths and stems of plants near the soil surface. Yellow nymphs become green as they reach full development. The nymphs form the namesake spittle by mixing air with the excretion of their alimentary canal. While perhaps unsightly, this most obvious symptom of the spittlebug's presence is not harmful to the host plant. Injury is caused rather by both nymphs and adults, who remove plant juices with their piercing-sucking mouthparts. Resulting damage includes stunted growth, shortened internodes, dwarfing, and a general loss of vitality. Strawberries, nursery stock, and legume forage crops are the spittlebug's favored hosts. Control infestations with insecticidal soap, preferably before adults begin laying eggs. Alternately, nymphs can be easily handpicked from small strawberry patches or dislodged by a jet of water.

Spodoptera Exigua
type: NamedIndividual
type: Crop Pest

Spodoptera Litura
type: NamedIndividual
type: Crop Pest
comment: Spodoptera Litura (Fabricius)

Spring
type: NamedIndividual
type: Season

Sri Lanka
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:LK
agricultural land: 26,200 sq. km
area: 65,610 km²
climate: tropical monsoon; northeast monsoon (December to March); southwest monsoon (June to October)
density: 319/km²
latitude: 80.7671
longitude: 7.874093
population: 21,128,773 (2008)
comment: Sri Lanka, officially the Democratic Socialist Republic of Sri Lanka, is an island country in South Asia, located in the Indian Ocean southwest of the Bay of Bengal and southeast of the Arabian Sea. Sri Lanka has been called “the pearl of the Indian ocean” because of its shape and location. It is geographically separated from the Indian subcontinent by the Gulf of Mannar and the Palk Strait. Sri Jayawardenepura Kotte is its legislative capital, and Colombo is its largest city and centre of commerce.

Stable Growth
type: NamedIndividual
type: Growth Stage
alternative label: Stable growth stage (SGS)

Stem-End Injury
type: NamedIndividual
type: Crop Disease
type: Crop Injure

Stem Elongation stage
type: NamedIndividual
type: Growth Stage
related to: Wheat
comment: Stage 3: Stem elongation or rosette growth, shoot development
comment: Stage 3: Stem elongation or rosette growth, shoot development 30 Beginning of stem elongation: pseudostem and tillers erect, first internode begins to elongate, top of inflorescence at least 1 cm above tillering node 31 First node at least 1 cm above tillering node 32 Node 2 at least 2 cm above node 1 33 Node 3 at least 2 cm above node 2 3 . Stages continuous till . . . 37 Flag leaf just visible, still rolled 39 Flag leaf stage: flag leaf fully unrolled, ligule just visible

Black Rust
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Barley
affects: Wheat
caused by: Puccinia Graminis Tritici
development: Primary infections are usually light and develop from wind-borne urediospores that may have travelled long distances. The disease can develop rapidly when free moisture (rain or dew) and moderate temperatures prevail. If temperatures average about 20C or more, the first generation of urediospores will be produced in 10-15 days. As plants mature, masses of black teliospores may be produced.
distribution: Stem rust can affect wheat, barley, triticale, and many other related grasses; it is found wherever temperate cereals are grown. The alternate hosts are Berberis and Mahonia spp.
important: If infection occurs during the early crop stages, the effects can be severe: reductions in tillering and losses in grain weight and quality. Under favorable conditions, complete crop loss can occur.
symptoms: Pustules (containing masses of urediospores) are dark reddish brown, and may occur on both sides of the leaves, on the stems, and on the spikes. With light infections the pustules are usually separate and scattered, but with heavy infections they may coalesce. Prior to pustule formation, "flecks" may appear. Before the spore masses break through the epidermis, the infection sites feel rough to the touch; as the spore masses break through, the surface tissues take on a ragged and torn appearance.
comment: Stem rust, also known as cereal rust, black rust, red rust or red dust,[3] is caused by the fungus Puccinia graminis, which causes significant disease in cereal crops. Crop species that are affected by the disease include bread wheat, durum wheat, barley and triticale.
label: Stem Rust

Stewart's Wilt
type: NamedIndividual
type: Bacterial Disease
type: Crop Disease
affects: Corn
management: Stewart's Wilt is a quarantined pathogen requiring phytosanitary certification in many countries. Because of this it is a serious issue in seed production fields. Genetic Resistance Resistant hybrids are commercially available and are the best management option. Chemical/Biological Control Seed applied in furrow, and foliar insecticides are effective in reducing Stewart's Wilt. However, cost can preclude chemical management in many situations.
pathogen: Pantoea stewartii Mergaert et. al. (Erwinia stewartii). Bacterial structures: non-motile, gram negative, rod shaped bacterium. This bacterium occurs throughout the corn belt from the mid-Atlantic states to Nebraska. Until recently, this pathogen was rare in Nebraska; in 1999 it was reported in at least 22 counties across Nebraska. The bacterium overwinters within and is vectored by the corn flea beetle (Pulicari chaetocnema).
symptoms: Hybrids and inbreds vary in susceptibility to Stewart's Wilt. There are two phases of the disease each linked to the insect vector. Symptoms of the first phase are observed soon after emergence of the corn plant. External symptoms include bleached leaf streaks that may run the length of the leaf. Internal symptoms include discoloration of vascular tissues, oozing of bacteria from the cut surface of a stem cross section, and discoloration and decay of the crown. When severe, seedlings may wilt and die. The second phase of Stewart's Wilt is a leaf blight that occurs after tasseling. Plants are usually not killed by this phase but significant leaf area can be killed predisposing the plants to stalk rot.
comment: Stewart's wilt is a serious bacterial disease of corn caused by the bacterium Pantoea stewartii. This bacterium affects plants, particularly types of maize such as sweet, flint, dent, flower, and popcorn.[1] The disease is also known as bacterial wilt or bacterial leaf blight and has shown to be quite problematic in sweet corn.[2][3] The disease is endemic in the mid-Atlantic and Ohio River Valley regions and in the southern portion of the Corn Belt.

Stink Bugs
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Barley
affects: Wheat
distribution: Stink bugs will feed on most cereals and grasses, as well as a large range of weeds (depending on the species). Stink bugs are of major economic importance in Asia Minor.
important: Losses due to stink bugs are highly variable and depend on the density of the insects, weather conditions, and duration of the crop growing period. Losses are due primarily to reduced baking quality.
life cycle: Stink bugs over-winter as adults and may diapause. They tend to hibernate under dead leaves and grass. In the spring they migrate to cereal hosts, mate, and lay eggs at various places on the plant. These hatch into nymphs that feed on the plant. Mild winters and low rainfall seem to favor outbreaks of the insects.
symptoms: Adult stink bugs feed on stem tissue or developing kernels. Saliva from this insect is toxic to the plant, and a single feeding puncture can kill a stem. Feeding on kernels during the milk dough stage will destroy the kernel, while feeding during later development stages will badly shrivel the grain. Feeding on the developing head may cause partial or total sterility. Adult stink bugs have a shield-shaped body and emit a disagreeable odor when crushed.
comment: The brown marmorated stink bug (BMSB) is considered an invasive species, or a pest of foreign origin, as it was introduced to the United States from Eastern Asia in the mid-1990s. It is also referred to as the yellow-brown or East Asian stink bug. The bug was first collected in the United States in Allentown, PA in the fall of 1996, but apparently not recognized or identified until September 2001. It quickly spread east to New Jersey, then Virginia by 2004, and now southward to the North Carolina border. Today, brown marmorated stink bugs are most prevalent in the mid-Atlanta region, but they have been identified in 44 states and the District of Columbia. The bug’s native range includes China, Japan, Korea and Taiwan. The stink bug earned its name from its tendency to release an odor when disturbed or when crushed. Many other insects have these same characteristics, including some species of ants, beetles and other bugs. Read on to learn about how to get rid of stink bugs and stink bug

Stollia Ventralis
type: NamedIndividual
type: Crop Pest
comment: Stollia Ventralis (Westwood)

Strawberry
type: NamedIndividual
type: Crop
type: Fruit Tree
harvesting: Fruit should be harvested regularly (every other day) when it is being produced by picking the berry along with the cap and 0.5 in of stem. remove the berries by pinching the stem. Harvest only fully ripe strawberries as they will not ripen further after harvest.
maintenance: Begin preparing the strawberry bed as early as possible in the Spring when the soil becomes workable. Prepare the soil for planting by adding organic matter such as compost or well-rotted manure. Plant the strawberry transplants 38-60 cm (15-24 in) apart depending on variety and allow 90-120 cm (36-48 in) between rows. Be careful to plant so that the crown of the plant (the point from which the leaves emerge) is at the soil surface. If planted too deep, the plants may rot and if planted too shallow the plants may not take root. After planting, water the plants thoroughly. In the first growing season after planting, the plants should not be allowed to produce fruit in order for the plants to concentrate their energy of vegetative growth. Pinch off any flowers as they are produced. Fruit can be harvested in the year after planting. When the desired density of plants is reached cut off any runners or cultivate with a hoe. Weeds should also be removed from the strawberry bed regularly. Strawberry plants also require adequate moisture and should receive 2.5-3.8 cm (1-1.5 in) of water per week either from rainfall or irrigation if needed. Strawberries will benefit from the addition of mulch which helps to conserve soil moisture and protects the plants from late frosts in Spring. If frost is forecast after planting the plants should be protected with row covers or other suitable material. Plants can also be protected with sprinkler irrigation. Row covers can also be used to protect the fruit from birds when fruiting.
propagation: It is generally considered to be more difficult to start strawberry plants from seed than it is to work with young plants. Depending on the variety of strawberry you have chosen, it may be necessary to cold treat your seeds before germination. This can be achieved by simply placing them in the freezer for 2-4 weeks before sowing. Strawberry seeds should be sown in trays containing a good quality, sterile seed starting mix. Sow the seeds to a depth of 6 mm (0.25 in) and keep the soil moist (but not wet) while the seeds germinate. Seeds should germinate in 2-3 weeks. When the seedlings reach 2.5-5.0 cm (1-2 in) in height, thin the seedlings if they are too close together and repot or transplant to the garden when they have 3 leaves. If planting outside be sure to harden the seedlings off before you put them in the ground. Runners Strawberries produce offspring on a sideshoot known as a “runnerâ€. Runners can be removed from the mother plant and relocated. Simply plant the runners to the desired final spacing in a bed prepared similarly to planting seeds.
requirements: Strawberries grow very well in cool temperate climates, at temperatures below 30oC (86oF)and require at least 8 hours of sunlight per day. They can be grown successfully in a wide variety of soils from sandy soils to silty clay but will grow best in a deep, well draining loam rich in organic matter. The optimum pH for their growth is between 5.5 and 6.5. If drainage is poor then it is recommended to plant strawberry on beds raised by a minimum of 15-20 cm (6-8 in).
uses: Strawberries are consumed as a fresh fruit or can be used to produce jams, jellies or preserves.
References: https://plantvillage.psu.edu/topics/strawberry/infos
comment: Strawberry is the name given to several plant species in the genus Fragaria, including Fragaria vesca (wild strawberry), Fragaria grandiflora, Fragaria magna, Fragaria chiloensis and Fragaria ananassa (or Fragaria x ananassa) which are grown for their edible fruit. Strawberry is an herbaceous perennial in the family Rosaceae. The plant has a short stem and trifoliate leaves which form a crown. close to the ground. The leaves display a variety of characteristic depending on the species, some are evergreen whereas some have leaves that will die and fall off in winter. The plant produces flower stalks from the crown and the the flowers are white in color. The fruit of the plant is red and fleshy with small seeds on the outside. Strawberry can grow 20-25 cm (8-10 in) in height and has an economic life of 2-4 years before the plants are replaced. Strawberry may also be referred to as garden strawberry and the plant is grown in most northern temperate regions of the world. The first domestic hybrids were created in Europe.

Stripe Rust
type: NamedIndividual
type: Crop Disease
affects: Wheat
symptoms: The first sign of stripe rust is the appearance of yellow streaks (pre-pustules), followed by small, bright yellow, elongated uredial pustules arranged in conspicuous rows on the leaves, leaf sheaths, glumes and awns (Fig. 3a-d). Mature pustules will break open and release yellow-orange masses of urediniospores. In some varieties, long, narrow yellow stripes will develop on leaves. The infected tissues may become brown and dry as the plant matures or becomes stressed (Fig. 4a-b). Severe early infection can result in plant stunting.
comment: Stripe rust (Puccinia striiformis f. sp. tritici) is an important disease of wheat (Triticum aestivum L.), especially in cool climates. Stripe rust of wheat is caused by the basidiomycete fungus Puccinia striiformis Westend f. sp. tritici Eriks & Henn (Syn. P. glumarum Erik. and Henn.) in the order Uredinales. It is considered to be an obligate parasite. The fungus produces bright yellow to orange urediniospores 20 to 30 um in diameter (Fig. 1a-b). These spores have thick and echinulated walls and are contained in sori or pustules on the plant (Fig. 2). Urenidiospore production usually is followed by teliospore production late in the growing season. No alternate host is known. The pathogen survives in wheat as dormant mycelium in cooler climates. Early season infections in the southeast are thought to be initiated by spores carried on wind currents from the south-central U.S. and Mexico. P. striiformis f. sp tritici primarily attacks wheat. A separate formae specialis (P. s. f. sp. hordei) attacks barley. Puccinia striiformis f. sp. tritici has numerous physiological races.

Strong Gale
type: NamedIndividual
type: Wind Type

Strong Wind
type: NamedIndividual
type: Wind Type

Strongyloides Variegatus
type: NamedIndividual
type: Crop Pest
comment: Strongyloides Variegatus (Fairmaire)

Boreal climate
type: NamedIndividual
type: Climate Type
comment: The subarctic climate (also called subpolar climate, or boreal climate) is a climate characterised by long, usually very cold winters, and short, cool to mild summers. It is found on large landmasses, away from the moderating effects of an ocean, generally at latitudes from 50o to 70oN poleward of the humid continental climates. Subarctic or boreal climates are the source regions for the cold air that affects temperate latitudes to the south in winter. These climates represent Köppen climate classification Dfc, Dwc, Dsc, Dfd, Dwd and Dsd.
label: Subarctic Climate
label: Subpolar climate

Subtropical Climate
type: NamedIndividual
type: Climate Type

Sudan
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:SD
agricultural land: 1.09 million sq. km
area: 2,505,813 km²
climate: hot and dry; arid desert; rainy season varies by region (April to November)
density: 14/km²
latitude: 30.209492
longitude: 12.855736
population: 39,379,358 (2007)
comment: Sudan, officially the Republic of the Sudan, is a country in Northeast Africa. It is bordered by Egypt to the north, Libya to the northwest, Chad to the west, the Central African Republic to the southwest, South Sudan to the south, Ethiopia to the southeast, Eritrea to the east, and the Red Sea to the northeast. Sudan has a population of 43 million and occupies 1,886,068 square kilometres, making it Africa's third-largest country and also the third-largest in the Arab league. It was also the largest country in Africa and the Arab league by area before the secession of South Sudan in 2011.

Summer
type: NamedIndividual
type: Season

Sunburn Disease
type: NamedIndividual
type: Crop Disease
comment: Abiotic sunburn refers to the damage caused to plants, bushes or trees by a combination of direct sunlight and excessive temperatures.

Superphosphate
type: NamedIndividual
type: Chemical Fertilizer
comment: Normal superphosphate has a relatively low concentration of phosphorous, however it is used in mixtures because of its low cost.

Suriname
type: NamedIndividual
type: Country
belongs to: South America
ISO code: ISO 3166-1:SR
agricultural land: 820 sq. km
area: 163,821 km²
climate: tropical; moderated by trade winds
density: 2.7/km²
latitude: -56.03203
longitude: 3.917845
population: 470,784 (2005)
comment: Suriname, officially known as the Republic of Suriname, is a country on the northeastern Atlantic coast of South America. It is bordered by the Atlantic Ocean to the north, French Guiana to the east, Guyana to the west and Brazil to the south. At just under 165,000 square kilometers, it is the smallest sovereign state in South America. Suriname has a population of approximately 575,990, most of whom live on the country's north coast, in and around the capital and largest city, Paramaribo.

Svalbard and Jan Mayen
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:SJ
area: 61,022 km²
climate: arctic, tempered by warm North Atlantic Current; cool summers, cold winters; North Atlantic Current flows along west and north coasts of Spitsbergen, keeping water open and navigable most of the year
population: 2,572
comment: Svalbard and Jan Mayen is a statistical designation defined by ISO 3166-1 for a collective grouping of two remote jurisdictions of Norway: Svalbard and Jan Mayen. While the two are combined for the purposes of the International Organization for Standardization category, they are not administratively related. This has further resulted in the country code top-level domain .sj being issued for Svalbard and Jan Mayen, and ISO 3166-2:SJ. The United Nations Statistics Division also uses this code, but has named it Svalbard and Jan Mayen Islands.

Eswatini
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:SZ
agricultural land: 12,220 sq. km
area: 17,364 km²
climate: varies from tropical to near temperate
density: 59/km²
latitude: 31.465683
longitude: -26.518373
population: 1,141,0001 (2007)
comment: Eswatini, officially the Kingdom of Eswatini, sometimes written in English as eSwatini, and formerly and still commonly known in English as Swaziland, is a landlocked country in Southern Africa. It is bordered by Mozambique to its northeast and South Africa to its north, west, and south. At no more than 200 kilometres north to south and 130 kilometres east to west, Eswatini is one of the smallest countries in Africa; despite this, its climate and topography are diverse, ranging from a cool and mountainous highveld to a hot and dry lowveld.
label: Kingdom of Eswatini
label: Swaziland

Sweden
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Temperate Climate
ISO code: ISO 3166-1:SE
agricultural land: 30,660 sq. km
area: 449,964 km²
climate: temperate in south with cold, cloudy winters and cool, partly cloudy summers; subarctic in north
density: 20/km²
latitude: 17.384787
longitude: 62.098297
population: 9,234,2094 (2008)
comment: Sweden, officially the Kingdom of Sweden, is a Nordic country in Northern Europe. It borders Norway to the west and north, Finland to the east, and is connected to Denmark in the southwest by a bridge-tunnel across the Öresund Strait. At 450,295 square kilometres, Sweden is the largest country in Northern Europe, the third-largest country in the European Union, and the fifth largest country in Europe. The capital city is Stockholm. Sweden has a total population of 10.4 million; and a low population density of 25 inhabitants per square kilometre . 87% of Swedes live in urban areas, which cover 1.5% of the entire land area. The highest concentration is in the central and southern half of the country.

Sweetpotato
type: NamedIndividual
type: Crop
type: Vegetable
comment: The sweet potato or sweetpotato (Ipomoea batatas) is a dicotyledonous plant that belongs to the bindweed or morning glory family, Convolvulaceae. Its large, starchy, sweet-tasting, tuberous roots are a root vegetable. The young shoots and leaves are sometimes eaten as greens. The sweet potato is distantly related to the common potato (Solanum tuberosum), both being in the order Solanales. The sweet potato, especially the orange variety, is often called a "yam" in parts of North America, but it is entirely unrelated to true yams.

Switzerland
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Dry summer climate
has climate: Temperate Climate
ISO code: ISO 3166-1:CH
agricultural land: 15,229 sq. km
area: 41,284 km²
climate: temperate, but varies with altitude; cold, cloudy, rainy/snowy winters; cool to warm, cloudy, humid summers with occasional showers
density: 181.4/km²
latitude: 8.224137
longitude: 46.813087
population: 7,667,700 (2009)
comment: Switzerland, officially the Swiss Confederation, is a country situated at the confluence of Western, Central, and Southern Europe. It is a federal republic composed of 26 cantons, with federal authorities based in Bern. Switzerland is a landlocked country bordered by Italy to the south, France to the west, Germany to the north, and Austria and Liechtenstein to the east. It is geographically divided among the Swiss Plateau, the Alps, and the Jura, spanning a total area of 41,285 km2, and land area of 39,997 km2 . While the Alps occupy the greater part of the territory, the Swiss population of approximately 8.5 million is concentrated mostly on the plateau, where the largest cities and economic centres are located, among them Zürich, Geneva and Basel. These cities are home to several offices of international organisations such as the headquarters of FIFA, the UN's second-largest Office, and the main building of the Bank for International Settlements. The main international airports of Switzerland are also located in these cities.

Sympiezomias Velatus
type: NamedIndividual
type: Crop Pest

Syria
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:SY
agricultural land: 138,640 sq. km
area: 185,180 km²
climate: mostly desert; hot, dry, sunny summers (June to August) and mild, rainy winters (December to February) along coast; cold weather with snow or sleet periodically in Damascus
density: 103/km²
latitude: 38.996815
longitude: 34.802074
population: 19,405,000 (2007)
comment: Syria, officially the Syrian Arab Republic, is a country in Western Asia, bordering Lebanon to the southwest, the Mediterranean Sea to the west, Turkey to the north, Iraq to the east, Jordan to the south, and Israel to the southwest. A country of fertile plains, high mountains, and deserts, Syria is home to diverse ethnic and religious groups, including the majority Syrian Arabs, Kurds, Turkmens, Assyrians, Armenians, Circassians, Mandeans, and Greeks. Religious groups include Sunnis, Christians, Alawites, Druze, Isma'ilis, Mandeans, Shiites, Salafis, and Yazidis. Arabs are the largest ethnic group, and Sunnis are the largest religious group.

Adaptive Neuro-Fuzzy Inference System
type: NamedIndividual
type: Computer System
Acronym: ANFIS
comment: Adaptive Neuro-Fuzzy Inference System (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system.

ArcGIS System
type: NamedIndividual
type: Computer System
Acronym: ArcGIS
comment: ArcGIS is a geographic information system (GIS) for working with maps and geographic information maintained by the Environmental Systems Research Institute (Esri). It is used for creating and using maps, compiling geographic data, analyzing mapped information, sharing and discovering geographic information, using maps and geographic information in a range of applications, and managing geographic information in a database.

Co-active Neuro-fuzzy Inference System
type: NamedIndividual
type: Computer System
Acronym: CANFIS
comment: Co-Active Neuro-Fuzzy Inference System is a method which combines fuzzy model and neural network in a manner to produce accurate results.

Computer Software System
type: NamedIndividual
type: Computer System
Acronym: CSS
comment: Computer Software System (CSS)

Computer Vision System
type: NamedIndividual
type: Computer System
Acronym: CVS
comment: Computer Vision System (CVS) is a computer system that focuses on processing, analyzing, and making sense of visual data (images or videos) in the same way that humans do.

Digital Electromechanical System
type: NamedIndividual
type: Computer System
Acronym: DES
comment: Digital electromechanical system (DES) is a complete system which measures soil bulk density.

Decision Support System
type: NamedIndividual
type: Computer System
Acronym: DSS
comment: A decision support system (DSS) is a computerized program used to support determinations, judgments, and courses of action in an organization or a business. A DSS sifts through and analyzes massive amounts of data, compiling comprehensive information that can be used to solve problems and in decision-making.
comment: Decision Support System (DSS)

Electro-mechanic Drive System
type: NamedIndividual
type: Computer System
Acronym: EMDS
comment: An electric motor drives via differential gear arrangement the track-driving wheels of the tracks of a full-track vehicle.

Expert System
type: NamedIndividual
type: Computer System
Acronym: ES
comment: An Expert System (ES) is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code.

Fuzzy Inference System
type: NamedIndividual
type: Computer System
Acronym: FIS
comment: A Fuzzy Inference System (FIS) is a system that uses fuzzy set theory to map inputs (features in the case of fuzzy classification) to outputs (classes in the case of fuzzy classification).

Farm Management Information System
type: NamedIndividual
type: Computer System
Acronym: FMIS
comment: A Farm Management Information System (FMIS) is a management information system designed to assist agricultural farmers to perform various tasks ranging from operational planning, implementation and documentation for assessment of performed field work.

Knowledge Based Systems
type: NamedIndividual
type: Computer System
Acronym: KBS
comment: Knowledge-based system (KBS) is a computer program that reasons and uses a knowledge base to solve complex problems. The term is broad and refers to many different kinds of systems. The one common theme that unites all knowledge based systems is an attempt to represent knowledge explicitly and a reasoning system that allows it to derive new knowledge. Thus, a knowledge-based system has two distinguishing features: a knowledge base and an inference engine.

Machine Vision System
type: NamedIndividual
type: Computer System
Acronym: MVS
comment: A Machine Vision System (MVS) is a type of technology that enables a computing device to inspect, evaluate and identify still or moving images. It is a field in computer vision and is quite similar to surveillance cameras, but provides automatic image capturing, evaluation and processing capabilities.

Spatial Decision Support System
type: NamedIndividual
type: Computer System
Acronym: FDSS
comment: A Spatial Decision Support System (SDSS) is an interactive, computer-based system designed to assist in decision making while solving a semi-structured spatial problem. It is designed to assist the spatial planner with guidance in making land use decisions.

WOrld FOod STudies (WOFOST)
type: NamedIndividual
type: Computer System
Acronym: WOFOST
comment: WOFOST (WOrld FOod STudies) is a simulation model for the quantitative analysis of the growth and production of annual field crops. With WOFOST, you can calculate attainable crop production, biomass, water use, etc. for a location given knowledge about soil, crop, weather and crop management (e.g. sowing date). WOFOST has been used by many researchers over the world and has been applied for many crops over a large range of climatic and management conditions (see documentation). WOFOST is one of the key components of the European MARS crop yield forecasting system. In the Global Yield Gap Atlas (GYGA) WOFOST is used to estimate the untapped crop production potential on existing farmland based on current climate and available soil and water resources. WOFOST originated in the framework of interdisciplinary studies on world food security and on the potential world food production by the Center for World Food Studies (CWFS) in cooperation with the Wageningen University & Research, Department of Theoretical Production Ecology (WAU-TPE) and the DLO-Center for Agrobiological Research and Soil Fertility (AB-DLO), Wageningen, the Netherlands. After cessation of CWFS in 1988, the DLO Winand Staring Centre (SC-DLO) has continued development of the model in co-operation with AB-DLO and WAU-TPE. Currently, the WOFOST model is maintained and further developed by Wageningen Environmental Research in co-operation with the Plant Production Systems Group of Wageningen University & Research and the Food Security unit of the Joint Research Centre in Italy.

Taiwan
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:TW
area: 36,190 km²
climate: tropical; marine; rainy season during southwest monsoon (June to August); cloudiness is persistent and extensive all year
density: 636.57/km²
latitude: 120.96052
longitude: 23.69781
population: 23,037,031 (2008)
comment: Taiwan, officially the Republic of China, is a country in East Asia. Neighbouring countries include the People's Republic of China to the northwest, Japan to the northeast, and the Philippines to the south. The main island of Taiwan has an area of 35,808 square kilometres, with mountain ranges dominating the eastern two-thirds and plains in the western third, where its highly urbanised population is concentrated. The capital is Taipei, which, along with New Taipei and Keelung, forms the largest metropolitan area of Taiwan. Other major cities include Kaohsiung, Taichung, Tainan and Taoyuan. With 23.57 million inhabitants, Taiwan is among the most densely populated countries.

Tajikistan
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:TJ
agricultural land: 48,550 sq. km
area: 143,100 km²
climate: midlatitude continental, hot summers, mild winters; semiarid to polar in Pamir Mountains
density: 45/km²
latitude: 71.26218
longitude: 38.85819
population: 7,215,7001 (1/2008)
comment: Tajikistan, officially the Republic of Tajikistan, is a landlocked country in Central Asia with an area of 143,100 km2 and an estimated population of 9,537,645 people. It is bordered by Afghanistan to the south, Uzbekistan to the west, Kyrgyzstan to the north and China to the east. The traditional homelands of the Tajik people include present-day Tajikistan as well as parts of Afghanistan and Uzbekistan.

Tan Spot
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Wheat
management: Cultural Practices A unique three-year conservation tillage rotation system called ecofarming (ecofallow) can be used to reduce the threat of tan spot. Ecofarming is a crop rotation system of controlling weeds and conserving soil moisture with minimum disturbance of crop residue. In this system, corn or sorghum is seeded directly into winter wheat stubble in a winter wheat-grain sorghum or corn-fallow rotation. In the ecofarming system a crop is planted directly into the residue of a different crop, rather than into the residue of the same crop. This crop rotation-fallow system effectively breaks disease cycles which involve host specific pathogens such the tan spot fungus that survive in crop residue. Chemical / Biological Control Foliar fungicides can be applied to control tan spot if the disease becomes serious during the crucial post-boot stage of wheat development.Crop scouting should begin early in the growing season and should continue on a regular basis until disease is detected or a decision to spray is made based on the observed disease level.
symptoms: Pseudothecia Tan spot symptoms usually appear in early April. On leaves, the disease first appears as small, tan to brown lens or diamond-shaped spots. Characteristic symptoms are elliptical to elongate spots that are tan colored, have a dark brown spot near the center and are surrounded by a yellow border. As the lesions increase in size they tend to coalesce, producing larger, irregular areas of dead tissue. When leaves are young and actively growing, the spots typically remain small. Where spots are abundant, leaves may yellow giving the field an overall yellow cast. The lower, more mature leaves are infected first and the pathogen spreads to the upper leaves as the disease progresses. As plants mature the fungus invades the straw where it produces pseudothecia in the fall. By mid-August, pseudothecia are visible on the stubble that remains after harvest and are diagnostic of tan spot. They feel like coarse sandpaper to the touch.
comment: Tan spot is caused by Pyrenophora tritici-repentis. The fungus can survive and reproduce on wheat straw. Early in the growing season, spores called ascospores are released from pseudothecia (tiny black, raised fruiting structures formed on wheat straw) and spread by wind or blowing rain. They are the primary source of infection of the lower, more mature leaves during tillering and early jointing. After the initial infection, the fungus spreads within a field or to neighboring fields by a different type of spore called a conidium that is produced in the tan spot lesions themselves.

Tanzania
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:TZ
agricultural land: 373,000 sq. km
area: 945,203 km²
climate: varies from tropical along coast to temperate in highlands
density: 41/km²
latitude: 34.885197
longitude: -6.368216
population: 40,000,000 (11/2006)
comment: Tanzania, officially the United Republic of Tanzania, is a country in East Africa within the African Great Lakes region. It borders Uganda to the north; Kenya to the northeast; Comoro Islands and the Indian Ocean to the east; Mozambique and Malawi to the south; Zambia to the southwest; and Rwanda, Burundi, and the Democratic Republic of the Congo to the west. Mount Kilimanjaro, Africa's highest mountain, is in northeastern Tanzania.

Oculimacula yallundae
type: NamedIndividual
type: Fungus
label: Pseudocercosporella herpotrichoides
label: Tapesia yallundae

Taproot
type: NamedIndividual
type: Root
comment: A taproot is a large, central, and dominant root from which other roots sprout laterally. Typically a taproot is somewhat straight and very thick, is tapering in shape, and grows directly downward. In some plants, such as the carrot, the taproot is a storage organ so well developed that it has been cultivated as a vegetable. The taproot system contrasts with the adventitious or fibrous root system of plants with many branched roots, but many plants that grow a taproot during germination go on to develop branching root structures, although some that rely on the main root for storage may retain the dominant taproot for centuries, for example Welwitschia.

Target Leaf Spot
type: NamedIndividual
type: Crop Disease
affects: Cotton
affects: Cucumber
affects: Tomato
caused by: Corynespora cassiicola
controls: Use resistant varieties when available. Implement a preventative fungicide spray program. In protected culture, good sanitation practices in and around structures will help avoid future infestations.
development: The fungus can exist on infected plant residue for at least two years, or on weed hosts. It is dispersed by air currents. Warm temperatures [25–35ºC (77–95ºF)] and long days are best for disease development, although infection takes place under humid and moderately cool temperatures [21–26ºC (70–80ºF)]. Also, fluctuating daily temperatures appear to favor disease development.
symptoms: The disease can be found on all cucurbits, although it is most common on cucumber. The first symptoms appear on older leaves as angular, yellow spots. In the open field, these spots enlarge and become circular with light brown centers and dark brown borders. Later these large spots become gray and drop out, giving a shot-hole or shredded appearance to the leaf. Under greenhouse conditions spots have light centers with rings of olive green tissue and yellow borders. Eventually, defoliation can occur. Spots on stems and petioles are more elongated, which helps to distinguish this disease from other diseases, which include anthracnose, downy mildew or angular leaf spot. Early infection at the blossom end results in darkened, shriveled fruit. Root and flower infection can also occur.
comment: Target leaf spot, is a severe leaf spot most common on cucumbers. Small yellow spots on leaf lamina develop into necrotic areas that eventually fall out leaving the leaf with a "shot-hole" appearance. Severe cases result in defoliation. Infected fruit are darkened and shriveled.

Target spot
type: NamedIndividual
type: Crop Disease
affects: Soybean

Tea anthracnose
type: NamedIndividual
type: Crop Disease
affects: Tea
comment: Anthracnose is a serious disease in global tea production, but there is no literature on the susceptibility of planting materials to this disease.

Tea brown blight
type: NamedIndividual
type: Crop Disease
affects: Tea

Temperate Climate
type: NamedIndividual
type: Climate Type
comment: Temperate climates of the Earth are characterized by relatively moderate mean annual temperatures, with average monthly temperatures above 10°C in their warmest months and above −3°C in their colder months.

TensorFlow
type: NamedIndividual
type: Toolkit
has algorithm: Artificial Neural Network (ANN)
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Deep Convolutional Neural Network (DCNN)
has algorithm: Recurrent neural network (RNN)
comment: TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.
comment: TensorFlow works well on images as well as sequence-based data. If you are a beginner in deep learning, or don’t have a solid understanding of mathematical concepts like linear algebra and calculus, then the steep learning curve of TensorFlow might be daunting. I totally understand that this aspect can be complex for folks who are just starting out. My suggestion would be to keep practicing, keep exploring the community, and keep reading articles to get the hang of TensorFlow. Once you have a good understanding of the framework, implementing deep learning models will be very easy for you.

Terrace
type: NamedIndividual
type: Topography

Tettigella Viridis
type: NamedIndividual
type: Crop Pest

TextBlob
type: NamedIndividual
type: Toolkit
inherits: Natural Language Toolkit (NLTK)
Acronym: TextBlob
comment: TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. TextBlob was designed based on NLTK. For the probationer, it is the best option to understand the complexities of NLP and designing prototypes for their projects. The tool enables sentiment analysis, tokenization, translation, phrase extraction, part-of-speech tagging, lemmatization, classification, spelling correction, etc.

Thailand
type: NamedIndividual
type: Country
belongs to: Asia
has climate: Tropical Savanna Climate
ISO code: ISO 3166-1:TH
agricultural land: 210,600 sq. km
area: 513,115 km²
climate: tropical; rainy, warm, cloudy southwest monsoon (mid-May to September); dry, cool northeast monsoon (November to mid-March); southern isthmus always hot and humid
density: 122/km²
latitude: 101.48974
longitude: 13.03886
population: 63,038,247 (12/2007)
comment: Thailand, officially the Kingdom of Thailand and formerly known as Siam, is a country in Southeast Asia. Located at the centre of the Indochinese Peninsula, it is composed of 76 provinces spanning 513,120 square kilometres, with a population of over 66 million people; Thailand is the world's 50th-largest country by land area and the 22nd-most-populous. The capital and largest city is Bangkok, a special administrative area. Thailand is bordered to the north by Myanmar and Laos, to the east by Laos and Cambodia, to the south by the Gulf of Thailand and Malaysia, and to the west by the Andaman Sea and the southern extremity of Myanmar. Its maritime boundaries include Vietnam in the Gulf of Thailand to the southeast, and Indonesia and India on the Andaman Sea to the southwest. Nominally, Thailand is a constitutional monarchy and parliamentary democracy; however, in recent history, its government has experienced multiple coups and periods of military dictatorships.

Thanatephorus Cucumeris
type: NamedIndividual
type: Crop Disease
comment: Thanatephorus cucumeris was long known as Rhizoctonia solani, the vegetative stage. It was long believed to be a sterile fungus, but recently it was discovered that the fungus produces basidiospores. These spores don’t play a role in the epidemics or dispersal of the fungus, only the mycelium is important in this context. Thanatephorus cucumeris is a soil-inhabiting fungus, that is very persistent in the soil. It overwinters as mycelium or sclerotia in the soil and on crop residues. It is also seed-borne. Thanatephorus cucumeris causes diseases such as phytophthora root rot and black leg.

Thiram
type: NamedIndividual
type: Pesticides
comment: Thiram is the simplest thiuram disulfide and the oxidized dimer of dimethyldithiocarbamate. It is used as a fungicide, ectoparasiticide to prevent fungal diseases in seed and crops and similarly as an animal repellent to protect fruit trees and ornamentals from damage by rabbits, rodents and deer. It is effective against Stem gall of coriander, damping off, smut of millet, neck rot of onion, etc. Thiram has been used in the treatment of human scabies, as a sun screen and as a bactericide applied directly to the skin or incorporated into soap.

Thistle
type: NamedIndividual
type: Crop Disease
type: Weeds
comment: Thistle is the common name of a group of flowering plants characterised by leaves with sharp prickles on the margins, mostly in the family Asteraceae. Prickles can also occur all over the plant – on the stem and on the flat parts of the leaves. These prickles are an adaptation that protects the plant from being eaten by herbivores. Typically, an involucre with a clasping shape similar to a cup or urn subtends each of a thistle's flowerheads.

Thee-Leaf Stage
type: NamedIndividual
type: Growth Stage
comment: The collar of the third leaf is visible. Note that once a leaf’s collar forms, the leaf no longer expands. This stage occurs 10 to 15 days after emergence, depending on weather conditions, when the plant is typically 3 to 4 inches tall.

Three Leaves
type: NamedIndividual
type: Growth Stage

Thrips
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Lemon
affects: Wheat
distribution: Several thrips species live exclusively on cereals, and on forage or weed grasses.
important: Thrips rarely cause serious damage, and it is unusual to find infestations at such a level as to warrant control.
life cycle: Eggs are inserted into or attached to host tissue. The generation time is very short, and there may be 10 or more generations per year. Heavy rains will usually destroy a high proportion of they population.
management: Management Strategies 1. Knock off thrips with a forceful spray of cold water in small gardens, preferably early in the morning. Use a nozzle that produces a fine spray of water, and thoroughly wash off the undersides of affected leaves. The control is most effective when repeated at least 3 times, either daily or every other day. 2. Keep plants well irrigated and adequately misted since thrips prefer a dry environment. 3. Prune and destroy injured and infested plants. Do not compost them. 4. Predacious spiders, mites, and pirate bugs help control thrips. Conserving naturally occurring populations of beneficial insects by avoiding persistent pesticides is important to biologically controlling thrips. 5. Aluminum foil and other disorienting mulches have been used with some success to reduce thrips infestations. 6. Two applications of pyrethrum applied 3 to 4 days apart will treat serious infestations. 7. Insecticidal soaps are effective for temporary reduction of thrips populations. Spray every 3 days for 2 weeks. To control iris thrips, spray insecticidal soap at the base of each plant every 5 to 7 days until the infestation has been controlled. To prevent thrips problems, soak iris divisions and gladiolus corms for 30 minutes in a hot water bath (110–125 degrees F). Dig up gladiolus bulbs early in the fall and let them cure in open trays in a shady location with good air flow such as a garage for a few weeks before soaking them. Then allow the bulbs to dry thoroughly before storing. 8. Spray infested plants with a systemic insecticide such as imidacloprid (Merit) since thrips frequently burrow inside the plant tissue. 9. If necessary, use chemical insecticides such as acephate (Orthene), bifenthrin, imidacloprid, or spinosad. Spray 2 or more times at intervals of 7-10 days to control thrips as they hatch. Organic Strategies Strategies 1, 2, 3, 4, and 5 are strictly organic approaches. For an organic approach to Strategy 7, consult the Organic Materials Review Insti…
symptoms: Thrips are small (1 mm long), brown or black insects with a tapering, segmented abdomen. They have piercing and sucking mouthparts and usually have two pairs of narrow wings. They are usually found behind the sheath of the flag leaf, feeding on the stem (69). However, leaves, stems, and heads may be attacked. Adults and nymphs both can cause damage and, if present in large numbers, may cause the tissue on which they are feeding to take on a silver coloration.
comment: Thrips are minute, slender-bodied insects less than 1/16 inch to about 3/16 inch in length. With magnification, you can identify the adults that range in color from yellow to black and have four long, narrow, fringed wings. The young nymphs are smaller, wingless, and range in color from yellow to white. Many thrips are plant feeders attacking flowers, leaves, fruit, twigs, or buds.

Thrips Scarring
type: NamedIndividual
type: Crop Disease
type: Crop Injure
comment: Thrips scarring is sometimes called “alligator skin.” Scarred fruit can continue to grow in size, although they are often smaller than normal, while the flesh remains healthy and green. Scarring from limb rub also causes fruit scarring that can be confused with injury from avocado thrips.

Thundersnow
type: NamedIndividual
type: Planetary Phenomena
comment: Thundersnow, also known as a winter thunderstorm or a thundersnowstorm, is an unusual kind of thunderstorm with snow falling as the primary precipitation instead of rain. It typically falls in regions of strong upward motion within the cold sector of an extratropical cyclone. Thermodynamically, it is not different from any other type of thunderstorm, but the top of the cumulonimbus cloud is usually quite low. In addition to snow, graupel or hail may fall as well.

Thunderstorm
type: NamedIndividual
type: Planetary Phenomena
comment: Thunderstorm, also known as an electrical storm or a lightning storm, is a storm characterized by the presence of lightning and its acoustic effect on the Earth's atmosphere, known as thunder.[1] Relatively weak thunderstorms are sometimes called thundershowers.

Tillering stage
type: NamedIndividual
type: Growth Stage
related to: Wheat
comment: Stage 2: Formation of side shoots, tillering 20 No tillers 21 Beginning of tillering: first tiller detectable 22 2 tillers detectable 23 3 tillers detectable 24 Stages continuous till . . . 29 End of tillering. Maximum no. of tillers detectable
alternative label: Stage 2: side shoots, tillering

Tillers
type: NamedIndividual
type: Machine

Timor-Leste
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:TL
area: 14,874 km²
density: 64/km²
latitude: 125.71039
longitude: -8.815956
population: 1,115,000 (2007)
comment: East Timor or Timor-Leste, officially the Democratic Republic of Timor-Leste, is an island country in Southeast Asia and Oceania. It comprises the eastern half of the island of Timor, the nearby islands of Atauro and Jaco, and Oecusse, an exclave on the northwestern side of the island surrounded by Indonesian West Timor. Australia is the country's southern neighbour, separated by the Timor Sea. The country's size is about 15,007 km2 .

Tobacco Streak Virus
type: NamedIndividual
type: Crop Disease
type: Viral Disease
type: Virus
affects: Chickpea
affects: Cotton
affects: Cranberry
affects: Peanut
affects: Soybean
life cycle: TSV purified from infected plants consists of a ss-RNA genome encapsulated by spheroid particles made up of coat protein subunits. The coat protein subunits play a key role in the life cycle of the virus. Once the virus has penetrated into the host cells, it uncoats and releases its viral genomic RNA into the cytoplasm and expressed proteins replicate. After replication, the dsRNA genome is synthesized from genomic ssRNA. Subgenomic RNA4 is translated producing capsid proteins and the new virus particles are assembled. The coat protein of TSV plays an important role in its life cycle. It protects the viral genome and plays a role in cell to cell movement. Once the virus becomes systemic, it can be transmitted by vectors. TSV requires a living plant to survive for a period of time. The virus may be transmitted by thrips vector, mechanical damage, pollen or dodder.
management: Control for TSV is difficult, and there are no chemical controls available for the virus. The best management practices are to maintain good sanitation, purchase certified seed, control insects that serve as vectors, and provide barrier crops. Sanitation: Remove plant debris after harvest or any plant material that may serve as a source of inoculum. Due to its wide host range, TSV may also infect many weed species, so keeping fields free of weeds is a good control strategy. Chemical Control: Preventative control of disease dispersal may be possible if insecticide is sprayed for the trips vector. Barrier Crops: One common form of management for viruses is to plant a desirable host for thrips around the cropping system. The thrips will insert their stylet and "clean" off any virus on their stylet before moving to the target crop.
comment: Tobacco streak virus (TSV) is a plant pathogenic virus of the family Bromoviridae, in the genus Ilavirus. It has a wide host range, with at least 200 susceptible species. TSV is generally more problematic in the tropics or warmer climates. TSV does not generally lead to epidemics, with the exception of sunflowers in India and Australia, and peanuts in India.

Togo
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:TG
agricultural land: 37,200 sq. km
area: 56,785 km²
climate: tropical; hot, humid in south; semiarid in north
density: 108/km²
latitude: 0.829684
longitude: 8.621697
population: 6,300,000 (2006)
comment: Togo, officially the Togolese Republic, is a country in West Africa bordered by Ghana to the west, Benin to the east and Burkina Faso to the north. The country extends south to the Gulf of Guinea, where its capital Lomé is located. Togo covers 57,000 square kilometres, making it one of the smallest countries in Africa, with a population of approximately 8 million, as well as one of the narrowest countries in the world with a width of less than 115 km between Ghana and its slightly larger eastern neighbor, Benin.

Tokelau
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:TK
area: 10 km²
climate: tropical; moderated by trade winds (April to November)
density: 115/km²
latitude: -171.85588
longitude: -8.967363
population: 1,449 (2007)
comment: Tokelau is a dependent territory of New Zealand in the southern Pacific Ocean. It consists of three tropical coral atolls: . They have a combined land area of 10 km2 . The capital rotates yearly among the three atolls. In addition to these three, Swains Island, which forms part of the same archipelago, is the subject of an ongoing territorial dispute; it is currently administered by the United States as part of American Samoa. Tokelau lies north of the Samoan Islands, east of Tuvalu, south of the Phoenix Islands, southwest of the more distant Line Islands, and northwest of the Cook Islands.

Tomato Mosaic Virus
type: NamedIndividual
type: Crop Disease
affects: Apple
affects: Cherry
affects: Pear
affects: Pepper
affects: Potato
management: There are numerous tomato varieties that are resistant to one or the other of the viruses. These are usually denoted in seed catalogs, often with the code ToMV after the variety name if resistant to tomato mosaic virus and TMV if resistant to tobacco mosaic virus. There are only a few varieties that are resistant to both viruses. Several popular rootstocks for grafted tomatoes can also confer resistance to varieties that may not normally be resistant.
symptoms: Overall, tomato mosaic virus symptoms can be varied and hard to distinguish from other common tomato viruses. - Mottled light and dark green on leaves. - If plants are infected early, they may appear yellow and stunted overall. - Leaves may be curled, malformed, or reduced in size. - Spots of dead leaf tissue may become apparent with certain cultivars at warm temperatures. - Fruits may ripen unevenly. - Reduced fruit number and size. - Yellowish rings may form if fruit ripens in warm weather. - Fruits may show internal browning just under the skin (brownwall).
comment: Tomato mosaic virus (ToMV) can cause yellowing and stunting of tomato plants resulting in loss of stand and reduced yield.

Tomato Spotted Wilt Virus (TSWV)
type: NamedIndividual
type: Crop Disease
management: Controlling this disease is difficult. The wide host range, which includes many perennial ornamentals and weeds, enables the virus to successfully overseason from one crop to the next. Additionally, efforts to control the insect vectors in agricultural fields have had little effect on TSWV. This is likely due to the fact that large populations of thrips may fly or be blown into treated fields from non-treated areas nearby. Controlling thrips is somewhat more effective in greenhouse situations. In greenhouses, however, growers should take care to avoid repeated sprays of similar insecticides because thrips are able to build up resistance to commonly used insecticides in a relatively short time. Rotating the insecticide class is the best approach to insect control. Control of thrips may be obtained with pyrethroids, carbamates, chlorinated hydrocarbons, organophosphates, and soaps. Insecticides are most effective when applied in the morning, when the thrips are most active and the chance for plant damage is reduced. Pesticide regulations change frequently, so check with your local county Extension office (http://aces.nmsu.edu/county/) for information on available insecticides. While eliminating this disease may not be possible, the incidence and severity of TSWV may be reduced by using cultural practices such as starting with virus-free plant material, removing all infected plants (since there is no cure once a plant has the disease), controlling weeds, and rotating crops. Some studies have also shown that using reflective mulches under plants may help to reduce infection. In greenhouses, it may be possible to greatly reduce the number of thrips entering the greenhouse by covering doors and air intakes with a fine mesh (400 mesh) cloth. Efforts are underway to breed cultivars with good horticultural characteristics that also exhibit tolerance to the virus.
symptoms: Symptoms of TSWV are numerous and varied. However, there are two fairly common symptoms for which this disease was named. First, the young leaves turn bronze and subsequently develop numerous small, dark spots. Second, the leaves often droop on the plant, creating a wilt-like appearance. Other symptoms include dieback of the growing tips, stunting, mottling, and dark streaking of the terminal stems. Affected plants may develop a one-sided growth habit or may be stunted completely. Plants that are affected early in the growing season often do not produce any fruit, while those infected after fruit set produce fruit with striking symptoms, including chlorotic concentric ring spots, raised bumps, uneven ripening, and deformation
transmission: TSWV is transmitted from infected plants to healthy plants by at least ten species of thrips. Thrips are tiny (approximately 1/16 of an inch) winged insects that feed on plants through sucking mouthparts. Thrips transmit the virus in a persistent propagative manner, which means that once the insect has picked up the virus, the virus replicates within the insect and the insect is able to transmit the virus for the remainder of its life. The virus is not passed on from adult to egg; however, progeny that develop on infected plants will quickly pick up the virus and be effective spreaders of the disease.
Acronym: TSWV
comment: Tomato spotted wilt virus (TSWV) is an important disease of many different crops grown in temperate and subtropical regions of the world. TSWV is a unique virus in a virus class by itself. The host range for TSWV is one of the widest known for plant viruses. It infects over 1,000 species in 85 families, including both monocots and dicots.

Tonga
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:TO
agricultural land: 310 sq. km
area: 748 km²
climate: tropical; modified by trade winds; warm season (December to May), cool season (May to December)
density: 153/km²
latitude: -174.95755
longitude: -19.093912
population: 112,000 (2005)
comment: Tonga, officially named the Kingdom of Tonga, is a Polynesian country, and also an archipelago comprising 169 islands, of which 36 are inhabited. The archipelago's total surface area is about 750 square kilometres scattered over 700,000 square kilometres of the southern Pacific Ocean. As of 2016, Tonga had a population of 100,651, 70% of whom resided on the main island, Tongatapu.

Tornado
type: NamedIndividual
type: Weather Phenomena
comment: A tornado is a rapidly rotating column of air that is in contact with both the surface of the Earth and a cumulonimbus cloud or, in rare cases, the base of a cumulus cloud. The windstorm is often referred to as a twister, whirlwind or cyclone, although the word cyclone is used in meteorology to name a weather system with a low-pressure area in the center around which, from an observer looking down toward the surface of the earth, winds blow counterclockwise in the Northern Hemisphere and clockwise in the Southern. Tornadoes come in many shapes and sizes, and they are often visible in the form of a condensation funnel originating from the base of a cumulonimbus cloud, with a cloud of rotating debris and dust beneath it. Most tornadoes have wind speeds less than 110 miles per hour (180 km/h), are about 250 feet (80 m) across, and travel a few miles (several kilometers) before dissipating. The most extreme tornadoes can attain wind speeds of more than 300 miles per hour (480 km/h), are more than two miles (3 km) in diameter, and stay on the ground for dozens of miles (more than 100 km).

Torrent
type: NamedIndividual
type: Topography

Aluminum by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Aluminum (Al)
unit: ppm
comment: Default transformation for Aluminum

Area by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Area
comment: Default transformation for Area

Crop Canopy Cover (CC)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Area
Acronym: CC
comment: Crop Canopy Cover (CC) is defined as the percentage of the total groundarea covered by the vertical projection of the crop. In other words, it is thepercent of the soil surface covered by plant foliage. It is sometimes referred to as vegetation crop area (VCA). Crop CC is considered an important metricfor crop yield prediction as well as an indicator of land use, degradation anddesertification

Biological Day by DPG
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Biological Day
References: Soltani, A., & Sinclair, T. R. (2012). Modeling Physiology of Crop Development. Cambridge, MA: Growth and Yield. CAB International.
Acronym: DPG
comment: Days of Potential Growth

Biomass by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Biomass
comment: Default transformation for Biomass

Above Ground Biomass (AGB)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Biomass
Acronym: AGB
comment: bove ground biomass (AGB) includes the plant stem, leaves, head, spike,seeds and foliage. Above ground dried biomass is the total dried mass of theorganic matter above the ground.

Aboveground Dry Biomass (AGDB)
type: NamedIndividual
type: Agriculture Transformation
inherits: Above Ground Biomass (AGB)
inherits: Biomass by Dried
is transformation of: Biomass
unit: g m-2
Acronym: AGDB

Aboveground Fresh Biomass (AGFB)
type: NamedIndividual
type: Agriculture Transformation
inherits: Above Ground Biomass (AGB)
inherits: Biomass by Fresh
is transformation of: Biomass
unit: g m-2
Acronym: AGFB

Biomass by Dried
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Biomass
comment: Transformation for Biomass by Dried Biomass value

Biomass by Fresh
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Biomass
comment: Transformation for Biomass by Fresh Biomass value

Stems Fresh Biomass (MFPA)
type: NamedIndividual
type: Agriculture Transformation
inherits: Biomass by Fresh
is transformation of: Biomass
References: Calada, Dario Brito, Solange Oliveira Rezende, and Mauro Sergio Teodoro. "Analysis of green manure decomposition parameters in northeast Brazil using association rule networks." Computers and Electronics in Agriculture 159 (2019): 34-41.
Acronym: MFPA
comment: Transformation for Biomass by MFPA

Root Fresh Biomass (MFR)
type: NamedIndividual
type: Agriculture Transformation
inherits: Biomass by Fresh
is transformation of: Biomass
References: Calada, Dario Brito, Solange Oliveira Rezende, and Mauro Sergio Teodoro. "Analysis of green manure decomposition parameters in northeast Brazil using association rule networks." Computers and Electronics in Agriculture 159 (2019): 34-41.
Acronym: MFR
comment: Transformation for Biomass by Root fresh biomass values (MFR)

Stems Dry Biomass (MSPA)
type: NamedIndividual
type: Agriculture Transformation
inherits: Biomass by Dried
is transformation of: Biomass
References: Calcada, Dario Brito, Solange Oliveira Rezende, and Mauro Sergio Teodoro. "Analysis of green manure decomposition parameters in northeast Brazil using association rule networks." Computers and Electronics in Agriculture 159 (2019): 34-41.
Acronym: MSPA
comment: Transformation for Biomass by MSPA (Stems dry biomass values)

Root Dry Biomass (MSR)
type: NamedIndividual
type: Agriculture Transformation
inherits: Biomass by Dried
is transformation of: Biomass
References: Calcada, Dario Brito, Solange Oliveira Rezende, and Mauro Sergio Teodoro. "Analysis of green manure decomposition parameters in northeast Brazil using association rule networks." Computers and Electronics in Agriculture 159 (2019): 34-41.
Acronym: MSR
comment: Transformation for Biomass by Root dry biomass values (MSR)

Boron by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Boron (B)
unit: ppm
comment: Default transformation for Boron

Boron by Tier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Boron (B)
unit: ppm
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Boron by Tier3
definition: [<15, "Low"], [15-25, "Medium"], [>25, "High"]

Boundary by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Boundary
comment: Default transformation for Boundary

Bulk Density by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Bulk Density
comment: Default transformation for BulkDensity

Bulk Density by Tier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Bulk Density
unit: g/cm3
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for BulkDensity by Tier 3
definition: [<2, "Low"], [1.0-1.3, "Medium"], [>1.3, "High"]

Calcium by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Calcium (Ca)
unit: ppm
comment: Default transformation for Calcium

Canal by Length
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Canal
related to: Irrigation
comment: Transformation for Canal by Length

Carbon Content (CC)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Carbon (C)
Acronym: CC

Total Carbon (TC)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Carbon (C)
Acronym: TC

Cation Exchange Capacity by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Cation Exchange Capacity
label: Default Transformation for CEC

Chlorophyll by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Chlorophyll
comment: Default transformation for Chlorophyll

Chromium by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Chromium (Cr)
comment: Default transformation for Chromium

Climate by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Climate
comment: Default transformation for Climate

Climate by Tier6
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Climate
References: C.W. Thornthwaite, "An approach toward a rational classification of climate", Geographical Review, 38 (1) (1948), pp. 55-94.
label: Transformation for Climate by Tier6
definition: {Amid, Semiamid, Dry subhumid, Moist subhumid, Humid, Perhumid}

CloudCover by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Cloud Cover
comment: Default Transformation for Cloud Cover

Red Green Blue Color (RGB)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Color

Copper by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Copper (Cu)
unit: ppm
comment: Default transformation for Copper (Cu)

Available Copper (ACu)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Copper (Cu)
Acronym: ACu

Copper by Tier5
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Copper (Cu)
unit: ppm
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Copper by Tier 5
definition: [<1.0, "Very low"], [1.0-1.4, "Low"], [1.5-1.7, "Medium"], [1.8-2.0, "High"], [>2, "Very high"]

Crop Density by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Density

Crop Disease by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Disease
comment: Default transformation for Crop disease

Crop Disease by Percentage
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Disease
comment: Transformation for CropDisease by Percentage

Crop Disease by Disease Severity
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Disease
Acronym: DS
comment: Disease severity (DS) levels at canopy scale

Crop Disease by Tier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Disease
label: Transformation for Crop Disease by Tier 3
definition: (1) Healthy, (2) Slightly infested, (3) Highly infested

Crop Disease by Tier4
type: NamedIndividual
type: Agriculture Transformation
has state: Healthy Trees
has state: Mildly-infected Trees
has state: Moderately-infected Trees
has state: Severely-infected Trees
is transformation of: Crop Disease
comment: Transformation for Crop Disease by Tier 4
definition: (1) Healthy trees (G0), (2) mildly-infected trees (G1), (3) moderately-infected trees (G2), (4) severely-infected trees (G3)

Crop Height by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Height
comment: Transformation_CropHeight

Crop Name by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
comment: Default transformation for Crop image

Crop Name by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Name
comment: Default transformation for Crop name

Crop Pest by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Pest
comment: Default transformation for Crop pest

Crop Pest by Count
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Pest

Crop Stress by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Stress
comment: Default transformation for Crop stress

Crop by Height
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop
is transformation of: Height
comment: Transformation for Crop Height

Crop by Length
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop

Crop by Name
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop
is transformation of: Crop Name
comment: Transformation for Crop Name

Crop by Radius
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop

Crop by Shape
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop
comment: Transformation for Crop Shape

Crop by Variety
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop
is transformation of: Variety
comment: Transformation for Crop Variety

Crop by Weight
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop
is transformation of: Weight
comment: Transformation for Crop Weight

Deep Soil by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Deep Soil
comment: Default transformation for Deep soil

Drilling Date
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Drilling Date
comment: Default transformation for Drilling date

Ear by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Ear
comment: Default transformation for Ear

Ear by Density
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Ear
unit: ear/m2
References: Yuan Zhang, Wenjia Yan, Bin Yang, Tianpeng Yang, Xiaohui Liu, "Estimation of rice yield from a C-band radar remote sensing image by integrating a physical scattering model and an optimization algorithm", Precision Agriculture, Volume 21, pages 245–263 (2020) , Springer.
comment: Ear by Density

Ear by Diameter
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Ear
unit: cm
References: Yuan Zhang, Wenjia Yan, Bin Yang, Tianpeng Yang, Xiaohui Liu, "Estimation of rice yield from a C-band radar remote sensing image by integrating a physical scattering model and an optimization algorithm", Precision Agriculture, Volume 21, pages 245–263 (2020) , Springer.
comment: Transformation for Ear by Diameter

Ear by Gravimetric
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Ear
References: Yuan Zhang, Wenjia Yan, Bin Yang, Tianpeng Yang, Xiaohui Liu, "Estimation of rice yield from a C-band radar remote sensing image by integrating a physical scattering model and an optimization algorithm", Precision Agriculture, Volume 21, pages 245–263 (2020) , Springer.
comment: Transformation for Ear based on Gravimetric

Ear by Length
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Ear
unit: cm
References: Yuan Zhang, Wenjia Yan, Bin Yang, Tianpeng Yang, Xiaohui Liu, "Estimation of rice yield from a C-band radar remote sensing image by integrating a physical scattering model and an optimization algorithm", Precision Agriculture, Volume 21, pages 245–263 (2020) , Springer.
comment: Transformation for Ear by Length

Ear by Weight
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Ear
comment: Transformation for Ear by Weight

Electrical Conductivity by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Electrical Conductivity

Electrical Conductivity by Tier 3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Electrical Conductivity
unit: mS/m
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Electrical conductivity by Tier 3
definition: [0.1-0.4, "Low"], [0.5-0.9, "Medium"], [>1.0, "High"]

Elevation by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Elevation
comment: Default transformation for Elevation

Roughness
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Elevation
References: M.F.J. Wilson, B. O’Connell, C. Brown, J.C. Guinan, A.J. Grehan, "Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope", Mar. Geod., 30 (2007), pp. 3-35
comment: Difference between maximum and minimum elevation

Evaporation by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Evaporation
comment: Default transformation for Evaporation

Evapotranspiration by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Evapotranspiration
comment: Default transformation for Evapotransporation

Exchangeable Calcium by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Exchangeable Calcium (Caex)
comment: Transformation for Exchangeable Calcium by default

Exchangeable Calcium by Tier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Exchangeable Calcium (Caex)
comment: Transformation for Exchangeable Calcium by Tier3

Exchangeable Magnesium by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Exchangeable Magnesium (Mgex)
comment: Transformation for Exchangeable Magnesium by Default

Exchangeable Potassium by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Exchangeable Potassium (Kex)
comment: Transformation for Exchangeable Potassium by Default

Exchangeable Potassium by Tier5
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Exchangeable Potassium (Kex)
comment: Transformation for Exchangeable Potassium by Tier5

Field Image by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Field Imagery
comment: Default transformation for Field image

Flower by Flowering
type: NamedIndividual
type: Agriculture Transformation
has state: Flowering
is transformation of: Flower
related to: Flowering
References: Calada, Dario Brito, Solange Oliveira Rezende, and Mauro Sergio Teodoro. "Analysis of green manure decomposition parameters in northeast Brazil using association rule networks." Computers and Electronics in Agriculture 159 (2019): 34-41.
comment: Transformation for Flower by Flowering

Frost by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Frost
comment: Default transformation for Frost

Fruit by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Fruit

Fruit by Counter
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Fruit

Beginning of Growing Season (BGS)
type: NamedIndividual
type: Agriculture Transformation
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Growing Season
unit: Day
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: BGS
comment: Beginning of Growing Season (BGS) is transformation for GrowingSeason by Date of Beginning of Growing Season

End of Growing Season (EGS)
type: NamedIndividual
type: Agriculture Transformation
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Growing Season
unit: Day
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: EGS
comment: End of Growing Season (EGS) is transformation for GrowingSeason by Date of end of growing season

Length of Growing Season (LGS)
type: NamedIndividual
type: Agriculture Transformation
defined in: Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
is transformation of: Growing Season
unit: Day
References: Audet, Rene, et al. "Atlas agroclimatique du Quebec. Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution." (2012).
Acronym: LGS
comment: Length of Growing Season (LGS) is transformation for GrowingSeason by Length of Growing Season

Growth Stage by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
References: Lancashire, Peter D., et al. "A uniform decimal code for growth stages of crops and weeds." Annals of applied Biology 119.3 (1991): 561-601.
comment: BBCH-scale Cereal growth staging scales
definition: 0: Germination, sprouting, bud development 1: Leaf development 2: Formation of side shoots, tillering 3: Stem elongation or rosette growth, shoot development 4: Development of harvestable vegetative plant parts, bolting 5: Inflorescene emergence, heading 6: Flowering 7: Development of fruit 8: Ripening or maturity of fruit and seed 9: Senescence, beginning of dormancy

Days of Booting (BO)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
related to: Booting stage

Days of Emergence (EM)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
related to: Emergence

Days of Flowering (FL)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
related to: Flowering

Days of Grain Filling (GF)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
related to: Grain Filling

Days of Heading (HE)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
related to: Heading

Days of Harvest (HV)
type: NamedIndividual
type: Agriculture Transformation

Days of Mature (MA)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
related to: Mature

Days of Milky (MI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
related to: Milky

Days of Ripening (RP)
type: NamedIndividual
type: Agriculture Transformation

Days of Sowing (SO)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
related to: Sowing

Days of Turning green (TG)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
related to: Turning green

Days of Tillering (TI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
related to: Tillering stage

Days of Transplanting (TR)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
related to: Transplanting

Growth Stage by Tier 5
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Growth Stage
related to: Maize
References: Lei Wang, Pengxin Wang, Shunlin Liang, Xuan Qi, LiLi, Lianxiang Xu, "Monitoring maize growth conditions by training a BP neural network with remotely sensed vegetation temperature condition index and leaf area index", Elsevier Computers and Electronics in Agriculture, Volume 160, May 2019, Pages 82-90
comment: Transformation for Growth stage - Tier 5
definition: {Seeding, Jointing, Heading, Milk, Mature stages}

Harvest Date by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Harvest Date
comment: Default transformation for Harvest date

Humidity by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Humidity
comment: Default transformation for Humidity

Temperature Hydration
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Hydration

Hydration Time
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Hydration

Brightness Index (BI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
References: S.S. Ray, J.P. Singh, G. Das, S. Panigrahy, "Use of high resolution remote sensing data for generating site-specific soil management plan", Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 35 (2004), pp. 127-132
comment: Average reflectance magnitude

Black White Image (BW)
type: NamedIndividual
type: Agriculture Transformation

Comprehensive Color Feature (CCF)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
Acronym: CCF
comment: Comprehensive Color Feature (CCF)

Coloration Index (CI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Imagery
Identifier: CI = (R-G) / (R+G)
References: S.S. Ray, J.P. Singh, G. Das, S. Panigrahy, "Use of high resolution remote sensing data for generating site-specific soil management plan", Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 35 (2004), pp. 127-132
Acronym: CI
comment: Soil color

Image by Color
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
References: Phadikar, Santanu, Jaya Sil, and Asit Kumar Das. "Rice diseases classification using feature selection and rule generation techniques." Computers and electronics in agriculture 90 (2013): 76-85.
comment: Transformation for Image by Color

Image by Colorname
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
comment: Transformation for Image by Colorname, such as Brown, Dark brown, Yellow, Green, Reddish, Pupple, Black, Grey brown, Red, Brick red, Orange, .etc.

Image by ConvNets
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
comment: Transformation for Image by ConvNets

Green Ratio, Hue and Intensity (GHI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
Acronym: GHI
comment: The color space of RGB unit images is converted into Green Ratio, Hue and Intensity (GHI) color space

Green Ratio, Hue and Luminance (GHL)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
Acronym: GHL
comment: The color space of RGB unit images is converted into Green Ratio, Hue and Luminance (GHL) color space

Green Ratio, Hue and Saturation (GHS)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
Acronym: GHS
comment: The color space of RGB unit images is converted into Green Ratio, Hue and Saturation (GHS) color space

Green Ratio, Hue, Saturation and Intensity (GHSI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
Acronym: GHSI
comment: The color space of RGB unit images is converted into Green Ratio, Hue, Saturation and Intensity (GHSI) color space

Greenness Index (GI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Imagery
References: S.S. Ray, J.P. Singh, G. Das, S. Panigrahy, "Use of high resolution remote sensing data for generating site-specific soil management plan", Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 35 (2004), pp. 127-132
Acronym: GI

Gray Color Image (Gray)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Imagery

Hue Index (HI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Imagery
References: S.S. Ray, J.P. Singh, G. Das, S. Panigrahy, "Use of high resolution remote sensing data for generating site-specific soil management plan", Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 35 (2004), pp. 127-132
Acronym: HI

Hue, Saturation and Intensity (HSI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
Acronym: HSI
comment: The color space of RGB unit images is converted into Hue, Saturation and Intensity (HSI) color space

Hue, Saturation and Luminance (HSL)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
Acronym: HSL
comment: The color space of RGB unit images is converted into Hue, Saturation and Luminance (HSL) color space

Image by HSV
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
Acronym: HSV
comment: HSL (hue, saturation, lightness) and HSV (hue, saturation, value) or HSB (hue, saturation, brightness) are alternative representations of the RGB color model, designed in the 1970s by computer graphics researchers to more closely align with the way human vision perceives color-making attributes.

Transformation Image Lab
type: NamedIndividual
type: Agriculture Transformation
Acronym: Lab

Image by Morphological
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
comment: Transformation for Image by Morphological Four morphological traits characterized the overall shape of the seedlings, namely seedling height, M_SH; seedling width, M_SW; aspect ratio, M_AR; and seedling length, M_SL.

Image by NIR
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Field Imagery
is transformation of: Imagery
is transformation of: Satellite Imagery
Acronym: NIR
comment: Transformation for Image by Image Near-Infrared (INR)

Image by ObjDetect
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
comment: Transformation for Image by Object Detection

Image by OverFeat
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Field Imagery
is transformation of: Imagery
References: Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. 2013. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229.

Image by Position
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
comment: Phadikar, Santanu, Jaya Sil, and Asit Kumar Das. "Rice diseases classification using feature selection and rule generation techniques." Computers and electronics in agriculture 90 (2013): 76-85.
comment: Transformation for Image by Position

Red Green Blue Color (RGB)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Field Imagery
is transformation of: Satellite Imagery
Acronym: RGB
comment: Transfermation_Image_RGB
comment: Transformation for Image by RGB

Red Green NIR Color (RGN)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Imagery
Acronym: RGN
comment: NIR band; Green: Red band; Blue: Green band

Redness Index (RI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Imagery
References: S.S. Ray, J.P. Singh, G. Das, S. Panigrahy, "Use of high resolution remote sensing data for generating site-specific soil management plan", Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 35 (2004), pp. 127-132
Acronym: RI
comment: Hematite content

Saturation Index (SI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Imagery
References: S.S. Ray, J.P. Singh, G. Das, S. Panigrahy, "Use of high resolution remote sensing data for generating site-specific soil management plan", Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 35 (2004), pp. 127-132
Acronym: SI

Image by SIL
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
Acronym: SIL
comment: The color space of RGB unit images is converted into Saturation, Intensity and Luminance (SIL) color space

Image by Shape
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
References: Phadikar, Santanu, Jaya Sil, and Asit Kumar Das. "Rice diseases classification using feature selection and rule generation techniques." Computers and electronics in agriculture 90 (2013): 76-85.
comment: Transformation for Image by Shape

Image by Size
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
References: M. Orru, O. Grillo, G. Lovicu, G. Venora, G. Bacchetta, "Morphological characterization of Vitis vinifera L. seeds by image analysis and comparison with archaeological remains", Vegetation History Archaeobotany, 22 (2013), pp. 231-242
comment: Transformation for Image by Size

Image by Texture
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Field Imagery
is transformation of: Imagery
is transformation of: Satellite Imagery
comment: Transformation for Image by Texture

YCbCr Color
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Imagery
Acronym: YCbCr
comment: YCbCr, Y′CbCr, or Y Pb/Cb Pr/Cr, also written as YCBCR or Y′CBCR, is a family of color spaces used as a part of the color image pipeline in video and digital photography systems.

Image by YIQ
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Crop Imagery
is transformation of: Imagery
Acronym: YIQ

Iron by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Iron (Fe)
unit: ppm
comment: Default transformation for Iron (Fe)

Available Iron (AFe)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Iron (Fe)
Acronym: Àe

Iron by Tier4
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Iron (Fe)
unit: ppm
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Iron by Tier 4
definition: [<3.0, "Low"], [3.0-3.4, "Medium"], [3.5-3.7, "High"], [>3.7, "Very high"]

Irradiation by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: IRRadiation
comment: Default Transformation for Irradiation

Irrigation by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Irrigation
comment: Default transformation for Irrigation

K2O by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Potassium oxide (K2O)
comment: Default transformation for K2O

Leaf by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
comment: Default transformation for Leaf

Leaf by Area
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Transformation for Leaf by Area

Leaf by Color
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Transformation for Leaf by Color

Leaf by Count
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
comment: Transformation for Leaf by number per plant

Leaf by FT
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Tearing Force (FT)

Leaf by Gravimetric
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Transformation for Leaf by Gravimetric

Leaf by LAITier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
comment: Leaf area index (LAI) is a dimensionless quantity that characterizes plant canopies. It is defined as the one-sided green leaf area per unit ground surface area (LAI = leaf area / ground area, m2 / m2) in broadleaf canopies.
definition: {High LAI, Medium LAI, Low LAI}

Leaf Spatial Location (LSL)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
Acronym: LSL
comment: Leaf Spatial Location (LSL) is the leaf location in the tree canopy, that is to say, LSL gives the spatial co-ordinates of the petiole–midrib junction point.

Leaf by Length
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
unit: cm
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Transformation for Leaf by Length

Leaf by Maturity
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Transformation for Leaf by Maturity
definition: {Young, Adult, Old}

Leaf by Perimeter
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Transformation for Leaf by Perimeter

Leaf by Roundness
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Transformation for Leaf by Roundness

Leaf by ST
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Tearing Strength (ST)

Leaf by Shape
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Transformation for Leaf by Shape
definition: Apex shape {Acuminate, Convex, Emarginate, Rounded, Straight, Truncate} Base shape {Complex, Convex, Cordate, Cuneate, Decurrent, Lobate, Rounded} Lamina shape {Cordate, Elliptical, Irregular, Lanceolate, Linear, Oblanceolate, Obovate, Ovate, Peltate}

Leaf by Slimness
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
comment: Transformation for Leaf by Slimness

Tree Area Index (TAI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
Acronym: TAI
comment: Leaf by TAI

Leaf by Thickness
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
unit: mm
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Transformation for Leaf Thickness

Leaf by Venation
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
definition: {Actinodromous; Campylodromous; Flabellate; Parallelodromous; Pinnate}

Leaf by Weight
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Transformation for Leaf Weight

Leaf by Width
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Leaf
unit: cm
References: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
comment: Transformation for Leaf Width

Lime by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Limes
unit: kg/ha
comment: Default transformation for Lime

Aspect
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Location

Distance from rivers (D2Ri)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Location
Acronym: D2Ri

Distance from roads (D2Ro)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Location
Acronym: D2Ro

Elevation
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Location

Lattitude of Location
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Location
comment: Latitude is a geographic coordinate that specifies the north–south position of a point on the Earth's surface.

Longitude of Location
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Location
comment: Longitude is given as an angular measurement ranging from 0° at the Prime Meridian to +180° eastward and −180° westward.

Slope
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Location

MGO by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Magnesium oxide (MgO)
comment: Default transformation for MgO

Magnesium by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Magnesium (Mg)
unit: ppm
comment: Default transformation for Magnesium (Mg)

Manganese by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Manganese (Mn)
unit: ppm
comment: Default transformation for Manganese (Mn)

Available Manganese (AMn)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Manganese (Mn)
Acronym: AMn

Manganese by Tier5
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Manganese (Mn)

Manganese by Tier5
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Manganese (Mn)
unit: ppm
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Manganese by Tier 5
definition: [<0.1, "Very low"], [0.5-0.9, "Low"], [1.0-1.4, "Medium"], [>1.4, "High"], [>7.5, "Very high"]

Maturity with 3 Stages
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Maturity
definition: - Immature: Green skin without yellow stripe - Partially Mature: Green skin with well-defined yellow stripe - Mature: Skin clearly yellow in colour and may or may not presence of small light green areas

Moisture Content by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Moisture
comment: Default transformation for Moisture content

Molybdenum by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Molybdenum (Mo)
unit: ppm
comment: Default transformation for Molybdeum (Mo)

Nitrogen by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
unit: ppm
comment: Default transformation for Nitrogen (N)

Nitrogen Agronomic Efficiency (NAE)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
related to: Nitrogen (N)
References: Song Chen, et al., "Effect of various crop rotations on rice yield and nitrogen use efficiency in paddy – upland systems in southeastern China", The Crop Journal Volume 6, Issue 6, December 2018, Elsevier B.V.
Acronym: NAE

Nitrogen Balance Degree (NBD)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
related to: Nitrogen (N)
References: Song Chen, et al., "Effect of various crop rotations on rice yield and nitrogen use efficiency in paddy – upland systems in southeastern China", The Crop Journal Volume 6, Issue 6, December 2018, Elsevier B.V.
Acronym: NBD

Nitrogen use efficiency (NUE)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
Acronym: NEU
comment: Nitrogen use efficiency (NUE) is the fraction of applied nitrogen that is absorbed and used by the plant. Improving a plant’s ability to utilize nitrogen is a key component in enhancing environmental sustainability. Today, improved plant breeding through the use of genetic engineering has the greatest potential to produce plants that will utilize fertilizer more efficiently than conventional varieties.

N Nutrition Nndex (NNI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
Acronym: NNI

Nitrogen Physiological Efficiency (NPE)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
related to: Nitrogen (N)
References: Song Chen, et al., "Effect of various crop rotations on rice yield and nitrogen use efficiency in paddy – upland systems in southeastern China", The Crop Journal Volume 6, Issue 6, December 2018, Elsevier B.V.
Acronym: NPE

Nitrogen Recovery Efficiency (NRE)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
related to: Nitrogen (N)
References: Song Chen, et al., "Effect of various crop rotations on rice yield and nitrogen use efficiency in paddy – upland systems in southeastern China", The Crop Journal Volume 6, Issue 6, December 2018, Elsevier B.V.
Acronym: NRE

Nitrogen in Fertilizer
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
Acronym: NinF

Nitrogen in Manure
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
Acronym: NinM

Nitrogen in Plant residue
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
Acronym: NinP
alternative label: Nitrogen in Biomass

Plant Nitrogen Concentration (PNC)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
Acronym: PNC

Plant N Uptake (PNU)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
Acronym: PNU

Total Nitrogen (TN)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
Acronym: TN

Ttotal Oxidized Nitrogen (TON)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
Acronym: TON

Nitrogen by Tier4
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
unit: ppm
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Nitrogen by Tier 4
definition: [0-150, "Low"], [151-280, "Medium"], [281-450, "High"], [>450, "Very high"]

Nitrogen by Tier6
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Nitrogen (N)
unit: ppm
comment: Transformation for Nitrogen by Tier 6
definition: [0.0, 150, "Very Low"], [150, 175, "Low"], [175, 200, "Medium"], [225, 250, "Moderately high"], [250, 300, "High"], [300, 999, "Very High"]

Organic Carbon by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Organic Carbon

Organic Carbon by Tier6
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Organic Carbon
comment: Transformation for Organic Carbon by Tier 6
definition: [0.00, 0.20, "Very Low"], [0.21, 0.40, "Low"], [0.41, 0.60, "Medium"], [0.61, 0.80, "Moderately high"], [0.81, 1.00, "High"], [1.00, 9.99, "Very High"]

Organic Matter by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Organic Matter
Acronym: OM
comment: Default transformation for Organic matter (OM)

Organic Matter by Tier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Organic Matter
unit: ppm
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Organic Matter by Tier 3
definition: [0.4-0.6, "Low"], [0.7-0.8, "Medium"], [>1.0, "High"]

Organic Matter by Tier6
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Organic Matter
unit: g/kg
References: National Soil Survey Office, "Chinese soil", China Agriculture Press, Beijing, China (1998)
comment: Transformation for Organic Matter by Tier 6
definition: { Level 1: >40 g/kg; Level 2: 30–40 g/kg; Level 3: 20–30 g/kg; Level 4: 10–20 g/kg; Level 5: 6–10 g/kg; Level 6: < 6 g/kg }

Oxygen by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Oxygen (O2)
comment: Default transformation for Oxygen (O)

Dissolved Oxygen (DO)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Oxygen (O2)
comment: Dissolved oxygen (DO) is a measure of how much oxygen is dissolved in the water - the amount of oxygen available to living aquatic organisms. The amount of dissolved oxygen in a stream or lake can tell us a lot about its water quality.

P2O5 by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Phosphorus Pentoxide (P2O5)
comment: Default transformation for P2O5

Pest Incidence by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Pest Incidence
comment: Default transformation for Pest incidence

Pest Incidence by Tier5
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Pest Incidence
unit: #
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Pest Incidence by Tier 5
definition: [<2, "Very low"], [2-3, "Low"], [4, "Medium"], [>8, "High"], [>10, "Very high"]

Phosphorus by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Phosphorus (P)
unit: ppm
comment: Default transformation for Phosphorus (P)

Available Phosphorus (AP)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Phosphorus (P)
Acronym: AP

Total Phosphorus (TP)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Phosphorus (P)
Acronym: TP

Phosphorus by Tier4
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Phosphorus (P)
unit: ppm
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Phosphorus by Tier 4
definition: [<10, "Low"], [10-14, "Medium"], [15-22, "High"], [>22, "Very high"]

Population in Agriculture (AP)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Population

Population Density (PD)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Population

Rural Population (RP)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Population

Potassium by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Potassium (K)
unit: ppm
comment: Default transformation for Potassium (K)

Available Potassium (AK)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Potassium (K)
Acronym: AK

Total Potassium (TK)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Potassium (K)
Acronym: TK

Potassium by Tier4
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Potassium (K)
unit: ppm
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Potassium by Tier 4
definition: [<100, "Low"], [100-150, "Medium"], [151-280, "High"], [>280, "Very high"]

Precipitation by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Precipitation
unit: mm
comment: Default transformation for Precipitation
comment: The total precipitation for cropping season (for example June to November) for each year of every district was calculated from the monthly mean precipitation of that year for a particular location.

Pressure by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Pressure
comment: Default Transformation for Pressure

Radiation by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Radiation
comment: Default transformation for Radiation

Rainfall by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Rainfall
comment: Default transformation for Rainfall

SO3 by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Sulfur Trioxide (SO3)
comment: Default transformation for SO3

Satellite Image by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Satellite Imagery
comment: Default transformation for Satellite images

Seed by Emergence
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Seed
References: Calada, Dario Brito, Solange Oliveira Rezende, and Mauro Sergio Teodoro. "Analysis of green manure decomposition parameters in northeast Brazil using association rule networks." Computers and Electronics in Agriculture 159 (2019): 34-41.
comment: Emergence (E)

Seed by PodRipening
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Seed
References: Calada, Dario Brito, Solange Oliveira Rezende, and Mauro Sergio Teodoro. "Analysis of green manure decomposition parameters in northeast Brazil using association rule networks." Computers and Electronics in Agriculture 159 (2019): 34-41.
comment: Transformation for Seed by PodRipening (V)

Seed Rate by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Seed
comment: Default transformation for Seed rate

Shallow Electrical Conductivity by Tier5
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Shallow Electrical Conductivity (ECsh)
comment: Transformation for ShallowElectricalConductivity by Tier5

Shallow Soil by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Shallow Soil
comment: Default transformation for Shallow soil

Slope by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Slope
comment: Default transformation for Slope

Slope by Angle
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Slope
comment: Transformation Slope by Angle

Slope by Height
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Slope
comment: Transformation for Slope by Height

Slope by Length
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Slope
comment: Transformation for Slope by Length

Soil Slope by Tier6
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Slope
References: De Reu, Jeroen., Bourgeois, J., Bats, M., Zwertvaegher, A., Gelorini, V., et al., 2013. "Application of the topographic position index to heterogeneous landscapes." Geomorphology, vol. 186, pp. 39–49.
comment: Transformation for Soil Slope by Tier6
definition: {Ridge, Upper slope, Middle slope, Flat area, Lower slope, Valley}

Sodium by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Sodium (Na)
comment: Default transformation for Sodium (So)

Soil Boron by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Boron
unit: ppm
comment: Default transformation for Soil boron (B). Range [0.0-10.0]

Soil Boron by Tier 6
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Boron
unit: ppm
References: Suchithra, M. S., and Maya L. Pai. "Improving the prediction accuracy of soilnutrient classification by optimizing extreme learning machine parameters." Informa-tion Processing in Agriculture 7.1 (2020): 72-82.
comment: Transformation for Soil Boron by Tier 6
definition: [0.00, 0.24, "Very Low"], [0.25, 0.50, "Low"], [0.50, 0.75, "Medium"], [0.75, 1.00, "Moderately high"], [1.00, 1.50, "High"], [1.50, 9.99, "Very High"]

Soil Calcium by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Calcium
unit: ppm
comment: Default transformation for Soil calcium (Ca)

Soil Clay by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Clay
comment: Default transformation for Soil clay

Soil Clay by Tier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Clay
comment: Transformation for SoilClay by Tier3

Soil Color by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Color
comment: Default transformation for Soil color

Soil Depth by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Depth
comment: Default transformation for Soil Depth

Soil Potasium by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Potasium
unit: ppm
comment: Default transformation for Soil Potasium (K)

Soil Potasium by Tier 6
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Potasium
unit: ppm
References: Suchithra, M. S., and Maya L. Pai. "Improving the prediction accuracy of soilnutrient classification by optimizing extreme learning machine parameters.", Informa-tion Processing in Agriculture 7.1 (2020): 72-82.
comment: Transformation for SoilK by Tier 6
definition: [0, 100, "Very Low"], [101, 150, "Low"], [151, 200, "Medium"], [201, 250, "Moderately high"], [251, 300, "High"], [301, 999, "Very High"]

Soil MG by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil MG
unit: ppm
comment: Default transformation for Soil MG

Soil Moisture by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Moisture
comment: Default transformation for Soil Moisture

Soil Moisture by Tier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Moisture
unit: %
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for SoilMoisture by Tier 3
definition: [<20, "Low"], [20-27, "Medium"], [>27, "High"]

Soil Nitrogen by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Nitrogen
unit: ppm
comment: Default transformation for Soil Nitrogen

Soil Phosphorus by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Phosphorus
unit: ppm
comment: Default transformation for Soil Phosphorus

Soil pH by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil pH
comment: Default transformation for Soil pH. Range [0.0 - 14.0]

Soil pH by Max
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil pH
References: Ngo, Quoc Hung, Nhien-An Le-Khac, and Tahar Kechadi. "Predicting Soil pH by Using Nearest Fields." International Conference on Innovative Techniques and Applications of Artificial Intelligence. Springer, Cham, 2019.
comment: Transformation for Soil pH by Maximum

Soil pH by Mean
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil pH
References: Ngo, Quoc Hung, Nhien-An Le-Khac, and Tahar Kechadi. "Predicting Soil pH by Using Nearest Fields." International Conference on Innovative Techniques and Applications of Artificial Intelligence. Springer, Cham, 2019.
comment: Transformation for Soil pH by Average

Soil pH by Min
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil pH
References: Ngo, Quoc Hung, Nhien-An Le-Khac, and Tahar Kechadi. "Predicting Soil pH by Using Nearest Fields." International Conference on Innovative Techniques and Applications of Artificial Intelligence. Springer, Cham, 2019.
comment: Transformation for Soil pH by Minimum

Soil pH by Tier10
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil pH

Soil pH by Tier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil pH
comment: Transformation for Soil pH by Tier 3
definition: [0.0, 6.0, 'Acidic'], [6.0, 8.0, 'Neutral'], [8.0, 14.0, 'Alkaline']

Soil pH by Tier4
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil pH
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Soil pH by Tier 4
definition: [<4.5, "Very low"], [4.5 - 5.4, "Low"], [5.5 - 6.4, "Medium"], [6.5 - 7.5, "High"]

Soil pH by Tier5
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil pH
comment: Transformation for Soil pH by Tier 5

Soil pH by Tier9
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil pH
References: Suchithra, M. S., and Maya L. Pai., "Improving the prediction accuracy of soilnutrient classification by optimizing extreme learning machine parameters.", Informa-tion Processing in Agriculture 7.1 (2020): 72-82.
comment: Transformation for Soil pH by Tier 9
definition: [0.0, 4.4, 'Strongly acidic'], [4.5, 5.5, 'Highly acidic'], [5.6, 6.5, 'Moderately acidic'], [6.6, 6.9, 'Slightly acidic'], [7.0, 7.0, 'Neutral'], [7.1, 8.0, 'Slightly alkaline'], [8.1, 9.0, 'Moderately alkaline'], [9.1, 10.0, 'Strongly alkaline'], [10.1, 14.0, 'Very strongly alkaline']

Soil Phosphorus by Tier6
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Phosphorus
References: Suchithra, M. S., and Maya L. Pai. "Improving the prediction accuracy of soilnutrient classification by optimizing extreme learning machine parameters.", Informa-tion Processing in Agriculture 7.1 (2020): 72-82.
comment: Transformation for SoilP by Tier 6
definition: [0.00, 7.00, "Very Low"], [7.10, 14.0, "Low"], [14.1, 21.0, "Medium"], [21.1, 28.0, "Moderately high"], [28.1, 35.0, "High"], [39.1, 99.0, "Very High"]

Soil Salinity by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Salinity
comment: Transformation for Soil Salinity

Soil Sand by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Sand
comment: Default transformation for soil sand

Soil Sand by Tier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Sand
comment: Transformation for Soil Sand by Tier3

Soil Silt by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Silt
comment: Default transformation for soil silt

Soil Surface by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Surface
comment: Default transformation for soil surface

Soil Temperature by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Temperature
comment: Default transformation for soil temperature

Soil Texture by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Texture
comment: Default transformation for soil texture

Clay by SoilTexture
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Texture

Sand by SoilTexture
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Texture

Silt by SoilTexture
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Texture

Soil Texture by Tier10
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Texture
comment: Transformation for SoilTexture by Tier10

Soil Texture by Tier9
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Texture
References: S.S. Rousseva, "Data transformations between soil texture schemes", Eur. J. Soil Sci., 48 (1997), pp. 748-758
comment: Transformation for SoilTexture by Tier9
definition: {loose sand, tight sand, sandy loam; light loam; medium loam; heavy loam; light clay; medium clay and heavy clay}

Soil Type by Tier5
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil
is transformation of: Soil Type
References: M.J. Aitkenhead, M.C. Coull, W. Towers, G. Hudson, H.I.J. Black, "Predicting soil chemical composition and other soil parameters from field observations using a neural network", Computers and Electronics in Agriculture, Volume 82, March 2012, pages 108-116, Elsevier B.V.
comment: Transformation for SoilType by Tier5
definition: {Alluvial, Calcareous, Brown earth, Gley, Podzol}

Soil Type by Tier7
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil
is transformation of: Soil Type
comment: Transformation for SoilType by Tier7
definition: OH (High Plastic Organic Soils), OL (Low Plastic Organic Soils), CL (Lean Clay), CH (Fat Clay), GC (Clayey Gravel), SM (Silty Sand), SC (Clayey Sand)

Soil Type by Tier9
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Soil Type
comment: Transformation for SoilType by Tier9
definition: {Felty soil, Dark felty soil, Frigid calcic soil, Frigid desert soil, Frigid frozen soil, Cold calcic soil, Cold brown calcic soil, Cold desert soil}

Solar Radiation by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Solar Radiation
comment: Default transformation for Solar radiation

Hyperspectral Image by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Satellite Imagery
is transformation of: Spectral Image
comment: Default transformation for Hyperspectral images
comment: Default transformation for Spectral images
label: Spectral Image by Default

Hyperspectral Image by Wavelength
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Spectral Image
Acronym: WL
comment: Transformation for HyperspectralImage by Wavelength
comment: Transformation for SpectralImage by wavelengths, for example 566, 673, 698, 764, 963, 1144, 1177, 1318, 1458 and 1508nm, etc.
label: Spectral Image by Wavelength

Stem by DC
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Stem
References: Calçada, Dario Brito, Solange Oliveira Rezende, and Mauro Sergio Teodoro. "Analysis of green manure decomposition parameters in northeast Brazil using association rule networks." Computers and Electronics in Agriculture 159 (2019): 34-41.
Acronym: DC
comment: Transformation for Stem by Diameters of the stems (DC)

Sulphur by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Sulphur (S)
unit: ppm
comment: Default transformation for Sulphur (S)

Avaliable Sulphur (AS)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Sulphur (S)
Acronym: AS

Total Sulphur (TS)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Sulphur (S)
Acronym: TS

Sulphur by Tier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Sulphur (S)
unit: ppm
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Sulphur by Tier 3
definition: [<10, "Low"], [10-20, "Medium"], [>20, "High"]

Sunshine by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Sunshine
comment: Default transformation for Sunshine

Tank by Count
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Tank
related to: Irrigation

Target Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
unit: ton/ha
comment: Default transformation for Target yield

Temperature by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Temperature
unit: oC
comment: Default transformation for Temperature.

Flow Direction (FlowDir)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Terrain
Acronym: FlowDir

Terrain by FlowPL
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Terrain
References: Wei Wu, Ai-Di Li, Xin-Hua He, Ran Ma, Hong-Bin Liu, Jia-Ke Lv, "A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China", 2018, Computers and Electronics in Agriculture, Volume 144, January 2018, pages 86-93, Elsevier B.V.
comment: Flow path length in SAGA (System for Automated Geoscientific Analyses)

Terrain by MRRTF
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Terrain
References: Wei Wu, Ai-Di Li, Xin-Hua He, Ran Ma, Hong-Bin Liu, Jia-Ke Lv, "A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China", 2018, Computers and Electronics in Agriculture, Volume 144, January 2018, pages 86-93, Elsevier B.V.
Acronym: MRRTF
comment: Multiresolution of the ridge top flatness index (MRRTFI) in SAGA (System for Automated Geoscientific Analyses)

Terrain by MRVBF
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Terrain
References: Wei Wu, Ai-Di Li, Xin-Hua He, Ran Ma, Hong-Bin Liu, Jia-Ke Lv, "A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China", 2018, Computers and Electronics in Agriculture, Volume 144, January 2018, pages 86-93, Elsevier B.V.
Acronym: MRVBF
comment: Multiresolution of valley bottom flatness index (MRVBFI) in SAGA (System for Automated Geoscientific Analyses)

Terrain by SPI
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Terrain
References: Wei Wu, Ai-Di Li, Xin-Hua He, Ran Ma, Hong-Bin Liu, Jia-Ke Lv, "A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China", 2018, Computers and Electronics in Agriculture, Volume 144, January 2018, pages 86-93, Elsevier B.V.
Acronym: SPI
comment: Stream Power Index in SAGA (System for Automated Geoscientific Analyses)

Terrain by TCILow
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Terrain
References: Wei Wu, Ai-Di Li, Xin-Hua He, Ran Ma, Hong-Bin Liu, Jia-Ke Lv, "A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China", 2018, Computers and Electronics in Agriculture, Volume 144, January 2018, pages 86-93, Elsevier B.V.
Acronym: TCILow
comment: Terrain Classification Index for Lowlands in SAGA (System for Automated Geoscientific Analyses)

Topographic Position Index (TPI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Terrain
Acronym: TPI

Terrain Ruggedness Index (TRI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Terrain
References: M.F.J. Wilson, B. O’Connell, C. Brown, J.C. Guinan, A.J. Grehan, "Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope", Mar. Geod., 30 (2007), pp. 3-35
References: S.J. Riley, "Index that quantifies topographic heterogeneity", Intermt. J. Sci., 5 (1999), pp. 23-27
Acronym: TRI

Topographical Wetness Index (TWI)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Terrain
References: Wei Wu, Ai-Di Li, Xin-Hua He, Ran Ma, Hong-Bin Liu, Jia-Ke Lv, "A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China", 2018, Computers and Electronics in Agriculture, Volume 144, January 2018, pages 86-93, Elsevier B.V.
Acronym: TWI
comment: Terrain by Topographical Wetness Index (TWI)

Terrain by VRM
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Terrain
References: Wei Wu, Ai-Di Li, Xin-Hua He, Ran Ma, Hong-Bin Liu, Jia-Ke Lv, "A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China", 2018, Computers and Electronics in Agriculture, Volume 144, January 2018, pages 86-93, Elsevier B.V.
Acronym: VRM
comment: Transformation for Terrain by VRM in SAGA (System for Automated Geoscientific Analyses)

Time by Day
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Time
comment: Day of year

Time by Month
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Time
comment: Month of Year

Time by Year
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Time
comment: Time by Year

Top Soil by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Top Soil
comment: Default transformation for Top soil

Topsoil by Depth
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Top Soil
comment: The outermost layer of the soil that is rich in organic matter and microorganisms.

Vanadium by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Vanadium (V)
comment: Default transformation for Vanadium

Vegetation by Tier5
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Vegetation
related to: Arable
related to: Forest
related to: Grassland
related to: Heathland
related to: Wet land
References: M.J. Aitkenhead, M.C. Coull, W. Towers, G. Hudson, H.I.J. Black, "Predicting soil chemical composition and other soil parameters from field observations using a neural network", Computers and Electronics in Agriculture, Volume 82, March 2012, pages 108-116, Elsevier B.V.
comment: Transformation for Vegetation by 5 types (five categories – arable, forest, grassland, heath and wetland)
definition: {Arable, Forest, Grassland, Heathland, Wetland}

Visibility by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Visibility
comment: Default transformation for Visibility

Dissolved Oxygen (DO)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Water
related to: Oxygen (O2)

Water pH
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Water

Weeds by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Weeds
comment: Default transformation for Weeds

Weeds by Area
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Weeds

Well by Count
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Well
related to: Irrigation

Wind Direction by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Wind Direction
comment: Default transformation for Wind direction

Wind Speed by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Wind Speed
comment: Default transformation for Wind speed

Yield by Default
type: NamedIndividual
type: Agriculture Transformation
has state: HighYield
is transformation of: Yield
comment: Default transformation for yield

Yield by Previous Season
type: NamedIndividual
type: Agriculture Transformation
has state: HighYield
is transformation of: Yield
comment: Transformation for Crop Yield by Historical Last Year

Yield by Two Seasons Ago
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
comment: Transformation for Crop Yield by Historical Last 2 Years

Yield by Three Seassons Ago
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
comment: Transformation for Crop Yield by Historical Last 3 Years

Apple Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
related to: Apple
comment: Default transformation for yield of Apple

Barley Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
related to: Barley
unit: ton/ha
comment: Default transformation for yield of Barley

Coffee Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
related to: Coffee
comment: Default transformation for yield of Coffee

Corn Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
related to: Corn
comment: Default transformation for yield of Corn

Yield by Count
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
comment: Yield by Count

Maize Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
related to: Maize
unit: ton/ha
comment: Default transformation for yield of Maize

Mango Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
related to: Mango
comment: Default transformation for yield of Mango

Oats Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
related to: Oats
comment: Default transformation for yield of Oats

Rape Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
comment: Default transformation for yield of Rape

Rice Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
related to: Rice
comment: Default transformation for yield of Rice The low yield was from 0.15 to 0.60 tonnes/hectare, moderate from 0.61 to 1.10 tonnes/hectare and high from 1.11 to 3.16 tonnes/hectare.
definition: [0.15, 0.60, "Low yield"], [0.61, 1.10, "Moderate yield"], [1.11, 3.16, "High yield"]

Soybean Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
related to: Soybean
comment: Default transformation for yield of Soybean

Strawberry Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
related to: Strawberry
comment: Default transformation for yield of Strawberry

Yield by Tier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
comment: Transformation for Yield by Tier 3
definition: {Low, Average, High}

Yield by Tier5
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
comment: Transformation for Yield by Tier 5

Yield by Tier6
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
comment: Transformation for Crop Yield by Trend 6
definition: {Very-Low, Low, Medium–low, Medium-high, High, Very-high}

Yield by Trend3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
comment: Transformation for Crop Yield by Trend 3
definition: {None, Increase, Decrease}

Yield by Weight
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
comment: Transformation for Yield by Weight

Wheat Yield by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
related to: Wheat
unit: ton/ha
comment: Default transformation for yield of Wheat

Wheat Yield by Tier3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Yield
related to: Wheat
comment: Transformation for Wheat Yield by Tier 3: - High yield (5.63–8.68 t/ha), - Low yield (0.65–3.86 t/ha), - Medium yield (3.86–5.63 t/ha)
definition: {high yield, medium yield, low yield}

Zinc by Default
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Zinc (Zn)
unit: ppm
comment: Default transformation for Zinc

Available Zinc (AZn)
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Zinc (Zn)
Acronym: AZn

Zinc by Tier 3
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Zinc (Zn)
unit: ppm
References: Natarajan, Rajathi, Jayashree Subramanian, and Elpiniki I. Papageorgiou. "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification." Computers and Electronics in Agriculture 127 (2016): 147-157.
comment: Transformation for Zinc by Tier 3
definition: [<20, "Low"], [20-27, "Medium"], [>27, "High"]

Zinc by Tier 5
type: NamedIndividual
type: Agriculture Transformation
is transformation of: Zinc (Zn)
comment: Transformation for Zinc by Tier 5

Transplanting
type: NamedIndividual
type: Growth Stage
comment: Transplanting is the process of moving a fully germinated seedling (or mature plant) and replanting it in a permanent location for the growing season. Transplanting is an important part of agribusiness in northern climates because it extends the flowering or fruit bearing season of many plants. In agriculture and gardening, transplanting or replanting is the technique of moving a plant from one location to another. Most often this takes the form of starting a plant from seed in optimal conditions, such as in a greenhouse or protected nursery bed, then replanting it in another, usually outdoor, growing location. This is common in market gardening and truck farming, where setting out or planting out are synonymous with transplanting. In the horticulture of some ornamental plants, transplants are used infrequently and carefully because they carry with them a significant risk of killing the plant.

Metrifonate
type: NamedIndividual
type: Insecticide
comment: Trichlorfon is a synthetic organochlorophosphate compound and organophosphate acetylcholinesterase inhibitor that is used as a pesticide. It is characterized as a white crystalline or colorless to yellow or pink waxy solid, and exposure occurs by inhalation, ingestion, or contact.
label: Trichlorofon 500g/l

Trinidad and Tobago
type: NamedIndividual
type: Country
belongs to: North America
ISO code: ISO 3166-1:TT
agricultural land: 540 sq. km
area: 5,128 km²
climate: tropical; rainy season (June to December)
density: 207.8/km²
latitude: -61.20666
longitude: 10.687224
population: 1,305,000 (2005)
comment: Trinidad and Tobago, officially the Republic of Trinidad and Tobago, is the southernmost island country in the Caribbean and is known for its fossil-fuel wealth. Consisting of the main islands Trinidad and Tobago, and numerous much smaller islands, it is situated 130 kilometres south of Grenada and 11 kilometres off the coast of northeastern Venezuela. It shares maritime boundaries with Barbados to the northeast, Grenada to the northwest and Venezuela to the south and west.

Tristeza disease
type: NamedIndividual
type: Crop Disease
type: Viral Disease
affects: Citrus
affects: Lemon
affects: Orange
caused by: Aphids
management: Quarantine procedures are used to control tristeza and prevent the pathogen from entering areas which are currently free of the disease.
symptoms: Light green foliage; poor new growth; leaves may be dropping from tree; young trees blooming early; severely infected trees are stunted and bushy in appearance with chlorotic leaves and brittle twigs; some strains of the virus cause elongated pits in the trunk and branches which give the wood a rope-like appearance.
comment: Disease spread from infected grafting material or by aphids.

Triticale
type: NamedIndividual
type: Cereal
type: Crop
sowing rate: 75kg per acre / 187.5kg per ha. (Recommended by www.cotswoldseeds.com)
uses: Used as a cereal crop in its own right and wild bird seed mixtures.
comment: Triticale is a hybrid or crossing of the cereal plants wheat and rye. The benefit of this crossing, is to combine the quality and yield potential of wheat, with the robust nature and wider growing conditions of rye. Currently there is some production for human consumption, however most is used for livestock forage. It may also form a constituent of farmland bird winter food mixtures.

Tropical Corn Rust
type: NamedIndividual
type: Crop Disease
comment: Common corn rust, caused by the fungus Puccinia sorghi, is the most frequently occurring of the two primary rust diseases of corn in the U.S., but it rarely causes significant yield losses in Ohio field (dent) corn. Occasionally field corn, particularly in the southern half of the state, does become severely affected when weather conditions favor the development and spread of the rust fungus. Sweet corn is generally more susceptible than field corn. In years with exceptionally cool summers, and especially on late-planted fields or sweet corn, yield losses may occur when the leaves at and above the ears become severely diseased before grain fill is complete.

Tropical Monsoon Climate
type: NamedIndividual
type: Climate Type
comment: An area of tropical monsoon climate (occasionally known as a tropical wet climate or a tropical monsoon and trade-wind littoral climate) is a type of climate that corresponds to the Köppen climate classification category "Am". Tropical monsoon climates have monthly mean temperatures above 18 oC (64 oF) in every month of the year and a dry season. Tropical monsoon climates is the intermediate climate between the wet Af (or tropical rainforest climate) and the drier Aw (or tropical savanna climate).
label: Tropical monsoon and trade-wind littoral climate
label: Tropical wet climate

Tropical Rainforest Climate
type: NamedIndividual
type: Climate Type
comment: A tropical rainforest climate is a tropical climate usually found within 10 to 15 degrees latitude of the equator, and has at least 60 millimetres (2.4 in) of rainfall every month of the year. Regions with this climate are typically designated Af by the Köppen climate classification. A tropical rainforest climate is typically hot, very humid, and wet.

Tropical Rust
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Cotton
affects: Maize
symptoms: Symptoms of the disease are mainly observed in the form of round to oval white pustules beneath the epidermis of the leaves. The pustules are organized in clusters parallel to the leaf veins on both sides of the leaf. As they enlarge, they become purple to black and eventually burst, leaving a conspicuous bright opening lesion at their center. When the attack is severe, pustules coalesce, leading to premature leaf fall. The fungus is highly destructive and can cause severe yield losses if it infects plants before the flowering stage.

Tropical Savanna Climate
type: NamedIndividual
type: Climate Type
comment: Tropical savanna climate or tropical wet and dry climate is a type of climate that corresponds to the Köppen climate classification categories Aw (for a dry winter) and As (for a dry summer). Tropical savanna climates have monthly mean temperatures above 18 oC (64 oF) in every month of the year and typically a pronounced dry season, with the driest month having less than 60 mm (2.36 inches) of precipitation and also less than 100- (AnnualPrecipitation(mm)/25) of precipitation.
label: Tropical wet and dry climate

Tropical Wet Climate
type: NamedIndividual
type: Climate Type

Tsunami
type: NamedIndividual
type: Weather Phenomena
comment: A tsunami is a series of waves in a water body caused by the displacement of a large volume of water, generally in an ocean or a large lake. Tsunami waves do not resemble normal undersea currents or sea waves because their wavelength is far longer. Rather than appearing as a breaking wave, a tsunami may instead initially resemble a rapidly rising tide.

Tundo
type: NamedIndividual
type: Crop Disease
affects: Wheat

Tundra Climate
type: NamedIndividual
type: Climate Type

Tunisia
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:TN
agricultural land: 100,720 sq. km
area: 163,610 km²
climate: temperate in north with mild, rainy winters and hot, dry summers; desert in south
density: 63/km²
latitude: 9.561548
longitude: 33.89218
population: 10,327,800 (2008)
comment: Tunisia, officially the Republic of Tunisia, is a country in the Maghreb region of North Africa. It is bordered by Algeria to the west and southwest, Libya to the southeast, and the Mediterranean Sea to the north and east; covering 163,610 km2, Tunisia is the smallest country in North Africa. It contains the eastern end of the Atlas Mountains and the northern reaches of the Sahara desert, with much of its remaining territory arable land. Its 1,300 km of coastline include the African conjunction of the western and eastern parts of the Mediterranean Basin. Tunisia's northernmost point, Cape Angela, is also the northernmost point of Africa. Its population was 11.7 million in 2019, and the capital and largest city is Tunis, located on the northeast coast, which lends the country its name.

Turkey
type: NamedIndividual
type: Country
belongs to: Middle East
has climate: Marine Climate
has climate: Dry summer climate
ISO code: ISO 3166-1:TR
agricultural land: 382,470 sq. km
area: 783,562 km²
climate: temperate; hot, dry summers with mild, wet winters; harsher in interior
density: 93/km²
latitude: 35.19066
longitude: 38.983345
population: 71,517,100 (2008)
comment: Turkey, officially the Republic of Turkey, is a transcontinental country straddling Southeastern Europe and Western Asia. It is bordered on its northwest by Greece and Bulgaria; north by the Black Sea; northeast by Georgia; east by Armenia, Azerbaijan, and Iran; southeast by Iraq; south by Syria and the Mediterranean Sea; and west by the Aegean Sea. Istanbul, which straddles Europe and Asia, is the country's largest city, while Ankara is the capital. Approximately 70 to 80 percent of the country's citizens are ethnic Turks, while the largest minority are Kurds at 20 percent.

Turkmenistan
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:TM
agricultural land: 3,600 sq. km
area: 488,100 km²
climate: tropical; hot, humid; distinct rainy and dry seasons
density: 9.9/km²
latitude: 59.562866
longitude: 38.968323
population: 5,110,023 (12/2006)
comment: Turkmenistan, also known as Turkmenia, is a sovereign country in Central Asia, bordered by Kazakhstan to the northwest, Uzbekistan to the north and east, Afghanistan to the southeast, Iran to the south and southwest and the Caspian Sea to the west. Ashgabat is the capital and largest city of the country. The population of the country is 6 million, the lowest of the Central Asian republics. Turkmenistan is one of the most sparsely populated nations in Asia. Citizens of Turkmenistan are known as Turkmenistanis, Turkmenians or Turkmens.

Turmeric
type: NamedIndividual
type: Crop
type: Vegetable
comment: Turmeric is a flowering plant, Curcuma longa of the ginger family, Zingiberaceae, the roots of which are used in cooking.

Turning green
type: NamedIndividual
type: Growth Stage

Turnip
type: NamedIndividual
type: Crop
affects: Alternaria Leaf Spot
description: Turnip, Brassica rapa, is an herbaceous annual or biennial plant in the family Brassicaceae grown for its edible roots and leaves. The plant possesses erect stems and 8–12 leaves forming a crown. The leaves are light green in color, hairy and thin. The plant produces light yellow flowers which are clustered at the top of a raceme and are often extended above the terminal buds. The leaves can reach 30.5–35.5 cm (12–14 in) in length, while the branching flower stems can reach 30.5–91.5 cm (12–36 in). The taproot of the plant is is a bulbous tuber, almost perfectly round, which is usually a mixture of purple, white and yellow. Turnip is usually grown as an annual and harvested after one growing season. Turnip may also be referred to as annual turnip and originates from Europe.
harvesting: Turnip greens can be harvested from the plant when the leaves are about 10 cm (10 in) tall. If growing turnips for both greens and roots then only remove 2–3 leaves from each plant. Roots are ready to harvest when they reach 2.5–7.5 cm (1–3 in) in diameter. Small turnips can often be harvested by gently pulling from the soil by hand but larger roots in heavier soil may need gently dug up with a garden fork. Store unwashed roots in a root cellar or basement.
maintenance: Water turnip plants evenly and keep the soil moist for optimum growth. Mulching the plants will help to conserve moisture in the soil. Pull any weeds as they appear, the soil can be cultivated down to 2.5–5.0 cm (2–3 in) when the plants are small but this should be reduced as the plants grow larger to prevent damage to delicate feeder roots.
propagation: Turnips are usually direct seeded and can be sown as soon as the soil is workable in the Spring. For a Fall harvest, sow seeds about 2 months before the first frost in your area. Prepare the soil for planting by loosening it with a fork to a depth of about 30–38 cm (12–15 in). Remove any large rocks if present. Incorporate 2–4 inches of compost into the soil prior to planting. Sow seeds by broadcasting and raking 13 mm (0.5 in) into the soil. Thin seedlings to a final spacing of 7.5–10 cm (3–4 in). Alternatively, seeds can be sown in rows spaced 30–45 cm (12–18 in) apart.
requirements: Turnips are cold hardy vegetables which can be grown early in the spring for a summer harvest or in the summer for harvesting in late fall. They prefer a fertile, well draining soil which has a pH between 6.0 and 7.0. The soil should have a loose texture for optimum root development. Turnips will grow best in full sun but will tolerate partial shade. The average daily temperature should fall between 10–18°C (50–65°F) for adequate growth.
uses: Turnips are eaten as a vegetable after cooking. The shoots and leaves can be eaten fresh in salads or the entire plant can be used as a forage for livestock.
References: https://plantvillage.psu.edu/topics/turnip/infos
comment: The turnip or white turnip (Brassica rapa subsp. rapa) is a root vegetable commonly grown in temperate climates worldwide for its white, fleshy taproot. The word turnip is a compound of turn as in turned/rounded on a lathe and neep, derived from Latin napus, the word for the plant.

Tuvalu
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:TV
agricultural land: 18 sq. km
area: 26 km²
climate: tropical; moderated by easterly trade winds (March to November); westerly gales and heavy rain (November to March)
density: 441/km²
latitude: 177.96413
longitude: -7.539093
population: 11,992 (2007)
comment: Tuvalu is an island country in the Polynesian subregion of Oceania, surrounded by the Pacific Ocean and situated about midway between Hawaii and Australia. The country lies east-northeast of the Santa Cruz Islands, northeast of Vanuatu, southeast of Nauru, south of Kiribati, west of Tokelau, northwest of Samoa and Wallis and Futuna, and north of Fiji. It is composed of three reef islands and six atolls spread out between the latitude of 5° to 10° south and longitude of 176° to 180°, west of the International Date Line. Tuvalu has a population of 10,507 . The total land area of the islands of Tuvalu is 26 square kilometres .

Typhoon
type: NamedIndividual
type: Weather Phenomena
comment: A typhoon is a mature tropical cyclone that develops between 180° and 100°E in the Northern Hemisphere. This region is referred to as the Northwestern Pacific Basin, and is the most active tropical cyclone basin on Earth, accounting for almost one-third of the world's annual tropical cyclones. For organizational purposes, the northern Pacific Ocean is divided into three regions: the eastern (North America to 140°W), central (140°W to 180°), and western (180° to 100°E). Like any tropical cyclone, there are few main requirements for typhoon formation and development: (1) sufficiently warm sea surface temperatures, (2) atmospheric instability, (3) high humidity in the lower to middle levels of the troposphere, (4) enough Coriolis effect to develop a low pressure center, (5) a pre-existing low level focus or disturbance, and (6) a low vertical wind shear.

Uganda
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:UG
agricultural land: 140,620 sq. km
area: 236,040 km²
climate: tropical; generally rainy with two dry seasons (December to February, June to August); semiarid in northeast
density: 119/km²
latitude: 32.30465
longitude: 1.365188
population: 30,900,000 (2007)
comment: Uganda, officially the Republic of Uganda, is a landlocked country in East-Central Africa. It is bordered to the east by Kenya, to the north by South Sudan, to the west by the Democratic Republic of the Congo, to the south-west by Rwanda, and to the south by Tanzania. The southern part of the country includes a substantial portion of Lake Victoria, shared with Kenya and Tanzania. Uganda is in the African Great Lakes region. Uganda also lies within the Nile basin and has a varied but generally a modified equatorial climate.

Ukraine
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:UA
agricultural land: 412,810 sq. km
area: 603,628 km²
climate: temperate continental; Mediterranean only on the southern Crimean coast; precipitation disproportionately distributed, highest in west and north, lesser in east and southeast; winters vary from cool along the Black Sea to cold farther inland; summers are warm across the greater part of the country, hot in the south
density: 77/km²
latitude: 31.17461
longitude: 48.3828
population: 46,179,226 (2008)
comment: Ukraine is a country in Eastern Europe. It is bordered by Russia to the east and north-east; Belarus to the north; Poland, Slovakia and Hungary to the west; and Romania, Moldova and the Black Sea to the south. Including Crimea, Ukraine has an area of 603,628 km2, and is the second-largest country in Europe after Russia. Excluding Crimea, Ukraine has a population of about 41.5 million, making it the eighth-most populous country in Europe. Its capital and largest city is Kyiv.

United Arab Emirates
type: NamedIndividual
type: Country
belongs to: Middle East
has climate: Desert Climate
ISO code: ISO 3166-1:AE
agricultural land: 3,973 sq. km
area: 83,600 km²
climate: desert; cooler in eastern mountains
density: 64/km²
latitude: 53.941357
longitude: 24.287956
population: 4,621,399 (2006)
comment: The United Arab Emirates, sometimes simply called the Emirates, is a country in Western Asia located at the eastern end of the Arabian Peninsula. It borders Oman and Saudi Arabia, and has maritime borders in the Persian Gulf with Qatar and Iran. It is a federal elective constitutional monarchy formed from a federation of seven emirates, consisting of Abu Dhabi, Ajman, Dubai, Fujairah, Ras Al Khaimah, Sharjah and Umm Al Quwain. Their boundaries have numerous enclaves within each other. Each emirate is governed by a ruler, who together form the Federal Supreme Council, and one of whom serves as President of the United Arab Emirates. In 2013, the UAE's population was 9.2 million, of which 1.4 million were Emirati citizens and 7.8 million were expatriates. The estimated population of the UAE in 2020 was 9.89 million.

United Kingdom
type: NamedIndividual
type: Country
belongs to: Europe
has climate: Oceanic Climate
has climate: Boreal climate
has climate: Temperate Climate
ISO code: ISO 3166-1:GB
agricultural land: 171,640 sq. km
area: 244,820 km²
climate: temperate; moderated by prevailing southwest winds over the North Atlantic Current; more than one-half of the days are overcast
density: 246/km²
latitude: -3.446868
longitude: 55.012936
population: 60,975,000 (2007)
comment: The United Kingdom of Great Britain and Northern Ireland, commonly known as the United Kingdom, or Britain, is a sovereign country in north-western Europe, off the north-­western coast of the European mainland. The United Kingdom includes the island of Great Britain, the north-­eastern part of the island of Ireland, and many smaller islands within the British Isles. Northern Ireland shares a land border with the Republic of Ireland. Otherwise, the United Kingdom is surrounded by the Atlantic Ocean, with the North Sea to the east, the English Channel to the south and the Celtic Sea to the south-west, giving it the 12th-longest coastline in the world. The Irish Sea separates Great Britain and Ireland. The total area of the United Kingdom is 94,000 square miles .

United States
type: NamedIndividual
type: Country
belongs to: North America
has climate: Alpine Climate
has climate: Desert Climate
has climate: Humid Continental Climate
has climate: Humid Subtropical Climate
has climate: Marine Climate
has climate: Polar Climate
has climate: Semi-arid Climate
has climate: Boreal climate
ISO code: ISO 3166-1:US
agricultural land: 4.11 million sq. km
area: 9,826,630 km²
climate: mostly temperate, but tropical in Hawaii and Florida, arctic in Alaska, semiarid in the great plains west of the Mississippi River, and arid in the Great Basin of the southwest; low winter temperatures in the northwest are ameliorated occasionally in January and February by warm chinook winds from the eastern slopes of the Rocky Mountains
density: 31/km²
latitude: -123.47032
longitude: 45.176384
population: 305,776,000 (2009)
comment: The United States of America, commonly known as the United States or America, is a country primarily located in North America. It consists of 50 states, a federal district, five major self-governing territories, 326 Indian reservations, and some minor possessions. At 3.8 million square miles, it is the world's third- or fourth-largest country by total area. With a population of more than 328 million people, it is the third most populous country in the world. The national capital is Washington, D.C., and the most populous city is New York City.

Urban area
type: NamedIndividual
type: LandUse

Urea
type: NamedIndividual
type: Chemical Fertilizer
comment: Urea, also known as carbamide, is an organic compound with chemical formula CO(NH2)2. This amide has two -NH2 groups joined by a carbonyl (C=O) functional group. Urea serves an important role in the metabolism of nitrogen-containing compounds by animals and is the main nitrogen-containing substance in the urine of mammals. It is a colorless, odorless solid, highly soluble in water, and practically non-toxic (LD50 is 15 g/kg for rats). Dissolved in water, it is neither acidic nor alkaline. The body uses it in many processes, most notably nitrogen excretion. The liver forms it by combining two ammonia molecules (NH3) with a carbon dioxide (CO2) molecule in the urea cycle. Urea is widely used in fertilizers as a source of nitrogen and is an important raw material for the chemical industry.

Urea and Ammonium Nitrate Solutions
type: NamedIndividual
type: Chemical Fertilizer

Uruguay
type: NamedIndividual
type: Country
belongs to: South America
ISO code: ISO 3166-1:UY
agricultural land: 143,780 sq. km
area: 176.215 km²
climate: warm temperate; freezing temperatures almost unknown
density: 19/km²
latitude: -55.758327
longitude: -32.565083
population: 3,477,779 (2008)
comment: Uruguay, officially the Oriental Republic of Uruguay, is a country in the southeastern region of South America. It borders Argentina to its west and southwest and Brazil to its north and northeast, with the Río de la Plata to the south and the Atlantic Ocean to the southeast. Uruguay is home to an estimated 3.51 million people, of whom 1.8 million live in the metropolitan area of its capital and largest city, Montevideo. With an area of approximately 176,000 square kilometers, Uruguay is geographically the second-smallest nation in South America, after Suriname.

Ustilago tritici
type: NamedIndividual
type: Fungus

Uzbekistan
type: NamedIndividual
type: Country
belongs to: Asia
ISO code: ISO 3166-1:UZ
agricultural land: 266,600 sq. km
area: 447,400 km²
climate: mostly midlatitude desert, long, hot summers, mild winters; semiarid grassland in east
density: 59/km²
latitude: 64.56445
longitude: 41.39476
population: 27,372,000 (2007)
comment: Uzbekistan, officially the Republic of Uzbekistan, is a country in Central Asia. It is surrounded by five landlocked countries: Kazakhstan to the north; Kyrgyzstan to the northeast; Tajikistan to the southeast; Afghanistan to the south and Turkmenistan to the south-west. Along with Liechtenstein, it is one of two doubly landlocked countries.

Visible near infrared reflectance spectroscopy (VNIRRS)
type: NamedIndividual
type: Near-infrared (NIR) Spectroscopy

Valley
type: NamedIndividual
type: Topography

Vanuatu
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:VU
agricultural land: 1,870 sq. km
area: 12,189 km²
climate: tropical; moderated by southeast trade winds from May to October; moderate rainfall from November to April; may be affected by cyclones from December to April
density: 17/km²
latitude: 168.37988
longitude: -16.661196
population: 215,446 (2008)
comment: Vanuatu, officially the Republic of Vanuatu, is an island country located in the South Pacific Ocean. The archipelago, which is of volcanic origin, is 1,750 kilometres east of northern Australia, 540 kilometres northeast of New Caledonia, east of New Guinea, southeast of the Solomon Islands, and west of Fiji.

Holy See
type: NamedIndividual
type: Country
belongs to: Europe
ISO code: ISO 3166-1:VA
area: 0.44 km²
density: 1780/km²
latitude: 12.453389
longitude: 41.902916
population: 900 (2008)
comment: Vatican City, officially the Vatican City State, is the Holy See's independent city state, an enclave within Rome, Italy. The Vatican City State, also known as The Vatican, became independent from Italy with the Lateran Treaty, and it is a distinct territory under "full ownership, exclusive dominion, and sovereign authority and jurisdiction" of the Holy See, itself a sovereign entity of international law, which maintains the city state's temporal, diplomatic, and spiritual independence. With an area of 49 hectares and a population of about 825, it is the smallest state in the world by both area and population. As governed by the Holy See, the Vatican City State is an ecclesiastical or sacerdotal-monarchical state ruled by the pope who is the bishop of Rome and head of the Catholic Church. The highest state functionaries are all Catholic clergy of various national origins. After the Avignon Papacy, the popes have mainly resided at the Apostolic Palace within what is now Vatican City, although at times residing instead in the Quirinal Palace in Rome or elsewhere.
label: Vatican City

Vegetable Grasshopper
type: NamedIndividual
type: Crop Pest
alternative label: Atractomorpha

Vegetative
type: NamedIndividual
type: Growth Stage

Venezuela
type: NamedIndividual
type: Country
belongs to: South America
ISO code: ISO 3166-1:VE
agricultural land: 212,500 sq. km
area: 916,445 km²
climate: tropical; hot, humid; more moderate in highlands
density: 30.2/km²
latitude: -66.58904
longitude: 6.472792
population: 28,199,822 (2/2008)
comment: Venezuela, officially the Bolivarian Republic of Venezuela, is a country on the northern coast of South America, consisting of a continental landmass and many islands and islets in the Caribbean Sea. It has a territorial extension of 916,445 km2, and the population of Venezuela was estimated at 28 million in 2019. The capital and largest urban agglomeration is the city of Caracas.

Verticillium Wilt
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Cotton
affects: Fruit Tree
life cycle: Verticillium species are opportunistic fungi that persist in the soil as saprophytes. The organism overwinters as mycelia or microsclerotia a dark, condensed mass of mycelium that collectively act as a propagule, which germinates under favorable conditions. Infection begins in the root area where the resting hyphae of Verticillium germinate and penetrate feeder roots. The fungus also can enter wounds in the root area. The mycelium of the fungus may grow slowly. The disease spreads within the plant by mycelium or spores called microconidea that travel in xylem vessels to other parts of the plant. Where the spores lodge, new hyphae grow and increase the infection. The infected plant tissue becomes necrotic (dead) because the vascular tissue is clogged with mycelium, conidia and by products of fungal metabolism. As a result water flow is restricted and the plant wilts. The necrotic tissue is what causes the dark streaks that are symptomatic of this wilt disease. The entire plant may die quickly or may die section by section over many years.
management: Management Strategies 1. Sanitation. Remove affected annuals and perennials or prune damaged areas of trees and shrubs. Pruning disease-damaged branches and foliage plus increasing the vigor of trees and shrubs may help to keep symptoms checked. Be sure to sterilize pruners between cuts. 2. Plant resistant or tolerant species. This is the best way to manage this disease. Fungicides are not generally effective or practical.
symptoms: Symptoms of verticillium wilt vary somewhat in different host species and also within species due to varying environmental conditions. These might include sudden wilting of small branches, yellowing of foliage, stunting of growth and premature defoliation. Sapwood of infected branches typically has olive-green to black streaks. Vascular tissue appears as a dark ring in cross sections or pin-point dark spots. The initial symptoms may occur on only one branch or may involve the entire plant. Oddly, following the initial symptoms, there may be no sign of the disease for several years, even though the infection continues to reduce plant vigor.
comment: Verticillium wilt is a fungal disease of over 300 host plants, including a wide range of garden and greenhouse crops in addition to woody ornamentals, most noticeably elms, magnolias, maples, redbud, and viburnums. (See following list for a more complete list of susceptible plants.) Caused by the soil-borne pathogens Verticillium dahliae and V. albo-atrum, these wilts are prevalent throughout the tropical and temperate regions of the world. They exist in the soil primarily as mycelia that infect belowground plant tissue. High summer temperatures tend to halt development of the disease. Groups of plants resistant to verticillium wilt include gymnosperms, monocots, members of the rose family, oaks, dogwoods, willows, rhododendrons, azaleas and others.

Very High Air Pollution
type: NamedIndividual
type: Air Pollution

Very Low Air Pollution
type: NamedIndividual
type: Air Pollution

Very Poor Quality
type: NamedIndividual
type: Quality

Vietnam
type: NamedIndividual
type: Country
belongs to: Asia
has climate: Temperate Climate
has climate: Tropical Monsoon Climate
ISO code: ISO 3166-1:VN
agricultural land: 108,420 sq. km
area: 331,690 km²
climate: tropical in south; monsoonal in north with hot, rainy season (May to September) and warm, dry season (October to March)
density: 253/km²
latitude: 105.80642
longitude: 15.898546
population: 86,116,559 (2008)
comment: Vietnam, officially the Socialist Republic of Vietnam, is a country in Southeast Asia and the easternmost country on the Indochinese Peninsula. With an estimated 97.8 million inhabitants as of 2020, it is the 16th most populous country in the world. Vietnam shares its land borders with China to the north, and Laos and Cambodia to the west. It shares its maritime borders with Thailand through the Gulf of Thailand, and the Philippines, Indonesia and Malaysia through the South China Sea. Its capital city is Hanoi, and its most populous city is Ho Chi Minh City.

Vinca Stem Canker
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
life cycle: The sexual stage of this fungus is known as Diaporthe vincae. The spores are produced during very rainy seasons. The fungus grows saprophytically throughout moist soil and commonly colonizes dead and dying plant material. The fungus is believed to overwinter as dormant mycelium and as specksized, brown to black fruiting bodies (pycnidia) on the dead stems and leaves. The cycle of infection, disease development, symptom expression, and re-infection may be repeated as long as cool, damp conditions prevail and the soil moisture remains high.
management: Management Strategies 1. Removal of debris. Remove infected plants and prune out diseased parts of the plant in the fall and spring. Discard this material but do not compost. Thin planting in the fall during dry weather to remove dense growth. 2. Purchase disease-free plants. For new beds, select and plant only vigorous, disease-free stock purchased from a reputable nursery. 3. Water. After transplanting, apply only enough water to maintain vigor. Avoid frequent watering if the soil is sufficiently moist for good growth. 4. Fungicides. Under conditions in the spring of extensive and frequent rainfall, fungicides can be applied to diseases beds every 7–10 days. Application should cover all leaves and stems to runoff. Thorough coverage is essential to be effective.
symptoms: Soon after new green shoots appear in spring, vinca stem canker, Phoma exigua, causes shoot tips to become dark brown, wilt, and die back to the soil surface. Some affected stems are nearly black. After several weeks, entire clumps of plants may wither and die. Small, black, dot-like, fruiting bodies develop on the surface of the diseased stem tissue.
comment: Vinca stem canker, Phoma exigua, is a fungal disease that is almost always prevalent during very rainy seasons. This fungal disease of periwinkle or ground myrtle causes shoot tips to become dark brown changing to nearly black as the disease progresses. Stem canker (blight) may be so severe that attempts to maintain vinca plantings fail, and the plants have to be removed and replaced with other non-susceptible ground covers. Damage is most prevalent in cool wet weather in spring and in autumn when soil moisture is high. Newly planted beds, where plantings are thin and not well established, are very susceptible. Incidence of the disease tapers off during the hot dry weather of summer; however, in prolonged periods of cool wet weather, new infections can occur. Stem canker can also be a severe problem in nurseries where plants are grown under mist propagation. The fungus grows and persists indefinitely in moist soil and plant debris. It is not unusual for new plants to appear healthy when purchased only to become diseased when planted. The causal fungus survives in infested plant debris which is difficult to remove.

Vineyard Snail
type: NamedIndividual
type: Crop Pest
alternative label: Cernuella Virgate

Violent Storm
type: NamedIndividual
type: Wind Type

Vitamin A
type: NamedIndividual
type: Vitamin

Vitamin B
type: NamedIndividual
type: Vitamin

Thiamine
type: NamedIndividual
type: Vitamin
label: Vitamin B1

Vitamin B12
type: NamedIndividual
type: Vitamin

Riboflavin
type: NamedIndividual
type: Vitamin
label: Vitamin B2

Vitamin B3
type: NamedIndividual
type: Vitamin

Pantothenic Acid
type: NamedIndividual
type: Vitamin
label: Vitamin B5

Vitamin B6
type: NamedIndividual
type: Vitamin

Vitamin B7
type: NamedIndividual
type: Vitamin

Folate
type: NamedIndividual
type: Vitamin
label: Vitamin B9

Vitamin D
type: NamedIndividual
type: Vitamin

Vitamin E
type: NamedIndividual
type: Vitamin

VizSeq
type: NamedIndividual
type: Toolkit
Acronym: VizSeq
comment: VizSeq is a Python toolkit for visual analysis on text generation tasks like machine translation, summarization, image captioning, speech translation and video description. It takes multi-modal sources, text references as well as text predictions as inputs, and analyzes them visually in Jupyter Notebook or a built-in Web App (the former has Fairseq integration). VizSeq also provides a collection of multi-process scorers as a normal Python package.

Volcanic Area
type: NamedIndividual
type: Topography

Volcano
type: NamedIndividual
type: Planetary Phenomena
comment: A volcano is a rupture in the crust of a planetary-mass object, such as Earth, that allows hot lava, volcanic ash, and gases to escape from a magma chamber below the surface.

Wadi
type: NamedIndividual
type: Topography

Wallis and Futuna
type: NamedIndividual
type: Country
belongs to: Oceania
ISO code: ISO 3166-1:WF
area: 264 km²
climate: tropical; hot, rainy season (November to April); cool, dry season (May to October); rains 2,500-3,000 mm per year (80% humidity); average temperature 26.6 degrees C
density: 51/km²
latitude: -177.16779
longitude: -13.801021
population: 13,484 (2008)
comment: Wallis and Futuna, officially the Territory of the Wallis and Futuna Islands is a French island collectivity in the South Pacific, situated between Tuvalu to the northwest, Fiji to the southwest, Tonga to the southeast, Samoa to the east, and Tokelau to the northeast.

Wasps
type: NamedIndividual
type: Crop Disease
type: Crop Pest
comment: Wasp is any insect of the narrow-waisted suborder Apocrita of the order Hymenoptera which is neither a bee nor an ant; this excludes the broad-waisted sawflies (Symphyta), which look somewhat like wasps but are in a separate suborder. The wasps do not constitute a clade, a complete natural group with a single ancestor, as their common ancestor is shared by bees and ants. Many wasps, those in the clade Aculeata, can sting their insect prey.

Water body
type: NamedIndividual
type: LandUse

Waterfall
type: NamedIndividual
type: Topography

Waterlogging Stress
type: NamedIndividual
type: Crop Stress
comment: Waterlogging is one of the main abiotic stresses suffered by plants. Inhibition of aerobic respiration during waterlogging limits energy metabolism and restricts growth and a wide range of developmental processes, from seed germination to vegetative growth and further reproductive growth.

Watershed
type: NamedIndividual
type: Topography

Weevils
type: NamedIndividual
type: Crop Disease
type: Crop Pest
comment: Weevils and curculios comprise a large family of small, typically snout-nosed brown or black beetles. They feed at night and hide under leaf debris or in the soil during the day. Their larvae are small whitish grubs which feed within the fruit, stem, or roots of plants. Eggs are typically laid on--and sometimes actually injected into--susceptible plants. Check suspect plants and their fruit for telltale zigzag paths. Often, many generations of these pests occur in a year. Flowering plants vulnerable to curculio and weevil damage include coralbells, iris, lily, and rose. Susceptible fruits and vegetables include apples, blueberries, cherries, peaches, pears, plums, raspberries, rhubarb and strawberries. Several nut trees are also prone to weevil damage, among them butternut, hickory, pecan and walnut. Some species of weevils carry the dreaded Dutch elm disease, although they are not the primary transmitters of this scourge. The best control strategies start with a regular inspection of susceptible plants. As soon as weevils appear, begin spraying weekly with pyrethrum or sabadilla, making sure to cover all leaf surfaces. Repel weevils by dusting around the base of the plant or thoroughly dusting the plant itself with diatomaceous earth. Commercially available predatory nematodes can help stop the reproduction of weevils. Effective preventive measures include weed control, cleanup and destruction of dead plants and any tree fruit or nuts that fall prematurely, and crop rotation. Attract predatory birds to your yard such as bluebirds, warblers, and wrens. Specific recommendations for flowering plants. Curculio and weevils damage flowering plants by eating holes or notches in leaves, and sometimes by rolling or curling them. They can defoliate plants, cause them to wilt or their crowns to become stunted or die. Rose curculio will eat into rosebuds which will dry up and fail to open. Since these insects sleep during the day, they can easily be shaken from plants and dest…

Weka
type: NamedIndividual
type: Toolkit
has algorithm: Apriori (Apriori)
has algorithm: Decision Tree (DT)
has algorithm: Decision Tree Regressor (DTR)
has algorithm: FPGrowth (FPGrowth)
has algorithm: Gaussian Mixture Model (GMM)
has algorithm: Hierarchical Clusterer (HC)
has algorithm: K-Means (KMeans)
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Multi-layer Perceptron (MLP)
has algorithm: Naive Bayes (NB)
has algorithm: Random Forest (RF)
has algorithm: Stochastic Gradient Descent (SGD)
has algorithm: Support Vector Machine (SVM)
comment: Waikato Environment for Knowledge Analysis (Weka), developed at the University of Waikato, New Zealand. It is free software licensed under the GNU General Public License, and the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques".

Western Sahara
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:EH
area: 266,000 km²
density: 1.3/km²
latitude: -12.886729
longitude: 24.221916
population: 382,617 (2007)
comment: Western Sahara is a disputed territory on the northwest coast and in the Maghreb region of North and West Africa. About 20% of the territory is controlled by the self-proclaimed Sahrawi Arab Democratic Republic, while the remaining 80% of the territory is occupied and administered by neighboring Morocco. Its surface area amounts to 266,000 square kilometres . It is one of the most sparsely populated territories in the world, mainly consisting of desert flatlands. The population is estimated at just over 500,000, of which nearly 40% live in Laayoune, the largest city in Western Sahara.

Wet land
type: NamedIndividual
type: Topography

Wheat Blast
type: NamedIndividual
type: Crop Disease
affects: Wheat
comment: Wheat blast is a fast-acting and devastating fungal disease that threatens food safety and security in tropical areas in South America and South Asia. Directly striking the wheat ear, wheat blast can shrivel and deform the grain in less than a week from the first symptoms, leaving farmers no time to act. The disease, caused by the fungus Magnaporthe oryzae pathotype triticum (MoT), can spread through infected seeds and survives on crop residues, as well as by spores that can travel long distances in the air.

Wheat Curl Mite
type: NamedIndividual
type: Crop Disease
comment: Wheat curl mites (Aceria tulipae) are tiny, almost microscopic plant feeding mites. They have two pairs of legs near the head which is perched atop a cigar-shaped body. Their favorite food, as the name suggests, is wheat, but they also infiltrate fields of onions and garlic. Wheat curl mites on plants become active in the spring and their populations virtually explode as temps increase; 75 to 85 degrees F. (23-29 C.) are prime reproduction temperatures. They lay their eggs in rows along leaf veins and when conditions are optimal, an entire generation can be completed in ten days. Read more at Gardening Know How: Wheat Curl Mite Control – Tips On Treating Wheat Curl Mites On Plants https://www.gardeningknowhow.com/plant-problems/pests/insects/wheat-curl-mite-control.htm

Wheat Streak Mosaic
type: NamedIndividual
type: Crop Disease
type: Viral Disease
affects: Wheat
management: Cultural Practices Wheat streak mosaic is controlled by cultural practices that minimize sources of mites and virus when winter wheat crops emerge in the fall. The key components of managing this disease are effective post harvest weed control and planting date. Control volunteer wheat and grassy weeds that emerge before harvest with tillage or herbicides at least two weeks before fall planting. In hailed areas, a community effort must be made to control volunteer wheat. Do not plant winter wheat too early for your growing area. Avoid planting winter wheat next to late maturing green corn fields since corn can be a carrier for both the mite and virus.
symptoms: Leaves of infected plants show a yellowed mosaic pattern of parallel discontinuous streaks. As symptoms, progress the leaves become mottled yellow. Plants infected before early tillering are stunted, discolored and rosetted. The extent of stunting and rosetting in a field gives some indication as to the severity of the disease and ultimate yield loss. The streaked mosaic pattern on leaves and the rosette appearance of plants are good field diagnostic symptoms. Mite-infested leaves tend to have their edges rolled inward toward the mid-rib. On a young plant the tip of a new leaf often is trapped in the rolled leaf immediately below it so that the developing leaf is curled black on itself, forming a loop. In most years, there is a gradation of intensity of symptoms across a field with the most yellowing on the side of the field adjacent to wheat stubble, corn, or areas with high populations of grassy weeds that are sources of the mites.
comment: The wheat streak mosaic virus (WSMV) is a member of the Potyviridae family of viruses. It occurs in most leaf cells as flexuous rods. The wheat curl mite, Aceria tosichella, vectors the virus in the field. It is also easily transmissible through sap by mechanical inoculation. The wheat curl mite vector feeds on young lush growth of wheat and certain grasses. Mites develop from eggs to adults within 8 to 10 days, and their numbers can increase markedly during relatively short periods when the environment is favorable.

WSMV
type: NamedIndividual
type: Virus
label: Wheat streak mosaic virus

White Backed Plant Hopper
type: NamedIndividual
type: Crop Pest

White Garden Snails
type: NamedIndividual
type: Crop Pest
alternative label: Theba Pisana

White Grubs
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Wheat
controls: Effective white grub control involves reaching these pests at their most vulnerable. Fall treatment targets newly hatched grubs while they're still small and close to the surface. Spring applications treat overwintered grubs as they return to roots and feed. GardenTech® brand offer a highly effective product that kills white grubs and adult scarab beetles by contact and keeps protecting for up to three months.
distribution: Many species of white grubs found throughout the world can attack wheat and many other plant species. Cereal crops may suffer significant damage if seeded into heavily infested grasslands.
important: When the roots are not completely destroyed, the plants may survive, but are stunted and fail to produce heads. However, the distribution and extent of attack is not uniform.
life cycle: White grubs are the larvae of May or June beetles. Eggs are deposited in the soil and the hatched larvae feed on roots. The duration of the larval stage varies from species to species.
symptoms: White grubs can partially or completely sever the roots of the host plants. This causes patches of wilting and dying wheat plants (especially at the seedling stage), symptoms that could be attributed to root rots. However, when stunted patches are observed, the surrounding soil should be examined for the larvae . When fully grown, the largest of these larvae may be several centimeters long and nearly one centimeter thick. The larvae have three pairs of legs on their thorax.
comment: White grubs are the immature larvae of several species of scarab beetles. These extremely destructive turf pests feed on grass roots just below the soil surface, severing roots and causing lawn grasses to brown and die. Japanese beetle larvae and masked chafer beetle larvae present the most serious grub threats to U.S. lawns. In their adult form, Japanese beetles also cause extensive damage to edible and ornamental plants, from vegetables to shrubs and trees.

White leaf spot
type: NamedIndividual
type: Crop Disease
affects: Cassava

Whiteflies
type: NamedIndividual
type: Crop Disease
type: Crop Pest
life cycle: Management Strategies 1. Inspection. Inspect all new plants planted in the garden for whiteflies. Inspect regularly and remove by hand older leaves that are heavily infested with whiteflies in a non-mobile nymphal stage. 2. Biological control. Whiteflies have many natural enemies in the garden setting including spiders, lady beetles, and lacewings. However, these predators are not effective if there is a frequent use of insecticides. 3. Mechanical control. The use of traps can be very helpful in controlling light infestations. Make yellow sticky traps about 12 x 6 inches and coat them with a sticky substance such as Tanglefoot, petroleum jelly, or heavy grade motor oil. Hang the traps vertically near the affected plants. The adults are attracted to yellow. Traps are also commercially available. Use one trap per plant. 4. Chemical control. Whiteflies have developed resistance to some chemicals. Because of this resistance, a certain product may work well in one area but not in another. Resistance may be delayed by alternating the types of chemicals used. In addition, the egg and non-feeding pupa stages are generally not as susceptible to insecticides as are the adults and nymphs. Consequently, eradication of a whitefly population usually requires four to five applications of a registered insecticide at five to seven day intervals. Be sure the applications are made to the lower leaf surface, and apply as soon as whiteflies are detected. Do not wait until populations become severe. Before using any insecticide for whitefly control, make sure that the site and target plant are both listed on the label. Follow label directions carefully. 5. Chemicals. Some of the over-the-counter chemicals which could be used are malathion, imidacloprid, permethrin, or pyrethrins. Insecticidal soap, petroleum oil, and insect growth regulators can also be effective. Organic Strategies Strategies 1, 2, and 3 are strictly organic approaches. For an organic approach to Strategy 5, consult the…
life cycle: The whitefly spends its winters in weeds and ornamental plants, migrating to crops and gardens in spring and summer. Once temperatures warm up in the summer, populations can build rapidly with the highest populations probably occurring in late summer. They lay their tiny eggs on the undersides of leaves. Adult females usually lay between 200 to 400 eggs. Sometimes the eggs are deposited in a circular pattern (not true in all species) in groups of 30 to 40 as the female will often keep her mouth part in the plant to feed while moving in a circle to deposit eggs. Within about a week the eggs hatch into flattened nymphs, called crawlers that wander about the plant. Soon, they insert their mouth part and begin to feed. The first stage has legs and antennae, but these are lost after the first molt and the flattened, oval-shaped larvae stay fixed at one feeding site. The last part of the fourth instar is the pupa. Adults emerge from the pupa through a Tshaped slit and soon mate and reproduce. The adults live about one month. Within a population, all life stages are present and generations often overlap.
symptoms: Identification of the whitefly is easy as a white cloud of insects rise from foliage when disturbed. The adults are about 1/16 to 1/10 inch in length, wedgeshaped, and appear powdery. Infested leaves may show little if any symptoms or may be mottled and yellow. Leaves will also become coated with a sticky substance called honeydew, which is secreted by feeding whitefly nymphs and adults. A black, sooty mold may sometimes grow on the honeydew. Sooty mold does not damage the leaf. The feeding, however, removes nutrients from the plant, which results in stunting, poor growth, defoliation, reduced yields, and sometimes death. On certain plants the silverleaf whitefly causes specific damage symptoms such as silvering of leaves on squash, irregular ripening in tomato, whitestalk in broccoli and cauliflower, light root in carrots, and white stem in poinsettia.
comment: If your plants are becoming weak and the leaves a yellow and dying, give them a good shake. If they release hordes of what appears to be flying dandruff, you have a whitefly problem (Note: in western states, leafhoppers are also called whiteflies.) Even without a good shaking out, whiteflies are clearly visible on plants. The adult is approximately the size of a pinhead, mothlike, dusty and white winged. The nymph is yellowish, legless, flat and oval; it may resemble scale at certain stages. The yellow, cone shaped eggs are laid on the underside of leaves. Both the nymphs and adult whiteflies weaken plants by sucking juices from leaves, buds and stems. In addition to being general weakened with yellowed, dying foliage, affected plants and fruit may be undersized and poorly colored. Another indication of a whitefly infestation is shiny, sticky honeydew covering fruit and leaves, an excretion which furthermore encourages growth of black fungus. Most fruits and vegetables are vulnerable to whitefly damage, and they are also common greenhouse pests. Since whitefly infestations are difficult to control once they have infested your garden plants, prevention is paramount. Inspect purchased bedding or vegetable plants carefully to avoid bringing home infested specimens. If whiteflies have previously plagued your vegetable and fruit crops, you may want to use a preventive spray of insecticidal soap early in the growing season. Otherwise, as soon as you spot a light infestation of whiteflies on any of your plants, spray with insecticidal soap every 2 to 3 days for 2 weeks, making sure to get undersides of leaves. For serious infestations, you may have to resort to pyrethrum, but do this only after you have tried soap spray. Destroy heavily infested annuals as soon as you detect the problem, because otherwise the whiteflies will spread quickly throughout the garden.

Whole Gale
type: NamedIndividual
type: Wind Type

Wind Scarring
type: NamedIndividual
type: Crop Disease
type: Crop Injure

Winter
type: NamedIndividual
type: Season

Wireworms
type: NamedIndividual
type: Crop Disease
type: Crop Pest
affects: Corn
affects: Potato
affects: Rye
affects: Strawberry
affects: Wheat
development: Larval stage can last between 1 and 5 years depending on species
distribution: Many species of wireworms are found throughout the world, all of which can attack wheat. These larvae are capable of attacking many different plant species as well.
important: Woreworms are among the most damaging soil-infesting insects. Damage is usually most severe where wheat has been seeded after fallow or after a number of years of grass.
life cycle: Wireworms are the larvae of click beetles, of which there are many species. The adult beetles (79) lay eggs in the soil, usually in the spring, and the larvae may take several years to develop prior to pupating, depending on species. Generations overlap so that all stages and sizes of larvae may be found in the soil at the same time.
management: Chemical control impossible in a standing crop, must be applied at preplanting or as a seed treatment; if wireworms are known to be present in soil fallow field during summer and till frequently to reduce numbers; rotate to non-host crop where possible; avoid planting susceptible crops after a wireworm infestation on cereals without either fallowing of applying appropriate pesticide
symptoms: Wireworm damage is very similar to that caused by other soil-inhabiting chewing insects; the only sure means of identifying wireworms as the causal agent is to find them in association with the damaged seedlings (77). The name "wireworm" refers to the tough, wire-like appearance of the larvae. They are 20-30 mm long and are often smooth, hard, and highly polished. They have three pairs of legs (78), and their color may vary from a rich cream to shades of brown. Wireworm larvae may attack wheat as soon as the crop is seeded, eating the endosperm of the kernels and leaving only the seed coat. A common sign of woreworm attack is the wilting and/or dying of a number of adjacent plants, either in a row or patch. The stems of affected seedlings will be chewed just above the seed. Death of seedlings; reduced stand; girdled stems and white heads; wireworm larvae can be found in soil when dug round the stem; larvae are yellow-brown, thin worms with shiny skin
comment: Wireworms are the larvae of click beetles. They are found throughout the United States, and are especially common on land that is poorly drained or has recently been in sod. Wireworms are 1/3 to 1 1/2" long, dark brown to yellowish, jointed, hard shelled and cylindrical. Unlike millipedes, with their many pairs of legs, wireworms have only 3 pairs, located directly behind the head. Wireworms chew on underground stems, roots, seeds and tubers of a wide variety of plants, causing them to wilt and die. They will feed on most garden fruits and vegetables, but prefer beets, beans (snap), carrots, corn, lettuce, muskmelons, onions, peas, potatoes, strawberries, and sweet potatoes.

Woody Perennial
type: NamedIndividual
type: Crop Disease
type: Weeds
life cycle: Many woody perennial weeds get their start in your garden from seeds, which blow in, come in in mulch or are brought in by birds or other animals. Once in the garden, some, such as black locust, can spread by roots underground or re-sprout from roots left in the ground even after most of the plant is removed.
management: Management Strategies 1. Proper maintenance and weeding.First, try and keep the weed plants from getting established. Use mulch, landscape cloth or other physical barriers to prevent seeds from becoming a problem. Weekly weeding keeps the flower bed free of volunteers. 2. Plant removal and stump treatment. Once the plants get established the best control is to dig up the invasive plant roots and all and discard it. If plants are too large or difficult to dig out, cut off the stem close to the ground leaving a stub or stump. Paint the stump with a stump remover or an herbicide containing 2,4-D such as Weed-B-Gone straight from the bottle and cover with soil. The herbicide will move down the stump and kill the roots of the plant. Use caution when using either of these products straight from the container as careless use can damage nearby plants, which may be sensitive to the herbicide. Alternatively, let foliage regrow on the stump to 12–18 inches then spray the foliage with a brush killer or one containing glyphosate (Roundup) with is absorbed through the leaves and will be translocated to the roots where it will kill the plant’s root. 3. Treat larger areas with herbicides. Selective herbicides such as Weed-B-Gone can be safely applied to woody perennial weeds growing in lawn areas but great care must be taken when using them in and around other broadleaf plants. Additional care must also be taken when using non-selective herbicides in all locations as overspray onto surrounding grass or broadleaf plants will cause damage. Always thoroughly read the label of the product you plan to use before applying. 4. Proper timing. If applying herbicides, apply when the weeds are most susceptible. Most perennial weeds are susceptible to herbicides when they flower, or in late summer and early fall. Timing varies for each plant so read the label carefully. Organic Strategies Strategy 1 is a strictly organic approach. Removal by hand, as suggested in Strategy 2, is an organic appr…
comment: Many plants that come back year after year and form a woody stem can become tenacious weeds in a garden. Some examples include: trees such as mulberry, ash, sweet gum, and black locust; shrubs such as honeysuckle, blackberry, and lespedeza; and vines such as poison ivy, honeysuckle, kudzu, Virginia creeper, trumpet creeper, clematis vine, wisteria, and English ivy. Left unchecked they can quickly overtake a garden or fencerow.

Chlorophyll Index RedEdge 710 (CIrededge710)
type: NamedIndividual
type: Satellite
label: WorldView-1

WorldView-2
type: NamedIndividual
type: Satellite

WorldView-3
type: NamedIndividual
type: Satellite
comment: WorldView-3 is the first multi-payload, super-spectral, high-resolution commercial satellite sensor operating at an altitude of 617 km. WorldView-3 satellite provides 31 cm panchromatic resolution, 1.24 m multispectral resolution, 3.7 m short wave infrared resolution (SWIR) and 30 m CAVIS resolution.

X-disease Phytoplasma
type: NamedIndividual
type: Crop Disease
affects: Cherry
affects: Peach
symptoms: Infection reduces fruit size and quality in sweet cherries. In contrast to Little cherry virus 2 where fruit often has little flavor, fruit from X-disease infected trees are often bitter. Fruit have reduced fructose, glucose, and sorbitol content and in some cases total phenolic content increases [1]. In addition to fruit symptoms you can see reduced growth/extension of infected limbs, sometimes leading to leaves crowding into dense clusters (rosette) when trees have been infected for multiple years. Symptoms in cherry - Small and misshapen fruit. - Poor color development. - Fruit lacking in flavor/ bitter. - Symptoms can be confused with unripe fruit until close to harvest. - Symptoms are restricted to one/a few branches unless trees have been infected for multiple years. Symptoms in peaches, plums, and nectarines - Yellowed curled leaves. - Leaf shot hole. - Small-deformed fruit. - Leaf yellowing symptoms on infected peaches and nectarines begin to appear about 2 months prior to harvest, and get progressively worse, with shot holes appearing as the season progresses. Symptom progression - Year 1-2: small fruit may be restricted to one branch, or cluster, fruit color may develop normally, or individual pale to white fruit may be observed. - Years 2-3: systemically infected tree, small fruit observed on multiple or all limbs, and poor color development is pronounced. - 4+ years: cultivar dependent, but characterized by reduced fruit yield, and dieback of limbs.
comment: X-disease phytoplasma, Little cherry virus 2 (LChV2) and Little cherry virus 1 (LChV1), cause small cherry symptoms often described as ‘X-disease’ and ‘Little Cherry Disease.’ Diseased cherry trees produce small, poorly colored, unmarketable fruit. In peaches, plums, and nectarines X-disease symptoms are typically yellowed curled leaves and shot hole as well as small-deformed fruit. X-disease is present across North America, throughout Washington State, and at epidemic levels in the Columbia River basin, with high incidence in Yakima, Benton, and Franklin counties, and present in Oregon in The Dalles area. X-disease (Western X) is not a new problem. It was first identified in cherry trees of WA State in 1946. In a 1947 survey, about 1% of cherry trees were found to be infected, and it has remained present ever since, fluctuating in frequency.

Xanthomonas campestris
type: NamedIndividual
type: Bacterial
alternative label: X. campestris pv. undulosa
alternative label: X. translucens, X. translucens flsp. undulosa
alternative label: Xanthomonas campestris pv. translucens

Xanthomonas Wilt
type: NamedIndividual
type: Crop Disease
affects: Banana
symptoms: The disease causes loss both through death of the plant and rotting of the fruit. The leaves gradually turn yellow and start looking lifeless as if they were melting under intense heat. They eventually turn brown and die. In flowering plants, the first symptoms of insect transmission are a drying rot and blackening of the male bud that start with the outer bracts and eventually extend to the rachis. The fruits ripen unevenly and prematurely, turning from green to yellow and black rapidly. The pulp of the rotting fruits shows rusty brown stains. Internal symptoms revealed by doing a cross-section of an infected pseudostem are yellow-orange streaking of the vascular tissues and the presence of a yellow bacterial ooze, which can also be seen from any other infected plant part.
comment: Xanthomonas wilt of banana - better known as BXW, and to a lesser extent as BBW (for banana bacterial wilt) - is a disease that causes a banana plant to rot from the inside out. The disease is triggered by the bacteria Xanthomonas vasicola pv. musacearum (Xvm), formerly known as Xanthomonas campestris pv. musacearum. Once they are inside the plant, these microscopic single-cell organisms multiply and form the slime that is visible when an infected plant is cut open. This bacterial ooze is the means by which the disease is transmitted by insects and cutting tools that have come in contact with it (see Modes of transmission).

Xanthomonas oryzae
type: NamedIndividual
type: Bacterial
comment: Xanthomonas oryzae is a species of Proteobacteria. The major host of the bacterium is rice. The species contains two pathovars which are not European: Xanthomonas oryzae pv. oryzae and Xanthomonas oryzae pv. oryzicola.

Yellow Leaf Curl
type: NamedIndividual
type: Crop Disease
caused by: YellowLeafCurlVirus
comment: Yellow leaf curl is a disease of tomato caused by Tomato yellow leaf curl virus. In March 2007, it was identified for the first time in California and currently has a limited distribution. Infected tomato plants initially show stunted and erect or upright plant growth; plants infected at an early stage of growth will show severe stunting. However, the most diagnostic symptoms are those in leaves. Leaves of infected plants are small and curl upward; and show strong crumpling and interveinal and marginal yellowing. The internodes of infected plants become shortened and, together with the stunted growth, plants often take on a bushy appearance, which is sometimes referred to as 'bonsai' or broccoli'-like growth. Flowers formed on infected plants commonly do not develop and fall off (abscise). Fruit production is dramatically reduced, particularly when plants are infected at an early age, and it is not uncommon for losses of 100% to be experienced in fields with heavily infected plants.

Yellow Nutsedge
type: NamedIndividual
type: Crop Disease
type: Weeds
management: In Lawns 1. Pulling the plants when they are young can give temporary control. Pull young plant when they are young in late spring to early summer before they begin forming new tubers mid summer through fall. Pulling will not, however, remove the underground nutlets so it is not a cure. Dormant tubers can survive in the soil for up to 3 years and will require regular pulling to prevent new tubers from being formed. Increasing the health of the lawn by mowing high, aerating, and fertilizing in the fall can help the grass better compete with nutsedge. Be sure to remove plants in the lawn and adjacent areas before they go to seed. Warmseason grasses compete better with nutsedge than do cool-season grasses. 2. Treat with an herbicide. Ortho Crabgrass & Nutgrass Killer, Basagran and Tenacity are effective. The herbicide Sedgehammer (Manage) provides the most effective control, but has more limited availability. Follow label directions and observe precautions. Two to three applications 4–7 days apart are generally required to kill existing patches of nutsedge. Spot treat only the areas that have nutsedge but follow-up treating will need to be done for 2-3 years as dormant tubers sprout and grow. Timing is also important. Treat beginning in late spring to early summer to kill plants before they begin forming new tubers, which occurs summer through fall. To prevent future problems, improve the health of the lawn to better compete with germinating nutsedge. In Planting Beds 1. Dig plants.In planting beds, dig nutsedge plants and remove the underground nutlets. Regular pulling will be necessary to effect control. 2. Spot treat with a chemical herbicide. Spot treatment with glyphosate (Roundup, Kleenup) gives good control. glyphosate (Roundup, Kleenup) will kill plants, root and all. It is nonselective and will kill or injure most plants it contacts. Use with care around desirable plants. Organic Strategies In lawns: Strategy 1 is a strictly organic approach. In planting beds:…
comment: Yellow nutsedge, Cyperus esculentus, is a common lawn and garden weed in Missouri. It is also referred to as nutgrass or watergrass. It is not a grass but rather a sedge. This is evident in the stem that is triangular in cross section, not round as in grasses. The leaves are bright green and have a waxy appearance. It grows faster than many lawn grasses so it is often noticed when it outgrows the surrounding grass. It will also remain a bright green in summer when surrounding lawn grass may be a lighter green. It thrives in low spots and high moisture areas that drain poorly, but can occur in drier sites as well. The plant produces feathery, umbrella-like flower clusters. The plant is perennial, reproducing by seed and underground tubers. The underground tubers or nutlets can remain dormant in the ground for several years.

Yellow Rice Borer
type: NamedIndividual
type: Crop Pest

Stripe Rust
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
affects: Wheat
development: Primary infections are caused by wind-borne urediospores that may have travelled long distances. The disease may develop rapidly when free moisture (rain or dew) occurs and temperatures range between 10-20C. At temperatures above 25C, the production of urediospores is reduced or ceases and black teliospores are often produced.
distribution: Stripe rust can attack wheat, barley, triticale, and many other related grasses. The disease is found in all highland and/or temperate areas where cereals are grown. No alternate host is known.
important: Severe infections can cause yield losses, mainly by reducing the number of kernels per spike, test weights, and kernel quality.
symptoms: The pustules of stripe rust, which, contain yellow to orange-yellow urediospores, usually form narrow stripes on the leaves. Pustules also can be found on leaf sheaths, necks, and glumes.
label: Yellow Rust

Yellow Sigatoka
type: NamedIndividual
type: Crop Disease
type: Fungal Disease
chemical control: The fungicides used to control black leaf streak can also be used against Sigatoka leaf spot. It is recommended to alternate between systemic and contact fungicides to delay the development of resistance to the fungicides. A forecasting system has been developed for Sigatoka leaf spot.
distribution: Like black leaf streak, Sigatoka leaf spot is globally distributed. It was first recorded on the Indonesian Island of Java by Zimmermann in 1902. Later, the disease was reported in the Sigatoka District on the island of Viti Levu, Fiji, in 1912. During the following 40 years, serious epidemics were reported, with distribution across growing regions of Asia, Africa, Central and South America and the Caribbean. In Brazil, where the pathogen occurs in all growing regions, it still imposes substantial costs to growers in the larger producing regions. Losses of up to 50% of production have been reported.
management: In regions where the pathogen is present, the inoculum can be maintained at a low level using cultural practices such as drainage, weeding, proper fertilization, planting density and deleafing to reduce inoculum. Sigatoka leaf spot can be effectively controlled by combining deleafing and chemical treatment.
symptoms: The first visible symptom is a slight discoloration between the leaf's secondary veins (A). Over time, these points develop into pale yellow streaks, brown streaks and elliptic necrotic spots arranged parallel to the secondary veins. The depressed grey center is surrounded by a yellow halo. As the disease progresses, the lesions coalesce and cover a large area of the leaf (F).
comment: Sigatoka leaf spot (popularly known as Yellow Sigatoka) is a fungal disease caused by Pseudocercospora musicola (formerly Mycosphaerella musicola ). It was the first leaf spot disease to have a global impact on bananas but has since been largely displaced by black leaf streak in many banana production areas. However, it can still cause considerable losses at higher altitudes and cooler temperatures, and is also typically a greater problem during rainy seasons in subtropical banana growing regions . The disease reduces the leaf's photosynthetic capacity, which affects bunch size. It also shortens the fruit's green life, the time between harvest and ripening.
alternative label: Sigatoka leaf spot

Yemen
type: NamedIndividual
type: Country
belongs to: Middle East
ISO code: ISO 3166-1:YE
agricultural land: 234,520 sq. km
area: 527,968 km²
climate: mostly desert; hot and humid along west coast; temperate in western mountains affected by seasonal monsoon; extraordinarily hot, dry, harsh desert in east
density: 42/km²
latitude: 48.173294
longitude: 15.35796
population: 23,013,376 (2008)
comment: Yemen, officially the Republic of Yemen, is a country at the southern end of the Arabian Peninsula in Western Asia. It is the second-largest Arab sovereign state in the peninsula, occupying 527,970 square kilometres . The coastline stretches for about 2,000 kilometres . Yemen's constitutionally stated capital is the city of Sanaa, but the city has been under Houthi rebel control since February 2015 as well as Aden, which is also controlled by the Southern Transitional Council since 2018. Its executive administration resides in Riyadh, Saudi Arabia.

Young
type: NamedIndividual
type: Growth Stage

Zambia
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:ZM
agricultural land: 234,350 sq. km
area: 752,618 km²
climate: tropical; modified by altitude; rainy season (October to April)
density: 16/km²
latitude: 27.852537
longitude: -13.151916
population: 11,668,000 (2005)
comment: Zambia, officially the Republic of Zambia, is a landlocked country at the crossroads of Central, Southern and East Africa. Its neighbours are the Democratic Republic of the Congo to the north, Tanzania to the north-east, Malawi to the east, Mozambique to the southeast, Zimbabwe and Botswana to the south, Namibia to the southwest, and Angola to the west. The capital city is Lusaka, located in the south-central part of Zambia. The population is concentrated mainly around Lusaka in the south and the Copperbelt Province to the northwest, the core economic hubs of the country.

Zimbabwe
type: NamedIndividual
type: Country
belongs to: Africa
ISO code: ISO 3166-1:ZW
agricultural land: 163,200 sq. km
area: 390,757 km²
climate: tropical; moderated by altitude; rainy season (November to March)
density: 33/km²
latitude: 29.146666
longitude: -19.013287
population: 13,349,0001 (2008)
comment: Zimbabwe, officially the Republic of Zimbabwe, is a landlocked country located in Southern Africa, between the Zambezi and Limpopo Rivers, bordered by South Africa, Botswana, Zambia and Mozambique. The capital and largest city is Harare. The second largest city is Bulawayo. A country of roughly 14 million people, Zimbabwe has 16 official languages, with English, Shona, and Ndebele the most common.

Zucchini
type: NamedIndividual
type: Crop
type: Vegetable
comment: The zucchini is a summer squash, a herbaceous vine whose fruit are harvested when their immature seeds and epicarp (rind) are still soft and edible. It is closely related, but not identical, to the marrow; its fruit may be called marrow when mature.

pSeven
type: NamedIndividual
type: Toolkit
comment: pSeven is a software platform that provides advanced tools for Design Exploration and Predictive Modeling, packaged in an easy-to-use graphical user interface. pSeven allows even non-math experts to solve challenging engineering problems and discover optimal designs just in a few clicks!

spaCy
type: NamedIndividual
type: Toolkit
Acronym: spaCy
comment: spaCy is built for advanced NLP in Python and Cython. The commercial open-source software was released under MIT license and supports custom models in PyTorch and TensorFlow. spaCy supports more than 60 languages and has trained pipelines for different languages and tasks. Its features include components for: Named entity recognition Part-of-speech tagging Dependency parsing Sentence segmentation Text classification Lemmatisation Morphological analysis Entity linking As the team behind spaCy says themselves, it has created an awesome ecosystem. Read more about its fast execution functionality

Bull’s Eye Rot
type: NamedIndividual
type: Crop Disease
affects: Apple
biological control: It is important to establish new plantings with clean stock free of visible cankers . Scout orchards, especially new ones, regularly for cankers. Early detection will help with management. In apple and pear orchards, prune and remove cankered branches to reduce overwintering inoculum and apply fungicides to dead bark to target potential surviving inoculum of Neofabraea spp. Water spreads the fungal inoculum and creates conditions conducive for fruit infection. Avoid overhead irrigationand , cycle over-tree cooling so that conditions do not remain wet in the canopy for prolonged periods of time. Ideally harvest fruit while they are dry and store immediately.
chemical control: Fungicides should be applied before fall rains at petal fall and pre-harvest. Refer to the Pacific Northwest Plant Disease Management Handbook for more information on pesticide use.
chemical control: Pre and postharvest applications of fungicides are key to effective bull’s eye rot management. Before making a spray decision, consider the cultivar and weather conditions occurring during that season, as these two factors have a great impact on BER development in storage. Even though fruit become more susceptible closer to harvest, infection by Neofabraea spp. may occur anytime between bloom and harvest. Consider your fungicide timing. Studies have shown that treatments applied 18 weeks before harvest were more effective in controlling BER than those applied two weeks before harvest. The most effective preharvest fungicide is Topsin-M (FRAC 1) but other fungicides, such Pristine (FRAC 7 + 11), have shown good efficacy levels. The multi-site fungicide Ziram applied within two weeks before harvest has shown variable levels of efficacy against bull’s eye rot in the Pacific Northwest but is a good fungicide to include in the spray program alone or tank-mixed with other fungicides to enhance control and mitigate fungicide resistance. Four different fungicides are currently registered to control postharvest decays of pome fruit (Table 2). Thiabendazole ( FRAC 1) is the most effective fungicide against bull’s eye rot followed by pyrimethanil (Penbotec). Fludioxonil (Scholar) has a low efficacy against bull’s eye rot and it is recommended to apply it in combination with thiabendazole, especially on susceptible cultivars or on lots with known bull’s eye rot history. Difenoconazole was registered for postharvest application on pome fruit in 2016 and has shown a better efficacy against bull’s eye rot compared to Scholar alone. Formulations exist to apply thiabendazole, pyrimethanil, and fludioxonil through thermonebulization (TNB, fog) exist already and a formulation for a fog application of Academy is pending. In Washington State, Academy can be applied via drench at harvest as long as packers can follow specific fungicide waste management practices (https://agr.wa.gov/waste…
life cycle: In central Washington State where the majority of apple and pear orchards are planted, BER is mainly caused by N. perennans, which seems to be better adapted to the dry and semi-arid climate. BER of pear in Oregon was found to be often caused by N. alba. Neofabraea perennans can cause perennial cankers, on the tree, on which the fungus overwinters as acervuli and from which conidia are released when conditions become favorable in the spring and summer of the following season. Besides cankers, Neofabraea species can overwinter, as asymptomatic infections, in mummified fruits, pruned wood, leaf litter, and on grass around the orchard (Kohl et al. 2018). On pear trees, Neofabraea spp. may survive on dead bark. Inoculum of Neofabraea spp. may also come from alternative hosts such as western crabapple (Malus fusca), which is thought to be the original host of anthracnose canker (Aguilar et al. 2017). The primary and secondary inoculums are water-splashed to limbs and fruits to cause infections between petal fall and harvest, when fruit are more susceptible. Infections are more frequent in rainy seasons and on late cultivars that tend to receive more rain. The fungus only infects fruit preharvest, and because of the dry growing season in the region, infections remain latent with symptoms expressed only after 3-4 months of storage. BER incidences as high as 20% can been seen especially in rainy seasons and on susceptible cultivars. BER does not spread from decayed to healthy fruits inside the bin in cold storage. The fungus grows between 0°C and 25°C (32°F-82°F), with the optimal temperature being 20°C (68°F). Growth declines significantly at temperatures around 30°C (>86°F) and above.
symptoms: BER symptoms of infections originating from lenticels show lesions that are depressed, circular, flat to slightly sunken with a light brown to dark brown halo around a lighter brown to tan center. Several spots may be visible on a single fruit. Infections that start from the stem-end or calyx-end will have the same coloration but are not sunken. Decayed tissue is dark brown in the inside with a dense consistency and a clear margin between decayed and healthy tissue. Usually, decayed tissue does not readily separate from the healthy tissue. BER lesions progress slowly in cold storage and as decay advances, cream-colored spore masses may appear at the center of the lesion (Fig.1). It is very difficult to differentiate between BER infections caused by the different Neofabraea species on fruit. Lesions of N. alba may look much paler than that of lesions caused by N. perennans, whereas N. malicorticis tend to show gray acervuli (small asexual fruiting body shaped like a hershy kiss) that are concentrically distributed at the center of the lesion.
comment: Bull’s eye rot (BER) is a continuous threat to apple and pear storability in the PNW and in many other growing regions. Recent surveys from Washington State showed that BER accounted for 8 to 10% of overall decayed fruit and 40% of the surveyed orchards had BER at frequencies ranging between 1% and 75% (Amiri and Ali 2016). Some cultivars, such as Golden Delicious and Pinata are highly susceptible to Neofabraea spp., but other cultivars such as Fuji, Cripps Pink, and Granny Smith can also be at risk, especially when rainy conditions occur before harvest.

Atlas agroclimatique du Quebec - Evaluation des opportunites et des risques agroclimatiques dans un climat en Evolution
type: NamedIndividual
type: Report
authors: Ct, H., Ouranos
creator: Ct, H., Ouranos
label: Report 001

EUMETNET/ECSN optional programme: European Climate Assessment & Dataset (ECA&D) Algorithm Theoretical Basis Document (ATBD), version 4.
type: NamedIndividual
type: Report
authors: Project team ECA&D
creator: Project team ECA&D
label: Report 002

AirPressure 1232
type: NamedIndividual
type: Air Pressure
is condition of: Regressor 1232

Altitude 0540
type: NamedIndividual
type: Altitude
is condition of: Regressor_0540

Aluminum 1073
type: NamedIndividual
type: Aluminum (Al)
is condition of: Regressor 1073

Anthocyanin 0034
type: NamedIndividual
type: Anthocyanin
is condition of: Classifier 0034

Area 0003
type: NamedIndividual
type: Area
is condition of: Regressor_0003

Area 0076
type: NamedIndividual
type: Area
is condition of: Regressor_0076

Area 0187
type: NamedIndividual
type: Area
is condition of: Regressor_0187

Area 1202
type: NamedIndividual
type: Area
is condition of: Regressor 1202

Area 1240
type: NamedIndividual
type: Area
has transformation: Gross Irrigated Area (GIA)
has transformation: Net Cropped Area (NCA)
has transformation: Net Irrigated Area (NIA)
is condition of: Regressor 1240

Area 2991
type: NamedIndividual
type: Area
is condition of: Regressor 2991

Area 3176
type: NamedIndividual
type: Area
is condition of: Classifier 3176

Area 3684
type: NamedIndividual
type: Area
has transformation: Crop Canopy Cover (CC)
is condition of: Regressor 3684

Area 3730
type: NamedIndividual
type: Area
has transformation: Land Cover (LC)
is condition of: Regressor 3730

Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters
type: NamedIndividual
type: Article
has knowledge model: Regressor_0001
abstract: The soil, Soul of Infinite Life, is the entity responsible for sustaining life on earth. In spite of significant advances in the service sector, agriculture remains the major provider of employment and source of revenue in India. Soil testing is a valuable tool for evaluating the available nutrient status of soil and helps to determine the proper amount of nutrients to be added to a given soil based on its fertility and crop needs. In the current study, the soil test report values are used to classify several significant soil features like village wise soil fertility indices of Available Phosphorus (P), Available Potassium (K), Organic Carbon (OC) and Boron (B), as well as the parameter Soil Reaction (pH). The classification and prediction of the village wise soil parameters aids in reducing wasteful expenditure on fertilizer inputs, increase profitability, save the time of chemical soil analysis experts, improves soil health and environmental quality. These five classification problems are solved using the fast learning classification technique known as Extreme Learning Machine (ELM) with different activation functions like gaussian radial basis, sine-squared, hyperbolic tangent, triangular basis, and hard limit. After the performance analysis of ELMs with diverse activation functions for these soil parameter classifications, the gaussian radial basis function attains the maximum performance for four out of five problems, which goes above 80% in most of the accuracy rate calculations in every problem, followed by hyperbolic tangent, hard limit, triangular basis, and sine-squared. However, the performance of the final classification problem, i.e. the pH classification, gives moderate values with the gaussian radial basis and best performance (near 90%), with the hyperbolic tangent.
author: M.S. Suchithra, Maya L. Pai,
publish year: 2020
creator: M.S. Suchithra, Maya L. Pai,
reference: M.S. Suchithra, Maya L. Pai,, "Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters", Information Processing in Agriculture, Volume 7, Issue 1, March 2020, Pages 72-82, Elsevier B.V.
Source: Information Processing in Agriculture, Volume 7, Issue 1, March 2020, Pages 72-82, Elsevier B.V.
label: Article 0001

Rice crop yield prediction in India using support vector machines
type: NamedIndividual
type: Article
has knowledge model: Regressor_0003
abstract: Food production in India is largely dependent on cereal crops including rice, wheat and various pulses. The sustainability and productivity of rice growing areas is dependent on suitable climatic conditions. Variability in seasonal climate conditions can have detrimental effect, with incidents of drought reducing production. Developing better techniques to predict crop productivity in different climatic conditions can assist farmer and other stakeholders in better decision making in terms of agronomy and crop choice. Machine learning techniques can be used to improve prediction of crop yield under different climatic scenarios. This paper presents the review on use of such machine learning technique for Indian rice cropping areas. This paper discusses the experimental results obtained by applying SMO classifier using the WEKA tool on the dataset of 27 districts of Maharashtra state, India. The dataset considered for the rice crop yield prediction was sourced from publicly available Indian Government records. The parameters considered for the study were precipitation, minimum temperature, average temperature, maximum temperature and reference crop evapotranspiration, area, production and yield for the Kharif season (June to November) for the years 1998 to 2002. For the present study the mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative squared error (RRSE) were calculated. The experimental results showed that the performance of other techniques on the same dataset was much better compared to SMO.
author: Niketa Gandhi; Leisa J. Armstrong; Owaiz Petkar; Amiya Kumar Tripathy
publish year: 2016
creator: Niketa Gandhi; Leisa J. Armstrong; Owaiz Petkar; Amiya Kumar Tripathy
reference: Niketa Gandhi; Leisa J. Armstrong; Owaiz Petkar; Amiya Kumar Tripathy, "Rice crop yield prediction in India using support vector machines ", 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2016, IEEE
Source: 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2016, IEEE
label: Article 0003

Predicting soil pH by using nearest fields.
type: NamedIndividual
type: Article
has knowledge model: Regressor_0004
abstract: In precision agriculture (PA), soil sampling and testing operation is prior to planting any new crop. It is an expensive operation since there are many soil characteristics to take into account. This paper gives an overview of soil characteristics and their relationships with crop yield and soil profiling. We propose an approach for predicting soil pH based on nearest neighbour fields. It implements spatial radius queries and various regression techniques in data mining. We use soil dataset containing about 4, 000 fields profiles to evaluate them and analyse their robustness. A comparative study indicates that LR, SVR, and GBRT techniques achieved high accuracy, with the R2 values of about 0.718 and MAE values of 0.29. The experimental results showed that the proposed approach is very promising and can contribute significantly to PA.
author: Quoc Hung Ngo, Nhien-An Le-Khac, and Tahar Kechadi
publish year: 2019
creator: Quoc Hung Ngo, Nhien-An Le-Khac, and Tahar Kechadi
reference: Quoc Hung Ngo, Nhien-An Le-Khac, and Tahar Kechadi, "Predicting soil pH by using nearest fields.", 29th SGAI International Conference on Artificial Intelligence,SGAI-AI 2019, LNAI 11927. Springer, 2019."
Source: 29th SGAI International Conference on Artificial Intelligence,SGAI-AI 2019, LNAI 11927. Springer, 2019."
label: Article 0004

Assessment of the agro-climatic indices to improve crop yield forecasting
type: NamedIndividual
type: Article
has knowledge model: Regressor_0005
abstract: Weather has a major impact on agriculture. Statistical models have been used to estimate or forecast crop yield from weather information. In this paper, a general statistical framework is developed in order to rank and quantify the information content of weather information. The methodology is tested over the US, for corn yield. The weather sensitivity of different corn production areas is first analyzed. More than fifty agro-climatic indices have been compared. The study shows that variability in yield is more likely to be due to weather variability in medium and low production areas. The two best yield predictors are the temperature and then the Standardized Precipitation-Evapotranspiration Index (SPEI) in July. While corn in the north Eastern regions is not significantly affected by weather variation, on the East coast, the weather-based mixed model is able to explain about 32% (corr = 0.57) of the yield variability, but in some particularly weather-sensitive states, such as Virginia, this number can reach 64% (corr = 0.80). Two applications are tested: yield estimation at the end of the year for monitoring purpose, and seasonal prediction. It is shown that some agro-climatic indices can significantly improve crop yield modeling when compared to simpler direct weather information (average correlation increase of 0.12 at the US scale).
author: Jordane A. Mathieu, Filipe Aires
publish year: 2018
creator: Jordane A. Mathieu, Filipe Aires
reference: Jordane A. Mathieu, Filipe Aires, "Assessment of the agro-climatic indices to improve crop yield forecasting", Agricultural and Forest Meteorology, 2018 , Elsevier
Source: Agricultural and Forest Meteorology, 2018 , Elsevier
label: Article 0005

Wheat yield prediction using machine learning and advanced sensing techniques
type: NamedIndividual
type: Article
has knowledge model: Classifier 0006
abstract: Understanding yield limiting factors requires high resolution multi-layer information about factors affecting crop growth and yield. Therefore, on-line proximal soil sensing for estimation of soil properties is required, due to the ability of these sensors to collect high resolution data (>1500 sample per ha), and subsequently reducing labor and time cost of soil sampling and analysis. The aim of this paper is to predict within field variation in wheat yield, based on on-line multi-layer soil data, and satellite imagery crop growth characteristics. Supervised self-organizing maps capable of handling existent information from different soil and crop sensors by utilizing an unsupervised learning algorithm were used. The performance of counter-propagation artificial neural networks (CP-ANNs), XY-fused Networks (XY-Fs) and Supervised Kohonen Networks (SKNs) for predicting wheat yield in a 22 ha field in Bedfordshire, UK were compared for a single cropping season. The self organizing models consisted of input nodes corresponded to feature vectors formed from normalized values of on-line predicted soil parameters and the satellite normalized difference vegetation index (NDVI). The output nodes consisted of yield isofrequency classes, which were predicted from the three trained networks. Results showed that cross validation based yield prediction of the SKN model for the low yield class exceeded 91% which can be considered as highly accurate given the complex relationship between limiting factors and the yield. The medium and high yield class reached 70% and 83% respectively. The average overall accuracy for SKN was 81.65%, for CP-ANN 78.3% and for XY-F 80.92%, showing that the SKN model had the best overall performance.
author: X.E. Pantazi, D. Moshou, T. Alexandridis, R.L. Whetton, A.M. Mouazen
publish year: 2016
creator: X.E. Pantazi, D. Moshou, T. Alexandridis, R.L. Whetton, A.M. Mouazen
reference: X.E. Pantazi, D. Moshou, T. Alexandridis, R.L. Whetton, A.M. Mouazen, "Wheat yield prediction using machine learning and advanced sensing techniques", Computers and Electronics in Agriculture, Volume 121, February 2016, pages 57-65, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 121, February 2016, pages 57-65, Elsevier B.V.
label: Article 0006

Application of Supervised Self Organising Models for Wheat Yield Prediction
type: NamedIndividual
type: Article
has knowledge model: Regressor_0010
abstract: The management of wheat yield behavior in agricultural areas is a very important task because it influences and specifies the wheat yield production. An efficient knowledge-based approach utilizing an efficient Machine Learning algorithm for characterizing wheat yield behavior is presented in this research work. The novelty of the method is based on the use of Supervised Self Organizing Maps to handle existent sensor information by using a supervised learning algorithm so as to assess measurement data and update initial knowledge. The advent of precision farming generates data which, because of their type and complexity, are not efficiently analyzed by traditional methods. The Supervised Self Organizing Maps have been proved from the literature efficient and flexible to analyze sensor information and by using the appropriate learning algorithms can update the initial knowledge. The Self Organizing models that are developed consisted of input nodes representing the main factors in wheat crop production such as biomass indicators, Organic Carbon (OC), pH, Mg, Total N, Ca, Cation Exchange Capacity (CEC), Moisture Content (MC) and the output weights represented the class labels corresponding to the predicted wheat yield.
author: Xanthoula Pantazi, Dimitrios Moshou, Abdul Mouazen, Boyan Kuang, Thomas Alexandridis
publish year: 2014
creator: Xanthoula Pantazi, Dimitrios Moshou, Abdul Mouazen, Boyan Kuang, Thomas Alexandridis
reference: Xanthoula Pantazi, Dimitrios Moshou, Abdul Mouazen, Boyan Kuang, Thomas Alexandridis, "Application of Supervised Self Organising Models for Wheat Yield Prediction", AIAI 2014: Artificial Intelligence Applications and InnovationsTechnology, book series (IFIPAICT, volume 436) Springer
Source: AIAI 2014: Artificial Intelligence Applications and InnovationsTechnology, book series (IFIPAICT, volume 436) Springer
label: Article 0010

A comparison of three methods for estimating leaf area index of paddy rice from optimal hyperspectral bands
type: NamedIndividual
type: Article
has knowledge model: Regressor_0012
abstract: This paper follows previous research that identified 15 hyperspectral wavebands that were suitable to estimate paddy rice leaf area index (LAI). The objectives of the study were to: (1) test the efficiency of the wavebands selected in the previous study, (2) to evaluate the potential of least squares support vector machines (LS-SVM) to estimate paddy rice LAI from canopy hyperspectral reflectance and (3) to compare multiple linear regression-MLR, partial least squares-PLS regression and LS-SVM to determine paddy rice LAI using the selected wavebands. In the study, measurements of hyperspectral reflectance (350–2500 nm) and corresponding LAI were made for a paddy rice canopy throughout the growing seasons. On the basis of the wavebands selected previously, models based on MLR, PLS and LS-SVM to estimate rice LAI were compared using the data from 123 observations, which were split randomly for model calibration (2/3) and validation (1/3). Root mean square errors (RMSEs) and the correlation coefficients (r) between measured and predicted LAI values from model calibration and validation were calculated to evaluate the quality of the models. The results showed that the LS-SVM model using the 15 selected wavebands produced more accurate estimates of paddy rice LAI than the PLS and MLR models. We concluded that the LS-SVM approach may provide a useful exploratory and predictive tool for estimating paddy rice LAI when applied to reflectance data using the 15 selected wavebands.
author: Fu-min Wang, Jing-feng Huang, Zhang-hua Lou
publish year: 2011
creator: Fu-min Wang, Jing-feng Huang, Zhang-hua Lou
reference: Fu-min Wang, Jing-feng Huang, Zhang-hua Lou, "A comparison of three methods for estimating leaf area index of paddy rice from optimal hyperspectral bands", Precision Agriculture, Volume 12, pages 439–447 (2011) , Springer
Source: Precision Agriculture, Volume 12, pages 439–447 (2011) , Springer
label: Article 0012

Analysis of green manure decomposition parameters in northeast Brazil using association rule networks
type: NamedIndividual
type: Article
has knowledge model: AssociationRule 0013
abstract: The food sector is one of the most critical areas of the economy, and consumers are seeking safer, more readily available, more affordable, and better quality food. Therefore, organic agriculture has become a possible approach for optimizing the characteristics of processed foods. Vegetables have essential uses as green manure, but the greatest difficulty encountered when using these species is related to the time required for their residues to decompose. Computational intelligence and data mining techniques are used widely in agricultural studies and for process improvement purposes. Association rules are used as data mining techniques to identify patterns in large databases, where the patterns identified are analyzed and transformed into a graph to facilitate further analyses. In this study, we employed the patterns identified by association rule networks (ARNs) to determine directly the key parameters related to the half-life decomposition times of legumes used as green manure in northeast Brazil. We validated this method by comparing the results obtained by the ARNs and a decision tree. The proposed approach obtained promising results, thereby demonstrating its capacity to describe a set of objective items and facilitate the development of more integrated hypotheses. Thus, it was possible to optimize the discovery of the key parameters related to green fertilizers in order to identify the best species according to its culture requirements and to enhance productivity.
author: Dario Brito Calcada, Solange Oliveira Rezende, Mauro Sergio Teodoro
publish year: 2019
creator: Dario Brito Calcada, Solange Oliveira Rezende, Mauro Sergio Teodoro
reference: Dario Brito Calcada, Solange Oliveira Rezende, Mauro Sergio Teodoro, "Analysis of green manure decomposition parameters in northeast Brazil using association rule networks", Computers and Electronics in Agriculture, Volume 159, April 2019, Pages 34-41, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 159, April 2019, Pages 34-41, Elsevier B.V.
label: Article 0013

Estimating wheat biomass by combining image clustering with crop height
type: NamedIndividual
type: Article
has knowledge model: Regressor_0015
abstract: Site-specific management strategies in wheat fields can be strongly enhanced with sensor technology that detects spatial changes of wheat biomass. The objectives of this study were to propose a multi-sensor approach combining a digital camera system that measures plant coverage with an arbitrary crop height measuring instrument for estimating wheat biomass. Digital images, fresh and dry biomass, and crop height measurements were taken at 180 sample points distributed over 4 fields between the BBCH growth stages 30-75. Plant coverage percentage was calculated by separating plant pixels from background pixels of NDVI and NIR images using image segmentation based on partitioning clustering. Performance of three clustering algorithms (k-means, partition around mediods (PAM), and fuzzy c-means) was analyzed. Plant coverage from image clustering was further related to fresh and dry biomass with and without crop height measurements using simple and multiple regression models. The performance of the three clustering algorithms was similar for estimating wheat biomass. NDVI image segmentation was highly obstructed by scattering effects especially at later BBCH stages through the presence of wheat ears, stems, and tilted leaves, whereas NIR image segmentation was generally good except with images that were taken at locations with very low plant coverage and dry soil crusts. Consequently, NIR image clustering yielded more accurate estimates of fresh and dry biomass (R2 = 0.79/0.68) than NDVI image clustering (R2 = 0.66/0.56) among the individual measurement runs on average. Still cloud conditions had some influence on NIR clustering. By pooling the complete set of sampling points from all measurement runs into a global model, the combination of image clustering with crop height was helpful. For fresh biomass, global model quality changed from R2 = 0.15 or R2 = 0.46 without crop height to R2 = 0.63 or R2 = 0.82 with crop height for NDVI or NIR, respectively. For dry biomass, cr…
author: Michael Schirrmann, André Hamdorf a, Andreas Garz, Anton Ustyuzhanin, Karl-Heinz Dammer
publish year: 2016
creator: Michael Schirrmann, André Hamdorf a, Andreas Garz, Anton Ustyuzhanin, Karl-Heinz Dammer
reference: Michael Schirrmann, André Hamdorf a, Andreas Garz, Anton Ustyuzhanin, Karl-Heinz Dammer, "Estimating wheat biomass by combining image clustering with crop height", Computers and Electronics in Agriculture, Volume 121, February 2016, Pages 374-384, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 121, February 2016, Pages 374-384, Elsevier B.V.
label: Article 0015

Rice diseases classification using feature selection and rule generation techniques.
type: NamedIndividual
type: Article
has knowledge model: Classifier 0016
abstract: Development of an automation system for classifying diseases of the infected plants is a growing research area in precision agriculture. The paper aims at classifying different types of rice diseases by extracting features from the infected regions of the rice plant images. Fermi energy based segmentation method has been proposed in the paper to isolate the infected region of the image from its background. Based on the field experts’ opinions, symptoms of the diseases are characterized using features like colour, shape and position of the infected portion and extracted by developing novel algorithms. To reduce complexity of the classifier, important features are selected using rough set theory (RST) to minimize the loss of information. Finally using selected features, a rule base classifier has been built that cover all the diseased rice plant images and provides superior result compare to traditional classifiers.
author: Phadikar, Santanu, Jaya Sil, and Asit Kumar Das
publish year: 2013
creator: Phadikar, Santanu, Jaya Sil, and Asit Kumar Das
reference: Phadikar, Santanu, Jaya Sil, and Asit Kumar Das, "Rice diseases classification using feature selection and rule generation techniques.", Computers and Electronics in Agriculture, Volume 90, January 2013, Pages 76-85, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 90, January 2013, Pages 76-85, Elsevier B.V.
label: Article 0016

Crop leaf disease recognition based on Self-Attention convolutional neural network.
type: NamedIndividual
type: Article
has knowledge model: Classifier 0017
abstract: The characteristics of the complex background in crop disease image, the small disease area, and the small contrast between the disease region and the background that easily causes confusion between them, seriously affect the recognition robustness and accuracy. To address these issues, we propose a Self-Attention Convolutional Neural Network (SACNN), which extracts effective features of crop disease spots to identify crop diseases. Our SACNN includes a basic network and a self-attention network: the basic network is for extracting the global features of the image, and the self-attention network is for obtaining the local features of the lesion area. Extensive experimental results show that the recognition accuracy of SACNN on AES-CD9214 and MK-D2 is 95.33% and 98.0%, respectively. The recognition accuracy of SACNN on MK-D2 has outperformed the state-of-the-art method by 2.9%, which implies that the CNN with self-attention can focus on the important areas of the image, and thus can improve the recognition accuracy. Adding different levels of noise to the AES-CD9214 test set shows the anti-interference ability and the strong robustness of SACNN. In addition, we discuss the influence of the location selection, channel size setting, network number and other aspects of the self-attention network on the recognition performance, in order to show the self-attention network working mechanism and provide inspiration for future research.
author: Weihui Zenga, Miao Li
publish year: 2020
creator: Weihui Zenga, Miao Li
reference: Weihui Zenga, Miao Li, "Crop leaf disease recognition based on Self-Attention convolutional neural network.", Computers and Electronics in Agriculture 172 (2020): 105341., Elsevier B.V.
Source: Computers and Electronics in Agriculture 172 (2020): 105341., Elsevier B.V.
label: Article 0017

Field and crop specific manure application on a dairy farm based on historical data and machine learning
type: NamedIndividual
type: Article
has knowledge model: Regressor_0018
abstract: abstract "An important factor in a circular agricultural system is the efficient use of animal manure. Until now, the applied quantity of manure is regulated by law at farm level, based on fixed phosphorus (P) application norms. However, a first step towards more efficient manure application is to better balance P input and output at field level by predicting future P yields. Machine learning techniques can be useful in this respect, because they can be trained with many variables without prior knowledge regarding their interrelationship. This study’s objective, therefore, was to predict P yields based on detailed records of on-farm data as recorded on an experimental farm combined with open source weather data. The dataset contained 657 records of annual crop yields per field between 1993 and 2016, and the boosted regression model was used for model development. Validation on the final five years of the dataset resulted in an RMSE of 7.3 kg P per ha per year, an R-squared of 0.46 and a correlation between observed and predicted values of 0.68, outperforming legal norms. We conclude that with the limited but detailed data available, prediction of P yield, and therewith, defining flexible P application norms before first manure application, is already feasible. This conclusion, together with the expected increasing availability of data through proximal and remote sensing technologies, opens the way to further improve nutrient management and move towards circular agriculture in the future.
author: Herman Mollenhorsta, Michel H.A. de Haan, Jouke Oenema, Claudia Kamphuis
publish year: 2020
creator: Herman Mollenhorsta, Michel H.A. de Haan, Jouke Oenema, Claudia Kamphuis
reference: Herman Mollenhorsta, Michel H.A. de Haan, Jouke Oenema, Claudia Kamphuis, "Field and crop specific manure application on a dairy farm based on historical data and machine learning", Computers and Electronics in Agriculture 175 (2020) 105599, Elsevier B.V.
Source: Computers and Electronics in Agriculture 175 (2020) 105599, Elsevier B.V.
label: Article 0018

A data-driven model simulating primary infection probabilities of cucumber downy mildew for use in early warning systems in solar greenhouses
type: NamedIndividual
type: Article
has knowledge model: Regressor_0019
abstract: Treatment during the primary infection phase is essential for controlling cucumber downy mildew in solar greenhouses. An early warning model applicable to this phase would represent a foundation for early warning systems for managing the disease and reducing pesticide usage. Based on the input parameters that were both readily available and appropriately limited in number, EWMPICDW (early warning model for primary infection of cucumber downy mildew in solar greenhouses) was developed based on monitoring data, early warning theory and plant disease epidemiology. The elaboration of this model included clarification of the meaning of warning, monitoring the warning indicators, forecasting the warning situation, tracing the warning sources and controlling the warning situation. The definition of warning included disease occurrence (yes or no) and probability. Because the leaf wetness duration (LWD) played an important role in disease warning systems for crops in solar greenhouses and was difficult to monitor, the leaf wetness sensor and RH threshold model were investigated and combined to form a practical estimation solution for LWD. Within the warning situation forecasting model system, the infection condition and incubation early warning submodels received the most attention. The infection condition early warning submodel was developed by using a threshold method based on the combination of LWD and mean temperature in LWD. The temperature was chosen as the warning indicator for incubation, and the incubation early warning submodel was defined using nonlinear regression methods. The warning sources traceability algorithm was developed in relation to expert knowledge and in terms of a mode of disaster mitigation that involved cutting the disaster chain from the headstream. The method for controlling the warning situation was based on good agricultural practices (GAP). The early warning model was implemented as a system and was evaluated using data for 4 years at two sit…
author: Chun-Jiang Zhao, Ming Li, Xin-Ting Yang, Chuan-Heng Sun, Jian-Ping Qian, Zeng-Tao Ji
publish year: 2011
creator: Chun-Jiang Zhao, Ming Li, Xin-Ting Yang, Chuan-Heng Sun, Jian-Ping Qian, Zeng-Tao Ji
reference: Chun-Jiang Zhao, Ming Li, Xin-Ting Yang, Chuan-Heng Sun, Jian-Ping Qian, Zeng-Tao Ji, "A data-driven model simulating primary infection probabilities of cucumber downy mildew for use in early warning systems in solar greenhouses", Computers and Electronics in Agriculture, Volume 76, Issue 2, May 2011, pages 306-315, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 76, Issue 2, May 2011, pages 306-315, Elsevier B.V.
label: Article 0019

A Band Math-ROC operation for early differentiation between Sclerotinia sclerotiorum and Botrytis cinerea in oilseed rape
type: NamedIndividual
type: Article
has knowledge model: Classifier 0022
abstract: Brassica napus (oilseed rape) is a major oilseed crop widely used in China. The hygrophanous lesions inflicted by Sclerotinia sclerotiorum and Botrytis cinerea to oilseed rape leaves are initially indistinguishable and wrong identification is possible, resulting in inadequate treatment. A new Band Math-ROC algorithm to identify S. sclerotiorum and B. cinerea infection on oilseed rape was developed in the research. The characteristic (ROC) curve was operated by using hyperspectral imaging technology combined with principal component analysis (PCA), least squares support vector machine (LS-SVM), Spectral Math and receiver. The algorithm is able to identify early-stage oilseed rape lesions. The oilseed rape leaves artificially infected with S. sclerotiorum and B. cinerea were collected and submitted to hyperspectral imaging, scanned at each of the initial three days after infection. To reduce the computational complexity, Band Math-ROC algorithm was applied and the characteristic bands (748.23 nm, 681.95 nm) were computed to extract characteristic values; finally, a ROC curve was created to select the optimal threshold, 4.5 for band ratio operation and 0.6 for band difference operation. The results show that the algorithm proposed in the research can effectively distinguish early lesions caused by S. sclerotiorum and B. cinerea in the first two days after infection. And the classification accuracy in the third day is lower.
author: Xing Xu, Yun Zhao
publish year: 2015
creator: Xing Xu, Yun Zhao
reference: Xing Xu, Yun Zhao, "A Band Math-ROC operation for early differentiation between Sclerotinia sclerotiorum and Botrytis cinerea in oilseed rape", Computers and Electronics in Agriculture, Volume 118, October 2015, pages 116-123, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 118, October 2015, pages 116-123, Elsevier B.V.
label: Article 0022

A deep-level region-based visual representation architecture for detecting strawberry flowers in an outdoor field
type: NamedIndividual
type: Article
has knowledge model: Classifier 0024
abstract: An accurate and robust strawberry flower representation and detection scheme is a key step to enable the reliable forecasting of fruit yield for use in precision agricultural applications. A state-of-the-art deep-level object detection framework which processes images through several layers using a region-based convolutional neural network (R-CNN) was developed to visually represent the instances of strawberry flowers in outdoor fields and improve the detection accuracy. A modified version of the visual geometry group 19 (VGG19) architecture, which had 47 layers, was used to represent the multiple scales of strawberry flower image features. The networks were trained entirely on 400 strawberry flower images and tested on another 100 images. Different region-based object detection methods, including the R-CNN, Fast R-CNN and Faster R-CNN, were used to represent the strawberry flower instances. The Faster R-CNN model achieved a better performance than the R-CNN and Fast R-CNN in detecting the instances and had a lower execution time. The detection accuracy of the Faster R-CNN model was 86.1%, which was higher than those of the R-CNN and Fast R-CNN models (63.4% and 76.7%, respectively). The experimental results showed the effectiveness of the deep-level Faster R-CNN framework for representing the strawberry flower instances under various camera view-points, different distances to flowers, overlaps, complex background illumination, blur, etc. The system developed for automatic and accurate strawberry flower detection provides an important and significant solution that enables subsequent applications to estimate the strawberry yield in outdoor fields.
author: P. Lin, W. S. Lee, Y. M. Chen, N. Peres, C. Fraisse
publish year: 2020
creator: P. Lin, W. S. Lee, Y. M. Chen, N. Peres, C. Fraisse
reference: P. Lin, W. S. Lee, Y. M. Chen, N. Peres, C. Fraisse, "A deep-level region-based visual representation architecture for detecting strawberry flowers in an outdoor field", Precision Agriculture, Volume 21, pages 387–402 (2020) , Springer
Source: Precision Agriculture, Volume 21, pages 387–402 (2020) , Springer
label: Article 0024

A comparative study of fine-tuning deep learning models for plant disease identification
type: NamedIndividual
type: Article
has knowledge model: Classifier 0028
abstract: Deep learning has recently attracted a lot of attention with the aim to develop a quick, automatic and accurate system for image identification and classification. In this work, the focus was on fine-tuning and evaluation of state-of-the-art deep convolutional neural network for image-based plant disease classification. An empirical comparison of the deep learning architecture is done. The architectures evaluated include VGG 16, Inception V4, ResNet with 50, 101 and 152 layers and DenseNets with 121 layers. The data used for the experiment is 38 different classes including diseased and healthy images of leafs of 14 plants from plantVillage. Fast and accurate models for plant disease identification are desired so that accurate measures can be applied early. Thus, alleviating the problem of food security. In our experiment, DenseNets has tendency’s to consistently improve in accuracy with growing number of epochs, with no signs of overfitting and performance deterioration. Moreover, DenseNets requires a considerably less number of parameters and reasonable computing time to achieve state-of-the-art performances. It achieves a testing accuracy score of 99.75% to beat the rest of the architectures. Keras with Theano backend was used to perform the training of the architectures.
author: Edna Chebet Too, Li Yujian, Sam Njuki, Liu Yingchun
publish year: 2019
creator: Edna Chebet Too, Li Yujian, Sam Njuki, Liu Yingchun
reference: Edna Chebet Too, Li Yujian, Sam Njuki, Liu Yingchun, "A comparative study of fine-tuning deep learning models for plant disease identification", Computers and Electronics in Agriculture, Volume 161, June 2019, pages 272-279, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 161, June 2019, pages 272-279, Elsevier B.V.
label: Article 0028

A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques
type: NamedIndividual
type: Article
has knowledge model: Regressor_0030
abstract: Improving the accuracy of harvest timing predictions offers an opportunity to sustainably improve soft fruit farming. Fruits are perishable, high-value and seasonal, and prices are typically time-sensitive. Harvesting is labour-intensive and increasingly expensive making accurate phenological predictions valuable for growers. We have developed and tested a novel framework for linking mesoscale weather forecasts to local crop microclimates using embedded autonomous sensors to produce bespoke phenological predictions, using strawberries as the model crop. Seedlings were planted in polytunnels, and environmental and yield data were collected throughout the growing season. Over 1.2 million datapoints were collected by networked microsensors which measured spatial and temporal variability in air temperature, relative humidity (RH), soil moisture and photosynthetically active radiation (PAR) irradiance. Fleeces were added to a subset of the plants to generate additional within-polytunnel variation. Trigonometric models transformed weather station data, which showed a relatively low agreement with polytunnel air temperature (R2 = 0.6) and RH (R2 = 0.5), into more accurate polytunnel-specific predictions for temperature and RH (both R2 = 0.8). Cumulative fruit yields followed logistic growth curves and the coefficients of these curves were dependent on micro-climatic conditions. After 10,000 iterations, machine learning adequately optimised the coefficients of these curves, including RH and air temperature into the fitted equation. Dataloggers measuring environmental data in-situ could infer model parameters using iterative training for novel fruit cultivars growing in different locations without a-priori phenological information. Reliance on manually measured yield data is a current limitation but if high-throughput technologies emerge then this process could be entirely automated. We have demonstrated that this framework can be used to predict fruit timing. Predictions co…
author: Mark A. Lee, Angelo Monteiro, Andrew Barclay, Jon Marcar, Mirena Miteva-Neagu, Joe Parker
publish year: 2020
creator: Mark A. Lee, Angelo Monteiro, Andrew Barclay, Jon Marcar, Mirena Miteva-Neagu, Joe Parker
reference: Mark A. Lee, Angelo Monteiro, Andrew Barclay, Jon Marcar, Mirena Miteva-Neagu, Joe Parker, "A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques", Computers and Electronics in Agriculture, Volume 168, January 2020, 105103
reference: Mark A. Lee, Angelo Monteiro, Andrew Barclay, Jon Marcar, Mirena Miteva-Neagu, Joe Parker, "A framework for predicting soft-fruit yields and phenology using embedded, networked microsensors, coupled weather models and machine-learning techniques", Computers and Electronics in Agriculture, Volume 168, January 2020, 105103, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 168, January 2020, 105103
Source: Computers and Electronics in Agriculture, Volume 168, January 2020, 105103, Elsevier B.V.
label: Article 0030

A framework for the management of agricultural resources with automated aerial imagery detection
type: NamedIndividual
type: Article
has knowledge model: Classifier 0031
abstract: The acquisition of data through remote sensing represents a significant advantage in agriculture, as it allows researchers to perform faster and cheaper inspections over large areas. Currently, extensive researches have been done on technical solutions that can benefit simultaneously from both: vast amounts of raw data (big data) extracted from satellite images and Unmanned Aerial Vehicle (UAV) and novel algorithms in Machine Learning for image processing. In this experiment, we provide an approach that fulfills the necessities of rapid food security, assessment, planning, exploitation, and management of agricultural resources by introducing a pipeline for the automatic localization and classification of four types of fruit trees (coconut, banana, mango, and papaya) and the segmentation of roads in the Kingdom of Tonga, using high-resolution aerial imagery (0.04 m).We used two supervised deep convolutional neural network (CNN): the first, to localize and classify trees (localization) and the second, to mask the streets from the aerial imagery for transportation purposes (semantic segmentation). Additionally, we propose auxiliary methods to determine the density of groupings of each of these trees species, based on the detection results from the localization task and render it in Density Maps that allow comprehending the condition of the agriculture site quickly. Ultimately, we introduce a method to optimize the harvesting of fruits, based on specific sceneries, such as maximum time, path length, and location of warehouses and security points.
author: Karla Saldana Ochoa, Zifeng Guo
publish year: 2019
creator: Karla Saldana Ochoa, Zifeng Guo
reference: Karla Saldana Ochoa, Zifeng Guo, "A framework for the management of agricultural resources with automated aerial imagery detection", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 53-69, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 162, July 2019, pages 53-69, Elsevier B.V.
label: Article 0031

A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images
type: NamedIndividual
type: Article
has knowledge model: Classifier 0033
abstract: Research reported in this paper aims to improve the extraction of cucumber leaf spot disease under complex backgrounds. An improved fuzzy C-means (FCM) algorithm is proposed in this paper. First, three runs of the marked-watershed algorithm, based on HSI space, are applied to isolate the target leaf. Second, the distance between the pixel xj and the cluster center vi is defined as ‖xj2-vi2‖. Third, the pixel's neighborhood mean gray value, which constitutes a two-dimensional vector with grayscale information, is calculated as a sample point, rather than FCM grayscale. Finally, the neighborhood mean gray value and pixel gray value are weighted by matrix w. To evaluate the robustness and accuracy of the proposed segmentation method, tests were conducted for 129 cucumber disease images in vegetable disease database. Results show that average segmentation error was only 0.12%. The proposed method provides an effective and robust segmentation means for sorting and grading apples in cucumber disease diagnosis, and it can be easily adapted for other imaging-based agricultural applications.
author: Xuebing Bai, Xinxing Li, Zetian Fu, Xiongjie Lv, Lingxian Zhang
publish year: 2017
creator: Xuebing Bai, Xinxing Li, Zetian Fu, Xiongjie Lv, Lingxian Zhang
reference: Xuebing Bai, Xinxing Li, Zetian Fu, Xiongjie Lv, Lingxian Zhang, "A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images", Computers and Electronics in Agriculture, Volume 136, 15 April 2017, pages 157-165, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 136, 15 April 2017, pages 157-165, Elsevier B.V.
label: Article 0033

A fuzzy inference system to model grape quality in vineyards
type: NamedIndividual
type: Article
has knowledge model: Classifier 0034
abstract: Fuzzy inference systems (FIS) are particularly suited for aggregating multiple data to feed multi-variables decision support systems. Moreover, grape quality is a complex concept that refers to the simultaneous achievement of optimal levels in many parameters, thus single berry attributes spatial data are not adequate to define grape suitability for a specific end use. The aim of the present study was to develop and validate a FIS to classify grape quality based on selected grape attributes in a commercial vineyard in Central Greece planted with Vitis vinifera cv. Agiorgitiko, during 2010, 2011 and 2012. The vineyard was sectioned in 48 cells sized 10 × 20 m; total soluble solids, titratable acidity, total skin anthocyanins and berry fresh weight were measured at harvest on the same grid and were used in the FIS as inputs to build linguistic rules based on expert knowledge. The result of the FIS was a numerical value (Grape Total Quality, GTQ) which corresponded to a fuzzy set of grape quality classes (very poor, poor, average, good, and excellent). The validation process for the proposed FIS consisted of two parts: a comparison of GTQ with an independent set of data by viticulture experts and a comparison with soil and grapevine properties to verify its spatial relevancy. The evaluation process showed high general agreement between GTQ and expert evaluation suggesting that the FIS was able to model expert knowledge successfully. Moreover, GTQ exhibited higher variability than the individual grape quality attributes in all years. Among individual grape components, anthocyanins and berry weight seemed to be more important in determining GTQ than total soluble solids and titratable acidity. According to the results, FIS could allow the aggregation of grape quality parameters into a single index providing grape growers with a valuable tool for classifying grape quality at harvest.
author: A. Tagarakis, S. Koundouras, E. I. Papageorgiou, Z. Dikopoulou, S. Fountas, T. A. Gemtos
publish year: 2014
creator: A. Tagarakis, S. Koundouras, E. I. Papageorgiou, Z. Dikopoulou, S. Fountas, T. A. Gemtos
reference: A. Tagarakis, S. Koundouras, E. I. Papageorgiou, Z. Dikopoulou, S. Fountas, T. A. Gemtos, "A fuzzy inference system to model grape quality in vineyards", Precision Agriculture, Volume 15, pages 555–578 (2014) , Springer
Source: Precision Agriculture, Volume 15, pages 555–578 (2014) , Springer
label: Article 0034

‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging
type: NamedIndividual
type: Article
has knowledge model: Classifier 0036
abstract: Hyperspectral (HS) imaging is becoming more important for agricultural applications. Due to its high spectral resolution, it exhibits excellent performance in disease identification of different crops. In this study, a novel method termed ‘extended spectral angle mapping (ESAM)’ was proposed to detect citrus greening disease (Huanglongbing or HLB), which is a very destructive disease of citrus. Firstly, the Savitzky–Golay smoothing filter was used to remove spectral noise within the data. A mask for tree canopy was built using support vector machine, to separate the tree canopies from the background. Pure endmembers of the masked dataset for healthy and HLB infected tree canopies were extracted using vertex component analysis. By utilizing the derived pure endmembers, spectral angle mapping was applied to differentiate between healthy and citrus greening disease infected areas in the image. Finally, most false positive detections were filtered out using red-edge position. An experiment was carried out using an HS image acquired by an airborne HS imaging system, and a multispectral image acquired by the WorldView-2 satellite, from the Citrus Research and Education Center, Lake Alfred, FL, USA. Ground reflectance measurement and coordinates for diseased trees were recorded. The experimental results were compared with another supervised method, Mahalanobis distance, and an unsupervised method, K-means, both of which showed a 63.6 % accuracy. The proposed ESAM performed better with a detection accuracy of 86 % than those two methods. These results demonstrated that the detection accuracy using HS image could be enhanced by focusing on the pure endmember extraction and the use of red-edge position, suggesting that there is a great potential of citrus greening disease detection using an HS image.
author: Han Li, Won Suk Lee, Ku Wang, Reza Ehsani, Chenghai Yang
publish year: 2014
creator: Han Li, Won Suk Lee, Ku Wang, Reza Ehsani, Chenghai Yang
reference: Han Li, Won Suk Lee, Ku Wang, Reza Ehsani, Chenghai Yang, "‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging", Precision Agriculture, Volume 15, pages 162–183 (2014) , Springer
Source: Precision Agriculture, Volume 15, pages 162–183 (2014) , Springer
label: Article 0036

A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm
type: NamedIndividual
type: Article
has knowledge model: Classifier 0037
abstract: Basal stem rot (BSR) disease in oil palm is caused by Ganoderma boninense fungus. This plant disease is deemed highly destructive and would cause substantial economic loss. The use of spectroscopy technique with the capacity to deal with a large amount of spectral data has gained growing attention as a robust method, particularly to identify the symptoms of plant disease in its initial stage. The dimensionality reduction is pivotal in the use of spectroscopy technique due to its improved prediction performance and optimum processing. Considering that, this study assessed the feasibility of utilising dielectric spectral properties to classify the severity levels of BSR disease in oil palm across a frequency range of 100 kHz–30 MHz. The support vector machine-feature selection (SVM-FS) and principal component analysis (PCA) were applied as data reduction methods. After selecting the optimum number of significant frequencies, this study proceeded to assess the effectiveness of the support vector machine (SVM) and quadratic discriminant analysis (QDA) classifiers in identifying the four different levels of BSR disease. The performance of both classifiers with and without data reduction methods was subsequently compared in terms of the classification accuracy, while the whole spectrum data served as part of the control method. The resultant outcomes revealed that the use of QDA classifier with PCA recorded the highest classification accuracy (up to 96.36%). As for the case of without using data reduction methods, the SVM classifier recorded the highest classification accuracy at only 79.55%. Conclusively, this study proved the significance of dimensionality reduction of dielectric spectral data for the classification of BSR disease in oil palm.
author: Alfadhl Yahya Khaled, Samsuzana Abd Aziz, Siti Khairunniza Bejo, Nazmi Mat Nawi, Diyana Jamaludin, Nur Ul Atikah Ibrahim
publish year: 2020
creator: Alfadhl Yahya Khaled, Samsuzana Abd Aziz, Siti Khairunniza Bejo, Nazmi Mat Nawi, Diyana Jamaludin, Nur Ul Atikah Ibrahim
reference: Alfadhl Yahya Khaled, Samsuzana Abd Aziz, Siti Khairunniza Bejo, Nazmi Mat Nawi, Diyana Jamaludin, Nur Ul Atikah Ibrahim, "A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm", Computers and Electronics in Agriculture, Volume 170, March 2020, 105288, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 170, March 2020, 105288, Elsevier B.V.
label: Article 0037

A leaf-based back propagation neural network for oleander (Nerium oleander L.) cultivar identification
type: NamedIndividual
type: Article
has knowledge model: Classifier 0039
abstract: Oleander (Nerium oleander L.) includes many cultivars differing for the combination of a high number of characters, therefore their identification is difficult and time consuming. Nomenclature is often inaccurate and not uniform and the commercialization of material under unreliable names or even without name but stating only flower colour and type is frequent. In this paper, a Backpropagation Neural Network (BPNN) based on the image analysis of oleander leaves was developed as support tool for cultivar identification. It was built using 18 morphometric and colorimetric leaf parameters of 880 leaves collected from 22 cultivars (40 leaves per cultivar). The model resulted to be an efficient, reliable, and rapid method for distinguishing genotypes. The percentage of leaves attributed to the correct class reached 97.50% considering the single cultivars, and 54.55% on the total of the analysed leaves. Twenty-one cultivars were identified with certainty, and similarities in leaf morphology between some genotypes were highlighted, too. The method requires care in the choice of the leaves, which must be healthy and well-developed, but it is objective and, being oleander an evergreen species, not season-dependent. The model could be implemented in efficiency by introducing more leaf parameters, or in speed and computer performance by selecting the most representative. In fact, a smaller BPNN based on eight selected leaf parameters resulted slightly less sensitive (45.45% of the leaves attributed to the correct cultivar) but faster (4.4 s vs 7.7 s using a standard computer), and it could be used for very numerous collections.
author: Ada Baldi, Camilla Pandolfi, Stefano Mancuso, Anna Lenzi
publish year: 2017
creator: Ada Baldi, Camilla Pandolfi, Stefano Mancuso, Anna Lenzi
reference: Ada Baldi, Camilla Pandolfi, Stefano Mancuso, Anna Lenzi, "A leaf-based back propagation neural network for oleander (Nerium oleander L.) cultivar identification", Computers and Electronics in Agriculture, Volume 142, Part B, November 2017, pages 515-520, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 142, Part B, November 2017, pages 515-520, Elsevier B.V.
label: Article 0039

A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.)
type: NamedIndividual
type: Article
has knowledge model: Classifier 0040
abstract: This paper introduces a least-squares support vector machine (LS-SVM) classifier to detect the degree of browning on mango fruits as a function of fractal dimension (FD) and L∗a∗b∗ values. Our results showed that the best classification accuracy of browning degree was up to 100% using the LS-SVM classifier based on FD and L∗a∗b∗ (γ = 6.13, σ2 = 9.36). However, the correct classification rates of 85.19% and 88.89% were achieved for the LS-SVM models based on FD (γ = 1.13, σ2 = 5.52) and based on L∗a∗b∗ (γ = 6.68, σ2 = 2.44), respectively. Therefore, this study indicated the possibility of developing a potentially useful classification tool using the LS-SVM combined with FD and L∗a∗b∗ values for classifying the degree of browning on mango fruits during processing, storage and distribution.
author: Hong Zheng, Hongfei Lu
publish year: 2012
creator: Hong Zheng, Hongfei Lu
reference: Hong Zheng, Hongfei Lu, "A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.)", Computers and Electronics in Agriculture, Volume 83, April 2012, pages 47-51, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 83, April 2012, pages 47-51, Elsevier B.V.
label: Article 0040

Yield prediction in apples using Fuzzy Cognitive Map learning approach
type: NamedIndividual
type: Article
has knowledge model: Regressor_0041
abstract: This work investigates the yield modeling and prediction process in apples (cv. Red Chief) using the dynamic influence graph of Fuzzy Cognitive Maps (FCMs). FCMs are ideal causal cognition tools for modeling and simulating dynamic systems. They gained momentum due to their simplicity, flexibility to model design, adaptability to different situations, and easiness of use. In general, they model the behavior of a complex system, have inference capabilities and can be used to predict new knowledge. In this work, a data driven non-linear FCM learning approach was chosen to categorize yield in apples, where very few decision making techniques were investigated. Through the proposed methodology, FCMs were designed and developed to represent experts’ knowledge for yield prediction and crop management. The developed FCM model consists of nodes linked by directed edges, where the nodes represent the main soil factors affecting yield, [such as soil texture (clay and sand content), soil electrical conductivity (EC), potassium (K), phosphorus (P), organic matter (OM), calcium (Ca) and zinc (Zn) contents], and the directed edges show the cause-effect (weighted) relationships between the soil properties and yield. The main purpose of this study was to classify apple yield using an efficient FCM learning algorithm, the non-linear Hebbian learning, and to compare it with the conventional FCM tool and benchmark machine learning algorithms. All algorithms have been implemented in the same data set of 56 cases measured in 2005 in an apple orchard located in central Greece. The analysis showed the superiority of the FCM learning approach in yield prediction.
author: E.I. Papageorgiou, K.D. Aggelopoulou, T.A. Gemtos, G.D. Nanos
publish year: 2013
creator: E.I. Papageorgiou, K.D. Aggelopoulou, T.A. Gemtos, G.D. Nanos
reference: E.I. Papageorgiou, K.D. Aggelopoulou, T.A. Gemtos, G.D. Nanos, "Yield prediction in apples using Fuzzy Cognitive Map learning approach", Computers and Electronics in Agriculture, Volume 91, February 2013, pages 19-29, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 91, February 2013, pages 19-29, Elsevier B.V.
label: Article 0041

A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring
type: NamedIndividual
type: Article
has knowledge model: Regressor_0042
abstract: Image-based remote sensing is one promising technique for precision crop management. In this study, the use of an ultra light aircraft (ULA) equipped with broadband imaging sensors based on commercial digital cameras was investigated to characterize crop nitrogen status in cases of combined nitrogen and water stress. The acquisition system was composed of two Canon® EOS 400D digital cameras: an original RGB camera measuring luminance in the Red, Green and Blue spectral bands, and a modified camera equipped with an external band-pass filter measuring luminance in the near-infrared. A 5 month experiment was conducted on a sugarcane (Saccharum officinarum) trial consisting of three replicates. In each replicate, two sugarcane cultivars were grown with two levels of water input (rainfed/irrigated) and three levels of nitrogen (0, 65 and 130 kg/ha). Six ULA flights, coupled with ground crop measurements, took place during the experiment. For nitrogen status characterisation, three indices were tested from the closed canopy: the normalised difference vegetation index (NDVI), the green normalised difference vegetation index (GNDVI), and a broadband version of the simple ratio pigment index (hereafter referred to as the SRPIb), calculated from the ratio between blue and red bands of the digital camera. The indices were compared with two nitrogen crop variables: leaf nitrogen content (NL) and canopy nitrogen content (NC). SRPIb showed the best correlation (R2 = 0.7) with NL, independently of the water and the N treatment. NDVI and GNDVI were best correlated with NC values with correlation coefficients of 0.7 and 0.64 respectively, but the regression coefficients were dependent on the water and N treatment. These results showed that SRPIb could characterise the nitrogen status of sugarcane crop, even in the case of combined stress, and that such acquisition systems are promising for crop nitrogen monitoring.
author: V. Lebourgeois, A. Begue, S. Labbe, M. Houlès, J. F. Martine
publish year: 2012
creator: V. Lebourgeois, A. Begue, S. Labbe, M. Houlès, J. F. Martine
reference: V. Lebourgeois, A. Begue, S. Labbe, M. Houlès, J. F. Martine, "A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring", Precision Agriculture, Volume 13, pages 525–541 (2012) , Springer
Source: Precision Agriculture, Volume 13, pages 525–541 (2012) , Springer
label: Article 0042

A low shot learning method for tea leaf’s disease identification
type: NamedIndividual
type: Article
has knowledge model: Classifier 0043
abstract: Tea leaf’s diseases seriously affect the yield and quality of tea. This paper presents a low shot learning method for tea leaf’s disease identification in order to prevent and control tea leaf’s diseases timely. By extracting the color and texture features, disease spots on tea leaf's disease images are segmented by using support vector machine (SVM) method. With segmented disease spot images as input, new training samples are generated by the improved conditional deep convolutional generative adversarial networks (C-DCGAN) for data augmentation, which are used to train VGG16 deep learning model to identify the tea leaf's diseases. Experimental results show that, SVM can segment disease spot images on the condition of low shot learning while retaining the edge information well, improved C-DCGAN can generate augmented images with the same data distribution as real disease spot images, the VGG16 deep learning model trained with augmented disease spot images can identify tea leaf’s diseases accurately, and the average identification accuracy of the proposed method reaches 90%, far exceeding classical low shot learning methods.
author: Gensheng Hu, Haoyu Wu, Yan Zhang, Mingzhu Wan
publish year: 2019
creator: Gensheng Hu, Haoyu Wu, Yan Zhang, Mingzhu Wan
reference: Gensheng Hu, Haoyu Wu, Yan Zhang, Mingzhu Wan, "A low shot learning method for tea leaf’s disease identification", Computers and Electronics in Agriculture, Volume 163, August 2019, 104852, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 163, August 2019, 104852, Elsevier B.V.
label: Article 0043

A method for recognition of locust age with few observations based on spectral analysis combined with chemical analysis
type: NamedIndividual
type: Article
has knowledge model: Regressor_0048
abstract: Locusts are one of the major pests in the world, causing huge losses to the economy, so it is very important to monitor them effectively. The age recognition of locusts is an important part of public opinion monitoring. At present, manual investigation, the visible-light image processing, and trapping methods are mainly used for monitoring. These methods are highly influenced by the environment and cost and have some limitations. In this paper, a method of recognizing the age of locusts in small samples based on spectral analysis and chemical analysis was proposed. Firstly, the hyperspectral scanning of 180 group locusts (including the 3rd age larva of locust, the 5th age larva of locust and the adult of locust) was carried out during the experiment. The data processing was performed by The Environment for Visualizing Images (ENVI) soft to extract the reflectivity data of the region of interest (ROI) in 400–1000 nm band. Secondly, we extracted and analyzed the chemical composition of the surface material of locusts, and finally found 2,4-ditert-butylphenol which can be used to distinguish different age locusts. We also obtained the content range of 2,4-ditert-butylphenol in the surface substances of three different age locusts through experiments. Finally, 180 sets of spectral data and material composition data were analyzed and processed to build a regression prediction model named MLRSVR between them. The model only needs to input the corresponding spectral data of characteristic bands to predict the content of 2,4-ditert-butylphenol on the locust body surface, so as to recognize the age of locust according to the age range of the composition content. The experiment in this paper shows that the regression prediction model MLRSVR constructed under the spectral data of the characteristic bands of 744 nm, 820 nm, 830 nm, and 885 nm has a good effect, in which R2 reaches 0.97. Compared with the spectral data processed in 400–1000 nm, the data amount is greatly reduced…
author: Jie Su, Jingfeng Gu, Xuchao Guo, Chengqi Liu, Xuesong Bo, Xiaoxue Liu, Han Zhou, Jing Cao, Shuhan Lu, Yinwei You, Lin Li
publish year: 2019
creator: Jie Su, Jingfeng Gu, Xuchao Guo, Chengqi Liu, Xuesong Bo, Xiaoxue Liu, Han Zhou, Jing Cao, Shuhan Lu, Yinwei You, Lin Li
reference: Jie Su, Jingfeng Gu, Xuchao Guo, Chengqi Liu, Xuesong Bo, Xiaoxue Liu, Han Zhou, Jing Cao, Shuhan Lu, Yinwei You, Lin Li, "A method for recognition of locust age with few observations based on spectral analysis combined with chemical analysis", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 450-458, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 162, July 2019, pages 450-458, Elsevier B.V.
label: Article 0048

A methodology for fresh tomato maturity detection using computer vision
type: NamedIndividual
type: Article
has knowledge model: Classifier 0051
abstract: Recent advancements in computer vision have provided opportunities for new applications in agriculture. Accurate yield estimation of fruit and vegetable crops is very important for better harvesting and marketing planning and logistics. This paper proposes a method for detecting the maturity levels (green, orange, and red) of fresh market tomatoes (Roma and Pear varieties) by combining the feature color value with the backpropagation neural network (BPNN) classification technique. A maturity detection device based on computer vision technology was designed specifically to acquire the tomato images in the lab. The tomato images were processed and the targets of the tomatoes were obtained based on the image processing technology. After that, the maximum inscribed circle of the tomato’s surface was identified as the color feature extraction area. The color feature extraction area was divided into five concentric circles (sub-domains). The average hue values of each sub-region were extracted as the feature color values and used to describe the maturity level of the samples. After that, the five feature color values were imported to the BPNN as input values to detect the maturity of the tomato samples. Analysis of the results shows that the average accuracy for detecting the three maturity levels of tomato samples using this method is 99.31%; and the standard deviation is 1.2%.
author: Peng Wan, Arash Toudeshki, Hequn Tan, Reza Ehsani
publish year: 2018
creator: Peng Wan, Arash Toudeshki, Hequn Tan, Reza Ehsani
reference: Peng Wan, Arash Toudeshki, Hequn Tan, Reza Ehsani, "A methodology for fresh tomato maturity detection using computer vision", Computers and Electronics in Agriculture, Volume 146, March 2018, pages 43-50, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 146, March 2018, pages 43-50, Elsevier B.V.
label: Article 0051

A comparison of object-based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery
type: NamedIndividual
type: Article
has knowledge model: Classifier 0053
abstract: In this study we evaluated several Earth observation methodologies for automatically delineating crop field boundaries from multi-temporal Sentinel-2 imagery. The methodology makes use of edge detection that is applied to multiple images acquired during a growing season and image segmentation to delimit agricultural fields, orchards and vineyards. Two edge detection operators (Canny and Scharr) and three image segmentation algorithms (watershed, multi-threshold and multi-resolution) were combined and evaluated, resulting in six experiments (scenarios). A rule-based (knowledge-based) classification was applied to the extracted image objects to discard uncultivated areas. Reference field boundaries, manually digitised from very high (10 cm) resolution aerial imagery, were used for quantitative accuracy assessments and qualitative comparisons. The quantitative accuracy assessment consisted of both area- and edge-based metrics. The results showed that the watershed segmentation, combined with Canny edge detection, produced the most accurate field boundaries with an OA of 92.9% and combined MAE of 24.5 m. The Scharr algorithm produced thicker edges, causing positional errors along the boundaries. The multi-resolution and multi-threshold segmentation algorithms produced boundary inaccuracies ranging from 1 to 3 pixels, largely due to the creation of thick boundary objects, which caused offsets of the extracted borders on both sides of the reference boundaries. The combination of Canny edge detection (performed on multiple Sentinel-2 images acquired during the growing season) and watershed segmentation is thus recommended for operational field boundary delineation.
author: Barry Watkins, Adriaan van Niekerk
publish year: 2019
creator: Barry Watkins, Adriaan van Niekerk
reference: Barry Watkins, Adriaan van Niekerk, "A comparison of object-based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery", Computers and Electronics in Agriculture, Volume 158, March 2019, pages 294-302, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 158, March 2019, pages 294-302, Elsevier B.V.
label: Article 0053

A model-based methodology to derive optimum nitrogen rates for rainfed crops – a case study for corn using STICS in Canada
type: NamedIndividual
type: Article
has knowledge model: Regressor_0054
abstract: While the application of nitrogen (N) fertilizer increases crop productivity, the amount of N not taken up by the crop is released as reactive N causing adverse environmental effects, which are associated with short-term climate variations and consist of nitrous oxide emissions, ammonia volatilization, and nitrate leaching. Finding the optimum rate of N application accounting for both economic and environmental aspects is challenging because it varies according to soil texture and climatic conditions. In this paper, a model-based methodology is developed to identify the ecophysiological optimum N rates at which the applied N leads to minimum N excess with little loss in maximum achievable yield. A slope-based method is proposed to identify the optimum nitrogen use efficiency (NUEopt) and to determine the corresponding optimum N rate (Nopt) for each growing season in given agroclimatic regions according to soil properties. It uses the yield predicted by a process-based crop model, long time series of climate data, and a newly proposed Mitscherlich Baule-plateau (MB-P) function. The NUEopt is identified by evaluating the linearity of the relationship between yield and Nopt, and the reduction in yield compared to maximum achievable yield for a given soil and various growing seasons. We illustrate the methodology and its performance by presenting corn yield response to N rate applications predicted by the STICS crop model for dominant soils with contrasting properties with 48–61 years of daily climate data from five major regions located along an agroclimatic gradient of the Mixedwood Plains ecozone in Canada (42.3°N 83°W - 46.8°N 71°W), which is the main grain corn production ecozone of Canada. Our case study in this ecozone indicates that a newly proposed yield MB-P function outperforms the two commonly used functions, i.e., linear-plateau, and MB functions. The proposed methodology provides valuable information such as the likelihood of achieving a yield in a given r…
author: Morteza Mesbah, Elizabeth Pattey, Guillaume Jego
publish year: 2017
creator: Morteza Mesbah, Elizabeth Pattey, Guillaume Jego
reference: Morteza Mesbah, Elizabeth Pattey, Guillaume Jego, "A model-based methodology to derive optimum nitrogen rates for rainfed crops – a case study for corn using STICS in Canada", Computers and Electronics in Agriculture, Volume 142, Part B, November 2017, pages 572-584, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 142, Part B, November 2017, pages 572-584, Elsevier B.V.
label: Article 0054

A multi-sensor approach for predicting biomass of extensively managed grassland
type: NamedIndividual
type: Article
has knowledge model: Regressor_0055
abstract: Leaf area index (LAI), ultrasonic sward height (USH) and common vegetation indices (VI) derived by spectral radiometric reflection data were collected on an experimental field site with three sward types comprising a pure stand of reed canary grass (Phalaris aruninacea), a legume grass mixture and a diversity mixture with thirty-six species in an extensive two cut management system. Sensor measurements and biomass samplings of 0.25 m2 subplots were conducted biweekly between May and October in 2009 and 2010. Different combinations of the sensor response values were used in multiple regression analysis to improve biomass (BM) predictions compared to exclusive sensors. Wavelength bands for sensor specific NDVI-type vegetation indices were selected from the hyperspectral data and evaluated for the biomass prediction as exclusive indices or in combination with LAI and USH. In the set of tested parameters, ultrasonic sward height was the best to predict biomass in single sensor approaches (R2 0.73–0.76). Inclusion of LAI improved the model performance and reduced the prediction accuracy by up to 30% for complex swards, while inclusion of vegetation indices resulted only in minor improvements compared to exclusive USH. LAI acted complementary to USH in a combined prediction model, correcting for overestimations of biomass in high swards. Prediction models using exclusive LAI were barely suited to predict biomass accurately (R2 0.36–0.44) but improved significantly when combined with waveband selected VIs (R2 < 0.8). Combining all three sensors did not significantly improve the model performance.
author: Bjorn Reddersen, Thomas Fricke, Michael Wachendorf
publish year: 2014
creator: Bjorn Reddersen, Thomas Fricke, Michael Wachendorf
reference: Bjorn Reddersen, Thomas Fricke, Michael Wachendorf, "A multi-sensor approach for predicting biomass of extensively managed grassland", Computers and Electronics in Agriculture, Volume 109, November 2014, pages 247-260, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 109, November 2014, pages 247-260, Elsevier B.V.
label: Article 0055

A multispectral machine vision system for invertebrate detection on green leaves
type: NamedIndividual
type: Article
has knowledge model: Classifier 0057
abstract: Detection and identification of invertebrate pests in farming fields is a prerequisite necessity for integrated pest management (IPM), however, current sensing technologies do not meet the requirements for IPM. Currently, farmers have to first sample pests and then manually count and identify them, in a way that is time-consuming, labour-intensive and error-prone. Machine vision technology has taken over part of the work in a more efficient and accurate manner. However, current machine vision systems (MVSs) have limitations in detecting pests on crops and the counting and identification are constrained in laboratories or pest traps, resulting in the exact time and locations of pests being unknown, hindering more proper decisions and efficient actions. In this study, we developed a multispectral MVS to detect common invertebrate pests on green leaves in natural environment. First, it was found that, besides visible light and near-infrared, the ultraviolet is a good indicator to distinguish green leaves from other materials. Then for multispectral or hyperspectral data processing, we proposed two models, one named normalised hypercube and another named hyper-hue, which are less affected by uneven illumination and can reflect data distribution, resulting in more accurate classification than the normal method of spectral angle mapper (SAM). Further, the relationship between spectral angle and the relative angle of hyper-hue was studied and it was found that usually, data of hyper-hue has larger inter-class distances which could contribute to better classification. At last, to solve the practical problems of image registration and real-time infield applications, instead of registering 2D images, the MVS created and registered 3D point clouds. In an experiment of detecting twelve types of common invertebrate pests on crops, the proposed MVS showed acceptable accuracy.
author: Huajian Liu, Javaan Singh Chahl
publish year: 2018
creator: Huajian Liu, Javaan Singh Chahl
reference: Huajian Liu, Javaan Singh Chahl, "A multispectral machine vision system for invertebrate detection on green leaves", Computers and Electronics in Agriculture, Volume 150, July 2018, pages 279-288, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 150, July 2018, pages 279-288, Elsevier B.V.
label: Article 0057

A multi-stage tracking for mustard rot disease combining surface meteorology and satellite remote sensing
type: NamedIndividual
type: Article
has knowledge model: Regressor_0058
abstract: Disease forecasting forms an integral part of crop protection for ensuring quality and quantity of production. In this paper, a new method of multi-stage tracking of Sclerotinia rot (Sclerotinia sclerotiorum) disease in a large mustard growing region over 5 km × 5 km (27.00–27.25°N; 77.25–77.50°E) in Bharatpur district of Rajasthan state of North-West India is demonstrated. In addition to surface weather data, post-facto analysis of 5-year (2003–2007) satellite-based data of surface reflectances in red (R), near infrared (NIR) and shortwave infrared (SWIR) bands, land surface temperature (LST) from Moderate Resolution Imaging Spectroradiometer (MODIS) AQUA at day (1:30 pm) and night time (1:30 am) LST, were done to characterize disease outbreak (stage-I) and persistence (stage-II). While stage-I evaluation was based on anomaly in minimum air temperatures and night time LST, stage-II evaluation was carried out using quadrant-based trapezoidal clusters between soil and canopy dryness indicators. Hyperspectral data on two dates from Hyperion sensor at EO-1 platform were used for two-step spectral discrimination to select bands and disease indices specific to rot. Among all the hyperspectral indices, a three-band rot index (ROTI) was found to be the better one in field scale rot discrimination (stage-III evaluation). The reduction in fractional canopy cover in diseased patches in 2005 as compared to a normal year (2007) indirectly validated the disease effect.
author: B.K. Bhattacharya, C. Chattopadhyay
publish year: 2013
creator: B.K. Bhattacharya, C. Chattopadhyay
reference: B.K. Bhattacharya, C. Chattopadhyay, "A multi-stage tracking for mustard rot disease combining surface meteorology and satellite remote sensing", Computers and Electronics in Agriculture, Volume 90, January 2013, pages 35-44, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 90, January 2013, pages 35-44, Elsevier B.V.
label: Article 0058

A new low-cost portable multispectral optical device for precise plant status assessment
type: NamedIndividual
type: Article
has knowledge model: Classifier 0062
abstract: Plant stress monitoring is of crucial importance to understand the plant response to environmental conditions, and has been widely applied in various fields including biology, agronomy, botany, and horticulture. A number of methods and instruments for plant stress monitoring have been proposed, most of which show significant disadvantages such as high cost, poor accuracy or complex operation that limit the use of such devices. In this paper, we propose a low cost, portable active multispectral optical device for precise plant stress detection and field mapping named Plant-O-Meter. The device has an integrated multispectral source that comprises light sources of the four most indicative wavelengths (850, 630, 535 and 465 nm), and enables simultaneous illumination of the whole plant. Sequential illumination and detection provide rapid reflectance measurements, which are wirelessly transmitted to android operated devices for processing and data storing. The device was tested in laboratory conditions comparing the Plant-O-Meter measurements with the image results from a SPECIM hyperspectral camera and a GreenSeeker handheld device and under field conditions with the GreenSeeker. The comparison revealed comparable performance, showing strong correlation with both the hyperspectral (R2 = 0.997) and the GreenSeeker handheld (R2 = 0.954 from the laboratory measurements and R2 = 0.886 from the field experiments), indicating that the device exhibits strong potential for accurate plant stress measurements. Moreover, owing to the very simple operating principle, the device represents a compact, cost-effective, and easy-to-operate solution.
author: Goran Kitić, Aristotelis Tagarakis, Norbert Cselyuszka, Marko Panić, Slobodan Birgermajer, Dušan Sakulski, Jovan Matović
publish year: 2019
creator: Goran Kitić, Aristotelis Tagarakis, Norbert Cselyuszka, Marko Panić, Slobodan Birgermajer, Dušan Sakulski, Jovan Matović
reference: Goran Kitić, Aristotelis Tagarakis, Norbert Cselyuszka, Marko Panić, Slobodan Birgermajer, Dušan Sakulski, Jovan Matović, "A new low-cost portable multispectral optical device for precise plant status assessment", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 300-308, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 162, July 2019, pages 300-308, Elsevier B.V.
label: Article 0062

A new proposal for automatic identification of multiple soybean diseases
type: NamedIndividual
type: Article
has knowledge model: Classifier 0068
abstract: This work proposes a combination of digital image processing techniques consisting of color moments, local binary patterns (LBP) and bag of visual words (BoVW) model for automatic detection of soybean diseases based on the analysis of color, texture and local characteristics of spots on affected leaves. The characteristics extracted after data collection and the application of the cited techniques are grouped and used as input to the support vector machine (SVM) for the purpose of disease classification. The images used in the development and validation of the proposed identification system were obtained from Digipathos, a database provided by the Brazilian Agricultural Research Agency (Embrapa), consisting of 354 images related to 8 typical soybean diseases, namely, bacterial blight, soybean rust, copper phytotoxicity, soybean mosaic, target spot, downy mildew, powdery mildew and Septoria brown spot. Performance analyses are presented separately for the techniques used in several stages of data processing and for the overall system consisting of the combination of these techniques and the SVM classifier. The proposed system achieves an success rate of 75.8%, representing an improvement of approximately 17% over results of methods in the existing literature.
author: Juliana Mariana Macedo Araujo, Zelia Myriam Assis Peixoto
publish year: 2019
creator: Juliana Mariana Macedo Araujo, Zelia Myriam Assis Peixoto
reference: Juliana Mariana Macedo Araujo, Zelia Myriam Assis Peixoto, "A new proposal for automatic identification of multiple soybean diseases", Computers and Electronics in Agriculture, Volume 167, December 2019, 105060, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 167, December 2019, 105060, Elsevier B.V.
label: Article 0068

A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method
type: NamedIndividual
type: Article
has knowledge model: Regressor_0074
abstract: The mathematical form to estimate the evaporation plays an important role in sustainable development and optimal water resources management of the countries facing water crisis. In this paper, a general regression analysis is proposed to estimate the daily pan evaporation with respect to input variables including the air temperature (T), relative humidity (R), wind speed (W) and sunshine hours (H) from two weather stations (i.e. Chahnimeh Zabol reservoirs and Iranshar station) in Iran. A conjugate gradient optimization method is used to calibrate the nonlinear mathematical function for accurate prediction of daily pan evaporation. Three mathematical functions with nonlinear forms are calibrated on data points of daily pan evaporation using proposed general regression analysis by proposed conjugate line search method. Then, the accuracies of three nonlinear models are compared with adaptive neuro-fuzzy inference system (ANFIS) and M5 Model Tree (M5Tree) models for two stations. Results indicated that that the conjugate gradient method was successfully applied to estimate the nonlinear models for predictions of daily pan evaporation with explicit mathematical function. The proposed mathematical models (RMSE of 4.14 mm for Chahnimeh and 1.94 mm for Iranshar) performed better than the ANFIS predictions for Iranshar (RMSE of 2.41 mm) and M5tree (RMSE of 4.31 mm for Chahnimeh and 2.19 mm for Iranshar).
author: Behrooz Keshtegar, Jamshid Piri, Ozgur Kisi
publish year: 2016
creator: Behrooz Keshtegar, Jamshid Piri, Ozgur Kisi
reference: Behrooz Keshtegar, Jamshid Piri, Ozgur Kisi, "A nonlinear mathematical modeling of daily pan evaporation based on conjugate gradient method", Computers and Electronics in Agriculture, Volume 127, September 2016, pages 120-130, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 127, September 2016, pages 120-130, Elsevier B.V.
label: Article 0074

A novel algorithm for damage recognition on pest-infested oilseed rape leaves
type: NamedIndividual
type: Article
has knowledge model: Regressor_0075
abstract: Cabbage caterpillar infestation of oilseed rape will leave wormholes on leaves. The percentage of wormholes’ area on leaf is an effective index to evaluate infestation seriousness. Hyperspectral imaging technology can be used to extract leaf from non-vegetation objects efficiently. Wormhole reconstruction can then be carried out for counting the wormholes’ area. The reconstruction of wormholes that are entirely within the leaf contour can be easily processed by holes filling function. However, it is difficult to process wormholes at the edge of a leaf. A novel location factor and an improved genetic-wavelet neural network reconstruction algorithm (G-WNNRA) have been proposed in this paper to process wormholes at the edge of a leaf. For the edge of a damaged leaf, the infested part represented by a hole at the edge and non-infested part should be distinguished automatically. Thus the novel location factor which was based on the first derivative of inverse function was used to develop test function for locating the infested part. Then the proposed G-WNNRA was constructed to reconstruct the missing part of an edge following the step of learning the non-infested part of the edge. The topological structure and parameters of the G-WNNRA was optimized by genetic algorithm and morlet wavelet function was applied as a transfer function. The points on non-infested part of edge were adopted as the training data set and the missing part of the edge were predicted. During the prediction, the points making up the reconstructed edge were chosen based on the output of the G-WNNRA. For performance comparison, wavelet neural network (WNN), genetic neural network (GNN) and back propagation neural network (BPNN) were tested on infested oilseed rape leaves and the RMSE of G-WNNRA was smaller than those of WNN, GNN and BPNN. The proposed location algorithm and G-WNNRA can be combined to reconstruct infested oilseed rape leaves.
author: Yun Zhao, Yong He, Xing Xu
publish year: 2012
creator: Yun Zhao, Yong He, Xing Xu
reference: Yun Zhao, Yong He, Xing Xu, "A novel algorithm for damage recognition on pest-infested oilseed rape leaves", Computers and Electronics in Agriculture, Volume 89, November 2012, pages 41-50, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 89, November 2012, pages 41-50, Elsevier B.V.
label: Article 0075

A novel approach for efficient crop yield prediction
type: NamedIndividual
type: Article
has knowledge model: Regressor_0076
abstract: Crop yield prediction is one of the challenging task in agricultural domain. Extensive research in agricultural domain has been carried out to predict better crop yield using the machine learning algorithm Artificial Neural Network (ANN) and statistical model Multiple Linear Regression (MLR). This article examines the intrinsic relationship between MLR and ANN. A hybrid MLR-ANN model has been proposed in this research work for efficient crop yield prediction. The proposed hybrid model is modeled to analyze the prediction accuracy when MLR intercept and coefficients were applied to initialize the ANN’s input layer weights and bias. Feed Forward Artificial Neural Network with Back Propagation training algorithm was used for predicting accurate paddy crop yield. In conventional ANN model, the weights and bias of input and hidden layer are initilized randomly. This hybrid MLR-ANN model, instead of random weights and bias initialization, the input layer weights and bias are initialized by using MLR’s coefficients and bias. The hybrid model prediction accuracy is compared with ANN, MLR, Support Vector Regression (SVR), k-Nearest Neighbour (KNN) and Random Forest (RF) models by using performance metrics. The computational time for both hybrid MLR-ANN and conventional ANN was calculated. The results show that the proposed hybrid MLR-ANN model gives better accuracy than the conventional models.
author: P.S. Maya Gopal, R. Bhargavi
publish year: 2019
creator: P.S. Maya Gopal, R. Bhargavi
reference: P.S. Maya Gopal, R. Bhargavi, "A novel approach for efficient crop yield prediction", Computers and Electronics in Agriculture, Volume 165, October 2019, 104968, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 165, October 2019, 104968, Elsevier B.V.
label: Article 0076

A novel approach for vegetation classification using UAV-based hyperspectral imaging
type: NamedIndividual
type: Article
has knowledge model: Classifier 0077
abstract: The use of unmanned aerial vehicle (UAV)-based spectral imaging offers considerable advantages in high-resolution remote-sensing applications. However, the number of sensors mountable on a UAV is limited, and selecting the optimal combination of spectral bands is complex but crucial for conventional UAV-based multispectral imaging systems. To overcome these limitations, we adopted a liquid crystal tunable filter (LCTF), which can transmit selected wavelengths without the need to exchange optical filters. For calibration and validation of the LCTF-based hyperspectral imaging system, a field campaign was conducted in the Philippines during March 28–April 3, 2016. In this campaign, UAV-based hyperspectral imaging was performed in several vegetated areas, and the spectral reflectances of 14 different ground objects were measured. Additionally, the machine learning (ML) approach using a support vector machine (SVM) model was applied to the obtained dataset, and a high-resolution classification map was then produced from the aerial hyperspectral images. The results clearly showed that a large amount of misclassification occurred in shaded areas due to the difference in spectral reflectance between sunlit and shaded areas. It was also found that the classification accuracy was drastically improved by training the SVM model with both sunlit and shaded spectral data. As a result, we achieved a classification accuracy of 94.5% in vegetated areas.
author: Tetsuro Ishida, Junichi Kurihara, Fra Angelico Viray, Shielo Baes Namuco, Enrico C. Paringit, Gay Jane Perez, Yukihiro Takahashi, Joel Joseph Marciano
publish year: 2018
creator: Tetsuro Ishida, Junichi Kurihara, Fra Angelico Viray, Shielo Baes Namuco, Enrico C. Paringit, Gay Jane Perez, Yukihiro Takahashi, Joel Joseph Marciano
reference: Tetsuro Ishida, Junichi Kurihara, Fra Angelico Viray, Shielo Baes Namuco, Enrico C. Paringit, Gay Jane Perez, Yukihiro Takahashi, Joel Joseph Marciano, "A novel approach for vegetation classification using UAV-based hyperspectral imaging", Computers and Electronics in Agriculture, Volume 144, January 2018, pages 80-85, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 144, January 2018, pages 80-85, Elsevier B.V.
label: Article 0077

A performance comparison of machine learning methods to estimate the fast-growing forest plantation yield based on laser scanning metrics
type: NamedIndividual
type: Article
has knowledge model: Regressor_0082
abstract: Machine learning models appear to be an attractive route towards tackling high-dimensional problems, particularly in areas where a lack of knowledge exists regarding the development of effective algorithms, and where programs must dynamically adapt to changing conditions. The objective of this study was to evaluate the performance of three machine learning tools for predicting stand volume of fast-growing forest plantations, based on statistical vegetation metrics extracted from an Airborne Laser Scanning (ALS) survey. The forests used in this study were composed of 1138 ha of commercial plantations that consisted of hybrids of Eucalyptus grandis and Eucalyptus urophylla, managed for pulp production. Three machine learning tools were implemented: neural network (NN), random forest (RF) and support vector regression (SV); and their performance was compared to a regression model (RM). The RF and the RM presented an RMSE in the leave-one-out cross-validation of 31.80 and 30.56 m3 ha−1 respectively. The NN and SV presented a higher RMSE than the others, equal to 64.44 and 65.30 m3 ha−1. The coefficient of determination and bias were similar to all modeling techniques. The ranking of ALS metrics based on their relative importance for the estimation of stand volume showed some differences. Rather than being limited to a subset of predictor variables, machine learning techniques explored the complete metrics set, looking for patterns between them and the dependent variable.
author: Eric Bastos Gorgens, Alessandro Montaghi, Luiz Carlos Estraviz Rodriguez
publish year: 2015
creator: Eric Bastos Gorgens, Alessandro Montaghi, Luiz Carlos Estraviz Rodriguez
reference: Eric Bastos Gorgens, Alessandro Montaghi, Luiz Carlos Estraviz Rodriguez, "A performance comparison of machine learning methods to estimate the fast-growing forest plantation yield based on laser scanning metrics", Computers and Electronics in Agriculture, Volume 116, August 2015, pages 221-227, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 116, August 2015, pages 221-227, Elsevier B.V.
label: Article 0082

A polarized hyperspectral imaging system for in vivo detection: Multiple applications in sunflower leaf analysis
type: NamedIndividual
type: Article
has knowledge model: Classifier 0083
abstract: This study aims to investigate the potential of an original polarized hyperspectral imaging (HSI) setup in the spectral domain of 400–1000 nm for sunflower leaves in real-world. Dataset 1 includes hypercubes of sunflower leaves in two varieties with different life growth stages, while Dataset 2 is comprised of healthy and contaminated sunflower leaves suffering from powdery mildew (PM) and/or septoria leaf spot (SLS). Cross polarised (R⊥), parallel polarised (R||) reflectance signals, RBS(R|| + R⊥) and RSS (R||-R⊥) spectra were obtained and used to develop partial least squares-discriminant analysis (PLS-DA) models. Surface information played an important role in separating two varieties of leaves due to the fact that the best model performance was achieved by using RSS mean spectra, while both surface and subsurface were equally important in classifying leaves between two major growth stages because model of RBS mean spectra outperformed other models. The best classification model for disease detection was achieved by using pixel R⊥ spectra with the correct classification rate (CCR) of 0.963 for both cross validation and prediction, meaning that subsurface spectral features were the most important to detect infected leaves. The resulting classification maps were also displayed to visualize the distribution of the infected regions on the leaf samples. The overall results obtained in this research showed that the developed polarized-HSI system coupled with multivariate analysis has considerable promise in agricultural real-world applications.
author: Jun-Li Xu, Alexia Gobrecht, Daphne Heran, Nathalie Gorretta, Marie Coque, Aoife A. Gowen, Ryad Bendoula, Da-Wen Sun
publish year: 2019
creator: Jun-Li Xu, Alexia Gobrecht, Daphne Heran, Nathalie Gorretta, Marie Coque, Aoife A. Gowen, Ryad Bendoula, Da-Wen Sun
reference: Jun-Li Xu, Alexia Gobrecht, Daphne Heran, Nathalie Gorretta, Marie Coque, Aoife A. Gowen, Ryad Bendoula, Da-Wen Sun, "A polarized hyperspectral imaging system for in vivo detection: Multiple applications in sunflower leaf analysis", Computers and Electronics in Agriculture, Volume 158, March 2019, pages 258-270, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 158, March 2019, pages 258-270, Elsevier B.V.
label: Article 0083

A prediction of leaf mechanical properties with data mining
type: NamedIndividual
type: Article
has knowledge model: Regressor_0084
abstract: The mechanical properties of the leaf are typically determined by mechanical testing approaches to study the leaf lifespan, plant anti-herbivore defences and ecological functions by considering habitat influences, environmental resources variation and species diversity. While the leaf morphology features were commonly used for plant recognition and plant disease detection with the aid of an automated inspection system. However, the influence of morphology features on leaf mechanical properties is vague. In this research, we investigated the effect of various morphological features on mechanical properties of leaf, followed by proposed a novel leaf mechanical properties prediction model using data mining techniques. A 600 × 22 feature vector was collected and examined using Pearson correlation analysis and Welch’s test to select the relevant features. The prediction on four mechanical properties indicators was performed with LinearRegression, KStar, DecisionTable and M5P algorithms in the Waikato Environment for Knowledge Analysis (WEKA). The experimental results show that numeric prediction for Tearing Force (FT) and Tearing Strength (ST) (RRSE ≈ 25%) were about two folds better as compared to Work-to-tear (WT) and Specific Work-to-tear (SWT) (RRSE ≈ 50%) in four algorithms tested. The best results achieved was the FT indicator prediction with M5P algorithms (RRSE = 23.12%). FT indicator prediction model adopted from M5P algorithms output was constructed.
author: Choo Wooi H'ng, Wei Ping Loh
publish year: 2019
creator: Choo Wooi H'ng, Wei Ping Loh
reference: Choo Wooi H'ng, Wei Ping Loh, "A prediction of leaf mechanical properties with data mining", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 162, July 2019, pages 669-676, Elsevier B.V.
label: Article 0084

A real-time fuzzy decision support system for alfalfa irrigation
type: NamedIndividual
type: Article
has knowledge model: Regressor_0089
abstract: An irrigation decision support system is a critical component in precision water management. In this study, a real-time irrigation decision support system (IDSS) based on a fuzzy inference system and a relevant software were developed for irrigation management in alfalfa. The proposed IDSS was a comprehensive model, with inputs of soil water, alfalfa growth and weather conditions. On the basis of soil moisture and difference of alfalfa height, the IDSS determined the most appropriate irrigation strategy in order to maintain the soil moisture above a predefined value. In the fuzzy inference system, all variables were fuzzified using triangular membership functions. In the process of fuzzification, the max-min inference and the rule-based Mamdani-type fuzzy modeling were adopted to generate the amount of irrigation. The IDSS was incorporated with weather forecasting and the models of alfalfa growth and soil water. It provided the predictions of alfalfa growth stages, height, and soil water status. The test results indicated that the prediction models made a good estimation of alfalfa growth stages and soil water with NRMSE of 8.28% and 6.29%, respectively. Eventually, the IDSS provided the timing and amounts of irrigation for high efficiency of the alfalfa irrigated system. The test in Zhuozhou, Hebei Province, China indicated that this IDSS is very promising in facilitating alfalfa irrigation and harvest management.
author: Maona Li, Ruixiu Sui, Yangyang Meng, Haijun Yan
publish year: 2019
creator: Maona Li, Ruixiu Sui, Yangyang Meng, Haijun Yan
reference: Maona Li, Ruixiu Sui, Yangyang Meng, Haijun Yan, "A real-time fuzzy decision support system for alfalfa irrigation", Computers and Electronics in Agriculture, Volume 163, August 2019, 104870, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 163, August 2019, 104870, Elsevier B.V.
label: Article 0089

A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network
type: NamedIndividual
type: Article
has knowledge model: Classifier 0091
abstract: Manual approaches to recognize cucumber diseases are often time-consuming, laborious and subjective. A deep convolutional neural network (DCNN) was proposed to conduct symptom-wise recognition of four cucumber diseases, i.e., anthracnose, downy mildew, powdery mildew, and target leaf spots. The symptom images were segmented from cucumber leaf images captured under field conditions. In order to decrease the chance of overfitting, data augmentation methods were utilized to enlarge the datasets formed by the segmented symptom images. With the augmented datasets containing 14,208 symptom images, the DCNN achieved good recognition results, with an accuracy of 93.4%. In order to compare the results of the DCNN, comparative experiments were conducted using conventional classifiers (Random Forest and Support Vector Machines), as well as AlexNet. Results showed that the DCNN was a robust tool for recognizing the cucumber diseases in field conditions.
author: Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhihong Gong, Zhongfu Sun
publish year: 2018
creator: Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhihong Gong, Zhongfu Sun
reference: Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhihong Gong, Zhongfu Sun, "A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network", Computers and Electronics in Agriculture, Volume 154, November 2018, pages 18-24, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 154, November 2018, pages 18-24, Elsevier B.V.
label: Article 0091

A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses
type: NamedIndividual
type: Article
has knowledge model: Classifier 0093
abstract: Early and accurate disease detection is essential for implementing timely disease management practices. Current disease detection tactics, like visual detection through scouting, are labor intensive, expensive, requires a level of expertise in pest identification, and, may result in subjective disease identification. Diagnosis based on visual symptoms is often compromised by the inability to differentiate between similar symptoms caused by different biotic and abiotic factors. In this paper, an automated early disease detection technique for avocado trees is presented and evaluated. This remote sensing technique can detect an important avocado disease, the laurel wilt (Lw) disease, and differentiate it from healthy trees (H), trees infected by phytophthora root rot (Prr), and trees with iron (Fe) and nitrogen (N) deficiencies. Detection of Lw disease in avocado trees, in early stage, is very difficult, because it has similar symptoms with other stress factors such as nutrient deficiency, salt damage, phytophthora root rot, etc. The proposed disease detection procedure contains several steps including image acquisition, image pre-processing, image segmentation, feature extraction and classification. For image acquisition, two cameras were utilized and evaluated: (i) a Tetracamera (6 bands Tetracam) and (ii) a modified Canon camera (3 bands); and two classification methods were studied: (a) neural network multilayer perceptron (MLP), and (ii) K- nearest neighbors, to detect Lw in asymptomatic stage and in late (symptomatic) stage. Additionally, two segmentation methods, region of interest (OVROI) and polygon region of interest (PROI), were utilized. The MLP classification method with the Tetracam was able to successfully detect Lw with an accuracy of 99% in asymptomatic (early) stage. Hence, low-cost remote technique can be utilized to differentiate healthy and unhealthy plants.
author: Jaafar Abdulridha, Reza Ehsani, Amr Abd-Elrahman, Yiannis Ampatzidis
publish year: 2019
creator: Jaafar Abdulridha, Reza Ehsani, Amr Abd-Elrahman, Yiannis Ampatzidis
reference: Jaafar Abdulridha, Reza Ehsani, Amr Abd-Elrahman, Yiannis Ampatzidis, "A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses", Computers and Electronics in Agriculture, Volume 156, January 2019, pages 549-557, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 156, January 2019, pages 549-557, Elsevier B.V.
label: Article 0093

A review of advanced machine learning methods for the detection of biotic stress in precision crop protection
type: NamedIndividual
type: Article
has knowledge model: Classifier 0094
abstract: Effective crop protection requires early and accurate detection of biotic stress. In recent years, remarkable results have been achieved in the early detection of weeds, plant diseases and insect pests in crops. These achievements are related both to the development of non-invasive, high resolution optical sensors and data analysis methods that are able to cope with the resolution, size and complexity of the signals from these sensors. Several methods of machine learning have been utilized for precision agriculture such as support vector machines and neural networks for classification (supervised learning); k-means and self-organizing maps for clustering (unsupervised learning). These methods are able to calculate both linear and non-linear models, require few statistical assumptions and adapt flexibly to a wide range of data characteristics. Successful applications include the early detection of plant diseases based on spectral features and weed detection based on shape descriptors with supervised or unsupervised learning methods. This review gives a short introduction into machine learning, analyses its potential for precision crop protection and provides an overview of instructive examples from different fields of precision agriculture.
author: Jan Behmann, Anne-Katrin Mahlein, Till Rumpf, Christoph Romer, Lutz Plümer
publish year: 2015
creator: Jan Behmann, Anne-Katrin Mahlein, Till Rumpf, Christoph Romer, Lutz Plümer
reference: Jan Behmann, Anne-Katrin Mahlein, Till Rumpf, Christoph Romer, Lutz Plümer, "A review of advanced machine learning methods for the detection of biotic stress in precision crop protection", Precision Agriculture, Volume 16, pages 239–260 (2015) , Springer
Source: Precision Agriculture, Volume 16, pages 239–260 (2015) , Springer
label: Article 0094

A segmentation method for disease spot images incorporating chrominance in Comprehensive Color Feature and Region Growing
type: NamedIndividual
type: Article
has knowledge model: Classifier 0098
abstract: In this research work, a segmentation method for disease spotted leaves using Advanced Comprehensive Color Feature (ACCF) and Region Growing method captured under real field condition is proposed. The captured diseased leaves have two main challenges one is clutter background and then uneven illumination, this issue makes the robust segmentation lower. Two methods namely Advanced Comprehensive Color Features (ACCF) and Region Growing method are used in this process for segmentation of disease spots to overcome those challenges. The Advanced Comprehensive Color Feature detection consists of different color spaces, color indexes and color to grayscale conversation using Singular Value Decomposition (SVD) which makes more powerful discrimination of disease spots from uneven illumination. In disease spot segmentation, region growing method is used for eliminating clutter background by interactively selecting growing seeds in ACCF map. The morphological operation is applied in the resultant region growing method. Under real field condition, the proposed method gives an average accuracy of 87% in segmentation.
author: N. Jothiaruna, K. Joseph Abraham Sundar, B. Karthikeyan
publish year: 2019
creator: N. Jothiaruna, K. Joseph Abraham Sundar, B. Karthikeyan
reference: N. Jothiaruna, K. Joseph Abraham Sundar, B. Karthikeyan, "A segmentation method for disease spot images incorporating chrominance in Comprehensive Color Feature and Region Growing", Computers and Electronics in Agriculture, Volume 165, October 2019, 104934, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 165, October 2019, 104934, Elsevier B.V.
label: Article 0098

A spectral correction method for multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application to nitrogen content assessment in wheat
type: NamedIndividual
type: Article
has knowledge model: AssociationRule 0102
abstract: In-field hyperspectral imagery is a promising tool for crop phenotyping or monitoring. In association with partial least square regression (PLS-R), it allows building high spatial resolution maps of the chemical content of plant leaves. However, several optical phenomena must be taken into account, due to their influence on collected spectral data. The most challenging is multiple scattering, produced when a leaf is partly illuminated by light reflection or transmission from neighboring leaves. It can induce bias in prediction results. This paper presents a method for multi-scattering correction. Its development has been based on simulation tools: a 3D canopy model of winter wheat was combined with light propagation modeling, in order to simulate the apparent reflectance of every visible leaf in the canopy for a given actual reflectance. Leaf nitrogen content (LNC) prediction has been considered. A data set of reflectance spectra associated with LNC values has been issued from real leaf measurements. A theoretical disturbance subspace representing the spectrum dispersion in the spectral space due to multi-scattering has then been built by considering polynomial combinations of the initial spectra, and a projection along this subspace has been applied to every simulated spectra. Using this strategy, a PLS-R model built on initial spectra was still satisfactory when applied to simulated spectra with multiple scattering. The method has then been applied to real plants in greenhouse and field conditions, and its prediction results compared with those of a standard PLS-R, confirming its efficiency in the presence of various lighting environments.
author: Nathalie Al Makdessi, Martin Ecarnot, Pierre Roumet, Gilles Rabatel
publish year: 2019
creator: Nathalie Al Makdessi, Martin Ecarnot, Pierre Roumet, Gilles Rabatel
reference: Nathalie Al Makdessi, Martin Ecarnot, Pierre Roumet, Gilles Rabatel, "A spectral correction method for multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application to nitrogen content assessment in wheat", Precision Agriculture, Volume 20, pages 237–259 (2019) , Springer
Source: Precision Agriculture, Volume 20, pages 237–259 (2019) , Springer
label: Article 0102

Wheat landraces identification through glumes image analysis
type: NamedIndividual
type: Article
has knowledge model: Classifier 0111
abstract: A new practical method able to identify wheat local landraces was implemented. It is based on computerized image analysis techniques and statistical identification, for the first time on the basis of glumes size, shape, colour and texture.Ears of 52 different Sicilian wheat landraces were reaped for three consecutive years. Digital images of the glumes were acquired, processed and analysed, measuring 138 quantitative morpho-colorimetic variables. The data were statistically analysed applying a Linear Discriminant Analysis. All the statistical comparisons, distinguished for systematic rank, given perfect identification performances; while an overall percentage of correct identification of 89.7% was reached when all the landraces were compared all together.Finally, the identification system was tested with an unknown glume sample, later entirely identified as Vallelunga, one of the Sicilian landraces.This work represents the first attempt of wheat landraces identification based on glume phenotypic characters, applying image analysis techniques. Considering the growing interest in local old wheat landraces, strongly linked to the renewed appreciation in traditional and typical local products, the obtained results support the application of the image analysis system not only for grading purposes, but also to define the product traceability, in order to get a “market card” for wheat landraces.
author: Oscar Grillo, Sebastiano Blangiforti, Gianfranco Venora
publish year: 2017
creator: Oscar Grillo, Sebastiano Blangiforti, Gianfranco Venora
reference: Oscar Grillo, Sebastiano Blangiforti, Gianfranco Venora, "Wheat landraces identification through glumes image analysis", Computers and Electronics in Agriculture, Volume 141, September 2017, pages 223-231, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 141, September 2017, pages 223-231, Elsevier B.V.
label: Article 0111

A weighted multivariate spatial clustering model to determine irrigation management zones
type: NamedIndividual
type: Article
has knowledge model: Cluster 0113
abstract: Management of agricultural fields according to spatial and temporal variability is an important aspect of precision agriculture. Precision management relies on division of a field into areas with homogeneous characteristics, management zones (MZs), which are likely affected by multiple, interrelated factors. We present a method, based on machine learning and spatial statistics, to analyze the spatial relationship between a set of variables and determine management zones in a vineyard. The method involves: (1) fitting a model that quantifies the relationship between multiple variables and yield; (2) fitting a model that quantifies the effect of the spatial variability of multiple variables on yield spatial characteristics; and (3) developing a weighted multivariate spatial clustering model as a method to determine MZs. Twelve variables were sampled for 3893 vines in the wine grape vineyard. These variables included soil properties, terrain characteristics, and environmental impact, as well as crop-condition, using indices calculated from remote sensing images. The predictor variables were spatially characterized using hot-spot analysis (Getis Ord Gi* Z-score values) to assess their spatial variability. A gradient boosted regression trees (BRT) algorithm was used to analyze the spatial multivariable effect on yield spatial characteristics. MZs were determined using multivariate K-means clustering, with relative weights given to the predictors, based on their relative influence on yield spatial variability provided by the BRT model. This method was compared to ordinary K-means clustering and K-means with spatial representation of the variables without weights using a dissimilarity index and spatial autocorrelation measures. Model performance was found to be very high and demonstrated that among the evaluated predictors, crop condition indices were the most important regressors for yield and its spatial characteristics. The weighted multivariate spatial clustering was f…
author: Noa Ohana-Levi, Idan Bahat, Aviva Peeters, Alexandra Shtein, Yishai Netzer, Yafit Cohen, Alon Ben-Gal
publish year: 2019
creator: Noa Ohana-Levi, Idan Bahat, Aviva Peeters, Alexandra Shtein, Yishai Netzer, Yafit Cohen, Alon Ben-Gal
reference: Noa Ohana-Levi, Idan Bahat, Aviva Peeters, Alexandra Shtein, Yishai Netzer, Yafit Cohen, Alon Ben-Gal, "A weighted multivariate spatial clustering model to determine irrigation management zones", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 719-731, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 162, July 2019, pages 719-731, Elsevier B.V.
label: Article 0113

A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China
type: NamedIndividual
type: Article
has knowledge model: Classifier 0114
abstract: The variability of soil properties plays a critical role in soil and water conversation engineering. In this study, different machine learning techniques were applied to identify the soil texture classes based on a set of terrain parameters in a small mountainous watershed located in the core areas of Three Gorges of Yangtze River, southwest China. For this, the support vector machines (SVMs) with polynomial and Gaussian radius basis functions, artificial neural network, and classification tree methods were compared. The most commonly used performance measures including overall accuracy, kappa index, receiver operating characteristics (ROC), and area under the ROC curve (AUC) were employed to evaluate the accuracy of the models for classification. The observed results showed a better performance under SVMs than under artificial neural network and classification tree algorithms. Moreover, SVM with polynomial function (SVM-poly) worked slightly better than SVM with Gaussian radius basis function. The overall accuracy, kappa statistic, and AUC of SVM-poly were 0.943, 0.79, and 0.944, respectively. Meanwhile, the classification accuracy was 0.794 for clay, 0.992 for loam, and 0.661 for sand under SVM-poly. Elevation, terrain classification index for lowlands, and flow path length were the most important terrain indicators affecting the variation in the soil texture class in the study area. These results showed that the support vector machines are feasible and reliable in the identification of soil texture classes.
author: Wei Wu, Ai-Di Li, Xin-Hua He, Ran Ma, Hong-Bin Liu, Jia-Ke Lv
publish year: 2018
creator: Wei Wu, Ai-Di Li, Xin-Hua He, Ran Ma, Hong-Bin Liu, Jia-Ke Lv
reference: Wei Wu, Ai-Di Li, Xin-Hua He, Ran Ma, Hong-Bin Liu, Jia-Ke Lv, "A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China", Computers and Electronics in Agriculture, Volume 144, January 2018, pages 86-93, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 144, January 2018, pages 86-93, Elsevier B.V.
label: Article 0114

A computer vision system for early stage grape yield estimation based on shoot detection
type: NamedIndividual
type: Article
has knowledge model: Classifier 0115
abstract: Counting grapevine shoots early in the growing season is critical for adjusting management practices but is challenging to automate due to a range of environmental factors.This paper proposes a completely automatic system for grapevine yield estimation, comprised of robust shoot detection and yield estimation based on shoot counts produced from videos. Experiments were conducted on four vine blocks across two cultivars and trellis systems over two seasons. A novel shoot detection framework is presented, including image processing, feature extraction, unsupervised feature selection and unsupervised learning as a final classification step. Then a procedure for converting shoot counts from videos to yield estimates is introduced.The shoot detection framework accuracy was calculated to be 86.83% with an F1-score of 0.90 across the four experimental blocks. This was shown to be robust in a range of lighting conditions in a commercial vineyard. The absolute predicted yield estimation error of the system when applied to four blocks over two consecutive years ranged from 1.18% to 36.02% when the videos were filmed around E-L stage 9.The developed system has an advantage over traditional PCD mapping techniques in that yield variation maps can be obtained earlier in the season, thereby allowing farmers to adjust their management practices for improved outputs. The unsupervised feature selection algorithm combined with unsupervised learning removed the requirement for any prior training or labeling, greatly enhancing the applicability of the overall framework and allows full automation of shoot mapping on a large scale in vineyards.
author: Scarlett Liu, Steve Cossell, Julie Tang, Gregory Dunn, Mark Whitty
publish year: 2017
creator: Scarlett Liu, Steve Cossell, Julie Tang, Gregory Dunn, Mark Whitty
reference: Scarlett Liu, Steve Cossell, Julie Tang, Gregory Dunn, Mark Whitty, "A computer vision system for early stage grape yield estimation based on shoot detection", Computers and Electronics in Agriculture, Volume 137, May 2017, pages 88-101, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 137, May 2017, pages 88-101, Elsevier B.V.
label: Article 0115

Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data
type: NamedIndividual
type: Article
has knowledge model: Regressor_0117
abstract: Proximal and remote sensors have proved their effectiveness for the estimation of several biophysical and biochemical variables, including yield, in many different crops. Evaluation of their accuracy in vegetable crops is limited. This study explored the accuracy of proximal hyperspectral and satellite multispectral sensors (Sentinel-2 and WorldView-3) for the prediction of carrot root yield across three growing regions featuring different cropping configurations, seasons and soil conditions. Above ground biomass (AGB), canopy reflectance measurements and corresponding yield measures were collected from 414 sample sites in 24 fields in Western Australia (WA), Queensland (Qld) and Tasmania (Tas), Australia. The optimal sensor (hyperspectral or multispectral) was identified by the highest overall coefficient of determination between yield and different vegetation indices (VIs) whilst linear and non-linear models were tested to determine the best VIs and the impact of the spatial resolution. The optimal regression fit per region was used to extrapolate the point source measurements to all pixels in each sampled crop to produce a forecasted yield map and estimate average carrot root yield (t/ha) at the crop level. The latter were compared to commercial carrot root yield (t/ha) obtained from the growers to determine the accuracy of prediction. The measured yield varied from 17 to 113 t/ha across all crops, with forecasts of average yield achieving overall accuracies (% error) of 9.2% in WA, 10.2% in Qld and 12.7% in Tas. VIs derived from hyperspectral sensors produced poorer yield correlation coefficients (R2 < 0.1) than similar measures from the multispectral sensors (R2 < 0.57, p < 0.05). Increasing the spatial resolution from 10 to 1.2 m improved the regression performance by 69%. It is impossible to non-destructively estimate the pre-harvest spatial yield variability of root vegetables such as carrots. Hence, this method of yield forecasting offers great benefit for …
author: Luz Angelica Suarez, Andrew Robson, John McPhee, Julie O’Halloran, Celia van Sprang
publish year: 2020
creator: Luz Angelica Suarez, Andrew Robson, John McPhee, Julie O’Halloran, Celia van Sprang
reference: Luz Angelica Suarez, Andrew Robson, John McPhee, Julie O’Halloran, Celia van Sprang, "Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data", Precision Agriculture (2020) , Springer
Source: Precision Agriculture (2020) , Springer
label: Article 0117

An analytical method for determination of quality parameters in cotton plumes by digital image and chemometrics
type: NamedIndividual
type: Article
has knowledge model: Regressor_0133
abstract: This paper proposes an analytical method based on the use of digital images and multivariate calibration for the determination of degree of yellowness (+b), reflectance (Rd) and content of wax (WAX) in samples of white cotton plumes and naturally colored plumes. Digital images acquisition of cotton plumes was carried out through a webcam and histograms containing frequency distributions of color index in red-green-blue (RGB), hue (H), saturation (S), value (V), and grayscale channels were obtained. Models of multivariate calibration based on Regression by partial least squares (PLS) using the complete histogram, as well as multiple linear regression (MLR) with the selection of variables by successive projections algorithm (SPA) were developed, validated and compared. Satisfactory prediction results were obtained for both models with Root Mean Square Error of Prediction (RMSEP) values varying from 0.76–0.83, 2.49–2.53% and 0.30–0.33% for +b, Rd and wax, respectively. According to a paired t-test at a 95% confidence level, no statistically significant difference was found between the predicted and reference values. An F-test at 95% confidence level does not indicate significant differences between the RMSEP values obtained with the full-histogram PLS and MLR-SPA models. It is a simple and low-cost method that does not use reagent, does not destroy the sample and performs analysis with comparable results to the reference method.
author: Maria Ivanda S. Gonçalves, Welma T.S. Vilar, Everaldo Paulo Medeiros, Marcio Jose Coelho Pontes
publish year: 2016
creator: Maria Ivanda S. Gonçalves, Welma T.S. Vilar, Everaldo Paulo Medeiros, Marcio Jose Coelho Pontes
reference: Maria Ivanda S. Gonçalves, Welma T.S. Vilar, Everaldo Paulo Medeiros, Marcio Jose Coelho Pontes, "An analytical method for determination of quality parameters in cotton plumes by digital image and chemometrics", Computers and Electronics in Agriculture, Volume 123, April 2016, pages 89-94, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 123, April 2016, pages 89-94, Elsevier B.V.
label: Article 0133

An approach for assessing the effects of site-specific fertilization on crop growth and yield of durum wheat in organic agriculture
type: NamedIndividual
type: Article
has knowledge model: Regressor_0134
abstract: Precision agriculture (PA) technologies allow us to assess field variability and support site-specific (SSP) application of inputs. The joint application of PA and organic farming practices might be synergetic. The objective of this 3-year study was to propose a multivariate statistical and geostatistical approach, to evaluate the effects of SSP nitrogen (N) fertilization on durum wheat in transition to organic farming. Soil parameters were measured to assess soil fertility level before the SSP fertilization on wheat, which was carried out by management zones in the third year. Radiometric measurements were performed with a hyperspectral spectroradiometer and N-uptake at anthesis and grain yield were determined. The expected values and 95 % confidence intervals of the soil parameters, N-uptake and yield data were estimated with polygon kriging for each management zone. Reflectance data were reduced through principal component analysis and the retained principal components were submitted to factorial co-kriging analysis to estimate orthogonal scale-dependent factors. Comparisons between N-uptake and yield and between the retained regionalized factors (F1) and yield were performed. The spatial pattern of F1 at shorter scales was mostly reproduced in the N-uptake map, suggesting the predictive capacity of hyperspectral data for crop N-status. Within-cluster variance for yield was reduced, quite probably as a combined effect of meteorological pattern and management. The preliminary results seem to be promising in the perspective of PA. Moreover, an inverse relationship between grain yield and crop N-status was observed.
author: M. Diacono, A. Castrignano, C. Vitti, A. M. Stellacci, L. Marino, C. Cocozza, D. De Benedetto, A. Troccoli, P. Rubino, D. Ventrella
publish year: 2014
creator: M. Diacono, A. Castrignano, C. Vitti, A. M. Stellacci, L. Marino, C. Cocozza, D. De Benedetto, A. Troccoli, P. Rubino, D. Ventrella
reference: M. Diacono, A. Castrignano, C. Vitti, A. M. Stellacci, L. Marino, C. Cocozza, D. De Benedetto, A. Troccoli, P. Rubino, D. Ventrella, "An approach for assessing the effects of site-specific fertilization on crop growth and yield of durum wheat in organic agriculture", Precision Agriculture, Volume 15, pages 479–498 (2014) , Springer
Source: Precision Agriculture, Volume 15, pages 479–498 (2014) , Springer
label: Article 0134

An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning
type: NamedIndividual
type: Article
has knowledge model: Regressor_0135
abstract: Many broadacre farmers have a time series of crop yield monitor data for their fields which are often augmented with additional data, such as soil apparent electrical conductivity surveys and soil test results. In addition there are now readily available national and global datasets, such as rainfall and MODIS, which can be used to represent the crop-growing environment. Rather than analysing one field at a time as is typical in precision agriculture research, there is an opportunity to explore the value of combining data over multiple fields/farms and years into one dataset. Using these datasets in conjunction with machine learning approaches allows predictive models of crop yield to be built. In this study, several large farms in Western Australia were used as a case study, and yield monitor data from wheat, barley and canola crops from three different seasons (2013, 2014 and 2015) that covered ~ 11 000 to ~ 17 000 hectares in each year were used. The yield data were processed to a 10 m grid, and for each observation point associated predictor variables in space and time were collated. The data were then aggregated to a 100 m spatial resolution for modelling yield. Random forest models were used to predict crop yield of wheat, barley and canola using this dataset. Three separate models were created based on pre-sowing, mid-season and late-season conditions to explore the changes in the predictive ability of the model as more within-season information became available. These time points also coincide with points in the season when a management decision is made, such as the application of fertiliser. The models were evaluated with cross-validation using both fields and years for data splitting, and this was assessed at the field spatial resolution. Cross-validated results showed the models predicted yield relatively accurately, with a root mean square error of 0.36 to 0.42 t ha−1, and a Lin’s concordance correlation coefficient of 0.89 to 0.92 at the field resolutio…
author: Patrick Filippi, Edward J. Jones, Niranjan S. Wimalathunge, Pallegedara D. S. N. Somarathna, Liana E. Pozza, Sabastine U. Ugbaje, Thomas G. Jephcott, Stacey E. Paterson, Brett M. Whelan, Thomas F. A. Bishop
publish year: 2019
creator: Patrick Filippi, Edward J. Jones, Niranjan S. Wimalathunge, Pallegedara D. S. N. Somarathna, Liana E. Pozza, Sabastine U. Ugbaje, Thomas G. Jephcott, Stacey E. Paterson, Brett M. Whelan, Thomas F. A. Bishop
reference: Patrick Filippi, Edward J. Jones, Niranjan S. Wimalathunge, Pallegedara D. S. N. Somarathna, Liana E. Pozza, Sabastine U. Ugbaje, Thomas G. Jephcott, Stacey E. Paterson, Brett M. Whelan, Thomas F. A. Bishop, "An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning", Precision Agriculture, Volume 20, pages 1015–1029 (2019) , Springer
Source: Precision Agriculture, Volume 20, pages 1015–1029 (2019) , Springer
label: Article 0135

An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data
type: NamedIndividual
type: Article
has knowledge model: Regressor_0136
abstract: Stem water potential seems to be a sensitive measure of plant water status. Nonetheless, it is a labour-intensive measurement and is not suited for automatic irrigation scheduling or control. This study describes the application of artificial neural networks to estimate stem water potential from soil moisture at different depths and standard meteorological variables, considering a limited data set. The experiment was carried out with ‘Navelina’ citrus trees grafted on ‘Cleopatra’ mandarin. Principal components analysis and multiple linear regression were used preliminarily to assess the relationships among observations and to propose other models to allow a comparative analysis, respectively. Two principal components account for the systematic data variation. The optimum regression equation of stem water potential considered temperature, relative humidity, solar radiation and soil moisture at 50 cm as input variables, with a determination coefficient of 0.852. When compared with their corresponding regression models, ANNs presented considerably higher performance accuracy (with an optimum determination coefficient of 0.926) due to a higher input-output mapping ability.
author: Pau Marti, Maria Gasque, Pablo Gonzalez-Altozano
publish year: 2013
creator: Pau Marti, Maria Gasque, Pablo Gonzalez-Altozano
reference: Pau Marti, Maria Gasque, Pablo Gonzalez-Altozano, "An artificial neural network approach to the estimation of stem water potential from frequency domain reflectometry soil moisture measurements and meteorological data", Computers and Electronics in Agriculture, Volume 91, February 2013, pages 75-86, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 91, February 2013, pages 75-86, Elsevier B.V.
label: Article 0136

An artificial neural network model for estimating Mentha crop biomass yield using Landsat 8 OLI
type: NamedIndividual
type: Article
has knowledge model: Regressor_0137
abstract: Yield forecasting is essential for management of the food and agriculture economic growth of a country. Artificial Neural Network (ANN) based models have been used widely to make precise and realistic forecasts, especially for the nonlinear and complicated problems like crop yield prediction, biomass change detection and crop evapo-transpiration examination. In the present study, various parameters viz. spectral bands of Landsat 8 OLI (Operational Land Imager) satellite data and derived spectral indices along with field inventory data were evaluated for Mentha crop biomass estimation using ANN technique of Multilayer Perceptron. The estimated biomass showed a good relationship (R2 = 0.762 and root mean square error (RMSE) = 2.74 t/ha) with field-measured biomass.
author: Mohammad Saleem Khan, Manoj Semwal, Ashok Sharma, Rajesh Kumar Verma
publish year: 2020
creator: Mohammad Saleem Khan, Manoj Semwal, Ashok Sharma, Rajesh Kumar Verma
reference: Mohammad Saleem Khan, Manoj Semwal, Ashok Sharma, Rajesh Kumar Verma, "An artificial neural network model for estimating Mentha crop biomass yield using Landsat 8 OLI", Precision Agriculture, Volume 21, pages 18–33 (2020) , Springer
Source: Precision Agriculture, Volume 21, pages 18–33 (2020) , Springer
label: Article 0137

Soil clustering by fuzzy c-means algorithm
type: NamedIndividual
type: Article
has knowledge model: Classifier 0140
abstract: In this study, hard k-means and fuzzy c-means algorithms are utilized for the classification of fine grained soils in terms of shear strength and plasticity index parameters. In order to collect data, several laboratory tests are performed on 120 undisturbed soil samples, which are obtained from Antalya region. Additionally, for the evaluation of the generalization ability of clustering analysis, 20 fine grained soil samples collected from the other regions of Turkey are also classified using the same clustering algorithms. Fuzzy c-means algorithm exhibited better clustering performance over hard k-means classifier. As expected, clustering analysis produced worse outcomes for soils collected from different regions than those of obtained from a specific region. In addition to its precise classification ability, fuzzy c-means approach is also capable of handling the uncertainty existing in soil parameters. As a result, fuzzy c-means clustering can be successfully applied to classify regional fine grained soils on the basis of shear strength and plasticity index parameters.
author: A.B.Goktepe, S.Altun, A.Sezer
publish year: 2005
creator: A.B.Goktepe, S.Altun, A.Sezer
reference: A.B.Goktepe, S.Altun, A.Sezer, "Soil clustering by fuzzy c-means algorithm", Advances in Engineering Software, Volume 36, Issue 10, October 2005, Pages 691-698
Source: Advances in Engineering Software, Volume 36, Issue 10, October 2005, Pages 691-698
label: Article 0140

Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements
type: NamedIndividual
type: Article
has knowledge model: Regressor_0144
abstract: Crop aboveground biomass estimates are critical for assessing crop growth and predicting yield. In order to ascertain the optimal methods for winter wheat biomass estimation, this study compared the utility of univariate techniques involving narrow band vegetation indices and red-edge position (REP), as well as multivariate calibration techniques involving the partial least square regression (PLSR) analyses using band depth parameters, and the combination of band depth parameters and hyperspectral indices including narrow band indices and REP. Narrow band indices were calculated in the form of normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI) using all possible two-band combinations for selecting optimal narrow band indices. Band depth, band depth ratio (BDR), normalized band depth index, and band depth normalized to area extracted from a red absorption region (550 nm–750 nm) were utilized as band depth parameters. The results indicated that: (1) Compared with the traditional NDVI and SAVI constructed with bands at 670 nm and 800 nm and REP, the selected narrow band indices (optimal NDVI-like and optimal SAVI-like) produced higher estimation accuracy of the winter wheat biomass; (2) the PLSR models based on band depth parameters produced lower root mean square error, relative to the models based on the selected narrow band indices; and (3) the PLSR model based on the combination of optimal NDVI-like and BDR produced the best estimated result of the winter wheat biomass (R2 = 0.84, RMSE = 0.177 kg/m2). The results of this study suggest that PLSR analysis using the combination of optimal NDVI-like and band depth parameters could significantly improve estimation accuracy of winter wheat biomass.
author: Yuanyuan Fu, Guijun Yang, Jihua Wang, Xiaoyu Song, Haikuan Feng
publish year: 2014
creator: Yuanyuan Fu, Guijun Yang, Jihua Wang, Xiaoyu Song, Haikuan Feng
reference: Yuanyuan Fu, Guijun Yang, Jihua Wang, Xiaoyu Song, Haikuan Feng, "Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements", Computers and Electronics in Agriculture, Volume 100, January 2014, pages 51-59, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 100, January 2014, pages 51-59, Elsevier B.V.
label: Article 0144

An effective biophysical indicator for opium yield estimation
type: NamedIndividual
type: Article
has knowledge model: Regressor_0149
abstract: Opium is a narcotic obtained from opium poppy and raw materials of heroin for illegal drug trade. Information on opium production is important for governments and international communities to understand the scale of illegal drug trade and to implement drug fighting policies. Opium production can be estimated from opium yield and poppy cultivation acreage. Poppy cultivation acreage in turn can be estimated using remote sensing technique. This study explores the feasibility of assessing opium yield using remote sensing technique. The objective of this study is to find an effective biophysical indicator that can be related to both opium yield estimation and remote sensing measurements. Data are obtained from three years ground experiments conducted in an official farm in North-Western China. Opium yield and main plant and environmental parameters of opium poppy are measured. A linear regression analysis measures the relationship between opium yield and main plant parameters. A strong correlation between opium yield and leaf area index (LAI) in flowering and harvesting periods of opium poppy is observed (R2 = 0.826 and 0.862 respectively). LAI is a good indicator during flowering and harvesting periods and can be used to build a model for opium yield estimation. Furthermore, LAI can be retrieved using remote sensing at a region scale. The study shows that remote sensing could be used to estimate opium yield in opium poppy concentrated regions, and that a more comprehensive and precise opium production information could be obtained for further analysis of illegal drug circulating trend and policy.
author: Kun Jia, Bingfang Wu, Yichen Tian, Qiangzi Li, Xin Du
publish year: 2011
creator: Kun Jia, Bingfang Wu, Yichen Tian, Qiangzi Li, Xin Du
reference: Kun Jia, Bingfang Wu, Yichen Tian, Qiangzi Li, Xin Du, "An effective biophysical indicator for opium yield estimation", Computers and Electronics in Agriculture, Volume 75, Issue 2, February 2011, pages 272-277, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 75, Issue 2, February 2011, pages 272-277, Elsevier B.V.
label: Article 0149

An intelligent mobile application for diagnosis of crop diseases in Pakistan using fuzzy inference system
type: NamedIndividual
type: Article
has knowledge model: Classifier 0158
abstract: South Asian countries are amongst the largest producers of crops with favourable climate conditions and fertile soil. However, traditional agricultural mechanisms are in place and inadequate effort has been put into exploit the usage of technology. One of the main problems being faced by agriculture sector in Pakistan and other developing countries is that crop diseases are not diagnosed timely and efficiently. Conventional methods for disease diagnosis in crops lead to less accurate and inefficient diagnosis, consequently leading to low productivity. In this paper, an intelligent approach for the diagnosis of crop diseases is proposed which is capable of working over Android mobile devices using fuzzy inference system as the main decision making engine at the backend. The system is capable enough to communicate to the farmers in Pakistan in their local language Urdu and assist them in diagnosing diseases in their crops. Agriculture experts in government sector can get equal benefit from it in diagnosis and prevention of crops diseases. It takes symptoms of the crops as input with a provision of vague input and generates the output in the form of diagnosed disease using its inference engine. The proposed system caters two main crops of Pakistan, cotton and wheat and is capable to diagnose their main diseases. The proposed system has been tested on a pool of 100 real crop problems and its inference engine has shown excellent performance in prediction of the right disease which is up to 99% accurate.
author: Muhammad Toseef, Malik Jahan Khan
publish year: 2018
creator: Muhammad Toseef, Malik Jahan Khan
reference: Muhammad Toseef, Malik Jahan Khan, "An intelligent mobile application for diagnosis of crop diseases in Pakistan using fuzzy inference system", Computers and Electronics in Agriculture, Volume 153, October 2018, pages 1-11, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 153, October 2018, pages 1-11, Elsevier B.V.
label: Article 0158

An IPSO-BP neural network for estimating wheat yield using two remotely sensed variables in the Guanzhong Plain, PR China
type: NamedIndividual
type: Article
has knowledge model: Regressor_0161
abstract: Early and accurate information of crop growth condition is vital for agricultural industry and food security, which gives rise to a strong demand for timely monitoring crop growth condition and estimating crop yields. This study selected the remotely sensed leaf area index (LAI) and vegetation temperature condition index (VTCI) which closely relate to crop growth and crop water stress as two key variables for indicating crop growth condition and estimating crop yields in the Guanzhong Plain, PR China. The single VTCI, the single LAI and the combination of VTCI and LAI at four growth stages of winter wheat (the turning green, jointing, heading-filling, and dough stages) were used as three input variable schemes of the back propagation (BP) neural network and the improved particle swarm optimization algorithm (IPSO)-BP neural network using a nonlinear decreasing inertia weight, respectively. The relative importance of the input variables to the output variable, yield of winter wheat, was used to determine the weight values of input variables at each growth stage. Based on the weights, the integrated index (I) was established, and then three linear regression models (weighted VTCIs, weighted LAIs, and I values) were established with yield data to estimate winter wheat yields. By calculating several statistical functions, i.e., coefficient of determination (R2) and probability value (P), the model between the I values and wheat yield performed better than those between the weighted VTCIs or weighted LAIs and wheat yields. The yield estimation model of I values by using the IPSO-BP neural network (R2 = 0.342) was found to be better than that using the BP neural network (R2 = 0.310). Therefore, we applied the model with better performance (R2 = 0.342) to map the regional winter wheat yields pixel by pixel in the Guanzhong Plain during 2011–2018, and analyzed the spatial and temporal characteristics of the estimated yields. Regarding the spatial distribution, the yields in…
author: Huiren Tian, Pengxin Wang, Kevin Tansey, Shuyu Zhang, Jingqi Zhang, Hongmei Li
publish year: 2020
creator: Huiren Tian, Pengxin Wang, Kevin Tansey, Shuyu Zhang, Jingqi Zhang, Hongmei Li
reference: Huiren Tian, Pengxin Wang, Kevin Tansey, Shuyu Zhang, Jingqi Zhang, Hongmei Li, "An IPSO-BP neural network for estimating wheat yield using two remotely sensed variables in the Guanzhong Plain, PR China", Computers and Electronics in Agriculture, Volume 169, February 2020, 105180, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 169, February 2020, 105180, Elsevier B.V.
label: Article 0161

An yield estimation in citrus orchards via fruit detection and counting using image processing
type: NamedIndividual
type: Article
has knowledge model: Classifier 0167
abstract: The overall goal of this study is to develop an effective, simple, aptly computer vision algorithm to detect and count citrus on the tree using image processing techniques, to estimate the yield, and to compare the yield estimation results obtained through several methods. This new citrus recognition and counting algorithm was utilized the color features (or schemes) to present an estimate of the citrus yield, and the corresponding models are developed to provide an early estimation of the citrus yield. Citrus images were taken from Jeju, South Korea during daylight and the citrus recognition and counting algorithm were tested on 84 images which were collected from 21 trees. The citrus counting algorithm consisted of the following steps: convert RGB image to HSV, thresholding, orange color detection, noise removal, watershed segmentation, and counting. Distance transform and marker-controlled watershed algorithms were evaluated for automated watershed segmentation in citrus fruits to obtain good result. A correlation coefficient R2 of 0.93 was obtained between the citrus counting algorithm and counting performed through human observation. The proposed algorithm showed great potential for early prediction of the yield of single citrus trees and the possibility of its uses for further fruit crops.
author: Ulzii-Orshikh Dorj, Malrey Lee, Sang-seok Yun
publish year: 2017
creator: Ulzii-Orshikh Dorj, Malrey Lee, Sang-seok Yun
reference: Ulzii-Orshikh Dorj, Malrey Lee, Sang-seok Yun, "An yield estimation in citrus orchards via fruit detection and counting using image processing", Computers and Electronics in Agriculture, Volume 140, August 2017, pages 103-112, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 140, August 2017, pages 103-112, Elsevier B.V.
label: Article 0167

Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat
type: NamedIndividual
type: Article
has knowledge model: Classifier 0172
abstract: Disease detection by means of hyperspectral reflectance is inevitably influenced by the spectral difference between foreside (adaxial surface) and backside (abaxial surface) of a leaf. Taking yellow rust disease in winter wheat as an example, the spectral differences between the foreside and backside of healthy and diseased wheat leaves at both jointing stage and grain filling stage were investigated based on spectral measurements with a large sample size. The spectral difference between leaf orientations was found to be confused with disease signals to some extent. Firstly, the original bands and spectral features (SFs) that were sensitive to the disease were identified through a correlation analysis. Then, to eliminate the influence of leaf orientation, a pairwise t test was used to screen for the orientation insensitive bands and SFs. By conducting an overlapping procedure, the bands/SFs that were sensitive to the disease yet insensitive to the leaf orientations were selected and tested for disease detection. The results suggested that the Ref525–745 nm, Ref1060–1068 nm, DEP920–1120, DEP1070–1320, AREA1070–1320, SR and NDVI at the jointing stage, and the Ref606–697 nm, Ref740–752 nm, WID550–770, SR, NDVI, GNDVI, RDVI, GI and MCARI at the grain filling stage were capable of eliminating the influence of leaf orientation, and were retained for disease detection. Given these features, models based on the partial least square regression analysis showed a better performance at the grain filling stage, with the R2 of 0.854 and RMSE of 0.104. This result indicated that reliable estimation of disease severity can be made until the grain filling stage. In the future, more attention should be given to leaf orientation when detecting disease at the canopy level.
author: Lin Yuan, Jing-Cheng Zhang, Ke Wang, Rebecca-W. Loraamm, Wen-Jiang Huang, Ji-Hua Wang, Jin-Ling Zhao
publish year: 2013
creator: Lin Yuan, Jing-Cheng Zhang, Ke Wang, Rebecca-W. Loraamm, Wen-Jiang Huang, Ji-Hua Wang, Jin-Ling Zhao
reference: Lin Yuan, Jing-Cheng Zhang, Ke Wang, Rebecca-W. Loraamm, Wen-Jiang Huang, Ji-Hua Wang, Jin-Ling Zhao, "Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat", Precision Agriculture, Volume 14, pages 495–511 (2013) , Springer
Source: Precision Agriculture, Volume 14, pages 495–511 (2013) , Springer
label: Article 0172

Analysis of transfer learning for deep neural network based plant classification models
type: NamedIndividual
type: Article
has knowledge model: Classifier 0175
abstract: Plant species classification is crucial for biodiversity protection and conservation. Manual classification is time-consuming, expensive, and requires experienced experts who are often limited available. To cope with these issues, various machine learning algorithms have been proposed to support the automated classification of plant species. Among these machine learning algorithms, Deep Neural Networks (DNNs) have been applied to different data sets. DNNs have been however often applied in isolation and no effort has been made to reuse and transfer the knowledge of different applications of DNNs. Transfer learning in the context of machine learning implies the usage of the results of multiple applications of DNNs. In this article, the results of the effect of four different transfer learning models for deep neural network-based plant classification is investigated on four public datasets. Our experimental study demonstrates that transfer learning can provide important benefits for automated plant identification and can improve low-performance plant classification models.
author: Aydin Kaya, Ali Seydi Keceli, Cagatay Catal, Hamdi Yalin Yalic, Huseyin Temucin, Bedir Tekinerdogan
publish year: 2019
creator: Aydin Kaya, Ali Seydi Keceli, Cagatay Catal, Hamdi Yalin Yalic, Huseyin Temucin, Bedir Tekinerdogan
reference: Aydin Kaya, Ali Seydi Keceli, Cagatay Catal, Hamdi Yalin Yalic, Huseyin Temucin, Bedir Tekinerdogan, "Analysis of transfer learning for deep neural network based plant classification models", Computers and Electronics in Agriculture, Volume 158, March 2019, pages 20-29, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 158, March 2019, pages 20-29, Elsevier B.V.
label: Article 0175

Apple detection during different growth stages in orchards using the improved YOLO-V3 model
type: NamedIndividual
type: Article
has knowledge model: Classifier 0180
abstract: Real-time detection of apples in orchards is one of the most important methods for judging growth stages of apples and estimating yield. The size, colour, cluster density, and other growth characteristics of apples change as they grow. Traditional detection methods can only detect apples during a particular growth stage, but these methods cannot be adapted to different growth stages using the same model. We propose an improved YOLO-V3 model for detecting apples during different growth stages in orchards with fluctuating illumination, complex backgrounds, overlapping apples, and branches and leaves. Images of young apples, expanding apples, and ripe apples are initially collected. These images are subsequently augmented using rotation transformation, colour balance transformation, brightness transformation, and blur processing. The augmented images are used to create training sets. The DenseNet method is used to process feature layers with low resolution in the YOLO-V3 network. This effectively enhances feature propagation, promotes feature reuse, and improves network performance. After training the model, the performance of the trained model is tested on a test dataset. The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model. The average detection time of the model is 0.304s per frame at 3000 × 3000 resolution, which can provide real-time detection of apples in orchards. Moreover, the YOLOV3-dense model can effectively provide apple detection under overlapping apples and occlusion conditions, and can be applied in the actual environment of orchards.
author: Yunong Tian, Guodong Yang, Zhe Wang, Hao Wang, En Li, Zize Liang
publish year: 2019
creator: Yunong Tian, Guodong Yang, Zhe Wang, Hao Wang, En Li, Zize Liang
reference: Yunong Tian, Guodong Yang, Zhe Wang, Hao Wang, En Li, Zize Liang, "Apple detection during different growth stages in orchards using the improved YOLO-V3 model", Computers and Electronics in Agriculture, Volume 157, February 2019, pages 417-426, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 157, February 2019, pages 417-426, Elsevier B.V.
label: Article 0180

Apple tree canopy leaf spatial location automated extraction based on point cloud data
type: NamedIndividual
type: Article
has knowledge model: Cluster 0182
abstract: A fine structure inside the canopy of the apple tree determines the light distribution and is one of the important factors affecting the quality and yield of apples. Leaf spatial location (LSL) is the spatial co-ordinates of the petiole–midrib junction point. In this paper, a Trimble TX8 was used to obtain 3D point clouds of the canopy in the flowering, leaf growth stage and stable growth stages of apple tree as the research object. An LSL extraction approach using density-based spatial clustering of applications with noise (DBSCAN) and layers K-mean and median (L-KaM) methods was proposed. Firstly, the DBSCAN clustering method based on adaptive parameters is used to separate single leaves from branches. Secondly, the same point cloud is sliced into layers, and the L-KaM method is used to fit the branch center line. Finally, the Euclidean distance of each point between the single leaf and the center line is determined, and the point with the smallest Euclidean distance is the LSL point. Field experiments show that the DBSCAN-L-KaM method proposed in this study is suitable for LSL extraction during the leaf growth and stable growth stages. The maximum Euclidean distance between the manual measurements’ actual LSL value and the values automatically extracted (ELD_MaA) was less than 9 mm, and the average ELD_MaA was 1.41 mm. The average extraction rate of tall spindle training system apple trees and free spindle training system apple trees were 89.90% and 51.75%, respectively. Using the method in this paper, we obtain the whole apple tree (3-year-old) LSL value in about 2.5–3 h. The method provides theoretical basis and systemic support for the analysis of the light-intensity distribution of apple trees and the internal structure details of fruit trees.
author: Cailing Guo, Gang Liu, Weijie Zhang, Juan Feng
publish year: 2019
creator: Cailing Guo, Gang Liu, Weijie Zhang, Juan Feng
reference: Cailing Guo, Gang Liu, Weijie Zhang, Juan Feng, "Apple tree canopy leaf spatial location automated extraction based on point cloud data", Computers and Electronics in Agriculture, Volume 166, November 2019, 104975, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 166, November 2019, 104975, Elsevier B.V.
label: Article 0182

Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables
type: NamedIndividual
type: Article
has knowledge model: Regressor_0183
abstract: The development of small unmanned aerial vehicles and advances in sensor technology have made consumer digital cameras suitable for the remote sensing of vegetation. In this context, monitoring the in-field variability of maize (Zea mays L.), characterized by high nitrogen fertilization rates, with a low-cost color-infrared airborne system could be the basis for a site-specific nitrogen (N) fertilization support system. An experimental field with different N treatments applied to silage maize was monitored during the years 2014 and 2015. Images of the field and reference destructive measurements of above ground biomass, its N concentration and N uptake were taken at V6 and V9 development stages. Classical normalized difference vegetation indices (NDVI) and the indices adjusted by crop ground cover were calculated and regressed against the measured variables. Finally, image colorgrams were used to explore the potential of band-related information in variable estimation. A colorgram is a linear signal that summarizes the color content of each digital image. It is composed of a sequence of the frequency distribution curves of the camera bands, of their related parameters and of results of the principal components analysis applied to each image. The best predictors were found to be the ground cover and the adjusted green-based NDVI: regression equation at V9 resulted in R2 of 0.7 and RRMSE < 25% in external validation. Colorgrams did not improve prediction performance due to the spectral limitations of the camera. Therefore, the feasibility of the method should be tested in future research. In spite of limitations of sensor setup, the modified camera was able to estimate maize biomass due to the very high spatial resolution. Since the above ground biomass is a robust proxy of N status, the modified camera could be a promising tool for a low-cost N fertilization support system.
author: Martina Corti, Daniele Cavalli, Giovanni Cabassi, Antonio Vigoni, Luigi Degano, Pietro Marino Gallina
publish year: 2019
creator: Martina Corti, Daniele Cavalli, Giovanni Cabassi, Antonio Vigoni, Luigi Degano, Pietro Marino Gallina
reference: Martina Corti, Daniele Cavalli, Giovanni Cabassi, Antonio Vigoni, Luigi Degano, Pietro Marino Gallina, "Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables", Precision Agriculture, Volume 20, pages 675–696 (2019) , Springer
Source: Precision Agriculture, Volume 20, pages 675–696 (2019) , Springer
label: Article 0183

Application of aerial remote sensing technology for detection of fire blight infected pear trees
type: NamedIndividual
type: Article
has knowledge model: Classifier 0185
abstract: Over the last few decades, Fire Blight (FB) has recognized as the most dangerous diseases of apple and pear trees in the world. Timely diagnosis is very important for the detection of this disease. Visual assessment and scouting are usually used for FB detection while these methods are time-consuming and labor-intensive. Remote sensing technology can be an alternative method for visual assessment of plant diseases. So, in this research, the capability of multispectral remote sensing was evaluated for FB disease diagnosis of pear orchards in leaf and tree crown levels. Ground multispectral imaging was carried out of healthy leaves (HEL) from healthy trees and non-symptomatic diseased leaves (NSL) and symptomatic diseased leaves (SDL. Aerial multispectral imagery of trees crown was carried out by unmanned aerial vehicle. Then preprocessing and processing of ground and aerial images were performed. Some vegetation indices were calculated to detect infected leaves. Among the studied indices, SIPI, RDVI, MCARI1, MCARI2, TVI, MTVI1, MTVI2, TCARI, PSRI and ARI indices were appropriate for early detection of FB in leaf level. Support vector machine (SVM) method was used for the detection of infected trees. The overall accuracy of the classification obtained 95.0%. Based on the results, it could be concluded that multispectral imaging in leaf and tree crown levels is a reliable method for the detection of FB infected pear trees spatially in the early stage.
author: Nikrooz Bagheri
publish year: 2020
creator: Nikrooz Bagheri
reference: Nikrooz Bagheri, "Application of aerial remote sensing technology for detection of fire blight infected pear trees", Computers and Electronics in Agriculture, Volume 168, January 2020, 105147, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 168, January 2020, 105147, Elsevier B.V.
label: Article 0185

Application of Artificial Neural Network modeling for optimization and prediction of essential oil yield in turmeric (Curcuma longa L.)
type: NamedIndividual
type: Article
has knowledge model: Regressor_0186
abstract: The essential oil obtained from rhizome of turmeric (Curcuma longa L.) is highly valued worldwide for its medicinal and cosmetic uses. Lack of requisite high oil containing genotypes and existing variation in the quality and quantity of essential oil with plant habitat and agro-climatic regions pose problem in commercialization of essential oil. Thus the present work was carried out for optimization and prediction of essential oil yield of turmeric at different agro climatic regions. An artificial neural network (ANN) based prediction model was developed by using the data of essential oil of 131 turmeric germplasms collected from 8 agro-climatic regions of Odisha and analysis of their soil and environmental factors. Each sample with 11 parameters was used for training and testing the ANN model. The results showed that multilayer-feed-forward neural networks with 12 nodes (MLFN-12) was the most suitable and reasonable model to use with R2 value of 0.88. This study indicates that ANN based prediction model is a suitable way of predicting oil yield at a new site and to optimize the yield of turmeric oil at a particular site by changing the changeable parameters of the prediction model and thus is of enough commercial significance.
author: Abdul Akbar, Ananya Kuanar, Jeetendranath Patnaik, Antaryami Mishra, Sanghamitra Nayak
publish year: 2018
creator: Abdul Akbar, Ananya Kuanar, Jeetendranath Patnaik, Antaryami Mishra, Sanghamitra Nayak
reference: Abdul Akbar, Ananya Kuanar, Jeetendranath Patnaik, Antaryami Mishra, Sanghamitra Nayak, "Application of Artificial Neural Network modeling for optimization and prediction of essential oil yield in turmeric (Curcuma longa L.)", Computers and Electronics in Agriculture, Volume 148, May 2018, pages 160-178, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 148, May 2018, pages 160-178, Elsevier B.V.
label: Article 0186

Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran
type: NamedIndividual
type: Article
has knowledge model: Regressor_0187
abstract: Tree survival and mortality play a critical role in forest ecosystems. Being able to predict the survival probability provides significant insights into forest management decision support. In this study, we employed an artificial neural network (ANN) to predict tree survival and mortality in an Oriental beech (Fagus orientalis Lipsky) forest in northern Iran, using long-term data and wide range of biotic (average tree diameter increment, and basal area in larger trees (BAL)) and abiotic variables (slope, average solar radiation during the growing season, wind velocity and direction, height above the nearest drainage point, topographic wetness index, temperature, and relative humidity). The ANN model revealed that the greatest survival rates belong to the trees with diameter >20–100 cm. An increase in competition leads to reduced survival, and the Carpinus betulus species showed a slightly lower survival rate than the other species. Further, our findings demonstrated that BAL and diameter increment have a strong correlation with the probability of tree survival in the study area. The ANN performance was evaluated using the R2 and RMSE metrics and compared to a multiple logistic regression that demonstrated the capability of the ANN model for the prediction of survival probability (R2 = 0.92; RMSE = 0.985). From these results, we recommend the employment of ANN, and perhaps other machine learning methods, for the prediction of survival probability in many different ecosystems around the world.
author: Mahmoud Bayat, Mansour Ghorbanpour, Rozita Zare, Abolfazl Jaafari, Binh Thai Pham
publish year: 2019
creator: Mahmoud Bayat, Mansour Ghorbanpour, Rozita Zare, Abolfazl Jaafari, Binh Thai Pham
reference: Mahmoud Bayat, Mansour Ghorbanpour, Rozita Zare, Abolfazl Jaafari, Binh Thai Pham, "Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran", Computers and Electronics in Agriculture, Volume 164, September 2019, 104929, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 164, September 2019, 104929, Elsevier B.V.
label: Article 0187

Application of GOCI-derived vegetation index profiles to estimation of paddy rice yield using the GRAMI rice model
type: NamedIndividual
type: Article
has knowledge model: Regressor_0189
abstract: In this paper, satellite remote sensing was used as the input parameter of the GRAMI rice model to evaluate its applicability for simulating paddy rice crop condition and yield assessment at the field scale. Especially, the world’s first Geostationary Ocean Color Imager (GOCI), which provides better temporal resolution than does MODIS, was applied to evaluate the estimation of intuitive paddy rice growth and development and to examine the feasibility for vegetation index profiles of the GRAMI rice model. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data at 500-m resolution were used as reference data to validate the quality of the crop growth and development data derived from GOCI. Field measurements of paddy rice at Chonnam National University, Gwangju, South Korea, were performed to determine initial parameters of the GRAMI rice model, which is used to optimize biophysical processes in the soil–crop–atmosphere system. For angular-dependent vegetation products, daily rolling time series of vegetation indices of GOCI and MODIS were estimated using semi-empirical BRDF modeling based on 16-day composite procedures. The observed temporal variation in GOCI vegetation indices (VIs) based on BAR (bidirectional reflectance distribution function adjusted reflectance) showed a similar growing pattern to the simulated VIs of the crop model, but MODIS showed a difference between measured and simulated VIs during the cloudy monsoon season. The rice yields predicted by integrating satellite data and the GRAMI rice model were compared with field measurements and showed reasonable agreement with reference to paddy rice productivity in the study area.
author: Jong-Min Yeom, Jonghan Ko, Hyun-Ok Kim
publish year: 2015
creator: Jong-Min Yeom, Jonghan Ko, Hyun-Ok Kim
reference: Jong-Min Yeom, Jonghan Ko, Hyun-Ok Kim, "Application of GOCI-derived vegetation index profiles to estimation of paddy rice yield using the GRAMI rice model", Computers and Electronics in Agriculture, Volume 118, October 2015, pages 1-8, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 118, October 2015, pages 1-8, Elsevier B.V.
label: Article 0189

Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis
type: NamedIndividual
type: Article
has knowledge model: Classifier 0193
abstract: Detecting plant health condition is an important step in controlling disease and insect stress in agricultural crops. In this study, we applied neural network and principal components analysis techniques for discriminating and classifying different fungal infection levels in rice (Oryza sativa L.) panicles. Four infection levels in rice panicles were used in the study: no infection condition, light and moderate infection caused by rice glume blight disease, and serious infection caused by rice false smut disease. Hyperspectral reflectance of rice panicles was measured through the wavelength range from 350 to 2500 nm with a portable spectroradiometer in the laboratory. The spectral response characteristics of rice panicles were analyzed, and principal component analysis (PCA) was performed to obtain the principal components (PCs) derived from different spectra processing methods, namely raw, inverse logarithmic, first, and second derivative reflectance. A learning vector quantization (LVQ) neural network classifier was employed to classify healthy, light, moderate, and serious infection levels. Classification accuracy was evaluated using overall accuracy and Kappa coefficient. The overall accuracies of LVQ with PCA derived from the raw, inverse logarithmic, first, and second derivative reflectance spectra for the validation dataset were 91.6%, 86.4%, 95.5%, and 100% respectively, and the corresponding Kappa coefficients were 0.887, 0.818, 0.939 and 1. Our results indicated that it is possible to discriminate different fungal infection levels of rice panicles under laboratory conditions using hyperspectral remote sensing data.
author: Zhan-Yu Liu, Hong-Feng Wu, Jing-Feng Huang
publish year: 2010
creator: Zhan-Yu Liu, Hong-Feng Wu, Jing-Feng Huang
reference: Zhan-Yu Liu, Hong-Feng Wu, Jing-Feng Huang, "Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis", Computers and Electronics in Agriculture, Volume 72, Issue 2, July 2010, pages 99-106, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 72, Issue 2, July 2010, pages 99-106, Elsevier B.V.
label: Article 0193

Wheat leaf rust detection at canopy scale under different LAI levels using machine learning techniques
type: NamedIndividual
type: Article
has knowledge model: Regressor_0198
abstract: Accurate diagnosis of wheat leaf rust is of high interest for precision farming. Spectral data have been increasingly employed to detect this disease at leaf or canopy scales; however, less attention has been paid to the variations of leaf area index (LAI). Therefore, in this study, identification of wheat leaf rust was investigated at canopy scale and under different LAI levels, namely high, medium and low. Four machine learning (ML) methods including ν-support vector regression (ν-SVR), boosted regression trees (BRT), random forests regression (RFR) and Gaussian process regression (GPR) were built to estimate disease severity (DS) levels at canopy scale, where the reflectance data were measured in field situ by using a spectroradiometer, in which records spectra from 350 to 2500 nm. Results showed that ν-SVR outperformed the other ML methods at all three LAI levels with the R2 measures all being around 0.99. The results, particularly, showed that the performances of the ML methods were improved with increasing LAI value, where RFR reported the worst R2 value of 0.79 (RMSE = 8.5%) at low LAI level. The variable importance obtained using BRT showed three distinct regions of wavelengths that were appropriate across different LAI levels. The results of this research confirmed that hyperspectral signature can be reliably considered to identify wheat leaf rust disease at different LAI levels. Moreover, performances of several spectral vegetation indices (SVIs) were compared with those of the ML techniques. The results showed that the SVIs were consistently outperformed by the ML methods, particularly at low LAI level in which the SVIs were adversely affected. Nevertheless, all the SVIs, except for the RVSI, performed moderately well at high and medium LAI levels.
author: Mohsen Azadbakht, Davoud Ashourloo, Hossein Aghighi, Soheil Radiom, Abbas Alimohammadi
publish year: 2019
creator: Mohsen Azadbakht, Davoud Ashourloo, Hossein Aghighi, Soheil Radiom, Abbas Alimohammadi
reference: Mohsen Azadbakht, Davoud Ashourloo, Hossein Aghighi, Soheil Radiom, Abbas Alimohammadi, "Wheat leaf rust detection at canopy scale under different LAI levels using machine learning techniques", Computers and Electronics in Agriculture, Volume 156, January 2019, pages 119-128, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 156, January 2019, pages 119-128, Elsevier B.V.
label: Article 0198

Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties
type: NamedIndividual
type: Article
has knowledge model: Regressor_0203
abstract: As a commodity for daily consumption, coffee plays a crucial role in the economy of several African, American and Asian countries; yet, the accurate prediction of coffee yield based on environmental, climatic and soil fertility conditions remains a challenge for agricultural system modellers. The ability of an Extreme Learning Machine (ELM) model to analyse soil fertility properties and to generate an accurate estimation of Robusta coffee yield was assessed in this study. The performance of 18 different ELM-based models with single and multiple combinations of the predictor variables based on the soil organic matter (SOM), available potassium, boron, sulphur, zinc, phosphorus, nitrogen, exchangeable calcium, magnesium, and pH, was evaluated. The ELM model’s performance was compared to that of existing predictive tools: Multiple Linear Regression (MLR) and Random Forest (RF). Individual model performance and inter-model performance comparisons were based on the root mean square error (RMSE), mean absolute error (MAE), Willmott’s Index (WI), Nash-Sutcliffe efficiency coefficient (ENS), and the Legates and McCabe’s Index (ELM) in the independent testing dataset. In the independent testing phase, an ELM model constructed with SOM, available potassium and available sulphur as predictor variables generated the most accurate coffee yield estimate (i.e., RMSE = 496.35 kg ha−1 or ±13.6%, and MAE = 326.40 kg ha−1 or ±7.9%). This contrasted with the less accurate MLR (RMSE = 1072.09 kg ha−1 and MAE = 797.60 kg ha−1) and RF (RMSE = 1087.35 kg ha−1 and MAE = 769.57 kg ha−1) model. Normalized metrics showed the ELM model’s ability to yield highly accurate results: WI = 0.9952, ENS = 0.406 and ELM = 0.431. In comparison to the MLR and RF models, the adoption of the ELM model as an improved class of artificial intelligence models for coffee yield prediction in smallholder farms in this study constitutes an original contribution to the agronomic sector, particularly with respect to …
author: Louis Kouadio, Ravinesh C. Deo, Vivekananda Byrareddy, Jan F. Adamowski, Shahbaz Mushtaq, Van Phuong Nguyen
publish year: 2018
creator: Louis Kouadio, Ravinesh C. Deo, Vivekananda Byrareddy, Jan F. Adamowski, Shahbaz Mushtaq, Van Phuong Nguyen
reference: Louis Kouadio, Ravinesh C. Deo, Vivekananda Byrareddy, Jan F. Adamowski, Shahbaz Mushtaq, Van Phuong Nguyen, "Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties", Computers and Electronics in Agriculture, Volume 155, December 2018, pages 324-338, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 155, December 2018, pages 324-338, Elsevier B.V.
label: Article 0203

Assessing Nitrogen and water status of winter wheat using a digital camera
type: NamedIndividual
type: Article
has knowledge model: Regressor_0209
abstract: In this research, winter wheat Nitrogen and water status assessment by a digital camera was studied. Field experiments were conducted during growing seasons, 2012, 2013 and 2014 in Potsdam, Germany. Different treatments of N fertilization and water regimes were applied to the plant. Aboveground biomass was sampled three times during each season, and analyzed for plant nitrogen content, fresh and dry biomasses, and water content. Color images of the plant canopy were taken in the field by a digital camera. The images were then processed and various features were derived. Multivariable models using the features and two different algorithms, Partial Least Square Regression (PLSR) and Random Forest (RF) were developed. The models were built for data of single sampling dates and combination of sampling dates of each season. In addition, spectral measurements were taken by a portable field spectroradiometer and a few vegetation indices (VIs) were calculated. Linear regression models were built between the VIs and the plant parameters. According to the results obtained, PLSR presented the strongest models for both single-date and combined-dates data. R2 (corresponding RMSE) of the models for N content and water content varied in the range of 0.77–0.91 (0.25–0.14), and 0.53–0.95 (0.75–0.36), respectively, for single-date data, and 0.76–0.89 (0.24–0.15), and 0.65–0.92 (1.49–1.66), respectively, for combined-dates data. Performance of RF was better for combined-dates than single-date data. The conclusion is that a combination of digital image processing with an appropriate machine learning method has a high potential for plant growth status assessment in the field.
author: H. Tavakoli, R. Gebbers
publish year: 2019
creator: H. Tavakoli, R. Gebbers
reference: H. Tavakoli, R. Gebbers, "Assessing Nitrogen and water status of winter wheat using a digital camera", Computers and Electronics in Agriculture, Volume 157, February 2019, pages 558-567, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 157, February 2019, pages 558-567, Elsevier B.V.
label: Article 0209

Weed detection in soybean crops using ConvNets
type: NamedIndividual
type: Article
has knowledge model: Classifier 0212
abstract: Weeds are undesirable plants that grow in agricultural crops, such as soybean crops, competing for elements such as sunlight and water, causing losses to crop yields. The objective of this work was to use Convolutional Neural Networks (ConvNets or CNNs) to perform weed detection in soybean crop images and classify these weeds among grass and broadleaf, aiming to apply the specific herbicide to weed detected. For this purpose, a soybean plantation was carried out in Campo Grande, Mato Grosso do Sul, Brazil, and the Phantom DJI 3 Professional drone was used to capture a large number of crop images. With these photographs, an image database was created containing over fifteen thousand images of the soil, soybean, broadleaf and grass weeds. The Convolutional Neural Networks used in this work represent a Deep Learning architecture that has achieved remarkable success in image recognition. For the training of Neural Network the CaffeNet architecture was used. Available in Caffe software, it consists of a replication of the well known AlexNet, network which won the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012). A software was also developed, Pynovisão, which through the use of the superpixel segmentation algorithm SLIC, was used to build a robust image dataset and classify images using the model trained by Caffe software. In order to compare the results of ConvNets, Support Vector Machines, AdaBoost and Random Forests were used in conjunction with a collection of shape, color and texture feature extraction techniques. As a result, this work achieved above 98% accuracy using ConvNets in the detection of broadleaf and grass weeds in relation to soil and soybean, with an accuracy average between all images above 99%.
author: Alessandro dos Santos Ferreira, Daniel Matte Freitas, Gercina Gonçalves da Silva, Hemerson Pistori, Marcelo Theophilo Folhes
publish year: 2017
creator: Alessandro dos Santos Ferreira, Daniel Matte Freitas, Gercina Gonçalves da Silva, Hemerson Pistori, Marcelo Theophilo Folhes
reference: Alessandro dos Santos Ferreira, Daniel Matte Freitas, Gercina Gonçalves da Silva, Hemerson Pistori, Marcelo Theophilo Folhes, "Weed detection in soybean crops using ConvNets", Computers and Electronics in Agriculture, Volume 143, December 2017, pages 314-324, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 143, December 2017, pages 314-324, Elsevier B.V.
label: Article 0212

Assessing the potential of data-driven models for estimation of long-term monthly temperatures
type: NamedIndividual
type: Article
has knowledge model: Regressor_0213
abstract: Having information on air temperature components consisting minimum (Tmin), maximum (Tmax) and mean (T) temperatures plays a crucial role in various aspects of agriculture such as agricultural meteorology, soil science, agronomy, etc. The present study explores the performance of four data-driven models including artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and multivariate adaptive regression splines (MARS) for estimation of long-term monthly Tmin, Tmax and T. For this purpose, the long-term monthly temperatures of 50 stations all over Iran were used. The data of 35 and 15 stations were utilized to train and test the models, respectively. To feed the models, the geographical information (latitude, longitude, altitude) and periodicity component (the number of months) were employed as input parameters. The obtained results demonstrated that the long-term monthly temperatures of the studied regions can be estimated as a function of geographical information and periodicity component. Comparing the overall performance of the models at training stage revealed that the ANN outperformed the other models for estimating the long-term monthly Tmin, Tmax and T. That's while the SVM, ANN and ANFIS had superiority over the others at testing stage for estimation of the long-term monthly Tmin, Tmax and T, respectively. Furthermore, the MARS model presented the weakest performance for estimating the long-term monthly temperatures at both training and testing stages.
author: Saeid Mehdizadeh
publish year: 2018
creator: Saeid Mehdizadeh
reference: Saeid Mehdizadeh, "Assessing the potential of data-driven models for estimation of long-term monthly temperatures", Computers and Electronics in Agriculture, Volume 144, January 2018, pages 114-125, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 144, January 2018, pages 114-125, Elsevier B.V.
label: Article 0213

Assessment of maize yield and phenology by drone-mounted superspectral camera
type: NamedIndividual
type: Article
has knowledge model: Regressor_0218
abstract: The capability of unmanned aerial vehicle (UAV) spectral imagery to assess maize yield under full and deficit irrigation is demonstrated by a Tetracam MiniMCA12 11 bands camera. The MiniMCA12 was used to image an experimental field of 19 maize hybrids. Yield prediction models were explored for different maize development stages, with the best model found using maize plant development stage reproductive 2 (R2) for both maize grain yield and ear weight (respective R2 values of 0.73 and 0.49, and root mean square error of validation (RMSEV) values of 2.07 and 3.41 metric tons per hectare using partial least squares regression (PLS-R) validation models). Models using vegetation indices for inputs rather than superspectral data showed similar R2 but higher RMSEV values, and produced best results for the R4 development stage. In addition to being able to predict yield, spectral models were able to distinguish between different development stages and irrigation treatments. These abilities potentially allow for yield prediction of maize plants whose development stage and water status are unknown.
author: Ittai Herrmann, Eyal Bdolach, Yogev Montekyo, Shimon Rachmilevitch, Philip A. Townsend, Arnon Karnieli
publish year: 2020
creator: Ittai Herrmann, Eyal Bdolach, Yogev Montekyo, Shimon Rachmilevitch, Philip A. Townsend, Arnon Karnieli
reference: Ittai Herrmann, Eyal Bdolach, Yogev Montekyo, Shimon Rachmilevitch, Philip A. Townsend, Arnon Karnieli, "Assessment of maize yield and phenology by drone-mounted superspectral camera", Precision Agriculture, Volume 21, pages 51–76 (2020) , Springer
Source: Precision Agriculture, Volume 21, pages 51–76 (2020) , Springer
label: Article 0218

Assessment of the optimal spectral bands for designing a sensor for vineyard disease detection: the case of ‘Flavescence dorée’
type: NamedIndividual
type: Article
has knowledge model: Classifier 0220
abstract: Disease detection and control is one of the main objectives of vineyard research in France. Manual monitoring of diseases is a time consuming operation especially when fields are large. Current research studies aim to develop automatic in-field systems to detect diseases. This study investigated the optimal spectral bands for the design of a dedicated high-resolution multispectral camera embedded on an unmanned aerial vehicle for identifying infected zones in a grapevine field. The target disease was Flavescence dorée, which is infectious, incurable and can result in considerable yield loss. An in-field spectrometry study was performed on four grapevine varieties in the Provence Alpes Côte d’Azur region in France. Two spectral analysis techniques were proposed for choosing the best spectral bands capable of discriminating healthy from diseased leaves. The first novel approach is a feature selection technique based on the successive projection algorithm (SPA), some spectral pre-processing techniques were jointly investigated. The second approach examines a set of traditional vegetation indices (VI). Support vector machine (SVM) and discriminant analysis (DA) are the two classifiers used in this paper and the accuracy of the results is compared for the two methods of analysis. The best models were computed as a function of the grapevine variety considered. The SPA technique performed better in general with respect to common VIs, the overall classification accuracy was more than 96%. Results demonstrated that employing a feature selection technique based on the SPA algorithm can provide a valid tool for determining the optimal bands that are sensitive to Flavescence dorée grapevine disease and assist in its identification. The benefit behind the presented procedure relies on the possibility of generalizing it for other infections and stresses or even for different crops.
author: H. Al-Saddik, J. C. Simon, F. Cointault
publish year: 2019
creator: H. Al-Saddik, J. C. Simon, F. Cointault
reference: H. Al-Saddik, J. C. Simon, F. Cointault, "Assessment of the optimal spectral bands for designing a sensor for vineyard disease detection: the case of ‘Flavescence dorée’", Precision Agriculture, Volume 20, pages 398–422 (2019) , Springer
Source: Precision Agriculture, Volume 20, pages 398–422 (2019) , Springer
label: Article 0220

Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance
type: NamedIndividual
type: Article
has knowledge model: Regressor_0222
abstract: Bacterial leaf blight (BLB) is an important vascular disease of irrigated rice and serious infestations may cause a significant loss of yield. This study analyzed hyperspectral canopy reflectance spectra of two rice cultivars with different susceptibilities to BLB to establish spectral models for assessing disease severity for future site-specific management. The results indicated that wavebands from 757 to 1039 nm were the most sensitive region of the spectrum for the moderately susceptible cultivar TNG 67, whereas most narrow bands showed a significant relationship for the highly susceptible cultivar TCS 10. All the spectral indices (SIs) calculated had significant relationships with proportions of infested area in cultivar TCS 10, but only two SIs correlated significantly with cultivar TNG 67. The relation between the severity of the disease and spectral reflectance for the less susceptible cultivar TNG 67 can be improved by using a multiple linear regression approach.
author: Chwen-Ming Yang
publish year: 2010
creator: Chwen-Ming Yang
reference: Chwen-Ming Yang, "Assessment of the severity of bacterial leaf blight in rice using canopy hyperspectral reflectance", Precision Agriculture, Volume 11, pages 61–81 (2010) , Springer
Source: Precision Agriculture, Volume 11, pages 61–81 (2010) , Springer
label: Article 0222

Automated early yield prediction in vineyards from on-the-go image acquisition
type: NamedIndividual
type: Article
has knowledge model: Regressor_0226
abstract: Early grapevine yield assessment provides information to viticulturists to help taking management decisions to achieve the desired grape quality and yield amount. In previous works, image analysis has been explored to this effect, but with systems performing either manually, on a single variety or close to harvest-time, when there are few rectifiable agronomic aspects. This study presents a solution based on image analysis for the non-invasive and in-field yield prediction in vines of several varieties, at phenological stages previous to veraison, around 100 days from harvest. To this end, an all-terrain vehicle (ATV) was modified with equipment to autonomously capture images of 30 vine segments of five different varieties at night-time. The images were analysed with a new image analysis algorithm based on mathematical morphology and pixel classification, which yielded overall average Recall and Precision values of 0.8764 and 0.9582, respectively. Finally, a model was calibrated to produce yield predictions from the number of detected berries in images with a Root-Mean-Square-Error per vine of 0.16 kg. This accuracy makes the proposed methodology ideal for early yield prediction as a very helpful tool for the grape and wine industry.
author: Arturo Aquino, Borja Millan, Maria-Paz Diago, Javier Tardaguila
publish year: 2018
creator: Arturo Aquino, Borja Millan, Maria-Paz Diago, Javier Tardaguila
reference: Arturo Aquino, Borja Millan, Maria-Paz Diago, Javier Tardaguila, "Automated early yield prediction in vineyards from on-the-go image acquisition", Computers and Electronics in Agriculture, Volume 144, January 2018, pages 26-36, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 144, January 2018, pages 26-36, Elsevier B.V.
label: Article 0226

Automatic classification of non-touching cereal grains in digital images using limited morphological and color features
type: NamedIndividual
type: Article
has knowledge model: Classifier 0238
abstract: Classification of cereal grains, namely; barley, oat, rye and wheat (Canada Western Amber Durum (CWAD) and Canada Western Red Spring (CWRS)) was performed using morphological and color features. Grain image boundary contours were extracted from the digital images of kernels, expressed as chain-coded points and then approximated by 13 elliptic Fourier coefficients. After normalization of the rotation and starting point of the contours, symmetrical standard coefficients were determined. The symmetrical Fourier index (SFX) of individual kernels was calculated from the product of the sum of absolute symmetrical coefficients and the circularity (roundness) index. Three geometric features, namely; aspect ratio (AR), major diameter (MD) and roundness (Ceq) were determined using ellipse fitting and Green’s transformation of curve integrals, respectively. The morphological classification model was defined using SFX, AR, MD, and Ceq. The color classification model was defined using color indices of individual kernels, which were calculated from the RGB color values of their images. The classification accuracies of different models were evaluated and compared. The combined model defined by morphological and color features achieved a classification accuracy of 98.5% for barley, 99.97% for CWRS, 99.93% for oat, and 100% for rye and CWAD.
author: H.K. Mebatsion, J. Paliwal, D.S. Jayas
publish year: 2013
creator: H.K. Mebatsion, J. Paliwal, D.S. Jayas
reference: H.K. Mebatsion, J. Paliwal, D.S. Jayas, "Automatic classification of non-touching cereal grains in digital images using limited morphological and color features", Computers and Electronics in Agriculture, Volume 90, January 2013, pages 99-105, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 90, January 2013, pages 99-105, Elsevier B.V.
label: Article 0238

Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic
type: NamedIndividual
type: Article
has knowledge model: Classifier 0239
abstract: In plants, there are different types of electrical signals involving changes in membrane potentials that could encode electrical information related to physiological states when plants are stimulated by different environmental conditions. A previous study analyzing traits of the dynamics of whole plant low-voltage electrical showed, for instance, that some specific frequencies that can be observed on plants growing under undisturbed conditions disappear after stress-like environments, such as cold, low light and osmotic stimuli. In this paper, we propose to test different methods of automatic classification in order to identify when different environmental cues cause specific changes in the electrical signals of plants. In order to verify such hypothesis, we used machine learning algorithms (Artificial Neural Networks, Convolutional Neural Network, Optimum-Path Forest, k-Nearest Neighbors and Support Vector Machine) together Interval Arithmetic. The results indicated that Interval Arithmetic and supervised classifiers are more suitable than deep learning techniques, showing promising results towards such research area.
author: Danillo Roberto Pereira, Joao Paulo Papa, Gustavo Francisco Rosalin Saraiva, Gustavo Maia Souza
publish year: 2018
creator: Danillo Roberto Pereira, Joao Paulo Papa, Gustavo Francisco Rosalin Saraiva, Gustavo Maia Souza
reference: Danillo Roberto Pereira, Joao Paulo Papa, Gustavo Francisco Rosalin Saraiva, Gustavo Maia Souza, "Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic", Computers and Electronics in Agriculture, Volume 145, February 2018, pages 35-42, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 145, February 2018, pages 35-42, Elsevier B.V.
label: Article 0239

Automatic cucumber recognition algorithm for harvesting robots in the natural environment using deep learning and multi-feature fusion
type: NamedIndividual
type: Article
has knowledge model: Classifier 0243
abstract: Mechanical harvesting requires agricultural robot to detect fruits automatically. However, effective and accurate detection of cucumber by computer vision system is still a challenge due to similarity between cucumber color and that of branches and leaves, shape irregularity and complex growing environment. To improve the practicability and accuracy of the automatic recognition models, this paper proposed a novel cucumber region detection method using multi-path convolutional neural network (MPCNN), combined with color component selection and support vector machine (SVM). In this method, the cucumber image was transformed into color space to obtain 15 color components and the weight information of relevant features was analyzed by I-RELIEF. In parallel, to remove part of the background area, the OSTU algorithm was applied to segment the G component and Maximally Stable Extrernal Regions (MSER) was used to obtain the mask image. In order to maximize the differences between cucumber and leaf, promoting the classification accuracy of SVM, the top three components of the weight were input into the deep learning module to extract and fuse features. In final, cucumber recognition was realized by combining SVM classification with mask image. The recognition results show that more than 90% pixels of cucumber images are correctly classified, and the misidentified pixels are less than 22%. The ratio between the two indicators is over 4, demonstrating the satisfactory performance of the proposed method and highlighting its promising applications in mechanical cucumber harvesting.
author: Shihan Mao, Yuhua Li, You Ma, Baohua Zhang, Jun Zhou
publish year: 2020
creator: Shihan Mao, Yuhua Li, You Ma, Baohua Zhang, Jun Zhou
reference: Shihan Mao, Yuhua Li, You Ma, Baohua Zhang, Jun Zhou, "Automatic cucumber recognition algorithm for harvesting robots in the natural environment using deep learning and multi-feature fusion", Computers and Electronics in Agriculture, Volume 170, March 2020, 105254, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 170, March 2020, 105254, Elsevier B.V.
label: Article 0243

Automatic detection and identification of brown stink bug, Euschistus servus, and southern green stink bug, Nezara viridula, (Heteroptera: Pentatomidae)
type: NamedIndividual
type: Article
has knowledge model: Classifier 0245
abstract: Stink bugs cost the southeastern US cotton industry millions of dollars each year in crop losses and control costs. These losses are reduced by strategic pesticide applications; however, current methods of monitoring these pests for making management decisions are time-consuming and costly. Therefore, improved methods to identify and monitor these bugs must be investigated in order to optimize pesticide applications. One such method would be to exploit the substrate-borne vibrational signals (SBVSs) of these insects. Recordings of SBVS for two prevalent regional pests, the brown stink bug, Euschistus servus, and southern green stink bug, Nezara viridula, were segmented into separate pulses of variable duration based on signal energy. For each pulse, the linear frequency cepstral coefficients, dominant frequency, and duration were calculated and used as features. These features were classified using a Gaussian mixture model (GMM) and a probabilistic neural network (PNN) to discriminate these SBVS from incidental sounds and SBVS of different species from each other. Detection of SBVS generated by brown stink bugs was performed with over 92% accuracy for single male–female pairs with both PNN and GMM and with over 86% accuracy for 30 individuals with both PNN and GMM. Detection of SBVS generated by southern green stink bugs was performed with up to 82.5% accuracy with PNN and 68.0% accuracy with GMM for 30 individuals. Also, both PNN and GMM were over 90% accurate in identifying SBVS of brown and southern green stink bugs. Concurrent detection of SBVS from noise and identification of SBVS of brown and southern green stink bugs was 83.3% accurate using PNN and 71.5% accurate using GMM. These results indicated the capability of detecting and identifying stink bug species using their SBVS.
author: B.D. Lampson, Y.J. Han, A. Khalilian, J. Greene, R.W. Mankin, E.G. Foreman
publish year: 2013
creator: B.D. Lampson, Y.J. Han, A. Khalilian, J. Greene, R.W. Mankin, E.G. Foreman
reference: B.D. Lampson, Y.J. Han, A. Khalilian, J. Greene, R.W. Mankin, E.G. Foreman, "Automatic detection and identification of brown stink bug, Euschistus servus, and southern green stink bug, Nezara viridula, (Heteroptera: Pentatomidae)", Computers and Electronics in Agriculture, Volume 91, February 2013, pages 154-159, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 91, February 2013, pages 154-159, Elsevier B.V.
label: Article 0245

Automatic detection of cereal rows by means of pattern recognition techniques
type: NamedIndividual
type: Article
has knowledge model: Classifier 0246
abstract: Automatic locating of weeds from fields is an active research topic in precision agriculture. A reliable and practical plant identification technique would enable the reduction of herbicide amounts and lowering of production costs, along with reducing the damage to the ecosystem. When the seeds have been sown row-wise, most weeds may be located between the sowing rows. The present work describes a clustering-based method for recognition of plantlet rows from a set of aerial photographs, taken by a drone flying at approximately ten meters. The algorithm includes three phases: segmentation of green objects in the view, feature extraction, and clustering of plants into individual rows. Segmentation separates the plants from the background. The main feature to be extracted is the center of gravity of each plant segment. A tentative clustering is obtained piecewise by applying the 2D Fourier transform to image blocks to get information about the direction and the distance between the rows. The precise sowing line position is finally derived by principal component analysis. The method was able to find the rows from a set of photographs of size 1452×969 pixels approximately in 0.11 s, with the accuracy of 94 per cent.
author: Henri Tenhunen, Tapio Pahikkala, Olli Nevalainen, Jukka Teuhola, Heta Mattila, Esa Tyystjarvi
publish year: 2019
creator: Henri Tenhunen, Tapio Pahikkala, Olli Nevalainen, Jukka Teuhola, Heta Mattila, Esa Tyystjarvi
reference: Henri Tenhunen, Tapio Pahikkala, Olli Nevalainen, Jukka Teuhola, Heta Mattila, Esa Tyystjarvi, "Automatic detection of cereal rows by means of pattern recognition techniques", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 677-688, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 162, July 2019, pages 677-688, Elsevier B.V.
label: Article 0246

Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach
type: NamedIndividual
type: Article
has knowledge model: Classifier 0247
abstract: One of the main problems in the post-harvest processing of citrus is the detection of visual defects in order to classify the fruit depending on their appearance. Species and cultivars of citrus present a high rate of unpredictability in texture and colour that makes it difficult to develop a general, unsupervised method able of perform this task. In this paper we study the use of a general approach that was originally developed for the detection of defects in random colour textures. It is based on a Multivariate Image Analysis strategy and uses Principal Component Analysis to extract a reference eigenspace from a matrix built by unfolding colour and spatial data from samples of defect-free peel. Test images are also unfolded and projected onto the reference eigenspace and the result is a score matrix which is used to compute defective maps based on the T2 statistic. In addition, a multiresolution scheme is introduced in the original method to speed up the process. Unlike the techniques commonly used for the detection of defects in fruits, this is an unsupervised method that only needs a few samples to be trained. It is also a simple approach that is suitable for real-time compliance. Experimental work was performed on 120 samples of oranges and mandarins from four different cultivars: Clemenules, Marisol, Fortune, and Valencia. The success ratio for the detection of individual defects was 91.5%, while the classification ratio of damaged/sound samples was 94.2%. These results show that the studied method can be suitable for the task of citrus inspection.
author: Fernando Lopez-Garcia, Gabriela Andreu-Garcia, Jose Blasco, Nuria Aleixos, Jose-Miguel Valiente
publish year: 2010
creator: Fernando Lopez-Garcia, Gabriela Andreu-Garcia, Jose Blasco, Nuria Aleixos, Jose-Miguel Valiente
reference: Fernando Lopez-Garcia, Gabriela Andreu-Garcia, Jose Blasco, Nuria Aleixos, Jose-Miguel Valiente, "Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach", Computers and Electronics in Agriculture, Volume 71, Issue 2, May 2010, pages 189-197, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 71, Issue 2, May 2010, pages 189-197, Elsevier B.V.
label: Article 0247

Automatic fruit count on coffee branches using computer vision
type: NamedIndividual
type: Article
has knowledge model: Classifier 0249
abstract: In this article, a non-destructive method is proposed to count the number of fruits on a coffee branch by using information from digital images of a single side of the branch and its growing fruits. In order to do this, 1018 coffee branches at different ripening stages. They had different numbers of fruits, harvest dates, were of different varieties, and were at different stages of coffee tree’s life. A Machine Vision System (MVS) was constructed, which was capable of counting and identifying harvestable and not harvestable fruits in a set of images corresponding to a specific coffee branch was constructed. This MVS consists of an image acquisition system, based on mobile devices (it does not require to control of the environmental conditions), and an image processing algorithm to classify and detect each one of the fruits in the acquired images. After obtaining information regarding the number of fruits identified by the MVS, linear estimation models were constructed between the detected fruits automatically and the ones observed on the coffee branch. These models were calculated for fruits in three categories: harvestable, not harvestable, and fruits whose maturation stage were disregarded. These models link the fruits that are counted automatically to the ones actually observed with an R2 higher than 0.93 one-to-one. Not only is the MVS used to estimate the number of fruits on the branch but also to estimate their maturation percentage and weight. The MVS was validated in four Variedad Castillo® coffee plots, in different stages of development and with different densities. We found that MVS neither overestimates nor underestimates the number of fruits and that it shows a correlation higher than 0.90 at early stages of crop development, when tree fruits are still not harvestable. The information obtained in this research will spawn a new generation of tools for coffee growers to use. It is an efficient, non-destructive, and low-cost method which offers useful info…
author: P.J. Ramos, F.A. Prieto, E.C. Montoya, C.E. Oliveros
publish year: 2017
creator: P.J. Ramos, F.A. Prieto, E.C. Montoya, C.E. Oliveros
reference: P.J. Ramos, F.A. Prieto, E.C. Montoya, C.E. Oliveros, "Automatic fruit count on coffee branches using computer vision", Computers and Electronics in Agriculture, Volume 137, May 2017, pages 9-22, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 137, May 2017, pages 9-22, Elsevier B.V.
label: Article 0249

Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold
type: NamedIndividual
type: Article
has knowledge model: Regressor_0252
abstract: An adaptive scheduling algorithm relying on a theoretical crop water stress index (CWSI) was developed to automatically irrigate apple trees. Unlike the traditional CWSI algorithm where the threshold is a constant value, in the present approach the threshold is dynamically determined by following the CWSI trend. A previous work on the energy budget analysis of a single apple leaf provided the base for calculating lower and upper boundaries of CWSI. To test the feasibility of the algorithm, it was applied to the thermal and meteorological data collected during the 2007 and 2008 growing seasons. A computer-based wireless control system was also developed to automatically schedule irrigations in three plots of apple trees in the 2013 growing season. In a small scale field experiment, two treatments were compared: (1) automatic irrigation using the new algorithm (CWSI-DT) and (2) irrigation scheduling based on weekly readings of neutron probe (NP). The soil water deficit under the CWSI-DT treatment was maintained within the well-watered range with no signs of over or under irrigation. This was better than the results in the NP treatment where there were occasions of under irrigation. Midday canopy and air temperature difference (ΔTm) exhibited a close agreement with midday stem water potential (Ψstem; R2 = 0.63, p<0.01). Normalizing ΔTm in the form of CWSI resulted in a much higher correlation between midday CWSI and midday Ψstem (R2 = 0.91, p<0.0001) suggesting CWSI as a reliable indicator of apple trees water status. The automatic control system running the new CWSI-DT algorithm was able to avoid over-irrigation under humid and cool weather conditions, and adapted itself to the changing conditions of the apple trees. The results of this study were promising in terms of using ground-based thermal sensing for automatic irrigation scheduling of sparse, discontinuous apple trees.
author: Yasin Osroosh, R. Troy Peters, Colin S. Campbell, Qin Zhang
publish year: 2015
creator: Yasin Osroosh, R. Troy Peters, Colin S. Campbell, Qin Zhang
reference: Yasin Osroosh, R. Troy Peters, Colin S. Campbell, Qin Zhang, "Automatic irrigation scheduling of apple trees using theoretical crop water stress index with an innovative dynamic threshold", Computers and Electronics in Agriculture, Volume 118, October 2015, pages 193-203, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 118, October 2015, pages 193-203, Elsevier B.V.
label: Article 0252

Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods
type: NamedIndividual
type: Article
has knowledge model: Regressor_0254
abstract: In low quality soils, as in the Indian state of Maharashtra, a sustainable land management practice is very important to enhance the soil quality and to maintain proper values for several nutrients that are relevant for an optimal crop yield. The evaluation of a soil fertility index for these nutrients and for each geographical place allows to create maps of village-wise fertility indices which are very useful for fertility management. An automatic prediction of such fertility indices would be very important to reduce the amount of chemical measurements of nutrients to be performed in different cultivation lands. The current study develops the prediction of fertility indices for soil organic carbon and four important soil nutrients (phosphorus pentoxide, iron, manganese and zinc) using almost all the available regression methods, specifically a collection of 76 regressors which belong to 20 families, including neural networks, deep learning, support vector regression, random forests, bagging and boosting, lasso and ridge regression, Bayesian models and more. The best results are achieved by the extremely randomized regression trees (extraTrees), with which achieve an acceptable prediction accuracy (average squared correlations between 0.57 and 0.70), being also relatively fast. Other regressors with high performance are random forests and regularized random forest, generalized boosting regression model and epsilon-support vector regression.
author: M.S. Sirsat, E. Cernadas, M. Fernandez-Delgado, S. Barro
publish year: 2018
creator: M.S. Sirsat, E. Cernadas, M. Fernandez-Delgado, S. Barro
reference: M.S. Sirsat, E. Cernadas, M. Fernandez-Delgado, S. Barro, "Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods", Computers and Electronics in Agriculture, Volume 154, November 2018, pages 120-133, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 154, November 2018, pages 120-133, Elsevier B.V.
label: Article 0254

Using optimal combination method and in situ hyperspectral measurements to estimate leaf nitrogen concentration in barley
type: NamedIndividual
type: Article
has knowledge model: Regressor_0257
abstract: Leaf nitrogen concentration (LNC), a good indicator of nitrogen (N) status in crops, is of special significance to diagnose nutrient stress and guide N fertilization in fields. Due to non-destructive and quick detectability, hyperspectral remote sensing plays a unique role in detecting LNC in crops. Barley, especially malting barley, is very demanding for N nutrition and requires timely monitoring and accurate estimation of N concentration in barley leaves. Hyperspectral techniques can help make effective diagnosis and facilitate dynamic regulation of plant N status. In this study, canopy reflectance spectra (between 350 and 1 050 nm) from 38 typical barley fields were measured as well as the corresponding LNC in Hailar Nongken, China’s Inner Mongolia Autonomous Region in July, 2010. Existing spectral indices that are considered to be good indicators for assessing N status in crops were selected to estimate LNC in barley. In addition, the optimal combination (OC) method was tested to extract the sensitive indices and first-order spectral derivative wavebands that are responsible for variation of leaf N in barley, and expected to develop some combination models for improving the accuracy of LNC estimates. The results showed that most of the selected indices (such as NPCI, PRI and DCNI) could adequately describe the dynamic changes of LNC in barley. The combined models based on OC performed better in comparison with the individual models using either spectral indices or first-order derivatives and the other methods (such as PCA). A combined model that integrated the first-order derivatives from five wavebands with OC performed well with R2 of 0.82 and RMSE of 0.50 for LNC in barley. This good correlation with ground measurements indicates that hyperspectral reflectance and the OC method have good potential for assessing N status in barley.
author: Xin-gang Xu, Chun-jiang Zhao, Ji-hua Wang, Jing-cheng Zhang, Xiao-yu Song
publish year: 2014
creator: Xin-gang Xu, Chun-jiang Zhao, Ji-hua Wang, Jing-cheng Zhang, Xiao-yu Song
reference: Xin-gang Xu, Chun-jiang Zhao, Ji-hua Wang, Jing-cheng Zhang, Xiao-yu Song, "Using optimal combination method and in situ hyperspectral measurements to estimate leaf nitrogen concentration in barley", Precision Agriculture, Volume 15, pages 227–240 (2014) , Springer
Source: Precision Agriculture, Volume 15, pages 227–240 (2014) , Springer
label: Article 0257

Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.)
type: NamedIndividual
type: Article
has knowledge model: Classifier 0258
abstract: In situ, non-destructive and real time mineral nutrient stress monitoring is an important aspect of precision farming for rational use of fertilizers. Studies have demonstrated the ability of remote sensing to monitor nitrogen (N) in many crops, phosphorus (P) and potassium (K) in very few crops and none so far to monitor sulphur (S). Specially designed (1) fertility gradient experiment and (2) test crop experiments were used to check the possibility of mineral N–P–S–K stress detection using airborne hyperspectral remote sensing. Leaf and canopy hyperspectral reflectance data and nutrient status at booting stage of the wheat crop were recorded. N–P–S–K sensitive wavelengths were identified using linear correlation analysis. Eight traditional vegetation indices (VIs) and three proposed (one for P and two for S) were evaluated for plant N–P–S–K predictability. A proposed VI (P_1080_1460) predicted P content with high and significant accuracy (correlation coefficient (r) 0.42 and root means square error (RMSE) 0.180 g m−2). Performance of the proposed S VI (S_660_1080) for S concentration and content retrieval was similar whereas prediction accuracies were higher than traditional VIs. Prediction accuracy of linear regressive models improved when biomass-based nutrient contents were considered rather than concentrations. Reflectance in the SWIR region was found to monitor N–P–S–K status in plants in combination with reflectance at either visible (VIS) or near infrared (NIR) region. Newly developed and validated spectral algorithms specific to N, P, S and K can further be used for monitoring in a wheat crop in order to undertake site-specific management.
author: G. R. Mahajan, R. N. Sahoo, R. N. Pandey, V. K. Gupta, Dinesh Kumar
publish year: 2014
creator: G. R. Mahajan, R. N. Sahoo, R. N. Pandey, V. K. Gupta, Dinesh Kumar
reference: G. R. Mahajan, R. N. Sahoo, R. N. Pandey, V. K. Gupta, Dinesh Kumar, "Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.)", Precision Agriculture, Volume 15, pages 499–522 (2014) , Springer
Source: Precision Agriculture, Volume 15, pages 499–522 (2014) , Springer
label: Article 0258

Banana detection based on color and texture features in the natural environment
type: NamedIndividual
type: Article
has knowledge model: Classifier 0259
abstract: Banana detection by picking robots in outdoor conditions is difficult due to the color similarity with leaves and stems. A method of banana detection in the natural environment based on color and texture features was performed in this study by using a regular red–green–blue color camera. First, part of the background was removed in HSV color space by analyzing the relationship between the S color component and V color component; this saved detection time and improved the detection efficiency. Then, the banana area was found by adopting support vector machine with local binary pattern features and histogram of oriented gradient features of the banana. Single-feature and multi-feature fusion with different classifiers were compared to find the most suitable classification algorithm for banana detection. A validation set containing 4400 samples was used to evaluate the proposed classification algorithm. The precision and recall of banana detection were 100%. A total of 120 photos under different illumination conditions were selected as the test set. The average single-scale detection rate based on the proposed algorithm was 89.63%, the average execution time was 1.325 s, and the shortest execution time was 0.343 s. At last, the multi-scale detection method based on the proposed algorithm was discussed to improve the detection accuracy. The results showed that the developed method can be applied to the detection of banana in plantations under different illumination and occlusion conditions.
author: Lanhui Fu, Jieli Duan, Xiangjun Zou, Guichao Lin, Shuaishuai Song, Bang Ji, Zhou Yang
publish year: 2019
creator: Lanhui Fu, Jieli Duan, Xiangjun Zou, Guichao Lin, Shuaishuai Song, Bang Ji, Zhou Yang
reference: Lanhui Fu, Jieli Duan, Xiangjun Zou, Guichao Lin, Shuaishuai Song, Bang Ji, Zhou Yang, "Banana detection based on color and texture features in the natural environment", Computers and Electronics in Agriculture, Volume 167, December 2019, 105057, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 167, December 2019, 105057, Elsevier B.V.
label: Article 0259

Biomass estimation from canopy measurements for leafy vegetables based on ultrasonic and laser sensors
type: NamedIndividual
type: Article
has knowledge model: Classifier 0264
abstract: This paper reports on the evaluation of a prototype sensor system embedded in a low-cost portable instrument for leaf vegetable production. The system involves circular scanning of crop canopies to identify crop biomass yield using distance sensors. The results of these scans are height profiles along an angular position from 0° to 360°, which are the input for the biomass estimation. Two experiments were developed to test the performance of the system’s prototype. The first experiment was conducted in a greenhouse with lettuce and kale. Biomass was estimated from the sensor system’s measurements resulting in the coefficient of determination (R2) for regression between measured and predicted biomass between 0.74 and 0.93, root mean squared error (RMSE) between 0.295 ln(g) and 0.441 ln(g), and percentage error between 25% and 55%. These values include both dry and fresh biomass for lettuce and kale. The second experiment was conducted in a spinach field of a commercial farm in Sherrington, Quebec, Canada. The R2 values were 0.78 and 0.94. The RMSE was, in turn, 4.18 t/ha and 2.16 t/ha. While only fresh biomass was considered for the spinach case, two approaches for processing laser-based height profiles are discussed: regression of profile-representative features and inference of a canopy density function. The results indicate that the developed sensor system would be a suitable tool for rapid assessment of fresh biomass in the field. Its application would be beneficial in the process of optimizing crop management logistics, comparing the performance of different varieties of crops, and detecting potential stresses in a field environment.
author: Roberto M. Buelvas, Viacheslav I. Adamchuk, Eko Leksono, Peter Tikasz, Mark Lefsrud, Jarek Holoszkiewicz
publish year: 2019
creator: Roberto M. Buelvas, Viacheslav I. Adamchuk, Eko Leksono, Peter Tikasz, Mark Lefsrud, Jarek Holoszkiewicz
reference: Roberto M. Buelvas, Viacheslav I. Adamchuk, Eko Leksono, Peter Tikasz, Mark Lefsrud, Jarek Holoszkiewicz, "Biomass estimation from canopy measurements for leafy vegetables based on ultrasonic and laser sensors", Computers and Electronics in Agriculture, Volume 164, September 2019, 104896, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 164, September 2019, 104896, Elsevier B.V.
label: Article 0264

BiomassID: A biomass type identification system for mobile devices
type: NamedIndividual
type: Article
has knowledge model: Classifier 0265
abstract: Biomass quality assessment is of great importance when one in the biomass industry needs to produce another energy product, such as biofuel or bioenergy, for instance. Usually, the biomass quality is determined using expensive devices, such as mass spectrometers, or complex chemical tests that may need several days to complete. Because of the high costs of such methods, people tend to forsake biomass quality assessment and move on to directly produce bioproducts from any kind of biomass. In this paper, a cheap and fast solution for biomass type identification is proposed and investigated. The quality of biomass can be inferred at a coarse level from the type of biomass. In the proposed approach, biomass type identification is treated as a texture classification problem. A texture classification system developed for mobile devices which is able to distinguish between four types of biomass texture images is presented in this paper. Several state of the art texture classification systems based on machine learning are evaluated in a series of experiments on a data set of biomass texture images. The experiments are conducted to determine the system that can offer the best trade-off between accuracy and speed, since the goal is to implement it on a mobile device with limited processing power and memory. In the end, the selected system can identify the type of biomass from pictures taken with a mobile device camera in a few seconds directly on the respective mobile device. The utility of the system is demonstrated through an iOS application that is freely available for download in the App Store at http://appstore.com/biomassid.
author: Radu Tudor Ionescu, Andreea-Lavinia Popescu, Marius Popescu, Dan Popescu
publish year: 2015
creator: Radu Tudor Ionescu, Andreea-Lavinia Popescu, Marius Popescu, Dan Popescu
reference: Radu Tudor Ionescu, Andreea-Lavinia Popescu, Marius Popescu, Dan Popescu, "BiomassID: A biomass type identification system for mobile devices", Computers and Electronics in Agriculture, Volume 113, April 2015, pages 244-253, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 113, April 2015, pages 244-253, Elsevier B.V.
label: Article 0265

Canopy spectral reflectance can predict grain nitrogen use efficiency in soft red winter wheat
type: NamedIndividual
type: Article
has knowledge model: Regressor_0280
abstract: Canopy spectral reflectance (CSR) is a cost-effective, rapid, and non-destructive remote sensing and selection tool that can be employed in high throughput plant phenotypic studies. The objectives of the current study were to evaluate the predictive potential of vegetative indices as a high-throughput phenotyping tool for nitrogen use efficiency in soft red winter wheat (SRWW) (Triticum aestivum L.) and determine the optimum growth stage for employing CSR. A panel of 281 regionally developed SRWW genotypes was screened under low and normal N regimes in two crop seasons for grain yield, N uptake, nitrogen use efficiency for yield (NUEY) and nitrogen use efficiency for protein (NUEP). Vegetative indices were calculated from CSR and the data were analyzed by year and over the 2 years. Multiple regression and Pearson’s correlation were used to obtain the best predictive models and vegetative indices. The chosen models explained 84 and 83 % of total variation in grain yield and N uptake respectively, over two crop seasons. Models further accounted for 85 and 77 % of total variation in NUEY, and 85, and 81 % of total variation in NUEP under low and normal N conditions, respectively. In general, yield, NUEY and NUEP had greater than 0.6 R2 values in 2011–2012 but not in 2012–2013. Differences between years are likely a result of saturation of CSR indices due to high biomass and crop canopy coverage in 2012–2013. Heading was found to be the most appropriate crop growth stage to sense SRWW CSR data for predicting grain yield, N uptake, NUEY, and NUEP.
author: K. Pavuluri, B. K. Chim, C. A. Griffey, M. S. Reiter, M. Balota, W. E. Thomason
publish year: 2015
creator: K. Pavuluri, B. K. Chim, C. A. Griffey, M. S. Reiter, M. Balota, W. E. Thomason
reference: K. Pavuluri, B. K. Chim, C. A. Griffey, M. S. Reiter, M. Balota, W. E. Thomason, "Canopy spectral reflectance can predict grain nitrogen use efficiency in soft red winter wheat", Precision Agriculture, Volume 16, pages 405–424 (2015) , Springer
Source: Precision Agriculture, Volume 16, pages 405–424 (2015) , Springer
label: Article 0280

CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features
type: NamedIndividual
type: Article
has knowledge model: Classifier 0282
abstract: In the agriculture farming business, plant diseases are the major reason for monetary misfortunes around the globe. It is an imperative factor, as it causes significant diminution in both quality and capacity of growing crops. Therefore, detection and taxonomy of various plants diseases is crucial, and it demands utmost attention. In plants, fruits act a major source of nutrients worldwide, however, various range of diseases adversely affect the production as well as the quality of the fruits. Therefore, utilization of an efficient machine vision technology not only detects the diseases at their early stages but also classify them accordingly. This research is primarily focusing on the detection and classification of various fruits diseases based on correlation coefficient and deep features (CCDF). The proposed technique incorporates two major steps of infected regions detection and finally feature extraction and classification. In the first step, initially contrast of input image is enhanced by utilizing a hybrid method - followed by proposed correlation coefficient-based segmentation method which separates the infected regions from the background. In the second step, two deep pre-trained models (VGG16, caffe AlexNet) are utilized for feature extraction of selected diseases (apple scab, apple rot, banana sigotka, banana cordial leaf spot, banana diamond leaf spot and deightoniella leaf and fruit spot). Parallel features fusion step is embedded to consolidate the extracted features prior to max-pooling step. Selection of most discriminant features are being performed using genetic algorithm before subjecting to the final stage of classification using mutli-class SVM. Experiments are being performed on publicly available datasets - plant village and CASC-IFW datasets to achieve the classification accuracy of 98.60%. Qualitative analysis of achieved results clearly shows that the proposed method outperforms several existing methods in terms of greater precision and im…
author: Muhammad Attique Khan, Tallha Akram, Muhammad Sharif, Muhammad Awais, Kashif Javed, Hashim Ali, Tanzila Saba
publish year: 2018
creator: Muhammad Attique Khan, Tallha Akram, Muhammad Sharif, Muhammad Awais, Kashif Javed, Hashim Ali, Tanzila Saba
reference: Muhammad Attique Khan, Tallha Akram, Muhammad Sharif, Muhammad Awais, Kashif Javed, Hashim Ali, Tanzila Saba, "CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features", Computers and Electronics in Agriculture, Volume 155, December 2018, pages 220-236, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 155, December 2018, pages 220-236, Elsevier B.V.
label: Article 0282

Changes in yield classification in a soybean-rice rotation
type: NamedIndividual
type: Article
has knowledge model: Classifier 0285
abstract: For yield based site-specific management to be successful in fields with crop rotations, changes in management zones between crops must be determined. The study objectives were to determine if yield classes change between crops within a rotation and whether soil properties can predict the yield classes or the year-to-year changes. A percentile classification method was used to categorize yearly soybean (Glycine max) and rice (Oryza sativa) yield in two fields with soybean-rice-soybean rotations into low, medium and high yield classes. There was little agreement in yield classifications between years. Yield class based on soil properties was predicted accurately by linear discriminant analysis in Field 1 20–67% of the time and in Field 2 13–83% of the time. Predictions in Field 1 were based on soil available Mg and P, elevation and the deep soil apparent electrical conductivity (ECa). Predictions in Field 2 were based on soil texture, soil available P, K and Mg, and pH. The linear discriminant analysis was also able to predict year-to-year changes in yield class. Changes in class in Field 1 could be predicted by total soil C and N, silt, and soil available Mg and P depending on the year. Soil texture, soil available P, K and Mg, total soil C and pH, elevation and deep soil ECa predicted yield changes in Field 2 depending on the year. The results of this study indicate only limited success at management zone definition in a soybean-rice rotation. Further investigation is needed with other crop rotation sequences to verify the findings of this study.
author: M. S. Cox, P. D. Gerard
publish year: 2010
creator: M. S. Cox, P. D. Gerard
reference: M. S. Cox, P. D. Gerard, "Changes in yield classification in a soybean-rice rotation", Precision Agriculture, Volume 11, pages 507–519 (2010) , Springer
Source: Precision Agriculture, Volume 11, pages 507–519 (2010) , Springer
label: Article 0285

Citrus disease recognition based on weighted scalable vocabulary tree
type: NamedIndividual
type: Article
has knowledge model: Classifier 0290
abstract: Citrus Huanglongbing (HLB) is a destructive disease in citrus production that causes huge economic damage to citrus producers and related industries in the world. Early and accurate detection of HLB is a critical management step to control the spread of this disease. However, existing HLB detection methods cannot be widely adopted in citrus production due to long-time and high-cost detection period in specific laboratory environments. In view of this, a fast-response and low-cost computer vision technique is investigated for diagnosing HLB in citrus leaves. Specifically, the Gaussian mixture density (GMD) is performed to extract the leaf object from the citrus image, followed by the feature extraction and recognition of the existence of HLB in the leaf based on scalable vocabulary tree. A citrus leaf image dataset is constructed, and the experimental results show that the proposed HLB recognition method with GMD object extraction performs 95–100 % accuracy within 1 s.
author: Xiao-Ling Deng, Zhen Li, Xiao-Ling Deng, Tian-Sheng Hong
publish year: 2014
creator: Xiao-Ling Deng, Zhen Li, Xiao-Ling Deng, Tian-Sheng Hong
reference: Xiao-Ling Deng, Zhen Li, Xiao-Ling Deng, Tian-Sheng Hong, "Citrus disease recognition based on weighted scalable vocabulary tree", Precision Agriculture, Volume 15, pages 321–330 (2014) , Springer
Source: Precision Agriculture, Volume 15, pages 321–330 (2014) , Springer
label: Article 0290

Soil organic carbon and particle sizes mapping using vis–NIR, EC and temperature mobile sensor platform
type: NamedIndividual
type: Article
has knowledge model: Regressor_0299
abstract: Soil organic carbon (SOC) is an important parameter in the climate change mitigation strategies and it is crucial for the function of ecosystems and agriculture. Particle size fractions affect strongly the physical and chemical properties of soil and thus also SOC. Conventional analyses of SOC and particle sizes are costly limiting the detailed characterization of soil spatial variability and fine resolution mapping. Mobile sensors provide an alternative approach to soil analysis. They offer densely spaced georeferenced data in a cost-effective manner. In this study, two agricultural fields (Voulund1 and Voulund2) in Denmark were mapped with the Veris mobile sensor platform (MSP). MSP collected simultaneously visible near infrared spectra (vis–NIR; 350–2200 nm), electrical conductivity (EC: shallow; 0–30 cm, deep; 0–90 cm), and temperature measurements. Fuzzy k-means clustering was applied to the obtained spectra to partition the fields and to select representative samples for calibration purposes. Calibration samples were analyzed for SOC and particle sizes (clay, silt and sand) using conventional wet chemistry analysis. The objectives of this study were to determine whether it is the single sensors or the fusion of sensor data that provides the best predictive ability of the soil properties in question. Using partial least square regression (PLS) excellent calibration results were generated for all soil properties with a ratio of performance to deviation (RPD) values above 2. The best predictive ability for SOC was obtained using a fusion of sensor data. The calibration models based on vis–NIR spectra and temperature resulted in RMSECV = 0.14% and R2 = 0.94 in Voulund1. In Voulund2, the combination of EC, temperature and spectral data generated a SOC model with RMSECV = 0.17% and R2 = 0.93. The highest predictive ability for clay was obtained using spectral data only in Voulund1 (RMSECV = 0.34% and R2 = 0.76). Whereas in Voulund2, improved results were obtained af…
author: Maria Knadel, Anton Thomsen, Kirsten Schelde, Mogens Humlekrog Greve
publish year: 2015
creator: Maria Knadel, Anton Thomsen, Kirsten Schelde, Mogens Humlekrog Greve
reference: Maria Knadel, Anton Thomsen, Kirsten Schelde, Mogens Humlekrog Greve, "Soil organic carbon and particle sizes mapping using vis–NIR, EC and temperature mobile sensor platform", Computers and Electronics in Agriculture, Volume 114, June 2015, pages 134-144, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 114, June 2015, pages 134-144, Elsevier B.V.
label: Article 0299

Classification of maize seeds of different years based on hyperspectral imaging and model updating
type: NamedIndividual
type: Article
has knowledge model: Classifier 0300
abstract: Seed classification and identification exhibit potential for detecting seed purity and increasing crop yield. In this study, hyperspectral imaging was employed to develop classification methods for maize seeds. A total of 2000 seeds, including four varieties of maize seeds of different years, were evaluated. Hyperspectral reflectance images were acquired between 400 nm and 1000 nm. Classification models based on the mean spectral features of seeds were developed using least squares support vector machine (LSSVM). Model updating using incremental support vector data description was also applied to update the LSSVM model online and ensure accurate identification of maize seeds of different years. The classification accuracy of the LSSVM model combined with model updating reached 94.4% and was 10.3% higher than that of other non-updated models. This study showed that combined hyperspectral imaging and model updating could be an effective method for classification of seeds of different years.
author: Min Huang, Jinya Tang, Bao Yang, Qibing Zhu
publish year: 2016
creator: Min Huang, Jinya Tang, Bao Yang, Qibing Zhu
reference: Min Huang, Jinya Tang, Bao Yang, Qibing Zhu, "Classification of maize seeds of different years based on hyperspectral imaging and model updating", Computers and Electronics in Agriculture, Volume 122, March 2016, pages 139-145, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 122, March 2016, pages 139-145, Elsevier B.V.
label: Article 0300

Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation
type: NamedIndividual
type: Article
has knowledge model: Classifier 0301
abstract: This study investigated the relationships between sunflower yield and crop multi-temporal spectral data obtained from aerial photographs, land elevation and the presence of Ridolfia segetum weed. Conventional-color and color-infrared airborne photographs were taken at three dates corresponding to the vegetative, flowering and senescent crop stages. Descriptive and statistical methods were applied to every spatial variable to extract the influence of each component on the sunflower yield variability. Principal components and regression models were used to explore the potential of the multi-spectral variables from the airborne photographs to predict the sunflower yield map at every studied date. Higher sunflower yield was found in areas with lower elevation. These areas were also predominantly free of weed infestation. The Normalized Difference Vegetation Index derived from the image taken at crop vegetative stage was strongly correlated to crop yield. A very poor correlation was detected between the sunflower yield and all the multi-spectral variables studied in the flowering and the senescence crop stages. A map with three zones of yield was predicted with 67.81% of overall accuracy using the stepwise-model equation formed by the green and red bands and the two vegetation indices obtained at vegetative crop stage. The selected multi-spectral data taken in early season (mid-May), plus the additional knowledge of weed presence and field elevation, could provide valuable spatial information to estimate the yield crop variability in the studied fields. This estimation might aid in the development of adequate spatially variable management strategies in the months prior to the sunflower harvest.
author: Jose M. Pena-Barragan, Francisca Lopez-Granados, Montserrat Jurado-Exposito, Luis Garcia-Torres
publish year: 2010
creator: Jose M. Pena-Barragan, Francisca Lopez-Granados, Montserrat Jurado-Exposito, Luis Garcia-Torres
reference: Jose M. Pena-Barragan, Francisca Lopez-Granados, Montserrat Jurado-Exposito, Luis Garcia-Torres, "Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation", Precision Agriculture, Volume 11, pages 568–585 (2010) , Springer
Source: Precision Agriculture, Volume 11, pages 568–585 (2010) , Springer
label: Article 0301

Classifying rapeseed varieties using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS)
type: NamedIndividual
type: Article
has knowledge model: Classifier 0306
abstract: This study proposed a methodology for classification of rapeseed varieties using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS). For this purpose, principal components analysis (PCA) was first used to reveal the separation of three varieties of rapeseeds, and then partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were employed for the classification task. The overall classification error rates (ERs) of prediction set were 7.5% and 0 for the models of PLS-DA and SVM, respectively. Furthermore, successive projections algorithm (SPA) was adopted to choose an appropriate variable subset as the inputs of PLS-DA and SVM. Both SPA-PLS-DA and SPA-SVM models gave improved predictive accuracy with significantly reduced model variables. The results of this study had showed the good performance of FTIR-PAS as a rapid, non-destructive and objective tool for classifying varieties of rapeseeds.
author: Yuzhen Lu, Changwen Du, Changbing Yu, Jianmin Zhou
publish year: 2014
creator: Yuzhen Lu, Changwen Du, Changbing Yu, Jianmin Zhou
reference: Yuzhen Lu, Changwen Du, Changbing Yu, Jianmin Zhou, "Classifying rapeseed varieties using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS)", Computers and Electronics in Agriculture, Volume 107, September 2014, pages 58-63, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 107, September 2014, pages 58-63, Elsevier B.V.
label: Article 0306

Combination of artificial neural networks and fractal theory to predict soil water retention curve
type: NamedIndividual
type: Article
has knowledge model: Regressor_0309
abstract: Despite good progress in developing pedotransfer functions (PTFs), the input variables that are more preferable in a PTF have not been yet determined clearly. Among the modeling techniques to characterize soil structure, those using fractal theory are in majority. For the first time, fractal parameters were used as predictors to estimate the water content at different matric suctions using artificial neural networks (ANNs). PTFs were developed to estimate soil water retention curve (SWRC) from a dataset of 148 soil samples from North West of Iran. Including geometric mean (dg), geometric standard deviation (sg), and median diameter (Md) of particle size distribution as input parameters significantly enhanced the PTFs’ accuracy and increased the coefficient of determination (R2) by up to 5.5%. Fractal parameters of particle size distribution (PSDFPs) were used as predictors and it improved the accuracy and reliability by decreasing root mean square error (RMSE) by up to 30% for water content at h value of 5 kPa (θ5 kPa) and by up to 12.5% for water content at h value of 50 kPa (θ50 kPa). Entering the fractal parameters of aggregate size distribution (ASDFPs) in the models raised the accuracy at most soil matric suctions (h) and caused up to 6.7% reduction in the RMSE. Their impacts were significant at θ25 kPa and θ50 kPa. The network architectures were unique and problem specific with respect to the output layer transfer functions and number of hidden neurons. Adding PSDFPs and ASDFPs to the input parameters of the proper ANN models could improve the estimation of SWRC, significantly.
author: Hossein Bayat, Mohammad Reza Neyshaburi, Kourosh Mohammadi, Nader Nariman-Zadeh, Mahdi Irannejad, Andrew S. Gregory
publish year: 2013
creator: Hossein Bayat, Mohammad Reza Neyshaburi, Kourosh Mohammadi, Nader Nariman-Zadeh, Mahdi Irannejad, Andrew S. Gregory
reference: Hossein Bayat, Mohammad Reza Neyshaburi, Kourosh Mohammadi, Nader Nariman-Zadeh, Mahdi Irannejad, Andrew S. Gregory, "Combination of artificial neural networks and fractal theory to predict soil water retention curve", Computers and Electronics in Agriculture, Volume 92, March 2013, pages 92-103, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 92, March 2013, pages 92-103, Elsevier B.V.
label: Article 0309

Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying
type: NamedIndividual
type: Article
has knowledge model: Regressor_0310
abstract: This study seeks to investigate the potential of using combined computer vision (CV) and laser-induced backscattering imaging (LLBI) in monitoring the quality attributes of sweet potato during drying. CV and backscattered images of 4 mm thickness sweet potato slices were captured after every one-hour of drying, at drying temperatures of 50–70 °C. Reference quality properties, such as moisture content, L∗, a∗ and b∗ colour coordinates were measured hourly under the same drying conditions. Principal component analysis (PCA) and partial least square regression (PLS) were applied to the extracted combined CV (based on RGB) and backscattering imaging parameters to analyse the quality changes of sweet potato during drying. The results showed that there was significant effect of drying temperature and time on combined CV and backscattering imaging parameters. The combined optical method showed good correlation with moisture content and colour properties i.e L∗ and a∗ of sweet potato with R2 > 0.7. Specifically, the redness (a∗) gave the highest coefficient of determination (R2) of 0.80, while the moisture ratio (MR) showed the lowest root mean square error of validation (RMSEV) with the value of 0.18. Thus, this study has shown that combined CV and backscattering imaging parameters can serve as a non-destructive tool for detecting the changes in quality parameters of sweet potato during drying.
author: Daniel I. Onwude, Norhashila Hashim, Khalina Abdan, Rimfiel Janius, Guangnan Chen
publish year: 2018
creator: Daniel I. Onwude, Norhashila Hashim, Khalina Abdan, Rimfiel Janius, Guangnan Chen
reference: Daniel I. Onwude, Norhashila Hashim, Khalina Abdan, Rimfiel Janius, Guangnan Chen, "Combination of computer vision and backscattering imaging for predicting the moisture content and colour changes of sweet potato (Ipomoea batatas L.) during drying", Computers and Electronics in Agriculture, Volume 150, July 2018, pages 178-187, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 150, July 2018, pages 178-187, Elsevier B.V.
label: Article 0310

Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture
type: NamedIndividual
type: Article
has knowledge model: Classifier 0311
abstract: Integrated Pest Management (IPM) lies at the core of the current efforts to reduce the use of deleterious chemicals in greenhouse agriculture. IPM strategies rely on the early detection and continuous monitoring of pest populations, a critical task that is not only time-consuming but also highly dependent on human judgement and therefore prone to error. In this study, we propose a novel approach for the detection and monitoring of adult-stage whitefly (Bemisia tabaci) and thrip (Frankliniella occidentalis) in greenhouses based on the combination of an image-processing algorithm and artificial neural networks. Digital images of sticky traps were obtained via an image-acquisition system. Detection of the objects in the images, segmentation, and morphological and color property estimation was performed by an image-processing algorithm for each of the detected objects. Finally, classification was achieved by means of a feed-forward multi-layer artificial neural network. The proposed whitefly identification algorithm achieved high precision (0.96), recall (0.95) and F-measure (0.95) values, whereas the thrip identification algorithm obtained similar precision (0.92), recall (0.96) and F-measure (0.94) values.
author: Karlos Espinoza, Diego L. Valera, Jose A. Torres, Alejandro Lopez, Francisco D. Molina-Aiz
publish year: 2016
creator: Karlos Espinoza, Diego L. Valera, Jose A. Torres, Alejandro Lopez, Francisco D. Molina-Aiz
reference: Karlos Espinoza, Diego L. Valera, Jose A. Torres, Alejandro Lopez, Francisco D. Molina-Aiz, "Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture", Computers and Electronics in Agriculture, Volume 127, September 2016, pages 495-505, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 127, September 2016, pages 495-505, Elsevier B.V.
label: Article 0311

Combined use of hyperspectral VNIR reflectance spectroscopy and kriging to predict soil variables spatially
type: NamedIndividual
type: Article
has knowledge model: Regressor_0313
abstract: Hyperspectral visible near infrared reflectance spectroscopy (VNIRRS) and geostatistical methods are considered for precision soil mapping. This study evaluated whether VNIR or geostatistics, or their combined use, could provide efficient approaches for assessing the soil spatially and associated reductions in sample size using soil samples from a 32 ha area (800 × 400 m) in northern Turkey. Soil variables considered were CaCO3, organic matter, clay, sand and silt contents, pH, electrical conductivity, cation exchange capacity (CEC) and exchangeable cations (Ca, Mg, Na and K). Cross-validation was used to compare the two approaches using all grid data (n = 512), systematic selections of 13, 25 and 50% of the data and random selections of 13 and 25% for calibration; the remaining data were used for validation. Partial least squares regression (PLSR) analysis was used for calibrating soil properties from first derivative VNIR reflectance spectra (VNIRRS), whereas ordinary-, co- and regression-kriging were used for spatial prediction. The VNIRRS-PLSR method provided better prediction results than ordinary kriging for soil organic matter, clay and sand contents, (R2 values of 0.56–0.73, 0.79–0.85, 0.65–0.79, respectively) and smaller root mean squared errors of prediction (values of 2.7–4.1, 37.4–43, 46.9–61, respectively). The EC, pH, Na, K and silt content were predicted poorly by both approaches because either the variables showed little variation or the data were not spatially correlated. Overall, the prediction accuracy of VNIRRS-PLSR was not affected by sample size as much as it was for ordinary kriging. Cokriging (COK) and regression kriging (RK) were applied to a combination of values predicted by VNIR reflectance spectroscopy and measured in the laboratory to improve the accuracy of prediction of the soil properties. The results showed that both COK and RK with VNIRRS estimates improved the predictions of soil variables compared to VNIRRS and OK. The combined u…
author: A. Volkan Bilgili, Fevzi Akbas, Harold M. van Es
publish year: 2011
creator: A. Volkan Bilgili, Fevzi Akbas, Harold M. van Es
reference: A. Volkan Bilgili, Fevzi Akbas, Harold M. van Es, "Combined use of hyperspectral VNIR reflectance spectroscopy and kriging to predict soil variables spatially", Precision Agriculture, Volume 12, pages 395–420 (2011) , Springer
Source: Precision Agriculture, Volume 12, pages 395–420 (2011) , Springer
label: Article 0313

Combined use of in situ hyperspectral vegetation indices for estimating pasture biomass at peak productive period for harvest decision
type: NamedIndividual
type: Article
has knowledge model: Regressor_0314
abstract: Since the typical destructive methods for measuring aboveground biomass (AGB) have many limitations, a variety of non-destructive techniques have been developed. In this paper, the potential of ground-based hyperspectral remote-sensed data for non-destructive assessment of semi-arid pasture AGB at the peak productive period was investigated. The reflectance spectrometric and AGB data were sampled at the end of the growing season (almost peak biomass) over two locations at pastures in the southern Horqin sandy land, eastern Inner Mongolia, China. All combinations (two-band and three-band) of narrow bands in the forms of simple ratio vegetation index (SRVI), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) were used in a linear regression analysis against AGB. The predictive performance of the stepwise multiple linear regression (SMLR) using 4 best VIs as input variables was compared with the performance of multivariate partial least squares regression (PLSR) using all reflectance bands as input variables to estimate AGB. It was observed that the relationship between AGB and single band spectral reflectance was low, while the estimation performance of the best VIs based on all available wavebands was improved considerably. In addition, the best VIs based on all available wavebands had considerably better fitting performance than those based on traditionally used wavebands for estimating AGB. In comparison to PLSR using the full individual reflectance as input variables, SMLR using the best VIs as input variables performed much better, with the maximum decrease in RMSECV of 37% and the relative mean absolute errors always below 12.5%. The study demonstrated the high potential to estimate pasture AGB, which is a proxy for pasture forage yield, at the peak productive period using a hyperspectral technique.
author: Xin Tong, Limin Duan, Tingxi Liu, Vijay P. Singh
publish year: 2019
creator: Xin Tong, Limin Duan, Tingxi Liu, Vijay P. Singh
reference: Xin Tong, Limin Duan, Tingxi Liu, Vijay P. Singh, "Combined use of in situ hyperspectral vegetation indices for estimating pasture biomass at peak productive period for harvest decision", Precision Agriculture, Volume 20, pages 477–495 (2019) , Springer
Source: Precision Agriculture, Volume 20, pages 477–495 (2019) , Springer
label: Article 0314

Combining discriminant analysis and neural networks for corn variety identification
type: NamedIndividual
type: Article
has knowledge model: Classifier 0315
abstract: Variety identification is an indispensable tool to assure grain purity and quality. Based on machine vision and pattern recognition, five China corn varieties were identified according to their external features. Images of non-touching corn kernels were acquired using a flat scanner. A total of 17 geometric features, 13 shape and 28 color features were extracted from color images of corn kernels. Two optimal feature sets were generated by stepwise discriminant analysis, and used as inputs to classifiers. A two-stage classifier combining distance discriminant and a back propagation neural network (BPNN) was built for identification. On the first stage, corn kernels were divided into three types: white, yellow and mixed corn by distance discriminant analysis. And then different varieties in the same type were identified by an improved BPNN classifier. The classification accuracies of BAINUO 6, NONGDA 86, NONGDA 108, GAOYOU 115, and NONGDA 4967 were 100, 94, 92, 88 and 100%, respectively.
author: Xiao Chen, Yi Xun, Wei Li, Junxiong Zhang
publish year: 2010
creator: Xiao Chen, Yi Xun, Wei Li, Junxiong Zhang
reference: Xiao Chen, Yi Xun, Wei Li, Junxiong Zhang, "Combining discriminant analysis and neural networks for corn variety identification", Computers and Electronics in Agriculture, Volume 71, Supplement 1, April 2010, pages S48-S53, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 71, Supplement 1, April 2010, pages S48-S53, Elsevier B.V.
label: Article 0315

A comparison of partial least squares (PLS) and sparse PLS regressions for predicting yield of Swiss chard grown under different irrigation water sources using hyperspectral data
type: NamedIndividual
type: Article
has knowledge model: Regressor_0316
abstract: There is an increasing demand for fresh vegetables such as Swiss chard in cognisance of their nutritive value. Early prediction of Swiss chard yield provides a valuable knowledge base for product management decisions like pre-harvest planning, post-harvest handing, food policy, and marketing. Consequently, the objective of the present study was to investigate the use of hyperspectral data in predicting yield of Swiss chard grown under different irrigation water sources. Swiss chard ground-based hyperspectral data were collected at canopy level using a handheld spectroradiometer at 2 and 2.5 months after planting. Reflectance spectra were transformed to their first-order derivative and partial least squares (PLS) and sparse PLS (SPLS) regressions (R) were used for data analysis. Results showed that 95% and 97% of Swiss chard fresh and dry yields variation, respectively could be explained. SPLSR outperformed PLSR models for predicting Swiss chard fresh and dry yields. Results further showed that models developed using data collected when the crop was 2.5 months old were more accurate than models derived using a 2-month old crop data, except the SPLSR model for predicting dry yield. Fresh yield estimates could be accurately modelled (root mean square error: RMSE = 23.97% of the mean, Nash–Sutcliffe efficiency: NSE = 0.93). However, Swiss chard dry yield could not be reliably predicted (at minimum RMSE = 35.00% of the mean and a maximum NSE of 0.60 were obtained). This study demonstrates the potential of hyperspectral data in predicting Swiss chard fresh yield using combined irrigation treatments data sets. The study offers insight to the potential of large-scale prediction and estimation of Swiss chard yield using space borne and/or airborne hyperspectral data.
author: Elfatih M. Abdel-Rahman, Onisimo Mutanga, John Odindi, Elhadi Adam, Alfred Odindo, Riyad Ismail
publish year: 2014
creator: Elfatih M. Abdel-Rahman, Onisimo Mutanga, John Odindi, Elhadi Adam, Alfred Odindo, Riyad Ismail
reference: Elfatih M. Abdel-Rahman, Onisimo Mutanga, John Odindi, Elhadi Adam, Alfred Odindo, Riyad Ismail, "A comparison of partial least squares (PLS) and sparse PLS regressions for predicting yield of Swiss chard grown under different irrigation water sources using hyperspectral data", Computers and Electronics in Agriculture, Volume 106, August 2014, pages 11-19, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 106, August 2014, pages 11-19, Elsevier B.V.
label: Article 0316

Combining spatial and spectral information to estimate chlorophyll contents of crop leaves with a field imaging spectroscopy system
type: NamedIndividual
type: Article
has knowledge model: Regressor_0317
abstract: Imaging spectroscopy is widely used in weed recognition, pest monitoring, agricultural product quality control and other precision agricultural fields. In the present study, an in-house-designed/developed field imaging spectroscopy system (FISS, 380–870 nm) was used to obtain the imaging spectra of soybean leaves at 344 wavelengths. The spatial and spectral information including the entropy, mean reflectivity and standard deviation of the leaf images at different wavelengths were extracted; the chlorophyll content was retrieved using multiple linear regression (MLR) together with the spatial information and spectral information, and the results were compared with the results derived with the Analytical Spectral Devices (ASD, FieldSpecFR spectrometer, Analytical Spectral Devices Inc., USA) data that were generated using conventional single sensor spectrometers. The results demonstrated that the entropy, standard deviation and other features of the image were very good indicators of the leaf chlorophyll content, confirming the idea that spatial information can be used to retrieve chlorophyll content, with an accuracy equivalent to that of spectral information, and can provide information that spectral reflectivity cannot provide. Thus, integrating spatial information and spectral information can greatly improve the chlorophyll content retrieval accuracy and reduce the estimation errors by 20 %. Due to the unique measurement method and image-spectrum-in-one feature, the field imaging spectroscopy system (FISS) data can be conveniently used to achieve accurate chlorophyll content retrieval, and the retrieval error was reduced by 30–45 % compared with that for the ASD data. FISS data and the proposed method of integrating both spectral and spatial information of imaging spectroscopy have potential advantages in quantitative spectral analysis applied in agricultural biochemistry related fields.
author: Bo Liu, Wenjing Shen, Yue-min Yue, Ru Li, Qingxi Tong, Bing Zhang
publish year: 2017
creator: Bo Liu, Wenjing Shen, Yue-min Yue, Ru Li, Qingxi Tong, Bing Zhang
reference: Bo Liu, Wenjing Shen, Yue-min Yue, Ru Li, Qingxi Tong, Bing Zhang, "Combining spatial and spectral information to estimate chlorophyll contents of crop leaves with a field imaging spectroscopy system", Precision Agriculture, Volume 18, pages 491–506 (2017) , Springer
Source: Precision Agriculture, Volume 18, pages 491–506 (2017) , Springer
label: Article 0317

Combining ultrasonic sward height and spectral signatures to assess the biomass of legume–grass swards
type: NamedIndividual
type: Article
has knowledge model: Regressor_0318
abstract: In binary mixtures of either white clover (Trifolium repens L.), red clover (Trifolium pratense L.) or lucerne (Medicago sativa L.) with perennial ryegrass (Lolium perenne L.) as well as in pure swards of each single species, biomass has been assessed measuring sward height with an ultrasonic distance sensor and spectral-radiometric reflections. Measurements and sampling of reference data were conducted along a wide range of biomass levels on 0.25 m2 subplots at 17 dates in 4 growth periods defined by 4 main cuts between September 2005 and September 2006. To improve biomass (BM) predictions on exclusive ultrasonic sward height (USH) by complementation of vegetation indices (VIs), a collection of existing and hyperspectral VIs have been evaluated in combination with USH. While red/NIR-based VIs performed suboptimal, indices representing bands related to water absorption or the NIR-shoulder showed better predictions. Best prediction accuracies were achieved by a combination of USH with sward-specifically selected 1 nm bands using the normalized spectral vegetation index (NDSI) reaching R2-values of 0.83 in common swards and 0.88–0.90 for species-specific calibrations, respectively. Broadening of bands up to 100 nm did only marginally reduce prediction accuracies. Using fixed NDSI bands selected from common swards instead of sward specific selected ones, did not significantly reduce prediction accuracy. It is identified that VIs act complementarily to USH and can avoid overestimations of BM frequently observed in grassland by the exclusive use of USH. Both, bandwidth flexibility and fixed NDSI band configurations can facilitate a configuration of sensors for legume–grass swards in a wide range of yield levels.
author: Thomas Fricke, Michael Wachendorf
publish year: 2013
creator: Thomas Fricke, Michael Wachendorf
reference: Thomas Fricke, Michael Wachendorf, "Combining ultrasonic sward height and spectral signatures to assess the biomass of legume–grass swards", Computers and Electronics in Agriculture, Volume 99, November 2013, pages 236-247, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 99, November 2013, pages 236-247, Elsevier B.V.
label: Article 0318

Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat
type: NamedIndividual
type: Article
has knowledge model: Regressor_0321
abstract: Hyperspectral data sets contain useful information for characterizing vegetation canopies not previously available from multi-spectral data sources. However, to make full use of the information content one has to find ways for coping with the strong multi-collinearity in the data. The redundancy directly results from the fact that only a few variables effectively control the vegetation signature. This low dimensionality strongly contrasts with the often more than 100 spectral channels provided by modern spectroradiometers and through imaging spectroscopy. With this study we evaluated three different chemometric techniques specifically designed to deal with redundant (and small) data sets. In addition, a widely used 2-band vegetation index was chosen (NDVI) as a baseline approach. A multi-site and multi-date field campaign was conducted to acquire the necessary reference observations. On small subplots the total canopy chlorophyll content was measured and the corresponding canopy signature (450–2500 nm) was recorded (nobs = 42). Using this data set we investigated the predictive power and noise sensitivity of stepwise multiple linear regression (SMLR) and two ‘full spectrum’ methods: principal component regression (PCR) and partial least squares regression (PLSR). The NDVI was fitted to the canopy chlorophyll content using an exponential relation. For all techniques, a jackknife approach was used to obtain cross-validated statistics. The PLSR clearly outperformed all other techniques. PLSR gave a cross-validated RMSE of 51 mg m−2 (Rcv2=0.82) for canopy chlorophyll contents ranging between 38 and 475 mg m−2 (0.99 ≤ LAI ≤ 8.74 m2 m−2). The lowest accuracy was achieved using PCR (RMSEcv = 82 mg m−2 and Rcv2=0.57). The NDVI, even using chlorophyll optimized band settings, could not reach the accuracy of PLSR. Regarding the sensitivity to artificially created (white) noise, PCR showed some advantages, whereas SMLR was the most sensitive chemometric technique. For relative…
author: Clement Atzberger, Martine Guerif, Frederic Baret, Willy Werner
publish year: 2010
creator: Clement Atzberger, Martine Guerif, Frederic Baret, Willy Werner
reference: Clement Atzberger, Martine Guerif, Frederic Baret, Willy Werner, "Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat", Computers and Electronics in Agriculture, Volume 73, Issue 2, August 2010, pages 165-173, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 73, Issue 2, August 2010, pages 165-173, Elsevier B.V.
label: Article 0321

Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat
type: NamedIndividual
type: Article
has knowledge model: Classifier 0326
abstract: Detection of yellow rust is of great importance in disease control and reducing the use of fungicide. Spectral analysis is an important method for disease detection in terms of remote sensing. In this study, an emerging spectral analysis method known as continuous wavelet analysis (CWA) was examined and compared with several conventional spectral features for the detection of yellow rust disease at a leaf level. The leaf spectral measurements were made by a spectroradiometer at both Zodaks 37 and 70 stages with a large sample size. The results showed that the wavelet features were able to capture the major spectral signatures of yellow rust, and exhibited considerable potential for disease detection at both growth stages. Both the accuracies of the univariate and multivariate models suggested that wavelet features outperformed conventional spectral features in quantifying disease severity at leaf level. Optimal accuracies returned a coefficient of determination (R2) of 0.81 and a root mean square error (RMSE) of 0.110 for pooled data at both stages. Furthermore, wavelet features showed a stronger response to the yellow rust at Zodaks 70 stage than at Zodaks 37 stage, indicating reliable estimation of disease severity can be made until the Zodaks 70 stage.
author: Jingcheng Zhang, Lin yuan, Ruiliang Pu, Rebecca W. Loraamm, Guijun Yang, Jihua Wang
publish year: 2014
creator: Jingcheng Zhang, Lin yuan, Ruiliang Pu, Rebecca W. Loraamm, Guijun Yang, Jihua Wang
reference: Jingcheng Zhang, Lin yuan, Ruiliang Pu, Rebecca W. Loraamm, Guijun Yang, Jihua Wang, "Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat", Computers and Electronics in Agriculture, Volume 100, January 2014, pages 79-87, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 100, January 2014, pages 79-87, Elsevier B.V.
label: Article 0326

Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation
type: NamedIndividual
type: Article
has knowledge model: Regressor_0328
abstract: The most important meteorological variables influencing plant growth are temperature, moisture, and solar radiation. Because it is scarcely gauged at meteorological stations in Turkey, solar radiation is commonly estimated by artificial neural networks (ANN), adaptive network-fuzzy inference system (ANFIS), multiple linear regression (MLR) models, or by empirical equations relating it to available meteorological data at monthly periods composed differently for each one of 12 months. In general, the explanatory meteorological data comprise month number, extraterrestrial radiation, average air temperature, average relative humidity, average sunshine duration, and daylight hours. Such data together with solar radiation measured by the Turkish State Meteorological Service (MGM) at 163 stations having records of at least 20 years are used in monthly units in developing the models. First, as a result of a variance inflation factors analysis, calendar month number (M), extraterrestrial radiation (Ra), average air temperature (Tmean), and average relative humidity (RHmean) are determined to be meaningful explanatory variables for estimation of solar radiation. Second, various combinations of input variables are dissected using the ANFIS and ANN models. Next, the accuracies of the ANFIS, ANN, and MLR models, and of Angstrom, Abdalla, Bahel, and Hargreaves–Samani empirical equations are compared with each other. The final results show that the ANN model performs better than the ANFIS and MLR models and the empirical equations in estimating solar radiation in Turkey.
author: Hatice Citakoglu
publish year: 2015
creator: Hatice Citakoglu
reference: Hatice Citakoglu, "Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation", Computers and Electronics in Agriculture, Volume 118, October 2015, pages 28-37, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 118, October 2015, pages 28-37, Elsevier B.V.
label: Article 0328

Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes
type: NamedIndividual
type: Article
has knowledge model: Regressor_0336
abstract: Monitoring agricultural drought utilizing the least variables can be useful at the areas with just rain gauge stations, especially in Iran, which is located in arid belt of the world. In this study, all of the stations having long duration data in Iran (31 stations with more than 59 years’ data), have been used to calculate three drought indices: (1) Palmer Drought Severity Index (PDSI); (2) Standardized Precipitation Index (SPI); (3) Multivariate Standardized Precipitation Index (MSPI). According to the recommendation of world meteorological organization (WMO), Palmer index has been defined as the reference index and SPI9 (9-month time window of SPI), SPI10, SPI11 & SPI12, and also MSPI9–12 (9–12 months’ time window), was evaluated by Palmer index. The results showed significant relation between SPI11 or SPI12 and PDSI in Iran’s climates. MSPI9–12 has a significant relation with PDSI too, and the correlation of MSPI with Palmer Index was found to be stronger than the SPI, in all stations. The relation between MSPI and PDSI is also stronger in extra-arid climates, and weaker in humid and per-humid areas. So, MSPI can be used for agricultural drought monitoring in the places where there are restrictions in meteorological dataset (when precipitation data are only available). In the second part, prediction of MSPI9–12 has been done using Adaptive Neuro-Fuzzy Inference System (ANFIS) merged with bio-inspired optimization algorithms. The meta-heuristic models implemented are ANFIS-ACO (ANFIS merged with Ant Colony Optimization), ANFIS-GA (ANFIS merged with Genetic Algorithm) and ANFIS-PSO (ANFIS merged with Particle Swarm Optimization). The models had their best predictions in arid, semi-arid and Mediterranean climates while humid climate provided the weakest predictions. Among the mentioned algorithms, the ACO and GA had the best performances to optimize ANFIS; they improved the ANFIS’s accuracy by 45.9% and 43.2%, respectively.
author: Pouya Aghelpour, Hadigheh Bahrami-Pichaghchi, Ozgur Kisi
publish year: 2020
creator: Pouya Aghelpour, Hadigheh Bahrami-Pichaghchi, Ozgur Kisi
reference: Pouya Aghelpour, Hadigheh Bahrami-Pichaghchi, Ozgur Kisi, "Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes", Computers and Electronics in Agriculture, Volume 170, March 2020, 105279, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 170, March 2020, 105279, Elsevier B.V.
label: Article 0336

Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions
type: NamedIndividual
type: Article
has knowledge model: Classifier 0351
abstract: Convolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment. As these models grow both in the number of training images and in the number of supported crops and diseases, there exist the dichotomy of (1) generating smaller models for specific crop or, (2) to generate a unique multi-crop model in a much more complex task (especially at early disease stages) but with the benefit of the entire multiple crop image dataset variability to enrich image feature description learning.In this work we first introduce a challenging dataset of more than one hundred-thousand images taken by cell phone in real field wild conditions. This dataset contains almost equally distributed disease stages of seventeen diseases and five crops (wheat, barley, corn, rice and rape-seed) where several diseases can be present on the same picture.When applying existing state of the art deep neural network methods to validate the two hypothesised approaches, we obtained a balanced accuracy (BAC=0.92) when generating the smaller crop specific models and a balanced accuracy (BAC=0.93) when generating a single multi-crop model.In this work, we propose three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network. This combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks. The crop-conditional plant disease classification network that incorporates the contextual information by concatenation at the embedding vector level obtains a balanced accuracy of 0.98 improving all previous methods and removing 71% of the miss-classifications of the former methods.
author: Artzai Picon, Maximiliam Seitz, Aitor Alvarez-Gila, Patrick Mohnke, Amaia Ortiz-Barredo, Jone Echazarra
publish year: 2019
creator: Artzai Picon, Maximiliam Seitz, Aitor Alvarez-Gila, Patrick Mohnke, Amaia Ortiz-Barredo, Jone Echazarra
reference: Artzai Picon, Maximiliam Seitz, Aitor Alvarez-Gila, Patrick Mohnke, Amaia Ortiz-Barredo, Jone Echazarra, "Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions", Computers and Electronics in Agriculture, Volume 167, December 2019, 105093, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 167, December 2019, 105093, Elsevier B.V.
label: Article 0351

Crop pest classification based on deep convolutional neural network and transfer learning
type: NamedIndividual
type: Article
has knowledge model: Classifier 0353
abstract: The growth of most important field crops such as rice, wheat, maize, soybean, and sugarcane are affected due to attack of various pests and the crop production is reduced due to different types of insects. The classification and identification of all types of crop insects correctly is a difficult task for the farmers due to the similar appearance in the earlier stage of crop growth. To address this issue, Convolutional neural network (CNN) with deep architectures is being applied as it performs automatic feature extraction and learns complex high-level features in image classification applications. This study proposed an efficient deep CNN model to classify insect species on three publicly available insect datasets. The National Bureau of Agricultural Insect Resources (NBAIR) dataset used as first insect dataset that consists of 40 classes of field crop insect images, while the second and third dataset (Xie1, Xie2) contains 24 and 40 classes of insects respectively. The proposed model was evaluated and compared with pre-trained deep learning architectures such as AlexNet, ResNet, GoogLeNet and VGGNet for insect classification. Transfer learning was applied to fine-tune the pre-trained models. The data augmentation techniques such as reflection, scaling, rotation, and translation are also applied to prevent the network from overfitting. The effectiveness of hyperparameters was analysed in the proposed model to improve accuracy. The highest classification accuracy of 96.75, 97.47, and 95.97% was achieved in proposed CNN model for NBAIR insect dataset (40 classes), Xie1 (24 classes) insect dataset and Xie2 (40 classes) insect dataset respectively. The results demonstrated that the proposed CNN model is effective in classifying various types of insects in field crops than pre-trained models and can be implemented in the agriculture sector for crop protection.
author: K. Thenmozhi, U. Srinivasulu Reddy
publish year: 2019
creator: K. Thenmozhi, U. Srinivasulu Reddy
reference: K. Thenmozhi, U. Srinivasulu Reddy, "Crop pest classification based on deep convolutional neural network and transfer learning", Computers and Electronics in Agriculture, Volume 164, September 2019, 104906, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 164, September 2019, 104906, Elsevier B.V.
label: Article 0353

Crop pest recognition in natural scenes using convolutional neural networks
type: NamedIndividual
type: Article
has knowledge model: Classifier 0354
abstract: Crop diseases and insect pests are major agricultural problems worldwide, because the severity and extent of their occurrence causes significant crop losses. In addition, traditional crop pests recognition methods are limited, ineffective, and time-consuming due to the manual selection of the useful feature sets. This paper introduces a crop pest recognition method that accurately recognizes ten common species of crop pests by applying several deep convolutional neural networks (CNNs). The main contributions of this paper are (1) a manually collected and validated crop pest dataset is described and shared; (2) a fine-tuned GoogLeNet model is proposed to deal with the complicated backgrounds presented by farmland scenes, with pest classification results better than the original model; and (3) the fine-tuned GoogLeNet model obtains an improvement of 6.22% compared to the state-of-the-art method. As a result, the proposed model has the potential to be applied in real-world applications and further motivate research on crop disease identification.
author: Yanfen Li, Hanxiang Wang, L. Minh Dang, Abolghasem Sadeghi-Niaraki, Hyeonjoon Moon
publish year: 2020
creator: Yanfen Li, Hanxiang Wang, L. Minh Dang, Abolghasem Sadeghi-Niaraki, Hyeonjoon Moon
reference: Yanfen Li, Hanxiang Wang, L. Minh Dang, Abolghasem Sadeghi-Niaraki, Hyeonjoon Moon, "Crop pest recognition in natural scenes using convolutional neural networks", Computers and Electronics in Agriculture, Volume 169, February 2020, 105174, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 169, February 2020, 105174, Elsevier B.V.
label: Article 0354

Crop type mapping using spectral–temporal profiles and phenological information
type: NamedIndividual
type: Article
has knowledge model: Classifier 0356
abstract: Spatially explicit multi-year crop information is required for many environmental applications. The study presented here proposes a hierarchical classification approach for per-plot crop type identification that is based on spectral–temporal profiles and accounts for deviations from the average growth stage timings by incorporating agro-meteorological information in the classification process. It is based on the fact that each crop type has a distinct seasonal spectral behavior and that the weather may accelerate or delay crop development. The classification approach was applied to map 12 crop types in a 14,000 km2 catchment area in Northeast Germany for several consecutive years. An accuracy assessment was performed and compared to those of a maximum likelihood classification. The 7.1% lower overall classification accuracy of the spectral–temporal profiles approach may be justified by its independence of ground truth data. The results suggest that the number and timing of image acquisition is crucial to distinguish crop types. The increasing availability of optical imagery offering a high temporal coverage and a spatial resolution suitable for per-plot crop type mapping will facilitate the continuous refining of the spectral–temporal profiles for common crop types and different agro-regions and is expected to improve the classification accuracy of crop type maps using these profiles.
author: Saskia Foerster, Klaus Kaden, Michael Foerster, Sibylle Itzerott
publish year: 2012
creator: Saskia Foerster, Klaus Kaden, Michael Foerster, Sibylle Itzerott
reference: Saskia Foerster, Klaus Kaden, Michael Foerster, Sibylle Itzerott, "Crop type mapping using spectral–temporal profiles and phenological information", Computers and Electronics in Agriculture, Volume 89, November 2012, pages 30-40, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 89, November 2012, pages 30-40, Elsevier B.V.
label: Article 0356

Crop water status assessment in controlled environment using crop reflectance and temperature measurements
type: NamedIndividual
type: Article
has knowledge model: Classifier 0357
abstract: Crop water status is an important parameter for plant growth and yield performance in greenhouses. Thus, early detection of water stress is essential for efficient crop management. The dynamic response of plants to changes of their environment is called ‘speaking plant’ and multisensory platforms for remote sensing measurements offer the possibility to monitor in real-time the crop health status without affecting the crop and environmental conditions. Therefore, aim of this work was to use crop reflectance and temperature measurements acquired remotely for crop water status assessment. Two different irrigation treatments were imposed in tomato plants grown in slabs filed with perlite, namely tomato plants under no irrigation for a certain period; and well-watered plants. The plants were grown in a controlled growth chamber and measurements were carried out during August and September of 2014. Crop reflectance measurements were carried out by two types of sensors: (i) a multispectral camera measuring the radiation reflected in three spectral bands centred between 590–680, 690–830 and 830–1000 nm regions, and (ii) a spectroradiometer measuring the leaf reflected radiation from 350 to 2500 nm. Based on the above measurements several crop indices were calculated. The results showed that crop reflectance increased due to water deficit with the detected reflectance increase being significant about 8 h following irrigation withholding. The results of a first derivative analysis on the reflectance data showed that the spectral regions centred at 490–510, 530–560, 660–670 and 730–760 nm could be used for crop status monitoring. In addition, the results of the present study point out that sphotochemical reflectance index, modified red simple ratio index and modified ratio normalized difference vegetation index could be used as an indicator of plant water stress, since their values were correlated well with the substrate water content and the crop water stress index; the last …
author: A. Elvanidi, N. Katsoulas, T. Bartzanas, K. P. Ferentinos, C. Kittas
publish year: 2017
creator: A. Elvanidi, N. Katsoulas, T. Bartzanas, K. P. Ferentinos, C. Kittas
reference: A. Elvanidi, N. Katsoulas, T. Bartzanas, K. P. Ferentinos, C. Kittas, "Crop water status assessment in controlled environment using crop reflectance and temperature measurements", Precision Agriculture, Volume 18, pages 332–349 (2017) , Springer
Source: Precision Agriculture, Volume 18, pages 332–349 (2017) , Springer
label: Article 0357

Crop yield prediction with deep convolutional neural networks
type: NamedIndividual
type: Article
has knowledge model: Regressor_0359
abstract: Using remote sensing and UAVs in smart farming is gaining momentum worldwide. The main objectives are crop and weed detection, biomass evaluation and yield prediction. Evaluating machine learning methods for remote sensing based yield prediction requires availability of yield mapping devices, which are still not very common among farmers. In this study Convolutional Neural Networks (CNNs) – a deep learning methodology showing outstanding performance in image classification tasks – are applied to build a model for crop yield prediction based on NDVI and RGB data acquired from UAVs. The effect of various aspects of the CNN such as selection of the training algorithm, depth of the network, regularization strategy, and tuning of the hyperparameters on the prediction efficiency are tested. Using the Adadelta training algorithm, L2 regularization with early stopping and a CNN with 6 convolutional layers, mean absolute error (MAE) in yield prediction of 484.3 kg/ha and mean absolute percentage error (MAPE) of 8.8% was achieved for data acquired during the early period of the growth season (i.e., in June of 2017, growth phase <25%) with RGB data. When using data acquired later in July and August of 2017 (growth phase >25%), MAE of 624.3 kg/ha (MAPE: 12.6%) was obtained. Significantly, the CNN architecture performed better with RGB data than the NDVI data.
author: Petteri Nevavuori, Nathaniel Narra, Tarmo Lipping
publish year: 2019
creator: Petteri Nevavuori, Nathaniel Narra, Tarmo Lipping
reference: Petteri Nevavuori, Nathaniel Narra, Tarmo Lipping, "Crop yield prediction with deep convolutional neural networks", Computers and Electronics in Agriculture, Volume 163, August 2019, 104859, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 163, August 2019, 104859, Elsevier B.V.
label: Article 0359

Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild
type: NamedIndividual
type: Article
has knowledge model: Classifier 0367
abstract: Fungal infection represents up to 50% of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks.In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita). The analysis was done using different mobile devices, and more than 8178 images were captured in two pilot sites in Spain and Germany during 2014,2015 and 2016. Obtained results reveal an overall improvement of the balanced accuracy from 0.78 (Johannes et al., 2017) up to 0.87 under exhaustive testing, and balanced accuracies greater than 0.96 on a pilot test performed in Germany.
author: Artzai Picon, Aitor Alvarez-Gila, Maximiliam Seitz, Amaia Ortiz-Barredo, Jone Echazarra, Alexander Johannes
publish year: 2019
creator: Artzai Picon, Aitor Alvarez-Gila, Maximiliam Seitz, Amaia Ortiz-Barredo, Jone Echazarra, Alexander Johannes
reference: Artzai Picon, Aitor Alvarez-Gila, Maximiliam Seitz, Amaia Ortiz-Barredo, Jone Echazarra, Alexander Johannes, "Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild", Computers and Electronics in Agriculture, Volume 161, June 2019, pages 280-290, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 161, June 2019, pages 280-290, Elsevier B.V.
label: Article 0367

Deep learning – Method overview and review of use for fruit detection and yield estimation
type: NamedIndividual
type: Article
has knowledge model: Regressor_0369
abstract: A review of developments in the rapidly developing field of deep learning is presented. Recommendations are made for original contributions to the literature, as opposed to formulaic applications of established methods to new application areas (e.g., to new crops), including the use of standard metrics (e.g., F1 score, the harmonic mean between Precision and Recall) for model comparison involving binary classification. A recommendation for the provision and use of publically available fruit-in-orchard image sets is made, to allow method comparisons and for implementation of transfer learning for deep learning models trained on the large public generic datasets. Emphasis is placed on practical aspects for application of deep learning models for the task of fruit detection and localisation, in support of tree crop load estimation. Approaches to the extrapolation of tree image counts to orchard yield estimation are also reviewed, dealing with the issue of occluded fruit in imaging. The review is intended to assist new users of deep learning image processing techniques, and to influence the direction of the coming body of application work on fruit detection.
author: Anand Koirala, Kerry B. Walsh, Zhenglin Wang, Cheryl McCarthy
publish year: 2019
creator: Anand Koirala, Kerry B. Walsh, Zhenglin Wang, Cheryl McCarthy
reference: Anand Koirala, Kerry B. Walsh, Zhenglin Wang, Cheryl McCarthy, "Deep learning – Method overview and review of use for fruit detection and yield estimation", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 219-234, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 162, July 2019, pages 219-234, Elsevier B.V.
label: Article 0369

Deep learning for classification and severity estimation of coffee leaf biotic stress
type: NamedIndividual
type: Article
has knowledge model: Classifier 0371
abstract: Biotic stress consists of damage to plants through other living organisms. The efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability. Agricultural sustainability promotes the development of new technologies that allow the reduction of environmental impacts, greater accessibility to farmers and, consequently, increased productivity. The use of computer vision with deep learning methods allows the early and correct identification of the stress-causing agent. So, corrective measures can be applied as soon as possible to mitigate the problem. The objective of this work is to design an effective and practical system capable of identifying and estimating the stress severity caused by biotic agents on coffee leaves. The proposed approach consists of a multi-task system based on convolutional neural networks. In addition, we have explored the use of data augmentation techniques to make the system more robust and accurate. Computational experiments performed with the proposed system using the ResNet50 architecture obtained an accuracy of 95.24% for the biotic stress classification and 86.51% for severity estimation. Moreover, it was found that by classifying only the symptoms, the results were greater than 97%. The experimental results indicate that the proposed system might be a suitable tool to assist both experts and farmers in the identification and quantification of biotic stresses in coffee plantations.
author: Jose G.M. Esgario, Renato A. Krohling, Jose A. Ventura
publish year: 2020
creator: Jose G.M. Esgario, Renato A. Krohling, Jose A. Ventura
reference: Jose G.M. Esgario, Renato A. Krohling, Jose A. Ventura, "Deep learning for classification and severity estimation of coffee leaf biotic stress", Computers and Electronics in Agriculture, Volume 169, February 2020, 105162, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 169, February 2020, 105162, Elsevier B.V.
label: Article 0371

Deep learning models for plant disease detection and diagnosis
type: NamedIndividual
type: Article
has knowledge model: Classifier 0375
abstract: In this paper, convolutional neural network models were developed to perform plant disease detection and diagnosis using simple leaves images of healthy and diseased plants, through deep learning methodologies. Training of the models was performed with the use of an open database of 87,848 images, containing 25 different plants in a set of 58 distinct classes of [plant, disease] combinations, including healthy plants. Several model architectures were trained, with the best performance reaching a 99.53% success rate in identifying the corresponding [plant, disease] combination (or healthy plant). The significantly high success rate makes the model a very useful advisory or early warning tool, and an approach that could be further expanded to support an integrated plant disease identification system to operate in real cultivation conditions.
author: Konstantinos P. Ferentinos
publish year: 2018
creator: Konstantinos P. Ferentinos
reference: Konstantinos P. Ferentinos, "Deep learning models for plant disease detection and diagnosis", Computers and Electronics in Agriculture, Volume 145, February 2018, pages 311-318, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 145, February 2018, pages 311-318, Elsevier B.V.
label: Article 0375

Depthwise separable convolution architectures for plant disease classification
type: NamedIndividual
type: Article
has knowledge model: Classifier 0383
abstract: Convolutional neural network has a huge partake and is still a dominating tool in the field of computer vision. In this study, we introduce a model with depthwise separable convolution architecture for plant disease detection based on images of leaves. We present two versions of depthwise separable convolution comprising two varieties of building blocks. Training and testing of the models were performed on a subset of publicly available PlantVillage dataset of 82,161 images containing 55 distinct classes of healthy and diseased plants. These depthwise separable convolutions achieved less accuracy and high gain in convergence speed. Several models were trained and tested, of which Reduced MobileNet achieved a classification accuracy of 98.34% with 29 times fewer parameters compared to VGG and 6 times lesser than that of MobileNet. However, MobileNet outperformed existing models with 36.03% accuracy when testing the model on a set of images taken under conditions different from those of the images used for training. Thin models were also introduced, which showed effective trade-off between latency and accuracy. The satisfactory accuracy and small size of this model makes it suitable for real-time crop diagnosis in resource constrained mobile devices.
author: Kamal KC, Zhendong Yin, Mingyang Wu, Zhilu Wu
publish year: 2019
creator: Kamal KC, Zhendong Yin, Mingyang Wu, Zhilu Wu
reference: Kamal KC, Zhendong Yin, Mingyang Wu, Zhilu Wu, "Depthwise separable convolution architectures for plant disease classification", Computers and Electronics in Agriculture, Volume 165, October 2019, 104948, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 165, October 2019, 104948, Elsevier B.V.
label: Article 0383

Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics
type: NamedIndividual
type: Article
has knowledge model: Regressor_0389
abstract: Stakeholders, policy makers, government planners and agricultural market participants in Central Eastern Europe require accurate and timely information about wheat and maize yield and production. The study site, the lowlands (altitude below 200 m) of the Tisza river catchment is by far the most important wheat and corn producing region in the Carpathian basin, and even in Central Eastern Europe. The conventional sampling of on-field data and data processing for crop forecasting requires significant amounts of time before official reports can be released. Several studies have shown that wheat and maize yield can be effectively forecast using satellite remote sensing. In this study, a freely available MODIS NDVI satellite data based wheat and maize yield forecasting methodology was developed and evaluated for estimating yield losses effected by drought.Wheat and maize yield was derived by regressing reported yield values against time series of 15 different peak-season MODIS-derived NDVI. The lowest RMSE values at the river basin level for both wheat and maize yield forecast versus reported yield were found when using at least six or more years of training data. Wheat forecast for the 2000 to 2015 growing seasons were within 0.819% and 19.08% of final reported yield values. Maize forecast at county level for the 2000 to 2015 growing seasons were within 0.299% and 17.14% of final reported yield values. The Nash–Sutcliffe efficiency index (E1) is positive with E1 = 0.322 in the case of wheat forecast, and with E1 = 0.401 in the case of maize forecast, which means the developed and evaluated forecasting method performs acceptable forecast efficiency. Nevertheless the occurrence of extreme drought or extreme precipitation can alter the forecasting efficiency resulting over or underestimation. Overall statement, which based on MODIS NDVI, possible yield losses can easily be forecasted 6–8 weeks before harvesting and applying simple threshold levels, yield losses can be mapp…
author: Attila Nagy, Janos Feher, Janos Tamas
publish year: 2018
creator: Attila Nagy, Janos Feher, Janos Tamas
reference: Attila Nagy, Janos Feher, Janos Tamas, "Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics", Computers and Electronics in Agriculture, Volume 151, August 2018, pages 41-49, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 151, August 2018, pages 41-49, Elsevier B.V.
label: Article 0389

Detecting Bakanae disease in rice seedlings by machine vision
type: NamedIndividual
type: Article
has knowledge model: Classifier 0393
abstract: Bakanae disease, or “foolish seedling”, is a seed-borne disease of rice (Oryza sativa L.). Infected plants can yield empty panicles or perish, resulting in a loss of grain yield. The disease occurs most frequently when contaminated seeds are used. Once the seeds are contaminated, the pathogen Fusarium fujikuroi spreads in the field. Therefore, infected plants must be screened at early developmental stages. This work proposes an approach to nondestructively distinguish infected and healthy seedlings at the age of 3 weeks using machine vision. Seeds of the rice cultivars Tainan 11 and Toyonishiki were inoculated with a conidial suspension of F. fujikuroi. The seedling were cultivated in an incubator for 3 weeks. The images of infected and control seedlings were acquired using flatbed scanners to quantify their morphological and color traits. Support vector machine (SVM) classifiers were developed for distinguishing the infected and healthy seedlings. A genetic algorithm was used for selecting essential traits and optimal model parameters for the SVM classifiers. The proposed approach distinguished infected and healthy seedlings with an accuracy of 87.9% and a positive predictive value of 91.8%.
author: Chia-Lin Chung, Kai-Jyun Huang, Szu-Yu Chen, Ming-Hsing Lai, Yu-Chia Chen, Yan-Fu Kuo
publish year: 2016
creator: Chia-Lin Chung, Kai-Jyun Huang, Szu-Yu Chen, Ming-Hsing Lai, Yu-Chia Chen, Yan-Fu Kuo
reference: Chia-Lin Chung, Kai-Jyun Huang, Szu-Yu Chen, Ming-Hsing Lai, Yu-Chia Chen, Yan-Fu Kuo, "Detecting Bakanae disease in rice seedlings by machine vision", Computers and Electronics in Agriculture, Volume 121, February 2016, pages 404-411, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 121, February 2016, pages 404-411, Elsevier B.V.
label: Article 0393

Detecting creeping thistle in sugar beet fields using vegetation indices
type: NamedIndividual
type: Article
has knowledge model: Classifier 0394
abstract: In this article, we address the problem of thistle detection in sugar beet fields under natural, outdoor conditions. In our experiments, we used a commercial color camera and extracted vegetation indices from the images. A total of 474 field images of sugar beet and thistles were collected and divided into six different groups based on illumination, scale and age. The feature set was made up of 14 indices. Mahalanobis Distance (MD) and Linear Discriminant Analysis (LDA) were used to classify the species. Among the features, excess green (ExG), green minus blue (GB) and color index for vegetation extraction (CIVE) offered the highest average accuracy, above 90%. The feature set was reduced to four important indices following a PCA analysis, but the classification accuracy was similar to that obtained by only combining ExG and GB which was around 95%, still better than an individual index. Stepwise linear regression selected nine out of 14 features and offered the highest accuracy of 97%. The results of LDA and MD were fairly close, making them both equally preferable. Finally, the results were validated by annotating images containing both sugar beet and thistles using the trained classifiers. The validation experiments showed that sunlight followed by the size of the plant, which is related to its growth stage, are the two most important factors affecting the classification. In this study, the best results were achieved for images of young sugar beet (in the seventh week) under a shade.
author: Wajahat Kazmi, Francisco Jose Garcia-Ruiz, Jon Nielsen, Jesper Rasmussen, Hans Jorgen Andersen
publish year: 2015
creator: Wajahat Kazmi, Francisco Jose Garcia-Ruiz, Jon Nielsen, Jesper Rasmussen, Hans Jorgen Andersen
reference: Wajahat Kazmi, Francisco Jose Garcia-Ruiz, Jon Nielsen, Jesper Rasmussen, Hans Jorgen Andersen, "Detecting creeping thistle in sugar beet fields using vegetation indices", Computers and Electronics in Agriculture, Volume 112, March 2015, pages 10-19, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 112, March 2015, pages 10-19, Elsevier B.V.
label: Article 0394

Detecting fruit surface wetness using a custom-built low-resolution thermal-RGB imager
type: NamedIndividual
type: Article
has knowledge model: Classifier 0396
abstract: Sweet cherry fruit cracking caused by seasonal rains is a major source of crop loss in the U.S. Pacific Northwest region and around the globe. In-field monitoring of cherry fruit surface wetness and temperature is, therefore, very important in fruit loss management. To determine the feasibility of low-resolution thermal-RGB imagery for detecting sweet cherry surface wetness, an experiment was carried out in plots of Skeena and Selah cherry varieties with Y-trellised and vertical architecture, respectively, at the Roza Farm of Washington State University, Prosser, WA. To wet cherries, 5 mm of rain was applied by running a rain simulator for 4 min (1.25 mm min−1) above canopies. Rainwater samples were collected using five rain gauges to quantify the applied amount of water. The in-field sensing setup included two custom-built thermal-RGB imagers, a microclimate-measuring unit and two leaf wetness sensors. The imagers were installed at a height of 2.1 m above the ground surface and were about 20 cm from the target cherries. The leaf wetness sensors were next to the cherries in the field of view of the imagers. A custom computer vision algorithm was developed and used to identify leaves and cherries in thermal and RGB images and extract the surface temperatures. The relationship between raw and normalized surface temperature, and wetness level and duration was investigated. The applicability of normalized cherry surface and air temperature difference was also studied. The results revealed that low-resolution thermal-RGB imagery can be used for detecting cherry fruit wetness level and duration. There was also a high correlation between the surface temperature of leaves and cherry fruits during the wetness period suggesting the temperature of leaves as reliable surrogate for cherry surface wetness and temperature monitoring. By utilizing the proposed imagery-based system, decision aid tools may be developed for efficient rainwater removal to prevent fruit cracking.
author: Yasin Osroosh, R. Troy Peters
publish year: 2019
creator: Yasin Osroosh, R. Troy Peters
reference: Yasin Osroosh, R. Troy Peters, "Detecting fruit surface wetness using a custom-built low-resolution thermal-RGB imager", Computers and Electronics in Agriculture, Volume 157, February 2019, pages 509-517, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 157, February 2019, pages 509-517, Elsevier B.V.
label: Article 0396

Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements
type: NamedIndividual
type: Article
has knowledge model: Regressor_0397
abstract: Powdery mildew (Blumeria graminis) is one of the most destructive diseases, which has a significant impact on the production of winter wheat. Detecting powdery mildew via spectral measurement and analysis is a possible alternative to traditional methods in obtaining the spatial distribution information of the disease. In this study, hyperspectral reflectances of normal and powdery mildew infected leaves were measured with a spectroradiometer in a laboratory. A total of 32 spectral features (SFs) were extracted from the lab spectra and examined through a correlation analysis and an independent t-test associated with the disease severity. Two regression models: multivariate linear regression (MLR) and partial least square regression (PLSR) were developed for estimating the disease severity of powdery mildew. In addition, the fisher linear discriminant analysis (FLDA) was also adopted for discriminating the three healthy levels (normal, slightly-damaged and heavily-damaged) of powdery mildew with the extracted SFs. The experimental results indicated that (1) most SFs showed a clear response to powdery mildew; (2) for estimating the disease severity with SFs, the PLSR model outperformed the MLR model, with a relative root mean square error (RMSE) of 0.23 and a coefficient of determination (R2) of 0.80 when using seven components; (3) for discrimination analysis, a higher accuracy was produced for the heavily-damaged leaves by FLDA with both producer’s and user’s accuracies over 90%; (4) the selected broad-band SFs revealed a great potential in estimating the disease severity and discriminating severity levels. The results imply that multispectral remote sensing is a cost effective method in the detection and mapping of powdery mildew.
author: Jing-Cheng Zhang, Rui-liang Pu, Ji-hua Wang, Wen-jiang Huang, Lin Yuan, Ju-hua Luo
publish year: 2012
creator: Jing-Cheng Zhang, Rui-liang Pu, Ji-hua Wang, Wen-jiang Huang, Lin Yuan, Ju-hua Luo
reference: Jing-Cheng Zhang, Rui-liang Pu, Ji-hua Wang, Wen-jiang Huang, Lin Yuan, Ju-hua Luo, "Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements", Computers and Electronics in Agriculture, Volume 85, July 2012, pages 13-23, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 85, July 2012, pages 13-23, Elsevier B.V.
label: Article 0397

Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection
type: NamedIndividual
type: Article
has knowledge model: Classifier 0398
abstract: In agriculture, plant diseases are primarily responsible for the reduction in production which causes economic losses. In plants, citrus is used as a major source of nutrients like vitamin C throughout the world. However, ‘Citrus’ diseases badly effect the production and quality of citrus fruits. From last decade, the computer vision and image processing techniques have been widely used for detection and classification of diseases in plants. In this article, we propose a hybrid method for detection and classification of diseases in citrus plants. The proposed method consists of two primary phases; (a) detection of lesion spot on the citrus fruits and leaves; (b) classification of citrus diseases. The citrus lesion spots are extracted by an optimized weighted segmentation method, which is performed on an enhanced input image. Then, color, texture, and geometric features are fused in a codebook. Furthermore, the best features are selected by implementing a hybrid feature selection method, which consists of PCA score, entropy, and skewness-based covariance vector. The selected features are fed to Multi-Class Support Vector Machine (M-SVM) for final citrus disease classification. The proposed technique is tested on Citrus Disease Image Gallery Dataset, Combined dataset (Plant Village and Citrus Images Database of Infested with Scale), and our own collected images database. We used these datasets for detection and classification of citrus diseases namely anthracnose, black spot, canker, scab, greening, and melanose. The proposed technique outperforms the existing methods and achieves 97% classification accuracy on citrus disease image gallery dataset, 89% on combined dataset and 90.4% on our local dataset.
author: Muhammad Sharif, Muhammad Attique Khan, Zahid Iqbal, Muhammad Faisal Azam, M. Ikram Ullah Lali, Muhammad Younus Javed
publish year: 2018
creator: Muhammad Sharif, Muhammad Attique Khan, Zahid Iqbal, Muhammad Faisal Azam, M. Ikram Ullah Lali, Muhammad Younus Javed
reference: Muhammad Sharif, Muhammad Attique Khan, Zahid Iqbal, Muhammad Faisal Azam, M. Ikram Ullah Lali, Muhammad Younus Javed, "Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection", Computers and Electronics in Agriculture, Volume 150, July 2018, pages 220-234, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 150, July 2018, pages 220-234, Elsevier B.V.
label: Article 0398

Detection and counting of flowers on apple trees for better chemical thinning decisions
type: NamedIndividual
type: Article
has knowledge model: Classifier 0399
abstract: Accurate chemical thinning of apple trees requires estimation of their blooming intensity, and determination of the blooming peak date. Performing this task, as of today, requires human experts to be present in the orchards for the entire blossom period or extrapolate using a single observation. Since experts are rare and in high demand, there is a need to automate this process. The system presented in this paper is able to estimate the blooming intensity and the blooming peak date from a sequence of tree images, with close-to-human accuracy. For this purpose, a two years dataset was collected in 2014–2015, partially tagged for the flowers location and completely annotated for blooming intensity. Using this dataset, an algorithm was developed and trained with three stages: a visual flower detector based on a deep convolutional neural network, followed by a blooming level estimator, and a peak blooming day finding algorithm. Despite the challenging conditions, the trained detector was able to detect flowers on trees with an Average Precision (AP) score of 0.68, which is on a par with contemporary results of other objects in detection benchmarks. The blooming estimator was based on a linear regression component, which used the number of flowers detected and related statistics to estimate the blooming intensity. The Pearson correlation between the algorithm blooming estimation and human judgments of several experts indicated high agreement levels (0.78–0.93) which were similar to the correlations measured among the human experts. Moreover, the developed estimator was relatively stable across multiple years. The developed peak date finding algorithm identified correctly the orchard’s blooming peak date, which was used to determine the thinning date in the current practice (the entire orchard is thinned in the same day). Experiments testing the algorithm’s ability to find a blooming peak date for each tree independently showed encouraging results, which may lead upon ref…
author: Guy Farjon, Omri Krikeb, Aharon Bar Hillel, Victor Alchanatis
publish year: 2020
creator: Guy Farjon, Omri Krikeb, Aharon Bar Hillel, Victor Alchanatis
reference: Guy Farjon, Omri Krikeb, Aharon Bar Hillel, Victor Alchanatis, "Detection and counting of flowers on apple trees for better chemical thinning decisions", Precision Agriculture, Volume 21, pages 503–521 (2020) , Springer
Source: Precision Agriculture, Volume 21, pages 503–521 (2020) , Springer
label: Article 0399

Detection and differentiation between potato (Solanum tuberosum) diseases using calibration models trained with non-imaging spectrometry data
type: NamedIndividual
type: Article
has knowledge model: Classifier 0401
abstract: The proportion of light at wavelengths across the electromagnetic spectrum that is either absorbed, transmitted or reflected from a plant leaf is dependent on leaf structure, physiology and biochemistry. Since these elements are influenced by pests, pathogens and their associated induced diseases, the detection, differentiation and diagnosis of plant diseases is theoretically possible by non-destructive analysis of the light reflected from plant leaves. In this study the utility of analysis of light over the visible and near-infrared (400–1000 nm) portion of the spectrum to detect and distinguish between several economically important potato diseases, using either Partial Least Squares and BackPropagation Neural Network spectral calibration models was explored. Models could detect and distinguish between diseases with obvious foliar symptoms (blackleg and late blight), even pre-symptomatically, correctly classifying spectra from greenhouse experiments with an accuracy of 84.6%. When these diseases were analysed separately, models could distinguish between spectra from healthy and pre-symptomatic leaves, plus three classes of late blight lesion advancement with 92% accuracy. For blackleg, models distinguished between spectra from healthy, pre-symptomatic foliage and plants expressing blackleg symptoms with a 74.6% classification accuracy. However, models trained on spectra from whole-plant readings from field trials did not have this level of accuracy, with an R2 between target and model values of 0.66 for late blight, 0.31 for blackleg symptoms and 0.41 for healthy foliage. Regardless of greenhouse or field environment, models failed to detect or distinguish between diseases with subtle foliar impacts (black dot, powdery scab and Rhizoctonia diseases). While deployment of hand-held spectrometers for disease detection on a broad-acre scale is impractical, these findings could underpin methods to analyse hyperspectral imaging data with sub-plant resolution for incorpo…
author: Damian Bienkowski, Matt J. Aitkenhead, Alison K. Lees, Christopher Gallagher, Roy Neilson
publish year: 2019
creator: Damian Bienkowski, Matt J. Aitkenhead, Alison K. Lees, Christopher Gallagher, Roy Neilson
reference: Damian Bienkowski, Matt J. Aitkenhead, Alison K. Lees, Christopher Gallagher, Roy Neilson, "Detection and differentiation between potato (Solanum tuberosum) diseases using calibration models trained with non-imaging spectrometry data", Computers and Electronics in Agriculture, Volume 167, December 2019, 105056, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 167, December 2019, 105056, Elsevier B.V.
label: Article 0401

Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis
type: NamedIndividual
type: Article
has knowledge model: Classifier 0402
abstract: Kernel discriminant analysis (KDA) can be used as a feasible strategy for identifying plant stresses, especially for detection of pests and diseases, considering the highly nonlinear distribution of hyperspectral absorption features that respond to biophysical variations in plants caused by foliar lesions. However, traditional computation of the kernel projection features produced by hyperspectral data is always affected by redundant information among the numerous wavelengths, subsequently leading to dimension disaster. In order to alleviate this problem, the aim of this study is to propose a spectral vegetation indices-based kernel discriminant approach (SVIKDA) for the detection and classification of yellow rust, aphid, and powdery mildew in winter wheat at the leaf and canopy level. Leaf and canopy level hyperspectral reflectance datasets were measured with a total of 314 and 187 samples, respectively. Fourteen Spectral Vegetation Indices (SVIs) related to foliar biophysical variations were employed as the input sample space; then, by using correlation analysis and independent t-tests, redundant information among SVIs was removed. Subsequently, a Gaussian kernel function was utilized to cast discriminant analysis into a nonlinear framework. Finally, using 5-fold cross validation, performance and transferability of this approach were evaluated. Our results revealed that the SVIKDA outperformed conventional linear discriminant approach on detection and classification among healthy wheat leaves and leaves infected with yellow rust, aphids, and powdery mildew. At the leaf level, the classification returned the overall accuracies (OA) of 82.9%, 89.2%, 87.9% for three occurrence levels, i.e. slight, moderate, and severe (Kappa > 0.85). Depending on the types and severities of infestations, the classification accuracy was between 76% and 95%; At the canopy level, the multiple classifications between healthy leaves and leaves with damages from the three different infesta…
author: Yue Shi, Wenjiang Huang, Juhua Luo, Linsheng Huang, Xianfeng Zhou
publish year: 2017
creator: Yue Shi, Wenjiang Huang, Juhua Luo, Linsheng Huang, Xianfeng Zhou
reference: Yue Shi, Wenjiang Huang, Juhua Luo, Linsheng Huang, Xianfeng Zhou, "Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis", Computers and Electronics in Agriculture, Volume 141, September 2017, pages 171-180, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 141, September 2017, pages 171-180, Elsevier B.V.
label: Article 0402

Detection of anthracnose in tea plants based on hyperspectral imaging
type: NamedIndividual
type: Article
has knowledge model: Classifier 0405
abstract: Anthracnose (Gloeosporium theae-sinesis Miyake) is an important and common foliar disease in tea plants and is a severe threat to tea quality and production. Hyperspectral imaging technology enables non-invasive, objective detection of the damages ca by foliar disease and offers significant potential for plant disease prevention and phenotyping. This study proposes a novel method for detecting anthracnose in tea plants based on hyperspectral imaging. By analyzing the spectral sensitivity, we identified disease-sensitive bands at 542, 686, and 754 nm and used these bands to create two new disease indices: the Tea Anthracnose Ratio Index (TARI) and the Tea Anthracnose Normalized Index (TANI). Based on an optimized set of spectral features, a strategy combining unsupervised classification and adaptive two-dimensional thresholding was developed to detect disease scabs. Compared with traditional pixel-based classification methods, the proposed method was not affected by leaf background differences and thereby provides an effective means for disease identification and damage analysis. The validation results gave an overall accuracy of 98% for identifying the disease at the leaf level and 94% at the pixel level. These results suggest that automated and accurate detection of anthracnose-infected tea leaves is possible by using hyperspectral imaging for practical tea-plant protection.
author: Lin Yuan, Peng Yan, Wenyan Han, Yanbo Huang, Bin Wang, Jingcheng Zhang, Haibo Zhang, Zhiyan Bao
publish year: 2019
creator: Lin Yuan, Peng Yan, Wenyan Han, Yanbo Huang, Bin Wang, Jingcheng Zhang, Haibo Zhang, Zhiyan Bao
reference: Lin Yuan, Peng Yan, Wenyan Han, Yanbo Huang, Bin Wang, Jingcheng Zhang, Haibo Zhang, Zhiyan Bao, "Detection of anthracnose in tea plants based on hyperspectral imaging", Computers and Electronics in Agriculture, Volume 167, December 2019, 105039, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 167, December 2019, 105039, Elsevier B.V.
label: Article 0405

Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers
type: NamedIndividual
type: Article
has knowledge model: Classifier 0407
abstract: Hyperspectral signatures can provide abundant information regarding health status of crops; however it is difficult to discriminate between biotic and abiotic stress. In this study, the case of simultaneous occurrence of yellow rust disease symptoms and nitrogen stress was investigated by using hyperspectral features from a ground based hyperspectral imaging system. Hyperspectral images of healthy and diseased plant canopies were taken at Rothamsted Research, UK by a Specim V10 spectrograph. Five wavebands of 20 nm width were utilized for accurate identification of each of the stress and healthy plant conditions. The technique that was developed used a hybrid classification scheme consisting of hierarchical self organizing classifiers. Three different architectures were considered: counter-propagation artificial neural networks, supervised Kohonen networks (SKNs) and XY-fusion. A total of 12 120 spectra were collected. From these 3 062 (25.3%) were used for testing. The results of biotic and abiotic stress identification appear to be promising, reaching more than 95% for all three architectures. The proposed approach aimed at sensor based detection of diseased and stressed plants so that can be treated site specifically contributing to a more effective and precise application of fertilizers and fungicides according to specific plant’s needs.
author: Xanthoula Eirini Pantazi, Dimitrios Moshou, Roberto Oberti, Jon West, Abdul Mounem Mouazen, Dionysios Bochtis
publish year: 2017
creator: Xanthoula Eirini Pantazi, Dimitrios Moshou, Roberto Oberti, Jon West, Abdul Mounem Mouazen, Dionysios Bochtis
reference: Xanthoula Eirini Pantazi, Dimitrios Moshou, Roberto Oberti, Jon West, Abdul Mounem Mouazen, Dionysios Bochtis, "Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers", Precision Agriculture, Volume 18, pages 383–393 (2017) , Springer
Source: Precision Agriculture, Volume 18, pages 383–393 (2017) , Springer
label: Article 0407

Detection of grapevine leafroll disease based on 11-index imagery and ant colony clustering algorithm
type: NamedIndividual
type: Article
has knowledge model: Classifier 0412
abstract: Grapevine leafroll disease (GLD) is a virus disease that quickly propagates through vineyards under appropriate weather conditions and can reduce grape production worldwide by nearly 60 %. Therefore, the accurate diagnosis and reliable evaluation of GLD distribution, particularly at the early stage of GLD infection, is important to prevent the spread of this disease. This study applied the ant colony clustering algorithm (ACCA) to detect GLD spectral anomalies on 4 GLD-infected vineyards according to multi-spectral imagery for precision disease management. GLD was classified into three stages: GLD1, GLD2 and GLD3 according to its infection severity. An 11-index feature vector and its stacked image were generated to enhance the spectral differences and spectral discrimination between diseased and healthy grapevines. ACCA was then designed to solve the fuzziness of the multi-spectral image for GLD-infected grapevines and successfully identify GLD from healthy grapevines. Finally, a field survey with 49 samples and pixel purity index technology were applied to validate the effectiveness, efficiency and accuracy of ACCA. Field results indicated that an early stage of the GLD infection (GLD1) could be successfully discriminated from GLD2-, GLD3- and non-infected grapevines. The classification accuracies of non-, GLD1-, GLD2- and GLD3-infected grapevines were 94.4, 75, 84.6 and 83.3 %, respectively. Hence, the method based on an 11-index image and ACCA may significantly detect GLD at an early stage from healthy grapevines for precision disease management at the field level.
author: Jingwei Hou, Longtang Li, Jie He
publish year: 2016
creator: Jingwei Hou, Longtang Li, Jie He
reference: Jingwei Hou, Longtang Li, Jie He, "Detection of grapevine leafroll disease based on 11-index imagery and ant colony clustering algorithm", Precision Agriculture, Volume 17, pages 488–505 (2016) , Springer
Source: Precision Agriculture, Volume 17, pages 488–505 (2016) , Springer
label: Article 0412

Detection of orchard citrus fruits using a monocular machine vision-based method for automatic fruit picking applications
type: NamedIndividual
type: Article
has knowledge model: Classifier 0416
abstract: Due to the variable illumination conditions and occlusion produced by neighbouring fruits and other background participants, vision systems are important in accurately and reliably detecting mature citrus in natural orchard environments for automatic fruit picking applications. A robust citrus fruit detection method based on a monocular vision system was proposed. An adaptive enhanced red and green chromatic map was generated from an illumination-compensated image, which was obtained using block-based local homomorphic filtering. Otsu thresholding, morphology operation, marker-controlled watershed transform and convex hull operation methods were then used in combination to locate potential citrus regions from the chromatic map. Local texture information was extracted from the potential regions using local binary patterns and fed to a histogram intersection kernel-based support vector machine to make the final decision. The performance of the proposed method was evaluated on 127 test images captured in two citrus orchards on both sunny and cloudy days. Under strict PASCAL criteria, the recall rate of correctly detected citrus was greater than 0.86, with 13 false detections.
author: J.J. Zhuang, S.M. Luo, C.J. Hou, Y. Tang, Y. He, X.Y. Xue
publish year: 2018
creator: J.J. Zhuang, S.M. Luo, C.J. Hou, Y. Tang, Y. He, X.Y. Xue
reference: J.J. Zhuang, S.M. Luo, C.J. Hou, Y. Tang, Y. He, X.Y. Xue, "Detection of orchard citrus fruits using a monocular machine vision-based method for automatic fruit picking applications", Computers and Electronics in Agriculture, Volume 152, September 2018, pages 64-73, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 152, September 2018, pages 64-73, Elsevier B.V.
label: Article 0416

Detection of peanut leaf spots disease using canopy hyperspectral reflectance
type: NamedIndividual
type: Article
has knowledge model: Classifier 0417
abstract: Leaf spot is one of the most destructive diseases, which has a significant impact on the peanut production. Detecting leaf spot via spectral measurement and analysis is a possible alternative to traditional methods in detecting the spatial distribution of this disease. In this study, we identified sensitive bands and derived hyperspectral vegetation index specific to leaf spot detection. Hyperspectral canopy reflectance spectra of peanut cultivars susceptibilities to leaf spot were measured at two experimental sites in 2017. The normalized difference spectral index (NDSI) was derived based on their correlation with disease index (DI) in the leaf spectrum between 325 nm and 1075 nm. The results showed that canopy spectral reflectance decreased significantly in the near-infrared regions (NIR) as DI increased (r < −0.90). The spectral index for detecting leaf spot in peanut were LSI: (NDSI (R938, R761)) with R2 values of up to 0.68 for the regression model. The high fit between the observed and estimated values indicates that the DI detecting model based on the index could be used in peanut leaf spot detection in the absence of other stresses causing unhealthy symptoms. The results of this study show that it will provide a reliable, effective and accurate method for detecting leaf spot diseases in peanut through the analysis of hyperspectral data in the future.
author: Tingting Chen, Jialei Zhang, Yong Chen, Shubo Wan, Lei Zhang
publish year: 2019
creator: Tingting Chen, Jialei Zhang, Yong Chen, Shubo Wan, Lei Zhang
reference: Tingting Chen, Jialei Zhang, Yong Chen, Shubo Wan, Lei Zhang, "Detection of peanut leaf spots disease using canopy hyperspectral reflectance", Computers and Electronics in Agriculture, Volume 156, January 2019, pages 677-683, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 156, January 2019, pages 677-683, Elsevier B.V.
label: Article 0417

Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques
type: NamedIndividual
type: Article
has knowledge model: Classifier 0421
abstract: Early and accurate diagnosis is a critical first step in mitigating losses caused by plant diseases. An incorrect diagnosis can lead to improper management decisions, such as selection of the wrong chemical application that could potentially result in further reduced crop health and yield. In tomato, initial disease symptoms may be similar even if caused by different pathogens, for example early lesions of target spot (TS) caused by the fungus Corynespora cassicola and bacterial spot (BS) caused by Xanthomonas perforans. In this study, hyperspectral imaging (380–1020 nm) was utilized in laboratory and field (collected by an unmanned aerial vehicle; UAV) settings to detect both diseases. Tomato leaves were classified into four categories: healthy, asymptomatic, early and late disease development stages. Thirty-five spectral vegetation indices (VIs) were calculated to select an optimum set of indices for disease detection and identification. Two classification methods were utilized: (i) multilayer perceptron neural network (MLP), and (ii) stepwise discriminant analysis (STDA). Best wavebands selection was considered in blue (408–420 nm), red (630–650 nm) and red edge (730–750 nm). The most significant VIs that could distinguish between healthy leaves and diseased leaves were the photochemical reflectance index (PRI) for both diseases, the normalized difference vegetation index (NDVI850) for BS in all stages, and the triangular vegetation index (TVI), NDVI850 and chlorophyll index green (Chl green) for TS asymptomatic, TS early and TS late disease stage respectively. The MLP classification method had an accuracy of 99%, for both BS and TS, under field (UAV-based) and laboratory conditions.
author: Jaafar Abdulridha, Yiannis Ampatzidis, Sri Charan Kakarla, Pamela Roberts
publish year: 2019
creator: Jaafar Abdulridha, Yiannis Ampatzidis, Sri Charan Kakarla, Pamela Roberts
reference: Jaafar Abdulridha, Yiannis Ampatzidis, Sri Charan Kakarla, Pamela Roberts, "Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques", Precision Agriculture (2019) , Springer
Source: Precision Agriculture (2019) , Springer
label: Article 0421

Detection of waterlogging stress based on hyperspectral images of oilseed rape leaves (Brassica napus L.)
type: NamedIndividual
type: Article
has knowledge model: Classifier 0423
abstract: The objective of this study was to investigate the utility of hyperspectral images (HSIs) for the detection of oilseed rape waterlogging stress. We assessed two oilseed rape varieties, the non-hybrid NingYou 22 (NY 22) and hybrid NingZa 19 (NZ 19), and HSIs of oilseed rape leaves under different durations of waterlogging stress (0, 3, and 6 days) were collected to build three datasets (NY 22, NZ 19, and both combined). We extracted red–green–blue (RGB) images and visible and near-infrared (VNIR 400–1000 nm) spectra from a region of interest (ROI) in each HSI. Quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), and support vector machine (SVM) classifiers were used to build classification models for comparing images and spectra of samples under different waterlogging levels among the three datasets, and to conduct training and prediction. From each dataset, 70% of the images were used for training, and the remaining 30% were used for testing. In the classification of full-wavelength HSIs (400–1000 nm), QDA and SVM exhibited high multivariate classification accuracy, reaching 77.37% and 95.90% accuracy, respectively. In contrast, KNN displayed low accuracy, but good identification and prediction ability for variety NZ 19. Six optimal wavebands of 529, 641, 698, 749, 856, and 979 nm were used as input for successive projections algorithm (SPA) classification and analysis. The QDA mode had better classification performance, with identification accuracies of 100% and 94.44%, respectively. Overall, the VNIR classification results exceeded those of image classification. These results show that hyperspectral imaging technology is feasible and useful for the detection of oilseed rape waterlogging stress.
author: Ji'An Xia, HongXin Cao, YuWang Yang, WeiXin Zhang, Qian Wan, Lei Xu, DaoKuo Ge, WenYu Zhang, YaQi Ke, Bo Huang
publish year: 2019
creator: Ji'An Xia, HongXin Cao, YuWang Yang, WeiXin Zhang, Qian Wan, Lei Xu, DaoKuo Ge, WenYu Zhang, YaQi Ke, Bo Huang
reference: Ji'An Xia, HongXin Cao, YuWang Yang, WeiXin Zhang, Qian Wan, Lei Xu, DaoKuo Ge, WenYu Zhang, YaQi Ke, Bo Huang, "Detection of waterlogging stress based on hyperspectral images of oilseed rape leaves (Brassica napus L.)", Computers and Electronics in Agriculture, Volume 159, April 2019, pages 59-68, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 159, April 2019, pages 59-68, Elsevier B.V.
label: Article 0423

Determination of dry matter content of tea by near and middle infrared spectroscopy coupled with wavelet-based data mining algorithms
type: NamedIndividual
type: Article
has knowledge model: Regressor_0426
abstract: To explore the potential of near and middle infrared spectroscopy application in fast determination of dry matter content (DMC) of tea through the whole process from fresh tea leaf, semi-manufactured tea and to finished tea, samples from seven stages of the tea process were collect and the research was conducted based on data mining algorithms. Kubelka–Munk transform and spectral pre-treatment were adopted for elimination of disturbances caused by irregular appearance of intact tea in diffuse reflectance mode. A wavelet-based data mining algorithm composed of wavelet packet transform and statistical analysis (WPT–SA) was proposed to extract and optimize spectral feature from full-spectrum data. Another data mining algorithm of kernel principal component analysis (KPCA) was also employed for a performance comparison. Regression models were respectively established based on the full-spectrum data, wavelet spectral feature and kernel principal component. Statistical analysis revealed that the wavelet parameters (basis function and scale) were significant for these R2 and RMSE of determination model and the optimization of wavelet parameters were vital for application of WPT. Modeling results showed that the regression model based on the wavelet spectral feature outperformed the other models, and the optimal regression model obtained a high R2 of 0.9556, and a low root mean square error of 0.0501. These results indicate that it is feasible to measure DMC of tea in different processing procedures using near and middle infrared spectroscopy, and the proposed feature optimization algorithm (WPT–SA) is an effective data mining approach for enhancing the capability of spectral measurement.
author: Xiaoli Li, Liubin Luo, Yong He, Ning Xu
publish year: 2013
creator: Xiaoli Li, Liubin Luo, Yong He, Ning Xu
reference: Xiaoli Li, Liubin Luo, Yong He, Ning Xu, "Determination of dry matter content of tea by near and middle infrared spectroscopy coupled with wavelet-based data mining algorithms", Computers and Electronics in Agriculture, Volume 98, October 2013, pages 46-53, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 98, October 2013, pages 46-53, Elsevier B.V.
label: Article 0426

Vineyard yield estimation by combining remote sensing, computer vision and artificial neural network techniques
type: NamedIndividual
type: Article
has knowledge model: Regressor_0427
abstract: In viticulture, it is critical to predict productivity levels of the different vineyard zones to undertake appropriate cropping practices. To overcome this challenge, the final yield was predicted by combining vegetation indices (VIs) to sense the health status of the crop and by computer vision to obtain the vegetated fraction cover (Fc) as a measure of plant vigour. Multispectral imagery obtained from an unmanned aerial vehicle (UAV) is used to obtain VIs and Fc, which are used together with artificial neural networks (ANN) to model the relationship between VIs, Fc and yield. The proposed methodology was applied in a vineyard, where different irrigation and fertilisation doses were applied. The results showed that using computer vision techniques to differentiate between canopy and soil is necessary in precision viticulture to obtain accurate results. In addition, the combination of VIs (reflectance approach) and Fc (geometric approach) to predict vineyard yield results in higher accuracy (root mean square error (RMSE) = 0.9 kg vine−1 and relative error (RE) = 21.8% for the image when close to harvest) compared to the simple use of VIs (RMSE = 1.2 kg vine−1 and RE = 28.7%). The implementation of machine learning techniques resulted in much more accurate results than linear models (RMSE = 0.5 kg vine−1 and RE = 12.1%). More precise yield predictions were obtained when images were taken close to the harvest date, although promising results were obtained at earlier stages. Given the perennial nature of grapevines and the multiple environmental and endogenous factors determining yield, seasonal calibration for yield prediction is required.
author: Rocio Ballesteros, Diego S. Intrigliolo, Jose F. Ortega, Juan M. Ramirez-Cuesta, Ignacio Buesa, Miguel A. Moreno
publish year: 2020
creator: Rocio Ballesteros, Diego S. Intrigliolo, Jose F. Ortega, Juan M. Ramirez-Cuesta, Ignacio Buesa, Miguel A. Moreno
reference: Rocio Ballesteros, Diego S. Intrigliolo, Jose F. Ortega, Juan M. Ramirez-Cuesta, Ignacio Buesa, Miguel A. Moreno, "Vineyard yield estimation by combining remote sensing, computer vision and artificial neural network techniques", Precision Agriculture (2020) , Springer
Source: Precision Agriculture (2020) , Springer
label: Article 0427

Developing a fused vegetation temperature condition index for drought monitoring at field scales using Sentinel-2 and MODIS imagery
type: NamedIndividual
type: Article
has knowledge model: Regressor_0439
abstract: Time-series high spatial resolution drought monitoring is essential for effective agricultural management. For more than a decade, the multiyear vegetation temperature condition index (VTCI) based on the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) has been applied to regional drought monitoring. However, the spatial resolutions of AVHRR and MODIS (1 km) often represent mixtures of built-up areas and different vegetation or crop types. In this paper, a framework is proposed to obtain a ten-day interval multiyear VTCI at field scales, which is fused from Sentinel-2 data with a fine spatial resolution (20 m) and ten-day interval Terra MODIS data with a coarse spatial resolution using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM). This framework includes the disaggregation of the Terra MODIS land surface temperature (LST) using Sentinel-2 data and digital elevation model (DEM) data, the spatiotemporal fusion of normalized difference vegetation index (NDVI) and LST, and drought monitoring by the fused VTCI. The accuracy of this framework for regional drought monitoring was tested in the Guanzhong Plain of China. The results indicate that 1) the Sentinel-2 biophysical products combined with DEM data based on support vector regression can accurately disaggregate the Terra MODIS LST; 2) the ESTARFM has good capability to fuse the NDVI and LST derived from Sentinel-2 and Terra MODIS; 3) the multiyear spatiotemporally fused VTCI, a quantitative drought monitoring index at field scales, can be calculated by using the linear regression between the single-year fused VTCI based on the data in 2018 and the multiyear Terra MODIS VTCI based on an 18-year data record; and 4) the spatiotemporal fusion of the multiyear VTCI can significantly improve the drought monitoring accuracy in the winter wheat and woodland area of the Guanzhong Plain. From mid-March to early May, the multiyear spatiote…
author: Xijia Zhou, Pengxin Wang, Kevin Tansey, Shuyu Zhang, Hongmei Li, Lei Wang
publish year: 2020
creator: Xijia Zhou, Pengxin Wang, Kevin Tansey, Shuyu Zhang, Hongmei Li, Lei Wang
reference: Xijia Zhou, Pengxin Wang, Kevin Tansey, Shuyu Zhang, Hongmei Li, Lei Wang, "Developing a fused vegetation temperature condition index for drought monitoring at field scales using Sentinel-2 and MODIS imagery", Computers and Electronics in Agriculture, Volume 168, January 2020, 105144, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 168, January 2020, 105144, Elsevier B.V.
label: Article 0439

Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse
type: NamedIndividual
type: Article
has knowledge model: Regressor_0449
abstract: Inadequate water application often decreases yield and grain quality. Existing methods using single, localized soil moisture or canopy temperature measurements do not account for crop water stress on both a high spatial and temporal resolution for precision irrigation water management decisions and scheduling. Therefore, this study was conducted to understand the feasibility of thermal cameras in order to quantify high resolution spatial canopy temperatures in relation to soil moisture. The objectives of this study were to deploy a thermal infrared imaging system (TIRIS) for high spatial and temporal monitoring of corn canopy temperature in greenhouse, test camera durability and measurement accuracy during full-season crop development, remove background temperatures with image segmentation, and sample individual plants to investigate full-season crop water stress versus soil moisture content. A TIRIS was developed using a lightweight uncooled thermal camera. Corn plants were divided into well-watered and water-stressed irrigation zones to observe stress from water deficits. Canopy temperatures were used to develop empirical canopy and air temperature deficit versus vapor pressure deficit linear regressions. Results showed that the TIRIS system maintained measurement accuracy of ±0.62 °C (α = 0.05) while compensating for changing ambient greenhouse conditions. Canopy and air temperature deficit versus vapor pressure deficit regression equations revealed that the predicted canopy temperature was closely related to characteristic water use. Results of the 80-day study demonstrated that 82% of soil moisture variation was explained by the crop water stress index (CWSI) values between 0.6 and 1.0. Results indicated that the CWSI derived by remotely measuring canopy temperature using TIRIS can be used as an alternate irrigation scheduling method in order to quantify spatial and temporal soil moisture variability.
author: Devin L. Mangus, Ajay Sharda, Naiqian Zhang
publish year: 2016
creator: Devin L. Mangus, Ajay Sharda, Naiqian Zhang
reference: Devin L. Mangus, Ajay Sharda, Naiqian Zhang, "Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse", Computers and Electronics in Agriculture, Volume 121, February 2016, pages 149-159, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 121, February 2016, pages 149-159, Elsevier B.V.
label: Article 0449

Development and field evaluation of a machine vision based in-season weed detection system for wild blueberry
type: NamedIndividual
type: Article
has knowledge model: Regressor_0451
abstract: The wild blueberry crop requires the heavy application of agrochemicals for the proper crop yield and weed control. An integrated machine vision based weed detection system was developed to target goldenrod weed spot-specifically. Color co-occurrence matrices and statistical classifiers were used for the goldenrod detection. The linear and quadratic classifiers were developed using different reduced sub-sets of textural features. The classifiers were evaluated using accuracy, specificity, sensitivity, and false negative ratio at Laboratory scale. Performance of the developed weed detection system was also evaluated in two wild blueberry fields. The results indicated that quadratic classifier with DM-HSISD model showed the best performance at laboratory scale with the classification accuracies of 94.98% and 93.80% for training and testing datasets, respectively. Another quadratic classifier (with DM-HSI model) containing all 39 textural features was found to be the second best with the accuracies of 94.29% and 91.46% on training and test datasets, respectively. The linear classifiers didn’t perform well compare to their respective quadratic counter-parts. Field performance of the developed system equipped with the quadratic DM-HSISD classifier indicated no significant difference between variable rate (VR) and uniform application (UA) in terms of mean percentage area coverage (PAC) for the targeted goldenrod spots in both fields. However, a significant difference was observed between mean PAC of VR and UA applications for the non-targeted wild blueberry spots. The potential and actual chemical savings were in ranges between 46.71% and 74.83% and 30.12% and 60.58% depending on the weed and sprayed area, respectively. These results demonstrated that the developed weed detection system has potential for targeted application of agrochemicals to control goldenrod in wild blueberry fields.
author: Tanzeel U. Rehman, Qamar U. Zaman, Young K. Chang, Arnold W. Schumann, Kenneth W. Corscadden
publish year: 2019
creator: Tanzeel U. Rehman, Qamar U. Zaman, Young K. Chang, Arnold W. Schumann, Kenneth W. Corscadden
reference: Tanzeel U. Rehman, Qamar U. Zaman, Young K. Chang, Arnold W. Schumann, Kenneth W. Corscadden, "Development and field evaluation of a machine vision based in-season weed detection system for wild blueberry", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 1-13, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 162, July 2019, pages 1-13, Elsevier B.V.
label: Article 0451

Development of an artificial cloud lighting condition system using machine vision for strawberry powdery mildew disease detection
type: NamedIndividual
type: Article
has knowledge model: Classifier 0467
abstract: Strawberry plants have been facing a significant proportion of diseases during cultivation, scattered throughout the field, emphasizing the need for proper diseases management. Powdery mildew is one of the major fungal strawberry disease which is typically responsible for approximately 30–70% loss of yields. The aim of this study was to develop a machine vision based artificial cloud lighting condition system for detecting strawberry powdery mildew leaf disease. The artificial cloud lighting condition system was developed consisting of custom software, two µEye colour cameras, a black cloth cover, real time kinematics-global positioning system and a ruggedized laptop computer and mounted on a mobile platform. The custom software was developed in C# programming language. The colour co-occurrence matrix based texture analysis was used to extract image features and discriminant analysis (quadratic) for classification. The study proposed mobile platform of artificial cloud lighting condition for image acquisition is beneficial. It showed higher detection accuracies of 95.26%, 95.45% and 95.37% for recall, precision and F-measure, respectively compared to 81.54%, 72% and 75.95% of recall, precision and F-measure, respectively with acquired images at natural cloud lighting condition. The feature selection results suggested the PM_GHSI feature model was best fit for this study. This study also revealed that the image acquisition speed (1.5 km h−1) and working depth (300 mm) are suitable for strawberry powdery mildew disease detection in real-time field condition.
author: Md. Sultan Mahmud, Qamar U. Zaman, Travis J. Esau, Gordon W. Price, Balakrishnan Prithiviraj
publish year: 2019
creator: Md. Sultan Mahmud, Qamar U. Zaman, Travis J. Esau, Gordon W. Price, Balakrishnan Prithiviraj
reference: Md. Sultan Mahmud, Qamar U. Zaman, Travis J. Esau, Gordon W. Price, Balakrishnan Prithiviraj, "Development of an artificial cloud lighting condition system using machine vision for strawberry powdery mildew disease detection", Computers and Electronics in Agriculture, Volume 158, March 2019, pages 219-225, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 158, March 2019, pages 219-225, Elsevier B.V.
label: Article 0467

Development of an in-season estimate of yield potential utilizing optical crop sensors and soil moisture data for winter wheat
type: NamedIndividual
type: Article
has knowledge model: Regressor_0474
abstract: When utilizing optical sensors to make in-season agronomic recommendations in winter wheat, one parameter often required is the in-season grain yield potential at the time of sensing. Current estimates use an estimate of biomass, such as normalized difference vegetation index (NDVI), and growing degree days (GDDs) from planting to NDVI data collection. The objective of this study was to incorporate soil moisture data to improve the ability to predict final grain yield in-season. Crop NDVI, GDDs that were adjusted based upon if there was adequate water for crop growth, and the amount of soil profile (0–0.80 m) water were incorporated into a multiple linear regression model to predict final grain yield. Twenty-two site-years of N fertility trials with in-season grain yield predictions for growth stages ranging from Feekes 3 to 10 were utilized to calibrate the model. Three models were developed: one for all soil types, one for loamy soil textured sites, and one for coarse soil textured sites. The models were validated with 11 independent site-years of NDVI and weather data. The results indicated there was no added benefit to having separate models based upon soil types. Typically, the models that included soil moisture, more accurately predicted final grain yield. Across all site years and growth stages, yield prediction estimates that included soil moisture had an R2 = 0.49, while the current model without a soil moisture adjustment had an R2 = 0.40.
author: Jacob T. Bushong, Jeremiah L. Mullock, Eric C. Miller, William R. Raun, Arthur R. Klatt, D. Brian Arnall
publish year: 2016
creator: Jacob T. Bushong, Jeremiah L. Mullock, Eric C. Miller, William R. Raun, Arthur R. Klatt, D. Brian Arnall
reference: Jacob T. Bushong, Jeremiah L. Mullock, Eric C. Miller, William R. Raun, Arthur R. Klatt, D. Brian Arnall, "Development of an in-season estimate of yield potential utilizing optical crop sensors and soil moisture data for winter wheat", Precision Agriculture, Volume 17, pages 451–469 (2016) , Springer
Source: Precision Agriculture, Volume 17, pages 451–469 (2016) , Springer
label: Article 0474

Development of artificial intelligence based systems for prediction of hydration characteristics of wheat
type: NamedIndividual
type: Article
has knowledge model: Regressor_0475
abstract: Hydration characteristics (moisture content, moisture ratio and hydration rate) of wheat kernel during soaking process were studied and simulated. Hydration procedure was performed at five different experimental water temperatures (30, 40, 50, 60 and 70 (°C)) with respect to hydration time changes. Hydration characteristics of samples were modeled using one of the most popular simulation frameworks of artificial intelligence, adaptive neuro-fuzzy inference system (ANFIS). A comparison was also made between results of the best developed ANFIS model and those of the another well-known artificial intelligence technique, artificial neural network (ANN). The hydration temperature and time were used as input parameters and moisture content, moisture ratio and hydration rate were taken as output parameters of the intelligent modeling frameworks. To attain the best model with the highest predictive ability, developed models were compared based on statistical parameters of coefficient of determination, root mean square error and mean relative deviation modulus. According to the results, although the best framework of both ANFIS and ANN were able to accurately predict hydration characteristics, the best ANN simulation framework with a simple structure (2–4–3) was easier to use than the ANFIS with three different structures. ANN surface plots also illustrated that increasing the hydration temperature and time increased moisture content and decreased moisture ratio. Moreover, ANN modeling results indicated that the hydration rate increased in the initial and decreased in the middle and final phase of hydration procedure.
author: S.M. Shafaei, A. Nourmohamadi-Moghadami, S. Kamgar
publish year: 2016
creator: S.M. Shafaei, A. Nourmohamadi-Moghadami, S. Kamgar
reference: S.M. Shafaei, A. Nourmohamadi-Moghadami, S. Kamgar, "Development of artificial intelligence based systems for prediction of hydration characteristics of wheat", Computers and Electronics in Agriculture, Volume 128, October 2016, pages 34-45, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 128, October 2016, pages 34-45, Elsevier B.V.
label: Article 0475

Development of spectral indices for identifying glyphosate-resistant weeds
type: NamedIndividual
type: Article
has knowledge model: Classifier 0478
abstract: Glyphosate as the most common and widely-used herbicide in agricultural crops has resulted in the explosion of resistant weeds around the world. An early detection of resistant weeds before observing the visible symptoms of glyphosate application in weeds can contribute to the crop protection in precision agriculture. This paper aims at developing and evaluating spectral weed indices (SWIs) to identify glyphosate-resistant weeds 72 h after herbicide application. A greenhouse experiment was conducted on three common weed species, namely, kochia (Kochia scoparia), ragweed (Ambrosia artemisiifolia L.) and waterhemp (Amaranthus rudis), including resistant and susceptible types to collect canopy spectral reflectance after glyphosate spraying. To generate SWI, the best weighted combination of single wavelength and a normalized wavelength difference in the range of 450–920 nm was established. Relief-F algorithm selected the most discriminative feature wavelengths, and two band normalized differences to differentiate weeds with higher degree of resistance, and changes in the hyperspectral signature caused by glyphosate application. The performance of optimized SWIs on resistant weeds identification was assessed by employing machine learning Random Forest (RF) method. The RF classification model achieved a classification accuracy of 96%, 100% and 97% in detecting resistant kochia, ragweed and waterhemp, respectively. A comparison between introduced SWIs in this study and previously published hyperspectral vegetation indices (VIs) indicated that the SWIs provided better agreement with real data in glyphosate-resistant weeds identification.
author: Alimohammad Shirzadifar, Sreekala Bajwa, John Nowatzki, Jamileh Shojaeiarani
publish year: 2020
creator: Alimohammad Shirzadifar, Sreekala Bajwa, John Nowatzki, Jamileh Shojaeiarani
reference: Alimohammad Shirzadifar, Sreekala Bajwa, John Nowatzki, Jamileh Shojaeiarani, "Development of spectral indices for identifying glyphosate-resistant weeds", Computers and Electronics in Agriculture, Volume 170, March 2020, 105276, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 170, March 2020, 105276, Elsevier B.V.
label: Article 0478

Diagnosis of bacterial spot of tomato using spectral signatures
type: NamedIndividual
type: Article
has knowledge model: Regressor_0481
abstract: Ultraviolet, visible, and near-infrared reflectance spectroscopy was used to determine the disease severity of tomato (Lycopersicon esculentum) leaves infected with Xanthomonas perforans, the causal agent of bacterial leaf spot of tomato. Chemometric methods were used to identify significant wavelengths and create spectral-based prediction models. Significant wavelengths were identified through analysis of the B-matrix from partial least squares (PLS) regression, analysis of a correlation coefficient spectrum, and through the use of a stepwise multiple linear regression (SMLR) procedure. These analysis methods revealed several significant regions wavelengths and produced predictive models of disease severity based on absorbance spectra. The best model predicted the disease severity of the validation data set with a root mean square difference (RMSD) of 4.9% and a coefficient of determination (R2) of 0.82. The results of this initial study indicate the potential for the use of spectral technology to detect bacterial leaf spot of tomato in the field.
author: C.D. Jones, J.B. Jones, W.S. Lee
publish year: 2010
creator: C.D. Jones, J.B. Jones, W.S. Lee
reference: C.D. Jones, J.B. Jones, W.S. Lee, "Diagnosis of bacterial spot of tomato using spectral signatures", Computers and Electronics in Agriculture, Volume 74, Issue 2, November 2010, pages 329-335, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 74, Issue 2, November 2010, pages 329-335, Elsevier B.V.
label: Article 0481

Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching
type: NamedIndividual
type: Article
has knowledge model: Classifier 0491
abstract: In a multidisciplinary scheme linking computer science with agricultural engineering, a novel approach based on orientation code matching (OCM) for robust, continuous, and site-specific observations of disease development in sugar beet plants is presented. Differing from conventional plant disease detection approaches, we introduce the robust template matching method of OCM in this paper to not only realize continuous and site-specific observations of disease progress, but also to demonstrate its excellent robustness for non-rigid plant object searching in scene illumination, translation, slight rotation, and occlusion changes. Furthermore, a single-feature two-dimensional xy-color histogram is proposed and input into support vector machine (SVM) classifier for pixel-wise disease classification and quantification. Experimental results with high precision and recall rates demonstrate the feasibility and potential of our proposed algorithm, which could be further implemented in real sugar beet fields with robust detection and precise quantization of foliar disease development, for better analysis of disease mechanism and optimal fungicide-spraying management.
author: Rong Zhou, Shun’ichi Kaneko, Fumio Tanaka, Miyuki Kayamori, Motoshige Shimizu
publish year: 2014
creator: Rong Zhou, Shun’ichi Kaneko, Fumio Tanaka, Miyuki Kayamori, Motoshige Shimizu
reference: Rong Zhou, Shun’ichi Kaneko, Fumio Tanaka, Miyuki Kayamori, Motoshige Shimizu, "Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching", Computers and Electronics in Agriculture, Volume 108, October 2014, pages 58-70, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 108, October 2014, pages 58-70, Elsevier B.V.
label: Article 0491

Dynamic Bayesian network for crop growth prediction in greenhouses
type: NamedIndividual
type: Article
has knowledge model: Regressor_0495
abstract: The paper presents an Internet-of-Things based agricultural decision support system for crop growth. A dynamic Bayesian network (DBN) relates indicative parameters of crop development to environmental control parameters via unobserved (hidden) Markov states. The expectation-maximization algorithm is used to track the states and to learn the parameters of the DBN. The steady state information is then used to derive a predictor for the measurement data a few days ahead. The proposed DBN avoids time-consuming training cultivation cycles, as only data of the current cultivation cycle are available to the algorithm.Three cultivation cycles of lettuce have been used to test the performance of the proposed DBN. The environmental parameters were temperature, solar irradiance and vapor-pressure deficit. The measurement data include evapotranspiration at granularity equal one day, and leaf-area index and dry weight, at granularity equal one week. It turned out that accurate measurement data prediction a few days ahead is possible even if the number of data samples is low.
author: A. Kocian, D. Massa, S. Cannazzaro, L. Incrocci, S. Di Lonardo, P. Milazzo, S. Chessa
publish year: 2020
creator: A. Kocian, D. Massa, S. Cannazzaro, L. Incrocci, S. Di Lonardo, P. Milazzo, S. Chessa
reference: A. Kocian, D. Massa, S. Cannazzaro, L. Incrocci, S. Di Lonardo, P. Milazzo, S. Chessa, "Dynamic Bayesian network for crop growth prediction in greenhouses", Computers and Electronics in Agriculture, Volume 169, February 2020, 105167, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 169, February 2020, 105167, Elsevier B.V.
label: Article 0495

Early cherry fruit pathogen disease detection based on data mining prediction
type: NamedIndividual
type: Article
has knowledge model: Classifier 0497
abstract: Today’s world depends largely on information and communication technologies. These technologies are in use in different areas of human life and work. Each day, more examples of possible application of information and communications technology are being discovered. In most cases, computer science is used to solve complex problems which have mathematical background. The most important and challenging job in agriculture is plant protection. This is due to its complexity and the lack of specialized tools that could predict when the conditions for specific infections are fulfilled. In this paper authors use different mathematics-based techniques for data processing and prediction of possible fruit disease infection. Six significant weather variables and one variable representing the month in the year are selected as predictor variables. Implemented techniques are compared with each other in order to select the best. Prediction includes two most important diseases of cherry fruit: monilinia laxa and coccomyces hiemalis. Data sets used in this research include data of eight year time period, collected over the region of Toplica in Serbia. The best achieved prediction accuracy is 95.8%. Additionally, the same implemented methods can be applied on other fruit species and other diseases for which data are known.
author: Milos Ilic, Sinisa Ilic, Srdjan Jovic, Stefan Panic
publish year: 2018
creator: Milos Ilic, Sinisa Ilic, Srdjan Jovic, Stefan Panic
reference: Milos Ilic, Sinisa Ilic, Srdjan Jovic, Stefan Panic, "Early cherry fruit pathogen disease detection based on data mining prediction", Computers and Electronics in Agriculture, Volume 150, July 2018, pages 418-425, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 150, July 2018, pages 418-425, Elsevier B.V.
label: Article 0497

Early detection of Fusarium infection in wheat using hyper-spectral imaging
type: NamedIndividual
type: Article
has knowledge model: Regressor_0498
abstract: Infections of wheat, rye, oat and barley by Fusarium ssp. are serious problems worldwide due to the mycotoxins, potentially produced by the fungi. In 2005, limit values were issued by the EU commission to avoid health risks by mycotoxins, both for humans and animals. This increased the need to develop tools for early detection of infections. Occurrence of Fusarium-caused head blight disease can be detected by spectral analysis (400–1000 nm) before harvest. With this information, farmers could recognize Fusarium contaminations. They could, therefore, harvest the grains separately and supply it to other utilizations, if applicable. In the present study, wheat plants were analyzed using a hyper-spectral imaging system under laboratory conditions. Principal component analysis (PCA) was applied to differentiate spectra of diseased and healthy ear tissues in the wavelength ranges of 500–533 nm, 560–675 nm, 682–733 nm and 927–931 nm, respectively. Head blight could be successfully recognized during the development stages (BBCH-stages) 71–85. However, the best time for disease determination was at the beginning of medium milk stage (BBCH 75). Just after start of flowering (BBCH 65) and, again, in the fully ripe stage (BBCH 89), distinction by spectral analysis is impossible. With the imaging analysis method ‘Spectral Angle Mapper’ (SAM) the degree of disease was correctly classified (87%) considering an error of visual rating of 10%. However, SAM is time-consuming. It involves both the analysis of all spectral bands and the setup of reference spectra for classification. The application of specific spectral sub-ranges is a very promising alternative. The derived head blight index (HBI), which uses spectral differences in the ranges of 665–675 nm and 550–560 nm, can be a suitable outdoor classification method for the recognition of head blight. In these experiments, mean hit rates were 67% during the whole study period (BBCH 65–89). However, if only the optimal classification…
author: E. Bauriegel, A. Giebel, M. Geyer, U. Schmidt, W.B. Herppich
publish year: 2011
creator: E. Bauriegel, A. Giebel, M. Geyer, U. Schmidt, W.B. Herppich
reference: E. Bauriegel, A. Giebel, M. Geyer, U. Schmidt, W.B. Herppich, "Early detection of Fusarium infection in wheat using hyper-spectral imaging", Computers and Electronics in Agriculture, Volume 75, Issue 2, February 2011, pages 304-312, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 75, Issue 2, February 2011, pages 304-312, Elsevier B.V.
label: Article 0498

Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms
type: NamedIndividual
type: Article
has knowledge model: Classifier 0499
abstract: The hyperspectral imaging technique was used for the non-destructive detection of tomato spotted wilt virus (TSWV) infection in tobacco at an early stage. Spectra ranging from 400 to 1000 nm with 128 bands from inoculated and healthy tobacco plants were analyzed by using three wavelength selection methods (successive projections algorithm (SPA), boosted regression tree (BRT), and genetic algorithm (GA)), and four machine learning (ML) techniques (boosted regression tree (BRT), support vector machine (SVM), random forest (RF), and classification and regression tress (CART)). The results indicated that the models built by the BRT algorithm using the wavelengths selected by SPA as the input variables obtained the best outcome for the 10-fold cross-validation with the mean overall accuracy of 85.2% and area under receiver operating curve (AUC) of 0.932. The band selection results and variable contribution analysis in BRT modeling jointly showed that the near-infrared (NIR) spectral region is informative and important for the differentiation of infected and healthy tobacco leaves. Different stages of post-inoculation were split according to the molecular identification and visual observation. The classification results at different stages indicated that the hyperspectral imaging data combined with ML methods and wavelength selection algorithms can be used for the early detection of TSWV in tobacco, both at the presymptomatic stage and during the period before the systematic infection can be detected by the molecular identification approach.
author: Qing Gu, Li Sheng, Tianhao Zhang, Yuwen Lu, Zhijun Zhang, Kefeng Zheng, Hao Hu, Hongkui Zhou
publish year: 2019
creator: Qing Gu, Li Sheng, Tianhao Zhang, Yuwen Lu, Zhijun Zhang, Kefeng Zheng, Hao Hu, Hongkui Zhou
reference: Qing Gu, Li Sheng, Tianhao Zhang, Yuwen Lu, Zhijun Zhang, Kefeng Zheng, Hao Hu, Hongkui Zhou, "Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms", Computers and Electronics in Agriculture, Volume 167, December 2019, 105066, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 167, December 2019, 105066, Elsevier B.V.
label: Article 0499

Early detection of water stress in maize based on digital images
type: NamedIndividual
type: Article
has knowledge model: Classifier 0500
abstract: Early water stress detection is of great significance in precision plant breeding and agricultural production. In the field, outdoor cameras would be an applicable tool for early drought stress detection with high-resolution images. Based on image analysis, we presented a model to detect water stress of maize in the early stage. In the red-green-blue (RGB) color space, a simple linear classifier was proposed to extract green vegetation from maize images. After color image segmentation, fourteen-dimensional color and texture features were extracted from each image. Three water treatment levels (well-watered, reduced watered and drought stressed) were applied to maize plants. We adopted a two-stage detection model trained with different feature subsets to evaluate the water stress. The water stress detection model was based on a supervised learning algorithm, gradient boosting decision tree (GBDT). The recognition accuracy of three water treatments (ATWT) was 80.95% and the accuracy of water stress (AWS) reached 90.39%. Results showed that the proposed method had an effective detection performance between water suitability and water stress conditions in the maize fields.
author: Shuo Zhuang, Ping Wang, Boran Jiang, Maosong Li, Zhihong Gong
publish year: 2017
creator: Shuo Zhuang, Ping Wang, Boran Jiang, Maosong Li, Zhihong Gong
reference: Shuo Zhuang, Ping Wang, Boran Jiang, Maosong Li, Zhihong Gong, "Early detection of water stress in maize based on digital images", Computers and Electronics in Agriculture, Volume 140, August 2017, pages 461-468, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 140, August 2017, pages 461-468, Elsevier B.V.
label: Article 0500

Early powdery mildew detection system for application in greenhouse automation
type: NamedIndividual
type: Article
has knowledge model: Classifier 0501
abstract: Early disease detection in greenhouses is an important component of an integrated management strategy. An automated process can be more accurate and cost effective. Automated disease detection of plants in the field is complex due to an uncontrolled environment, and the scale at which detection must be done. This paper presents a powdery mildew detection machine vision system for use in greenhouses. The system uses augmented lighting to remove clutter and reduce the search space, and a Hough forest to detect early powdery mildew. Field testing on greenhouse tomato plants has showed detection rates of 85%.
author: Patrick Wspanialy, Medhat Moussa
publish year: 2016
creator: Patrick Wspanialy, Medhat Moussa
reference: Patrick Wspanialy, Medhat Moussa, "Early powdery mildew detection system for application in greenhouse automation", Computers and Electronics in Agriculture, Volume 127, September 2016, pages 487-494, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 127, September 2016, pages 487-494, Elsevier B.V.
label: Article 0501

Early prediction of durum wheat yield in Spain using radial basis functions interpolation models based on agroclimatic data
type: NamedIndividual
type: Article
has knowledge model: Regressor_0502
abstract: Durum wheat is an important crop in semi-arid Mediterranean regions as Andalusia, an autonomous community in the southern part of Spain. Accurate early predictions of durum wheat yield can provide precious information for within-season adjustment of crop managing as well as for economical and political stakeholders. In this study, an alternative methodology to mechanistic crop models is proposed for within-season early prediction of durum wheat yield in Spain based on estimates for its larger producer community, Andalusia. The proposed Radial Basis Functions (RBF) interpolation models are based on the sown area and a large number of climatic variables. Global warming and increasing occurrence of extreme weather events are only two of the factors that make crop yield forecast extremely difficult as they can lead to an increased interannual yield variability. Nevertheless, the RBF models proposed presented good quality yield predictions clearly outperforming multivariate linear models used as benchmark. Moreover, RBF models’ predictions made four months prior to harvest are able to capture the trend of the yield series as well as near-harvest predictions.
author: H. Rocha, J.M. Dias
publish year: 2019
creator: H. Rocha, J.M. Dias
reference: H. Rocha, J.M. Dias, "Early prediction of durum wheat yield in Spain using radial basis functions interpolation models based on agroclimatic data", Computers and Electronics in Agriculture, Volume 157, February 2019, pages 427-435, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 157, February 2019, pages 427-435, Elsevier B.V.
label: Article 0502

Early season detection of rice plants using RGB, NIR-G-B and multispectral images from unmanned aerial vehicle (UAV)
type: NamedIndividual
type: Article
has knowledge model: Classifier 0503
abstract: Crop plant detection is vital for mapping crop planting area and extracting pure crop canopy information. In this study, three cameras (RGB, color infrared (NIR-G-B) and multispectral (MS) camera) were mounted on a multi-rotor unmanned aerial vehicle (UAV) to obtain images of rice canopy at the early growth stages (tillering, jointing and initial booting stages). We proposed a new decision tree (DT) combining texture features (mean and variance (C.V)) and spectral features (TS-DT) for rice plants detection within UAV images. First, the image was classified into the pure class and the mixed class based on the C.V value. Then the pure class was classified into rice plants and road by the DN or reflectance value in red band. The mixed class was classified into rice plants and background (soil, water and duckweed) through comparing each pixel value to the mean value within the moving window. The results showed that TS-DT exhibited an averaged high classification accuracy with overall accuracy (OA) and kappa coefficient (KC) of 91.25%, 0.86, 92.88%, 0.86 and 93.53%, 0.88 for RGB, NIR and MS image among the early three growth stages, respectively. The highest estimation accuracy was obtained at booting stage and the lowest was at tillering stage. Compared with the traditional classification methods, the TS-DT method achieved an improved estimation accuracy of 5.2–26.71% in OA and 0.06–0.40 in KC. Therefore, this TS-DT method is a reliable approach for crop plants detection using UAV imagery.
author: Hengbiao Zheng, Xiang Zhou, Jiaoyang He, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, Yongchao Tian
publish year: 2020
creator: Hengbiao Zheng, Xiang Zhou, Jiaoyang He, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, Yongchao Tian
reference: Hengbiao Zheng, Xiang Zhou, Jiaoyang He, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, Yongchao Tian, "Early season detection of rice plants using RGB, NIR-G-B and multispectral images from unmanned aerial vehicle (UAV)", Computers and Electronics in Agriculture, Volume 169, February 2020, 105223, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 169, February 2020, 105223, Elsevier B.V.
label: Article 0503

Effects of changing simulated sky cover on hyperspectral reflectance measurements for dry matter yield and forage quality prediction
type: NamedIndividual
type: Article
has knowledge model: Regressor_0510
abstract: Sensor based analysis methods to assess dry matter yield and nutritive values of legume–grass swards are time and labour saving and can facilitate a site-specific forage management. Nevertheless, in-field measurements, based on canopy reflectance are highly dependent on weather conditions, like, e.g. wind or clouds. This study was conducted to explore the potential of field spectral measurements for a non destructive prediction of dry matter yield (DM), metabolisable energy (ME), ash content (XA), and crude protein (XP), of a binary legume–grass mixture (Trifolium pratense L. and Lolium multiflorum L.) under changing weather conditions. Five different degrees of sky cover were simulated by shadowing measurement plots with layers of cotton to reduce incoming radiation at different growth stages (leaf developing to flowering). Additionally, a halogen lamp was established over the plots to examine the influence of an artificial light source on the spectral response under changing cloud stages. Modified partial least squares (MPLS) regression was used for analysis of the hyperspectral data set (350–2500 nm). Artificial illumination led to spectral interferences of solar radiation and additional light, and hence, partly reduced prediction accuracies. In contrast, prediction accuracy increased, when solar radiation was completely excluded. Coefficients of determination (RSQcal) range from 0.87 to 0.94 without artificial illumination and from 0.86 to 0.94 with artificial illumination for DM yield and nutritive values, respectively.
author: Daniela Perbandt, Thomas Fricke, Michael Wachendorf
publish year: 2010
creator: Daniela Perbandt, Thomas Fricke, Michael Wachendorf
reference: Daniela Perbandt, Thomas Fricke, Michael Wachendorf, "Effects of changing simulated sky cover on hyperspectral reflectance measurements for dry matter yield and forage quality prediction", Computers and Electronics in Agriculture, Volume 73, Issue 2, August 2010, pages 230-239, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 73, Issue 2, August 2010, pages 230-239, Elsevier B.V.
label: Article 0510

Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images
type: NamedIndividual
type: Article
has knowledge model: Regressor_0525
abstract: Monitoring the above ground biomass (AGB) in winter oilseed rape is important for improving the agronomic management efficiency and predicting yield to ensure edible oil and biofuel supplies. The Yangtze River Basin in China accounts for one-fifth of the rapeseed yield in the world. However, the fragmented farming lands in the Yangtze River Basin make it difficult to accurately monitor winter oilseed rape growth at large scale using the medium-resolution satellite data. The low-altitude unmanned aerial vehicle (UAV) provides a feasible way to accurately and non-destructively estimate AGB in winter oilseed rape at the plot level. In this study, we evaluated the contributions of vegetation indices (VIs) and texture metrics, derived from multispectral images captured by the camera mounted on a UAV, to predict the AGB in winter oilseed rape. The AGB was estimated with (1) multiple VIs and (2) multiple VIs and texture metrics. The partial least square regression (PLSR) and random forest (RF) regression models were trained with datasets of the experiment in 2016–2017 and 2017–2018, and then applied to predict AGB of the 2018–2019 growth season. Results demonstrated that the incorporation of texture metrics to both PLSR and RF models provided more accurate estimations of AGB in winter oilseed rape than the models based solely on VIs. The accuracy of the AGB predicted by the RF regression model using VIs and texture metrics (RMSE = 274.18 kg/ha for the validation dataset) was slightly higher than the results of the PLSR model (RMSE = 284.09 kg/ha for the validation dataset). According to the evaluation of the important variables, the red edge chlorophyll index (CIred edge) and ratio vegetation index (RVI) were selected as the most important input features by PLSR and RF regression models. The normalized difference vegetation index (NDVI) contrast was selected as an important texture metrics for the AGB prediction, indicating that NDVI contrast could be a sensitive indicator…
author: Yinuo Liu, Shishi Liu, Jing Li, Xinyi Guo, Shanqin Wang, Jianwei Lu
publish year: 2019
creator: Yinuo Liu, Shishi Liu, Jing Li, Xinyi Guo, Shanqin Wang, Jianwei Lu
reference: Yinuo Liu, Shishi Liu, Jing Li, Xinyi Guo, Shanqin Wang, Jianwei Lu, "Estimating biomass of winter oilseed rape using vegetation indices and texture metrics derived from UAV multispectral images", Computers and Electronics in Agriculture, Volume 166, November 2019, 105026, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 166, November 2019, 105026, Elsevier B.V.
label: Article 0525

Estimating mango crop yield using image analysis using fruit at ‘stone hardening’ stage and night time imaging
type: NamedIndividual
type: Article
has knowledge model: Regressor_0526
abstract: This paper extends a previous study on the use of image analysis to automatically estimate mango crop yield (fruit on tree) (Payne et al., 2013). Images were acquired at night, using artificial lighting of fruit at an earlier stage of maturation (‘stone hardening’ stage) than for the previous study. Multiple image sets were collected during the 2011 and 2012 seasons. Despite altering the settings of the filters in the algorithm presented in the previous study (based on colour segmentation using RGB and YCbCr, and texture), the less mature fruit were poorly identified, due to a lower extent of red colouration of the skin. The algorithm was altered to reduce its dependence on colour features and to increase its use of texture filtering, hessian filtering in particular, to remove leaves, trunk and stems. Results on a calibration set of images (2011) were significantly improved, with 78.3% of fruit detected, an error rate of 10.6% and an R2 value (machine vision to manual count) of 0.63. Further application of the approach on validation sets from 2011 and 2012 had mixed results, with issues related to variation in foliage characteristics between sets. It is proposed the detection approaches within both of these algorithms be used as a ‘toolkit’ for a mango detection system, within an expert system that also uses user input to improve the accuracy of the system.
author: A. Payne, K. Walsh, P. Subedi, D. Jarvis
publish year: 2014
creator: A. Payne, K. Walsh, P. Subedi, D. Jarvis
reference: A. Payne, K. Walsh, P. Subedi, D. Jarvis, "Estimating mango crop yield using image analysis using fruit at ‘stone hardening’ stage and night time imaging", Computers and Electronics in Agriculture, Volume 100, January 2014, pages 160-167, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 100, January 2014, pages 160-167, Elsevier B.V.
label: Article 0526

Estimating nitrogen concentration in rape from hyperspectral data at canopy level using support vector machines
type: NamedIndividual
type: Article
has knowledge model: Regressor_0527
abstract: The estimation of nitrogen concentration from remotely sensed data has been the subject of some work. However, few studies have addressed the effective model for monitoring nitrogen status at canopy level using Support Vector Machines (SVM). The present study is focused on the assessment of an estimation model for nitrogen concentration of rape canopy with hyperspectral data. Two types of estimation model, the traditional statistical method based on stepwise linear regression (SLR) and the emerging computationally powerful techniques based on support vector machines were applied The Root Mean Square Error (RMSE) and T values were used to assess their predictability. The results show that a better agreement between the observed and the predicted nitrogen concentration were obtained by using the SVM model. Compared to the SLR model, the SVM model improved the results by lowering RMSE by 11.86–21.13 %, and by increasing T by 20.00–29.41 % for different spectral transformations. The study demonstrated the potential of SVM to estimate nitrogen concentration using canopy level hyperspectral data and it was concluded that SVM may provide a useful exploratory and predictive tool when applied to canopy-level hyperspectral reflectance data for monitoring nitrogen status of rape.
author: Fumin Wang, Jingfeng Huang, Yuan Wang, Zhanyu Liu, Fayao Zhang
publish year: 2013
creator: Fumin Wang, Jingfeng Huang, Yuan Wang, Zhanyu Liu, Fayao Zhang
reference: Fumin Wang, Jingfeng Huang, Yuan Wang, Zhanyu Liu, Fayao Zhang, "Estimating nitrogen concentration in rape from hyperspectral data at canopy level using support vector machines", Precision Agriculture, Volume 14, pages 172–183 (2013) , Springer
Source: Precision Agriculture, Volume 14, pages 172–183 (2013) , Springer
label: Article 0527

Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging
type: NamedIndividual
type: Article
has knowledge model: Regressor_0533
abstract: Accurate and rapid diagnosis of nitrogen status in fruit trees on an individual tree basis is a prerequisite for precision orchard nutrient management. This study presents a rapid and non-destructive approach for estimation and mapping of nitrogen content in apple trees at both leaf and canopy levels. An ImSpector V10 system was used to collect hyperspectral images (400–1000 nm) for both apple leaves and canopies. Nitrogen content in apple leaves was measured by Vario EL cube. Raw reflectance and first derivative reflectance were used to relate to leaf nitrogen content. Partial least squares (PLS) regression and multiple linear regression (MLR) analyses were performed to estimate nitrogen content from reflectance. The results showed that both PLS and MLR models achieved reasonable predictive accuracy (PLS and MLR models based on raw reflectance: R2 = 0.7728 and 0.7843 (p < 0.001); PLS and MLR models based on first derivative reflectance: R2 = 0.7745 and 0.774 (p < 0.001)). However, the MLR model based on raw reflectance demonstrated its advantage over the PLS models as well as the MLR model based on first derivative reflectance, because it only used 4 key wavelengths (505, 560, 675 and 705 nm) while the other models were based on either the full wavelengths (132 wavelengths) or more narrowband wavelengths adjacent to the selected key wavelengths. Furthermore, nitrogen distribution maps at both leaf and canopy levels were generated based on the nitrogen contents estimated by the MLR model based on raw reflectance. This new approach may be potentially applied to precision apple orchard nutrient management.
author: Xujun Ye, Shiori Abe, Shuhuai Zhang
publish year: 2020
creator: Xujun Ye, Shiori Abe, Shuhuai Zhang
reference: Xujun Ye, Shiori Abe, Shuhuai Zhang, "Estimation and mapping of nitrogen content in apple trees at leaf and canopy levels using hyperspectral imaging", Precision Agriculture, Volume 21, pages 198–225 (2020) , Springer
Source: Precision Agriculture, Volume 21, pages 198–225 (2020) , Springer
label: Article 0533

Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression
type: NamedIndividual
type: Article
has knowledge model: Regressor_0534
abstract: The objective of this study was to compare performance of partial least square regression (PLSR) and best narrowband normalize nitrogen vegetation index (NNVI) linear regression models for predicting N concentration and best narrowband normalize different vegetation index (NDVI) for end of season biomass yield in bioenergy crop production systems. Canopy hyperspectral data was collected using an ASD FieldSpec FR spectroradiometer (350–2500 nm) at monthly intervals in 2012 and 2013. The cropping systems evaluated in the study were perennial grass {mixed grass [50 % switchgrass (Panicum virgatum L.), 25 % Indian grass “Cheyenne” (Sorghastrum nutans (L.) Nash) and 25 % big bluestem “Kaw” (Andropogon gerardii Vitman)] and switchgrass “Alamo”} and high biomass sorghum “Blade 5200” (Sorghum bicolor (L.) Moench) grown under variable N applications rates to estimate biomass yield and quality. The NNVI was computed with the wavebands pair of 400 and 510 nm for the high biomass sorghum and 1500 and 2260 nm for the perennial grass that were strongly correlated to N concentration for both years. Wavebands used in computing best narrowband NDVI were highly variable, but the wavebands from the red edge region (710–740 nm) provided the best correlation. Narrowband NDVI was weakly correlated with final biomass yield of perennial grass (R2 = 0.30 and RMSE = 1.6 Mg ha−1 in 2012 and R2 = 0.37 and RMSE = 4.0 Mg ha−1, but was strongly correlated for the high biomass sorghum in 2013 (R2 = 0.72 and RMSE = 4.6 Mg ha−1). Compared to the best narrowband VI, the RMSE of the PLSR model was 19–41 % lower for estimating N concentration and 4.2–100 % lower for final biomass. These results indicates that PLSR might be best for predicting the final biomass yield using spectral sample obtained in June to July, but narrowband NNVI was more robust and useful in predicting N concentration.
author: A. J. Foster, V. G. Kakani, J. Mosali
publish year: 2017
creator: A. J. Foster, V. G. Kakani, J. Mosali
reference: A. J. Foster, V. G. Kakani, J. Mosali, "Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression", Precision Agriculture, Volume 18, pages 192–209 (2017) , Springer
Source: Precision Agriculture, Volume 18, pages 192–209 (2017) , Springer
label: Article 0534

Estimation of fruit load in mango orchards: tree sampling considerations and use of machine vision and satellite imagery
type: NamedIndividual
type: Article
has knowledge model: Regressor_0536
abstract: In current best commercial practice, pre-harvest fruit load predictions of mango orchards are provided based on a manual count of fruit number on up to 5% of trees within each block. However, the variability in fruit number per tree (coefficient of variation, CV, from 27 to 93% across ten orchards) was demonstrated to be such that the best case commercial sampling practice was inadequate for reliable estimation (to an error of 54–82 fruit/tree, and percentage error, PE, of 10% at a probability of 0.95). These results highlight the need for alternative methods for estimation of orchard fruit load. Pre-harvest fruit load was estimated for a case study orchard of 469 trees using (i) count of a sample of trees, (ii) in-field machine vision and (iii) correlation to a tree spectral index estimated using high resolution satellite imagery. A count of 5% of trees (23) in the trial orchard resulted in a PE of 31% (error of 37 fruit/tree), with a count of 157 trees required to achieve a PE of 10% (error of 12 fruit/tree). Sampling effort to achieve a PE of 10% was decreased by only 10% by sampling from aspatial k-means tree classifications based on machine vision derived fruit counts of all trees. Clustering based on tree attributes of canopy volume and trunk circumference was not helpful in decreasing sampling effort as these attributes were poorly correlated to fruit load (R2 = 0.21 and 0.17, respectively). In-field multi-view machine vision-based estimation of fruit load per tree achieved a R2 = 0.97 and a RMSE = 14.8 fruit/tree against harvest fruit count per tree for a set of 18 trees (average = 88; SD = 82 fruit/tree), using a faster region convolutional neural network trained the previous season. The relationship between WorldView-3 (WV3) satellite spectral reflectance characteristics of sampled trees and fruit number was characterised by a R2 = 0.66 and a RMSE = 56.1 fruit/tree. For this orchard, for which the actual fruit harvest was 56,720 fruit, the estimate based o…
author: N. T. Anderson, J. P. Underwood, M. M. Rahman, A. Robson, K. B. Walsh
publish year: 2019
creator: N. T. Anderson, J. P. Underwood, M. M. Rahman, A. Robson, K. B. Walsh
reference: N. T. Anderson, J. P. Underwood, M. M. Rahman, A. Robson, K. B. Walsh, "Estimation of fruit load in mango orchards: tree sampling considerations and use of machine vision and satellite imagery", Precision Agriculture, Volume 20, pages 823–839 (2019) , Springer
Source: Precision Agriculture, Volume 20, pages 823–839 (2019) , Springer
label: Article 0536

Estimation of leaf nitrogen concentration in wheat using the MK-SVR algorithm and satellite remote sensing data
type: NamedIndividual
type: Article
has knowledge model: Regressor_0537
abstract: The appropriate spectral vegetation indices can be used to rapidly and non-destructively estimate the leaf nitrogen concentration (LNC) in wheat for on-farm wheat management. However, the accuracy of estimation should be further improved. Previous studies focused on developing vegetation indices, but research about modeling algorithms were limited. In this study, multiple-kernel support vector regression (MK-SVR) was used to assess the LNC in wheat based on satellite remote sensing data. The objectives of this study were to (1) investigate the applicability of the MK-SVR algorithm for remotely estimating the LNC in wheat, (2) test the performance of the MK-SVR regression model, and (3) compare the performance of the MK-SVR algorithm with multiple linear regression (MLR), partial least squares (PLS), artificial neural networks (ANNs), and single-kernel SVR (SK-SVR) algorithms for wheat LNC estimation. In-situ LNC data over four years at different sites in Jiangsu Province of China were measured during the jointing, booting, and anthesis stages; one HJ-CCD image of wheat was obtained during each stage. Vegetation indices were calculated based on these images, and correlations between vegetation indices and LNC data were measured. Finally, a MK-SVR model whose inputs were vegetation indices was established to estimate the LNC during each stage. The results showed that the MK-SVR model performed well in estimating LNC. The coefficients of determination (R2) of the estimated-versus-measured LNC values for the three stages were respectively 0.73, 0.82, and 0.75, meanwhile, the corresponding root mean square errors (RMSE) and the relative RMSE were respectively 0.13 and 6.6%, 0.21 and 7.7%, and 0.20 and 6.5%. Thus, the MK-SVR algorithm provides an effective way to improve the prediction accuracy of LNC in wheat on a large scale.
author: Liai Wang, Xudong Zhou, Xinkai Zhu, Wenshan Guo
publish year: 2017
creator: Liai Wang, Xudong Zhou, Xinkai Zhu, Wenshan Guo
reference: Liai Wang, Xudong Zhou, Xinkai Zhu, Wenshan Guo, "Estimation of leaf nitrogen concentration in wheat using the MK-SVR algorithm and satellite remote sensing data", Computers and Electronics in Agriculture, Volume 140, August 2017, pages 327-337, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 140, August 2017, pages 327-337, Elsevier B.V.
label: Article 0537

Estimation of leaf wetness duration for greenhouse roses using a dynamic greenhouse climate model in Zimbabwe
type: NamedIndividual
type: Article
has knowledge model: Regressor_0538
abstract: Leaf wetness duration (LWD) is one of the most critical parameters involved in the development of plant diseases, since many pathogens require the presence of free water on plant organs to infect foliar tissue. For this reason LWD monitoring is extremely important in crop protection, particularly through the use of weather-related disease forecasting models. Because of the difficulties involved in the measurement of leaf wetness duration, simulation models, based on agrometeorological parameters, are often used as alternatives to field measurement. Furthermore, in greenhouse crop production, these LWD models can be used in predictive control of the greenhouse climate as to suppress the development and propagation of infectious plant diseases. In this study, the Gembloux Dynamic Greenhouse Climate Model (GDGCM) was applied to derive estimates of LWD and hence potential disease incidence, for a rose crop in a naturally ventilated Azrom type greenhouse in Zimbabwe. The model LWD estimates were compared both with data measured by sensors and with visual inspections of LWD conducted during the period May 2007–April 2008. When the GDGCM outputs were used to estimate LWD, the best agreement between measured and predicted LWDs (RMSE = 3.2 h d−1) were obtained when LWD was calculated as the number of hours that the dew point depression of the air remained below 2.3 °C (for wetness onset) and 2.5 °C (for drying off). The good agreement between measured and predicted LWD showed that the GDGCM can be used to predict LWD with acceptable accuracy. This could be of considerable value in helping to predict possible outbreaks of certain climate related diseases, like downy mildew, powdery mildew and botrytis. This information can be used by growers to decide on the optimal precautions to take to prevent possible epidemics of these diseases.
author: E. Mashonjowa, F. Ronsse, M. Mubvuma, J.R. Milford, J.G. Pieters
publish year: 2013
creator: E. Mashonjowa, F. Ronsse, M. Mubvuma, J.R. Milford, J.G. Pieters
reference: E. Mashonjowa, F. Ronsse, M. Mubvuma, J.R. Milford, J.G. Pieters, "Estimation of leaf wetness duration for greenhouse roses using a dynamic greenhouse climate model in Zimbabwe", Computers and Electronics in Agriculture, Volume 95, July 2013, pages 70-81, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 95, July 2013, pages 70-81, Elsevier B.V.
label: Article 0538

Estimation of mango crop yield using image analysis – Segmentation method
type: NamedIndividual
type: Article
has knowledge model: Regressor_0539
abstract: This paper presents an approach to count mango fruit from daytime images of individual trees for the purpose of a machine vision based estimation of mango crop yield. Images of mango trees were acquired over a three day period, 3 weeks before commercial harvest occurred. The fruit load of each of fifteen trees was manually counted, and these trees were imaged on four sides. Correlation between tree counts and manual image counts was strong (R2 = 0.91 for two sides). A further 555 trees were imaged on one side only. For these images, pixels were segmented into fruit and background pixels using colour segmentation in the RGB and YCbCr colour ranges and a texture segmentation based on adjacent pixel variability. Resultant blobs were counted to obtain a per image mango count. Across a set of 555 images (with mean ± standard deviation of fruit per tree of 32.3 ± 14.3), a linear regression, (y = 0.582x − 0.20, R2 = 0.74, bias adjusted root mean square error of prediction = 7.7) was achieved on the machine vision count relative to the image count. The algorithm decreased in effectiveness as the number of fruit on the tree increased, and when imaging conditions involved direct sunlight. Approaches to reduce the impact of fruit load and lighting conditions are discussed.
author: A.B. Payne, K.B. Walsh, P.P. Subedi, D. Jarvis
publish year: 2013
creator: A.B. Payne, K.B. Walsh, P.P. Subedi, D. Jarvis
reference: A.B. Payne, K.B. Walsh, P.P. Subedi, D. Jarvis, "Estimation of mango crop yield using image analysis – Segmentation method", Computers and Electronics in Agriculture, Volume 91, February 2013, pages 57-64, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 91, February 2013, pages 57-64, Elsevier B.V.
label: Article 0539

Estimation of mean monthly air temperatures in Turkey
type: NamedIndividual
type: Article
has knowledge model: Regressor_0540
abstract: One of the most important climatic factors influencing growth, development and yield of crops, which involve a countless number of biochemical reactions, is temperature. It is also one of the most effective explanatory variables of the evapotranspiration estimation models such as Hargreaves, Rich and Thornthwaite needed for irrigation scheduling policies. Using artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models, means of maximum, minimum, and average monthly temperatures are estimated as a function of geographical coordinates and month number for any location in Turkey. The monthly data measured by the General Directorate of State Meteorological Works at 275 stations having records of at least 20 years are used in developing the models. The latitude, longitude, and altitude of the location, and the month number are used as the input variables, and each of the mean monthly maximum, minimum, and average air temperatures is computed as the output variable. The observed values are compared versus those predicted by the ANN, ANFIS, and MLR models by evaluating their errors, and as a result the ANFIS model turns out to be the best.
author: Murat Cobaner, Hatice Citakoglu, Ozgur Kisi, Tefaruk Haktanir
publish year: 2014
creator: Murat Cobaner, Hatice Citakoglu, Ozgur Kisi, Tefaruk Haktanir
reference: Murat Cobaner, Hatice Citakoglu, Ozgur Kisi, Tefaruk Haktanir, "Estimation of mean monthly air temperatures in Turkey", Computers and Electronics in Agriculture, Volume 109, November 2014, pages 71-79, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 109, November 2014, pages 71-79, Elsevier B.V.
label: Article 0540

Estimation of rice yield from a C-band radar remote sensing image by integrating a physical scattering model and an optimization algorithm
type: NamedIndividual
type: Article
has knowledge model: Regressor_0543
abstract: Accurate estimation of rice yield with remotely sensed data plays a role in ensuring food security at local or national scales. In this study, an estimation scheme integrating a rice canopy scattering model (RCSM) and a genetic algorithm optimization tool (GAOT) was proposed on a basis of radar remote sensing technology. A C-band Radarsat-2 synthetic aperture radar (SAR) image acquired at rice maturity stage in Northeast, China was tested to simulate three yield-related rice parameters via an integrated RCSM-GAOT scheme, with a parallel computing environment. Rice yield was then estimated via a regression analysis by linking the simulated parameters to rice ear weights. Results showed that three parameters, ear length, ear diameter and ear density, can be simulated by the RCSM-GAOT scheme with a simulation error of 1.2 cm, 0.11 cm and 29 ears/m2, respectively. Rice yield is estimated with an average error of 0.28 kg/m2 and 0.22 kg/m2 for fresh ear weight and dry ear weight, respectively. In comparison with the need of multiple SAR data acquisitions in past studies, this study demonstrates the capability of one time Radarsat-2 quad-polarized SAR image in regional rice yield estimation. At the same time, it also indicates that the proposed RCSM-GAOT scheme has a potential in future precision agriculture applications.
author: Yuan Zhang, Wenjia Yan, Bin Yang, Tianpeng Yang, Xiaohui Liu
publish year: 2020
creator: Yuan Zhang, Wenjia Yan, Bin Yang, Tianpeng Yang, Xiaohui Liu
reference: Yuan Zhang, Wenjia Yan, Bin Yang, Tianpeng Yang, Xiaohui Liu, "Estimation of rice yield from a C-band radar remote sensing image by integrating a physical scattering model and an optimization algorithm", Precision Agriculture, Volume 21, pages 245–263 (2020) , Springer
Source: Precision Agriculture, Volume 21, pages 245–263 (2020) , Springer
label: Article 0543

Estimation of surface soil organic matter using a ground-based active sensor and aerial imagery
type: NamedIndividual
type: Article
has knowledge model: Regressor_0545
abstract: Active canopy sensors are currently being studied as a tool to assess crop N status and direct in-season N applications. The objective of this study was to use a variety of strategies to evaluate the capability of an active sensor and a wide-band aerial image to estimate surface soil organic matter (OM). Grid soil samples, active sensor reflectance and bare soil aerial images were obtained from six fields in central Nebraska before the 2007 and 2008 growing seasons. Six different strategies to predict OM were developed and tested by dividing samples randomly into calibration and validation datasets. Strategies included uniform, interpolation, universal, field-specific, intercept-adjusted and multiple-layer prediction models. By adjusting regression intercept values for each field, OM was predicted using a single sensor or image data layer. Across all fields, the uniform and universal prediction models resulted in less accurate predictions of OM than any of the other methods tested. The most accurate predictions of OM were obtained using interpolation, field-specific and intercept-adjusted strategies. Increased accuracy in mapping soil OM using an active sensor or aerial image may be achieved by acquiring the data when there is minimal surface residue or where it has been excluded from the sensor’s field-of-view. Alternatively, accuracy could be increased by accounting for soil moisture content with supplementary sensors at the time of data collection, by focusing on the relationship between soil reflectance and soil OM content in the 0–1 cm soil depth or through the use of a subsurface active optical sensor.
author: D. F. Roberts, V. I. Adamchuk, J. F. Shanahan, R. B. Ferguson, J. S. Schepers
publish year: 2011
creator: D. F. Roberts, V. I. Adamchuk, J. F. Shanahan, R. B. Ferguson, J. S. Schepers
reference: D. F. Roberts, V. I. Adamchuk, J. F. Shanahan, R. B. Ferguson, J. S. Schepers, "Estimation of surface soil organic matter using a ground-based active sensor and aerial imagery", Precision Agriculture, Volume 12, pages 82–102 (2011) , Springer
Source: Precision Agriculture, Volume 12, pages 82–102 (2011) , Springer
label: Article 0545

Estimation of yield and quality of legume and grass mixtures using partial least squares and support vector machine analysis of spectral data
type: NamedIndividual
type: Article
has knowledge model: Regressor_0549
abstract: The project aim was to estimate N uptake (Nup), dry matter yield (DMY) and crude protein concentration (CP) of forage crops both during typical harvest times and at a very early developmental stage. Canopy spectral reflectance of legume and grass mixtures was measured in Sweden using a commercialized radiometer (400–1000 nm range). In total, 377 plant samples were tested in-situ in different grass and legume mixtures (6 grass species and 2 clover species) across two years, two locations and five N rates. Two mathematical methods, namely partial least squares (PLS) and support vector machine (SVM) were used to build prediction models between Nup, DMY and CP, and canopy spectral reflectance. Of the total 377 samples, 251 were randomly selected and used for calibration, and the remaining 126 samples were used as an independent dataset for validation. Results showed that the performance of SVM was better than PLS (based on mean absolute error (MAE) for both calibration and validation datasets) for the estimation of all investigated variables. Results for the validation set showed that the MAEs of PLS and SVM for Nup estimation were 17 and 9.2 kg/ha, respectively. The MAEs of PLS and SVM for DMY estimation were 587 and 283 kg/ha, respectively. The MAEs of PLS and SVM for CP estimation were 2.8 and 1.8%, respectively. In addition, a subsample, which corresponded to an early developmental stage, was analysed separately with PLS and SVM as for the whole dataset. Results showed that SVM was better than PLS for the estimation of all investigated variables. The high performance of SVM to estimate legume and grass mixture N uptake and dry matter yield could provide support for varying management decisions including fertilization and timing of harvest.
author: Zhenjiang Zhou, Julien Morel, David Parsons, Sergey V. Kucheryavskiy, Anne-Maj Gustavsson
publish year: 2019
creator: Zhenjiang Zhou, Julien Morel, David Parsons, Sergey V. Kucheryavskiy, Anne-Maj Gustavsson
reference: Zhenjiang Zhou, Julien Morel, David Parsons, Sergey V. Kucheryavskiy, Anne-Maj Gustavsson, "Estimation of yield and quality of legume and grass mixtures using partial least squares and support vector machine analysis of spectral data", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 246-253, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 162, July 2019, pages 246-253, Elsevier B.V.
label: Article 0549

Evaluating chlorophyll density in winter oilseed rape (Brassica napus L.) using canopy hyperspectral red-edge parameters
type: NamedIndividual
type: Article
has knowledge model: Classifier 0550
abstract: Accurate assessments of chlorophyll density (ChD) using hyperspectral techniques are important for effective evaluation of plant productivity and precise nitrogen (N) management in winter oilseed rape. To develop a quantitative estimation model for determining ChD in winter oilseed rape, field experiments with different N fertilizer levels were conducted over two successive years by measuring canopy hyperspectral reflectance and ChD at various developmental stages. The relationships between two types of parameters (existing red-edge spectral parameters and newly-developed red-edge area parameters) and ChD were investigated to determine the optimal red-edge spectral parameters (ORSPs) for ChD predictions. The Noise Equivalent (NE) model was adopted to evaluate the sensitivity and accuracy of the ORSPs for detecting changes in ChD across different growth stages. The results indicated that canopy hyperspectral reflectance and its first derivative spectra significantly varied with the levels of N fertilization. A strong correlation also existed between canopy reflectance data and ChD. Using a training dataset, the best results for assessing ChD status were observed when using the newly-developed red-edge area parameter, which indicated a difference between the double-peak areas based on the position of the main peak (DIDRmid). DIDRmid was the ORSP and exhibited a significant exponential relationship with ChD, with a coefficient of determination (R2) of 0.88 and a standard error (SE) of 0.312. Tests conducted on the independent validation dataset showed that DIDRmid can be used to accurately predict ChD in oilseed rape, with a relative root mean square error (RRMSE) of 0.091 and a mean relative error (MRE) of 7.22%. Additionally, this ORSP also had relatively lower NE values and higher sensitivity and accuracy with respect to ChD estimation. Consequently, the ChD of winter oilseed rape can be stably estimated with the hyperspectral red-edge methods established in this st…
author: Lantao Li, Tao Ren, Yi Ma, Quanquan Wei, Shanqin Wang, Xiaokun Li, Rihuan Cong, Shishi Liu, Jianwei Lu
publish year: 2016
creator: Lantao Li, Tao Ren, Yi Ma, Quanquan Wei, Shanqin Wang, Xiaokun Li, Rihuan Cong, Shishi Liu, Jianwei Lu
reference: Lantao Li, Tao Ren, Yi Ma, Quanquan Wei, Shanqin Wang, Xiaokun Li, Rihuan Cong, Shishi Liu, Jianwei Lu, "Evaluating chlorophyll density in winter oilseed rape (Brassica napus L.) using canopy hyperspectral red-edge parameters", Computers and Electronics in Agriculture, Volume 126, August 2016, pages 21-31, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 126, August 2016, pages 21-31, Elsevier B.V.
label: Article 0550

Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado
type: NamedIndividual
type: Article
has knowledge model: Classifier 0553
abstract: Laurel wilt (Lw) disease is an exotic and lethal disease that can kill laurel family trees very fast. It is vectored by the redbay ambrosia beetle that prefers to live and lay eggs inside avocado trees (among other plants). Lw disease continues to expand in Florida posing a major threat to the avocado industry. Early and accurate disease detection is very critical in this case to remove infected trees and distinguish Lw disease from other diseases or disorders with similar symptoms. Herein, we present a nondestructive remote sensing method to detect Lw-infected avocado trees (in early and late stage) and discriminate them from healthy and other factors that cause similar symptoms, such as iron and nitrogen deficiencies, by using a portable spectral data collection system (visible – near infrared; 400–970 nm). Two data sets were collected in 10 nm and 40 nm spectral resolution, and 23 vegetation indices (VIs) were calculated to detect Lw-affected trees by using two classification methods: decision tree (DT) and multilayer perceptron (MLP) neural networks. Additionally, the optimal wavelengths and VIs to discriminate healthy, Lw-infected and avocado trees with iron and nitrogen deficiencies were identified. The results showed that it was possible to detect Lw-infected trees at early stage and distinguish them from other biotic and abiotic factors with high accuracy (around 100%) using the MLP method. Poorer results were achieved with DTs. The optimum 10 nm wide bands and VIs selected for the Lw-detection were found in the red, red-edge and NIR bands.
author: Jaafar Abdulridha, Yiannis Ampatzidis, Reza Ehsani, Ana I. de Castro
publish year: 2018
creator: Jaafar Abdulridha, Yiannis Ampatzidis, Reza Ehsani, Ana I. de Castro
reference: Jaafar Abdulridha, Yiannis Ampatzidis, Reza Ehsani, Ana I. de Castro, "Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado", Computers and Electronics in Agriculture, Volume 155, December 2018, pages 203-211, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 155, December 2018, pages 203-211, Elsevier B.V.
label: Article 0553

Evaluating the performance of xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in five fruit tree species
type: NamedIndividual
type: Article
has knowledge model: Classifier 0554
abstract: This study assessed the capability of several xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in a commercial farm consisting of five fruit tree crop species with contrasting phenology and canopy architecture. Plots irrigated and non-irrigated for eight days of each species were used to promote a range of plant water status. Multi-spectral and thermal images were acquired from an unmanned aerial system while concomitant measurements of stomatal conductance (gs), stem water potential (Ψs) and photosynthesis were taken. The Normalized Difference Vegetation Index (NDVI), red-edge ratio (R700/R670), Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil Adjusted Vegetation Index (TCARI/OSAVI), the Photochemical Reflectance Index using reflectance at 530 (PRI) and 515 nm [PRI(570–515)] and the normalized PRI (PRInorm) were obtained from the narrow-band multi-spectral images and the relationship with the in-field measurements explored. Results showed that within the Prunus species, Ψs yielded the best correlations with PRI and PRI(570–515) (R2 = 0.53) in almond trees, with TCARI/OSAVI (R2 = 0.88) in apricot trees and with PRInorm, R700/R670 and NDVI (R2 from 0.72 to 0.88) in peach trees. Weak or no correlations were found for the Citrus species due to the low level of water stress reached by the trees. Results from the sensitivity analysis pointed out the canopy temperature (Tc) and PRI(570–515) as the first and second most sensitive indicators to the imposed water conditions in all the crops with the exception of apricot trees, in which Ψs was the most sensitive indicator at midday. PRInorm was the least sensitive index among all the water stress indicators studied. When all the crops were analyzed together, PRI(570–515) and NDVI were the indices that better correlations yielded with Crop Water Stress Index, gs and, particularly, Ψs (R2 = 0.61 and 0.65, respectively). This work demonstrated the fe…
author: C. Ballester, P. J. Zarco-Tejada, E. Nicolas, J. J. Alarcon, E. Fereres, D. S. Intrigliolo, V. Gonzalez-Dugo
publish year: 2018
creator: C. Ballester, P. J. Zarco-Tejada, E. Nicolas, J. J. Alarcon, E. Fereres, D. S. Intrigliolo, V. Gonzalez-Dugo
reference: C. Ballester, P. J. Zarco-Tejada, E. Nicolas, J. J. Alarcon, E. Fereres, D. S. Intrigliolo, V. Gonzalez-Dugo, "Evaluating the performance of xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in five fruit tree species", Precision Agriculture, Volume 19, pages 178–193 (2018) , Springer
Source: Precision Agriculture, Volume 19, pages 178–193 (2018) , Springer
label: Article 0554

Evaluation of Citrus Gummosis disease dynamics and predictions with weather and inversion based leaf optical model
type: NamedIndividual
type: Article
has knowledge model: Regressor_0559
abstract: One of the major threats for crops around the world due to pest and diseases, which can impact the health, economy, environment, and society at large. In general, several issues related to crop yield improvement arises due to insufficient and inadequate knowledge. Therefore, there is a need to develop viable models that incorporate various weather-soil-plant factors, which can give better understanding of the crop and enable timely interventions for yield improvement. To overcome Citrus Gummosis disease related issues and increase the Citrus productivity, seven different datasets Temperature (T), Humidity (Rh), Rainfall (R), Soil Moisture (SM), Soil Temperature (ST), Leaf Area Index (LAI) and Chlorophyll (Cab) were used. Considering various plant, soil and environmental factors, the Citrus Gummosis prediction model has been developed with the multi-source datasets from June 2014 to November 2016 using Support vector regression (SVR) and multilinear regression (MLR). The research is carried out for healthy (5–10 Yrs. and 11–15 Yrs.) and unhealthy (5–10 Yrs. and 11–15 Yrs.) age group of plants. Inverse PROSAIL model has been simulated for retrieving citrus Cab and LAI values. These values were validated with the actual field data. Both the weather and soils based disease prediction models has been developed and validated with MLR and SVR. Further, the influence of Gummosis disease on plant parameters was also studies with the new contribution of Biophysical variables (LAI and Cab) based statistical prediction model. The SVR model gave fairly good performance as compared to MLR. In addition to the separate models a the combined scenario approach (Integrated Gummosis Disease Forecast Model: IGDFM) is designed to understand the interconnectivity of the parametric conditions (weather-soil- plant parameters) with disease physiology with respect to different age group of the plants. The RMSE of proposed approach for higher age group plants (i.e. 11–15 years) in the combined…
author: Mrunalini R. Badnakhe, Surya S. Durbha, Adinarayana Jagarlapudi, Rajendra M. Gade
publish year: 2018
creator: Mrunalini R. Badnakhe, Surya S. Durbha, Adinarayana Jagarlapudi, Rajendra M. Gade
reference: Mrunalini R. Badnakhe, Surya S. Durbha, Adinarayana Jagarlapudi, Rajendra M. Gade, "Evaluation of Citrus Gummosis disease dynamics and predictions with weather and inversion based leaf optical model", Computers and Electronics in Agriculture, Volume 155, December 2018, pages 130-141, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 155, December 2018, pages 130-141, Elsevier B.V.
label: Article 0559

Evaluation of mid-season sensor based nitrogen fertilizer recommendations for winter wheat using different estimates of yield potential
type: NamedIndividual
type: Article
has knowledge model: Regressor_0561
abstract: Optical sensors, coupled with mathematical algorithms, have proven effective at determining more accurate mid-season nitrogen (N) fertilizer recommendations in winter wheat. One parameter required in making these recommendations is in-season grain yield potential at the time of sensing. Four algorithms, with different methods for determining grain yield potential, were evaluated for effectiveness to predict final grain yield and the agronomic optimum N rate (AONR) at 34 site-years. The current N fertilizer optimization algorithm (CNFOA) outperformed the other three algorithms at predicting yield potential with no added N and yield potential with added N (R2 = 0.46 and 0.25, respectively). However, no differences were observed in the amount of variability accounted for among all four algorithms in regards to predicting the AONR. Differences were observed in that the CNFOA and proposed N fertilizer optimization algorithm (PNFOA), under predicted the AONR at approximately 75 % of the site-years; whereas, the generalized algorithm (GA) and modified generalized algorithm (MGA) recommended N rates under the AONR at about 50 % of the site-years. The PNFOA was able to determine N rate recommendations within 20 kg N ha−1 of the AONR for half of the site-years; whereas, the other three algorithms were only able recommend within 20 kg N ha−1 of the AONR for about 40 % of the site-years. Lastly, all four algorithms reported more accurate N rate recommendations compared to non-sensor based methodologies and can more precisely account for the year to year variability in grain yields due to environment.
author: Jacob T. Bushong, Jeremiah L. Mullock, Eric C. Miller, William R. Raun, D. Brian Arnall
publish year: 2016
creator: Jacob T. Bushong, Jeremiah L. Mullock, Eric C. Miller, William R. Raun, D. Brian Arnall
reference: Jacob T. Bushong, Jeremiah L. Mullock, Eric C. Miller, William R. Raun, D. Brian Arnall, "Evaluation of mid-season sensor based nitrogen fertilizer recommendations for winter wheat using different estimates of yield potential", Precision Agriculture, Volume 17, pages 470–487 (2016) , Springer
Source: Precision Agriculture, Volume 17, pages 470–487 (2016) , Springer
label: Article 0561

Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields
type: NamedIndividual
type: Article
has knowledge model: Regressor_0566
abstract: Remote sensing-based nitrogen (N) management has been evaluated in many crops. The water background and wide range of varieties in rice (Oryza sativa), are unique features that require additional consideration when using sensor technology. The commonly calculated normalized difference vegetation index is of limited use when the crop has reached complete canopy closure. The objective of this research was to evaluate mid-season agronomic parameter and grain yield prediction models along with the effect of water background and of different varieties using a red- and red-edge-based vegetation index. Varieties × N trials were established at the LSU AgCenter Rice Research Station located in Crowley, Louisiana in 2011 and 2012. Canopy spectral reflectance under clear and turbid water, biomass yield, N content, plant coverage, and water depth were collected each week for three consecutive weeks beginning 2 weeks before panicle differentiation. Grain yield was also determined. Water turbidity had an influence on spectral reflectance when canopy coverage was less than 50 %. While water depth influenced red reflectance, this was not carried over when reflectance was transformed to vegetation indices. The red-edge-based vegetation indices, especially those computed by ratio, had stronger relationships with measured agronomic parameters as compared with red-based indices. Furthermore, the effect of variety on the yield prediction model was observed using derivative-based red-edge indices but not with other ratio-based indices. Future researches should focus on developing a generalized yield prediction model using ratio-based red-edge indices across different varieties to extend its applicability in production fields.
author: Yumiko Kanke, Brenda Tubana, Marilyn Dalen, Dustin Harrell
publish year: 2016
creator: Yumiko Kanke, Brenda Tubana, Marilyn Dalen, Dustin Harrell
reference: Yumiko Kanke, Brenda Tubana, Marilyn Dalen, Dustin Harrell, "Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields", Precision Agriculture, Volume 17, pages 507–530 (2016) , Springer
Source: Precision Agriculture, Volume 17, pages 507–530 (2016) , Springer
label: Article 0566

Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems
type: NamedIndividual
type: Article
has knowledge model: Cluster 0571
abstract: Elucidating the biophysical mechanisms governing the exchange of water vapor between land and the atmosphere is particularly crucial for addressing water scarcity under climate change. Owing to the rapid development of machine learning techniques, a series of powerful tools have been proposed over the past two decades, allowing the scientific community to obtain new insights into the patterns of evapotranspiration (ET) on different spatial scales ranging from ecosystem to global. The primary focus of this study was to investigate the feasibility and effectiveness of both extreme learning machine (ELM) and adaptive neuro-fuzzy inference system (ANFIS) to estimate the daily ET with flux tower observations in four main types of ecosystems. A comparative research was undertaken to evaluate the potential of the models compared with the conventional artificial neural network and support vector machine models. All the developed models were evaluated according to the following performance indices: coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root mean square error and mean absolute error. The results showed that all the applied models had high performance for modeling daily ET (e.g., R2 = 0.9398–0.9593 and NSE = 0.8877–0.9147 in forest ecosystem). Among the applied ELM models, the three hybrid ELM methods outperformed the original ELM method in most cases at the four sites and the computational time required for learning these ELM models has been considerably reduced. The subtractive clustering and fuzzy c-means clustering algorithms for ANFIS generally performed better than the grid partitioning algorithm. It was concluded that the advanced ELM and ANFIS models can be recommended as important complements to traditional methods due to their robustness and flexibility. Moreover, significant difference regarding the modeling performance existed among the four major ecosystems types. The models generally achieved the best performance in forest ecosystem,…
author: Xianming Dou, Yongguo Yang
publish year: 2018
creator: Xianming Dou, Yongguo Yang
reference: Xianming Dou, Yongguo Yang, "Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems", Computers and Electronics in Agriculture, Volume 148, May 2018, pages 95-106, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 148, May 2018, pages 95-106, Elsevier B.V.
label: Article 0571

Extended model prediction of high-resolution soil organic matter over a large area using limited number of field samples
type: NamedIndividual
type: Article
has knowledge model: Regressor_0576
abstract: Detailed soil organic matter (SOM) spatial distribution maps are essential for soil management and forestry operations. However, mapping of spatial SOM distribution over a large area is a difficult challenge, especially in regions where field samples are difficult to obtain. The objective of this research was to develop a two-stage approach to map SOM content with 10 m-resolution in Yunfu, South China with an area of 7785 km2. In the first stage, using 10-fold cross-validation 511 artificial neural network (ANN) models were built to map SOM content based on 318 field samples from three of five sub-areas of Yunfu (ANN model area). Results indicated that the optimal ANN model with six DEM-derived variables as model inputs, i.e. ANN6, had a good model performance in ANN model area, 5.6 g/kg of root mean squared error (RMSE), 0.81 of R2, and 84.1% of relative overall accuracy (ROA) ± 10%, and the best generalization capability in the rest two of five sub-areas of Yunfu (extended model area), with 7.7 g/kg of RMSE, 0.58 of R2, and 60.7% of ROA ± 10%. In the second stage, using the reverse k-fold cross-validation extended models were developed to adapt ANN6-produced SOM content to fit field samples in the extended model areas. Results indicated the optimal extended model only required 20% of 386 field samples (5-fold) to build a stable and significant linear relationship between ANN6-produced SOM content and measured SOM content from the extended model area, and improved model accuracy with 9–21% of RMSE, 28–29% of R2, and 6–21% of ROA ± 10%. Thus, the two-stage method is a viable way to generate SOM content over a large area with limited number of field samples.
author: Zhengyong Zhao, Qi Yang, Dongxiao Sun, Xiaogang Ding, Fan-Rui Meng
publish year: 2020
creator: Zhengyong Zhao, Qi Yang, Dongxiao Sun, Xiaogang Ding, Fan-Rui Meng
reference: Zhengyong Zhao, Qi Yang, Dongxiao Sun, Xiaogang Ding, Fan-Rui Meng, "Extended model prediction of high-resolution soil organic matter over a large area using limited number of field samples", Computers and Electronics in Agriculture, Volume 169, February 2020, 105172, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 169, February 2020, 105172, Elsevier B.V.
label: Article 0576

Extreme learning machine based prediction of daily dew point temperature
type: NamedIndividual
type: Article
has knowledge model: Regressor_0577
abstract: The dew point temperature is a significant element particularly required in various hydrological, climatological and agronomical related researches. This study proposes an extreme learning machine (ELM)-based model for prediction of daily dew point temperature. As case studies, daily averaged measured weather data collected for two Iranian stations of Bandar Abass and Tabass, which enjoy different climate conditions, were used. The merit of the ELM model is evaluated against support vector machine (SVM) and artificial neural network (ANN) techniques. The findings from this research work demonstrate that the proposed ELM model enjoys much greater prediction capability than the SVM and ANN models so that it is capable of predicting daily dew point temperature with very favorable accuracy. For Tabass station, the mean absolute bias error (MABE), root mean square error (RMSE) and correlation coefficient (R) achieved for the ELM model are 0.3240 °C, 0.5662 °C and 0.9933, respectively, while for the SVM model the values are 0.7561 °C, 1.0086 °C and 0.9784, respectively and for the ANN model are 1.0324 °C, 1.2589 °C and 0.9663, respectively. For Bandar Abass station, the MABE, RMSE and R for the ELM model are 0.5203 °C, 0.6709 °C and 0.9877, respectively whereas for the SVM model the values are 1.0413 °C, 1.2105 °C and 0.9733, and for the ANN model are 1.3205 °C, 1.5530 °C and 0.9617, respectively. The study results convincingly advocate that ELM can be employed as an efficient method to predict daily dew point temperature with much higher precision than the SVM and ANN techniques.
author: Kasra Mohammadi, Shahaboddin Shamshirband, Shervin Motamedi, Dalibor Petković, Roslan Hashim, Milan Gocic
publish year: 2015
creator: Kasra Mohammadi, Shahaboddin Shamshirband, Shervin Motamedi, Dalibor Petković, Roslan Hashim, Milan Gocic
reference: Kasra Mohammadi, Shahaboddin Shamshirband, Shervin Motamedi, Dalibor Petković, Roslan Hashim, Milan Gocic, "Extreme learning machine based prediction of daily dew point temperature", Computers and Electronics in Agriculture, Volume 117, September 2015, pages 214-225, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 117, September 2015, pages 214-225, Elsevier B.V.
label: Article 0577

Grading of ripening stages of red banana using dielectric properties changes and image processing approach
type: NamedIndividual
type: Article
has knowledge model: Classifier 0619
abstract: In this research work, the dielectric properties of red banana fruit are studied at different ripening temperatures for developing a rapid and non-destructive assessment method to measure the ripening stages of red banana. A 5 volt sine wave AC power supply and a rectangular parallel plate capacitor circuit are used to measure the difference in dielectric properties caused by the introduction of a red banana in between the plates. The values of properties like capacitance and relative permittivity are increased continuously whereas impedance and admittance are decreased gradually with increase in ripening stages of red banana. In image processing approach, Noise Reductant Local Binary Pattern (NRLBP), Local Binary Pattern (LBP), Completed Local Binary Pattern (CLBP) based techniques are used for red banana’s ripening grade determination. The processing stages involved are enhancement, Binary Pattern generation and classification. The variant Binary patterns are tested on noisy as well as noiseless condition and the results are compared. A novel enhancement technique for banana ripening grade determination is proposed based on segmentation using Tsallis entropy. Also novel idea on the automation of q parameter involved in Tsallis Entropy is implemented. The threshold parameter of the Noise Reductant Local Binary Pattern (NRLBP) varied and its effect on classification rate is studied. A new modification is proposed and implemented on NRLBP to accommodate uniform background and areas with the image. Classification is done using Chi-Square distance/nearest neighbor and Fuzzy C means (FCM) clustering. The results are compared and superiority of FCM method for banana ripening grade determination is noted.
author: Asutosh Mohapatra, S. Shanmugasundaram, R. Malmathanraj
publish year: 2017
creator: Asutosh Mohapatra, S. Shanmugasundaram, R. Malmathanraj
reference: Asutosh Mohapatra, S. Shanmugasundaram, R. Malmathanraj, "Grading of ripening stages of red banana using dielectric properties changes and image processing approach", Computers and Electronics in Agriculture, Volume 143, December 2017, pages 100-110, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 143, December 2017, pages 100-110, Elsevier B.V.
label: Article 0619

Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association
type: NamedIndividual
type: Article
has knowledge model: Classifier 0620
abstract: Agricultural applications such as yield prediction, precision agriculture and automated harvesting need systems able to infer the crop state from low-cost sensing devices. Proximal sensing using affordable cameras combined with computer vision has seen a promising alternative, strengthened after the advent of convolutional neural networks (CNNs) as an alternative for challenging pattern recognition problems in natural images. Considering fruit growing monitoring and automation, a fundamental problem is the detection, segmentation and counting of individual fruits in orchards. Here we show that for wine grapes, a crop presenting large variability in shape, color, size and compactness, grape clusters can be successfully detected, segmented and tracked using state-of-the-art CNNs. In a test set containing 408 grape clusters from images taken on a trellis-system based vineyard, we have reached an F1-score up to 0.91 for instance segmentation, a fine separation of each cluster from other structures in the image that allows a more accurate assessment of fruit size and shape. We have also shown as clusters can be identified and tracked along video sequences recording orchard rows. We also present a public dataset containing grape clusters properly annotated in 300 images and a novel annotation methodology for segmentation of complex objects in natural images. The presented pipeline for annotation, training, evaluation and tracking of agricultural patterns in images can be replicated for different crops and production systems. It can be employed in the development of sensing components for several agricultural and environmental applications.
author: Thiago T. Santos, Leonardo L. de Souza, Andreza A. dos Santos, Sandra Avila
publish year: 2020
creator: Thiago T. Santos, Leonardo L. de Souza, Andreza A. dos Santos, Sandra Avila
reference: Thiago T. Santos, Leonardo L. de Souza, Andreza A. dos Santos, Sandra Avila, "Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association", Computers and Electronics in Agriculture, Volume 170, March 2020, 105247, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 170, March 2020, 105247, Elsevier B.V.
label: Article 0620

Grapevine variety identification using “Big Data” collected with miniaturized spectrometer combined with support vector machines and convolutional neural networks
type: NamedIndividual
type: Article
has knowledge model: Classifier 0622
abstract: Several experiments have been previously reported suggesting that the application of spectroscopy and machine learning allows the identification of grapevine varieties, however, up to now, the maximum number of varieties separated was twenty and the total number of sample spectra used does not go beyond the few hundreds. The present work aim is to answer the question: Is it possible to separate one variety from an enlarged group of other varieties when the number of samples is also significantly increased? With this in mind, a total of 35,833 spectra from leaves of 626 plants from 64 varieties were gathered for the study. This is a non-trivial evolution from previous works because it originates an increase in the variability of spectra which brings in a higher risk that a significant percentage of spectra of different varieties are equal and cannot be separated. Simultaneously, it was studied if a miniaturized and easy to use spectrometer could deliver data whose quality was enough to allow varieties separation even with data being collected in the field, non-destructively, and under uncontrolled solar lighting. This data was used to build support vector machines and convolutional neural networks for separating Touriga Nacional from 63 other varieties (including Touriga Franca) or Touriga Franca from 63 varieties (including Touriga Nacional), and the classification efficiencies are analysed.
author: Armando M. Fernandes, Andrei B. Utkin, Jose Eiras-Dias, Jorge Cunha, Jose Silvestre, Pedro Melo-Pinto
publish year: 2019
creator: Armando M. Fernandes, Andrei B. Utkin, Jose Eiras-Dias, Jorge Cunha, Jose Silvestre, Pedro Melo-Pinto
reference: Armando M. Fernandes, Andrei B. Utkin, Jose Eiras-Dias, Jorge Cunha, Jose Silvestre, Pedro Melo-Pinto, "Grapevine variety identification using “Big Data” collected with miniaturized spectrometer combined with support vector machines and convolutional neural networks", Computers and Electronics in Agriculture, Volume 163, August 2019, 104855, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 163, August 2019, 104855, Elsevier B.V.
label: Article 0622

Ground-level hyperspectral imagery for detecting weeds in wheat fields
type: NamedIndividual
type: Article
has knowledge model: Classifier 0623
abstract: Site-specific weed management can allow more efficient weed control from both an environmental and an economic perspective. Spectral differences between plant species may lead to the ability to separate wheat from weeds. The study used ground-level image spectroscopy data, with high spectral and spatial resolutions, for detecting annual grasses and broadleaf weeds in wheat fields. The image pixels were used to cross-validate partial least squares discriminant analysis classification models. The best model was chosen by comparing the cross-validation confusion matrices in terms of their variances and Cohen’s Kappa values. This best model used four classes: broadleaf, grass weeds, soil and wheat and resulted in Kappa of 0.79 and total accuracy of 85 %. Each of the classes contains both sunlit and shaded data. The variable importance in projection method was applied in order to locate the most important spectral regions for each of the classes. It was found that the red-edge is the most important region for the vegetation classes. Ground truth pixels were randomly selected and their confusion matrix resulted in a Kappa of 0.63 and total accuracy of 72 %. The results obtained were reasonable although the model used wheat and weeds from different growth stages, acquisition dates and fields. It was concluded that high spectral and spatial resolutions can provide separation between wheat and weeds based on their spectral data. The results show feasibility for up-scaling the spectral methods to air or spaceborne sensors as well as developing ground-level application.
author: I. Herrmann, U. Shapira, S. Kinast, A. Karnieli, D. J. Bonfil
publish year: 2013
creator: I. Herrmann, U. Shapira, S. Kinast, A. Karnieli, D. J. Bonfil
reference: I. Herrmann, U. Shapira, S. Kinast, A. Karnieli, D. J. Bonfil, "Ground-level hyperspectral imagery for detecting weeds in wheat fields", Precision Agriculture, Volume 14, pages 637–659 (2013) , Springer
Source: Precision Agriculture, Volume 14, pages 637–659 (2013) , Springer
label: Article 0623

Hybrid learning of fuzzy cognitive maps for sugarcane yield classification
type: NamedIndividual
type: Article
has knowledge model: Classifier 0633
abstract: Sugarcane is one of India’s most important renewable commercial crops. The sugarcane cultivation and sugar industry plays a vital role towards socio-economic development in the rural areas by creating higher income and employment opportunities. Early detection and management of problems associated with sugarcane yield indicators enables the decision makers and planners to decide import or export policies. In this work, a hybrid approach using fuzzy cognitive map (FCM) learning algorithms for sugarcane yield classification is proposed, combining the key aspects of Data Driven Nonlinear Hebbian Learning (DDNHL) algorithm and Genetic Algorithm (GA) called FCM-DDNHL-GA. The FCM model developed for the proposed study includes various soil and climate parameters which influence the precision agriculture application of sugarcane yield prediction. The classification accuracies and inference capabilities of the hybrid learning algorithm of FCMs are analyzed and compared with some well-known machine learning algorithms for sugarcane yield monitoring application. Experimental results show the superiority of the hybrid learning approach by providing significantly higher classification accuracy.
author: Rajathi Natarajan, Jayashree Subramanian, Elpiniki I. Papageorgiou
publish year: 2016
creator: Rajathi Natarajan, Jayashree Subramanian, Elpiniki I. Papageorgiou
reference: Rajathi Natarajan, Jayashree Subramanian, Elpiniki I. Papageorgiou, "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification", Computers and Electronics in Agriculture, Volume 127, September 2016, pages 147-157, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 127, September 2016, pages 147-157, Elsevier B.V.
label: Article 0633

Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars
type: NamedIndividual
type: Article
has knowledge model: Classifier 0634
abstract: To use remotely sensed spectral data for determining rates and timing of variable rate nitrogen (N) applications at a commercial scale, the most reliable indicators of crop N status must be determined. This study evaluated the ability of hyperspectral remote sensing to predict N stress in potatoes (Solanum tuberosum) during two growing seasons (2010 and 2011). Spectral data were evaluated using ground based measurements of leaf N concentration. Two canopy-scale hyperspectral images were acquired with an AISA-Eagle hyperspectral camera in both years. The experiment included five N treatments with varying rates and timing of N fertilizer and two potato cultivars, Russet Burbank (RB) and Alpine Russet (AR). Partial Least Squares regression (PLS) models resulted in the best prediction of leaf N concentration (R2 = 0.79, Root Mean Square Error of Cross Validation (RMSECV) = 14% across dates for RB; R2 = 0.77, RMSECV = 13% across dates for AR). Applying the Nitrogen Sufficiency Index (NSI) formula to spectral indices/models made them mostly insensitive to the effects of cultivar. The most promising technique for determining N stress in potato based on spectral indices was found to be the MERIS Terrestrial Chlorophyll Index (MTCI) due to a combination of relatively high R2 values, lower RMSECVs, and high accuracy assessment. Pairwise comparison tests from the means separation showed that spectral indices/models from the imagery resulted in more statistically significant groupings of crop stress levels for the spectra than leaf N concentration because canopy-scale spectral data are affected by both tissue N concentration and biomass. The results of this study suggest that upon proper sensor calibration, canopy-scale spectral data may be the most sensitive tool available to detect N status of a potato crop.
author: Tyler J. Nigon, David J. Mulla, Carl J. Rosen, Yafit Cohen, Victor Alchanatis, Joseph Knight, Ronit Rud
publish year: 2015
creator: Tyler J. Nigon, David J. Mulla, Carl J. Rosen, Yafit Cohen, Victor Alchanatis, Joseph Knight, Ronit Rud
reference: Tyler J. Nigon, David J. Mulla, Carl J. Rosen, Yafit Cohen, Victor Alchanatis, Joseph Knight, Ronit Rud, "Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars", Computers and Electronics in Agriculture, Volume 112, March 2015, pages 36-46, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 112, March 2015, pages 36-46, Elsevier B.V.
label: Article 0634

Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield
type: NamedIndividual
type: Article
has knowledge model: Regressor_0636
abstract: While hyperspectral sensors describe plant canopy reflectance in greater detail than multispectral sensors, they also suffer from issues with data redundancy and spectral autocorrelation. Data mining techniques that extract relevant spectral features from hyperspectral data will aid the development of novel sensors for plant trait estimation. The objectives of this research were to (1) compare broad-band reflectance, narrow-band reflectance, and spectral derivatives for estimation of durum wheat traits in the field and (2) develop a genetic algorithm to identify the most relevant spectral features for durum wheat trait estimation. Experiments at Maricopa, Arizona during the winters of 2010–2011 and 2011–2012 tested six durum wheat cultivars with six split-applied nitrogen (N) fertilization rates. Durum wheat traits, including leaf area index, canopy dry weight, and plant N content, were measured from destructive biomass samples on four occassions in each growing season. Grain yield and grain N content were also measured. Canopy spectral reflectance data in 701 narrow wavebands from 350 nm to 1050 nm were collected weekly using a field spectroradiometer. First- and second-order spectral derivatives were calculated using Savitzky-Golay filtering. The narrow-band data were also used to estimate reflectance in broad wavebands, as typically collected by two commercial multispectral instruments. Partial least squares regression (PLSR) compared the ability of each spectral data set to estimate each measured durum wheat trait. A genetic algorithm was developed to mine narrow-band canopy reflectance and spectral derivative data for spectral features that improved estimates of durum wheat traits. Multispectral data in 4 broad bands estimated leaf area index, canopy dry weight, and plant N content with root mean squared errors of cross validation (RMSECV) between 33.0% and 67.6%, while hyperspectral data in 701 narrow bands reduced RMSECV to values between 19.3% and 36.3%. Use…
author: K.R. Thorp, G. Wang, K.F. Bronson, M. Badaruddin, J. Mon
publish year: 2017
creator: K.R. Thorp, G. Wang, K.F. Bronson, M. Badaruddin, J. Mon
reference: K.R. Thorp, G. Wang, K.F. Bronson, M. Badaruddin, J. Mon, "Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield", Computers and Electronics in Agriculture, Volume 136, 15 April 2017, pages 1-12, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 136, 15 April 2017, pages 1-12, Elsevier B.V.
label: Article 0636

Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis)
type: NamedIndividual
type: Article
has knowledge model: Regressor_0637
abstract: The reflectance from rice (Oryza sativa L.) leaves and canopy damaged by rice leaf folder (RLF), Cnaphalocrocis medinalis (Guenée) was studied at the booting stage in order to establish a monitoring method for RLF based on hyperspectral data. The results showed that reflectance from rice leaves significantly decreased in the green (530–570 nm) and near infrared (700–1000 nm) regions, and significantly increased in the blue (450–520 nm) and red (580–700 nm) regions as the leaf-roll rate of rice increased. Reflectance from rice canopy significantly decreased in the spectral regions from 737 to 1000 nm as the infestation scale of RLF increased, and the most correlation appeared at 938 nm. Seven spectral regions 503–521, 526–545, 550–568, 581–606, 688–699, 703–715, and 722–770 nm at leaf-level, and one region 747–754 nm at canopy-level were found to be sensitive bands to exhibit the damage severity in rice by RLF. The position of the red edge peak remarkably moved to blue region, and the amplitude and area of the red edge significantly decreased when rice leaves were severely infected by RLF. Thirty-eight spectral indices at leaf-level and 29 indices at canopy-level were found to be sensitive to leaf-roll rate and infestation scale in rice, respectively. The linear regression models were built to detect the leaf-roll rate (0.0–1.0) and infestation scale (0–5) in rice using leaf- and canopy-level reflectance data. The root mean square error of the model was only 0.059 and 0.22 for the leaf-roll rate and infestation scale, respectively. These results suggested that the hyperspectral reflectance was potential to detect RLF damage severity in rice.
author: Jianrong Huang, Huaijian Liao, Yubo Zhu, Jiayi Sun, Qihua Sun, Xiangdong Liu
publish year: 2012
creator: Jianrong Huang, Huaijian Liao, Yubo Zhu, Jiayi Sun, Qihua Sun, Xiangdong Liu
reference: Jianrong Huang, Huaijian Liao, Yubo Zhu, Jiayi Sun, Qihua Sun, Xiangdong Liu, "Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis)", Computers and Electronics in Agriculture, Volume 82, March 2012, pages 100-107, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 82, March 2012, pages 100-107, Elsevier B.V.
label: Article 0637

Hyperspectral indices for assessing damage by the solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton
type: NamedIndividual
type: Article
has knowledge model: Regressor_0642
abstract: Phenacoccus solenopsis Tinsley, a native of North America, is a widespread exotic mealybug infesting cotton, Gossypium spp. in several countries. Monitoring of this pest is generally undertaken through regular field surveys, which is labour intensive, time consuming and error prone. Alternately, radiometry is a reliable technique for rapid and non-destructive assessment of plant health. Thus, a study was conducted to characterize reflectance spectra of cotton plants with known mealybug infestation levels (grade-0 is healthy and grade-4 is severe), and seek to identify specific narrow wavelengths sensitive to mealybug damage. Reflectance measurements were made in the spectral range of 350–2500 nm using a hyperspectral radiometer. Significant differences were found in green, near infrared and short wave infrared spectral regions for plants with early stages of P. solenopsis infestation, and for plants showing higher grades of infestation these differences extended to all the regions except blue. A significant reduction in total chlorophyll (12.83–35.83%) and relative water content (1.93–23.49%) was observed in the infested plants. Reflectance sensitivity analysis of the hyperspectral data revealed wavelengths centered at 492, 550, 674, 768 and 1454 nm as most sensitive to mealybug damage. Mealybug Stress Indices (MSIs) were developed using two or three wavelengths, tested using multinomial logistic regression (MLR) analysis and compared with other indices published earlier. Results showed that the MSIs were superior (R2 = 0.82) to all other spectral vegetation indices tested. Further, the proposed MLR models corresponding to each MSIs were validated using two independent field data sets. The overall percent correct classification of cotton plants into different mealybug damage severity grades was in the range of 38.3 and 54.9. High classification accuracy for grade-1 (81.8%) showed that models are capable of early detection of mealybug damage. Results of this study co…
author: Mathyam Prabhakar, Yenumula G. Prasad, Sengottaiyan Vennila, Merugu Thirupathi, Gudapati Sreedevi, G. Ramachandra Rao, Bandi Venkateswarlu
publish year: 2013
creator: Mathyam Prabhakar, Yenumula G. Prasad, Sengottaiyan Vennila, Merugu Thirupathi, Gudapati Sreedevi, G. Ramachandra Rao, Bandi Venkateswarlu
reference: Mathyam Prabhakar, Yenumula G. Prasad, Sengottaiyan Vennila, Merugu Thirupathi, Gudapati Sreedevi, G. Ramachandra Rao, Bandi Venkateswarlu, "Hyperspectral indices for assessing damage by the solenopsis mealybug (Hemiptera: Pseudococcidae) in cotton", Computers and Electronics in Agriculture, Volume 97, September 2013, pages 61-70, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 97, September 2013, pages 61-70, Elsevier B.V.
label: Article 0642

Hyperspectral remote sensing of grapevine drought stress
type: NamedIndividual
type: Article
has knowledge model: Classifier 0644
abstract: In karst landscapes stony soils have little water holding capacity; the rational use of water for irrigation therefore plays an important management role. Because the water holding capacity is not homogenous, precision agriculture approaches would enable better management decisions. This research was carried out in an experimental vineyard grown in an artificially transformed karst terrain in Dalmatia, Croatia. The experimental design included four water treatments in three replicates: (1) fully irrigated, based on 100% crop evapotranspiration (ETc) application (N100); (2 and (3) deficit irrigation, based on 75% and 50% ETc applications (N75 and N50, respectively); and (4) non-irrigated (N0). Hyperspectral images of grapevines were taken in the summer of 2016 using two spectral-radiance (W sr−1 m−2) calibrated cameras, covering wavelengths from 409 to 988 nm and 950 to 2509 nm. The four treatments were grouped into a new set consisting of: (1) drought (N0); and (2) irrigated (the remaining three treatments: N100, N75, and N50). The images were analyzed using Partial Least Squares-Discriminant Analysis (PLS-DA), and treatments were classified using PLS-Single Vector Machines (PLS-SVM). PLS-SVM demonstrated the capability to determine levels of grapevine drought or irrigated treatments with an accuracy of more than 97%. PLS-DA identified relevant wavelengths, which were linked to O–H, C–H, and N–H stretches in water, carbohydrates and proteins. The study presents the applicability of hyperspectral imaging for drought stress assessment in grapevines, even though temporal variability needs to be taken into account for early detection.
author: M. Zovko, U. Žibrat, M. Knapič, M. Bubalo Kovačić, D. Romić
publish year: 2019
creator: M. Zovko, U. Žibrat, M. Knapič, M. Bubalo Kovačić, D. Romić
reference: M. Zovko, U. Žibrat, M. Knapič, M. Bubalo Kovačić, D. Romić, "Hyperspectral remote sensing of grapevine drought stress", Precision Agriculture, Volume 20, pages 335–347 (2019) , Springer
Source: Precision Agriculture, Volume 20, pages 335–347 (2019) , Springer
label: Article 0644

Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging
type: NamedIndividual
type: Article
has knowledge model: Classifier 0651
abstract: Healthy wheat kernels and wheat kernels damaged by the feeding of the insects: rice weevil (Sitophilus oryzae), lesser grain borer (Rhyzopertha dominica), rusty grain beetle (Cryptolestes ferrugineus), and red flour beetle (Tribolium castaneum) were scanned using a near-infrared (NIR) hyperspecrtal imaging system (700–1100 nm wavelength range) and a colour imaging system. Dimensionality of hyperspectral data was reduced and statistical and histogram features were extracted from NIR images of significant wavelengths and given as input to three statistical discriminant classifiers (linear, quadratic, and Mahalanobis) and a back propagation neural network (BPNN) classifier. A total of 230 features (colour, textural, and morphological) were extracted from the colour images and the most contributing features were selected and used as input to the statistical and BPNN classifiers. The quadratic discriminant analysis (QDA) classifier gave the highest accuracy and correctly identified 96.4% healthy and 91.0–100.0% insect-damaged wheat kernels using the top 10 features from 230 colour image features combined with hyperspectral image features.
author: Chandra B. Singh, Digvir S. Jayas, Jitendra Paliwal, Noel D.G. White
publish year: 2010
creator: Chandra B. Singh, Digvir S. Jayas, Jitendra Paliwal, Noel D.G. White
reference: Chandra B. Singh, Digvir S. Jayas, Jitendra Paliwal, Noel D.G. White, "Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging", Computers and Electronics in Agriculture, Volume 73, Issue 2, August 2010, pages 118-125, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 73, Issue 2, August 2010, pages 118-125, Elsevier B.V.
label: Article 0651

Identification of purple spot disease on asparagus crops across spatial and spectral scales
type: NamedIndividual
type: Article
has knowledge model: Classifier 0656
abstract: Purple spot caused by the fungus Stemphylium vesicarium is a disease that causes heavy damage in asparagus crops. It infects the leaves and stem and results in degradation of the quality in the harvested asparagus shoots, lowering their commercial value. Precautionary spraying with fungicides is practiced to prevent its appearance, posing a threat to consumers and the environment. The aim of this work was to evaluate the ability of visible and near-infrared field spectroscopy (UNISPEC-DC) to identify the appearance of the purple spot symptoms at the level of asparagus plants and the ability of very high resolution (Pleiades-1A) and wide area (Landsat 8) satellite images to map fungal distribution within asparagus fields and across the river plain, respectively. Field measurements were used as reference to select the optimum infection identification models. Results indicate that field spectroscopy and wide area remote sensing can be used to create sufficiently accurate quantification models of disease severity in asparagus plants (R2 = 0.83 and RMSE = 9.68%) or across asparagus fields (R2 = 0.85 and RMSE = 8.26%). The accuracy drops when using satellite data to quantify infection within field (R2 = 0.34 and RMSE = 13.13%). The contributing wavelengths, bands and indices can be utilized to provide timely information to farmers and agronomists in order to support precision plant protection applications.
author: I. Navrozidis, T.K. Alexandridis, A. Dimitrakos, A.L. Lagopodi, D. Moshou, G. Zalidis
publish year: 2018
creator: I. Navrozidis, T.K. Alexandridis, A. Dimitrakos, A.L. Lagopodi, D. Moshou, G. Zalidis
reference: I. Navrozidis, T.K. Alexandridis, A. Dimitrakos, A.L. Lagopodi, D. Moshou, G. Zalidis, "Identification of purple spot disease on asparagus crops across spatial and spectral scales", Computers and Electronics in Agriculture, Volume 148, May 2018, pages 322-329, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 148, May 2018, pages 322-329, Elsevier B.V.
label: Article 0656

Identifying and characterizing yield limiting soil factors with the aid of remote sensing and data mining techniques
type: NamedIndividual
type: Article
has knowledge model: Regressor_0659
abstract: Soil provides crop with nutrients, water and root support. But, soils vary a great deal in terms of origin, appearance, characteristics and production capacity. Better understanding of the causality between yield and yield-limiting soil factor(s) is essential for site-specific crop management. The objectives of this study were deriving a spatiotemporal yield trend map of a 144 km2 paddy rice growing region located at an alluvial plain in southwestern Taiwan from satellite images and exploring the potential yield-limiting soil factor(s) in conjunction with general soil survey data. Due to the complexity of data sets, classification and regression trees analysis (CART) was used to relate soil characteristics to yield classes in the spatiotemporal yield trend map, and followed by comparisons of soil characteristics between those consistently-high and -low yielding areas to explore the interactions between yields and soil properties. Through the above data mining analysis, high soil pH, severe leaching loss of applied nitrogen fertilizers, and excessive reductive root environment were suspected to be the major soil related low-yielding mechanisms spread within studied region. Soil characteristics that induced these low-yielding mechanisms were identified and mapped. Error analysis indicated that 61.8 % of the consistently low-yield areas could be correctly identified by just a few soil characteristics. Improvements of management practices to alleviate the negative effects on yields were also proposed based on the identified low yielding mechanisms. Our study highlighted the pressing need and possible methodologies to adjust management strategies for narrowing yield variability and increasing crop production.
author: Yi-Ping Wang, Yuan Shen
publish year: 2015
creator: Yi-Ping Wang, Yuan Shen
reference: Yi-Ping Wang, Yuan Shen, "Identifying and characterizing yield limiting soil factors with the aid of remote sensing and data mining techniques", Precision Agriculture, Volume 16, pages 99–118 (2015) , Springer
Source: Precision Agriculture, Volume 16, pages 99–118 (2015) , Springer
label: Article 0659

Identifying barley varieties by computer vision
type: NamedIndividual
type: Article
has knowledge model: Classifier 0660
abstract: Visual discrimination between barley varieties is difficult, and it requires training and experience. The development of automatic methods based on computer vision could have positive implications for the food processing industry. In the brewing industry, varietal uniformity is crucial for the production of high quality malt. The varietal purity of thousands of tons of grain has to be inspected upon purchase in the malt house.This paper evaluates the effectiveness of identification of barley varieties based on image-derived shape, color and texture attributes of individual kernels. Varieties can be determined by means of discriminant analysis, including reduction of feature space dimensionality, linear classifier ensembles and artificial neural networks, with high balanced accuracy ranging from 67% to 86%. The study demonstrated that classification results can be significantly improved by standardizing individual kernel images in terms of their anteroposterior and dorsoventral orientation and performing additional analyses of wrinkled regions.
author: Piotr M. Szczypinski, Artur Klepaczko, Piotr Zapotoczny
publish year: 2015
creator: Piotr M. Szczypinski, Artur Klepaczko, Piotr Zapotoczny
reference: Piotr M. Szczypinski, Artur Klepaczko, Piotr Zapotoczny, "Identifying barley varieties by computer vision", Computers and Electronics in Agriculture, Volume 110, January 2015, pages 1-8, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 110, January 2015, pages 1-8, Elsevier B.V.
label: Article 0660

Identifying blueberry fruit of different growth stages using natural outdoor color images
type: NamedIndividual
type: Article
has knowledge model: Classifier 0661
abstract: This study was conducted to identify blueberry fruit of different growth stages using natural outdoor images toward the development of a blueberry yield mapping system. As blueberries usually contain different maturity stages in a same branch, identification of blueberry fruit and their maturity stages from different background is very important for yield mapping. In this study, maturity stages of the fruit were divided into four categories: mature (m), near-mature (nm), near-young (ny) and young (y). A stepwised algorithm, termed ‘color component analysis based detection (CCAD)’ method, was developed and validated to identify blueberry fruit using outdoor color images. Firstly, a dataset was built using manually cropped pixels from training images. Three color components, red (R), blue (B) and hue (H), were selected using the forward feature selection algorithm (FFSA), and used to separate all fruit of four maturity stages from background through different classifiers. In this work, not only the traditional classifiers such as K-nearest neighbor (KNN), and naïve Bayesian classification (NBC) were used, but another newly introduced ‘supervised K-means clustering classifier (SK-means)’ was also developed and applied to the dataset. In the second step, classifiers were built to separate a group of ‘mature & near-mature’ fruit from a group of ‘near-young & young’ fruit from all fruit pixels. Finally, classifiers were developed to separate mature fruit from near-mature fruit, and near-young fruit from young fruit. The classifiers obtained from these different steps were then applied to validation images, resulting in final identification. Cross validation was conducted using these different classifiers and their results were compared. KNN classifier yielded the highest classification accuracy (85–98%) from the validation set of the prebuilt pixel dataset collected from the training images in all separations. An one-way ANOVA was used to compare the performance of the th…
author: Han Li, Won Suk Lee, Ku Wang
publish year: 2014
creator: Han Li, Won Suk Lee, Ku Wang
reference: Han Li, Won Suk Lee, Ku Wang, "Identifying blueberry fruit of different growth stages using natural outdoor color images", Computers and Electronics in Agriculture, Volume 106, August 2014, pages 91-101, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 106, August 2014, pages 91-101, Elsevier B.V.
label: Article 0661

Image processing algorithms for infield single cotton boll counting and yield prediction
type: NamedIndividual
type: Article
has knowledge model: Regressor_0668
abstract: Cotton boll number is an important component of fiber yield, arguably the most important phenotypic trait to plant breeders and growers alike. In addition, boll number provides a better understanding on the physiological and genetic mechanisms of crop growth and development, facilitating timely decisions on crop management to maximize profit. Traditional in-field cotton boll number counting by visual inspection is time consuming and labor-intensive. In this work, we presented novel image processing algorithms for automatic single cotton boll recognition and counting under natural illumination in the field. A digital camera mounted on a robot platform was used to acquire images with a 45° downward angle on three different days before harvest. A double-thresholding with region growth algorithm combining color and spatial features was applied to segment bolls from background, and three geometric-feature-based algorithms were developed to estimate boll number. Line features detected by linear Hough Transform and the minimum boundary distance between two regions were used to merge disjointed regions split by branches and burrs, respectively. The area and the elongation ratio between major and minor axes were used to separate bolls overlapping in clusters. A total of 210 images captured under sunny and cloudy illumination conditions on three days were used to validate the performance of the cotton boll recognition method, with an F1 score of around 0.98; whereas, the best accuracy for boll counting was around 84.6%. At the whole plot level, fifteen plots were used to build a linear regression model between the estimated boll number and the overall fiber yield with a R2 value of 0.53. The performance was evaluated by another ten plots with a mean absolute percentage error of 8.92% and a root mean square error of 99 g. The methodology developed in this study provides a means to estimate cotton boll number from color images under field conditions and would be helpful to pred…
author: Shangpeng Sun, Changying Li, Andrew H. Paterson, Peng W. Chee, Jon S. Robertson
publish year: 2019
creator: Shangpeng Sun, Changying Li, Andrew H. Paterson, Peng W. Chee, Jon S. Robertson
reference: Shangpeng Sun, Changying Li, Andrew H. Paterson, Peng W. Chee, Jon S. Robertson, "Image processing algorithms for infield single cotton boll counting and yield prediction", Computers and Electronics in Agriculture, Volume 166, November 2019, 104976, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 166, November 2019, 104976, Elsevier B.V.
label: Article 0668

Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove
type: NamedIndividual
type: Article
has knowledge model: Classifier 0673
abstract: Recognition and detection of green immature citrus fruit more accurately and efficiently in groves under natural illumination conditions provides a promising benefit for growers to plan application of nutrients during the fruit maturing stages and estimate their yield and profit prior to the harvesting period. The goal of this study was to develop a robust and fast algorithm to detect and count immature green citrus fruit in individual trees from colour images acquired with different fruit sizes and under various illumination conditions. Adaptive Red and Blue chromatic map (ARB) was created and combined with the Hue image extracted after histogram equalization (HEH). The sum of absolute transformed difference (SATD), a block-matching method, was applied to detect potential fruit pixels. After OR operation of the results obtained from colour and SATD analysis which kept as many fruit pixels as possible, a kernel support vector machine (SVM) classifier was built with same learning sets used for different classification stages to remove false positives based on five selected texture features. The algorithm was evaluated with a set of testing images, and achieved more than 83% recognition accuracy. The proposed method can provide a more efficient way for green citrus identification in a grove using colour images.
author: Chuanyuan Zhao, Won Suk Lee, Dongjian He
publish year: 2016
creator: Chuanyuan Zhao, Won Suk Lee, Dongjian He
reference: Chuanyuan Zhao, Won Suk Lee, Dongjian He, "Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove", Computers and Electronics in Agriculture, Volume 124, June 2016, pages 243-253, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 124, June 2016, pages 243-253, Elsevier B.V.
label: Article 0673

Immature green citrus fruit detection using color and thermal images
type: NamedIndividual
type: Article
has knowledge model: Classifier 0674
abstract: Citrus fruit detection is one of the most important and challenging steps in citrus yield mapping. The distinct color differences between the ripe fruit and leaves allowed previously-described imaging-based methods to achieve good results. However, immature green citrus fruit detection, which aims to provide valuable information for citrus yield mapping at earlier stages is much more difficult because the fruit and leaf colors are very similar. This study combines color and thermal images for immature green fruit detections. Experiments identified optimal conditions for thermal imaging. A multimodal imaging platform was built to integrate color and thermal cameras. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color-Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. An increase in recall rate from 78.1% when using only color images to 90.4% after fusing the color and thermal images was obtained at similar precision rates, and an increase in precision rate from 86.6% to 95.5% was obtained at similar recall rates. The fusion of the color and thermal images effectively improved immature green citrus fruit detection.
author: H. Gan, W.S. Lee, V. Alchanatis, R. Ehsani, J.K. Schueller
publish year: 2018
creator: H. Gan, W.S. Lee, V. Alchanatis, R. Ehsani, J.K. Schueller
reference: H. Gan, W.S. Lee, V. Alchanatis, R. Ehsani, J.K. Schueller, "Immature green citrus fruit detection using color and thermal images", Computers and Electronics in Agriculture, Volume 152, September 2018, pages 117-125, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 152, September 2018, pages 117-125, Elsevier B.V.
label: Article 0674

Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification
type: NamedIndividual
type: Article
has knowledge model: Classifier 0676
abstract: The problem of automatic recognition of plant diseases has been historically based on conventional machine learning techniques such as Support Vector Machines, Multilayer Perceptron Neural Networks and Decision Trees. However, the prevailing approach has shifted to the application of deep learning concepts, with focus on Convolutional Neural Networks (CNNs). In general, this kind of technique requires large datasets containing a wide variety of conditions to work properly. This is an important limitation, given the many challenges involved in the construction of a suitable image database. In this context, this study investigates how the size and variety of the datasets impact the effectiveness of deep learning techniques applied to plant pathology. This investigation was based on an image database containing 12 plant species, each presenting very different characteristics in terms of number of samples, number of diseases and variety of conditions. Experimental results indicate that while the technical constraints linked to automatic plant disease classification have been largely overcome, the use of limited image datasets for training brings many undesirable consequences that still prevent the effective dissemination of this type of technology.
author: Jayme Garcia Arnal Barbedo
publish year: 2018
creator: Jayme Garcia Arnal Barbedo
reference: Jayme Garcia Arnal Barbedo, "Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification", Computers and Electronics in Agriculture, Volume 153, October 2018, pages 46-53, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 153, October 2018, pages 46-53, Elsevier B.V.
label: Article 0676

Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery
type: NamedIndividual
type: Article
has knowledge model: Regressor_0677
abstract: Crop aboveground biomass (AGB) is one of the most important indicators in crop breeding and crop management, and can be used for crop yield prediction. A number of vegetation indices (VIs) have been proposed to estimate crop biomass, but they perform poorly at high biomass levels and are easily affected by background materials. Texture analysis has been proved to be an efficient approach in forest biomass estimation, but has never been applied to crops with low-altitude unmanned aerial vehicle (UAV) images. The objective of this study was to improve rice AGB estimation by combining textural and spectral analysis of UAV imagery. A two-year rice experiment was conducted in 2015 and 2016, involving different nitrogen (N) rates, planting densities and rice cultivars with three replicates. A six-band multispectral (MS) camera was mounted on a UAV to acquire rice canopy images at critical stages during the rice growing seasons and concurrent field samplings were taken. Simple regression and stepwise multiple linear regression models were developed between biomass data from the two-year experiment and image parameters derived from four different types of feature sets. These features represented commonly used VIs, texture parameters, normalization of texture measurements (normalized difference texture index, NDTI) and combinations of VIs and NDTIs. Finally, all the regression models were evaluated by cross-validation over pooled data with the coefficient of determination (R2) and the root mean square error (RMSE). Results demonstrated that the optimized soil adjusted vegetation index (OSAVI) exhibited the best relationship with AGB for the whole season (R2 = 0.63) and post-heading stages (R2 = 0.65). Red-edge-based indices yielded best performance (R2 > 0.70) only for the growth stages before heading. The texture measurement mean (MEA) from the NIR band was the best among the eight candidates in AGB estimation. Texture index (NDTI (MEA800, MEA550)) was superior to all the e…
author: Hengbiao Zheng, Tao Cheng, Meng Zhou, Dong Li, Xia Yao, Yongchao Tian, Weixing Cao, Yan Zhu
publish year: 2019
creator: Hengbiao Zheng, Tao Cheng, Meng Zhou, Dong Li, Xia Yao, Yongchao Tian, Weixing Cao, Yan Zhu
reference: Hengbiao Zheng, Tao Cheng, Meng Zhou, Dong Li, Xia Yao, Yongchao Tian, Weixing Cao, Yan Zhu, "Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery", Precision Agriculture, Volume 20, pages 611–629 (2019) , Springer
Source: Precision Agriculture, Volume 20, pages 611–629 (2019) , Springer
label: Article 0677

Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices
type: NamedIndividual
type: Article
has knowledge model: Classifier 0680
abstract: In this study, we investigated the possibility of using ground-based remote sensing technology to estimate powdery mildew disease severity in winter wheat. Using artificially inoculated fields, potted plants, and disease nursery tests, we measured the powdery mildew canopy spectra of varieties of wheat at different levels of incidence and growth stages to investigate the disease severity. The results showed that the powdery mildew sensitive bands were between 580 and 710 nm. The best two-band vegetation index that correlated with wheat powdery mildew between 400 and 1000 nm wavelength were the normalized spectrum 570–590 and 536–566 nm bands for the ratio index, and 568–592 and 528–570 nm for the normalized difference index. The coefficients of determination (R 2) for both were almost the same. The optimum dual-green vegetation index was constructed based on a calculation of the ratio and normalized difference between the normalized spectrum within the two green bands. The coefficients of determination (R 2) of DGSR (584, 550) (dual-green simple ratio) and DGND (584, 550) (dual-green normalized difference) were both 0.845. The inverse models of disease severity performed well in the test process at the canopy scale, and indicated that, compared with the traditional vegetation indices of Lwidth, mND705, ND (SDr, SDb), SIPI, and GNDVI, the novel dual-green indices greatly improved the remote sensing detection of wheat powdery mildew disease. Following these results, combined disease severity and canopy spectra were shown to be of enormous value when applied to the accurate monitoring, prevention, and control of crop diseases.
author: Wei Feng, Wenying Shen, Li He, Jianzhao Duan, Binbin Guo, Yingxue Li, Chenyang Wang, Tiancai Guo
publish year: 2016
creator: Wei Feng, Wenying Shen, Li He, Jianzhao Duan, Binbin Guo, Yingxue Li, Chenyang Wang, Tiancai Guo
reference: Wei Feng, Wenying Shen, Li He, Jianzhao Duan, Binbin Guo, Yingxue Li, Chenyang Wang, Tiancai Guo, "Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices", Precision Agriculture, Volume 17, pages 608–627 (2016) , Springer
Source: Precision Agriculture, Volume 17, pages 608–627 (2016) , Springer
label: Article 0680

Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor
type: NamedIndividual
type: Article
has knowledge model: Classifier 0684
abstract: In-season site-specific nitrogen (N) management is a promising strategy to improve crop N use efficiency and reduce risks of environmental contamination. To successfully implement such precision management strategies, it is important to accurately estimate yield potential without additional topdressing N application (YP0) as well as precisely assess the responsiveness to additional N application (RI) during the growing season. Previous research has mainly used normalized difference vegetation index (NDVI) or ratio vegetation index (RVI) obtained from GreenSeeker active crop canopy sensor with two fixed bands in red and near-infrared (NIR) spectrums to estimate these two parameters. The development of three-band Crop Circle active sensor provides a potential to improve in-season estimation of YP0 and RI. The objectives of this study were twofold: (1) identify important vegetation indices obtained from Crop Circle ACS-470 sensor for estimating rice YP0 and RI; and (2) evaluate their potential improvements over GreenSeeker NDVI and RVI. Four site-years of field N rate experiments were conducted in 2012 and 2013 at the Jiansanjiang Experiment Station of China Agricultural University located in Northeast China. The GreenSeeker and Crop Circle ACS-470 active canopy sensor with green, red edge, and NIR bands were used to collect rice canopy reflectance data at different key growth stages. The results indicated that both the GreenSeeker (best R2 = 0.66 and 0.70, respectively) and Crop Circle (best R2 = 0.71 and 0.77, respectively) sensors worked well for estimating YP0 and RI at the stem elongation stage. At the booting stage, Crop Circle red edge optimized soil adjusted vegetation index (REOSAVI, R2 = 0.82) and green ratio vegetation index (R2 = 0.73) explained 26 and 22 % more variability in YP0 and RI, respectively, than GreenSeeker NDVI or RVI. At the heading stage, the GreenSeeker sensor indices became saturated and consequently could not be used for YP0 or RI estimati…
author: Qiang Cao, Yuxin Miao, Jianning Shen, Weifeng Yu, Fei Yuan, Shanshan Cheng, Shanyu Huang, Hongye Wang, Wen Yang, Fengyan Liu
publish year: 2016
creator: Qiang Cao, Yuxin Miao, Jianning Shen, Weifeng Yu, Fei Yuan, Shanshan Cheng, Shanyu Huang, Hongye Wang, Wen Yang, Fengyan Liu
reference: Qiang Cao, Yuxin Miao, Jianning Shen, Weifeng Yu, Fei Yuan, Shanshan Cheng, Shanyu Huang, Hongye Wang, Wen Yang, Fengyan Liu, "Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor", Precision Agriculture, Volume 17, pages 136–154 (2016) , Springer
Source: Precision Agriculture, Volume 17, pages 136–154 (2016) , Springer
label: Article 0684

In-field cotton detection via region-based semantic image segmentation
type: NamedIndividual
type: Article
has knowledge model: Classifier 0688
abstract: Crop detection from the images taken in the natural field is not only a complex task, but also stands an important data for precision agriculture in obtaining crop growth information of field crops. This paper reports on a novel in-field cotton detection via region-based semantic image segmentation with two perspectives of observation, including unsupervised region generation and supervised semantic labeling prediction. First, simple linear iterative clustering (SLIC) and density-based spatial clustering of applications with noise (DBSCAN) on Wasserstein distance are employed to generate regions, with superiority in edge-preserving and density contrast distribution. Then histogram-based color and texture features extracted from each region proposal are passed to random forest, achieving semantic labeling prediction on in-field cotton images. Finally, to evaluate the robustness and accuracy of the proposed method on cotton detection, 46 test images taken from the year 2012 to 2015 are utilized to compare the proposed method with other well-established methodologies based on four metrics. Experiments and comparisons demonstrate that our method outperforms other mentioned methods with highest mean values and lowest standard deviations. Furthermore, the method can also detect the boll opening stage automatically, which provides support for precision agriculture. The dataset and source code will be made available online.
author: Yanan Li, Zhiguo Cao, Hao Lu, Yang Xiao, Yanjun Zhu, Armin B. Cremers
publish year: 2016
creator: Yanan Li, Zhiguo Cao, Hao Lu, Yang Xiao, Yanjun Zhu, Armin B. Cremers
reference: Yanan Li, Zhiguo Cao, Hao Lu, Yang Xiao, Yanjun Zhu, Armin B. Cremers, "In-field cotton detection via region-based semantic image segmentation", Computers and Electronics in Agriculture, Volume 127, September 2016, pages 475-486, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 127, September 2016, pages 475-486, Elsevier B.V.
label: Article 0688

In-field detection of Alternaria solani in potato crops using hyperspectral imaging
type: NamedIndividual
type: Article
has knowledge model: Classifier 0689
abstract: Automatic detection of early blight caused by Alternaria solani could promote a drastic reduction in the consumption of plant protection agents and the related production losses. A proximal sensing platform was constructed and calibrated for acquiring high resolution hyperspectral images in the field, and used to accurately map Alternaria lesions. High resolution canopy reflectance images were obtained for 32 potato plants that had been infected with A. solani and 32 healthy reference plants. Spectral classifiers like partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) based on PCA scores were tested to discriminate affected and non-affected pixels. Both spectral classifiers performed well at pixel level with accuracies above 0.92. The NIR region (750 nm) was identified as the most discriminative part of the spectrum for detecting the lesions. As the disease pressure is typically expressed as the number of lesions per area, the accuracy was also evaluated at this level. This indicated a considerable number of false detections at the edges of the leaves and the leaf axils. Therefore, a decision tree was designed based on expert knowledge about the shape of Alternaria lesions, and used to post-process the classified images. This reduced the number of false detections, increasing the precision from 0.17 to 0.22 at the expense of a reduction in recall from 0.88 to 0.84. This leaves considerable room for improvement in the classification accuracy at the object level. We learned that (1) few, broad wavelengths are sufficient and (2) spatial context is essential for the detection of lesions caused by Alternaria infection. The application of more powerful object classification techniques such as convolutional neural networks to enhance the model performance by efficiently encapsulating the spatial context in the classifier might further improve the detection performance. This could pave the way to UAV or tractor based Alternaria mapping.
author: Ruben Van De Vijver, Koen Mertens, Kurt Heungens, Ben Somers, David Nuyttens, Irene Borra-Serrano, Peter Lootens, Isabel Roldan-Ruiz, Jürgen Vangeyte, Wouter Saeys
publish year: 2020
creator: Ruben Van De Vijver, Koen Mertens, Kurt Heungens, Ben Somers, David Nuyttens, Irene Borra-Serrano, Peter Lootens, Isabel Roldan-Ruiz, Jürgen Vangeyte, Wouter Saeys
reference: Ruben Van De Vijver, Koen Mertens, Kurt Heungens, Ben Somers, David Nuyttens, Irene Borra-Serrano, Peter Lootens, Isabel Roldan-Ruiz, Jürgen Vangeyte, Wouter Saeys, "In-field detection of Alternaria solani in potato crops using hyperspectral imaging", Computers and Electronics in Agriculture, Volume 168, January 2020, 105106, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 168, January 2020, 105106, Elsevier B.V.
label: Article 0689

Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning
type: NamedIndividual
type: Article
has knowledge model: Cluster 0701
abstract: Research into crop growth models at the spatial scale is of great significance for evaluating crop growth, predicting grain yield and studying global climate change. Coupling spatial remote sensing (RS) data can effectively promote the simulation of growth models at spatial scales. However, the integration of RS data and crop models to produce a coupled model based on pixel by pixel requires a large amount of calculations. Simulation zone partitioning is used to separate and cluster the large area into a few relatively uniform zones. Then, the growth model can run on the basis of these units. This method both reflects spatial heterogeneity and avoids repeated simulations of regions with similar attributes, improving the simulation efficiency. In this study, simulation partitioning was performed using soil nutrient indices (organic matter content, total nitrogen content and available potassium content) and corresponding spatial characteristics of wheat growth, as indicated by RS data. A coupled model, integrating RS information and the WheatGrow model, using vegetation indices as the coupling parameters (based on the Particle Swarm Optimization algorithm and PROSAIL model), was developed. The aim was to realize accurate prediction of wheat growth parameters and grain yield at the spatial scale. Good zone partitions were obtained by partitioning with the spatial combination of soil nutrient indices and the wheat canopy vegetation index, calculated during the main growth (jointing, heading and filling) stages. The variation coefficients of each index within individual simulation sub-zones were much smaller than those of the indices across the whole area. An analysis of variance showed that the indices were significantly different between the simulation sub-zones, which indicated that appropriate simulated sub-zones had been defined. The minimum root mean square error of the leaf area index, leaf nitrogen accumulation and yield between the predicted values and the value…
author: Caili Guo, Ling Zhang, Xiang Zhou, Yan Zhu, Weixing Cao, Xiaolei Qiu, Tao Cheng, Yongchao Tian
publish year: 2018
creator: Caili Guo, Ling Zhang, Xiang Zhou, Yan Zhu, Weixing Cao, Xiaolei Qiu, Tao Cheng, Yongchao Tian
reference: Caili Guo, Ling Zhang, Xiang Zhou, Yan Zhu, Weixing Cao, Xiaolei Qiu, Tao Cheng, Yongchao Tian, "Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning", Precision Agriculture, Volume 19, pages 55–78 (2018) , Springer
Source: Precision Agriculture, Volume 19, pages 55–78 (2018) , Springer
label: Article 0701

Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield
type: NamedIndividual
type: Article
has knowledge model: Regressor_0702
abstract: Widespread adoption of precision agriculture requires timely acquisition of low-cost, high quality soil and crop yield maps. Integration of remotely sensed data and machine learning algorithms offers cost-and time-effective approach for spatial prediction of soil properties and crop yield compared to conventional approaches. The objectives of this study were to: (i) evaluate the role of remotely sensed images; (ii) compare the performance of various machine learning algorithms; and (iii) identify the importance of remotely sensed image-derived variables, in spatial prediction of soil properties and corn yield. This study integrated field based data on five soil properties (i.e., soil organic matter (SOM), cation exchange capacity (CEC), magnesium (Mg), potassium (K), and pH) and yield monitor based corn yield data with multispectral aerial images and topographic data, both collected in 2013, from seven fields at the Molly Caren Farm near London, Ohio. Digital elevation model data, at a resolution of 1 m, was used to derive topographic properties of the fields. Multispectral images collected at bare-soil conditions, at a resolution 0.30 m, were used to derive soil and vegetation indices. Models developed for prediction of soil properties and corn yield using linear regression (LM) and five machine learning algorithms (i.e., Random Forest (RF); Neural Network (NN); Support Vector Machine (SVM) with radial and linear kernel functions; Gradient Boosting Model (GBM); and Cubist (CU)) were evaluated in terms of coefficient of determination (R2) and root mean square error (RMSE). Machine learning algorithms were found to outperform LM algorithm for most of the times with a higher R2 and lower RMSE. Based on models for seven fields, on average, NN provided the highest accuracy for SOM (R2 = 0.64, RMSE = 0.44) and CEC (R2 = 0.67, RMSE = 2.35); SVM for K (R2 = 0.21, RMSE = 0.49) and Mg (R2 = 0.22, RMSE = 4.57); and GBM for pH (R2 = 0.15, RMSE = 0.62). For corn yield, RF consi…
author: Sami Khanal, John Fulton, Andrew Klopfenstein, Nathan Douridas, Scott Shearer
publish year: 2018
creator: Sami Khanal, John Fulton, Andrew Klopfenstein, Nathan Douridas, Scott Shearer
reference: Sami Khanal, John Fulton, Andrew Klopfenstein, Nathan Douridas, Scott Shearer, "Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield", Computers and Electronics in Agriculture, Volume 153, October 2018, pages 213-225, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 153, October 2018, pages 213-225, Elsevier B.V.
label: Article 0702

Investigating temporal and spatial patterns of cranberry yield in New Jersey fields
type: NamedIndividual
type: Article
has knowledge model: Regressor_0709
abstract: Cranberries are grown in sensitive wetland ecosystems and precision farming could be beneficial to reduce agro-chemical pollution and increase production without expanding area. Precision farming requires knowledge of the variation of yield within-fields but cranberry harvesting methods produce only one yield value per field unless an expensive pre-harvest berry count is done. Co-operatives and extension services have an important role in precision farming to: (1) determine important factors affecting yield patterns within a growing region and (2) identify fields that would benefit most from future intensive survey. This paper reports a study to investigate temporal and spatial patterns in useable and poor quality cranberry yield for the New Jersey (NJ), USA growing region. Principal components analysis indicated that mean growing season temperature is important for understanding temporal patterns in useable yield and maximum temperatures and precipitation for poor quality yield. Multiple linear regression showed that some cultivars were susceptible to disease and poor quality yield in years with high maximum growing season temperatures. Analysis of spatial patterns using area to area and area to point kriging, local cluster analysis and geographically weighted regression helped identify clusters of fields that were consistently yielding or alternated between high and low yielding. They also showed differences between owners and soil types particularly in hot or wet years showing the different response to soil types to weather and the potential for improvement in irrigation practices by some owners. The methods used should be useful for other growing regions and crops, particularly where there are no yield monitors.
author: R. Kerry, P. Goovaerts, D. Gimenez, P. V. Oudemans
publish year: 2017
creator: R. Kerry, P. Goovaerts, D. Gimenez, P. V. Oudemans
reference: R. Kerry, P. Goovaerts, D. Gimenez, P. V. Oudemans, "Investigating temporal and spatial patterns of cranberry yield in New Jersey fields", Precision Agriculture, Volume 18, pages 507–524 (2017) , Springer
Source: Precision Agriculture, Volume 18, pages 507–524 (2017) , Springer
label: Article 0709

Jujube yield prediction method combining Landsat 8 Vegetation Index and the phenological length
type: NamedIndividual
type: Article
has knowledge model: Regressor_0716
abstract: It is challenging to generate a time series of vegetation indices from moderate spatial resolution Landsat Thematic Mapper images (Landsat 8) for crop yield forecasting. In addition, crop yields are correlated with phenology information, especially the fruit filling days. The objectives of this study were to identify the phenology time for making a reliable jujube yield prediction, more importantly, explore an approach that used the length of phenology growth period to improve remotely sensed estimates of inter-annual variability for yields. The best time for making jujube yield prediction was found to be during the fruit filling period, showing higher correlation coefficient (r) between vegetation indices and yields. The average NDVI for 14th and 15th half-months represented a better performance for yield prediction, with a highest r value of 0.87 for NDVI, 0.82 for SAVI, 0.73 for NDWI and 0.73 for EVI, respectively. The potential of using Landsat-NDVI for jujube yield estimation, combined with the phenological length, was preliminarily proved based on 200 observations of individual jujube orchards, showing a validated R2 of 0.85, 0.80 and 0.67, RMSE of 0.61, 0.78 and 0.85 t ha−1 for 2013, 2014 and 2016, respectively. Furthermore, the phenological adjusted model was further evaluated by inter-annual official statistic data, with R2 and RMSE values ranging from 0.38 to 0.53, and 0.31 to 0.47 t ha−1, respectively. The proposed method showed better performance between years when the fruit filling days differed greatly than the leave-one-year-out method, which was verified to well fit to jujube yield monitoring and mapping two months before harvest.
author: Tiecheng Bai, Nannan Zhang, Benoit Mercatoris, Youqi Chen
publish year: 2019
creator: Tiecheng Bai, Nannan Zhang, Benoit Mercatoris, Youqi Chen
reference: Tiecheng Bai, Nannan Zhang, Benoit Mercatoris, Youqi Chen, "Jujube yield prediction method combining Landsat 8 Vegetation Index and the phenological length", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 1011-1027, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 162, July 2019, pages 1011-1027, Elsevier B.V.
label: Article 0716

Understanding rat occurrences in oil palm plantation using high-resolution satellite image and GIS data
type: NamedIndividual
type: Article
has knowledge model: Regressor_0720
abstract: Rats are an important vertebrate pest of oil palm in Malaysia. This damage translates into substantial economic loss to the oil palm plantation sector. We used habitat factors dervived from GIS data to develop a logistic regression model to predict rat occurrence in a FELDA oil palm plantation in Sabah, Malaysia. Our logistic regression results predicted a high occurrence of rats in young palms areas (<3 years) and in areas near the plantation roads. The rat occurrence areas were further examined using a detailed land cover types generated from a high-resolution satellite image (GeoEye, spatial resolution: 0.5 m). Our results showed that the combination of bare or open areas bushes mixed with partial shades of young palms is important to the survival of Rattus rattus diardii, a highly adaptable rat species that dominated the plantation. The combined use of GIS and high-resolution satellite remote sensing techniques enhanced our understanding of rat occurrence in oil palm plantation. Our results suggest these techniques can be used to predict rat occurrence and can be used to guide the rat baiting program in the oil palm plantation.
author: M.-H. Phua, C. W. Chong, A. H. Ahmad, M. N. Hafidzi
publish year: 2018
creator: M.-H. Phua, C. W. Chong, A. H. Ahmad, M. N. Hafidzi
reference: M.-H. Phua, C. W. Chong, A. H. Ahmad, M. N. Hafidzi, "Understanding rat occurrences in oil palm plantation using high-resolution satellite image and GIS data", Precision Agriculture, Volume 19, pages 42–54 (2018) , Springer
Source: Precision Agriculture, Volume 19, pages 42–54 (2018) , Springer
label: Article 0720

Land suitability assessments for yield prediction of cassava using geospatial fuzzy expert systems and remote sensing
type: NamedIndividual
type: Article
has knowledge model: Regressor_0721
abstract: Cassava has the potential to be a promising crop that can adapt to changing climatic conditions in Indonesia due to its low water requirement and drought tolerance. However, inappropriate land selection decisions limit cassava yields and increase production-related costs to farmers. As a root crop, yield prediction using vegetation indices and biophysical properties is essential to maximize the yield of cassava before harvesting. Therefore, the purpose of this research was to develop a yield prediction model based on suitable areas that assess with land suitability analysis (LSA). For LSA, the priority indicators were identified using a fuzzy expert system combined with a multicriteria decision method including ecological categories. Furthermore, the yield prediction method was developed using satellite remote sensing datasets. In this analysis, Sentinel-2 datasets were collected and analyzed in SNAP® and ArcGIS® environments. The multisource database of ecological criteria for cassava production was built using the fuzzy membership function. The results showed that 42.17% of the land area was highly suitable for cassava production. Then, in the highly suitable area, the yield prediction model was developed using the vegetation indices based on Sentinel-2 datasets with 10 m resolution for the accuracy assessment. The vegetation indices were used to predict cassava growth, biophysical condition, and phenology over the growing seasons. The NDVI, SAVI, IRECI, LAI, and fAPAR were used to develop the model for predicting cassava growth. The generated models were validated using regression analysis between observed and predicted yield. As the vegetation indices, NDVI showed higher accuracy in the yield prediction model (R2 = 0.62) compared to SAVI and IRECI. Meanwhile, LAI had a higher prediction accuracy (R2 = 0.70) than other biophysical properties, fAPAR. The combined model using NDVI, SAVI, IRECI, LAI, and fAPAR reported the highest accuracy (R2 = 0.77). The ground tr…
author: Riska Ayu Purnamasari, Ryozo Noguchi, Tofael Ahamed
publish year: 2019
creator: Riska Ayu Purnamasari, Ryozo Noguchi, Tofael Ahamed
reference: Riska Ayu Purnamasari, Ryozo Noguchi, Tofael Ahamed, "Land suitability assessments for yield prediction of cassava using geospatial fuzzy expert systems and remote sensing", Computers and Electronics in Agriculture, Volume 166, November 2019, 105018, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 166, November 2019, 105018, Elsevier B.V.
label: Article 0721

Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery
type: NamedIndividual
type: Article
has knowledge model: Regressor_0725
abstract: The Leaf Area Index (LAI) is an ecophysiology key parameter characterising the canopy-atmosphere interface where most of the energy fluxes are exchanged. However, producing maps for managing the spatial and temporal variability of LAI in large croplands with traditional techniques is typically laborious and expensive. The objective of this paper is to evaluate the reliability of LAI estimation by processing dense 3D point clouds as a cost-effective alternative to traditional LAI assessments. This would allow for high resolution, extensive and fast mapping of the index, even in hilly and not easily accessible regions. In this setting, the 3D point clouds were generated from UAV-based multispectral imagery and processed by using an innovative methodology presented here. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for canopy thickness, height and leaf density distribution along the wall. For the validation of the estimated LAI, an experiment was conducted in a vineyard in Piedmont: the leaf area of 704 vines was manually measured by the inclined point quadrant approach and six UAV flights were contextually performed to acquire the aerial images. The vineyard LAI estimated by the proposed methodology showed to be correlated with the ones obtained by the traditional manual method. Indeed, the obtained R2 value of 0.82 can be considered fully adequate, compatible to the accuracy of the reference LAI manual measurement.
author: L. Comba, A. Biglia, D. Ricauda Aimonino, C. Tortia, E. Mania, S. Guidoni, P. Gay
publish year: 2019
creator: L. Comba, A. Biglia, D. Ricauda Aimonino, C. Tortia, E. Mania, S. Guidoni, P. Gay
reference: L. Comba, A. Biglia, D. Ricauda Aimonino, C. Tortia, E. Mania, S. Guidoni, P. Gay, "Leaf Area Index evaluation in vineyards using 3D point clouds from UAV imagery", Precision Agriculture (2019) , Springer
Source: Precision Agriculture (2019) , Springer
label: Article 0725

Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENμS satellite
type: NamedIndividual
type: Article
has knowledge model: Regressor_0726
abstract: Relationships between leaf spectral reflectance at 400–900 nm and nitrogen levels in potato petioles and leaves were studied. Five nitrogen (N) fertilizer treatments were applied to build up levels of nitrogen variation in potato fields in Israel in spring 2006 and 2007. Reflectance of leaves was measured in the field over a spectral range of 400–900 nm. The leaves were sampled and analyzed for petiole NO3–N and leaf percentage N (leaf-%N). Prediction models of leaf nitrogen content were developed based on an optical index named transformed chlorophyll absorption reflectance index (TCARI) and on partial least squares regression (PLSR). Prediction models were also developed based on simulated bands of the future VENμS satellite (Vegetation and Environment monitoring on a New Micro-Satellite). Leaf spectral reflectance correlated better with leaf-%N than with petiole NO3–N. The TCARI provided strong correlations with leaf-%N, but only at the tuber-bulking stage. The PLSR analysis resulted in a stronger correlation than TCARI with leaf-%N. An R2 of 0.95 (p < 0.01) and overall accuracy of 80.5% (Kappa = 74%) were determined for both vegetative and tuber-bulking periods. The simulated VENμS bands gave a similar correlation with leaf-%N to that of the spectrometer spectra. The satellite has significant potential for spatial analysis of nitrogen levels with inexpensive images that cover large areas every 2 days.
author: Y. Cohen, V. Alchanatis, Y. Zusman, Z. Dar, D. J. Bonfil, A. Karnieli, A. Zilberman, A. Moulin, V. Ostrovsky, A. Levi, R. Brikman, M. Shenker
publish year: 2010
creator: Y. Cohen, V. Alchanatis, Y. Zusman, Z. Dar, D. J. Bonfil, A. Karnieli, A. Zilberman, A. Moulin, V. Ostrovsky, A. Levi, R. Brikman, M. Shenker
reference: Y. Cohen, V. Alchanatis, Y. Zusman, Z. Dar, D. J. Bonfil, A. Karnieli, A. Zilberman, A. Moulin, V. Ostrovsky, A. Levi, R. Brikman, M. Shenker, "Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENμS satellite", Precision Agriculture, Volume 11, pages 520–537 (2010) , Springer
Source: Precision Agriculture, Volume 11, pages 520–537 (2010) , Springer
label: Article 0726

Machine vision-based automatic disease symptom detection of onion downy mildew
type: NamedIndividual
type: Article
has knowledge model: Classifier 0743
abstract: The effective crop management is major issue in recent agriculture because the cultivation area per farmer is increasing consistently while the aging-related reductions in the labor force. To manage crop cultivation effectively, it needs automatic monitoring in farmland. This paper presents an image-based field monitoring system for automatically crop monitoring and consists of constructing field monitoring system for periodic capturing of onion field images, training the deep neural network model for detecting the disease symptom, and evaluating performance of the developed system. The field monitoring system was composed of a PTZ camera, a motor system, wireless transceiver, and image logging module. The deep learning model was trained based on weakly supervised learning method that can classify and localize objects only with image-level annotation. It is effective to recognize crop disease symptom which has ambiguous boundary. The model was trained using captured onion images using the filed monitoring system, and 6 classes including the disease symptom were classified. The detected disease symptom was localized from background through thresholding of the class activation map. The 60% of maximum value in class activation map was determined as an Optimal threshold for disease symptom localization. Identification performance of disease symptom was evaluated using mAP metric by IoU. The results show that the mAP at IoU criteria 0.5, which should have over 50% overlap, was the highest in all models from 74.1 to 87.2. The results showed that the developed field monitoring system could automatically detect onion disease symptoms in real-time.
author: Wan-Soo Kim, Dae-Hyun Lee, Yong-Joo Kim
publish year: 2020
creator: Wan-Soo Kim, Dae-Hyun Lee, Yong-Joo Kim
reference: Wan-Soo Kim, Dae-Hyun Lee, Yong-Joo Kim, "Machine vision-based automatic disease symptom detection of onion downy mildew", Computers and Electronics in Agriculture, Volume 168, January 2020, 105099, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 168, January 2020, 105099, Elsevier B.V.
label: Article 0743

Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery
type: NamedIndividual
type: Article
has knowledge model: Classifier 0747
abstract: The application of remote sensing technology and precision agriculture in the oil palm industry is in development. This study investigated the potential of high resolution QuickBird satellite imagery, which has a synoptic overview, for detecting oil palms infected by basal stem rot disease and for mapping the disease. Basal stem rot disease poses a major threat to the oil palm industry, especially in Indonesia. It is caused by Ganoderma boninense and the symptoms can be seen on the leaf and basal stem. At present there is no effective control for this disease and early detection of the infection is essential. A detailed, accurate and rapid method of monitoring the disease is needed urgently. This study used QuickBird imagery to detect the disease and its spatial pattern. Initially, oil palm and non oil palm object segmentation based on the red band was used to map the spatial pattern of the disease. Secondly, six vegetation indices derived from visible and near infrared bands (NIR) were used for to identify palms infected by the disease. Finally, ground truth from field sampling in four fields with different ages of plant and degrees of infection was used to assess the accuracy of the remote sensing approach. The results show that image segmentation effectively delineated areas infected by the disease with a mapping accuracy of 84%. The resulting maps showed two patterns of the disease; a sporadic pattern in fields with older palms and a dendritic pattern in younger palms with medium to low infection. Ground truth data showed that oil palms infected by basal stem rot had a higher reflectance in the visible bands and a lower reflectance in the near infrared band. Different vegetation indices performed differently in each field. The atmospheric resistant vegetation index and green blue normalized difference vegetation index identified the disease with an accuracy of 67% in a field with 21 year old palms and high infection rates. In the field of 10 year old palms with …
author: Heri Santoso, Totok Gunawan, Retnadi Heru Jatmiko, Witjaksana Darmosarkoro, Budiman Minasny
publish year: 2011
creator: Heri Santoso, Totok Gunawan, Retnadi Heru Jatmiko, Witjaksana Darmosarkoro, Budiman Minasny
reference: Heri Santoso, Totok Gunawan, Retnadi Heru Jatmiko, Witjaksana Darmosarkoro, Budiman Minasny, "Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery", Precision Agriculture, Volume 12, pages 233–248 (2011) , Springer
Source: Precision Agriculture, Volume 12, pages 233–248 (2011) , Springer
label: Article 0747

Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle
type: NamedIndividual
type: Article
has knowledge model: Classifier 0748
abstract: Characterizing the spatial variability in water status across vineyards is a prerequisite for precision irrigation. The crop water stress index (CWSI) indicator was used to map the spatial variability in water deficits across an 11-ha ‘Pinot noir’ vineyard. CWSI was determined based on canopy temperatures measured with infrared temperature sensors placed on top of well-watered and water-stressed grapevines in 2009 and 2010. CWSI was correlated with leaf water potential (ΨL) (R2 = 0.83). This correlation was also tested with results from high resolution airborne thermal imagery. An unmanned aerial vehicle equipped with a thermal camera was flown over the vineyard at 07:30, 09:30, and 12:30 h (solar time) on 31 July 2009. At about the same time, ΨL was measured in 184 grapevines. The image obtained at 07:30 was not useful because it was not possible to separate soil from canopy temperatures. Using the airborne data, the correlation between CWSI and ΨL had an R2 value of 0.46 at 09:30 h and of 0.71 at 12:30 h, suggesting that the latter was the more favorable time for obtaining thermal images that were linked with ΨL values. A sensitivity analysis of varying pixel size showed that a 0.3 m pixel was needed for precise CWSI mapping. The CWSI maps thus obtained by airborne thermal imagery were effective in assessing the spatial variability of water stress across the vineyard.
author: J. Bellvert, P. J. Zarco-Tejada, J. Girona, E. Fereres
publish year: 2014
creator: J. Bellvert, P. J. Zarco-Tejada, J. Girona, E. Fereres
reference: J. Bellvert, P. J. Zarco-Tejada, J. Girona, E. Fereres, "Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle", Precision Agriculture, Volume 15, pages 361–376 (2014) , Springer
Source: Precision Agriculture, Volume 15, pages 361–376 (2014) , Springer
label: Article 0748

Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq
type: NamedIndividual
type: Article
has knowledge model: Regressor_0765
abstract: To model an agriculture process for any region, it is significantly essential to accurately simulate the reference evaporation (ETo) from the available regional meteorological parameters. Nine models, including five data mining algorithms and four adaptive neuro-fuzzy inference systems (ANFISs), were tested for their ability to predict ETo at meteorological stations in Baghdad and Mosul (Iraq). Various weather parameters (e.g., wind speed, sunshine hours, rainfall, maximum and minimum temperature and relative humidity) were recorded and employed as explanatory variables in the models. Pearson correlation analysis showed ETo to have the closest correlation with sunshine hours, maximum and minimum temperatures, and relative humidity. The modeling performance was assessed using the statistical measures of coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), percentage of bias (PBIAS), and the ratio of RMSE to the standard deviation of observations (RSR). Investigations on the modeling accuracy with different input parameter combinations showed that, despite the different structures of the models, no single input combination showed a consistent modeling outcome. Fewer variables were necessary to achieve the same high predictive power for the models developed for the Baghdad station than for those developed for the Mosul station. For both stations, the ANFIS-GA model generally showed the greatest predictive power whereas the random tree algorithm showed the poorest. Moreover, hybrid models showed a higher predictive power than the individual models.
author: Khabat Khosravi, Prasad Daggupati, Mohammad Taghi Alami, Salih Muhammad Awadh, Mazen Ismaeel Ghareb, Mehdi Panahi, Binh Thai Pham, Fatemeh Rezaie, Chongchong Qi, Zaher Mundher Yaseen
publish year: 2019
creator: Khabat Khosravi, Prasad Daggupati, Mohammad Taghi Alami, Salih Muhammad Awadh, Mazen Ismaeel Ghareb, Mehdi Panahi, Binh Thai Pham, Fatemeh Rezaie, Chongchong Qi, Zaher Mundher Yaseen
reference: Khabat Khosravi, Prasad Daggupati, Mohammad Taghi Alami, Salih Muhammad Awadh, Mazen Ismaeel Ghareb, Mehdi Panahi, Binh Thai Pham, Fatemeh Rezaie, Chongchong Qi, Zaher Mundher Yaseen, "Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq", Computers and Electronics in Agriculture, Volume 167, December 2019, 105041, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 167, December 2019, 105041, Elsevier B.V.
label: Article 0765

Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm
type: NamedIndividual
type: Article
has knowledge model: Classifier 0772
abstract: Basal stem rot (BSR), caused by Ganoderma boninense is known as the most destructive disease in oil palm plantations in Southeast Asia. Ganoderma could reduce the productivity of oil palm plantations and potentially reduce the market value of palm oil in Malaysia. Early disease management of Ganoderma could prevent production losses and reduce the use of chemicals. This study focuses on the development of a statistical model for the discrimination of Ganoderma infestation on oil palm trees at different stages using a Fourier transform infrared (FT-IR) spectroscopic technique. Leaf samples of healthy, mild, moderately, and severely-infected trees were measured using FT-IR spectrometers to obtain absorbance data from the range of 2.55–25.05 μm s (3921–399 cm−1). The samples were analyzed with and without dilution with KBr. After pre-processing (baseline correction and normalization), the Savitzky–Golay method was used to calculate first and second derivatives. Then, for the preprocessed raw, first derivatives and second derivatives datasets, principal component analysis was performed to reduce the dimensionality of the data. The selected principal component scores were used in classification using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (kNN) and Naive-Bayes (NB) multivariate classification algorithms. The algorithms were tested to classify the leaf samples into four levels of disease severity. The results demonstrated that when samples were prepared with KBr, the LDA-based model resulted in the highest average overall classification accuracy of 92%, with individual classification accuracies greater than about 90% using the pre-processed raw dataset. This verifies the potential of mid-infrared spectroscopy for Ganoderma detection in early symptomless stages of infection in oil palm.
author: Shohreh Liaghat, Shattri Mansor, Reza Ehsani, Helmi Zulhaidi Mohd Shafri, Sariah Meon, Sindhuja Sankaran
publish year: 2014
creator: Shohreh Liaghat, Shattri Mansor, Reza Ehsani, Helmi Zulhaidi Mohd Shafri, Sariah Meon, Sindhuja Sankaran
reference: Shohreh Liaghat, Shattri Mansor, Reza Ehsani, Helmi Zulhaidi Mohd Shafri, Sariah Meon, Sindhuja Sankaran, "Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm", Computers and Electronics in Agriculture, Volume 101, February 2014, pages 48-54, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 101, February 2014, pages 48-54, Elsevier B.V.
label: Article 0772

Using RapidEye imagery to identify within-field variability of crop growth and yield in Ontario, Canada
type: NamedIndividual
type: Article
has knowledge model: Classifier 0782
abstract: Remote sensing has been recognized as a cost-effective way to detect the spatial and temporal variability of crop growth and productivity. In this study, multispectral RapidEye images were used to delineate homogeneous zones of soil and crop development in two fields in Ontario, Canada, one planted with canola (Brassica napus L.) and the other with spring wheat (Triticum aestivum L.). The two fields received different levels of nitrogen (N) treatments during the pre-planting land preparation phase. Soil textures, mineral nitrogen content and crop yield were used to interpret the results of zone delineation. The analysis of variance (ANOVA) tests revealed that the high-resolution RapidEye data, particularly the imagery acquired at peak crop growth stages (i.e. when leaf area index (LAI) is high), provided valuable information for delineating within-field variability of crop growth and yield. Further analysis showed that for both crops, the spatial patterns of crop growth condition varied throughout the growth cycle, revealing different impacts of soil properties and N fertilization on the crops. In particular, during peak growth stage, the within-field variability was most strongly affected by the pre-planting N application and had the strongest correlation with crop yield. These results suggest that high-resolution satellite data (e.g., RapidEye) could assist in making decisions on optimal N fertilization for enhanced crop productivity.
author: Taifeng Dong, Jiali Shang, Jiangui Liu, Budong Qian, Qi Jing, Baoluo Ma, Ted Huffman, Xiaoyuan Geng, Abdoul Sow, Yichao Shi, Francis Canisius, Xianfeng Jiao, John M. Kovacs, Dan Walters, Jeff Cable, Jeff Wilson
publish year: 2019
creator: Taifeng Dong, Jiali Shang, Jiangui Liu, Budong Qian, Qi Jing, Baoluo Ma, Ted Huffman, Xiaoyuan Geng, Abdoul Sow, Yichao Shi, Francis Canisius, Xianfeng Jiao, John M. Kovacs, Dan Walters, Jeff Cable, Jeff Wilson
reference: Taifeng Dong, Jiali Shang, Jiangui Liu, Budong Qian, Qi Jing, Baoluo Ma, Ted Huffman, Xiaoyuan Geng, Abdoul Sow, Yichao Shi, Francis Canisius, Xianfeng Jiao, John M. Kovacs, Dan Walters, Jeff Cable, Jeff Wilson, "Using RapidEye imagery to identify within-field variability of crop growth and yield in Ontario, Canada", Precision Agriculture, Volume 20, pages 1231–1250 (2019) , Springer
Source: Precision Agriculture, Volume 20, pages 1231–1250 (2019) , Springer
label: Article 0782

Modeling yield response to crop management using convolutional neural networks
type: NamedIndividual
type: Article
has knowledge model: Regressor_0790
abstract: Predicting crop yield response to management and environmental variables is a crucial step towards nutrient management optimization. With the increase in the amount of data generated by agricultural machinery, more sophisticated models are necessary to get full advantage of such data. In this work, we propose a Convolutional Neural Network (CNN) to capture relevant spatial structures of different attributes and combine them to model yield response to nutrient and seed rate management. Nine on-farm experiments on corn fields are used to construct a suitable dataset to train and test the CNN model. Four architectures combining input attributes at different stages in the network are evaluated and compared to the most commonly used predictive models. Results show a reduction in the test dataset RMSE up to 68% when compared to multiple linear regression and up to 29% when compared to a random forest. We also demonstrate that higher variability associated with the spatial structure of the data takes the most advantage of this framework.
author: Alexandre Barbosa, Rodrigo Trevisan, Naira Hovakimyan, Nicolas F. Martin
publish year: 2020
creator: Alexandre Barbosa, Rodrigo Trevisan, Naira Hovakimyan, Nicolas F. Martin
reference: Alexandre Barbosa, Rodrigo Trevisan, Naira Hovakimyan, Nicolas F. Martin, "Modeling yield response to crop management using convolutional neural networks", Computers and Electronics in Agriculture, Volume 170, March 2020, 105197, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 170, March 2020, 105197, Elsevier B.V.
label: Article 0790

Monitoring maize growth conditions by training a BP neural network with remotely sensed vegetation temperature condition index and leaf area index
type: NamedIndividual
type: Article
has knowledge model: Regressor_0799
abstract: Crop water stress and vegetation status are critical parameters and should be proposed as input variables of an integrated model for crop productivity and yield estimation. In this study, to improve the monitoring of the regional maize growth conditions in the North China Plain, PR China, the remotely sensed vegetation temperature condition index (VTCI) and leaf area index (LAI) at five growth stages of maize (the seeding, jointing, heading, milk and mature stages) during 2010–2015 were generated as inputs of three-layer back propagation (BP) artificial neural networks (ANNs) with different numbers of nodes in the hidden layer to estimate the crop growth. Among these BP ANN models, an architecture with 12 nodes in the hidden layer provided the best training (RMSE = 755.7 kg/ha, MSE = 0.023) and testing (RMSE = 644.3 kg/ha, MSE = 0.037) performance and was selected to simulate values of the integrated growth monitoring index of maize (IGMIM) and to map the regional maize growth conditions pixel by pixel in the North China Plain during 2010–2018. The spatiotemporal characteristics displayed by the maize growth maps based on the IGMIM showed that the best year was 2016, the worst year was 2015, and maize growth in different parts of the plain varied accordingly with variations in the meteorological conditions. Thus, the information reflected by the IGMIM was in good agreement with the actual results. To further validate the accuracy of the integrated index, the correlations between the values of the IGMIM and several growth-related variables, including the measured yield, planting density, plant height and relative soil humidity at the 0–10 cm layer, at thirteen meteorological stations from 2010 to 2012 were analyzed, and the results were meaningful and presented a significant linear relationship. Thus, the BP ANN-based model has the ability to integrate information reflected by multiple maize growth-related factors at each growth stage and provides a better quantifica…
author: Lei Wang, Pengxin Wang, Shunlin Liang, Xuan Qi, Li Li, Lianxiang Xu
publish year: 2019
creator: Lei Wang, Pengxin Wang, Shunlin Liang, Xuan Qi, Li Li, Lianxiang Xu
reference: Lei Wang, Pengxin Wang, Shunlin Liang, Xuan Qi, Li Li, Lianxiang Xu, "Monitoring maize growth conditions by training a BP neural network with remotely sensed vegetation temperature condition index and leaf area index", Computers and Electronics in Agriculture, Volume 160, May 2019, pages 82-90, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 160, May 2019, pages 82-90, Elsevier B.V.
label: Article 0799

Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing
type: NamedIndividual
type: Article
has knowledge model: Classifier 0801
abstract: Till date, the remote sensing research on crop nutrient monitoring has focused mainly on biomass and nitrogen (N) estimation and only a few attempts had been made to characterize and monitor macronutrients other than N. Field experiments were undertaken to study the remote detection of macronutrient status of rice using hyperspectral remote sensing. The variability in soil available N, phosphorus (P) and sulphur (S) and their content in plants were created using artificial fertility gradient design. The leaf and canopy hyperspectral reflectance was captured from variable macronutrient status vegetation. Linear correlation analysis between the spectral reflectance and plant nutrient status revealed significantly (p < 0.05) higher correlation coefficient at 670, 700, 730, 1090, 1260, 1460 nm for the nutrient under study. Published and proposed vegetation indices (VIs) were tested for canopy N, P and S prediction. The results of the investigation revealed that, published VIs (NDVI hyper and NDVI broadbands) could retrieve canopy N with higher accuracy, but not P and S. The predictability of the visible and short wave infrared based VI NRI1510 ((R1510 − R660)/(R1510 + R660)) was the highest (r = 0.81, p < 0.01) for predicting N. Based on the outcomes of linear correlation analysis new VIs were proposed for remote detection of P and S. Proposed VI P_670_1260 ((R1260 − R670)/(R1260 + R670)) retrieved canopy P status with higher prediction accuracy (r = 0.67, p < 0.01), whereas significantly higher canopy S prediction (r = 0.58, p < 0.01) was obtained using VI S_670_1090 ((R1090 − R670)/(R1090 + R670)). The proposed spectral algorithms could be used for real time and site-specific N, P and S management in rice. Nutrient specific wavelengths, identified in the present investigation, could be used for developing relatively low-cost sensors of hand-held instruments to monitor N, P and S status of rice plant.
author: G. R. Mahajan, R. N. Pandey, R. N. Sahoo, V. K. Gupta, S. C. Datta, Dinesh Kumar
publish year: 2017
creator: G. R. Mahajan, R. N. Pandey, R. N. Sahoo, V. K. Gupta, S. C. Datta, Dinesh Kumar
reference: G. R. Mahajan, R. N. Pandey, R. N. Sahoo, V. K. Gupta, S. C. Datta, Dinesh Kumar, "Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing", Precision Agriculture, Volume 18, pages 736–761 (2017) , Springer
Source: Precision Agriculture, Volume 18, pages 736–761 (2017) , Springer
label: Article 0801

Monitoring plant diseases and pests through remote sensing technology: A review
type: NamedIndividual
type: Article
has knowledge model: Classifier 0803
abstract: Plant diseases and pests endanger agriculture and forestry significantly around the world. The implementation of non-contact, highly-efficient, and affordable methods for detecting and monitoring plant diseases and pests over vast areas could greatly facilitate plant protection. In this respect, different forms of remote sensing methods have been introduced for detecting and monitoring plant diseases and pests in many ways. This review outlines the state-of-the-art research achievements in relation to sensing technologies, feature extraction, and monitoring algorithms that have been conducted at multiple scales. Based on their characteristics and maturity in detecting and monitoring plant diseases and pests, sensing systems are classified into groups that include: visible & near-infrared spectral sensors (VIS-NIR); fluorescence and thermal sensors; and synthetic aperture radar (SAR) and light detection and ranging (Lidar) systems. Based on the data acquired from these remote sensing systems and sensitivity analysis, a variety of remote sensing features are proposed and identified as surrogates in the detection and monitoring processes. They include (1) optical, fluorescence, and thermal parameters; (2) image-based landscape features; and (3) features associated with habitat suitability. We also review the algorithms that link the remote sensing features with the occurrence of plant diseases and pests for identifying, differentiating and determining severity of diseases and pests over large areas. The algorithms including statistical discriminant analyses, machine learning algorithms, regression-based models and spectral unmixing algorithms using data collected at a single time or multiple times. Finally, according to the review, we provide a general framework to facilitate the monitoring of an unknown disease or pest highlighting future challenges and trends.
author: Jingcheng Zhang, Yanbo Huang, Ruiliang Pu, Pablo Gonzalez-Moreno, Lin Yuan, Kaihua Wu, Wenjiang Huang
publish year: 2019
creator: Jingcheng Zhang, Yanbo Huang, Ruiliang Pu, Pablo Gonzalez-Moreno, Lin Yuan, Kaihua Wu, Wenjiang Huang
reference: Jingcheng Zhang, Yanbo Huang, Ruiliang Pu, Pablo Gonzalez-Moreno, Lin Yuan, Kaihua Wu, Wenjiang Huang, "Monitoring plant diseases and pests through remote sensing technology: A review", Computers and Electronics in Agriculture, Volume 165, October 2019, 104943, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 165, October 2019, 104943, Elsevier B.V.
label: Article 0803

Multimodal neural network with clustering-based drop for estimating plant water stress
type: NamedIndividual
type: Article
has knowledge model: Regressor_0813
abstract: Decision-making with low-cost data is an attractive approach in the field of agriculture as it aids to solve the difficulty of inheriting advanced cultivation technologies. To provide expertise in the decision-making process for stress cultivation, precision irrigation based on plant water stress is required to steadily produce high-quality fruits. Single low-cost data namely, single-modal data, is used in the traditional approach. However, for advanced cultivation, multimodal data such as physiological and meteorological data is required. In this study, we propose a multimodal neural network with clustering-based drop (C-Drop) for accurate estimation of plant water stress, as it is an index for irrigation decision-making, using plant image and environmental data. Our proposed method extracts temporal multimodal features from leaf wilting features (physiological data) using environmental features (meteorological data) as an attention mechanism of a multimodal neural network that includes long short-term memory layers. Moreover, the proposed neural network with C-drop realizes a novel end-to-end deep learning architecture in consideration of environmental conditions. On evaluating this method against the existing methods, the proposed method was found to improve the accuracy of plant water stress estimation by 21% for mean absolute error and root-mean-squared error, thereby indicating that this method is precise and stable for the plant water stress estimation. The performance of our proposed method to support precision irrigation will allow new-age farmers to produce high-quality fruits steadily.
author: Kazumasa Wakamori, Ryosuke Mizuno, Gota Nakanishi, Hiroshi Mineno
publish year: 2020
creator: Kazumasa Wakamori, Ryosuke Mizuno, Gota Nakanishi, Hiroshi Mineno
reference: Kazumasa Wakamori, Ryosuke Mizuno, Gota Nakanishi, Hiroshi Mineno, "Multimodal neural network with clustering-based drop for estimating plant water stress", Computers and Electronics in Agriculture, Volume 168, January 2020, 105118, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 168, January 2020, 105118, Elsevier B.V.
label: Article 0813

Multiple view image analysis of freefalling U.S. wheat grains for damage assessment
type: NamedIndividual
type: Article
has knowledge model: Classifier 0815
abstract: Currently, inspection of wheat in the United States for grade and class is performed by human visual analysis. This is a time consuming operation typically taking several minutes for each sample. Digital imaging research has addressed this issue over the past two decades, with success in recognition of differing wheat classes and distinguishing wheat from non-wheat species. Detection of wheat kernel defects caused either by damage or disease has been a greater challenge. A study was undertaken using high-speed digital imaging to detect damaged U.S. grown kernels in freefall, one kernel at a time. The system is composed of hardware (camera, lighting, power supplies, and data acquisition card) and associated software for instrument control, data collection, and analysis. It was designed to capture images of freefalling kernels at opposing angles through the use of optical grade mirrors. Parameterization was performed on kernel morphological and textural characteristics of three views (primary and two reflections), whereupon these terms were used to develop classification models for sound and damaged classes. Fifty samples of hard red and white wheat subjected to weather-related damage during plant development were examined. Parametric (linear discriminant analysis, LDA) and non-parametric (k-nearest neighbor, KNN) classification models were tested to determine the image features that best fostered recognition of kernel damage (mold, pre-harvest sprouting, and black tip). The morphological features used in classification included area, projected volume, perimeter, ellipse eccentricity, and major and minor axis lengths. Textural features from calculated gray level co-occurrence matrices (including contrast, correlation, energy, homogeneity) as well as entropy were also considered, as were elliptic Fourier descriptors (truncated Fourier series functions that defined the contour of border in each view). The results indicate that with a combination of two morphological and…
author: Stephen R. Delwiche, I-Chang Yang, Robert A. Graybosch
publish year: 2013
creator: Stephen R. Delwiche, I-Chang Yang, Robert A. Graybosch
reference: Stephen R. Delwiche, I-Chang Yang, Robert A. Graybosch, "Multiple view image analysis of freefalling U.S. wheat grains for damage assessment", Computers and Electronics in Agriculture, Volume 98, October 2013, pages 62-73, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 98, October 2013, pages 62-73, Elsevier B.V.
label: Article 0815

New perspectives on plant disease characterization based on deep learning
type: NamedIndividual
type: Article
has knowledge model: Classifier 0832
abstract: The control of plant diseases is a major challenge to ensure global food security and sustainable agriculture. Several recent studies have proposed to improve existing procedures for early detection of plant diseases through modern automatic image recognition systems based on deep learning. In this article, we study these methods in detail, especially those based on convolutional neural networks. We first examine whether it is more relevant to fine-tune a pre-trained model on a plant identification task rather than a general object recognition task. In particular, we show, through visualization techniques, that the characteristics learned differ according to the approach adopted and that they do not necessarily focus on the part affected by the disease. Therefore, we introduce a more intuitive method that considers diseases independently of crops, and we show that it is more effective than the classic crop-disease pair approach, especially when dealing with disease involving crops that are not illustrated in the training database. This finding therefore encourages future research to rethink the current de facto paradigm of crop disease categorization.
author: Sue Han Lee, Herve Goëau, Pierre Bonnet, Alexis Joly
publish year: 2020
creator: Sue Han Lee, Herve Goëau, Pierre Bonnet, Alexis Joly
reference: Sue Han Lee, Herve Goëau, Pierre Bonnet, Alexis Joly, "New perspectives on plant disease characterization based on deep learning", Computers and Electronics in Agriculture, Volume 170, March 2020, 105220, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 170, March 2020, 105220, Elsevier B.V.
label: Article 0832

Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyperspectral imaging: growing degree-days integrated model
type: NamedIndividual
type: Article
has knowledge model: Regressor_0834
abstract: Ground-based hyperspectral imaging was applied to rice plants at the panicle initiation stage to estimate nitrogen content. We developed a partial least squares regression (PLSR) model that incorporated both the reflectance and growing degree-days (GDD) to account for differences in growing temperature conditions across a 3-year period. The acquired images were divided into two components: (1) the rice plant and (2) other elements (e.g., irrigation water, soil background) by using the GreenNDVI − NDVI equation. Rice plant reflectance (Ref RICE ) was calculated as the ratio of rice plant reflectance to that of a reference board. Three types of PLSR models were constructed: 1-year, 2-year, and 2-year GDD. Mutual estimation was used to infer the predictive power of the three models, which was calculated by estimating the values for the other years. The root mean square error of prediction (RMSE) of the mutual estimation for the 1- and 2-year PLSR models was high because of overestimation and underestimation. In contrast, the RMSE of the mutual estimation for the 2-year GDD PLSR models clearly decreased. It was inferred that hyperspectral imaging at 400–1000 nm could not predict variation in the amount of growth caused by weather variation expressed as GDD. This study indicates that the combination of reflectance and temperature data could be used to potentially construct an adaptable model to identify variance in growing conditions.
author: Hiroyuki Onoyama, Chanseok Ryu, Masahiko Suguri, Michihisa Iida
publish year: 2015
creator: Hiroyuki Onoyama, Chanseok Ryu, Masahiko Suguri, Michihisa Iida
reference: Hiroyuki Onoyama, Chanseok Ryu, Masahiko Suguri, Michihisa Iida, "Nitrogen prediction model of rice plant at panicle initiation stage using ground-based hyperspectral imaging: growing degree-days integrated model", Precision Agriculture, Volume 16, pages 558–570 (2015) , Springer
Source: Precision Agriculture, Volume 16, pages 558–570 (2015) , Springer
label: Article 0834

Optical sensing for early spring freeze related blueberry bud damage detection: Hyperspectral imaging for salient spectral wavelengths identification
type: NamedIndividual
type: Article
has knowledge model: Classifier 0855
abstract: The floral tissue of blueberry buds is very sensitive to the freezing and can be damaged during early spring freeze events. Such damage results in loss of yield to the commercial blueberry producers. Therefore, this study investigated the feasibility of injured blueberry buds damage detection during early spring freeze using hyperspectral imaging (HSI) technique. Imaged were the buds from bud swell and early pink stage for two growing seasons. Prior to imaging, buds in 2017 were subjected to freezing temperatures of −7 to −16 °C and −2 to −11 °C at respective growth stages. In 2018 season, buds were frozen at −11 to −20 °C and −4 to −13 °C at two respective sampling date for bud swell stage and at −2 to −11 °C for early pink growth stage. The HSI data was obtained using a push broom type system in the spectral wavelength ranges of 517–1729 nm. For the imaged buds, each of the indivisual buds on sampled shoot was considered as one sample, and the average spectra of such bud within 540 to 1599 nm was extracted for further analysis. Principal component analysis and successive projection algorithms were applied to select key wavelengths related to bud damage symptoms. Common wavelengths of 615, 673, 690, 756, 979 and 1467 nm were identified for both the growth stages. Partial least squares discriminant analysis was performed to identify normal and injured bud samples with the selected common wavelengths. Performance of the models manifested good results with better sensitivity (>0.80), specificity (>0.75), the area under the receiver operating characteristic curve (>0.84) and accuracy (>0.75) attributes for 2017, 2018 seasons and two seasons combined test dataset.
author: Zongmei Gao, Yanru Zhao, Lav R. Khot, Gwen-Alyn Hoheisel, Qin Zhang
publish year: 2019
creator: Zongmei Gao, Yanru Zhao, Lav R. Khot, Gwen-Alyn Hoheisel, Qin Zhang
reference: Zongmei Gao, Yanru Zhao, Lav R. Khot, Gwen-Alyn Hoheisel, Qin Zhang, "Optical sensing for early spring freeze related blueberry bud damage detection: Hyperspectral imaging for salient spectral wavelengths identification", Computers and Electronics in Agriculture, Volume 167, December 2019, 105025, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 167, December 2019, 105025, Elsevier B.V.
label: Article 0855

Particle Swarm Optimization based incremental classifier design for rice disease prediction
type: NamedIndividual
type: Article
has knowledge model: Classifier 0874
abstract: Increase of huge amount of data in every application demands an incremental learning technique for data analysis. One of such data analysis task in dynamic environment is to design an incremental classifier for decision making and consequently updating the knowledge base of the overall system. Classifier construction depicts extraction of interesting patterns from the large repository of data and predicts the future trends based on the existing patterns. The time complexity of the classification system increases gradually and the system becomes inefficient while it is learned repeatedly for adding new group of data with the existing one in a certain interval of time. Without learning the same classifier for the whole data, if the knowledge of old data extracted by the classifier is used together with the new group of data to design the updated classifier, called incremental classifier, then time complexity reduces drastically. In the paper, the concepts of Particle Swarm Optimization technique and Association Rule Mining are used to design an incremental rule based classification system. The incremental classifier is suitable to apply on rice disease dataset for disease prediction as the characteristics of rice diseases change in time due to change of climate, biological, and geographical factors. The proposed method has been applied on both simulated rice disease dataset and benchmark datasets and the classification accuracy is measured and compared with various state of the art classification algorithms. The method is also evaluated based on some statistical measures and statistical test is done to establish its significance and effectiveness.
author: Shampa Sengupta, Asit K. Das
publish year: 2017
creator: Shampa Sengupta, Asit K. Das
reference: Shampa Sengupta, Asit K. Das, "Particle Swarm Optimization based incremental classifier design for rice disease prediction", Computers and Electronics in Agriculture, Volume 140, August 2017, pages 443-451, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 140, August 2017, pages 443-451, Elsevier B.V.
label: Article 0874

PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network
type: NamedIndividual
type: Article
has knowledge model: Classifier 0877
abstract: The plant disease diagnosis and severity estimation are a very challenging research field in the agriculture sector. In this work, we present a robust image-based Plant Disease Diagnosis and Severity Estimation Network (PD2SE-Net), which contains a residual structure and shuffle units. The aim of this paper is to design a more excellent and practical diagnosis system for plant diseases. The common plant disease diagnosis, disease severity estimation, are simultaneously addressed by the proposed PD2SE-Net. In addition, the data augmentation and visualization of convolutional neural networks (CNNs) are exploited in this paper to improve the accuracy and accelerate the excellent selection of hyper-parameters during the training period. To the best of our knowledge, this report for the first time describes a computer-assisted approach that can simultaneously estimate disease severity, recognize species, and classify disease for plants base on deep learning. The proposed PD2SE-Net50 consists of the ResNet50 architecture as the basic model and shuffle units as the auxiliary structures, and it achieves excellent comprehensive performances (overall accuracies of 0.91, 0.99 and 0.98 for the disease severity estimation, plant species recognition and plant disease classification, respectively) over the existing approaches. As a diagnosis expert, our system exploits the multivariate nature of plant leaves in order to deliver an outstanding classification performance with a low computational cost. The experimental results demonstrate the feasibility and effectiveness of our network.
author: Qiaokang Liang, Shao Xiang, Yucheng Hu, Gianmarc Coppola, Dan Zhang, Wei Sun
publish year: 2019
creator: Qiaokang Liang, Shao Xiang, Yucheng Hu, Gianmarc Coppola, Dan Zhang, Wei Sun
reference: Qiaokang Liang, Shao Xiang, Yucheng Hu, Gianmarc Coppola, Dan Zhang, Wei Sun, "PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network", Computers and Electronics in Agriculture, Volume 157, February 2019, pages 518-529, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 157, February 2019, pages 518-529, Elsevier B.V.
label: Article 0877

Pest identification via deep residual learning in complex background
type: NamedIndividual
type: Article
has knowledge model: Classifier 0880
abstract: Agricultural pests severely affect both agricultural production and the storage of crops. To prevent damage caused by agricultural pests, the pest category needs to be correctly identified and targeted control measures need to be taken; therefore, it is important to develop an agricultural pest identification system based on computer vision technology. To achieve pest identification with the complex farmland background, a pest identification method is proposed that uses deep residual learning. Compared to support vector machine and traditional BP neural networks, the pest image recognition accuracy of this method is noticeably improved in the complex farmland background. Furthermore, in comparison to plain deep convolutional neural networks such as Alexnet, the recognition performance in this method was further improved after optimized by deep residual learning. A classification accuracy of 98.67% for 10 classes of crop pest images with complex farmland background was achieved. Accordingly, the method has a high value of practical application, and can be integrated with currently used agricultural networking systems into actual agricultural pest control tasks.
author: Xi Cheng, Youhua Zhang, Yiqiong Chen, Yunzhi Wu, Yi Yue
publish year: 2017
creator: Xi Cheng, Youhua Zhang, Yiqiong Chen, Yunzhi Wu, Yi Yue
reference: Xi Cheng, Youhua Zhang, Yiqiong Chen, Yunzhi Wu, Yi Yue, "Pest identification via deep residual learning in complex background", Computers and Electronics in Agriculture, Volume 141, September 2017, pages 351-356, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 141, September 2017, pages 351-356, Elsevier B.V.
label: Article 0880

Predicting soil chemical composition and other soil parameters from field observations using a neural network
type: NamedIndividual
type: Article
has knowledge model: Regressor_0905
abstract: Characterisation of soils in relation to a particular use or classification system is often heavily dependant on their chemical properties, requiring detailed, time-consuming and often expensive laboratory analysis. If it were possible to gain even partial knowledge of the status of a soil in terms of parameters that normally require this kind of analysis, but instead to be able to do so, based on simple field observation information, then an observer in the field would be able to more effectively characterise the soil they were investigating. Using data from the NSIS (National Soil Inventory of Scotland) database, we have produced a neural network model that predicts a wide range of soil chemical and physical properties with varying accuracy levels. This neural network model is supplied with field observation inputs that require only a limited degree of training to determine, and limited field equipment. These inputs include colour, texture classification and site information (topography, climate and vegetation). Several model outputs are predicted with a high degree of accuracy, including organic matter content, Mg, Ca, Ni, total base saturation and pH amongst others. We discuss the outputs that are predicted well and those that are not in terms of their relationships to the model input parameters and their significance within the soil, and consider possible uses and limitations of this prediction system.
author: M.J. Aitkenhead, M.C. Coull, W. Towers, G. Hudson, H.I.J. Black
publish year: 2012
creator: M.J. Aitkenhead, M.C. Coull, W. Towers, G. Hudson, H.I.J. Black
reference: M.J. Aitkenhead, M.C. Coull, W. Towers, G. Hudson, H.I.J. Black, "Predicting soil chemical composition and other soil parameters from field observations using a neural network", Computers and Electronics in Agriculture, Volume 82, March 2012, pages 108-116, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 82, March 2012, pages 108-116, Elsevier B.V.
label: Article 0905

Predicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images
type: NamedIndividual
type: Article
has knowledge model: Regressor_0911
abstract: Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha−1 and RMSE = 1532 kg ha−1, respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to estimate yield at field scale with very high accuracy due to large RMSE, the methodologies are suitable to predict harvest volumes at large agricultural areas or at country level, and to construct synthetic yield maps reflecting within field variability. Higher temporal resolution of images would improve accuracy in estimating yield and spatial variability at field scale.
author: Deborah V. Gaso, Andres G. Berger, Veronica S. Ciganda
publish year: 2019
creator: Deborah V. Gaso, Andres G. Berger, Veronica S. Ciganda
reference: Deborah V. Gaso, Andres G. Berger, Veronica S. Ciganda, "Predicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images", Computers and Electronics in Agriculture, Volume 159, April 2019, pages 75-83, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 159, April 2019, pages 75-83, Elsevier B.V.
label: Article 0911

Prediction of cotton lint yield from phenology of crop indices using artificial neural networks
type: NamedIndividual
type: Article
has knowledge model: Regressor_0915
abstract: A primary utility of satellite remote sensing technology is monitoring and assessment of agricultural lands for determining the area, amount, type, and quality of crop production. Since the mid-1970s agricultural scientists have sought to advance this utility through development of precision agriculture (PA) methods and technologies. Consequently, PA has taken advantage of freely available medium-spatial resolution remote sensing technology and instrumented fields to monitor crop biomass, phenology, and yield of crops at the sub-field to larger scales. The main goal of this study was to determine cotton lint yield in a 73-ha irrigated field in western Tennessee using remote sensing technology. We used two growing seasons (2013 and 2014) of Landsat 8 transformed to 8 input predictors including Red, near infra-red (NIR), the simple ratio (SR), normalized difference vegetation index (NDVI), green NDVI (GNDVI), and the tasselled cap transformation’s greenness, wetness, and soil brightness indices: GI, WI, and SBI, respectively, as proxies for cotton lint yield and crop phenology (in this study all input predictors are being referred to as crop indices, CIs). We used artificial neural network (ANN) approach to generate 61,200 models relating individual CIs and CI phenology to field estimates of lint yield to predict and map the field’s cotton lint yield in two cropping seasons. The correlation between cotton lint yield and CIs ranged from -0.20 to 0.60 in 2013 and from −0.79 to 0.84 in 2014. The best ANN models were in 2013 (r = 0.68 and the normalized MAE = 11%) and 2014 (r = 0.86 and the normalized MAE = 8%) growing seasons. The WI and GI were the best CI predictors of cotton lint yield, and overall for the early to mid-season prediction, CI phenologies had better performance than single date CI models. Consequently, we recommend the use of Landsat 8 derived WI or GI phenology to predict crop yields.
author: Amir Haghverdi, Robert A. Washington-Allen, Brian G. Leib
publish year: 2018
creator: Amir Haghverdi, Robert A. Washington-Allen, Brian G. Leib
reference: Amir Haghverdi, Robert A. Washington-Allen, Brian G. Leib, "Prediction of cotton lint yield from phenology of crop indices using artificial neural networks", Computers and Electronics in Agriculture, Volume 152, September 2018, pages 186-197, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 152, September 2018, pages 186-197, Elsevier B.V.
label: Article 0915

Prediction of leaf area index in almonds by vegetation indexes
type: NamedIndividual
type: Article
has knowledge model: Regressor_0919
abstract: Three levels of scale for determining leaf area index (LAI) were explored within an almond orchard of alternating rows of Nonpareil and Monterey varieties using hemispherical photography and mule lightbar (MLB) at ground level up to airborne and satellite imagery. We compared LAI estimates of 56 fisheye photos strategically placed in the orchard to validate 500,000 MLB point scans of a small portion of the aisles between tree rows to water and vegetation indexes of MASTER (MODIS/ASTER simulator) and Landsat 5 imagery. The high correlation of fisheye photo LAI to MLB LAI estimates establishes this new method against the measurement standard within the plant community while significantly increasing sample size. MLB LAI and MASTER vegetation indexes, such as NDWI (normalized difference water index), GMI (Gitelson–Merzlyak index) and NDVI (normalized difference vegetation index), were highly correlated (R2 = 0.90). In addition, a high correlation (R2 = 0.80) between the MLB measured LAI and selected Landsat derived vegetation indexes (VI) was found. This scaling and validation of LAI estimate expands the spatial area and frequency of determination for time series analysis of crop phenology studies.
author: Jose L. Zarate-Valdez, Michael L. Whiting, Bruce D. Lampinen, Samuel Metcalf, Susan L. Ustin, Patrick H. Brown
publish year: 2012
creator: Jose L. Zarate-Valdez, Michael L. Whiting, Bruce D. Lampinen, Samuel Metcalf, Susan L. Ustin, Patrick H. Brown
reference: Jose L. Zarate-Valdez, Michael L. Whiting, Bruce D. Lampinen, Samuel Metcalf, Susan L. Ustin, Patrick H. Brown, "Prediction of leaf area index in almonds by vegetation indexes", Computers and Electronics in Agriculture, Volume 85, July 2012, pages 24-32, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 85, July 2012, pages 24-32, Elsevier B.V.
label: Article 0919

Remote sensing and machine learning for crop water stress determination in various crops: a critical review
type: NamedIndividual
type: Article
has knowledge model: Regressor_0969
abstract: The remote sensing (RS) technique is less cost- and labour- intensive than ground-based surveys for diverse applications in agriculture. Machine learning (ML), a branch of artificial intelligence (AI), provides an effective approach to construct a model for regression and classification of a multivariate and non-linear system. Without being explicitly programmed, machine learning models learn from training data, i.e., past experience. Machine learning, when applied to remotely sensed data, has the potential to evolve a real-time farm-specific management system to reinforce farmers' ability to make appropriate decisions. Recently, the use of machine learning techniques combined with RS data has reshaped precision agriculture in many ways, such as crop identification, yield prediction and crop water stress assessment, with better accuracy than conventional RS methods. As agriculture accounts for approximately 70% of the worldwide water withdrawals, it must be used in the most efficient way to obtain maximum yields and food production. The use of water management and irrigation based on plant water stress have been demonstrated to not only save water but also increase yield. To date, RS and ML-based results have encouraged farmers and decision-makers to adopt this technology to meet global food demands. This phenomenon has led to the much-needed interest of researchers in using ML to improve agriculture outcomes. However, the use of ML for the potential evaluation of water stress continues to be unexplored and the existing methods can still be greatly improved. This study aims to present an overall review of the widely used methods for crop water stress monitoring using remote sensing and machine learning and focuses on future directions for researchers.
author: Shyamal S. Virnodkar, Vinod K. Pachghare, V. C. Patil, Sunil Kumar Jha
publish year: 2020
creator: Shyamal S. Virnodkar, Vinod K. Pachghare, V. C. Patil, Sunil Kumar Jha
reference: Shyamal S. Virnodkar, Vinod K. Pachghare, V. C. Patil, Sunil Kumar Jha, "Remote sensing and machine learning for crop water stress determination in various crops: a critical review", Precision Agriculture (2020) , Springer
Source: Precision Agriculture (2020) , Springer
label: Article 0969

Research on deep learning in apple leaf disease recognition
type: NamedIndividual
type: Article
has knowledge model: Classifier 0971
abstract: The main reason affecting apple production is the occurrence of apple leaf diseases, which causes huge economic losses every year. Therefore, it is of great significance to study the identification of apple leaf diseases. Based on DenseNet-121 deep convolution network, three methods of regression, multi-label classification and focus loss function were proposed to identify apple leaf diseases. In this paper, the apple leaf image data set, including 2462 images of six apple leaf diseases, were used for data modeling and method evaluation. The proposed methods achieved 93.51%, 93.31% and 93.71% accuracy on the test set respectively, which were better than the traditional multi-classification method based on cross-entropy loss function with an accuracy of 92.29%.
author: Yong Zhong, Ming Zhao
publish year: 2020
creator: Yong Zhong, Ming Zhao
reference: Yong Zhong, Ming Zhao, "Research on deep learning in apple leaf disease recognition", Computers and Electronics in Agriculture, Volume 168, January 2020, 105146, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 168, January 2020, 105146, Elsevier B.V.
label: Article 0971

Segmenting ears of winter wheat at flowering stage using digital images and deep learning
type: NamedIndividual
type: Article
has knowledge model: Classifier 0993
abstract: Segmenting ears of winter wheat from canopy images was considered to be an important procedure prior to the extraction of related traits. Current segmentation method based on computer vision was susceptible to noise, which is limited in practical applications. In this study, a two-stage segmentation method for ears of winter wheat based on digital images of unit ground area and the state-of-the-art deep learning techniques was proposed. In the coarse segmentation stage, a deep convolutional neural network (DCNN) was constructed to classify the superpixels generated by entropy rate superpixel algorithm, achieving the coarse results. In the fine segmentation stage, a fully convolutional network (FCN) allowing pixel-wise semantic segmentation was constructed to eliminate the non-ear pixels in the coarse results. To compare the results of the proposed two-stage segmentation method, conventionally adopted methods for image segmentation were used. Results showed that the proposed two-stage segmentation method was able to accurately segmenting ears of winter wheat from canopy images captured at flowering stage (Qseg = 0.7197, F1 score = 83.70%, SSIM = 0.8605), outperforming the other compared methods. Generalization tests were conducted to evaluate the utility of the proposed two-stage segmentation method. Results showed that the two-stage segmentation method was still capable of accurately segmenting ears of winter wheat, even though the performance slightly decreased. Change of winter wheat cultivar and lack of descriptive information were two factors that could degrade the performance of the two-stage segmentation method. Tests of the methods on Unmanned Aerial Vehicle (UAV) based RGB images showed the Fully Convolutional Network stride 8 predictions (FCN-8s) had a good chance to achieve satisfactory performances on UAV based canopy images.
author: Juncheng Ma, Yunxia Li, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhihong Gong, Weihua Jiao
publish year: 2020
creator: Juncheng Ma, Yunxia Li, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhihong Gong, Weihua Jiao
reference: Juncheng Ma, Yunxia Li, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhihong Gong, Weihua Jiao, "Segmenting ears of winter wheat at flowering stage using digital images and deep learning", Computers and Electronics in Agriculture, Volume 168, January 2020, 105159, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 168, January 2020, 105159, Elsevier B.V.
label: Article 0993

Site-specific early season potato yield forecast by neural network in Eastern Canada
type: NamedIndividual
type: Article
has knowledge model: Regressor 1011
abstract: Deterministic potato (Solanum tuberosum L.) growth models hardly rely on driving seasonal field variables that directly characterize spatial variation of plant growth. For example, the SUBSTOR model computes the leaf area index (LAI) as an auxiliary variable from meteorological conditions and soil properties. Empirical models may account for seasonal LAI functions and accurately predict potato yield. The objective was to evaluate multiple linear regression (MLR) and neural networks (NN) as predictive models of potato yield. Using data from several replicated on-farm experiments conducted over 3 years, model performance was evaluated for their capacity to forecast tuber yields 9, 10 and 11 weeks before harvest compared to SUBSTOR. A 3-input NN using LAI functions and cumulative rainfall yielded the most accurate estimations and forecasts of tuber yields. This NN showed that tuber yield of contrasting zones was mostly a function of meteorological conditions prevailing during the first 5–8 weeks after planting. Subsequent development of tubers was essentially controlled by biomass allocation to tubers. The NN models were more coherent than MLR and SUBSTOR for two reasons: (1) the use of seasonal LAI directly as input rather than computed as an auxiliary variable and (2) the non-linearity of the modeling process resulting in more accurate estimation of the temporal discontinuities of potato tuber growth. This model showed potential for application in precision agriculture by accounting for temporal and spatial real-time climatic and crop data.
author: Jerome G. Fortin, François Anctil, Leon-etienne Parent, Martin A. Bolinder
publish year: 2011
creator: Jerome G. Fortin, François Anctil, Leon-etienne Parent, Martin A. Bolinder
reference: Jerome G. Fortin, François Anctil, Leon-etienne Parent, Martin A. Bolinder, "Site-specific early season potato yield forecast by neural network in Eastern Canada", Precision Agriculture, Volume 12, pages 905–923 (2011) , Springer
Source: Precision Agriculture, Volume 12, pages 905–923 (2011) , Springer
label: Article 1011

SLIC_SVM based leaf diseases saliency map extraction of tea plant
type: NamedIndividual
type: Article
has knowledge model: Classifier 1015
abstract: For the purpose of improving the extraction of tea plant leaf disease saliency map under complex backgrounds, a new algorithm combining SLIC (Simple Linear Iterative Cluster) with SVM (Support Vector Machine) is proposed in this paper. Firstly, super-pixel block is obtained by SLIC algorithm, significant point is detected by Harris algorithm, and fuzzy salient region contour is extracted by employing convex hull method. Secondly, the four-dimensional texture features of super-pixel blocks in salient regions and background areas are extracted, and then the classification map is obtained by classifying the super-pixel blocks with the help of SVM classifier. Lastly, the morphological and algebraic operations are implemented for repairing classified super-pixel blocks. As a result, one accurate saliency map of tea plant leaf disease image is obtained. Through testing based on 261 diseased images, the quality evaluation index, the accuracy, precision, recall and F-value are 98.5%, 96.8%, 98.6% and 97.7%, respectively. It demonstrates that the proposed method performs better than the other three SLIC-based algorithms in visual effects and quality assessment index. Such conclusion can be drawn that the proposed method can effectively extract tea plant leaf disease saliency map from complex background. Consequently, this research is expected to lay a good basis for the study of tea plant leaf disease identification. Last but not the least, the proposed method has good potential that extracts saliency map of crops or plants disease.
author: Yunyun Sun, Zhaohui Jiang, Liping Zhang, Wei Dong, Yuan Rao
publish year: 2019
creator: Yunyun Sun, Zhaohui Jiang, Liping Zhang, Wei Dong, Yuan Rao
reference: Yunyun Sun, Zhaohui Jiang, Liping Zhang, Wei Dong, Yuan Rao, "SLIC_SVM based leaf diseases saliency map extraction of tea plant", Computers and Electronics in Agriculture, Volume 157, February 2019, pages 102-109, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 157, February 2019, pages 102-109, Elsevier B.V.
label: Article 1015

Spatial and multi-depth temporal soil temperature assessment by assimilating satellite imagery, artificial intelligence and regression based models in arid area
type: NamedIndividual
type: Article
has knowledge model: Regressor 1034
abstract: Soil temperature is a noteworthy factor, particularly in farming and land treatment of organic wastes, since development of organic system is firmly controlled by soil temperature. Furthermore, soil temperature has impacts on the physical, substance, and microbiological forms that occur in soil. In this study, for estimation of multi-depth soil temperature, the most reliable combination of input variables for model development was achieved using gamma test technique. The most suitable training and testing data length i.e. training data set 11,529 (01 January 1975 to 25 July 2006; 81%) and testing data set 2716 (26 July 2006 to 31 December 2013; 19%) of the most efficient input variables were determined by means of M-test technique. Multi-depth soil temperature model was achieved by means of multiple linear regression (MLR), multilayer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS) techniques. After comparing results of all models, it was found that the ANFIS technique with generalized bell membership function had the best performing results when compared to MLR and MLP for all cases i.e. 5 cm, 10 cm and 20 cm depths of soil. Potential of gamma test technique was evaluated by means of sensitivity analysis. The spatial variation of land surface temperature (LST) was assessed by remote sensing (RS) and GIS technique and also the qualitative and quantitative performances were evaluated for determining the reliable and accurate spatial variation of LST. In qualitative evaluation, it was observed that the split-window (SW) approach overestimated the LST with coefficient of determination 0.8654. According to the quantitative evaluation based on root mean square error, correlation coefficient and coefficient of efficiency values between observed and estimated LST, the spatial variation of LST was found to be accurate over the Udaipur City, Rajasthan.
author: Vijay Kumar Singh, Bhaskar Pratap Singh, Ozgur Kisi, Daniel Prakash Kushwaha
publish year: 2018
creator: Vijay Kumar Singh, Bhaskar Pratap Singh, Ozgur Kisi, Daniel Prakash Kushwaha
reference: Vijay Kumar Singh, Bhaskar Pratap Singh, Ozgur Kisi, Daniel Prakash Kushwaha, "Spatial and multi-depth temporal soil temperature assessment by assimilating satellite imagery, artificial intelligence and regression based models in arid area", Computers and Electronics in Agriculture, Volume 150, July 2018, pages 205-219, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 150, July 2018, pages 205-219, Elsevier B.V.
label: Article 1034

Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors
type: NamedIndividual
type: Article
has knowledge model: Classifier 1040
abstract: The Russian wheat aphid (RWA) Diuraphis noxia (Mordvilko) is a major pest of winter wheat and barley in the United States. RWA induces stress to the wheat crop by damaging plant foliage, lowering the greenness of plants, and affecting productivity. The utilization of multispectral remote sensing is effective at detecting plant stress in agricultural crops. Stress to wheat plants detected in fields can be caused by several factors that can vary spatially in their presence and intensity across a field. Stress can result from factors such as nutrient deficiency, drought, diseases, and pests that can occur individually or collectively. The present study investigated the potential of using spatial pattern metrics derived from multispectral images in combination with topographic and edaphic variables to identify a set of variables to differentiate the stress induced by RWA from other stress causing factors. A discriminant function analysis was applied to 15 discriminating variables. A set of 13 variables were retained to develop a model to differentiate the three types of stress. Overall, 97 percent of patches of stress used to validate the model were correctly categorized. Stressed patches caused by RWA were 98 percent correctly classified, patches caused by drought were 94 percent correctly classified, and patches caused by agronomic conditions were 99 correctly classified. It is possible to discriminate stress induced by RWA from other stress causing factors in multispectral data when spatial attributes of the stress causing factors are incorporated in the analysis.
author: Georges F. Backoulou, Norman C. Elliott, Kristopher Giles, Mpho Phoofolo, Vasile Catana, Mustafa Mirik, Jerry Michels
publish year: 2011
creator: Georges F. Backoulou, Norman C. Elliott, Kristopher Giles, Mpho Phoofolo, Vasile Catana, Mustafa Mirik, Jerry Michels
reference: Georges F. Backoulou, Norman C. Elliott, Kristopher Giles, Mpho Phoofolo, Vasile Catana, Mustafa Mirik, Jerry Michels, "Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors", Computers and Electronics in Agriculture, Volume 78, Issue 2, September 2011, pages 123-129, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 78, Issue 2, September 2011, pages 123-129, Elsevier B.V.
label: Article 1040

Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy
type: NamedIndividual
type: Article
has knowledge model: Classifier 1044
abstract: Basal stem rot (BSR) is a prominent plant disease caused by Ganoderma boninense fungus, which infects oil palm plantations leading to large economic losses in palm oil production. There is need for novel disease detection techniques that can be used to reduce the oil palm losses due to BSR. Thus, this paper investigated the feasibility of utilizing electrical properties such as impedance, capacitance, dielectric constant, and dissipation factor in early detection of BSR disease in oil palm tree. Leaf samples from different oil palm trees (healthy, mild, moderate, and severely-infected) were collected and measured using a solid test fixture (16451B, Keysight Technologies, Japan) connected to an impedance analyzer (4294A, Agilent Technologies, Japan) at a frequency range of 100 kHz–30 MHz with 300 spectral interval. Genetic algorithm (GA), random forest (RF), and support vector machine-feature selection (SVM-FS) were used to analyze the electrical properties of the dataset and the most significant frequencies were selected. Following the selection of significant frequencies, the features were evaluated using two classifiers, support vector machine (SVM) and artificial neural networks (ANN) to determine the overall and individual class classification accuracies. The selection model comparative feature analysis demonstrated that the best statistical indicators with overall accuracy (88.64%), kappa (0.8480) and low mean absolute error (0.1652) were obtained using significant frequencies produced by SVM-FS model. The results indicated that the SVM classifier shows better performance as compared to ANN classifier. The results also showed that the classes, features selection models, and the electrical properties were found to be significantly different (p < .1). The impedance values were highly classified by Ganoderma disease at different levels of severity with overall accuracies of more than 80%. Impedance can be considered as the best electrical properties that can be us…
author: Alfadhl Yahya Khaled, Samsuzana Abd Aziz, Siti Khairunniza Bejo, Nazmi Mat Nawi, Idris Abu Seman
publish year: 2018
creator: Alfadhl Yahya Khaled, Samsuzana Abd Aziz, Siti Khairunniza Bejo, Nazmi Mat Nawi, Idris Abu Seman
reference: Alfadhl Yahya Khaled, Samsuzana Abd Aziz, Siti Khairunniza Bejo, Nazmi Mat Nawi, Idris Abu Seman, "Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy", Computers and Electronics in Agriculture, Volume 144, January 2018, pages 297-309, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 144, January 2018, pages 297-309, Elsevier B.V.
label: Article 1044

Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection
type: NamedIndividual
type: Article
has knowledge model: Classifier 1047
abstract: Remote sensing approaches are of increasing importance for agricultural applications, particularly for the support of selective agricultural measures that increase the productivity of crop stands. In contrast to multi-spectral image data, hyperspectral data has been shown to be highly suitable for the detection of crop growth anomalies, since they allow a detailed examination of stress-dependent changes in certain spectral ranges. However, the entire spectrum covered by hyperspectral data is probably not needed for discrimination between healthy and stressed plants. To define an optimal sensor-based system or a data product designed for crop stress detection, it is necessary to know which spectral wavelengths are significantly affected by stress factors and which spectral resolution is needed. In this study, a single airborne hyperspectral HyMap dataset was analyzed for its potential to detect plant stress symptoms in wheat stands induced by a pathogen infection. The Bhattacharyya distance (BD) with a forward feature search strategy was used to select relevant bands for the differentiation between healthy and fungal infected stands. Two classification algorithms, i.e. spectral angle mapper (SAM) and support vector machines (SVM) were used to classify the data covering an experimental field. Thus, the original dataset as well as datasets reduced to several band combinations as selected by the feature selection approach were classified. To analyze the influence of the spectral resolution on the detection accuracy, the original dataset was additionally stepwise spectrally resampled and a feature selection was carried out on each step. It is demonstrated that just a few phenomenon-specific spectral features are sufficient to detect wheat stands infected with powdery mildew. With original spectral resolution of HyMap, the highest classification accuracy could be obtained by using only 13 spectral bands with a Kappa coefficient of 0.59 in comparison to Kappa 0.57 using al…
author: Thorsten Mewes, Jonas Franke, Gunter Menz
publish year: 2011
creator: Thorsten Mewes, Jonas Franke, Gunter Menz
reference: Thorsten Mewes, Jonas Franke, Gunter Menz, "Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection", Precision Agriculture, Volume 12, Article number: 795 (2011) , Springer
Source: Precision Agriculture, Volume 12, Article number: 795 (2011) , Springer
label: Article 1047

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations
type: NamedIndividual
type: Article
has knowledge model: Regressor 1049
abstract: Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (R2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as R2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommenda…
author: Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabian G. Fernandez, David W. Franzen, Carrie A.M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan
publish year: 2019
creator: Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabian G. Fernandez, David W. Franzen, Carrie A.M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan
reference: Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabian G. Fernandez, David W. Franzen, Carrie A.M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan, "Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations", Computers and Electronics in Agriculture, Volume 164, September 2019, 104872, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 164, September 2019, 104872, Elsevier B.V.
label: Article 1049

The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling
type: NamedIndividual
type: Article
has knowledge model: Regressor 1073
abstract: Crop yield models can assist decision makers within any agro-industrial supply chain, even with regard to decisions that are unrelated to the crop production. Considering the characteristics of the mechanisms and data related to yield, data mining techniques are suitable candidates for modelling. The use of these techniques within a context with feature engineering, feature selection, and proper tuning can further improve performance beyond a simple replacement of multiple linear regression. To evaluate the impact of the different steps in the mentioned context, we evaluated sugarcane (Saccharum spp.) yield modelling with data obtained from a sugarcane mill. For a combination of six techniques, tuning, feature selection, and feature engineering, leading to 66 combinations, we assessed final model performance. Average performance across combinations resulted in a mean absolute error (MAE) of 6.42 Mg ha−1. Using different techniques led to a range of MAE from 4.57 to 8.80 Mg ha−1 on average. The best and worst performances for an individual model were MAEs of 4.11 and 9.00 Mg ha−1. Models with lower performance were close to simply predicting yield from the average yield for each number of cuts (MAE of 9.86 Mg ha−1). Tuning and feature engineering reduced the MAE on average by 1.17 and 0.64 Mg ha−1, respectively. Feature selection removed nearly 40% of the features but increased the MAE by 0.19 Mg ha−1. The performance of models was improved by simple strategies such as decomposing weather attributes and detailing fertilisation. Evaluation of feature importance provided by the RReliefF feature selection algorithm was used to explain the performance gains. If empirical models are needed, they will rely on using advanced techniques, but they will need proper algorithm tuning and feature engineering to extract most of the information from datasets. Based on the results, we recommend following the presented workflow for the development of yield models.
author: Felipe F. Bocca, Luiz Henrique Antunes Rodrigues
publish year: 2016
creator: Felipe F. Bocca, Luiz Henrique Antunes Rodrigues
reference: Felipe F. Bocca, Luiz Henrique Antunes Rodrigues, "The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling", Computers and Electronics in Agriculture, Volume 128, October 2016, pages 67-76, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 128, October 2016, pages 67-76, Elsevier B.V.
label: Article 1073

The potential of automatic methods of classification to identify leaf diseases from multispectral images
type: NamedIndividual
type: Article
has knowledge model: Classifier 1077
abstract: Three methods of automatic classification of leaf diseases are described based on high-resolution multispectral stereo images. Leaf diseases are economically important as they can cause a loss of yield. Early and reliable detection of leaf diseases has important practical relevance, especially in the context of precision agriculture for localized treatment with fungicides. We took stereo images of single sugar beet leaves with two cameras (RGB and multispectral) in a laboratory under well controlled illumination conditions. The leaves were either healthy or infected with the leaf spot pathogen Cercospora beticola or the rust fungus Uromyces betae. To fuse information from the two sensors, we generated 3-D models of the leaves. We discuss the potential of two pixelwise methods of classification: k-nearest neighbour and an adaptive Bayes classification with minimum risk assuming a Gaussian mixture model. The medians of pixelwise classification rates achieved in our experiments are 91% for Cercospora beticola and 86% for Uromyces betae. In addition, we investigated the potential of contextual classification with the so called conditional random field method, which seemed to eliminate the typical errors of pixelwise classification.
author: Sabine D. Bauer, Filip Korč, Wolfgang Forstner
publish year: 2011
creator: Sabine D. Bauer, Filip Korč, Wolfgang Forstner
reference: Sabine D. Bauer, Filip Korč, Wolfgang Forstner, "The potential of automatic methods of classification to identify leaf diseases from multispectral images", Precision Agriculture, Volume 12, pages 361–377 (2011) , Springer
Source: Precision Agriculture, Volume 12, pages 361–377 (2011) , Springer
label: Article 1077

Using boosted tree regression and artificial neural networks to forecast upland rice yield under climate change in Sahel
type: NamedIndividual
type: Article
has knowledge model: Regressor 1107
abstract: Climate drivers are key stress factors affecting upland rice yields in Sahel because the region is vulnerable to unfavorable weather and has a very low adaptive capacity. This study modeled upland rice yield responses to climate factors using multiple linear regression, boosted tree regression, and artificial neural networks (ANNs). Four ANNs were explored: ANNMLP (multilayer perceptron), ANNPNN (probabilistic neural network), ANNGFF (generalized feedforward), and ANNLR (linear regression). Then the modeled rice yield function was calibrated and tested against the observed yield data and climate variables of three provinces of Burkina Faso, West Africa. The global climate model (GCM) outputs under the AR4-SR-A1B, A2, and B1 mean ensemble CO2 emissions scenarios were then downscaled and used as input of the calibrated yield response model, in order to forecast yield trends over 2052. The results are three-fold: first, rain (R = 0.402) is the most dominant climate driver in Sahel, followed by the maximum and minimum temperatures (R = -0.313 and R = -0.237, respectively), which clearly reduce yield. Second, the ANNPNN (R = 0.952, MSE = 0.033 ton/ha, NMSE = 0.109 ton/ha, MAE = 0.115 ton/ha) has a great capability in rice yield responses function modeling outperforming boosted tree (R = 0.920, MSE = 0.077 ton/ha, NMSE = 0.208 ton/ha, MAE = 0.223 ton/ha) and the multiple linear regression (R = 0.385, MSE = 0.259 ton/ha, NMSE = 0.852 ton/ha, MAE = 0.340 ton/ha). All linear models performed unsatisfactorily. Third, the projected yields showed a gap of 57.29% with the site-recorded maximum average yields over 2052. From application of ANNPNN, we anticipate that site-specific rice yield may substantially decline with climate change, as rainfall is projected to decrease while temperatures increase. These results should assist in identifying priority adaptation measures for Sahel, such as village rainwater catchment basins supplemented with adapted irrigation technologies, to e…
author: Lei Zhang, Seydou Traore, Jiankun Ge, Yanbin Li, Shunsheng Wang, Ge Zhu, Yuanlai Cui, Guy Fipps
publish year: 2019
creator: Lei Zhang, Seydou Traore, Jiankun Ge, Yanbin Li, Shunsheng Wang, Ge Zhu, Yuanlai Cui, Guy Fipps
reference: Lei Zhang, Seydou Traore, Jiankun Ge, Yanbin Li, Shunsheng Wang, Ge Zhu, Yuanlai Cui, Guy Fipps, "Using boosted tree regression and artificial neural networks to forecast upland rice yield under climate change in Sahel", Computers and Electronics in Agriculture, Volume 166, November 2019, 105031, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 166, November 2019, 105031, Elsevier B.V.
label: Article 1107

Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale
type: NamedIndividual
type: Article
has knowledge model: Classifier 1111
abstract: Efficient crop protection management requires timely detection of diseases. The rapid development of remote sensing technology provides a possibility of spatial continuous monitoring of crop diseases over a large area. In this study, to monitor powdery mildew in winter wheat in an area where a severe disease infection occurred, the capability of high resolution (6 m) multi-spectral satellite imagery, SPOT-6, in disease mapping was assessed and validated using field survey data. Based on a rigorous feature selection process, five disease sensitive spectral features: green band, red band, normalized difference vegetation index, triangular vegetation index, and atmospherically-resistant vegetation index were selected from a group of candidate spectral features/variables. A spectral correction was processed on the selected features to eliminate possible baseline effect across different regions. Then, the disease mapping method was developed based on a spectral angle mapping technique. By validating against a set of field survey data, an overall mapping accuracy of 78 % and kappa coefficient of 0.55 were achieved. Such a moderate but practically acceptable accuracy suggests that the high resolution multi-spectral satellite image data would be of great potential in crop disease monitoring.
author: Lin Yuan, Ruiliang Pu, Jingcheng Zhang, Jihua Wang, Hao Yang
publish year: 2016
creator: Lin Yuan, Ruiliang Pu, Jingcheng Zhang, Jihua Wang, Hao Yang
reference: Lin Yuan, Ruiliang Pu, Jingcheng Zhang, Jihua Wang, Hao Yang, "Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale", Precision Agriculture, Volume 17, pages 332–348 (2016) , Springer
Source: Precision Agriculture, Volume 17, pages 332–348 (2016) , Springer
label: Article 1111

Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate
type: NamedIndividual
type: Article
has knowledge model: Classifier 1124
abstract: Glyphosate is a non-selective, systemic herbicide highly toxic to sensitive plant species. Its use has seen a significant increase due to the increased adoption of genetically modified glyphosate-resistant crops since the mid-1990s. Glyphosate application for weed control in glyphosate-resistant crops can drift onto an off-target area, causing unwanted injury to non-glyphosate resistant plants. Thus, early detection of crop injury from off-target drift of herbicide is critical in crop production. In non-glyphosate-resistant plants, glyphosate causes a reduction in chlorophyll content and metabolic disturbances. These subtle changes may be detectable by plant reflectance, which suggests the possibility of using optical remote sensing for early detection of drift damage to plants. In order to determine the feasibility of using optical remote sensing, a greenhouse study was initiated to measure the canopy reflectance of soybean plants using a portable hyperspectral image sensor. Non-glyphosate resistant soybean (Glycine max L. Merr.) plants were treated with glyphosate using a pneumatic track sprayer in a spray chamber. The three treatment groups were control (0 kg ae/ha), low dosage (0.086 kg ae/ha), and high dosage (0.86 kg ae/ha), each with four 2-plant pots. Hyperspectral images were taken at 4, 24, 48, and 72 h after application. The extracted canopy reflectance data was analyzed with vegetation indices. The results indicated that a number of vegetation indices could identify crop injury at 24 h after application, at which time visual inspection could not distinguish between glyphosate injured and non-treated plants. To improve the results a modified method of spectral derivative analysis was proposed and applied to find that the method produced better results than the vegetation indices. Four selected first derivatives at wavelength 519, 670, 685, and 697 nm could potentially differentiate crop injury at 4 h after treatment. The overall false positive rate was lo…
author: Haibo Yao, Yanbo Huang, Zuzana Hruska, Steven J. Thomson, Krishna N. Reddy
publish year: 2012
creator: Haibo Yao, Yanbo Huang, Zuzana Hruska, Steven J. Thomson, Krishna N. Reddy
reference: Haibo Yao, Yanbo Huang, Zuzana Hruska, Steven J. Thomson, Krishna N. Reddy, "Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate", Computers and Electronics in Agriculture, Volume 89, November 2012, pages 145-157, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 89, November 2012, pages 145-157, Elsevier B.V.
label: Article 1124

Weed identification based on K-means feature learning combined with convolutional neural network
type: NamedIndividual
type: Article
has knowledge model: Classifier 1149
abstract: Aiming at the problem that unstable identification results and weak generalization ability in feature extraction based on manual design features in weed identification, this paper take the soybean seedlings and its associated weeds as the research object, and construct a weed identification model based on K-means feature learning combined with Convolutional neural network. Combining advantages of multilayer and fine-turning of parameters of the convolutional neural network, this paper set k-means unsupervised feature learning as pre-training process, and replaced the random initialization weights of traditional CNN parameters. This method make the parameters can be obtained more reasonable values before optimization to gain higher weed identification accuracy. The experimental results show that this method with K-means pre-training achieved 92.89% accuracy, beyond 1.82% than convolutional neural network with random initialization and 6.01% than the two layer network without fine-tuning. Our results suggest that identification accuracy might be improved by fine-tuning of parameters.
author: JingLei Tang, Dong Wang, ZhiGuang Zhang, LiJun He, Jing Xin, Yang Xu
publish year: 2017
creator: JingLei Tang, Dong Wang, ZhiGuang Zhang, LiJun He, Jing Xin, Yang Xu
reference: JingLei Tang, Dong Wang, ZhiGuang Zhang, LiJun He, Jing Xin, Yang Xu, "Weed identification based on K-means feature learning combined with convolutional neural network", Computers and Electronics in Agriculture, Volume 135, 1 April 2017, pages 63-70, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 135, 1 April 2017, pages 63-70, Elsevier B.V.
label: Article 1149

Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery
type: NamedIndividual
type: Article
has knowledge model: Classifier 1154
abstract: The use of a low-cost five-band multispectral camera (RedEdge, MicaSense, USA) and a low-altitude airborne platform is investigated for the detection of plant stress caused by yellow rust disease in winter wheat for sustainable agriculture. The research is mainly focused on: (i) determining whether or not healthy and yellow rust infected wheat plants can be discriminated; (ii) selecting spectral band and Spectral Vegetation Index (SVI) with a strong discriminating capability; (iii) developing a low-cost yellow rust monitoring system for use at farmland scales. An experiment was carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where aerial multispectral images under different developmental stages of yellow rust were captured by an Unmanned Aerial Vehicle at an altitude of 16–24 m with a ground resolution of 1–1.5 cm/pixel. An automated yellow rust detection system is developed by learning (via random forest classifier) from labelled UAV aerial multispectral imagery. Experimental results indicate that: (i) good classification performance (with an average Precision, Recall and Accuracy of 89.2%, 89.4% and 89.3%) was achieved by the developed yellow rust monitoring at a diseased stage (45 days after inoculation); (ii) the top three SVIs for separating healthy and yellow rust infected wheat plants are RVI, NDVI and OSAVI; while the top two spectral bands are NIR and Red. The learnt system was also applied to the whole farmland of interest with a promising monitoring result. It is anticipated that this study by seamlessly integrating low-cost multispectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at farmland scales.
author: Jinya Su, Cunjia Liu, Matthew Coombes, Xiaoping Hu, Conghao Wang, Xiangming Xu, Qingdong Li, Lei Guo, Wen-Hua Chen
publish year: 2018
creator: Jinya Su, Cunjia Liu, Matthew Coombes, Xiaoping Hu, Conghao Wang, Xiangming Xu, Qingdong Li, Lei Guo, Wen-Hua Chen
reference: Jinya Su, Cunjia Liu, Matthew Coombes, Xiaoping Hu, Conghao Wang, Xiangming Xu, Qingdong Li, Lei Guo, Wen-Hua Chen, "Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery", Computers and Electronics in Agriculture, Volume 155, December 2018, pages 157-166, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 155, December 2018, pages 157-166, Elsevier B.V.
label: Article 1154

Wine grape cultivar influence on the performance of models that predict the lower threshold canopy temperature of a water stress index
type: NamedIndividual
type: Article
has knowledge model: Regressor 1156
abstract: The calculation of a thermal based Crop Water Stress Index (CWSI) requires an estimate of canopy temperature under non-water stressed conditions (Tnws). The objective of this study was to assess the influence of different wine grape cultivars on the performance of models that predict Tnws. Stationary infrared sensors were used to measure the canopy temperature of the wine grape cultivars Malbec, Syrah, Chardonnay and Cabernet franc under well-watered conditions over multiple years and modeled as a function of climatic parameters – solar radiation, air temperature, relative humidity and wind speed using multiple linear regression and neural network modeling. Despite differences among cultivars in Tnws, both models provided good prediction results when all cultivars were collectively modeled. For all cultivars, prediction error variance was lower in neural network models developed from cultivar-specific datasets than regression models developed from multi-cultivar datasets. Overall, the cultivar-specific models had less prediction error variance than multi-cultivar models. Multi-cultivar models generally resulted in prediction bias whereas cultivar-specific models eliminated the prediction bias. All predictive models had an uncertainty of ±0.1 in calculation of the CWSI despite significantly different prediction error variance between models.
author: B.A. King, K.C. Shellie
publish year: 2018
creator: B.A. King, K.C. Shellie
reference: B.A. King, K.C. Shellie, "Wine grape cultivar influence on the performance of models that predict the lower threshold canopy temperature of a water stress index", Computers and Electronics in Agriculture, Volume 145, February 2018, pages 122-129, Elsevier B.V.
Source: Computers and Electronics in Agriculture, Volume 145, February 2018, pages 122-129, Elsevier B.V.
label: Article 1156

Yield prediction with machine learning algorithms and satellite images
type: NamedIndividual
type: Article
has knowledge model: Regressor 1201
abstract: BACKGROUND: Barley is one of the strategic agricultural products available in the world, and yield prediction is important for ensuring food security. One way of estimating a product is to use remote sensing data in conjunction with field data and meteorological data. One of the main issues surrounding this comprises the use of machine learning techniques to create a multiresource data-based estimation model. Many studies have been conducted on barley yield prediction from planting to harvest. Still, the effect of different time intervals on yield prediction has not been investigated. Furthermore, the effect of different periods on yield prediction has not been investigated. RESULTS: In the present study, the whole growth period was divided into three parts. Using one of the major barley production areas in Iran, the performance of the proposed model was evaluated. In the first step, a model for integrating field data, remote sensing data and meteorological data was prepared. The results obtained show that, among the four machine learning methods implemented, the gaussian process regression algorithm performed best and estimated yield with r2 = 0.84, root mean square error = 737 kg ha−1 and mean absolute = 650 kg ha−1 , 1 month before harvest. CONCLUSION: It was found that the estimation results change depending on different agricultural zones and temporal training settings. The findings of the present study provide a powerful potential tool for the yield prediction of barley using multisource data and machine learning.
author: Alireza Sharif
publish year: 2020
creator: Alireza Sharif
reference: Alireza Sharif, "Yield prediction with machine learning algorithms and satellite images", Journal of the Science of Food and Agriculture
Source: Journal of the Science of Food and Agriculture
label: Article 1201

Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest
type: NamedIndividual
type: Article
has knowledge model: Regressor 1202
abstract: Crop yield estimates over large areas are conventionally made using weather observations, but a comprehensive understanding of the effects of various environmental indicators, observation frequency, and the choice of prediction algorithm remains elusive. Here we present a thorough assessment of county-level maize yield prediction in U.S. Midwest using six statistical/machine learning algorithms (Lasso, Support Vector Regressor, Random Forest, XGBoost, Long-short term memory (LSTM), and Convolutional Neural Network (CNN)) and an extensive set of environmental variables derived from satellite observations, weather data, land surface model results, soil maps, and crop progress reports. Results show that seasonal crop yield forecasting benefits from both more advanced algorithms and a large composite of information associated with crop canopy, environmental stress, phenology, and soil properties (i.e. hundreds of features). The XGBoost algorithm outperforms other algorithms both in accuracy and stability, while deep neural networks such as LSTM and CNN are not advantageous. The compositing interval (8-day, 16-day or monthly) of time series variable does not have significant effects on the prediction. Combining the best algorithm and inputs improves the prediction accuracy by 5% when compared to a baseline statistical model (Lasso) using only basic climatic and satellite observations. Reasonable county-level yield foresting is achievable from early June, almost four months prior to harvest. At the national level, early-season (June and July) prediction from the best model outperforms that of the United States Department of Agriculture (USDA) World Agricultural Supply and Demand Estimates (WASDE). This study provides insights into practical crop yield forecasting and the understanding of yield response to climatic and environmental conditions.
author: Yanghui Kang, Mutlu Ozdogan, Xiaojin Zhu, Zhiwei Ye, Christopher Hain, Martha Anderson
publish year: 2020
creator: Yanghui Kang, Mutlu Ozdogan, Xiaojin Zhu, Zhiwei Ye, Christopher Hain, Martha Anderson
reference: Yanghui Kang, Mutlu Ozdogan, Xiaojin Zhu, Zhiwei Ye, Christopher Hain, Martha Anderson, "Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest", Environmental Research Letters
Source: Environmental Research Letters
label: Article 1202

Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data
type: NamedIndividual
type: Article
has knowledge model: Regressor 1203
abstract: Traditional potato growth models evidence certain limitations, such as the cost of obtaining the input data required to run the models, the lack of spatial information in some instances, or the actual quality of input data. In order to address these issues, we develop a model to predict potato yield using satellite remote sensing. In an effort to offer a good predictive model that improves the state of the art on potato precision agriculture, we use images from the twin Sentinel 2 satellites (European Space Agency—Copernicus Programme) over three growing seasons, applying different machine learning models. First, we fitted nine machine learning algorithms with various pre-processing scenarios using variables from July, August and September based on the red, red-edge and infra-red bands of the spectrum. Second, we selected the best performing models and evaluated them against independent test data. Finally, we repeated the previous two steps using only variables corresponding to July and August. Our results showed that the feature selection step proved vital during data pre-processing in order to reduce multicollinearity among predictors. The Regression Quantile Lasso model (11.67% Root Mean Square Error, RMSE; R2 = 0.88 and 9.18% Mean Absolute Error, MAE) and Leap Backwards model (10.94% RMSE, R2 = 0.89 and 8.95% MAE) performed better when predictors with a correlation coefficient > 0.5 were removed from the dataset. In contrast, the Support Vector Machine Radial (svmRadial) performed better with no feature selection method (11.7% RMSE, R2 = 0.93 and 8.64% MAE). In addition, we used a random forest model to predict potato yields in Castilla y León (Spain) 1–2 months prior to harvest, and obtained satisfactory results (11.16% RMSE, R2 = 0.89 and 8.71% MAE). These results demonstrate the suitability of our models to predict potato yields in the region studied.
author: Diego Gómez, Pablo Salvador, Julia Sanz, Jose Luis Casanova
publish year: 2019
creator: Diego Gómez, Pablo Salvador, Julia Sanz, Jose Luis Casanova
reference: Diego Gómez, Pablo Salvador, Julia Sanz, Jose Luis Casanova, "Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data", Journals on Remote Sensing, MDPI
Source: Journals on Remote Sensing, MDPI
label: Article 1203

Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images
type: NamedIndividual
type: Article
has knowledge model: Classifier 1205
abstract: Deep learning framework for paddy crop field image classification Adopted pre - trained VGG-16 CNN model for paddy crop stress classification Classification of 12 categories of biotic and abiotic paddy crop stresses The trained models achieve an average accuracy of 92.89 % . Tool for implementing crop management strategiesAbstractOn - time recognition and early control of the stresses in the paddy crops at the booting growth stage is the key to prevent qualitative and quantitative loss of agricultural yield . The conventional paddy crop stress recognition and classification activities invariably rely on human experts identifying visual symptoms as a means of categorization . This process is admittedly subjective and error - prone , which in turn can lead to incorrect actions being taken in stress management decisions . The work presented in this paper aims to design a deep convolutional neural network ( DCNN ) framework for automatic recognition and classification of various biotic and abiotic paddy crop stresses using the field images . The work has adopted the pre - trained VGG-16 CNN model for the automatic classification of stressed paddy crop images captured during the booting growth stage . The trained models achieve an average accuracy of 92.89 % on the held - out dataset , demonstrating the technical feasibility of using the deep learning approach utilizing 30,000 field images of 5 different paddy crop varieties with 12 different stress categories ( including healthy / normal ) . The proposed work finds applications in developing the decision support systems and mobile applications for automating the field crop and resource management practices .
author: Basavaraj S. Anami, Naveen N. Malvade, Surendra Palaiah
publish year: 2020
creator: Basavaraj S. Anami, Naveen N. Malvade, Surendra Palaiah
reference: Basavaraj S. Anami, Naveen N. Malvade, Surendra Palaiah, "Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images", Artificial Intelligence in AgricultureVolume 4, 2020, Elsevier B.V.
Source: Artificial Intelligence in AgricultureVolume 4, 2020, Elsevier B.V.
label: Article 1205

Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data
type: NamedIndividual
type: Article
has knowledge model: Regressor 1206
abstract: Relationship between the NDVI and cereal grain yield in Central Europe as studied . Strong correlation NDVI and grain yield was observed for period between March and May. Increase in the NDVI in spring by 0.1 increases grain yield by about 1.1–2.6 t / ha . Grain yield can be predicted at the regional level 3–4 months before the harvest . AbstractEarth observation satellite technologies allow the status of crops to be assessed at various geographical scales . The most popular vegetation index for analyzing this status is the Normalized Difference Vegetation Index ( NDVI ) . This article studies the relationship between the NDVI and cereal - grain yield in selected regions ( corresponding to NUTS2 levels ) of four Central European countries : Poland , Germany , the Czech Republic , and Slovakia . The remote sensing data cover the vegetation seasons ( from the beginning of March until the end of June ) of 2012–2016 , and were obtained using the Moderate Resolution Imaging Spectroradiometer ( MODIS ) sensor of NASA 's Terra satellite . The average NDVI of the data was calculated for both the entire area and the arable - land area of the regions ( according to the Corine Land Cover program ) . Statistical analysis includes correlation and linear regression analyses between cereal grain yield and mean NDVI for entire area and arable land of the regions in each year of the study . They are strongly correlated from the beginning of March until mid - May ( most of the correlation coefficients for arable land are between 0.50 and 0.85 ) , the strongest relationship being around the beginning of April in the Czech Republic and Slovakia ( most of the correlation coefficients are between 0.80 and 1.00 ) . In most regions of Poland , the relationships are quite strong , but in German regions they are weak and inconsistent . Regression coefficients ( slopes ) for relationships between the NDVI in the beginning of April and the grain yield of cereals range from 10.8 to 26.2 . This …
author: Ewa Panek, Dariusz Gozdowski
publish year: 2020
creator: Ewa Panek, Dariusz Gozdowski
reference: Ewa Panek, Dariusz Gozdowski, "Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data", Remote Sensing Applications: Society and EnvironmentVolume 17, January 2020, Elsevier B.V.
Source: Remote Sensing Applications: Society and EnvironmentVolume 17, January 2020, Elsevier B.V.
label: Article 1206

Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index ☆
type: NamedIndividual
type: Article
has knowledge model: Regressor 1207
abstract: Accurate estimation of biomass is necessary for evaluating crop growth and predicting crop yield . Biomass is also a key trait in increasing grain yield by crop breeding . The aims of this study were ( i ) to identify the best vegetation indices for estimating maize biomass , ( ii ) to investigate the relationship between biomass and leaf area index ( LAI ) at several growth stages , and ( iii ) to evaluate a biomass model using measured vegetation indices or simulated vegetation indices of Sentinel 2A and LAI using a deep neural network ( DNN ) algorithm . The results showed that biomass was associated with all vegetation indices . The three - band water index ( TBWI ) was the best vegetation index for estimating biomass and the corresponding R2 , RMSE , and RRMSE were 0.76 , 2.84 t ha−1 , and 38.22 % respectively . LAI was highly correlated with biomass ( R2 = 0.89 , RMSE = 2.27 t ha−1 , and RRMSE = 30.55 % ) . Estimated biomass based on 15 hyperspectral vegetation indices was in a high agreement with measured biomass using the DNN algorithm ( R2 = 0.83 , RMSE = 1.96 t ha−1 , and RRMSE = 26.43 % ) . Biomass estimation accuracy was further increased when LAI was combined with the 15 vegetation indices ( R2 = 0.91 , RMSE = 1.49 t ha−1 , and RRMSE = 20.05 % ) . Relationships between the hyperspectral vegetation indices and biomass differed from relationships between simulated Sentinel 2A vegetation indices and biomass . Biomass estimation from the hyperspectral vegetation indices was more accurate than that from the simulated Sentinel 2A vegetation indices ( R2 = 0.87 , RMSE = 1.84 t ha−1 , and RRMSE = 24.76 % ) . The DNN algorithm was effective in improving the estimation accuracy of biomass . It provides a guideline for estimating biomass of maize using remote sensing technology and the DNN algorithm in this region .
author: Xiuliang Jin, Zhenhai Li, Haikuan Feng, Zhibin Ren, Shaokun Li
publish year: 2020
creator: Xiuliang Jin, Zhenhai Li, Haikuan Feng, Zhibin Ren, Shaokun Li
reference: Xiuliang Jin, Zhenhai Li, Haikuan Feng, Zhibin Ren, Shaokun Li, "Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index ☆", The Crop JournalVolume 8, Issue 1, February 2020, Elsevier B.V.
Source: The Crop JournalVolume 8, Issue 1, February 2020, Elsevier B.V.
label: Article 1207

Effect of various crop rotations on rice yield and nitrogen use efficiency in paddy – upland systems in southeastern China
type: NamedIndividual
type: Article
has knowledge model: Regressor 1208
abstract: To evaluate the effects of various rotation systems on rice grain yield and N use efficiency , a paddy – upland cropping experiment ( 2013–2016 ) was conducted in southeastern China . The experiment was designed using six different rice––winter crop rotations : rice – fallow ( RF ) , rice – wheat ( RW ) , rice – potato with rice straw mulch ( RP ) , rice – green manure ( Chinese milk vetch ; RC – G ) , rice – oilseed rape ( RO ) , and rice – green manure crop ( oilseed rape with fresh straw incorporated into soil at flowering ; RO – G ) and three N rates , N0 ( 0 kg N ha−1 ) , N1 ( 142.5 kg N ha−1 ) , and N2 ( 202.5 kg N ha−1 ) . Average rice yields in the RF ( 5.93 t ha−1 ) rotation were significantly lower than those in the rotations with winter crops ( 7.20–7.48 t ha−1 ) under the N0 treatment , suggesting that incorporation of straw might be more effective for increasing soil N than winter fallow . The rice yield differences among the rotations varied by year with the N input . In general , the grain yields in the RP and RO – G rotations – were respectively 11.6–28.5 % and 14.80–37.19 % higher than those in the RF in plots with N applied . Increasing the N rate may have tended to minimize the average yield gap between the RF and the other rotations ; the yield gaps were 18.55 % , 4.14 % , and 0.23 % in N0 , N1 , and N2 , respectively . However , the N recovery efficiency in the RF was significantly lower than that in other rotations , except for 2015 under both N1 and N2 rates , a finding that implies a large amount of chemical N loss . No significant differences in nitrogen agronomic efficiency ( NAE ) and physiological efficiency ( NPE ) were found between the rotations with legume ( RC – G ) and non – legume ( RO and RW ) winter crops , a result that may be due partly to straw incorporation . For this reason , we concluded that the return of straw could reduce differences in N use efficiency between rotations with and without legume crops . The degree of sync…
author: Song Chen, Shaowen Liu, Xi Zheng, Min Yin, Guang Chu, Chunmei Xu, Jinxiang Yan, Liping Chen, Danying Wang, Xiufu Zhang
publish year: 2018
creator: Song Chen, Shaowen Liu, Xi Zheng, Min Yin, Guang Chu, Chunmei Xu, Jinxiang Yan, Liping Chen, Danying Wang, Xiufu Zhang
reference: Song Chen, Shaowen Liu, Xi Zheng, Min Yin, Guang Chu, Chunmei Xu, Jinxiang Yan, Liping Chen, Danying Wang, Xiufu Zhang, "Effect of various crop rotations on rice yield and nitrogen use efficiency in paddy – upland systems in southeastern China", The Crop JournalVolume 6, Issue 6, December 2018, Elsevier B.V.
Source: The Crop JournalVolume 6, Issue 6, December 2018, Elsevier B.V.
label: Article 1208

Genetic dissection of grain elements predicted by hyperspectral imaging associated with yield - related traits in a wild barley NAM population
type: NamedIndividual
type: Article
has knowledge model: Regressor 1209
abstract: Hyperspectral imaging enables reliable estimation of grain nutrient concentrations . Wild barley offers a broad variation for grain nutrient concentrations . Identification of genomic regions associated with nutrient concentrations . QTL Hotspots simultaneously affect multiple grain nutrients . Wild HvGA20ox2 alleles increase several grain nutrients without yield loss . AbstractEnhancing the accumulation of essential mineral elements in cereal grains is of prime importance for combating human malnutrition . Biofortification by breeding holds great potential for improving nutrient accumulation in grains . However , conventional breeding approaches require element analysis of many grain samples , which causes high costs . Here we applied hyperspectral imaging to estimate the concentration of 15 grain elements ( C , B , Ca , Cd , Cu , Fe , K , Mg , Mn , Mo , N , Na , P , S , Zn ) in high - throughput in the wild barley nested association mapping ( NAM ) population HEB-25 , comprising 1,420 BC1S3 lines derived from crossing 25 wild barley accessions with the cultivar ‘ Barke ’ . Nutrient concentrations varied largely with a multitude of lines having higher micronutrient concentration than ‘ Barke ’ . In a genome - wide association study ( GWAS ) , we located 75 quantitative trait locus ( QTL ) hotspots , whereof many could be explained by major genes such as NO APICAL MERISTEM-1 ( NAM-1 ) and PHOTOPERIOD 1 ( Ppd - H1 ) . The GWAS approach revealed exotic alleles that were able to increase grain element concentrations . Remarkably , a QTL linked to GIBBERELLIN 20 OXIDASE 2 ( HvGA20ox2 ) significantly increased several grain elements without yield loss . We conclude that introgressing promising exotic alleles into elite breeding material can assist in improving the nutritional value of barley grains .
author: Paul Herzig, Andreas Backhaus, Udo Seiffert, Nicolaus von Wirén, Klaus Pillen, Andreas Maurer
publish year: 2019
creator: Paul Herzig, Andreas Backhaus, Udo Seiffert, Nicolaus von Wirén, Klaus Pillen, Andreas Maurer
reference: Paul Herzig, Andreas Backhaus, Udo Seiffert, Nicolaus von Wirén, Klaus Pillen, Andreas Maurer, "Genetic dissection of grain elements predicted by hyperspectral imaging associated with yield - related traits in a wild barley NAM population", Plant ScienceVolume 285, August 2019, Elsevier B.V.
Source: Plant ScienceVolume 285, August 2019, Elsevier B.V.
label: Article 1209

Digital mapping of soil organic matter stocks using Random Forest modeling in a semi - arid steppe ecosystem
type: NamedIndividual
type: Article
has knowledge model: Regressor 1210
abstract: Spatial prediction of soil organic matter is a global challenge and of particular importance for regions with intensive land use and where availability of soil data is limited . This study evaluated a Digital Soil Mapping ( DSM ) approach to model the spatial distribution of stocks of soil organic carbon ( SOC ) , total carbon ( Ctot ) , total nitrogen ( Ntot ) and total sulphur ( Stot ) for a data - sparse , semi - arid catchment in Inner Mongolia , Northern China . Random Forest ( RF ) was used as a new modeling tool for soil properties and Classification and Regression Trees ( CART ) as an additional method for the analysis of variable importance . At 120 locations soil profiles to 1 m depth were analyzed for soil texture , SOC , Ctot , Ntot , Stot , bulk density ( BD ) and pH. On the basis of a digital elevation model , the catchment was divided into pixels of 90 m × 90 m and for each cell , predictor variables were determined : land use unit , Reference Soil Group ( RSG ) , geological unit and 12 topography - related variables . Prediction maps showed that the highest amounts of SOC , Ctot , Ntot and Stot stocks are stored under marshland , steppes and mountain meadows . River - like structures of very high elemental stocks in valleys within the steppes are partly responsible for the high amounts of SOC for grasslands ( 81–84 % of total catchment stocks ) . Analysis of variable importance showed that land use , RSG and geology are the most important variables influencing SOC storage . Prediction accuracy of the RF modeling and the generated maps was acceptable and explained variances of 42 to 62 % and 66 to 75 % , respectively . A decline of up to 70 % in elemental stocks was calculated after conversion of steppe to arable land confirming the risk of rapid soil degradation if steppes are cultivated . Thus their suitability for agricultural use is limited .
author: Martin Wiesmeier, Frauke Barthold, Benjamin Blank, Ingrid Kögel-Knabner
publish year: 2011
creator: Martin Wiesmeier, Frauke Barthold, Benjamin Blank, Ingrid Kögel-Knabner
reference: Martin Wiesmeier, Frauke Barthold, Benjamin Blank, Ingrid Kögel-Knabner, "Digital mapping of soil organic matter stocks using Random Forest modeling in a semi - arid steppe ecosystem", Plant and Soil volume 340, pages7–24(2011) , Springer
Source: Plant and Soil volume 340, pages7–24(2011) , Springer
label: Article 1210

Performance of chlorophyll prediction indices for Eragrostis tef at Sentinel-2 MSI and Landsat-8 OLI spectral resolutions
type: NamedIndividual
type: Article
has knowledge model: Regressor 1211
abstract: As remotely sensed data becomes more readily available around the world , satellites such as Landsat-8 and Sentinel-2 have great potential to support precision agriculture . Sensors with high spectral and spatial resolutions are particularly optimal for limited land resource farmers to improve land management . The objective of this short communication is to assess the performance of Sentinel-2 and Landsat-8 multispectral bands for chlorophyll prediction using indices that were originally developed using imaging spectroscopy / hyperspectral data . Remotely sensed chlorophyll content measures are often utilized as a proxy of plant health . Performance of a group of chlorophyll prediction indices is tested for tef ( Eragrostis tef ) , an endemic grass crop native to Ethiopia that forms a major component of Ethiopian diets and is grown by limited land resource farmers . Hyperspectral reflectance data captured in situ at the canopy level were convolved into bands approximating Landsat-8 and Sentinel-2 sensors , and a suite of chlorophyll prediction indices were computed and regressed against chlorophyll content . Results show that simple pigment indices employing wavelengths corresponding to the blue and ultra - blue bands performed best for predicting chlorophyll . The red - edge index computed using the Sentinel-2 bands also performed well . These findings suggest that publicly available , multispectral imagery can potentially substitute for hyperspectral data in chlorophyll prediction indices , thereby improving the accessibility of precision agriculture methods .
author: K. Colton Flynn, Amy E. Frazier, Sintayehu Admas
publish year: 2020
creator: K. Colton Flynn, Amy E. Frazier, Sintayehu Admas
reference: K. Colton Flynn, Amy E. Frazier, Sintayehu Admas, "Performance of chlorophyll prediction indices for Eragrostis tef at Sentinel-2 MSI and Landsat-8 OLI spectral resolutions", Precision Agriculture volume 21, pages1057–1071(2020) , Springer
Source: Precision Agriculture volume 21, pages1057–1071(2020) , Springer
label: Article 1211

Sensitivity of leaf chlorophyll empirical estimators obtained at Sentinel-2 spectral resolution for different canopy structures
type: NamedIndividual
type: Article
has knowledge model: Regressor 1212
abstract: A comparison of the sensitivity of canopy scale estimators of leaf chlorophyll , obtainable with Sentinel-2 spectral resolution , to soil , canopy and leaf mesophyll factors , was addressed . The analysis of a synthetic dataset , generated simulating the reflectance in the 1–4 LAI range of canopies for the main general classes of leaf inclination ( i.e. erectophile , plagiophile , spherical , planophile and extremophile ) and for different soil types was used for such a purpose . The synthetic dataset was obtained using the PROSPECT5 - 4SAIL model in the direct mode with a large variety of soil backgrounds . Additionally an experimental dataset including airborne hyperspectral data gathered during ESA ( European Space Agency ) campaigns SPARC and AGRISAR , was employed to simulate Sentinel-2 spectral and spatial resolution , to confirm model results . Analysis of the synthetic and experimental datasets indicated that : ( i ) the CVI ( Chlorophyll Vegetation Index ) , relying only on visible and NIR ( Near Infra - Red ) bands and obtainable at 10 m spatial resolution , can be used as leaf chlorophyll estimator , at growth stages suitable for nitrogen fertilizer topdressings , for all canopy structures except for erectophile canopies ; ( ii ) better results can be obtained by using different indices for different leaf architectures , with TCI / OSAVI ( Triangular Chlorophyll Index / Optimized Soil Adjusted Vegetation Index ) performing better for erectophile canopies , whereas MTCI ( MERIS Terrestrial Chlorophyll Index ) provides better results for planophile canopies , despite the fact that these indices require bands obtainable at 20 m spatial resolution from Sentinel-2 data .
author: M. Vincini, F. Calegari, R. Casa
publish year: 2016
creator: M. Vincini, F. Calegari, R. Casa
reference: M. Vincini, F. Calegari, R. Casa, "Sensitivity of leaf chlorophyll empirical estimators obtained at Sentinel-2 spectral resolution for different canopy structures", Precision Agriculture volume 17, pages313–331(2016) , Springer
Source: Precision Agriculture volume 17, pages313–331(2016) , Springer
label: Article 1212

Prediction of citrus yield from airborne hyperspectral imagery
type: NamedIndividual
type: Article
has knowledge model: Regressor 1213
abstract: Recent advances in spectral imaging technology have enabled the development of models that estimate various crop parameters from spectral imagery data . We developed partial least square ( PLS ) models to predict fruit yield of Satsuma mandarin using airborne hyperspectral imagery obtained several months before harvesting . Hyperspectral images in the 72 visible and near - infrared ( NIR ) wavelengths ( from 407 to 898 nm ) were acquired over a citrus orchard during the early growing seasons of 2003 , 2004 and 2005 . The canopy features of individual trees were identified using pixel - based average spectral reflectance values for all 72 wavelengths from the acquired images . The acquired canopy features were then used as prediction variables to develop yield prediction models . These were developed using three techniques : ( 1 ) normalized difference vegetation index ( NDVI ) , simple ratio ( SR ) and photochemical reflectance index ( PRI ) , ( 2 ) conventional multiple linear regression ( MLR ) models , and ( 3 ) PLS regression models . As we intended to predict yield several months before the harvesting season ( generally late December ) , the conventional techniques ( vegetation indices and MLR ) did not predict well . In contrast , PLS models gave successful predictions for the three years . These results confirmed the hypothesized correlation between canopy features and citrus yield . The successful forecasting of yields several months or even one year ahead of the harvest season is expected to contribute to planning harvest schedules , generating prescription maps for dealing with fluctuations of yield in specific trees , control measures , and management practices .
author: Xujun Ye, Kenshi Sakai, Masafumi Manago, Shin-ichi Asada, Akira Sasao
publish year: 2007
creator: Xujun Ye, Kenshi Sakai, Masafumi Manago, Shin-ichi Asada, Akira Sasao
reference: Xujun Ye, Kenshi Sakai, Masafumi Manago, Shin-ichi Asada, Akira Sasao, "Prediction of citrus yield from airborne hyperspectral imagery", Precision Agriculture volume 8, pages111–125(2007) , Springer
Source: Precision Agriculture volume 8, pages111–125(2007) , Springer
label: Article 1213

Mapping Maize Cropping Patterns in Dak Lak , Vietnam Through MODIS EVI Time Series
type: NamedIndividual
type: Article
has knowledge model: Classifier 1214
abstract: Land use maps specifying up - to - date acreage information on maize ( Zea mays L. ) cropping patterns are required by many stakeholders in Vietnam . Government statistics , however , lag behind by one year , and the official land use maps are only updated at 5-year intervals . The aim of this study was to apply the Savitzky – Golay algorithm to reconstruct noisy Enhanced Vegetation Index ( EVI ) time series ( 2003–2018 ) from Terra Moderate Resolution Imaging Spectroradiometer ( MODIS ) Vegetation Indices ( MOD13Q1 ) to allow timely detection of changes in maize crop phenology , and then to employ a linear kernel Support Vector Machine ( SVM ) classifier on the reconstructed EVI time series to prepare the present - day maize cropping pattern map of Dak Lak province of Vietnam . The method was able to specify the spatial extent of areas cropped to maize with an overall map accuracy of 79 % and could also differentiate the areas cropped to maize just once versus twice annually . The by - district mapped maize acreage shows a good agreement with the official governmental data , with a 0.93 correlation coefficient ( r ) and a root mean square deviation ( RMSD ) of 1624 ha .
author: Ha Thi Thu Nguyen, Loc Van Nguyen, C.A.J.M (Kees) de Bie, Ignacio A. Ciampitti, Duc Anh Nguyen, Minh Van Nguyen, Luciana Nieto, Rai Schwalbert, Long Viet Nguyen
publish year: 2020
creator: Ha Thi Thu Nguyen, Loc Van Nguyen, C.A.J.M (Kees) de Bie, Ignacio A. Ciampitti, Duc Anh Nguyen, Minh Van Nguyen, Luciana Nieto, Rai Schwalbert, Long Viet Nguyen
reference: Ha Thi Thu Nguyen, Loc Van Nguyen, C.A.J.M (Kees) de Bie, Ignacio A. Ciampitti, Duc Anh Nguyen, Minh Van Nguyen, Luciana Nieto, Rai Schwalbert, Long Viet Nguyen, "Mapping Maize Cropping Patterns in Dak Lak , Vietnam Through MODIS EVI Time Series", Agronomy 2020, 10(4), 478; https://doi.org/10.3390/agronomy10040478
Source: Agronomy 2020, 10(4), 478; https://doi.org/10.3390/agronomy10040478
label: Article 1214

Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs
type: NamedIndividual
type: Article
has knowledge model: Regressor 1215
abstract: In this study ANFIS models were developed to model wheat yield on the basis of energy inputs . The results of ANFIS model were compared with artificial neural networks . Results showed that ANFIS model can predict yield relatively better than ANN model . AbstractEnergy is regarded as one of the most important elements in agricultural sector . During the last decades energy consumption in agriculture has increased , so finding the relationship between energy consumption and crop yields in agricultural production can help to achieve sustainable agriculture . In this study several adaptive neuro - fuzzy inference system ( ANFIS ) models were evaluated to predict wheat grain yield on the basis of energy inputs . Moreover , artificial neural networks ( ANNs ) were developed and the obtained results were compared with ANFIS models . For the best ANFIS structure gained in this study , R , RMSE and MAPE were calculated as 0.976 , 0.046 and 0.4 , respectively . The developed ANN was a multilayer perceptron ( MLP ) with eleven neurons in the input layer , two hidden layers with 32 and 10 neurons and one neuron ( wheat grain yield ) in the output layer . For the best ANN model , R , RMSE and MAPE were computed as 0.92 , 0.9 and 0.1 , respectively . The results illustrated that ANFIS model can predict the yield more precisely than ANN .
author: Benyamin Khoshnevisan, Shahin Rafiee, Mahmoud Omid, Hossein Mousazadeh
publish year: 2014
creator: Benyamin Khoshnevisan, Shahin Rafiee, Mahmoud Omid, Hossein Mousazadeh
reference: Benyamin Khoshnevisan, Shahin Rafiee, Mahmoud Omid, Hossein Mousazadeh, "Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs", Information Processing in AgricultureVolume 1, Issue 1, August 2014, Elsevier B.V.
Source: Information Processing in AgricultureVolume 1, Issue 1, August 2014, Elsevier B.V.
label: Article 1215

Insect classification and detection in field crops using modern machine learning techniques
type: NamedIndividual
type: Article
has knowledge model: Classifier 1216
abstract: The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious food . Crop insect detection is a challenging task for farmers as a significant portion of the crops are damaged , and the quality is degraded due to the pest attack . Traditional insect identification has the drawback of requiring well - trained taxonomists to identify insects based on morphological features accurately . Experiments were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural networks ( ANN ) , support vector machine ( SVM ) , k - nearest neighbors ( KNN ) , naive bayes ( NB ) and convolutional neural network ( CNN ) model . This paper presents the insect pest detection algorithm that consists of foreground extraction and contour identification to detect the insects for Wang , Xie , Deng , and IP102 datasets in a highly complex background . The 9-fold cross - validation was applied to improve the performance of the classification models . The highest classification rate of 91.5 % and 90 % was achieved for nine and 24 class insects using the CNN model . The detection performance was accomplished with less computation time for Wang , Xie , Deng , and IP102 datasets using insect pest detection algorithm . The comparison results with the state - of - the - art classification algorithms exhibited considerable improvement in classification accuracy , computation time performance while apply more efficiently in field crops to recognize the insects . The results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture .
author: Thenmozhi Kasinathan, Dakshayani Singaraju, Srinivasulu Reddy Uyyala
publish year: 2020
creator: Thenmozhi Kasinathan, Dakshayani Singaraju, Srinivasulu Reddy Uyyala
reference: Thenmozhi Kasinathan, Dakshayani Singaraju, Srinivasulu Reddy Uyyala, "Insect classification and detection in field crops using modern machine learning techniques", Information Processing in AgricultureAvailable online 8 October 2020, Elsevier B.V.
Source: Information Processing in AgricultureAvailable online 8 October 2020, Elsevier B.V.
label: Article 1216

Research on insect pest image detection and recognition based on bio-inspired methods
type: NamedIndividual
type: Article
has knowledge model: Classifier 1217
abstract: Insect pest recognition and detection are vital for food security, a stable agricultural economy and quality of life. To realise rapid detection and recognition of insect pests, methods inspired by human visual system were proposed in this paper. Inspired by human visual attention, Saliency Using Natural statistics model (SUN) was used to generate saliency maps and detect region of interest (ROI) in a pest image. To extract the invariant features for representing the pest appearance, we extended the bio-inspired Hierarchical Model and X (HMAX) model in the following ways. Scale Invariant Feature Transform (SIFT) was integrated into the HMAX model to increase the invariance to rotational changes. Meanwhile, Non-negative Sparse Coding (NNSC) is used to simulate the simple cell responses. Moreover, invariant texture features were extracted based on Local Configuration Pattern (LCP) algorithm. Finally, the extracted features were fed to Support Vector Machines (SVM) for recognition. Experimental results demonstrated that the proposed method had an advantage over the compared methods: HMAX, Sparse Coding and Natural Input Memory with Bayesian Likelihood Estimation (NIMBLE), and was comparable to the Deep Convolutional Network. The proposed method has achieved a good result with a recognition rate of 85.5% and could effectively recognise insect pest under complex environments. The proposed method has provided a new approach for insect pest detection and recognition.
author: Limiao Denga, Yanjiang Wang, Zhongzhi Han, Renshi Yu
publish year: 2018
creator: Limiao Denga, Yanjiang Wang, Zhongzhi Han, Renshi Yu
reference: Limiao Denga, Yanjiang Wang, Zhongzhi Han, Renshi Yu, "Research on insect pest image detection and recognition based on bio-inspired methods", Biosystems Engineering, Volume 169, May 2018, Pages 139-148, Elsevier B.V.
Source: Biosystems Engineering, Volume 169, May 2018, Pages 139-148, Elsevier B.V.
label: Article 1217

Ip102: A large-scale benchmark dataset for insect pest recognition
type: NamedIndividual
type: Article
has knowledge model: Classifier 1218
abstract: Insect pests are one of the main factors affecting agricultural product yield. Accurate recognition of insect pests facilitates timely preventive measures to avoid economic losses. However, the existing datasets for the visual classification task mainly focus on common objects, e.g., flowers and dogs. This limits the application of powerful deep learning technology on specific domains like the agricultural field. In this paper, we collect a large-scale dataset named IP102 for insect pest recognition. Specifically, it contains more than 75, 000 images belonging to 102 categories, which exhibit a natural long-tailed distribution. In addition, we annotate about 19, 000 images with bounding boxes for object detection. The IP102 has a hierarchical taxonomy and the insect pests which mainly affect one specific agricultural product are grouped into the same upperlevel category. Furthermore, we perform several baseline experiments on the IP102 dataset, including handcrafted and deep feature based classification methods. Experimental results show that this dataset has the challenges of interand intra- class variance and data imbalance. We believe our IP102 will facilitate future research on practical insect pest control, fine-grained visual classification, and imbalanced learning fields. We make the dataset and pre-trained models publicly available at https://github.com/ xpwu95/IP102.
author: Wu, Xiaoping, Chi Zhan, Yu-Kun Lai, Ming-Ming Cheng, and Jufeng Yang.
publish year: 2019
creator: Wu, Xiaoping, Chi Zhan, Yu-Kun Lai, Ming-Ming Cheng, and Jufeng Yang.
reference: Wu, Xiaoping, Chi Zhan, Yu-Kun Lai, Ming-Ming Cheng, and Jufeng Yang., "Ip102: A large-scale benchmark dataset for insect pest recognition", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8787-8796. 2019.
Source: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8787-8796. 2019.
label: Article 1218

Classification of yield affecting biotic and abiotic paddy crop stresses using field images
type: NamedIndividual
type: Article
has knowledge model: Classifier 1219
abstract: On - time recognition and early control of the stresses in the paddy crops at the booting growth stage is the key to prevent qualitative and quantitative loss of agricultural yield . The conventional paddy crop stress identification and classification activities invariably rely on human experts to identify visual symptoms as a means of categorization . This process is admittedly subjective and error - prone , which in turn may lead to incorrect action in stress management decisions . The proposed work presented in this paper aims to develop an automated computer vision system for the recognition and classification of paddy crop stress types from the field images using the state - of - the - art color features . The work examines the impact of eleven stress types , two biotic and nine abiotic stresses , on five different paddy crop varieties during the booting growth stage using field images and analyzes the stress responses in terms of color variations using lower - order color moments and two visual color descriptors defined by the MPEG-7 standard , the Dominant Color Descriptor ( DCD ) and Color Layout Descriptor ( CLD ) . The Sequential Forward Floating Selection ( SFFS ) algorithm has been employed to reduce the overlapping between the features . Three different classifiers , the Back Propagation Neural Network ( BPNN ) , the Support Vector Machine ( SVM ) , and the k - Nearest Neighbor ( k - NN ) have been deployed to distinguish among stress types . The average stress classification accuracies of 89.12 % , 84.44 % and 76.34 % have been achieved using the BPNN , SVM , and k - NN classifiers , respectively . The proposed work finds application in the development of decision support systems and mobile apps for the automation of crop and resource management practices in the field of agricultural science .
author: Basavaraj S. Anami, Naveen N. Malvade, Surendra Palaiah
publish year: 2020
creator: Basavaraj S. Anami, Naveen N. Malvade, Surendra Palaiah
reference: Basavaraj S. Anami, Naveen N. Malvade, Surendra Palaiah, "Classification of yield affecting biotic and abiotic paddy crop stresses using field images", Information Processing in AgricultureVolume 7, Issue 2, June 2020, Elsevier B.V.
Source: Information Processing in AgricultureVolume 7, Issue 2, June 2020, Elsevier B.V.
label: Article 1219

Maturity status classification of papaya fruits based on machine learning and transfer learning approach
type: NamedIndividual
type: Article
has knowledge model: Classifier 1220
abstract: Papaya ( Carica papaya ) is a tropical fruit having commercial importance because of its high nutritive and medicinal value . The packaging of papaya fruit as per its maturity status is an essential task in the fruit industry . The manual grading of papaya fruit based on human visual perception is time - consuming and destructive . The objective of this paper is to suggest a novel non - destructive maturity status classification of papaya fruits . The paper suggested two approaches based on machine learning and transfer learning for classification of papaya maturity status . Also , a comparative analysis is carried out with different methods of machine learning and transfer learning . The experimentation is carried out with 300 papaya fruit sample images which includes 100 of each three maturity stages . The machine learning approach includes three sets of features and three classifiers with their different kernel functions . The features and classifiers used in machine learning approaches are local binary pattern ( LBP ) , histogram of oriented gradients ( HOG ) , Gray Level Co - occurrence Matrix ( GLCM ) and k - nearest neighbour ( KNN ) , support vector machine ( SVM ) , Naïve Bayes respectively . The transfer learning approach includes seven pre - trained models such as ResNet101 , ResNet50 , ResNet18 , VGG19 , VGG16 , GoogleNet and AlexNet . The weighted KNN with HOG feature outperforms other machine learning - based classification model with 100 % of accuracy and 0.0995 s training time . Again , among the transfer learning approach based classification model VGG19 performs better with 100 % accuracy and 1 min 52 s training time with consideration of early stop training . The proposed classification method for maturity classification of papaya fruits , i.e. VGG19 based on transfer learning approach achieved 100 % accuracy which is 6 % more than the existing method .
author: Santi Kumari Behera, Amiya Kumar Rath, Prabira Kumar Sethy
publish year: 2020
creator: Santi Kumari Behera, Amiya Kumar Rath, Prabira Kumar Sethy
reference: Santi Kumari Behera, Amiya Kumar Rath, Prabira Kumar Sethy, "Maturity status classification of papaya fruits based on machine learning and transfer learning approach", Information Processing in AgricultureAvailable online 20 May 2020, Elsevier B.V.
Source: Information Processing in AgricultureAvailable online 20 May 2020, Elsevier B.V.
label: Article 1220

Detection of plant leaf diseases using image segmentation and soft computing techniques
type: NamedIndividual
type: Article
has knowledge model: Classifier 1221
abstract: Agricultural productivity is something on which economy highly depends . This is the one of the reasons that disease detection in plants plays an important role in agriculture field , as having disease in plants are quite natural . If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality , quantity or productivity is affected . For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States . Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops , and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves . This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases . It also covers survey on different diseases classification techniques that can be used for plant leaf disease detection . Image segmentation , which is an important aspect for disease detection in plant leaf disease , is done by using genetic algorithm .
author: Vijai Singh, A.K. Misra
publish year: 2017
creator: Vijai Singh, A.K. Misra
reference: Vijai Singh, A.K. Misra, "Detection of plant leaf diseases using image segmentation and soft computing techniques", Information Processing in AgricultureVolume 4, Issue 1, March 2017, Elsevier B.V.
Source: Information Processing in AgricultureVolume 4, Issue 1, March 2017, Elsevier B.V.
label: Article 1221

Forecasting soil temperature at multiple - depth with a hybrid artificial neural network model coupled - hybrid firefly optimizer algorithm
type: NamedIndividual
type: Article
has knowledge model: Regressor 1222
abstract: Forecasting soil temperature at multiple depths is considered to be a core decision - making task for examining future changes in surface and sub - surface meteorological processes , land – atmosphere energy exchange , resilient agricultural systems for improved crop health and eco - environmental risk assessment . The aim of this paper is to estimate monthly soil temperature ( ST ) at multiple depth : 5 , 10 , 20 , 50 and 100 cm with a hybrid multi - layer perceptron algorithm integrated with the firefly optimizer algorithm ( MLP - FFA ) . To develop the hybrid MLP - FFA model , the monthly ST and relevant meteorological variables for the city of Adana ( Turkey ) are collated for the period of 2000–2007 . Construction of hybrid MLP - FFA model is drawn upon a limited set of predictors , denoted as soil depth , periodicity ( or the respective month ) , air temperature , pressure and solar radiation , while the objective variable for MLP - FFA model is the forecasted ST at multiple depths . To the evaluate MLP - FFA , statistical metrics applied to test the model ’s performance are : the root mean square error ( RMSE ) , mean absolute error ( MAE ) , mean absolute percentage error ( MAPE ) and mean bias error ( MBE ) where the sign of the difference is also considered . In conjunction with statistical metrics , a Taylor diagram is utilized to visualize the degree of similarity between the observed and forecasted soil moisture . In terms of the forecasted results , the hybrid MLP - FFA model is seen to outperform the standalone MLP model . The optimal MLP - FFA is attained for soil temperature forecasting at a depth of 20 cm ( RMSE , MAPE of 0.546 ° C , 2.40 % ) whereas the optimal MLP is attained for soil temperature forecasting at a depth of 50 cm ( RMSE of 0.544 ° C , 2.21 % ) . Conclusively , the study advocates through statistical metrics attained the better utility of the hybrid MLP - FFA hybrid model . Given its superior performance , it is ascertained that the…
author: Saeed Samadianfard, Mohammad Ali Ghorbani, Babak Mohammadi
publish year: 2018
creator: Saeed Samadianfard, Mohammad Ali Ghorbani, Babak Mohammadi
reference: Saeed Samadianfard, Mohammad Ali Ghorbani, Babak Mohammadi, "Forecasting soil temperature at multiple - depth with a hybrid artificial neural network model coupled - hybrid firefly optimizer algorithm", Information Processing in AgricultureVolume 5, Issue 4, December 2018, Elsevier B.V.
Source: Information Processing in AgricultureVolume 5, Issue 4, December 2018, Elsevier B.V.
label: Article 1222

Improving operational maize yield forecasting in Hungary
type: NamedIndividual
type: Article
has knowledge model: Regressor 1223
abstract: Improving the MARS - Crop Yield Forecasting System 's Hungarian maize yield forecast . Best crop model - based predictors related to water limited leaf area and biomass . Limited benefit for national yield forecasts from regressions at regional level . Best yield forecasts were associated with critical phenological stages . Best yield forecasts had lead times of 5 to 8 weeks before maize harvest . AbstractIn most landscapes , accurate crop yield forecasting depends on a quantitative understanding of the relation between past weather , management and crop yield variability . We evaluated and improved the regression - based crop yield forecasting methodology currently employed in the MARS - Crop Yield Forecasting System ( M - CYFS ) for maize in Hungary . We quantified the effect of : 1 ) different statistical trends ; 2 ) different crop growth simulation model outputs providing weekly predictors ; 3 ) yield prediction lead times ; and 4 ) spatial aggregation on the forecast accuracy as evaluated against statistical yield from 1993 to 2012 . The LOESS ( locally weighted scatterplot smoothing ) trend provided the lowest root mean square error ( RMSE ) in describing the yield time - series compared to the quadratic and linear trend . Using the WOFOST crop model - based predictors to explain the yield residuals derived with each of the three trends , the lowest RMSEs were obtained with the Water Limited Leaf Area Index ( WLLAI ) and Water Limited Above Ground Biomass ( WLB ) predictors in combination with the LOESS trend . The LOESS trend was used to evaluate the effect of spatially aggregating subnational yield forecasts . During the first half of the crop cycle there are only marginal differences between the NUTS0 ( national ) , NUTS1 ( supra - regional ) , NUTS2 ( regional ) , and NUTS3 ( sub - regional ) level . However , the NUTS0 forecast had a slightly lower accuracy from the start of flowering and onwards , indicating the possible benefit of maintaining spatial d…
author: Attila Bussay, Marijn van der Velde, Davide Fumagalli, Lorenzo Seguini
publish year: 2015
creator: Attila Bussay, Marijn van der Velde, Davide Fumagalli, Lorenzo Seguini
reference: Attila Bussay, Marijn van der Velde, Davide Fumagalli, Lorenzo Seguini, "Improving operational maize yield forecasting in Hungary", Agricultural SystemsVolume 141, December 2015, Elsevier B.V.
Source: Agricultural SystemsVolume 141, December 2015, Elsevier B.V.
label: Article 1223

Prediction of kiwifruit orchard characteristics from satellite images
type: NamedIndividual
type: Article
has knowledge model: Regressor 1224
abstract: The dry matter content of New Zealand kiwifruit is currently measured using destructive testing of a 90-fruit sample . Dry matter content varies from 14 to 20 % with a standard deviation of 1.05 % in the test measurement . This work investigates the use of multispectral data from satellite images of kiwifruit orchards to predict the dry matter of both green and gold kiwifruit . A novel method is developed that reduces the four - dimensional satellite data to three - dimensional unit color vectors and these show a strong linear relationship with measured dry matter . Regression on the data from a set of 20 ‘ training ’ orchards yielded predictions of dry matter with a standard deviation of 0.76 % . When the resulting model was applied to nine test orchards the standard deviation of predicted dry matter was 0.73 % . The prediction of dry matter was accurate even when applied to images taken when the fruit was too immature for the standard dry matter measurement test . Therefore , satellite image data may provide a more accurate and non - destructive alternative to the standard 90-fruit test method . It can also provide a way to visualize the variation in dry matter content of the fruit in an orchard ; showing regions that would benefit from remedial action and defining areas where fruit harvesting is optimal . Several vegetation indices , derived from satellite image data , are reported in the literature . The strength of the linear correlation between dry matter and twelve common vegetation indices was tested but was found to be much weaker than the correlation developed in this research .
author: Linda Mills, Rory Flemmer, Claire Flemmer, Huub Bakker
publish year: 2019
creator: Linda Mills, Rory Flemmer, Claire Flemmer, Huub Bakker
reference: Linda Mills, Rory Flemmer, Claire Flemmer, Huub Bakker, "Prediction of kiwifruit orchard characteristics from satellite images", Precision Agriculture volume 20, pages911–925(2019) , Springer
Source: Precision Agriculture volume 20, pages911–925(2019) , Springer
label: Article 1224

Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield
type: NamedIndividual
type: Article
has knowledge model: Regressor 1225
abstract: Spectral unmixing techniques can be used to quantify crop canopy cover within each pixel of an image and have the potential for mapping the variation in crop yield . This study applied linear spectral unmixing to airborne hyperspectral imagery to estimate the variation in grain sorghum yield . Airborne hyperspectral imagery and yield monitor data recorded from two sorghum fields were used for this study . Both unconstrained and constrained linear spectral unmixing models were applied to the hyperspectral imagery with sorghum plants and bare soil as two endmembers . A pair of plant and soil spectra derived from each image and another pair of ground - measured plant and soil spectra were used as endmember spectra to generate unconstrained and constrained soil and plant cover fractions . Yield was positively related to the plant fraction and negatively related to the soil fraction . The effects of variation in endmember spectra on estimates of cover fractions and their correlations with yield were also examined . The unconstrained plant fraction had essentially the same correlations ( r ) with yield among all pairs of endmember spectra examined , whereas the unconstrained soil fraction and constrained plant and soil fractions had r - values that were sensitive to the spectra used . For comparison , all 5151 possible narrow - band normalized difference vegetation indices ( NDVIs ) were calculated from the 102-band images and related to yield . Results showed that the best plant and soil fractions provided better correlations than 96.3 and 99.9 % of all the NDVIs for fields 1 and 2 , respectively . Since the unconstrained plant fraction could represent yield variation better than most narrow - band NDVIs , it can be used as a relative yield map especially when yield data are not available . These results indicate that spectral unmixing applied to hyperspectral imagery can be a useful tool for mapping the variation in crop yield .
author: Chenghai Yang, James H. Everitt, Joe M. Bradford
publish year: 2007
creator: Chenghai Yang, James H. Everitt, Joe M. Bradford
reference: Chenghai Yang, James H. Everitt, Joe M. Bradford, "Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield", Precision Agriculture volume 8, pages279–296(2007) , Springer
Source: Precision Agriculture volume 8, pages279–296(2007) , Springer
label: Article 1225

Applicability of support vector machines and adaptive neurofuzzy inference system for modeling potato crop evapotranspiration
type: NamedIndividual
type: Article
has knowledge model: Regressor 1226
abstract: Estimation of crop evapotranspiration ( ETC ) for certain crops such as potato is very important for irrigation planning , irrigation scheduling and irrigation systems management . The primary focus of this study was to investigate the accuracy of the adaptive neurofuzzy inference system ( ANFIS ) and support vector machines ( SVM ) for potato ETC estimation when lysimeter measurements or the complete weather data for applying the FAO method are not available . The estimates of the ANFIS and SVM models were compared with the empirical equations of Blaney – Criddle , Makkink , Turc , Priestley – Taylor , Hargreaves and Ritchie . The performances of the different SVM and ANFIS models were evaluated by comparing the corresponding values of root mean square error ( RMSE ) , mean absolute error ( MAE ) and correlation coefficient ( r ) . The drawn conclusions confirmed that the SVM and ANFIS models could provide more accurate ETC estimates than the empirical equations . Overall , the minimum RMSE ( 0.042 mm / day ) and MAE ( 0.031 mm / day ) values and the maximum r value ( 0.98 ) were obtained using the SVM model with mean air temperature , relative humidity , solar radiation , sunshine hours and wind speed as inputs .
author: Hossein Tabari, Christopher Martinez, Azadeh Ezani, P. Hosseinzadeh Talaee
publish year: 2013
creator: Hossein Tabari, Christopher Martinez, Azadeh Ezani, P. Hosseinzadeh Talaee
reference: Hossein Tabari, Christopher Martinez, Azadeh Ezani, P. Hosseinzadeh Talaee, "Applicability of support vector machines and adaptive neurofuzzy inference system for modeling potato crop evapotranspiration", Irrigation Science volume 31, pages575–588(2013) , Springer
Source: Irrigation Science volume 31, pages575–588(2013) , Springer
label: Article 1226

An effective neural network and fuzzy time series - based hybridized model to handle forecasting problems of two factors
type: NamedIndividual
type: Article
has knowledge model: AssociationRule 1227
has knowledge model: Cluster 1227
abstract: Fuzzy time series forecasting method has been applied in several domains , such as stock market price , temperature , sales , crop production and academic enrollments . In this paper , we introduce a model to deal with forecasting problems of two factors . The proposed model is designed using fuzzy time series and artificial neural network . In a fuzzy time series forecasting model , the length of intervals in the universe of discourse always affects the results of forecasting . Therefore , an artificial neural network- based technique is employed for determining the intervals of the historical time series data sets by clustering them into different groups . The historical time series data sets are then fuzzified , and the high - order fuzzy logical relationships are established among fuzzified values based on fuzzy time series method . The paper also introduces some rules for interval weighing to defuzzify the fuzzified time series data sets . From experimental results , it is observed that the proposed model exhibits higher accuracy than those of existing two - factors fuzzy time series models .
abstract: Traditional agricultural systems are challenged by globally declining resources resulting from climate change and growing population . Alternative agricultural practices such as aquaponics ( includes crop plant and aquatic species ) and hydroponics ( includes crop plant only ) have the potential to generate high yield per unit area using limited land , water , and no soil . A soilless agricultural study was conducted at the Georgia Southern University , Statesboro , GA , USA from August to November , 2015 . The growth , yield , quality , and nutrition of basil ( Ocimum basilicum L. ) cultivar Aroma 2 , were compared between aquaponic and hydroponic systems using crayfish ( Procambarus spp . ) as the aquatic species . Non - circulating floating raft systems were designed using 95 L polyethylene tanks . Equal amounts of start - up fertilizer dose were applied to both systems . The objective was to understand how the additional nutritional dynamics associated with crayfish influence the basil crop . Both fresh and dry basil plant weights were collected after harvest , followed by leaf nutrient analysis . Leaf chlorophyll content , water pH , nitrogen and temperature were measured periodically . Aquaponic basil ( AqB ) showed 14 % , 56 % , and 65 % more height , fresh weight , and dry weight , respectively , compared to hydroponic basil ( HyB ) . It is logical to assume that crayfish waste ( excreta and unconsumed feed ) has supplied the additional nutrients to AqB , resulting in greater growth and yield . The chlorophyll content ( plant quality ) or leaf nutrients , however , did not differ between AqB and HyB. Further research is needed to investigate aquaponic crayfish yield , overall nutritional dynamics , cost - benefit ratio , and other plant characteristics under soilless systems .
author: Pritpal Singh, Bhogeswar Borah
author: Subhrajit Saha, Amber Monroe, Martin R. Day
publish year: 2014
publish year: 2016
creator: Pritpal Singh, Bhogeswar Borah
creator: Subhrajit Saha, Amber Monroe, Martin R. Day
reference: Pritpal Singh, Bhogeswar Borah, "An effective neural network and fuzzy time series - based hybridized model to handle forecasting problems of two factors", Knowledge and Information Systems volume 38, pages669–690(2014) , Springer
reference: Subhrajit Saha, Amber Monroe, Martin R. Day, "Growth , yield , plant quality and nutrition of basil ( Ocimum basilicum L. ) under soilless agricultural systems", Annals of Agricultural SciencesVolume 61, Issue 2, December 2016, Elsevier B.V.
Source: Annals of Agricultural SciencesVolume 61, Issue 2, December 2016, Elsevier B.V.
Source: Knowledge and Information Systems volume 38, pages669–690(2014) , Springer
title: Growth , yield , plant quality and nutrition of basil ( Ocimum basilicum L. ) under soilless agricultural systems
label: Article 1227

Accurate prediction of sugarcane yield using a random forest algorithm
type: NamedIndividual
type: Article
has knowledge model: Regressor 1228
abstract: Foreknowledge about sugarcane crop size can help industry members make more informed decisions . There exists many different combinations of climate variables , seasonal climate prediction indices , and crop model outputs that could prove useful in explaining sugarcane crop size . A data mining method like random forests can cope with generating a prediction model when the search space of predictor variables is large . Research that has investigated the accuracy of random forests to explain annual variation in sugarcane productivity and the suitability of predictor variables generated from crop models coupled with observed climate and seasonal climate prediction indices is limited . Simulated biomass from the APSIM ( Agricultural Production Systems sIMulator ) sugarcane crop model , seasonal climate prediction indices and observed rainfall , maximum and minimum temperature , and radiation were supplied as inputs to a random forest classifier and a random forest regression model to explain annual variation in regional sugarcane yields at Tully , in northeastern Australia . Prediction models were generated on 1 September in the year before harvest , and then on 1 January and 1 March in the year of harvest , which typically runs from June to November . Our results indicated that in 86.36 % of years , it was possible to determine as early as September in the year before harvest if production would be above the median . This accuracy improved to 95.45 % by January in the year of harvest . The R - squared of the random forest regression model gradually improved from 66.76 to 79.21 % from September in the year before harvest through to March in the same year of harvest . All three sets of variables — ( i ) simulated biomass indices , ( ii ) observed climate , and ( iii ) seasonal climate prediction indices — were typically featured in the models at various stages . Better crop predictions allows farmers to improve their nitrogen management to meet the demands of the new cr…
author: Yvette Everingham, Justin Sexton, Danielle Skocaj, Geoff Inman-Bamber
publish year: 2016
creator: Yvette Everingham, Justin Sexton, Danielle Skocaj, Geoff Inman-Bamber
reference: Yvette Everingham, Justin Sexton, Danielle Skocaj, Geoff Inman-Bamber, "Accurate prediction of sugarcane yield using a random forest algorithm", Agronomy for Sustainable Development volume 36, Article number: 27 (2016) , Springer
Source: Agronomy for Sustainable Development volume 36, Article number: 27 (2016) , Springer
label: Article 1228

Crop Yield Prediction Using Multitemporal UAV Data and Spatio - Temporal Deep Learning Models
type: NamedIndividual
type: Article
has knowledge model: Regressor 1229
abstract: Unmanned aerial vehicle ( UAV ) based remote sensing is gaining momentum worldwide in a variety of agricultural and environmental monitoring and modelling applications . At the same time , the increasing availability of yield monitoring devices in harvesters enables input - target mapping of in - season RGB and crop yield data in a resolution otherwise unattainable by openly availabe satellite sensor systems . Using time series UAV RGB and weather data collected from nine crop fields in Pori , Finland , we evaluated the feasibility of spatio - temporal deep learning architectures in crop yield time series modelling and prediction with RGB time series data . Using Convolutional Neural Networks ( CNN ) and Long - Short Term Memory ( LSTM ) networks as spatial and temporal base architectures , we developed and trained CNN - LSTM , convolutional LSTM and 3D - CNN architectures with full 15 week image frame sequences from the whole growing season of 2018 . The best performing architecture , the 3D - CNN , was then evaluated with several shorter frame sequence configurations from the beginning of the season . With 3D - CNN , we were able to achieve 218.9 kg / ha mean absolute error ( MAE ) and 5.51 % mean absolute percentage error ( MAPE ) performance with full length sequences . The best shorter length sequence performance with the same model was 292.8 kg / ha MAE and 7.17 % MAPE with four weekly frames from the beginning of the season .
author: Petteri Nevavuori, Nathaniel Narra, Petri Linna, Tarmo Lipping
publish year: 2020
creator: Petteri Nevavuori, Nathaniel Narra, Petri Linna, Tarmo Lipping
reference: Petteri Nevavuori, Nathaniel Narra, Petri Linna, Tarmo Lipping, "Crop Yield Prediction Using Multitemporal UAV Data and Spatio - Temporal Deep Learning Models", Remote Sens. 2020, 12(23), 4000; https://doi.org/10.3390/rs12234000
Source: Remote Sens. 2020, 12(23), 4000; https://doi.org/10.3390/rs12234000
label: Article 1229

Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms
type: NamedIndividual
type: Article
has knowledge model: Regressor 1230
abstract: Proximal sensing techniques can potentially survey soil and crop variables responsible for variations in crop yield . The full potential of these precision agriculture technologies may be exploited in combination with innovative methods of data processing such as machine learning ( ML ) algorithms for the extraction of useful information responsible for controlling crop yield . Four ML algorithms , namely linear regression ( LR ) , elastic net ( EN ) , k - nearest neighbor ( k - NN ) , and support vector regression ( SVR ) , were used to predict potato ( Solanum tuberosum ) tuber yield from data of soil and crop properties collected through proximal sensing . Six fields in Atlantic Canada including three fields in Prince Edward Island ( PE ) and three fields in New Brunswick ( NB ) were sampled , over two ( 2017 and 2018 ) growing seasons , for soil electrical conductivity , soil moisture content , soil slope , normalized - difference vegetative index ( NDVI ) , and soil chemistry . Data were collected from 39–40 30 × 30 m2 locations in each field , four times throughout the growing season , and yield samples were collected manually at the end of the growing season . Four datasets , namely PE-2017 , PE-2018 , NB-2017 , and NB-2018 , were then formed by combing data points from three fields to represent the province data for the respective years . Modeling techniques were employed to generate yield predictions assessed with different statistical parameters . The SVR models outperformed all other models for NB-2017 , NB-2018 , PE-2017 , and PE-2018 dataset with RMSE of 5.97 , 4.62 , 6.60 , and 6.17 t / ha , respectively . The performance of k - NN remained poor in three out of four datasets , namely NB-2017 , NB-2018 , and PE-2017 with RMSE of 6.93 , 5.23 , and 6.91 t / ha , respectively . The study also showed that large datasets are required to generate useful results using either model . This information is needed for creating site - specific management zones for p…
author: Farhat Abbas, Hassan Afzaal, Aitazaz A. Farooque, Skylar Tang
publish year: 2020
creator: Farhat Abbas, Hassan Afzaal, Aitazaz A. Farooque, Skylar Tang
reference: Farhat Abbas, Hassan Afzaal, Aitazaz A. Farooque, Skylar Tang, "Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms", Agronomy 2020, 10(7), 1046; https://doi.org/10.3390/agronomy10071046
Source: Agronomy 2020, 10(7), 1046; https://doi.org/10.3390/agronomy10071046
label: Article 1230

Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification
type: NamedIndividual
type: Article
has knowledge model: Classifier 1231
abstract: Southern Corn Rust ( SCR ) is one of the most destructive diseases in corn production , significantly affecting corn quality and yields globally . Field - based fast , nondestructive diagnosis of SCR is critical for smart agriculture applications to reduce pesticide use and ensure food safety . The development of spectral disease indices ( SDIs ) , based on in situ leaf reflectance spectra , has proven to be an effective method in detecting plant diseases in the field . However , little is known about leaf spectral signatures that can assist in the accurate diagnosis of SCR , and no SDIs - based model has been reported for the field - based SCR monitoring . Here , to address those issues , we developed SDIs - based monitoring models to detect SCR - infected leaves and classify SCR damage severity . In detail , we first collected in situ leaf reflectance spectra ( 350–2500 nm ) of healthy and infected corn plants with three severity levels ( light , medium , and severe ) using a portable spectrometer . Then , the RELIEF - F algorithm was performed to select the most discriminative features ( wavelengths ) and two band normalized differences for developing SDIs ( i.e. , health index and severity index ) in SCR detection and severity classification , respectively . The leaf reflectance spectra , most sensitive to SCR detection and severity classification , were found in the 572 nm , 766 nm , and 1445 nm wavelength and 575 nm , 640 nm , and 1670 nm wavelength , respectively . These spectral features were associated with leaf pigment and leaf water content . Finally , by employing a support vector machine ( SVM ) , the performances of developed SCR - SDIs were assessed and compared with 38 stress - related vegetation indices ( VIs ) identified in the literature . The SDIs - based models developed in this study achieved an overall accuracy of 87 % and 70 % in SCR detection and severity classification , 1.1 % and 8.3 % higher than the other best VIs - based model under stu…
author: Ran Meng, Zhengang Lv, Jianbing Yan, Gengshen Chen, Feng Zhao, Linglin Zeng, Binyuan Xu
publish year: 2020
creator: Ran Meng, Zhengang Lv, Jianbing Yan, Gengshen Chen, Feng Zhao, Linglin Zeng, Binyuan Xu
reference: Ran Meng, Zhengang Lv, Jianbing Yan, Gengshen Chen, Feng Zhao, Linglin Zeng, Binyuan Xu, "Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification", Remote Sens. 2020, 12(19), 3233; https://doi.org/10.3390/rs12193233
Source: Remote Sens. 2020, 12(19), 3233; https://doi.org/10.3390/rs12193233
label: Article 1231

Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat - Producing Regions of China with Deep Learning Approaches
type: NamedIndividual
type: Article
has knowledge model: Regressor 1232
abstract: Timely and accurate forecasting of crop yields is crucial to food security and sustainable development in the agricultural sector . However , winter wheat yield estimation and forecasting on a regional scale still remains challenging . In this study , we established a two - branch deep learning model to predict winter wheat yield in the main producing regions of China at the county level . The first branch of the model was constructed based on the Long Short - Term Memory ( LSTM ) networks with inputs from meteorological and remote sensing data . Another branch was constructed using Convolution Neural Networks ( CNN ) to model static soil features . The model was then trained using the detrended statistical yield data during 1982 to 2015 and evaluated by leave - one - year - out - validation . The evaluation results showed a promising performance of the model with the overall R 2 and RMSE of 0.77 and 721 kg / ha , respectively . We further conducted yield prediction and uncertainty analysis based on the two - branch model and obtained the forecast accuracy in one month prior to harvest of 0.75 and 732 kg / ha . Results also showed that while yield detrending could potentially introduce higher uncertainty , it had the advantage of improving the model performance in yield prediction .
author: Xinlei Wang, Jianxi Huang, Quanlong Feng, Dongqin Yin
publish year: 2020
creator: Xinlei Wang, Jianxi Huang, Quanlong Feng, Dongqin Yin
reference: Xinlei Wang, Jianxi Huang, Quanlong Feng, Dongqin Yin, "Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat - Producing Regions of China with Deep Learning Approaches", Remote Sens. 2020, 12(11), 1744; https://doi.org/10.3390/rs12111744
Source: Remote Sens. 2020, 12(11), 1744; https://doi.org/10.3390/rs12111744
label: Article 1232

A Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks , Remote Sensing , and Deep Learning : Case Study of the Caturra Variety in Colombia
type: NamedIndividual
type: Article
has knowledge model: Classifier 1233
abstract: Agricultural activity has always been threatened by the presence of pests and diseases that prevent the proper development of crops and negatively affect the economy of farmers . One of these pests is Coffee Leaf Rust ( CLR ) , which is a fungal epidemic disease that affects coffee trees and causes massive defoliation . As an example , this disease has been affecting coffee trees in Colombia ( the third largest producer of coffee worldwide ) since the 1980s , leading to devastating losses between 70 % and 80 % of the harvest . Failure to detect pathogens at an early stage can result in infestations that cause massive destruction of plantations and significantly damage the commercial value of the products . The most common way to detect this disease is by walking through the crop and performing a human visual inspection . As a result of this problem , different research studies have proven that technological methods can help to identify these pathogens . Our contribution is an experiment that includes a CLR development stage diagnostic model in the Coffea arabica , Caturra variety , scale crop through the technological integration of remote sensing ( through drone capable multispectral cameras ) , wireless sensor networks ( multisensor approach ) , and Deep Learning ( DL ) techniques . Our diagnostic model achieved an F1-score of 0.775 . The analysis of the results revealed a p - value of 0.231 , which indicated that the difference between the disease diagnosis made employing a visual inspection and through the proposed technological integration was not statistically significant . The above shows that both methods were significantly similar to diagnose the disease .
author: David Velásquez, Alejandro Sánchez , Sebastian Sarmiento , Mauricio Toro , Mikel Maiza, Basilio Sierra
publish year: 2020
creator: David Velásquez, Alejandro Sánchez , Sebastian Sarmiento , Mauricio Toro , Mikel Maiza, Basilio Sierra
reference: David Velásquez, Alejandro Sánchez , Sebastian Sarmiento , Mauricio Toro , Mikel Maiza, Basilio Sierra , "A Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks , Remote Sensing , and Deep Learning : Case Study of the Caturra Variety in Colombia", Appl. Sci. 2020, 10(2), 697; https://doi.org/10.3390/app10020697
Source: Appl. Sci. 2020, 10(2), 697; https://doi.org/10.3390/app10020697
label: Article 1233

Assessing Correlation of High - Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs
type: NamedIndividual
type: Article
has knowledge model: Classifier 1234
abstract: The aim of this study was to use small unmanned aerial vehicles ( UAVs ) for determining high - resolution normalized difference vegetation index ( NDVI ) values . Subsequently , these results were used to assess their correlations with fertilizer application levels and the yields of rice and wheat crops . For multispectral sensing , we flew two types of small UAVs ( DJI Phantom 4 and DJI Phantom 4 Pro ) — each equipped with a compact multispectral sensor ( Parrot Sequoia ) . The information collected was composed of numerous RGB orthomosaic images as well as reflectance maps with spatial resolution greater than a ground sampling distance of 10.5 cm . From 223 UAV flight campaigns over 120 fields with a total area coverage of 77.48 ha , we determined that the highest efficiency for the UAV - based remote sensing measurement was approximately 19.8 ha per 10 min while flying 100 m above ground level . During image processing , we developed and used a batch image alignment algorithm — a program written in Python language – to calculate the NDVI values in experimental plots or fields in a batch of NDVI index maps . The color NDVI distribution maps of wide rice fields identified differences in stages of ripening and lodging - injury areas , which accorded with practical crop growth status from aboveground observation . For direct - seeded rice , variation in the grain yield was most closely related to that in the NDVI at the early reproductive and late ripening stages . For wheat , the NDVI values were highly correlated with the yield ( R 2 = 0.601–0.809 ) from the middle reproductive to the early ripening stages . Furthermore , using the NDVI values , it was possible to differentiate the levels of fertilizer application for both rice and wheat . These results indicate that the small UAV - derived NDVI values are effective for predicting yield and detecting fertilizer application levels during rice and wheat production .
author: Senlin Guan, Koichiro Fukami, Hitoshi Matsunaka, Midori Okami, Ryo Tanaka, Hiroshi Nakano, Tetsufumi Sakai, Keiko Nakano, Hideki Ohdan, Kimiyasu Takahashi
publish year: 2019
creator: Senlin Guan, Koichiro Fukami, Hitoshi Matsunaka, Midori Okami, Ryo Tanaka, Hiroshi Nakano, Tetsufumi Sakai, Keiko Nakano, Hideki Ohdan, Kimiyasu Takahashi
reference: Senlin Guan, Koichiro Fukami, Hitoshi Matsunaka, Midori Okami, Ryo Tanaka, Hiroshi Nakano, Tetsufumi Sakai, Keiko Nakano, Hideki Ohdan, Kimiyasu Takahashi, "Assessing Correlation of High - Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs", Remote Sens. 2019, 11(2), 112; https://doi.org/10.3390/rs11020112
Source: Remote Sens. 2019, 11(2), 112; https://doi.org/10.3390/rs11020112
label: Article 1234

Crop Yield Estimation Using Time - Series MODIS Data and the Effects of Cropland Masks in Ontario , Canada
type: NamedIndividual
type: Article
has knowledge model: Regressor 1235
abstract: This study investigated the estimation of grain yields of three major annual crops in Ontario ( corn , soybean , and winter wheat ) using MODIS reflectance data extracted with a general cropland mask and crop - specific masks . Time - series two - band enhanced vegetation index ( EVI2 ) was derived from the 8 day composite 250 m MODIS reflectance data from 2003 to 2016 . Using a general cropland mask , the strongest positive linear correlation between crop yields and EVI2 was observed at the end of July to early August , whereas a negative correlation was observed in spring . Using crop - specific masks , the time of the strongest positive linear correlation for winter wheat was found between mid - May and early June , corresponding to peak growth stages of the crop . EVI2 derived at peak growth stages of a crop provided good predictive capability for grain yield estimation , with considerable inter - annual variation . A multiple linear regression model was established for county - level yield estimation using EVI2 at peak growth stages and the year as independent variables . The model accounted for the spatiotemporal variability of grain yields of about 30 % and 47 % for winter wheat , 63 % and 65 % for corn , and 59 % and 64 % for soybean using the general cropland mask and crop - specific masks , respectively . A negative correlation during the spring indicated that vegetation index extracted using a general cropland mask should be used with caution in regions with mixed crops , as factors other than the growth conditions of the targeted crops may also be captured by remote sensing data .
author: Jiangui Liu, Jiali Shang, Budong Qian, Ted Huffman, Yinsuo Zhang, Taifeng Dong, Qi Jing, Tim Martin
publish year: 2019
creator: Jiangui Liu, Jiali Shang, Budong Qian, Ted Huffman, Yinsuo Zhang, Taifeng Dong, Qi Jing, Tim Martin
reference: Jiangui Liu, Jiali Shang, Budong Qian, Ted Huffman, Yinsuo Zhang, Taifeng Dong, Qi Jing, Tim Martin, "Crop Yield Estimation Using Time - Series MODIS Data and the Effects of Cropland Masks in Ontario , Canada", Remote Sens. 2019, 11(20), 2419; https://doi.org/10.3390/rs11202419
Source: Remote Sens. 2019, 11(20), 2419; https://doi.org/10.3390/rs11202419
label: Article 1235

Combining Multi - Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States
type: NamedIndividual
type: Article
has knowledge model: Regressor 1236
abstract: Winter wheat ( Triticum aestivum L. ) is one of the most important cereal crops , supplying essential food for the world population . Because the United States is a major producer and exporter of wheat to the world market , accurate and timely forecasting of wheat yield in the United States ( U.S. ) is fundamental to national crop management as well as global food security . Previous studies mainly have focused on developing empirical models using only satellite remote sensing images , while other yield determinants have not yet been adequately explored . In addition , these models are based on traditional statistical regression algorithms , while more advanced machine learning approaches have not been explored . This study used advanced machine learning algorithms to establish within - season yield prediction models for winter wheat using multi - source data to address these issues . Specifically , yield driving factors were extracted from four different data sources , including satellite images , climate data , soil maps , and historical yield records . Subsequently , two linear regression methods , including ordinary least square ( OLS ) and least absolute shrinkage and selection operator ( LASSO ) , and four well - known machine learning methods , including support vector machine ( SVM ) , random forest ( RF ) , Adaptive Boosting ( AdaBoost ) , and deep neural network ( DNN ) , were applied and compared for estimating the county - level winter wheat yield in the Conterminous United States ( CONUS ) within the growing season . Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018 , with the results demonstrating that the machine learning approaches performed better than the linear regression models , with the best performance being achieved using the AdaBoost model ( R2 = 0.86 , RMSE = 0.51 t / ha , MAE = 0.39 t / ha ) . Additionally , the results showed that combining data from multiple sources outperformed single source sate…
author: Yumiao Wang, Zhou Zhang, Luwei Feng, Qingyun Du, Troy Runge
publish year: 2020
creator: Yumiao Wang, Zhou Zhang, Luwei Feng, Qingyun Du, Troy Runge
reference: Yumiao Wang, Zhou Zhang, Luwei Feng, Qingyun Du, Troy Runge, "Combining Multi - Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States", Remote Sens. 2020, 12(8), 1232; https://doi.org/10.3390/rs12081232
Source: Remote Sens. 2020, 12(8), 1232; https://doi.org/10.3390/rs12081232
label: Article 1236

Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques
type: NamedIndividual
type: Article
has knowledge model: Classifier 1237
abstract: Spatial variability in a crop field creates a need for precision agriculture . Economical and rapid means of identifying spatial variability is obtained through the use of geotechnology ( remotely sensed images of the crop field , image processing , GIS modeling approach , and GPS usage ) and data mining techniques for model development . Higher - end image processing techniques are followed to establish more precision . The goal of this paper was to investigate the strength of key spectral vegetation indices for agricultural crop yield prediction using neural network techniques . Four widely used spectral indices were investigated in a study of irrigated corn crop yields in the Oakes Irrigation Test Area research site of North Dakota , USA . These indices were : ( a ) red and near - infrared ( NIR ) based normalized difference vegetation index ( NDVI ) , ( b ) green and NIR based green vegetation index ( GVI ) , ( c ) red and NIR based soil adjusted vegetation index ( SAVI ) , and ( d ) red and NIR based perpendicular vegetation index ( PVI ) . These four indices were investigated for corn yield during 3 years ( 1998 , 1999 , and 2001 ) and for the pooled data of these 3 years . Initially , Back - propagation Neural Network ( BPNN ) models were developed , including 16 models ( 4 indices * 4 years including the data from the pooled years ) to test for the efficiency determination of those four vegetation indices in corn crop yield prediction . The corn yield was best predicted using BPNN models that used the means and standard deviations of PVI grid images . In all three years , it provided higher prediction accuracies , coefficient of determination ( r2 ) , and lower standard error of prediction than the models involving GVI , NDVI , and SAVI image information . The GVI , NDVI , and SAVI models for all three years provided average testing prediction accuracies of 24.26 % to 94.85 % , 19.36 % to 95.04 % , and 19.24 % to 95.04 % , respectively while the PVI models f…
author: Sudhanshu Sekhar Panda, Daniel P. Ames, Suranjan Panigrahi
publish year: 2010
creator: Sudhanshu Sekhar Panda, Daniel P. Ames, Suranjan Panigrahi
reference: Sudhanshu Sekhar Panda, Daniel P. Ames, Suranjan Panigrahi, "Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques", Remote Sens. 2010, 2(3), 673-696; https://doi.org/10.3390/rs2030673
Source: Remote Sens. 2010, 2(3), 673-696; https://doi.org/10.3390/rs2030673
label: Article 1237

Prediction of Maize Yield at the City Level in China Using Multi - Source Data
type: NamedIndividual
type: Article
has knowledge model: Regressor 1238
abstract: Maize is a widely grown crop in China , and the relationships between agroclimatic parameters and maize yield are complicated , hence , accurate and timely yield prediction is challenging . Here , climate , satellite data , and meteorological indices were integrated to predict maize yield at the city - level in China from 2000 to 2015 using four machine learning approaches , e.g. , cubist , random forest ( RF ) , extreme gradient boosting ( Xgboost ) , and support vector machine ( SVM ) . The climate variables included the diffuse flux of photosynthetic active radiation ( PDf ) , the diffuse flux of shortwave radiation ( SDf ) , the direct flux of shortwave radiation ( SDr ) , minimum temperature ( Tmn ) , potential evapotranspiration ( Pet ) , vapor pressure deficit ( Vpd ) , vapor pressure ( Vap ) , and wet day frequency ( Wet ) . Satellite data , including the enhanced vegetation index ( EVI ) , normalized difference vegetation index ( NDVI ) , and adjusted vegetation index ( SAVI ) from the Moderate Resolution Imaging Spectroradiometer ( MODIS ) , were used . Meteorological indices , including growing degree day ( GDD ) , extreme degree day ( EDD ) , and the Standardized Precipitation Evapotranspiration Index ( SPEI ) , were used . The results showed that integrating all climate , satellite data , and meteorological indices could achieve the highest accuracy . The highest estimated correlation coefficient ( R ) values for the cubist , RF , SVM , and Xgboost methods were 0.828 , 0.806 , 0.742 , and 0.758 , respectively . The climate , satellite data , or meteorological indices inputs from all growth stages were essential for maize yield prediction , especially in late growth stages . R improved by about 0.126 , 0.117 , and 0.143 by adding climate data from the early , peak , and late - period to satellite data and meteorological indices from all stages via the four machine learning algorithms , respectively . R increased by 0.016 , 0.016 , and 0.017 when adding s…
author: Xinxin Chen, Lan Feng, Rui Yao, Xiaojun Wu, Jia Sun, Wei Gong
publish year: 2021
creator: Xinxin Chen, Lan Feng, Rui Yao, Xiaojun Wu, Jia Sun, Wei Gong
reference: Xinxin Chen, Lan Feng, Rui Yao, Xiaojun Wu, Jia Sun, Wei Gong, "Prediction of Maize Yield at the City Level in China Using Multi - Source Data", Remote Sens. 2021, 13(1), 146; https://doi.org/10.3390/rs13010146
Source: Remote Sens. 2021, 13(1), 146; https://doi.org/10.3390/rs13010146
label: Article 1238

Drought Stress Detection in Juvenile Oilseed Rape Using Hyperspectral Imaging with a Focus on Spectra Variability
type: NamedIndividual
type: Article
has knowledge model: Classifier 1239
abstract: Hyperspectral imaging ( HSI ) has been gaining recognition as a promising proximal and remote sensing technique for crop drought stress detection . A modelling approach accounting for the treatment effects on the stress indicators ’ standard deviations was applied to proximal images of oilseed rape — a crop subjected to various HSI studies , with the exception of drought . The aim of the present study was to determine the spectral responses of two cultivars , ‘ Cadeli ’ and ‘ Viking ’ , representing distinctive water management strategies , to three types of watering regimes . Hyperspectral data cubes were acquired at the leaf level using a 2D frame camera . The influence of the experimental factors on the extent of leaf discolorations , vegetation index values , and principal component scores was investigated using Bayesian linear models . Clear treatment effects were obtained primarily for the vegetation indexes with respect to the watering regimes . The mean values of RGI , MTCI , RNDVI , and GI responded to the difference between the well - watered and water - deprived plants . The RGI index excelled among them in terms of effect strengths , which amounted to −0.96[−2.21,0.21 ] and −0.71[−1.97,0.49 ] units for each cultivar . A consistent increase in the multiple index standard deviations , especially RGI , PSRI , TCARI , and TCARI / OSAVI , was associated with worsening of the hydric regime . These increases were captured not only for the dry treatment but also for the plants subjected to regeneration after a drought episode , particularly by PSRI ( a multiplicative effect of 0.33[0.16,0.68 ] for ‘ Cadeli ’ ) . This result suggests a higher sensitivity of the vegetation index variability measures relative to the means in the context of the oilseed rape drought stress diagnosis and justifies the application of HSI to capture these effects . RGI is an index deserving additional scrutiny in future studies , as both its mean and standard deviation were affected by …
author: Wiktor R. Żelazny, Jan Lukáš
publish year: 2020
creator: Wiktor R. Żelazny, Jan Lukáš
reference: Wiktor R. Żelazny, Jan Lukáš, "Drought Stress Detection in Juvenile Oilseed Rape Using Hyperspectral Imaging with a Focus on Spectra Variability", Remote Sens. 2020, 12(20), 3462; https://doi.org/10.3390/rs12203462
Source: Remote Sens. 2020, 12(20), 3462; https://doi.org/10.3390/rs12203462
label: Article 1239

Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications
type: NamedIndividual
type: Article
has knowledge model: Regressor 1240
abstract: Predicting crop yield based on the environmental , soil , water and crop parameters has been a potential research topic . Deep - learning - based models are broadly used to extract significant crop features for prediction . Though these methods could resolve the yield prediction problem there exist the following inadequacies : Unable to create a direct non - linear or linear mapping between the raw data and crop yield values ; and the performance of those models highly relies on the quality of the extracted features . Deep reinforcement learning provides direction and motivation for the aforementioned shortcomings . Combining the intelligence of reinforcement learning and deep learning , deep reinforcement learning builds a complete crop yield prediction framework that can map the raw data to the crop prediction values . The proposed work constructs a Deep Recurrent Q - Network model which is a Recurrent Neural Network deep learning algorithm over the Q - Learning reinforcement learning algorithm to forecast the crop yield . The sequentially stacked layers of Recurrent Neural network is fed by the data parameters . The Q- learning network constructs a crop yield prediction environment based on the input parameters . A linear layer maps the Recurrent Neural Network output values to the Q - values . The reinforcement learning agent incorporates a combination of parametric features with the threshold that assist in predicting crop yield . Finally , the agent receives an aggregate score for the actions performed by minimizing the error and maximizing the forecast accuracy . The proposed model efficiently predicts the crop yield outperforming existing models by preserving the original data distribution with an accuracy of 93.7 % .
author: Dhivya Elavarasan, P. M. Durairaj Vincent
publish year: 2020
creator: Dhivya Elavarasan, P. M. Durairaj Vincent
reference: Dhivya Elavarasan, P. M. Durairaj Vincent, "Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 1240

Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R - CNN
type: NamedIndividual
type: Article
has knowledge model: Cluster 1241
abstract: Uniform plant row spacing in a paddy field is a critical requirement for rice seedling transplanting , as it affects subsequent field management and the crop yield . However , current transplanters are not able to meet this requirement due to the lack of accurate navigation systems . In this study , a plant row detection algorithm was developed to serve as a navigation system of a rice transplanter . The algorithm was based on the convolutional neural network ( CNN ) to identify and locate rice seedlings from field images . The agglomerative hierarchical clustering ( AHC ) was used to group rice seedlings into seedling rows which were then used to determine the navigation parameters . The accuracies of the navigation parameters were evaluated using test images . Results showed that the CNN - based algorithm successfully detected rice seedlings from field images and generated a reference line which was used to determine navigation parameters ( lateral distance and travel angle ) . Compared with mean absolute errors ( MAE ) test results , the CNN - based algorithm resulted in a deviation of 8.5 mm for the lateral distance and 0.50 ° for the travel angle , over the six intra - row seedling spacings tested . Relative to the test results , the CNN - based algorithm had 62 % lower error for the lateral distance and 57 % lower error for the travel angle when compared to a classical algorithm . These results demonstrated that the proposed algorithm had reasonably good accuracy and can be used for the rice transplanter navigation in real - time .
author: Shaomin Lin, Yu Jiang, Xueshen Chen, Asim Biswas, Shuai Li, Zihao Yuan, Hailin Wang, Long Qi
publish year: 2020
creator: Shaomin Lin, Yu Jiang, Xueshen Chen, Asim Biswas, Shuai Li, Zihao Yuan, Hailin Wang, Long Qi
reference: Shaomin Lin, Yu Jiang, Xueshen Chen, Asim Biswas, Shuai Li, Zihao Yuan, Hailin Wang, Long Qi, "Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R - CNN", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 1241

A Model for Yellow Tea Polyphenols Content Estimation Based on Multi - Feature Fusion
type: NamedIndividual
type: Article
has knowledge model: Classifier 1242
abstract: Polyphenols are one of the important ingredients determining the quality of tea , which play an important role in affecting tea quality standards and quality control . At present , NIR spectroscopy technology has been widely used in tea quality detection to achieve good results . However , due to the lack of spatial features , it is difficult to meet the accuracy requirements to comprehensively judge the quality of both inside and outside of tea . With the development of hyperspectral imaging system ( HIS ) , although progress has been made in the estimation of tea texture based on gray level co - occurrence matrix ( GLCM ) extraction , there are still some obstacles in practical applications . When the resolution is low , the texture features of the image are not significantly different , and a small number of features can not more fully represent the image features , resulting in lower model accuracy . When the resolution is higher , the increase in the dimension of the feature leads to a more complex model . Therefore , it is necessary to extract multi - scale features from the hyperspectral image while preserving the original information of the hyperspectral image . In this study , a multi - feature fusion method is proposed to improve the accuracy of tea polyphenols content estimation . The process includes multi - scale wavelet decomposition , and feature fusion based on wavelet coefficient features , GLCM texture features and wavelet texture features . Experiments were carried out on five kinds of yellow tea by comparing different models based on different features , including partial least squares regression ( PLSR ) and support vector regression ( SVR ) . Results indicated that the model based on multi - feature fusion is more accurate than the individual indicators , and models based on SVR perform better than PLSR .
author: Baohua Yang, Yue Zhu, Mengxuan Wang, Jingming Ning
publish year: 2019
creator: Baohua Yang, Yue Zhu, Mengxuan Wang, Jingming Ning
reference: Baohua Yang, Yue Zhu, Mengxuan Wang, Jingming Ning, "A Model for Yellow Tea Polyphenols Content Estimation Based on Multi - Feature Fusion", IEEE Access ( Volume: 7)
Source: IEEE Access ( Volume: 7)
label: Article 1242

Detection of Peach Disease Image Based on Asymptotic Non - Local Means and PCNN - IPELM
type: NamedIndividual
type: Article
has knowledge model: Classifier 1243
abstract: Aiming at the problems of noise , background interference and low detection in peach disease image , this paper proposes a detection method of peach disease based on the asymptotic non - local means ( ANLM ) image algorithm and the fusion of parallel convolution neural network ( PCNN ) and extreme learning machine ( ELM ) optimized by linear particle swarm optimization ( IPSO ) . Firstly , the method uses the ANLM image denoising algorithm to reduce the interference of the complex background in the image , then uses the parallel convolution neural network proposed by this paper to identify the characteristics of peach disease , uses the improved elu activation function instead of the conventional ReLu activation function , and uses the linear particle swarm optimized ELM ( IPELM ) proposed by this paper in the last layer instead of the traditional softmax layer , the update method is improved from two aspects to improve the convergence speed and accuracy of the network effectively . The results of 25513 images showed that the highest detection accuracy of brown rot , black spot , anthracnose , scab and normal peach were 89.02 , 90.56 , 85.37 , 86.70 and 89.91 percent respectively , which indicated that this method was an effective method for peach disease detection .
author: Shuangjie Huang, Guoxiong Zhou, Mingfang He, Aibin Chen, Wenzhuo Zhang, Yahui Hu
publish year: 2020
creator: Shuangjie Huang, Guoxiong Zhou, Mingfang He, Aibin Chen, Wenzhuo Zhang, Yahui Hu
reference: Shuangjie Huang, Guoxiong Zhou, Mingfang He, Aibin Chen, Wenzhuo Zhang, Yahui Hu, "Detection of Peach Disease Image Based on Asymptotic Non - Local Means and PCNN - IPELM", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 1243

A study on trade - offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time
type: NamedIndividual
type: Article
has knowledge model: Regressor 1244
abstract: Exploring trade - offs between spatial resolution and temporal sampling density . Accurate field yield estimates by a finer spatial resolution over thermal time . Accurate field yield estimates by an increasing pixel purity over thermal time . At regional scale , fields for the 100 m data suffered from a marginal penalty . Unlike 100 m , diminishing fields for the 300 m and 1 km data by increasing purity . AbstractWithin - season forecasting of crop yields is of great economic , geo - strategic and humanitarian interest . Satellite Earth Observation now constitutes a valuable and innovative way to provide spatio - temporal information to assist such yield forecasts . This study explores different configurations of remote sensing time series to estimate of winter wheat yield using either spatially finer but temporally sparser time series ( 5daily at 100 m spatial resolution ) or spatially coarser but denser ( 300 m and 1 km at daily frequency ) time series . Furthermore , we hypothesised that better yield estimations could be made using thermal time , which is closer to the crop physiological development . Time series of NDVI from the PROBA - V instrument , which has delivered images at a spatial resolution of 100 m , 300 m and 1 km since 2013 , were extracted for 39 fields for field and 56 fields for regional level analysis across Northern France during the growing season 2014 - 2015 . An asymmetric double sigmoid model was fitted on the NDVI series of the central pixel of the field . The fitted model was subsequently integrated either over thermal time or over calendar time , using different baseline NDVI thresholds to mark the start and end of the cropping season . These integrated values were used as a predictor for yield using a simple linear regression and yield observations at field level . The dependency of this relationship on the spatial pixel purity was analysed for the 100 m , 300 m and 1 km spatial resolution . At field level , depending on the spatial r…
author: Yetkin Özüm Durgun, Anne Gobin, Grégory Duveiller, Bernard Tychon
publish year: 2020
creator: Yetkin Özüm Durgun, Anne Gobin, Grégory Duveiller, Bernard Tychon
reference: Yetkin Özüm Durgun, Anne Gobin, Grégory Duveiller, Bernard Tychon, "A study on trade - offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time", International Journal of Applied Earth Observation and GeoinformationVolume 86, April 2020, Elsevier B.V.
Source: International Journal of Applied Earth Observation and GeoinformationVolume 86, April 2020, Elsevier B.V.
label: Article 1244

Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability
type: NamedIndividual
type: Article
has knowledge model: Regressor 1245
abstract: Increased availability of hyperspectral imagery necessitates the evaluation of its potential for precision agriculture applications . This study examined airborne hyperspectral imagery for mapping cotton ( Gossypium hirsutum L. ) yield variability as compared with yield monitor data . Hyperspectral images were acquired using an airborne imaging system from two cotton fields during the 2001 growing season , and yield data were collected from the fields using a cotton yield monitor . The raw hyperspectral images contained 128 bands between 457 and 922 nm . The raw images were geometrically corrected , georeferenced and resampled to 1 m resolution , and then converted to reflectance . Aggregation functions were then applied to each of the 128 bands to reduce the cell resolution to 4 m ( close to the cotton picker 's cutting width ) and 8 m. The yield data were also aggregated to the two grids . Correlation analysis showed that cotton yield was significantly related to the image data for all the bands except for a few bands in the transitional range from the red to the near - infrared region . Stepwise regression performed on the yield and hyperspectral data identified significant bands and band combinations for estimating yield variability for the two fields . Narrow band normalized difference vegetation indices derived from the significant bands provided better yield estimation than most of the individual bands . The stepwise regression models based on the significant narrow bands explained 61 % and 69 % of the variability in yield for the two fields , respectively . To demonstrate if narrow bands may be better for yield estimation than broad bands , the hyperspectral bands were aggregated into Landsat-7 ETM+ sensor 's bandwidths . The stepwise regression models based on the four broad bands explained only 42 % and 58 % of the yield variability for the two fields , respectively . These results indicate that hyperspectral imagery may be a useful data source for mapping…
author: Chenghai Yang, James H. Everitt, Joe M. Bradford, Dale Murden
publish year: 2004
creator: Chenghai Yang, James H. Everitt, Joe M. Bradford, Dale Murden
reference: Chenghai Yang, James H. Everitt, Joe M. Bradford, Dale Murden, "Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability", Precision Agriculture volume 5, pages445–461(2004) , Springer
Source: Precision Agriculture volume 5, pages445–461(2004) , Springer
label: Article 1245

Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent - Convolutional Neural Network ( R - CNN )
type: NamedIndividual
type: Article
has knowledge model: Classifier 1246
abstract: Understanding the use of current land cover , along with monitoring change over time , is vital for agronomists and agricultural agencies responsible for land management . The increasing spatial and temporal resolution of globally available satellite images , such as provided by Sentinel-2 , creates new possibilities for researchers to use freely available multi - spectral optical images , with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification ( LC&CC ) , agricultural monitoring and management , environment monitoring . Existing solutions dedicated to cropland mapping can be categorized based on per - pixel based and object - based . However , it is still challenging when more classes of agricultural crops are considered at a massive scale . In this paper , a novel and optimal deep learning model for pixel - based LC&CC is developed and implemented based on Recurrent Neural Networks ( RNN ) in combination with Convolutional Neural Networks ( CNN ) using multi - temporal sentinel-2 imagery of central north part of Italy , which has diverse agricultural system dominated by economic crop types . The proposed methodology is capable of automated feature extraction by learning time correlation of multiple images , which reduces manual feature engineering and modeling crop phenological stages . Fifteen classes , including major agricultural crops , were considered in this study . We also tested other widely used traditional machine learning algorithms for comparison such as support vector machine SVM , random forest ( RF ) , Kernal SVM , and gradient boosting machine , also called XGBoost . The overall accuracy achieved by our proposed Pixel R - CNN was 96.5 % , which showed considerable improvements in comparison with existing mainstream methods . This study showed that Pixel R - CNN based model offers a highly accurate way to assess and employ time - series data for multi - temporal classif…
author: Vittorio Mazzia, Aleem Khaliq, Marcello Chiaberge
publish year: 2019
creator: Vittorio Mazzia, Aleem Khaliq, Marcello Chiaberge
reference: Vittorio Mazzia, Aleem Khaliq, Marcello Chiaberge, "Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent - Convolutional Neural Network ( R - CNN )", Appl. Sci. 2020, 10(1), 238; https://doi.org/10.3390/app10010238
Source: Appl. Sci. 2020, 10(1), 238; https://doi.org/10.3390/app10010238
label: Article 1246

Green manures and NPK fertilizer effects on soil properties , growth , yield , mineral and vitamin C composition of okra ( Abelmoschus esculentus ( L. ) Moench )
type: NamedIndividual
type: Article
has knowledge model: Regressor 1250
abstract: The nutritional effects of green manure on soil and crop plants depends on residue quality . Hence field experiments were carried out during the cropping seasons of 2015 and 2016 to compare the impact of different green manures and NPK 15 - 15 - 15 fertilizer on soil properties , growth , yield , mineral and vitamin C composition of okra ( Abelmoschus esculentus ( L. ) Moench ) . The experiment each year consisted of four green manure ( GM ) types { Pawpaw ( Carica papaya L. ) leaves , Neem ( Azadirachta indica A. Juss . ) leaves , Moringa ( Moringa oleifera Lam . ) leaves , and Mesquite ( Prosopis africana Guill . , Perr . & A. Rich ) Taubert leaves } , NPK 15 - 15 - 15 fertilizer and a control . The six treatments were arranged in a randomized complete block design with four replications . Application of GMs reduced soil bulk density and increased soil organic matter ( OM ) , N , P , K , Ca , Mg , growth and yield of okra compared with the control . NPK fertilizer did not reduce soil bulk density and increase soil OM , but did increase soil N , P , K , Ca , Mg , growth and yield of okra compared with the control . Mesquite increased growth and yield of okra compared with NPK fertilizer and other GMs . This was due to increased availability of N and K in the soil at the level of this treatment . Using the means of the two years and compared with the control and NPK fertilizer , Mesquite increased pod yield of okra by 214 and 53 % , respectively . Also GMs and NPK fertilizer increased okra mineral and vitamin C contents compared with the control . Moringa had the best fruit quality in terms of higher K , Ca , Fe , Zn , Cu , and vitamin C contents compared with other GMs and NPK fertilizer . Therefore , for those that desire to grow okra for the quality of its fruits , Moringa ( Moringa oleifera ) is recommended , however for those that desire quantity , Mesquite ( Prosopis africana ) is recommended .
author: A.O. Adekiya, T.M. Agbede, C.M. Aboyeji, O. Dunsin, J.O. Ugbe
publish year: 2019
creator: A.O. Adekiya, T.M. Agbede, C.M. Aboyeji, O. Dunsin, J.O. Ugbe
reference: A.O. Adekiya, T.M. Agbede, C.M. Aboyeji, O. Dunsin, J.O. Ugbe, "Green manures and NPK fertilizer effects on soil properties , growth , yield , mineral and vitamin C composition of okra ( Abelmoschus esculentus ( L. ) Moench )", Journal of the Saudi Society of Agricultural SciencesVolume 18, Issue 2, April 2019, Elsevier B.V.
Source: Journal of the Saudi Society of Agricultural SciencesVolume 18, Issue 2, April 2019, Elsevier B.V.
label: Article 1250

Can just one - second measurement of chlorophyll a fluorescence be used to predict sulphur deficiency in radish ( Raphanus sativus L. sativus ) plants ?
type: NamedIndividual
type: Article
has knowledge model: Classifier 1262
abstract: Changes in photosynthetic apparatus become earlier than S deficiency visible effects . Sulphur deficiency decreased chlorophyll content and grana size . Sulphur deficiency decreased the electron transport rate . Some sensitive parameters of OJIP test were altered . Stress can be detected by Principal Component Analysis of numerous JIP parameters . AbstractNutrients deficiency ( ND ) in plants is one of the main factors adversely affecting plant growth and yield of agricultural and horticultural production . Usually detection of NDs in plants is based on visible symptoms or destructive chemical analysis . Recently chlorophyll a fluorescence is one of the most used approaches to investigate photosynthesis light - dependent reactions . It is widely accepted that it can inform about structural and functional changes in photosynthetic apparatus , especially under abiotic stresses including NDs . The aim of this research was to study the influence of sulphur deficiency on photosynthetic efficiency of two cultivars radish ( Raphanus sativus L. sativus ) – ‘ Fluo HF1 ’ and ‘ Suntella F1 ’ , and to assess the potential of chlorophyll a fluorescence measurements in early detection of this stress . Our results showed the sulphur deficiency has decreased the electron transport rate , chlorophyll content , grana size , and , at some degree , net photosynthesis and stomatal conductance . The changes in photosynthetic machinery performance were observed earlier than the visible effects of sulphur deficiency . However , the photosynthetic apparatus of radish plants was not considerably affected by this stress . Some sensitive parameters of OJIP test were altered but we were not able to identify the sulphur deficiency stress based on the alteration of only one of these parameters . We conclude that the detection of specific stress reactions of plants to sulphur deficiency should be based on integrative analysis of the response of the photosynthetic machinery . Such stress induced re…
author: Izabela A. Samborska, Hazem M. Kalaji, Leszek Sieczko, Wojciech Borucki, Radosław Mazur, Margarita Kouzmanova, Vasilij Goltsev
publish year: 2019
creator: Izabela A. Samborska, Hazem M. Kalaji, Leszek Sieczko, Wojciech Borucki, Radosław Mazur, Margarita Kouzmanova, Vasilij Goltsev
reference: Izabela A. Samborska, Hazem M. Kalaji, Leszek Sieczko, Wojciech Borucki, Radosław Mazur, Margarita Kouzmanova, Vasilij Goltsev, "Can just one - second measurement of chlorophyll a fluorescence be used to predict sulphur deficiency in radish ( Raphanus sativus L. sativus ) plants ?", Current Plant BiologyVolume 19, September 2019, Elsevier B.V.
Source: Current Plant BiologyVolume 19, September 2019, Elsevier B.V.
label: Article 1262

Predictive mapping of soil electrical conductivity as a Proxy of soil salinity in south - east of Algeria
type: NamedIndividual
type: Article
has knowledge model: Regressor 1289
abstract: Comparison of two different EC mapping techniques . Introduction of auxiliary variables related to EC to improve the interpolation results . Use of IK to individuate areas with a high probability of exceeding a salinity threshold . Production of estimated maps for regional planning and for future agricultural projects . AbstractIn semi - arid and arid areas soil salinity has adverse effects both on the environment and agricultural production . The region of Biskra ( South - East of Algeria ) underwent a strong agricultural transformation from traditional oasis agriculture to an almost exclusive production of dates involving market gardening throughout the year . The main goal was to predict the spatial variation of EC using geostatistics and a Geographic Information System ( GIS ) , comparing also the performance of two classical geostatistical interpolators - Ordinary Kriging ( OK ) , using only point data , and Cokriging ( CK ) , introducing also auxiliary variables to improve prediction accuracy ( SI gypsum and SO42− , obtained from the analysis of the chemical and geochemical processes of soil salinization ) . For this study , a total of 42 soil samples were randomly collected from topsoil ( 0–15 ​cm ) in the irrigated perimeter of El Ghrous , a representative rural community located in the west of Biskra . Aiming to better understand the processes that most influence the evolution of soil salinity in this area , some chemical parameters were determined , among which the electrical conductivity ( EC ) . Moreover , some terrain parameters were derived from a digital elevation model as auxiliary information , and Normalized Difference Vegetation Index ( NDVI ) was calculated from satellite imagery . The prediction efficiency of the methods was evaluated by calculating the mean error ( ME ) and the root mean square error ( RMSE ) . The resulting maps showed that soils in the study area are affected by salinization . Cross - validation results showed a better perfor…
author: Mohamed Amine Abdennour, Abdelkader Douaoui, Chiara Piccini, Manuel Pulido, Amel Bennacer, Abdelhamid Bradaï, Jesús Barrena, Ibrahim Yahiaoui
publish year: 2020
creator: Mohamed Amine Abdennour, Abdelkader Douaoui, Chiara Piccini, Manuel Pulido, Amel Bennacer, Abdelhamid Bradaï, Jesús Barrena, Ibrahim Yahiaoui
reference: Mohamed Amine Abdennour, Abdelkader Douaoui, Chiara Piccini, Manuel Pulido, Amel Bennacer, Abdelhamid Bradaï, Jesús Barrena, Ibrahim Yahiaoui, "Predictive mapping of soil electrical conductivity as a Proxy of soil salinity in south - east of Algeria", Environmental and Sustainability IndicatorsVolume 8, December 2020, Elsevier B.V.
Source: Environmental and Sustainability IndicatorsVolume 8, December 2020, Elsevier B.V.
label: Article 1289

A predictive machine learning application in agriculture : Cassava disease detection and classification with imbalanced dataset using convolutional neural networks
type: NamedIndividual
type: Article
has knowledge model: Classifier 1318
abstract: This work is inspired by Kaggle competition which was part of the Fine - Grained Visual Categorization workshop at CVPR 2019 ( Conference on Computer Vision and Pattern Recognition ) we participated in . It aimed at detecting cassava diseases using 5 fine - grained cassava leaf disease categories with 10,000 , labeled images collected during a regular survey in Uganda . Traditionally , this detection is done mostly through physical inspection and supervision of cassava plants in the garden by farmers or agricultural extension workers from NAADS ( National Agricultural Advisory Services ) and then reported to NARO ( National Agricultural Advisory Services ) for further analysis . However , this can be tiresome , capital intensive , and lacks the ability to detect cassava infection timely to help farmers apply preventive techniques to the non - infected cassava plants in order to improve on yields which subsequently increases African food basket leading to food security which fights famine . Using the dataset provided to train CNNs ( Convolutional Neural Networks ) to achieve high accuracy was very challenging due to two reasons : the dataset was small in size and has high - class imbalance being heavily biased towards CMD ( Cassava Mosaic Disease ) and CBB ( Cassava Brown Streak Virus Disease ) classes . Class imbalance is problematic in machine learning and exists in many domains . Note that , not all world data is balanced , in fact , most of the time you will not be extremely lucky to get a perfectly balanced real - world dataset , in recent years , a lot of research has been done for two - class problems such as fraudulent credit card and tumor detection among others . Interestingly , class imbalance in multi - class image datasets has received little attention . This paper , therefore , focused on techniques to achieve an accuracy score of over 93 % with class weight , SMOTE ( Synthetic Minority Over - sampling Technique ) and focal loss with deep convolutional …
author: G. Sambasivam, Geoffrey Duncan Opiyo
publish year: 2020
creator: G. Sambasivam, Geoffrey Duncan Opiyo
reference: G. Sambasivam, Geoffrey Duncan Opiyo, "A predictive machine learning application in agriculture : Cassava disease detection and classification with imbalanced dataset using convolutional neural networks", Egyptian Informatics JournalAvailable online 9 March 2020, Elsevier B.V.
Source: Egyptian Informatics JournalAvailable online 9 March 2020, Elsevier B.V.
label: Article 1318

Predicting soil organic carbon content in Spain by combining Landsat TM and ALOS PALSAR images
type: NamedIndividual
type: Article
has knowledge model: Regressor 1353
abstract: The spatial distribution of 30 m SOC content in Spain was estimated . Landsat TM , ALOS PALSAR , and LUCAS data in 2009 were integrated for SOC prediction . The proposed method could predict more accurate SOC content , especially for soils with high vegetation canopy density . AbstractSoil organic carbon ( SOC ) is the largest carbon pool and a key property of ecosystems . Compared with traditional field surveys , remote sensing ( RS ) represents a more efficient approach to mapping SOC , especially in larger - scale areas . Hyperspectral imagery provides a great potential for SOC prediction , but predicting the SOC content at a regional scale for a given year remains a great challenge . Multispectral RS images ( e.g. , Landsat images ) with middle spatial resolution can be used as an alternative to predict SOC amongst other images . However , multispectral images are commonly affected by cloud cover and lack information on the shallow soil surface and soil surface with vegetation . By contrast , synthetic aperture radar ( SAR ) images can capture shallow surface information , penetrate through vegetation and are not affected by cloud cover . In this study , a new approach was evaluated for the prediction of SOC content by integrating Landsat Thematic Mapper ( TM ) and Advanced Land Observing Satellite ( ALOS ) Phased Arrayed L - band Synthetic Aperture Radar ( PALSAR ) images . Mainland Spain ( hereafter Spain ) was used as the study site . Soil samples from the Land Use and Coverage Area frame Survey ( LUCAS ) European topsoil dataset in 2009 were utilized to fit a random forest ( RF ) model of the relationships between SOC content and several covariates in Spain . SOC prediction using only the Landsat TM image and only the ALOS PALSAR image was established as benchmarks to verify the efficiency of the proposed approach . The results for Spain showed that the integrated approach holds the highest prediction accuracy , with R2 , RPD and RMSE of 0.59 , 1.98 and 9.27…
author: Xia Wang, Yihang Zhang, Peter M. Atkinson, Huaiying Yao
publish year: 2020
creator: Xia Wang, Yihang Zhang, Peter M. Atkinson, Huaiying Yao
reference: Xia Wang, Yihang Zhang, Peter M. Atkinson, Huaiying Yao, "Predicting soil organic carbon content in Spain by combining Landsat TM and ALOS PALSAR images", International Journal of Applied Earth Observation and GeoinformationVolume 92, October 2020, Elsevier B.V.
Source: International Journal of Applied Earth Observation and GeoinformationVolume 92, October 2020, Elsevier B.V.
label: Article 1353

Predicting lake surface water phosphorus dynamics using process - guided machine learning
type: NamedIndividual
type: Article
has knowledge model: Regressor 1368
abstract: Annual lake phosphorus dynamic explained mostly by recycling and sedimentation Mass balance unable to fully reproduce lake phosphorus cycling Process - guided machine learning outperforms neural network or process alone Missing long - term trend in lake phosphorus likely due to changes in loadingABSTRACTPhosphorus ( P ) loading to lakes is degrading the quality and usability of water globally . Accurate predictions of lake P dynamics are needed to understand whole - ecosystem P budgets , as well as the consequences of changing lake P concentrations for water quality . However , complex biophysical processes within lakes , along with limited observational data , challenge our capacity to reproduce short - term lake dynamics needed for water quality predictions , as well as long - term dynamics needed to understand broad scale controls over lake P. Here we use an emerging paradigm in modeling , process - guided machine learning ( PGML ) , to produce a phosphorus budget for Lake Mendota ( Wisconsin , USA ) and to accurately predict epilimnetic phosphorus over a time range of days to decades . In our implementation of PGML , which we term a Process - Guided Recurrent Neural Network ( PGRNN ) , we combine a process - based model for lake P with a recurrent neural network , and then constrain the predictions with ecological principles . We test independently the process - based model , the recurrent neural network , and the PGRNN to evaluate the overall approach . The process - based model accounted for most of the observed pattern in lake P ; however it missed the long - term trend in lake P and had the worst performance in predicting winter and summer P in surface waters . The root mean square error ( RMSE ) for the process - based model , the recurrent neural network , and the PGRNN was 33.0 μg P L−1 , 22.7 μg P L−1 , and 20.7 μg P L−1 , respectively . All models performed better during summer , with RMSE values for the three models ( same order ) equal to 14.3 μg P L−…
author: Paul C. Hanson, Aviah B. Stillman, Xiaowei Jia, Anuj Karpatne, Hilary A. Dugan, Cayelan C. Carey, Joseph Stachelek, Nicole K. Ward, Yu Zhang, Jordan S. Read, Vipin Kumar
publish year: 2020
creator: Paul C. Hanson, Aviah B. Stillman, Xiaowei Jia, Anuj Karpatne, Hilary A. Dugan, Cayelan C. Carey, Joseph Stachelek, Nicole K. Ward, Yu Zhang, Jordan S. Read, Vipin Kumar
reference: Paul C. Hanson, Aviah B. Stillman, Xiaowei Jia, Anuj Karpatne, Hilary A. Dugan, Cayelan C. Carey, Joseph Stachelek, Nicole K. Ward, Yu Zhang, Jordan S. Read, Vipin Kumar, "Predicting lake surface water phosphorus dynamics using process - guided machine learning", Ecological ModellingVolume 430, 15 August 2020, Elsevier B.V.
Source: Ecological ModellingVolume 430, 15 August 2020, Elsevier B.V.
label: Article 1368

Electrical conductivity method for predicting yields of two yam ( Dioscorea alata ) cultivars in a coarse textured soil
type: NamedIndividual
type: Article
has knowledge model: Regressor 1369
abstract: Apparent Soil Electrical Conductivity ( ECa ) measurement is a rapid and accurate tool for measuring soil physical and chemical properties affecting crop productivity . This study uses ECa to predict yam yield . Soil ECa was measured at the depth of 0–15 cm , 15–30 cm and 30–45 cm using Miller 400D resistance meter with multi - electrode Wenner array . Soil samples were collected at the aforementioned depths and analyzed for selected physical and chemical properties . Two cultivars of water yam , Discorea alata L. ( TDa 00/00194 ( D1 ) and TDa 00/00006 ( D2 ) ) were planted and yield data were collected after harvesting . Data collected were analyzed using correlation , nonlinear and multiple linear regression analysis using Origin statistical software ( Pro . V.8.1 ) . Soil ECa at 0–15 cm correlated with the yields of D1 and D2 with correlations ( r ) of 0.83 and 0.84 , respectively . The relationship between ECa and D1 and D2 were best fit with a cubic function ( with r2 = 0.70 and 0.75 , respectively ) . A Multiple linear regression model showed the interactive effect of soil physical and chemical properties as it affected the yields . The generated models showed that soil properties needed for growth and yields of D1 and D2 are different . Therefore , farmers should not plant both cultivars into the same soil environment or use blanket fertilizer application to achieve optimum performance .
author: Mutair A. Akanji, Suarau O. Oshunsanya, Abdulrasoul Alomran
publish year: 2018
creator: Mutair A. Akanji, Suarau O. Oshunsanya, Abdulrasoul Alomran
reference: Mutair A. Akanji, Suarau O. Oshunsanya, Abdulrasoul Alomran, "Electrical conductivity method for predicting yields of two yam ( Dioscorea alata ) cultivars in a coarse textured soil", International Soil and Water Conservation ResearchVolume 6, Issue 3, September 2018, Elsevier B.V.
Source: International Soil and Water Conservation ResearchVolume 6, Issue 3, September 2018, Elsevier B.V.
label: Article 1369

Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network
type: NamedIndividual
type: Article
has knowledge model: Classifier 1371
abstract: Herbicide use is rising globally to enhance food production , causing harm to environment and the ecosystem . Precision agriculture suggests variable - rate herbicide application based on weed densities to mitigate adverse effects of herbicides . Accurate weed density estimation using advanced computer vision techniques like deep learning requires large labelled agriculture data . Labelling large agriculture data at pixel level is a time - consuming and tedious job . In this paper , a methodology is developed to accelerate manual labelling of pixels using a two - step procedure . In the first step , the background and foreground are segmented using maximum likelihood classification , and in the second step , the weed pixels are manually labelled . Such labelled data is used to train semantic segmentation models , which classify crop and background pixels as one class , and all other vegetation as the second class . This paper evaluates the proposed methodology on high - resolution colour images of canola fields and makes performance comparison of deep learning meta - architectures like SegNet and UNET and encoder blocks like VGG16 and ResNet-50 . ResNet-50 based SegNet model has shown the best results with mean intersection over union value of 0.8288 and frequency weighted intersection over union value of 0.9869 .
author: Muhammad Hamza Asad, Abdul Bais
publish year: 2020
creator: Muhammad Hamza Asad, Abdul Bais
reference: Muhammad Hamza Asad, Abdul Bais, "Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network", Information Processing in AgricultureVolume 7, Issue 4, December 2020, Elsevier B.V.
Source: Information Processing in AgricultureVolume 7, Issue 4, December 2020, Elsevier B.V.
label: Article 1371

Can just one - second measurement of chlorophyll a fluorescence be used to predict sulphur deficiency in radish ( Raphanus sativus L. sativus ) plants ?
type: NamedIndividual
type: Article
has knowledge model: Classifier 1373
abstract: Changes in photosynthetic apparatus become earlier than S deficiency visible effects . Sulphur deficiency decreased chlorophyll content and grana size . Sulphur deficiency decreased the electron transport rate . Some sensitive parameters of OJIP test were altered . Stress can be detected by Principal Component Analysis of numerous JIP parameters . AbstractNutrients deficiency ( ND ) in plants is one of the main factors adversely affecting plant growth and yield of agricultural and horticultural production . Usually detection of NDs in plants is based on visible symptoms or destructive chemical analysis . Recently chlorophyll a fluorescence is one of the most used approaches to investigate photosynthesis light - dependent reactions . It is widely accepted that it can inform about structural and functional changes in photosynthetic apparatus , especially under abiotic stresses including NDs . The aim of this research was to study the influence of sulphur deficiency on photosynthetic efficiency of two cultivars radish ( Raphanus sativus L. sativus ) – ‘ Fluo HF1 ’ and ‘ Suntella F1 ’ , and to assess the potential of chlorophyll a fluorescence measurements in early detection of this stress . Our results showed the sulphur deficiency has decreased the electron transport rate , chlorophyll content , grana size , and , at some degree , net photosynthesis and stomatal conductance . The changes in photosynthetic machinery performance were observed earlier than the visible effects of sulphur deficiency . However , the photosynthetic apparatus of radish plants was not considerably affected by this stress . Some sensitive parameters of OJIP test were altered but we were not able to identify the sulphur deficiency stress based on the alteration of only one of these parameters . We conclude that the detection of specific stress reactions of plants to sulphur deficiency should be based on integrative analysis of the response of the photosynthetic machinery . Such stress induced re…
author: Izabela A. Samborska, Hazem M. Kalaji, Leszek Sieczko, Wojciech Borucki, Radosław Mazur, Margarita Kouzmanova, Vasilij Goltsev
publish year: 2019
creator: Izabela A. Samborska, Hazem M. Kalaji, Leszek Sieczko, Wojciech Borucki, Radosław Mazur, Margarita Kouzmanova, Vasilij Goltsev
reference: Izabela A. Samborska, Hazem M. Kalaji, Leszek Sieczko, Wojciech Borucki, Radosław Mazur, Margarita Kouzmanova, Vasilij Goltsev, "Can just one - second measurement of chlorophyll a fluorescence be used to predict sulphur deficiency in radish ( Raphanus sativus L. sativus ) plants ?", Current Plant BiologyVolume 19, September 2019, Elsevier B.V.
Source: Current Plant BiologyVolume 19, September 2019, Elsevier B.V.
label: Article 1373

Integrated phenology and climate in rice yields prediction using machine learning methods
type: NamedIndividual
type: Article
has knowledge model: Regressor 1374
abstract: Rice yields predictions were fundamentally improved using machine Learning methods . Phenological variables substantially affected the rice yields by altering the carbon allocation processes . The relative importance of phenological variables were relatively large compared with climatic variables . The integrated phenological , climatic variables and geographical information was the best combination for yield predictions . AbstractRice ( Oryza sativa L. ) is a staple cereal crop and its demand is substantially increasing with the growth of the global population . Precisely predicting rice yields are of vital importance to ensure the food security in countries like China , where rice accounts for one - fifth of the total agricultural production . Previous studies found that the rice yields had been significantly impacted by climate change . In addition , phenological variables were found to be important factors concerning rice yields due to its fundamental role in carbon allocation between plant organs , but its impacts on rice yields were seldom evaluated . In this study , eleven combinations of phenology , climate and geography data were tested to predict the site - based rice yields using a traditional regression - based method ( MLR , multiple linear regression ) , and more advanced three machine learning ( ML ) methods : backpropagation neural network ( BP ) , support vector machine ( SVM ) and random forest ( RF ) . The results showed that ML methods were more precise than MLR method . The combination using the integrated phenology , climate during growing season and geographical information was better for yields predictions than other combinations across the ML methods , e.g. the difference RMSE ( R2 ) between prediction and observed rice yields were 800 ( 0.24 ) , 737 ( 0.33 ) , and 744 ( 0.31 ) kg / ha for BP , SVM and RF , respectively . The SVM had achieved the highest precisions in yield predictions and the phenological variables substantially improved th…
author: Yahui Guo, Yongshuo Fu, Fanghua Hao, Xuan Zhang, Wenxiang Wu, Xiuliang Jin, Christopher Robin Bryant, J. Senthilnath
publish year: 2021
creator: Yahui Guo, Yongshuo Fu, Fanghua Hao, Xuan Zhang, Wenxiang Wu, Xiuliang Jin, Christopher Robin Bryant, J. Senthilnath
reference: Yahui Guo, Yongshuo Fu, Fanghua Hao, Xuan Zhang, Wenxiang Wu, Xiuliang Jin, Christopher Robin Bryant, J. Senthilnath, "Integrated phenology and climate in rice yields prediction using machine learning methods", Ecological IndicatorsVolume 120, January 2021, Elsevier B.V.
Source: Ecological IndicatorsVolume 120, January 2021, Elsevier B.V.
label: Article 1374

Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment
type: NamedIndividual
type: Article
has knowledge model: Cluster 1399
abstract: A large number of countries depend on agriculture . However , the biggest challenge for farmers is the plant - related diseases . The most observed part of the plant is leaf but the segmentation of a diseased lesion leaf image suffers from uneven illumination and cluttered complex natural environment problems . To overcome these problems , the proposed methodology is based on the fusion of color balancing and superpixel . First , the input image transforms into a color - balanced image to eliminate the effects of uneven illumination . Second , compact regions are created from the transformed image using superpixel operation and the empirically derived threshold is applied on Histogram of Gradients ( HOG ) and color channels of superpixel to separate the unwanted background from leaf image . K - means clustering is applied to find a diseased infected image . An extended form of HOG , Pyramid of HOG ( PHOG ) with Gray Level Co - occurrence Matrix ( GLCM ) features are used to represent a diseased infected part . Finally , different classifiers are compared and Random Forest ( RF ) is selected to classify diseases . To assess the robustness of the proposed method , an experiment is conducted to test its accuracy effectively on natural images acquired from various farms , downloaded images from the Internet and Plant - Village color dataset . Results indicate that the proposed method achieved an accuracy of 93.12 % on a combined dataset using cross - validation to show that method is effective to classify diseases in the presence of a cluttered background . The proposed system is compared with ALDD method ( Dhaygude and Kumbhar , 2013 ) , MVBPDR ( Habib et al . , 2018 ) , AIMSD ( Araujo and Peixoto , 2019 ) and CCF ( Ma et al . , 2017 ) . The novelty of the proposed system lies in the fact that the proposed segmentation is effective for all the varied datasets and it is evaluated by True Positive Rate ( TPR ) , False Positive Rate ( FPR ) , False Negative Rate ( FNR ) ,…
author: Saiqa Khan, Meera Narvekar
publish year: 2020
creator: Saiqa Khan, Meera Narvekar
reference: Saiqa Khan, Meera Narvekar, "Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment", Journal of King Saud University - Computer and Information SciencesAvailable online 15 September 2020, Elsevier B.V.
Source: Journal of King Saud University - Computer and Information SciencesAvailable online 15 September 2020, Elsevier B.V.
label: Article 1399

Grading method of soybean mosaic disease based on hyperspectral imaging technology
type: NamedIndividual
type: Article
has knowledge model: Classifier 1405
abstract: Soybean is a crop with a long cultivation history that occupies an important position in agricultural production . Soybean mosaic virus disease ( SMV ) has caused a rapid decline in soybean yields , causing huge losses to the soybean industry , wherefrom its early detection is particularly important . This study proposes a new classification method for the early SMV , dividing its severity into grades 0 , 1 and 2 . In the case of a small number of experimental samples of soybeans , this study proposes a combined convolutional neural network and support vector machine ( CNN - SVM ) method for the early detection of SMV . Experimental results showed that the accuracy of the training set of the CNN - SVM model reached 96.67 % , and the accuracy rate of the test set reached 94.17 % . The experiment proved the feasibility of using the proposed CNN - SVM model to classify early SMV under the new classification method , and provided a new direction for early SMV detection based on hyperspectral images .
author: Jiangsheng Gui, Jingyi Fei, Zixian Wu, Xiaping Fu, Alou Diakite
publish year: 2020
creator: Jiangsheng Gui, Jingyi Fei, Zixian Wu, Xiaping Fu, Alou Diakite
reference: Jiangsheng Gui, Jingyi Fei, Zixian Wu, Xiaping Fu, Alou Diakite, "Grading method of soybean mosaic disease based on hyperspectral imaging technology", Information Processing in AgricultureAvailable online 10 November 2020, Elsevier B.V.
Source: Information Processing in AgricultureAvailable online 10 November 2020, Elsevier B.V.
label: Article 1405

Detection of banana plants and their major diseases through aerial images and machine learning methods : A case study in DR Congo and Republic of Benin
type: NamedIndividual
type: Article
has knowledge model: Classifier 1407
abstract: Aerial imagery datasets from Africa is used to develop ML models to classify banana . Random forest based ML model classifies banana with more than 90 % accuracy . Deep learning based mixed - model pipeline for banana disease detection is developed . AI - Powered disease surveillance system for Africa is discussed . AbstractFront - line remote sensing tools , coupled with machine learning ( ML ) , have a significant role in crop monitoring and disease surveillance . Crop type classification and a disease early warning system are some of these remote sensing applications that provide precise , timely , and cost - effective information at different spatial , temporal , and spectral resolutions . To our knowledge , most disease surveillance systems focus on a single - sensor based solutions and lagging the integration of multiple information sources . Moreover , monitoring larger landscapes using unmanned aerial vehicles ( UAV ) are challenging , and , therefore combining high resolution satellite imagery data with advanced machine learning ( ML ) models through the use of mobile apps could help detect and classify banana plants and provide more information on its overall health status . In this study , we classified banana under mixed - complex African landscapes through pixel - based classifications and ML models derived from multi - level satellite images ( Sentinel 2 , PlanetScope and WorldView-2 ) and UAV ( MicaSense RedEdge ) platforms . Our pixel - based classification from random forest ( RF ) model using combined features of vegetation indices ( VIs ) and principal component analysis ( PCA ) showed up to 97 % overall accuracy ( OA ) with less than 10 % omission and commission errors ( OE and CE ) and Kappa coefficient of 0.96 in high resolution multispectral images . We used UAV - RGB aerial images from DR Congo and Republic of Benin fields to develop a mixed - model system combining object detection model ( RetinaNet ) and a custom classifier for simultaneous…
author: Michael Gomez Selvaraj, Alejandro Vergara, Frank Montenegro, Henry Alonso Ruiz, Nancy Safari, Dries Raymaekers, Walter Ocimati, Jules Ntamwira, Laurent Tits, Aman Bonaventure Omondi, Guy Blomme
publish year: 2020
creator: Michael Gomez Selvaraj, Alejandro Vergara, Frank Montenegro, Henry Alonso Ruiz, Nancy Safari, Dries Raymaekers, Walter Ocimati, Jules Ntamwira, Laurent Tits, Aman Bonaventure Omondi, Guy Blomme
reference: Michael Gomez Selvaraj, Alejandro Vergara, Frank Montenegro, Henry Alonso Ruiz, Nancy Safari, Dries Raymaekers, Walter Ocimati, Jules Ntamwira, Laurent Tits, Aman Bonaventure Omondi, Guy Blomme, "Detection of banana plants and their major diseases through aerial images and machine learning methods : A case study in DR Congo and Republic of Benin", ISPRS Journal of Photogrammetry and Remote SensingVolume 169, November 2020, Elsevier B.V.
Source: ISPRS Journal of Photogrammetry and Remote SensingVolume 169, November 2020, Elsevier B.V.
label: Article 1407

Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques
type: NamedIndividual
type: Article
has knowledge model: Classifier 1410
abstract: Pine wilt disease ( PWD ) has recently caused substantial pine tree losses in Republic of Korea . PWD is considered a severe problem due to the importance of pine trees to Korean people , so this problem must be handled appropriately . Previously , we examined the history of PWD and found that it had already spread to some regions of Republic of Korea ; these became our study area . Early detection of PWD is required . We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD . Drone remote sensing was employed because it yields high - quality images and can easily reach the locations of pine trees . To differentiate healthy pine trees from those with PWD , we produced a land cover ( LC ) map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods , i.e. , artificial neural network ( ANN ) and support vector machine ( SVM ) . Furthermore , compared the accuracy of two types of Global Positioning System ( GPS ) data , collected using drone and hand - held devices , for identifying the locations of trees with PWD . We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD . In Anbi , the SVM had an overall accuracy of 94.13 % , which is 6.7 % higher than the overall accuracy of the ANN , which was 87.43 % . We obtained similar results in Wonchang , for which the accuracy of the SVM and ANN was 86.59 % and 79.33 % , respectively . In terms of the GPS data , we used two type of hand - held GPS device . GPS device 1 is corrected by referring to the benchmarks sited on both locations , while the GPS device 2 is uncorrected device which used the default setting of the GPS only . The data collected from hand - held GPS device 1 was better than those collected using hand - held GPS device 2 in Wonchang . However , in Anbi , we obtained better results from GPS device 2 than…
author: Mutiara Syifa, Sung-Jae Park, Chang-Wook Lee
publish year: 2020
creator: Mutiara Syifa, Sung-Jae Park, Chang-Wook Lee
reference: Mutiara Syifa, Sung-Jae Park, Chang-Wook Lee, "Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques", EngineeringVolume 6, Issue 8, August 2020, Elsevier B.V.
Source: EngineeringVolume 6, Issue 8, August 2020, Elsevier B.V.
label: Article 1410

Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm
type: NamedIndividual
type: Article
has knowledge model: Cluster 1412
abstract: In the agriculture field , one of the recent research topics is recognition and classification of diseases from the leaf images of a plant . The recognition of agricultural plant diseases by utilizing the image processing techniques will minimize the reliance on the farmers to protect the agricultural products . In this paper , Recognition and Classification of Paddy Leaf Diseases using Optimized Deep Neural Network with Jaya Algorithm is proposed . For the image acquisition the images of rice plant leaves are directly captured from the farm field for normal , bacterial blight , brown spot , sheath rot and blast diseases . In pre - processing , for the background removal the RGB images are converted into HSV images and based on the hue and saturation parts binary images are extracted to split the diseased and non - diseased part . For the segmentation of diseased portion , normal portion and background a clustering method is used . Classification of diseases is carried out by using Optimized Deep Neural Network with Jaya Optimization Algorithm ( DNN_JOA ) . In order to precise the stability of this approach a feedback loop is generated in the post processing step . The experimental results are evaluated and compared with ANN , DAE and DNN . The proposed method achieved high accuracy of 98.9 % for the blast affected , 95.78 % for the bacterial blight , 92 % for the sheath rot , 94 % for the brown spot and 90.57 % for the normal leaf image .
author: S. Ramesh, D. Vydeki
publish year: 2020
creator: S. Ramesh, D. Vydeki
reference: S. Ramesh, D. Vydeki, "Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm", Information Processing in AgricultureVolume 7, Issue 2, June 2020, Elsevier B.V.
Source: Information Processing in AgricultureVolume 7, Issue 2, June 2020, Elsevier B.V.
label: Article 1412

An evaluation of Landsat , Sentinel-2 , Sentinel-1 and MODIS data for crop type mapping
type: NamedIndividual
type: Article
has knowledge model: Classifier 1422
abstract: Over 95 % accuracy can be achieved with one sensor and representative training . Shortwave infrared bands are most useful for mapping soybean and corn . S2 red - edge bands provide valuable information . Harmonize SWIR and red - edge bands for Landsat and S2 can improve crop mapping . Improve S1 data acquisition is a priority for operational crop monitoring . AbstractGenerating crop type maps using satellite remote sensing requires robust data acquisition at both high spatial and temporal resolutions to resolve rapid phenological transition at the field scale . The increasing availability of freely - available , moderate - resolution satellite data such as the Landsat and Sentinel series of satellites offers an unprecedent opportunity for large - area crop type mapping . In this study , we evaluated the utility of Landsat ( 7&8 ) , Sentinel-2 ( A&B ) , Sentinel-1 ( A&B ) and the Moderate Resolution Imaging Spectroradiometer ( MODIS ) for mapping corn and soybean in the United States . We designed a series of classification experiments using these satellite data over a nationally distributed sample as input and the United States Department of Agriculture ( USDA ) Cropland Data Layer ( CDL ) as reference for training and accuracy assessment . A set of tests were performed with data from each satellite senor as input to derive the potential accuracy achievable by the satellite / sensor . In comparison , another set of tests were conducted with all data from all sensors as input to derive the combined accuracy as well as to evaluate the utility of each sensor , spectral band and acquisition date . Results showed that data from one satellite / sensor , either Landsat , Sentinel-2 or Sentinel-1 , could achieve 94.8–96.8 % accuracy , whereas the coarse - resolution MODIS produced about 92 % accuracy , for both corn and soybean . Combing data from all sensors marginally improved the accuracy to 97.0 % for both crops . Based on the criterion of deviation reduction in decisio…
author: Xiao-Peng Song, Wenli Huang, Matthew C. Hansen, Peter Potapov
publish year: 2021
creator: Xiao-Peng Song, Wenli Huang, Matthew C. Hansen, Peter Potapov
reference: Xiao-Peng Song, Wenli Huang, Matthew C. Hansen, Peter Potapov, "An evaluation of Landsat , Sentinel-2 , Sentinel-1 and MODIS data for crop type mapping", Science of Remote SensingVolume 3, June 2021, Elsevier B.V.
Source: Science of Remote SensingVolume 3, June 2021, Elsevier B.V.
label: Article 1422

Automated mapping of soybean and corn using phenology
type: NamedIndividual
type: Article
has knowledge model: Classifier 1423
abstract: For the two of the most important agricultural commodities , soybean and corn , remote sensing plays a substantial role in delivering timely information on the crop area for economic , environmental and policy studies . Traditional long - term mapping of soybean and corn is challenging as a result of the high cost of repeated training data collection , the inconsistency in image process and interpretation , and the difficulty of handling the inter - annual variability of weather and crop progress . In this study , we developed an automated approach to map soybean and corn in the state of Paraná , Brazil for crop years 2010–2015 . The core of the approach is a decision tree classifier with rules manually built based on expert interaction for repeated use . The automated approach is advantageous for its capacity of multi - year mapping without the need to re - train or re - calibrate the classifier . Time series MODerate - resolution Imaging Spectroradiometer ( MODIS ) reflectance product ( MCD43A4 ) were employed to derive vegetation phenology to identify soybean and corn based on crop calendar . To deal with the phenological similarity between soybean and corn , the surface reflectance of the shortwave infrared band scaled to a phenological stage was used to fully separate the two crops . Results suggested that the mapped areas of soybean and corn agreed with official statistics at the municipal level . The resultant map in the crop year 2012 was evaluated using an independent reference data set , and the overall accuracy and Kappa coefficient were 87.2 % and 0.804 respectively . As a result of mixed pixel effect at the 500 m resolution , classification results were biased depending on topography . In the flat , broad and highly - cropped areas , uncultivated lands were likely to be identified as soybean or corn , causing over - estimation of cropland area . By contrast , scattered crop fields in mountainous regions with dense natural vegetation tend to be overlooke…
author: Liheng Zhong, Lina Hu, Le Yu, Peng Gong, Gregory S. Biging
publish year: 2016
creator: Liheng Zhong, Lina Hu, Le Yu, Peng Gong, Gregory S. Biging
reference: Liheng Zhong, Lina Hu, Le Yu, Peng Gong, Gregory S. Biging, "Automated mapping of soybean and corn using phenology", ISPRS Journal of Photogrammetry and Remote SensingVolume 119, September 2016, Elsevier B.V.
Source: ISPRS Journal of Photogrammetry and Remote SensingVolume 119, September 2016, Elsevier B.V.
label: Article 1423

Machine vision based papaya disease recognition
type: NamedIndividual
type: Article
has knowledge model: Cluster 1425
abstract: Over the years little research has been performed for vision - based papaya disease recognition system in order to help distant farmers , most of whom require proper support for cultivation . Due to advancement of vision - based technology we find a good solution to this problem . Papaya disease recognition mainly involves two challenging problems : one is disease detection and another is disease classification . Considering this scenario , here we present an online machine vision - based agro - medical expert system that processes an image captured through mobile or handheld device and determines the diseases in order to help distant farmers to address the problem . Some experiments are performed to show the utility of the proposed expert system . First , we propose a set of features from the view point of distinguishing attributes . K - means clustering algorithm is used in order to segment out the disease - attacked region from the captured image and then required features are extracted to classify the diseases with the help of support vector machine . More than 90 % classification accuracy has been achieved , which appears to be good as well as promising by comparing performances obtained with recently reported relevant works .
author: Md. Tarek Habib, Anup Majumder, A.Z.M. Jakaria, Morium Akter, Mohammad Shorif Uddin, Farruk Ahmed
publish year: 2020
creator: Md. Tarek Habib, Anup Majumder, A.Z.M. Jakaria, Morium Akter, Mohammad Shorif Uddin, Farruk Ahmed
reference: Md. Tarek Habib, Anup Majumder, A.Z.M. Jakaria, Morium Akter, Mohammad Shorif Uddin, Farruk Ahmed, "Machine vision based papaya disease recognition", Journal of King Saud University - Computer and Information SciencesVolume 32, Issue 3, March 2020, Elsevier B.V.
Source: Journal of King Saud University - Computer and Information SciencesVolume 32, Issue 3, March 2020, Elsevier B.V.
label: Article 1425

Automated detection of individual clove trees for yield quantification in northeastern Madagascar based on multi - spectral satellite data
type: NamedIndividual
type: Article
has knowledge model: Classifier 1427
abstract: Single clove tree detection based on very high resolution optical satellite data A tree detection rate of 97.9 % was achieved using Circular Hough Transform . Clove trees can be distinguished from other trees using a Random Forest classifier . Clove bud yield for three production systems was estimated using reference data . AbstractThere is an increasing demand for clove products , mainly dried buds and essential oil on global markets . Consequently , the importance of clove trees as a provisioning service is increasing at the local level , particularly for smallholders cultivating clove trees as cash crops . Due to limited availability of data on local production , using remote sensing - based methods to quantify today 's clove production is of key interest . We estimated the clove bud yield in a study site in northeastern Madagascar by detecting individual clove trees and determining relevant production systems , including pasture and clove , clove plantation and agroforestry systems . We implemented an individual tree detection method based on two machine learning approaches . Specifically , we proposed using a circular Hough transform ( CHT ) for the automated detection of individual clove trees . Subsequently , we implemented a tree species classification method using a random forests ( RF ) classifier based on a set of features extracted for relevant trees in the above production systems . Finally , we classified and mapped different production systems . Based on the number of detected clove trees growing in a clove production system , we estimated the production system - dependent clove bud yield . Our results show that 97.9 % of all reference clove trees were detected using a CHT . Classifying clove and non - clove trees resulted in a producer accuracy of 70.7 % and a user accuracy of 59.2 % for clove trees . The classification of the clove production systems resulted in an overall accuracy of 77.9 % . By averaging different clove tree yield estimates obtain…
author: Sandra I.B. Roth, Reik Leiterer, Michele Volpi, Enrico Celio, Michael E. Schaepman, Philip C. Joerg
publish year: 2019
creator: Sandra I.B. Roth, Reik Leiterer, Michele Volpi, Enrico Celio, Michael E. Schaepman, Philip C. Joerg
reference: Sandra I.B. Roth, Reik Leiterer, Michele Volpi, Enrico Celio, Michael E. Schaepman, Philip C. Joerg, "Automated detection of individual clove trees for yield quantification in northeastern Madagascar based on multi - spectral satellite data", Remote Sensing of EnvironmentVolume 221, February 2019, Elsevier B.V.
Source: Remote Sensing of EnvironmentVolume 221, February 2019, Elsevier B.V.
label: Article 1427

Classification of some strategic crops in Egypt using multi remotely sensing sensors and time series analysis
type: NamedIndividual
type: Article
has knowledge model: Classifier 1430
abstract: The agricultural fields in Egypt are commonly distributed with relatively small sizes parcels that usually reduce the reliability of Agricultural statistics in surveying cropland . The use of remote sensing help in an accurate crop inventory under complex landscape conditions based on the spectral characteristics differences of crops . The current study was carried out in Abu El Matamir district , Behira Governorate , located in western Nile delta Egypt . The main objective of the current study is using time series analysis of remote sensing data in crop discrimination . In this study , 160 locations of ground truth points collected during different growth stages of summer season crops . Two different sensors images used in this study represented by single date image of RapidEye and multi - date Landsat 8 OLI satellites . The acquired satellite images from both sensors atmospherically and geometrically corrected . Moreover different vegetation indices calculated such as Normalized Difference Vegetation Index ( NDVI ) , Soil Adjusted Vegetation Index ( SAVI ) and Enhanced Vegetation Index ( EVI ) for cultivated crops in the study area during the whole growing season . Preliminary statistical analysis applied to the collected field data to show the distribution of the cultivated crops types . Moreover , unsupervised Iso - Data applied for multi - date Landsat 8 OLI images and calculated VI ’s series for overall growth season . Results showed higher overall kappa accuracy with 0.82 and 0.79 respectively . NDVI showed the best representation of the crop phenological changes during the crop growth season and showed higher accuracy in strategic crops discrimination than the single date image with higher spatial resolution .
author: Eslam Farg, Mohsen Nabil Ramadan, Sayed Medany Arafat
publish year: 2019
creator: Eslam Farg, Mohsen Nabil Ramadan, Sayed Medany Arafat
reference: Eslam Farg, Mohsen Nabil Ramadan, Sayed Medany Arafat, "Classification of some strategic crops in Egypt using multi remotely sensing sensors and time series analysis", The Egyptian Journal of Remote Sensing and Space ScienceVolume 22, Issue 3, December 2019, Elsevier B.V.
Source: The Egyptian Journal of Remote Sensing and Space ScienceVolume 22, Issue 3, December 2019, Elsevier B.V.
label: Article 1430

Computer vision - based apple grading for golden delicious apples based on surface features
type: NamedIndividual
type: Article
has knowledge model: Classifier 1438
abstract: In this paper , a computer vision - based algorithm for golden delicious apple grading is proposed which works in six steps . Non - apple pixels as background are firstly removed from input images . Then , stem end is detected by combination of morphological methods and Mahalanobis distant classifier . Calyx region is also detected by applying K - means clustering on the Cb component in YCbCr color space . After that , defects segmentation is achieved using Multi - Layer Perceptron ( MLP ) neural network . In the next step , stem end and calyx regions are removed from defected regions to refine and improve apple grading process . Then , statistical , textural and geometric features from refined defected regions are extracted . Finally , for apple grading , a comparison between performance of Support Vector Machine ( SVM ) , MLP and K - Nearest Neighbor ( KNN ) classifiers is done . Classification is done in two manners which in the first one , an input apple is classified into two categories of healthy and defected . In the second manner , the input apple is classified into three categories of first rank , second rank and rejected ones . In both grading steps , SVM classifier works as the best one with recognition rate of 92.5 % and 89.2 % for two categories ( healthy and defected ) and three quality categories ( first rank , second rank and rejected ones ) , among 120 different golden delicious apple images , respectively , considering K - folding with K = 5 . Moreover , the accuracy of the proposed segmentation algorithms including stem end detection and calyx detection are evaluated for two different apple image databases .
author: Payman Moallem, Alireza Serajoddin, Hossein Pourghassem
publish year: 2017
creator: Payman Moallem, Alireza Serajoddin, Hossein Pourghassem
reference: Payman Moallem, Alireza Serajoddin, Hossein Pourghassem, "Computer vision - based apple grading for golden delicious apples based on surface features", Information Processing in AgricultureVolume 4, Issue 1, March 2017, Elsevier B.V.
Source: Information Processing in AgricultureVolume 4, Issue 1, March 2017, Elsevier B.V.
label: Article 1438

Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks
type: NamedIndividual
type: Article
has knowledge model: Classifier 1457
abstract: Grape diseases are main factors causing serious grapes reduction . So it is urgent to develop an automatic identification method for grape leaf diseases . Deep learning techniques have recently achieved impressive successes in various computer vision problems , which inspires us to apply them to grape diseases identification task . In this paper , a united convolutional neural networks ( CNNs ) architecture based on an integrated method is proposed . The proposed CNNs architecture , i.e. , UnitedModel is designed to distinguish leaves with common grape diseases i.e. , black rot , esca and isariopsis leaf spot from healthy leaves . The combination of multiple CNNs enables the proposed UnitedModel to extract complementary discriminative features . Thus the representative ability of UnitedModel has been enhanced . The UnitedModel has been evaluated on the hold - out PlantVillage dataset and has been compared with several state - of - the - art CNN models . The experimental results have shown that UnitedModel achieves the best performance on various evaluation metrics . The UnitedModel achieves an average validation accuracy of 99.17 % and a test accuracy of 98.57 % , which can serve as a decision support tool to help farmers identify grape diseases .
author: Miaomiao Ji, Lei Zhang, Qiufeng Wu
publish year: 2020
creator: Miaomiao Ji, Lei Zhang, Qiufeng Wu
reference: Miaomiao Ji, Lei Zhang, Qiufeng Wu, "Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks", Information Processing in AgricultureVolume 7, Issue 3, September 2020, Elsevier B.V.
Source: Information Processing in AgricultureVolume 7, Issue 3, September 2020, Elsevier B.V.
label: Article 1457

A segmentation method for processing greenhouse vegetable foliar disease symptom images
type: NamedIndividual
type: Article
has knowledge model: Classifier 1460
abstract: Uneven illumination and clutter background were the most challenging problems to segmentation of disease symptom images . In order to achieve robust segmentation , a method for processing greenhouse vegetable foliar disease symptom images was proposed in this paper . The segmentation method was based on a decision tree which was constructed by a two - step coarse - to - fine procedure . Firstly , a coarse decision tree was built by the CART ( Classification and Regression Tree ) algorithm with a feature subset . The feature subset consisted of color features that was selected by Pearson ’s Rank correlations . Then , the coarse decision tree was optimized by pruning . Using the optimized decision tree , segmentation of disease symptom images was achieved by conducting pixel - wise classification . In order to evaluate the robustness and accuracy of the proposed method , an experiment was performed using greenhouse cucumber downy mildew images . Results showed that the proposed method achieved an overall accuracy of 90.67 % , indicating that the method was able to obtain robust segmentation of disease symptom images .
author: Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhongfu Sun
publish year: 2019
creator: Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhongfu Sun
reference: Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhongfu Sun, "A segmentation method for processing greenhouse vegetable foliar disease symptom images", Information Processing in AgricultureVolume 6, Issue 2, June 2019, Elsevier B.V.
Source: Information Processing in AgricultureVolume 6, Issue 2, June 2019, Elsevier B.V.
label: Article 1460

Machine learning for large - scale crop yield forecasting
type: NamedIndividual
type: Article
has knowledge model: Regressor 1487
abstract: Combined principles of crop modeling with machine learning for crop yield forecasting Designed a generic workflow emphasizing correctness , modularity and reusability Useful to explore benefits of machine learning in large - scale crop yield forecasting Tested the workflow by predicting regional crop yields for 3 countries and 5 crops Early season predictions were comparable with the MARS Crop Yield Forecasting SystemAbstractMany studies have applied machine learning to crop yield prediction with a focus on specific case studies . The data and methods they used may not be transferable to other crops and locations . On the other hand , operational large - scale systems , such as the European Commission 's MARS Crop Yield Forecasting System ( MCYFS ) , do not use machine learning . Machine learning is a promising method especially when large amounts of data are being collected and published . We combined agronomic principles of crop modeling with machine learning to build a machine learning baseline for large - scale crop yield forecasting . The baseline is a workflow emphasizing correctness , modularity and reusability . For correctness , we focused on designing explainable predictors or features ( in relation to crop growth and development ) and applying machine learning without information leakage . We created features using crop simulation outputs and weather , remote sensing and soil data from the MCYFS database . We emphasized a modular and reusable workflow to support different crops and countries with small configuration changes . The workflow can be used to run repeatable experiments ( e.g. early season or end of season predictions ) using standard input data to obtain reproducible results . The results serve as a starting point for further optimizations . In our case studies , we predicted yield at regional level for five crops ( soft wheat , spring barley , sunflower , sugar beet , potatoes ) and three countries ( the Netherlands ( NL ) , Germany ( DE ) , F…
author: Dilli Paudel, Hendrik Boogaard, Allard de Wit, Sander Janssen, Sjoukje Osinga, Christos Pylianidis, Ioannis N. Athanasiadis
publish year: 2021
creator: Dilli Paudel, Hendrik Boogaard, Allard de Wit, Sander Janssen, Sjoukje Osinga, Christos Pylianidis, Ioannis N. Athanasiadis
reference: Dilli Paudel, Hendrik Boogaard, Allard de Wit, Sander Janssen, Sjoukje Osinga, Christos Pylianidis, Ioannis N. Athanasiadis, "Machine learning for large - scale crop yield forecasting", Agricultural SystemsVolume 187, February 2021, Elsevier B.V.
Source: Agricultural SystemsVolume 187, February 2021, Elsevier B.V.
label: Article 1487

Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe
type: NamedIndividual
type: Article
has knowledge model: Regressor 1493
abstract: Crop model and meteorological variables are compared to yield variability . Crop model simulations allow to anticipate yields of crops exposed to drought . Impact of warm temperatures on yield during grain filling is reflected by WOFOST . Impacts of water excess on crop growth are not reproduced by the crop model . The simulated biomass of winter cereals at anthesis is poorly related to the yields . AbstractThe MARS - Crop Yield Forecasting System ( M - CYFS ) is used since 1993 to forecast the yields of all major crops in the European Union ( EU ) based on gridded runs of the WOFOST crop model . Using 28 years of observation , from 1988 to 2015 , we quantified the variability in crop yield reported by all 28 EU Member States ( MS ) that can be explained by each individual WOFOST crop model based predictors and a few simple meteorological variables . A linear regression is used as a screening tool to quantify the relationship between each predictor and the yield residuals from the trend throughout the crop cycle for 168 country / crop combinations , assuming the yield residuals from the trend depend on the inter - annual climate variability . The results are plotted and analyzed at different level : every 10 days for each country crop / combination and each predictor ; synthetized every 10 days for each country / crop combination keeping the predictor showing the best relationship with the yield residuals ; finally , the best predictor found for each MS during the entire growing season is used to evaluate the ability of the model to estimate yield variability of each crop at European scale . While 61 % of the grain maize ( Zea mays L. ) yield variability can be anticipated 80 days before harvest with the simulated water limited biomass for countries where rainfed maize prevails , 41 % of the soft wheat ( Triticum aestivum L. ) yield variability can be reproduced a month before harvest , the best estimates being obtained where wheat is predominantly exposed to water …
author: Rémi Lecerf, Andrej Ceglar, Raúl López-Lozano, Marijn Van Der Velde, Bettina Baruth
publish year: 2019
creator: Rémi Lecerf, Andrej Ceglar, Raúl López-Lozano, Marijn Van Der Velde, Bettina Baruth
reference: Rémi Lecerf, Andrej Ceglar, Raúl López-Lozano, Marijn Van Der Velde, Bettina Baruth, "Assessing the information in crop model and meteorological indicators to forecast crop yield over Europe", Agricultural SystemsVolume 168, January 2019, Elsevier B.V.
Source: Agricultural SystemsVolume 168, January 2019, Elsevier B.V.
label: Article 1493

Synergistic integration of optical and microwave satellite data for crop yield estimation
type: NamedIndividual
type: Article
has knowledge model: Regressor 1498
abstract: 1 . Optical and passive microwave data are complementary and useful to estimate crop yield . 2 . The time lag between EVI and VOD is proposed as a new metric for crop assessment . 3 . The EVI / VOD lag outperforms common single - sensor metrics for crop yield estimation . 4 . Machine learning further improves results by directly blending multisensor time series . 5 . Crop yield prediction is feasible up to four months before harvest . AbstractDeveloping accurate models of crop stress , phenology and productivity is of paramount importance , given the increasing need of food . Earth observation ( EO ) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way . In this study , we propose the combination of multisensor ( optical and microwave ) remote sensing data for crop yield estimation and forecasting using two novel approaches . We first propose the lag between Enhanced Vegetation Index ( EVI ) derived from MODIS and Vegetation Optical Depth ( VOD ) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor . Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD . This study considers two statistical methods , a regularized linear regression and its nonlinear extension called kernel ridge regression to directly estimate the county - level surveyed total production , as well as individual yields of the major crops grown in the region : corn , soybean and wheat . The study area includes the US Corn Belt , and we use agricultural survey data from the National Agricultural Statistics Service ( USDA - NASS ) for year 2015 for quantitative assessment . Results show that ( 1 ) the proposed EVI - VOD lag metric correlates well with crop yield and outperforms common single - sensor metrics for crop yield estimation ; ( 2 ) the statistical ( machine learning ) models wo…
author: Anna Mateo-Sanchis, Maria Piles, Jordi Muñoz-Marí, Jose E. Adsuara, Adrián Pérez-Suay, Gustau Camps-Valls
publish year: 2019
creator: Anna Mateo-Sanchis, Maria Piles, Jordi Muñoz-Marí, Jose E. Adsuara, Adrián Pérez-Suay, Gustau Camps-Valls
reference: Anna Mateo-Sanchis, Maria Piles, Jordi Muñoz-Marí, Jose E. Adsuara, Adrián Pérez-Suay, Gustau Camps-Valls, "Synergistic integration of optical and microwave satellite data for crop yield estimation", Remote Sensing of EnvironmentVolume 234, 1 December 2019, Elsevier B.V.
Source: Remote Sensing of EnvironmentVolume 234, 1 December 2019, Elsevier B.V.
label: Article 1498

Evaluation of the predicted error of the soil moisture retrieval from C - band SAR by comparison against modelled soil moisture estimates over Australia
type: NamedIndividual
type: Article
has knowledge model: Regressor 1566
abstract: The Sentinel-1 will carry onboard a C - band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution . The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring . Currently , updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode ( GM ) measurements from the Advanced Synthetic Aperture Radar ( ASAR ) onboard ENVISAT . The service demonstrates the potential of the C - band observations to monitor variations in soil moisture . Importantly , a retrieval error estimate is also available ; these are needed to assimilate observations into models . The retrieval error is estimated by propagating sensor errors through the retrieval model . In this work , the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid - based landscape hydrological model ( AWRA - L ) developed within the Australian Water Resources Assessment system ( AWRA ) . The ASAR GM retrieval error estimate , an assumed prior AWRA - L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error ( RMSE ) and the Pearson 's correlation coefficient R between the two datasets . These were compared with the RMSE calculated directly from the two datasets . The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement ; the RMSE was predicted within accuracy of 4 % of saturated soil moisture over 89 % of the Australian land mass . Predicted and calculated R maps corresponded within accuracy of 10 % over 61 % of the continent . The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimate…
author: Marcela Doubková, Albert I.J.M. Van Dijk, Daniel Sabel, Wolfgang Wagner, Günter Blöschl
publish year: 2012
creator: Marcela Doubková, Albert I.J.M. Van Dijk, Daniel Sabel, Wolfgang Wagner, Günter Blöschl
reference: Marcela Doubková, Albert I.J.M. Van Dijk, Daniel Sabel, Wolfgang Wagner, Günter Blöschl, "Evaluation of the predicted error of the soil moisture retrieval from C - band SAR by comparison against modelled soil moisture estimates over Australia", Remote Sensing of EnvironmentVolume 120, 15 May 2012, Elsevier B.V.
Source: Remote Sensing of EnvironmentVolume 120, 15 May 2012, Elsevier B.V.
label: Article 1566

Performance analysis of deep learning CNN models for disease detection in plants using image segmentation
type: NamedIndividual
type: Article
has knowledge model: Classifier 1585
abstract: Food security for the 7 billion people on earth requires minimizing crop damage by timely detection of diseases . Most deep learning models for automated detection of diseases in plants suffer from the fatal flaw that once tested on independent data , their performance drops significantly . This work investigates a potential solution to this problem by using segmented image data to train the convolutional neural network ( CNN ) models . As compared to the F - CNN model trained using full images , S - CNN model trained using segmented images more than doubles in performance to 98.6 % accuracy when tested on independent data previously unseen by the models even with 10 disease classes . Not only this , by using tomato plant and target spot disease type as an example , we show that the confidence of self - classification for S - CNN model improves significantly over F - CNN model . This research work brings applicability of automated methods closer to non - experts for timely detection of diseases .
author: Parul Sharma, Yash Paul Singh Berwal, Wiqas Ghai
publish year: 2020
creator: Parul Sharma, Yash Paul Singh Berwal, Wiqas Ghai
reference: Parul Sharma, Yash Paul Singh Berwal, Wiqas Ghai, "Performance analysis of deep learning CNN models for disease detection in plants using image segmentation", Information Processing in AgricultureVolume 7, Issue 4, December 2020, Elsevier B.V.
Source: Information Processing in AgricultureVolume 7, Issue 4, December 2020, Elsevier B.V.
label: Article 1585

Using SPOT data and leaf area index for rice yield estimation in Egyptian Nile delta
type: NamedIndividual
type: Article
has knowledge model: Regressor 1597
abstract: The objective of the current work is to generate statistical empirical rice yield estimation models under the local conditions of the Egyptian Nile delta . The methodology is based on regressing measured yield with satellite derived spectral information or leaf area index ( LAI ) . LAI field measurements and spectral information from SPOT data collected during two crop seasons are examined against measured yield to generate the yield models . Near - infrared and red bands , six vegetation indices and LAI of 100 points are used as the main inputs for the modeling process while 20 points of the same are used for validation process . Nine models are generated and tested against the observed yield . Comparing the generated models show relatively higher superiority of ( LAI - yield ) and ( infrared - yield ) models over the rest of the models with ( 0.061 ) and ( 0.090 ) as a standard error of estimate and ( 0.945 ) and ( 0.883 ) as coefficient of determinations between modeled and observed yield . The models are applicable a month before harvest for similar regions with same conditions .
author: M. Aboelghar, S. Arafat, M. Abo Yousef, M. El-Shirbeny, S. Naeem, A. Massoud, N. Saleh
publish year: 2011
creator: M. Aboelghar, S. Arafat, M. Abo Yousef, M. El-Shirbeny, S. Naeem, A. Massoud, N. Saleh
reference: M. Aboelghar, S. Arafat, M. Abo Yousef, M. El-Shirbeny, S. Naeem, A. Massoud, N. Saleh, "Using SPOT data and leaf area index for rice yield estimation in Egyptian Nile delta", The Egyptian Journal of Remote Sensing and Space ScienceVolume 14, Issue 2, December 2011, Elsevier B.V.
Source: The Egyptian Journal of Remote Sensing and Space ScienceVolume 14, Issue 2, December 2011, Elsevier B.V.
label: Article 1597

Fifty years of semi - dwarf spring wheat breeding at CIMMYT : Grain yield progress in optimum , drought and heat stress environments
type: NamedIndividual
type: Article
has knowledge model: AssociationRule 1631
abstract: Continuous breeding progress in grain yield from 1965–2014 in optimum , drought and heat environments . Drip irrigation system in high yielding environments can improve yield potential . A graphical LASSO estimation model expands the understanding of complex relationship between grain yield and other traits . Highest yielding varieties have employed different trait strategies to attain grain yield . AbstractGrain yield progress over 50 years of spring wheat breeding at the International Maize and Wheat Improvement Center ( CIMMYT ) was determined in field trials conducted during five crop seasons ( 2013–2017 ) at Norman E. Borlaug research station near Ciudad Obregon , Mexico . The trials included 30 varieties ( 24 bread wheat and 6 durum wheat ) released between 1965–2014 and were sown under managed optimum , drought , and heat stress conditions . The optimum irrigated environment had 3 management systems , flat sowing with weekly drip irrigation ( FDI ) , bed sowing with flood irrigation ( BFI ) , and flat sowing with flood irrigation ( FFI ) . The drought environment had 2 management systems , flat sowing with reduced irrigation ( FRI ) and flat sowing under severe drought stress ( FSD ) . The heat stress environment was sown in beds ( HFI ) three months later than the normally sown irrigated and drought environments . The rate of grain yield progress was estimated relative to Sonalika released in 1965 and Mexicali C75 released in 1975 for bread and durum wheat , respectively . Grain yield progress per year for bread wheat was , 31.2 kg ha−1 , 35.3 kg ha−1 , and 24.7 kg ha−1 in irrigated environments FDI , BFI , and FFI , respectively . In the stress environments , bread wheat grain yield progress was estimated as 25.6 kg ha−1 , 17.7 kg ha−1 , and 18.1 kg ha−1 per year in FRI , FSD , and HFI , respectively . For durum wheat , the grain yield progress was estimated as 29.6 kg ha−1 , 48.1 kg ha−1 , 18.8 kg ha−1 , and 29.8 kg ha−1 , per year in FDI , BFI , FFI , and…
author: Suchismita Mondal, Somak Dutta, Leonardo Crespo-Herrera, Julio Huerta-Espino, Hans J. Braun, Ravi P. Singh
publish year: 2020
creator: Suchismita Mondal, Somak Dutta, Leonardo Crespo-Herrera, Julio Huerta-Espino, Hans J. Braun, Ravi P. Singh
reference: Suchismita Mondal, Somak Dutta, Leonardo Crespo-Herrera, Julio Huerta-Espino, Hans J. Braun, Ravi P. Singh, "Fifty years of semi - dwarf spring wheat breeding at CIMMYT : Grain yield progress in optimum , drought and heat stress environments", Field Crops ResearchVolume 250, 1 May 2020, Elsevier B.V.
Source: Field Crops ResearchVolume 250, 1 May 2020, Elsevier B.V.
label: Article 1631

Near - infrared spectroscopy ( NIRS ) evaluation and regional analysis of Chinese faba bean ( Vicia faba L. )
type: NamedIndividual
type: Article
has knowledge model: Regressor 1633
abstract: To analyze the nutritional composition of faba bean ( Vicia faba L. ) seed , estimation models were developed for protein , starch , oil , and total polyphenol using near infrared spectroscopy ( NIRS ) . Two hundred and forty - four samples from twelve producing regions were measured in both milled powder and intact seed forms . Partial least squares ( PLS ) regression was applied for model development . The model based on ground seed powder was generally superior to that based on the intact seed . The optimal seed powder - based models for protein , starch , and total polyphenol had coefficients of correlation ( r2 ) of 0.97 , 0.93 and 0.89 , respectively . The relationship between nutrient contents and twelve producing areas was determined by two - step cluster analysis . Three distinct groupings were obtained with region - constituent features , i.e. , Group 1 of high oil , Group 2 of high protein , and Group 3 of high starch as well as total polyphenol . The clustering accuracy was 79.5 % . Moreover , the nutrition contents were affected by seeding date , longitude , latitude , and altitude of plant location . Cluster analysis revealed that the differences in the seed were strongly influenced by geographical factors .
author: Jiaojiao Wang, Hao Liu, Guixing Ren
publish year: 2014
creator: Jiaojiao Wang, Hao Liu, Guixing Ren
reference: Jiaojiao Wang, Hao Liu, Guixing Ren, "Near - infrared spectroscopy ( NIRS ) evaluation and regional analysis of Chinese faba bean ( Vicia faba L. )", The Crop JournalVolume 2, Issue 1, February 2014, Elsevier B.V.
Source: The Crop JournalVolume 2, Issue 1, February 2014, Elsevier B.V.
label: Article 1633

Partial substitution of organic nitrogen with synthetic nitrogen enhances rice yield , grain starch metabolism and related genes expression under the dual cropping system
type: NamedIndividual
type: Article
has knowledge model: Regressor 1648
abstract: Improving grain filling in the presernt farming systems is crucial where grain filling is a concern due to the extreme use of chemical fertilizers ( CF ) . A field experiment was conducted at the experimental station of Guangxi University , China in 2019 to test the hypothesis that cattle manure ( CM ) and poultry manure ( PM ) combined with CF could improve rice grain filling rate , yield , biochemical and qualitative attributes . A total of six treatments , i.e. , no fertilizer ( T1 ) , 100 % CF ( T2 ) , 60 % CM + 40 % CF ( T3 ) , 30 % CM + 70 % CF ( T4 ) , 60 % PM + 40 % CF ( T5 ) , and 30 % PM + 70 % CF ( T6 ) were used in this study . Results showed that the combined treatment T6increased starch metabolizing enzymes activity ( SMEs ) , such as ADP - glucose phosphorylase ( ADGPase ) by 8 and 12 % , soluble starch synthase ( SSS ) by 7 and 10 % , granule bound starch synthesis ( GBSS ) by 7 and 9 % , and starch branching enzyme ( SBE ) by 14 and 21 % in the early and late seasons , respectively , compared with T2 . Similarly , higher rice grain yield , grain filling rate , starch , and amylose content were also recorded in combined treatments . In terms of seasons , higher activity of SMEs , grain starch , and amylose content was noted in the late - season compared to the early season . The increment in these traits was mainly attributed to a lower temperature in the late season during the grain filling period . Furthermore , our results suggested that an increment in starch accumulation and grain filling rate were mainly associated with the enhanced sink capacity by regulating key enzyme activities involved in Suc - to - starch conversion . In - addition , RT - qPCR analysis showed higher expression levels of AGPS2b , SSS1 , GBSS1 , and GBSE11b genes , which resultantly increased the activities of SMEs during the grain filling period under combined treatments . Linear regression analysis revealed that the activity of ADGPase , SSS , GBSS , and SBE were highly p…
author: Anas Iqbal, Huimin Xie, Liang He, Shakeel Ahmad, Izhar Hussain, Haneef Raza, Abdullah Khan, Shangqin Wei, Zhao Quan, Ke Wu, Izhar Ali, Ligeng Jiang
publish year: 2021
creator: Anas Iqbal, Huimin Xie, Liang He, Shakeel Ahmad, Izhar Hussain, Haneef Raza, Abdullah Khan, Shangqin Wei, Zhao Quan, Ke Wu, Izhar Ali, Ligeng Jiang
reference: Anas Iqbal, Huimin Xie, Liang He, Shakeel Ahmad, Izhar Hussain, Haneef Raza, Abdullah Khan, Shangqin Wei, Zhao Quan, Ke Wu, Izhar Ali, Ligeng Jiang, "Partial substitution of organic nitrogen with synthetic nitrogen enhances rice yield , grain starch metabolism and related genes expression under the dual cropping system", Saudi Journal of Biological SciencesVolume 28, Issue 2, February 2021, Elsevier B.V.
Source: Saudi Journal of Biological SciencesVolume 28, Issue 2, February 2021, Elsevier B.V.
label: Article 1648

Use of spectral reflectance for indirect selection of yield potential and stability in Pacific Northwest winter wheat
type: NamedIndividual
type: Article
has knowledge model: Regressor 1682
abstract: Yield showed significant genetic correlations with spectral reflectance indices . Response to selection was generally high in moist cool rain - fed condition . Predictive power of yield models using selected indices ranged from 41 to 82 % . Integrated use of reflectance indices and grain yield increased selection efficiency . AbstractThe use of canopy spectral reflectance as a high throughput selection method has been recommended to augment genetic gain from yield based selection in highly variable environments . The objectives of this study were to estimate genotypic correlations between grain yield and spectral reflectance indices ( SRIs ) , and estimate heritability , expected response to selection , relative efficiency of indirect selection , and accuracy of yield predictive models in Pacific Northwest winter wheat ( Triticum aestivum L. ) under a range of moisture regimes . A diversity panel of 402 winter wheat genotypes ( 87 hard and 315 soft ) was grown in rain - fed and irrigated conditions across the eastern Washington in 2012 and 2013 . Canopy spectral reflectance measured at heading , milk , soft dough , and hard dough stages were used to derive several SRIs which generally had higher broad sense heritability ( H2 ) than yield per se . Grain yield and SRIs showed generally high genetic variability and response to selection in moist - cool rain - fed condition . Efficiency of indirect selection for yield using SRIs was high in drought environment and exceeded efficiency of yield - based selection in the soft winter subgroup . Normalized water band index ( NWI ) showed consistent response to selection across environments , higher genetic correlation with yield ( 0.51–0.80 , p < 0.001 ) , and highest indirect selection efficiency ( up to 143 % ) . A yield predictive model with one or more SRIs explained 41–82 % of total variation in grain yield ( p < 0.001 ) . The repeatability of genotypic performance between years increased when selection was conducted bas…
author: Shiferaw A. Gizaw, Kimberly Garland-Campbell, Arron H. Carter
publish year: 2016
creator: Shiferaw A. Gizaw, Kimberly Garland-Campbell, Arron H. Carter
reference: Shiferaw A. Gizaw, Kimberly Garland-Campbell, Arron H. Carter, "Use of spectral reflectance for indirect selection of yield potential and stability in Pacific Northwest winter wheat", Field Crops ResearchVolume 196, September 2016, Elsevier B.V.
Source: Field Crops ResearchVolume 196, September 2016, Elsevier B.V.
label: Article 1682

Grain yield , adaptation and progress in breeding for early - maturing and heat - tolerant wheat lines in South Asia
type: NamedIndividual
type: Article
has knowledge model: Regressor 1687
abstract: Each year from 2009 to 2014 , 28 newly developed early - maturing high - yielding CIMMYT wheat lines were evaluated across locations in South Asia . Maximum temperatures in ME5 ( continual high temperature stress regions ) and minimum temperature in ME1 ( terminal high temperature stress regions ) had significant impact on grain yield in South Asia . Significant negative genetic correlations of grain yield with days to heading . Early maturity has the potential to improve adaptation and maintenance of genetic gains in South Asia . AbstractMaintaining wheat productivity under the increasing temperatures in South Asia is a challenge . We focused on developing early maturing wheat lines as an adaptive mechanism in regions suffering from terminal heat stress and those areas that require wheat adapted to shorter cycles under continual high temperature stress . We evaluated the grain yield performance of early - maturing heat - tolerant germplasm developed by CIMMYT , Mexico at diverse locations in South Asia from 2009 to 2014 and estimated the breeding progress for high - yielding and early - maturing heat - tolerant germplasm in South Asia . Each year the trial comprised of 28 new entries , one CIMMYT check ( Baj ) and a local check variety . Locations were classified by mega environment ( ME ) ; ME1 being the temperate irrigated locations with terminal high temperature stress , and ME5 as hot , sub - tropical , irrigated locations . Grain yield ( GY ) , days to heading ( DTH ) and plant height ( PH ) were recorded at each location . Effect of temperature on GY was observed in both ME1 and ME5 . Across years , mean minimum temperatures in ME1 and mean maximum temperatures in ME5 during grain filling had significant negative association with GY . The ME1 locations were cooler that those in ME5 in the 5 years of evaluations and had a 1–2 t / ha higher GY . A mean reduction of 20 days for DTH and 20 cm in PH was observed in ME5 . Negative genetic correlations of −0.43 to −…
author: S. Mondal, R.P. Singh, E.R. Mason, J. Huerta-Espino, E. Autrique, A.K. Joshi
publish year: 2016
creator: S. Mondal, R.P. Singh, E.R. Mason, J. Huerta-Espino, E. Autrique, A.K. Joshi
reference: S. Mondal, R.P. Singh, E.R. Mason, J. Huerta-Espino, E. Autrique, A.K. Joshi, "Grain yield , adaptation and progress in breeding for early - maturing and heat - tolerant wheat lines in South Asia", Field Crops ResearchVolume 192, June 2016, Elsevier B.V.
Source: Field Crops ResearchVolume 192, June 2016, Elsevier B.V.
label: Article 1687

Transcriptome Comparison of Susceptible and Resistant Wheat in Response to Powdery Mildew Infection
type: NamedIndividual
type: Article
has knowledge model: Classifier 1693
abstract: Powdery mildew ( Pm ) caused by the infection of Blumeria graminis f. sp . tritici ( Bgt ) is a worldwide crop disease resulting in significant loss of wheat yield . To profile the genes and pathways responding to the Bgt infection , here , using Affymetrix wheat microarrays , we compared the leaf transcriptomes before and after Bgt inoculation in two wheat genotypes , a Pm - susceptible cultivar Jingdong 8 ( S ) and its near - isogenic line ( R ) carrying a single Pm resistant gene Pm30 . Our analysis showed that the original gene expression status in the S and R genotypes of wheat was almost identical before Bgt inoculation , since only 60 genes exhibited differential expression by P = 0.01 cutoff . However , 12 h after Bgt inoculation , 3014 and 2800 genes in the S and R genotype , respectively , responded to infection . A wide range of pathways were involved , including cell wall fortification , flavonoid biosynthesis and metabolic processes . Furthermore , for the first time , we show that sense - antisense pair genes might be participants in wheat - powdery mildew interaction . In addition , the results of qRT - PCR analysis on several candidate genes were consistent with the microarray data in their expression patterns . In summary , this study reveals leaf transcriptome changes before and after powdery mildew infection in wheat near - isogenic lines , suggesting that powdery mildew resistance is a highly complex systematic response involving a large amount of gene regulation .
author: Mingming Xin, Xiangfeng Wang, Huiru Peng, Yingyin Yao, Chaojie Xie, Yao Han, Zhongfu Ni, Qixin Sun
publish year: 2012
creator: Mingming Xin, Xiangfeng Wang, Huiru Peng, Yingyin Yao, Chaojie Xie, Yao Han, Zhongfu Ni, Qixin Sun
reference: Mingming Xin, Xiangfeng Wang, Huiru Peng, Yingyin Yao, Chaojie Xie, Yao Han, Zhongfu Ni, Qixin Sun, "Transcriptome Comparison of Susceptible and Resistant Wheat in Response to Powdery Mildew Infection", Genomics, Proteomics & BioinformaticsVolume 10, Issue 2, April 2012, Elsevier B.V.
Source: Genomics, Proteomics & BioinformaticsVolume 10, Issue 2, April 2012, Elsevier B.V.
label: Article 1693

Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging
type: NamedIndividual
type: Article
has knowledge model: Regressor 1708
abstract: Intact single green coffee beans were analysed for their moisture and fat content by HSI . NIR Hyperspectral imaging was applied to develop PLS calibrations for these constituents . The PLSR performance showed a performance comparable with traditional NIR instrumentation . A classification model was successfully applied by PLS - DA to discriminate Arabica vs Robusta . AbstractHyperspectral imaging ( 1000–2500 nm ) was used for rapid prediction of moisture and total lipid content in intact green coffee beans on a single bean basis . Arabica and Robusta samples from several growing locations were scanned using a “ push - broom ” system . Hypercubes were segmented to select single beans , and average spectra were measured for each bean . Partial Least Squares regression was used to build quantitative prediction models on single beans ( n = 320–350 ) . The models exhibited good performance and acceptable prediction errors of ∼0.28 % for moisture and ∼0.89 % for lipids . This study represents the first time that HSI - based quantitative prediction models have been developed for coffee , and specifically green coffee beans . In addition , this is the first attempt to build such models using single intact coffee beans . The composition variability between beans was studied , and fat and moisture distribution were visualized within individual coffee beans . This rapid , non - destructive approach could have important applications for research laboratories , breeding programmes , and for rapid screening for industry . Graphical abstract
author: Nicola Caporaso, Martin B. Whitworth, Stephen Grebby, Ian D. Fisk
publish year: 2018
creator: Nicola Caporaso, Martin B. Whitworth, Stephen Grebby, Ian D. Fisk
reference: Nicola Caporaso, Martin B. Whitworth, Stephen Grebby, Ian D. Fisk, "Rapid prediction of single green coffee bean moisture and lipid content by hyperspectral imaging", Journal of Food EngineeringVolume 227, June 2018, Elsevier B.V.
Source: Journal of Food EngineeringVolume 227, June 2018, Elsevier B.V.
label: Article 1708

Early detection of forest stress from European spruce bark beetle attack , and a new vegetation index : Normalized distance red & SWIR ( NDRS )
type: NamedIndividual
type: Article
has knowledge model: Classifier 1923
abstract: Sentinel-1 and 2 images were used to detect forest stress of bark beetle attacks . Temporal characteristics of the radar and spectral signal responses were analyzed . A novel vegetation index using a red and a SWIR band was proposed and validated . High detection accuracies were achieved before and during the attacks . The radar signal did not contribute significantly to the detection . AbstractThe European spruce bark beetle ( Ips typographus [ L. ] ) is one of the most damaging pest insects of European spruce forests . A crucial measure in pest control is the removal of infested trees before the beetles leave the bark , which generally happens before the end of June . However , stressed tree crowns do not show any significant color changes in the visible spectrum at this early - stage of infestation , making early detection difficult . In order to detect the related forest stress at an early stage , we investigated the differences in radar and spectral signals of healthy and stressed trees . How the characteristics of stressed trees changed over time was analyzed for the whole vegetation season , which covered the period before attacks ( April ) , early - stage infestation ( ‘ green - attacks ’ , May to July ) , and middle to late - stage infestation ( August to October ) . The results show that spectral differences already existed at the beginning of the vegetation season , before the attacks . The spectral separability between the healthy and infested samples did not change significantly during the ‘ green - attack ’ stage . The results indicate that the trees were stressed before the attacks and had spectral signatures that differed from healthy ones . These stress - induced spectral changes could be more efficient indicators of early infestations than the ‘ green - attack ’ symptoms . In this study we used Sentinel-1 and 2 images of a test site in southern Sweden from April to October in 2018 and 2019 . The red and SWIR bands from Sentinel-2 showed the highest…
author: Langning Huo, Henrik Jan Persson, Eva Lindberg
publish year: 2021
creator: Langning Huo, Henrik Jan Persson, Eva Lindberg
reference: Langning Huo, Henrik Jan Persson, Eva Lindberg, "Early detection of forest stress from European spruce bark beetle attack , and a new vegetation index : Normalized distance red & SWIR ( NDRS )", Remote Sensing of EnvironmentVolume 255, 15 March 2021, Elsevier B.V.
Source: Remote Sensing of EnvironmentVolume 255, 15 March 2021, Elsevier B.V.
label: Article 1923

Prediction of crude protein and oil content of soybeans using Raman spectroscopy
type: NamedIndividual
type: Article
has knowledge model: Regressor 1939
abstract: While conventional chemical analysis methods for food nutrients require time - consuming , labor - intensive , and invasive pretreatment procedures , Raman spectroscopy can be used to measure a variety of food components rapidly and non - destructively without supervision from experts once the instrument has been calibrated . The purpose of this study was to develop an optimal prediction model for determining the protein and oil contents of soybeans using a dispersive Raman spectroscopy method . In general , the crude oil content of soybeans is chemically determined using the Soxhlet extraction method , while the semimicro - Kjeldahl method and an auto protein analyzer have been used to assess crude protein content . In the present study , Raman spectra were measured in the 200–1800 cm−1 wavenumber range and partial least squares ( PLS ) analysis methods were used to develop optimal models for predicting the crude protein and oil contents of soybeans . The resultant PLS models that used the effective wavenumber regions determined by intermediate PLS ( iPLS ) method were better than those models developed using the entire wavenumber range under investigation . The Rp2 and SEP of the optimal PLS model for crude protein content were 0.916 and 0.636 % , respectively . Likewise , the Rp2 and SEP for crude oil content were 0.872 and 0.759 % , respectively . The result suggests that the conventional Raman techniques investigated in this study can be applied to the prediction of soybean crude protein and oil content .
author: Hoonsoo Lee, Byoung-Kwan Cho, Moon S. Kim, Wang-Hee Lee, Jagdish Tewari, Hanhong Bae, Soo-In Sohn, Hee-Youn Chi
publish year: 2013
creator: Hoonsoo Lee, Byoung-Kwan Cho, Moon S. Kim, Wang-Hee Lee, Jagdish Tewari, Hanhong Bae, Soo-In Sohn, Hee-Youn Chi
reference: Hoonsoo Lee, Byoung-Kwan Cho, Moon S. Kim, Wang-Hee Lee, Jagdish Tewari, Hanhong Bae, Soo-In Sohn, Hee-Youn Chi, "Prediction of crude protein and oil content of soybeans using Raman spectroscopy", Sensors and Actuators B: ChemicalVolume 185, August 2013, Elsevier B.V.
Source: Sensors and Actuators B: ChemicalVolume 185, August 2013, Elsevier B.V.
label: Article 1939

Evaluation of resistance to cassava mosaic disease in selected African cassava cultivars using combined molecular and greenhouse grafting tools
type: NamedIndividual
type: Article
has knowledge model: AssociationRule 1986
abstract: Cassava mosaic disease has a major impact on cassava production in sub - Saharan Africa . Cassava germplasm were screened for resistance to CMD by grafting tools . Eight of the 12 tested cultivars possessed the resistance gene CMD2 . The genetic background of cultivars is the key determinant in plants resisting CMD . Resistant cultivars identified in this research could help farmers control CMD damage . AbstractCassava mosaic disease ( CMD ) threatens cassava ( Manihot esculenta ) production in Africa . A total of 24 selected cultivars were screened against CMD using combined molecular and greenhouse grafting tools . Disease severity was recorded for 10 weeks after inoculation and the molecular markers associated with CMD2 were detected by PCR . CMD severity significantly differed ( P < 0.0001 ) among cultivars . Twelve cultivars were morphologically resistant and eight of these possessed CMD2 and four did not . These results suggest that there are several CMD - resistant cassava cultivars that could be recommended for on - farm production and for conservation and breeding programs .
author: Jerome Anani Houngue, Martine Zandjanakou-Tachin, Hermine Bille Ngalle, Justin S. Pita, Gilles Habib Todjro Cacaï, Sergine E. Ngatat, Joseph Martin Bell, Corneille Ahanhanzo
publish year: 2019
creator: Jerome Anani Houngue, Martine Zandjanakou-Tachin, Hermine Bille Ngalle, Justin S. Pita, Gilles Habib Todjro Cacaï, Sergine E. Ngatat, Joseph Martin Bell, Corneille Ahanhanzo
reference: Jerome Anani Houngue, Martine Zandjanakou-Tachin, Hermine Bille Ngalle, Justin S. Pita, Gilles Habib Todjro Cacaï, Sergine E. Ngatat, Joseph Martin Bell, Corneille Ahanhanzo, "Evaluation of resistance to cassava mosaic disease in selected African cassava cultivars using combined molecular and greenhouse grafting tools", Physiological and Molecular Plant PathologyVolume 105, January 2019, Elsevier B.V.
Source: Physiological and Molecular Plant PathologyVolume 105, January 2019, Elsevier B.V.
label: Article 1986

Automatic drought stress detection in grapevines without using conventional threshold values
type: NamedIndividual
type: Article
has knowledge model: Classifier 2129
abstract: AimsBecause the water status of grapevines strongly affects the quality of the grapes and resulting wine , automated and early drought stress detection is important . Plant measurements are very promising for detecting drought stress , but strongly depend on microclimatic changes . Therefore , conventional stress detection methods require threshold values which define when plants start sensing drought stress . There is however no unique method to define these values . In this study , we propose two techniques that overcome this limitation . MethodsTwo statistical methods were used to automatically distinguish between drought and microclimate effects , based on a short preceding full - irrigated period to extract plant behaviour under normal conditions : Unfold Principal Component Analysis ( UPCA ) and Functional Unfold Principal Component Analysis ( FUPCA ) . Both techniques aimed at detecting when measured sap flow rate or stem diameter variations in grapevine deviated from their normal behaviour due to drought stress . ResultsThe models based on sap flow rate had some difficulties to detect stress on days with low atmospheric demands , while those based on stem diameter variations did not show this limitation , but ceased detecting stress when the stem diameter levelled off after a period of severe shrinkage . Nevertheless , stress was successfully detected with both approaches days before visible symptoms appeared . ConclusionsUPCA and FUPCA based on plant indicators are therefore very promising for early stress detection .
author: Annelies Baert, Kris Villez, Kathy Steppe
publish year: 2013
creator: Annelies Baert, Kris Villez, Kathy Steppe
reference: Annelies Baert, Kris Villez, Kathy Steppe, "Automatic drought stress detection in grapevines without using conventional threshold values", Plant and Soil volume 369, pages439–452(2013) , Springer
Source: Plant and Soil volume 369, pages439–452(2013) , Springer
label: Article 2129

Effects of cadmium and salicylic acid on growth , spectral reflectance and photosynthesis of castor bean seedlings
type: NamedIndividual
type: Article
has knowledge model: Classifier 2166
abstract: Salicylic acid ( SA ) is a potent signaling molecule in plants and is involved in eliciting specific responses to biotic and abiotic stresses . The aim of this study is to investigate whether the exogenous application of SA can improve cadmium ( Cd ) induced inhibition of photosynthesis in castor bean ( Ricinus communis L. ) plants . The effects of SA and Cd on plant growth , spectral reflectance , pigment contents , chlorophyll fluorescence and gas exchange were examined in a hydroponic cultivation system . Results indicate that Cd exposure significantly decreased the dry biomass , photosynthetic rate ( Pn ) , stomatal conductance ( Gs ) , intercellular CO2 concentration ( Ci ) , pigment contents , quantum yield of PS II photochemistry ( Fv / Fm ) , and effective quantum yield of PS II ( ΦPS II ) in the plants . Pretreatment with SA alone reduced the biomass and Pn in castor bean plants , whereas pigment contents , Fv / Fm and ΦPS II remained unaffected . Reduced Gs , Ci and E , as well as increased stomatal limitation ( Ls ) and water use efficiency ( WUE ) , were observed in plants pretreated with 500 μM SA alone , whereas plants treated with 250 μM SA were unaffected . Under Cd stress , SA pretreatment led to a significant decrease in Pn , Gs , E , Ci , and chlorophyll contents ( Chl a , Chl b , Chl a+b , Car , Chl a / b ) , and an increase in Ls and WUE . Cd exposure enhanced spectral reflectance in the range 550–680 nm and 750–1,050 nm . It also decreased the normalized difference vegetation index ( chlNDI ) , the modified red edge simple ratio index ( mSR705 ) , the red edge position ( REP ) , water band index , and red / green ratio , whereas the structure independent pigment index ( SIPI ) was increased . Significant correlations ( P < 0.01 ) between spectral indices ( mSR705 , chlNDI , REP , red / green ratio ) and pigment contents . SA significantly worsened plant growth and photosynthesis in Cd - stressed castor bean plants , in which a stomatal limitati…
author: Caifeng Liu, Jiali Guo, Yanlan Cui, Tianfeng Lü, Xiaohuan Zhang, Gangrong Shi
publish year: 2011
creator: Caifeng Liu, Jiali Guo, Yanlan Cui, Tianfeng Lü, Xiaohuan Zhang, Gangrong Shi
reference: Caifeng Liu, Jiali Guo, Yanlan Cui, Tianfeng Lü, Xiaohuan Zhang, Gangrong Shi, "Effects of cadmium and salicylic acid on growth , spectral reflectance and photosynthesis of castor bean seedlings", Plant and Soil volume 344, pages131–141(2011) , Springer
Source: Plant and Soil volume 344, pages131–141(2011) , Springer
label: Article 2166

Analysis of bacterial communities in rhizosphere soil of healthy and diseased cotton ( Gossypium sp . ) at different plant growth stages
type: NamedIndividual
type: Article
has knowledge model: Classifier 2173
abstract: Bacterial communities in rhizosphere soil of healthy and diseased cotton ( infected by the pathogenic fungus Verticillium dahliae Kleb . ) were examined at different plant growth stages using T - RFLP and 16S rDNA clone library . At flowering and bolling , soil samples from the rhizosphere of healthy cotton had the highest richness , whereas the highest evenness was found in the rhizosphere of diseased cotton at boll opening . Acidobacteria and Proteobacteria were the main phyla in the cotton rhizosphere . Other phyla , including WS3 , Deinococcus - Thermus , Verrucomicrobia , Firmicutes , Bacteroidetes , Actinobacteria , Thermomicrobia , Gemmatimonadetes , Planctomycetes , Proteobacteria , Cyanobacteria and unclassified bacteria were also found in cotton rhizosphere soils . Deinococcus - Thermus and Firmicutes were only present in diseased cotton rhizospheres . PCA analysis based on the 16S rDNA clone library demonstrated that six sampling resolved three groups of assemblages .
author: Yan Zhang, Bing-Hai Du, Zhi-gang Jin, Zheng-hua Li, Hong-ning Song, Yan-Qin Ding
publish year: 2011
creator: Yan Zhang, Bing-Hai Du, Zhi-gang Jin, Zheng-hua Li, Hong-ning Song, Yan-Qin Ding
reference: Yan Zhang, Bing-Hai Du, Zhi-gang Jin, Zheng-hua Li, Hong-ning Song, Yan-Qin Ding, "Analysis of bacterial communities in rhizosphere soil of healthy and diseased cotton ( Gossypium sp . ) at different plant growth stages", Plant and Soil volume 339, pages447–455(2011) , Springer
Source: Plant and Soil volume 339, pages447–455(2011) , Springer
label: Article 2173

Fine root isotropy in Eucalyptus grandis plantations . Towards the prediction of root length densities from root counts on trench walls
type: NamedIndividual
type: Article
has knowledge model: Regressor 2183
abstract: The objectives of the study were to assess changes in fine root anisotropy and specific root lengths throughout the development of Eucalyptus grandis ( W. Hill ex Maiden ) plantations and to establish a predictive model of root length density ( RLD ) from root intercept counts on trench walls . Fine root densities ( < 1 mm in diameter ) were studied in 6- , 12- , 22- , 28- , 54- , 68- and 72-month - old E. grandis plantations established on deep Ferralsols in southern Brazil . Fine root intercepts were counted on 3 faces of 90–198 soil cubes ( 1 dm3 in volume ) in each stand and fine root lengths ( L ) were measured inside 576 soil cubes , sampled between the depths of 10 cm and 290 cm . The number of fine root intercepts was counted on one vertical face perpendicular to the planting row ( N t ) , one vertical face parallel to the planting row ( N l ) and one horizontal face ( N h ) , for each soil cube sampled . An overall isotropy of fine roots was shown by paired Student ’s t - tests between the numbers of fine roots intersecting each face of soil cubes at most stand ages and soil depths . Specific root lengths decreased with stand age in the upper soil layers and tended to increase in deep soil layers at the end of the rotation . A linear regression established between N t and L for all the soil cubes sampled accounted for 36 % of the variability of L. Such a regression computed for mean N t and L values at each sampling depth and stand age explained only 55 % of the variability , as a result of large differences in the relationship between L and N t depending on stand productivity . The equation RLD = 1.89*LAI*N t , where LAI was the stand leaf area index ( m2 m−2 ) and N t was expressed as the number of root intercepts per cm2 , made it possible to predict accurately ( R2 = 0.84 ) and without bias the mean RLDs ( cm cm−3 ) per depth in each stand , for the whole data set of 576 soil cubes sampled between 2 years of age and the end of the rotation .
author: Jérôme Maurice, Jean-Paul Laclau, Danilo Scorzoni Re, José Leonardo de Moraes Gonçalves, Yann Nouvellon, Jean-Pierre Bouillet, José Luiz Stape, Jacques Ranger, Maurel Behling, Jean-Louis Chopart
publish year: 2010
creator: Jérôme Maurice, Jean-Paul Laclau, Danilo Scorzoni Re, José Leonardo de Moraes Gonçalves, Yann Nouvellon, Jean-Pierre Bouillet, José Luiz Stape, Jacques Ranger, Maurel Behling, Jean-Louis Chopart
reference: Jérôme Maurice, Jean-Paul Laclau, Danilo Scorzoni Re, José Leonardo de Moraes Gonçalves, Yann Nouvellon, Jean-Pierre Bouillet, José Luiz Stape, Jacques Ranger, Maurel Behling, Jean-Louis Chopart, "Fine root isotropy in Eucalyptus grandis plantations . Towards the prediction of root length densities from root counts on trench walls", Plant and Soil volume 334, pages261–275(2010) , Springer
Source: Plant and Soil volume 334, pages261–275(2010) , Springer
label: Article 2183

Vegetation indices and NIR - SWIR spectral bands as a phenotyping tool for water status determination in soybean
type: NamedIndividual
type: Article
has knowledge model: Classifier 2297
abstract: Drought is one of the main limiting factors of soybean production . The great deal of time and effort that current available phenotyping methods demand hampers the selection of tolerant genotypes . Therefore , the development of techniques capable of determining the water status of plants in a fast and practical way may improve the ability to distinguish genotypes under water deficit conditions . The aim of this study was to correlate physiological variables such as relative water content and gas exchange measurements , with vegetation indices ( VIs ) and spectral bands in order to optimize tools for plant phenotyping . Two trials were carried out , one in a climatic chamber and one in the field . The soybean genotypes were submitted to water deficit and control ( irrigated ) conditions . The variables measured were relative water content , leaf temperature , photosynthesis , transpiration , stomatal conductance and internal CO2 content . The VIs NDWI ( 1000–1600 ) , NDWI ( 1000–2300 ) , NMDI , MSI and the spectral bands SWIR1600 , SWIR2300 , ρ1440 , ρ1920 , ρ1440+ρ1920 , ρ1920−ρ1440 and SWIR−ρ1440 were obtained using a hyperspectral sensor . According to the results , the physiological measurements , the VIs and the spectral bands were able to differentiate the water conditions to which the genotypes were submitted and , in some cases , the indices and bands were more sensitive than the physiological measures to detect genotype effect . All indices and bands were efficient in determining the water status of soybean plants . However , the SWIR indices were the most sensitive , allowing the differentiation of a greater number of genotypes with high accuracy .
author: P. Braga, L. G. T. Crusiol, M. R. Nanni, A. L. H. Caranhato, M. B. Fuhrmann, A. L. Nepomuceno, N. Neumaier, J. R. B. Farias, A. Koltun, L. S. A. Gonçalves, L. M. Mertz-Henning
publish year: 2021
creator: P. Braga, L. G. T. Crusiol, M. R. Nanni, A. L. H. Caranhato, M. B. Fuhrmann, A. L. Nepomuceno, N. Neumaier, J. R. B. Farias, A. Koltun, L. S. A. Gonçalves, L. M. Mertz-Henning
reference: P. Braga, L. G. T. Crusiol, M. R. Nanni, A. L. H. Caranhato, M. B. Fuhrmann, A. L. Nepomuceno, N. Neumaier, J. R. B. Farias, A. Koltun, L. S. A. Gonçalves, L. M. Mertz-Henning, "Vegetation indices and NIR - SWIR spectral bands as a phenotyping tool for water status determination in soybean", Precision Agriculture volume 22, pages249–266(2021) , Springer
Source: Precision Agriculture volume 22, pages249–266(2021) , Springer
label: Article 2297

A 3D white referencing method for soybean leaves based on fusion of hyperspectral images and 3D point clouds
type: NamedIndividual
type: Article
has knowledge model: Regressor 2298
abstract: In recent years , plant phenotyping technologies have been widely applied to evaluate complex plant traits such as morphology , physiology , ecology , biochemistry , tolerance , growth and yield . Hyperspectral / multispectral cameras , artificial lighting sources , mechanisms and computers together capture images of different species of plants . Due to the non - uniform intensity of lighting sources in different wavelengths , raw images need to be calibrated using white references . Flat white panels are typically scanned as a white reference . However , geometrical factors such as leaf tilt angles can not be calibrated by flat white references . In this publication , the effectiveness of using angled white reference to calibrate corresponding raw images was first demonstrated . Furthermore , a 3D white referencing library integrating different angles and spatial positions in the system of a hyperspectral camera and a Kinect V2 depth sensor was created . Thus , a pixel on the leaf surface can be calibrated by a point with the nearest tilt angle and spatial position in the 3D referencing library . The validating samples for this referencing library were soybean leaves grown in a greenhouse . The results showed that the reflectance spectra after 3D calibration were closer to the standard calibration ( flat leaf calibrated by flat white reference ) than the conventional flat white referencing calibration . Furthermore , the pixel - level normalized difference vegetation index ( NDVI ) distribution over the soybean leaf surface after 3D calibration was also closer to the standard calibration . This proposed 3D white referencing method had the potential to improve calibration quality of plant images . Integrating with LiDAR sensors , this new approach has an opportunity to be applied in field environments .
author: Libo Zhang, Jian Jin, Liangju Wang, Peikui Huang, Dongdong Ma
publish year: 2020
creator: Libo Zhang, Jian Jin, Liangju Wang, Peikui Huang, Dongdong Ma
reference: Libo Zhang, Jian Jin, Liangju Wang, Peikui Huang, Dongdong Ma, "A 3D white referencing method for soybean leaves based on fusion of hyperspectral images and 3D point clouds", Precision Agriculture volume 21, pages1173–1186(2020) , Springer
Source: Precision Agriculture volume 21, pages1173–1186(2020) , Springer
label: Article 2298

Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves
type: NamedIndividual
type: Article
has knowledge model: Classifier 2308
abstract: This paper investigated the possibility of discriminating tomato yellow leaf curl disease by a hyperspectral imaging technique . A hyperspecral imaging system collected hyperspectral images of both healthy and infected tomato leaves . The reflectance spectra , first derivative reflectance spectra and absolute reflectance difference spectra in the wavelength range of 500–1000 nm of both background and the leaf area were analyzed to select sensitive wavelengths and band ratios . 853 nm was selected to create a mask image for background segmentation , while 720 nm from the reflectance spectra , four peaks ( 560 , 575 , 712 , and 729 nm ) from the first derivative spectra and , four wavelengths with higher values ( 586 , 720 nm ) and lower values ( 690 , 840 nm ) in the absolute difference spectra were selected as a set of sensitive wavelengths . Four band ratio images ( 560/575 , 712/729 , 586/690 , and 720/840 nm ) were compared with four widely used vegetation indices ( VIs ) . 24 texture features were extracted using grey level co - occurrence matrix ( GLCM ) , respectively . The performance of each feature was evaluated by receiver operator characteristic ( ROC ) curve analysis . The best threshold values of each feature were calculated by Yonden ’s index . Mean value of correlation ( COR_MEAN ) extracted from the band ratio image ( 720/840 nm ) had the best performance , whose AUC value was 1.0 . The discrimination result for a validation set based on its best threshold value was 100 % . This research also demonstrated that multispectral images at 560 , 575 and 720 nm have a potential for detecting tomato yellow leaf curl virus infection in field applications .
author: Jinzhu Lu, Mingchuan Zhou, Yingwang Gao, Huanyu Jiang
publish year: 2018
creator: Jinzhu Lu, Mingchuan Zhou, Yingwang Gao, Huanyu Jiang
reference: Jinzhu Lu, Mingchuan Zhou, Yingwang Gao, Huanyu Jiang, "Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves", Precision Agriculture volume 19, pages379–394(2018) , Springer
Source: Precision Agriculture volume 19, pages379–394(2018) , Springer
label: Article 2308

Predicting cover crop biomass by lightweight UAS - based RGB and NIR photography : an applied photogrammetric approach
type: NamedIndividual
type: Article
has knowledge model: Regressor 2313
abstract: Easy - to - capture and robust plant status indicators are important factors when implementing precision agriculture techniques on fields . In this study , aerial red , green and blue color space ( RGB ) photography and near - infrared ( NIR ) photography was performed on an experimental field site with nine different cover crops . A lightweight unmanned aerial system ( UAS ) served as platform , consumer cameras as sensors . Photos were photogrammetrically processed to orthophotos and digital surface models ( DSMs ) . In a first validation step , the spatial precision of RGB orthophotos ( x and y , ± 0.1 m ) and DSMs ( z , ± 0.1 m ) was determined . Then , canopy cover ( CC ) , plant height ( PH ) , normalized differenced vegetation index ( NDVI ) , red edge inflection point ( REIP ) , and green red vegetation index ( GRVI ) were extracted . In a second validation step , the PHs derived from the DSMs were compared with ground truth ruler measurements . A strong linear relationship was observed ( R 2 = 0.80−0.84 ) . Finally , destructive biomass samples were taken and compared with the remotely - sensed characteristics . Biomass correlated best with plant height ( PH ) , and good approximations with linear regressions were found ( R 2 = 0.74 for four selected species , R 2 = 0.58 for all nine species ) . CC and the vegetation indices ( VIs ) showed less significant and less strong overall correlations , but performed well for certain species . It is therefore evident that the use of DSM - based PHs provides a feasible approach to a species - independent non - destructive biomass determination , where the performance of VIs is more species - dependent .
author: Lukas Roth, Bernhard Streit
publish year: 2018
creator: Lukas Roth, Bernhard Streit
reference: Lukas Roth, Bernhard Streit, "Predicting cover crop biomass by lightweight UAS - based RGB and NIR photography : an applied photogrammetric approach", Precision Agriculture volume 19, pages93–114(2018) , Springer
Source: Precision Agriculture volume 19, pages93–114(2018) , Springer
label: Article 2313

Spectral signatures of sugar beet leaves for the detection and differentiation of diseases
type: NamedIndividual
type: Article
has knowledge model: Classifier 2339
abstract: This study examines the potential of hyperspectral sensor systems for the non - destructive detection and differentiation of plant diseases . In particular , a comparison of three fungal leaf diseases of sugar beet was conducted in order to facilitate a simplified and reproducible data analysis method for hyperspectral vegetation data . Reflectance spectra ( 400–1050 nm ) of leaves infected with the fungal pathogens Cercospora beticola , Erysiphe betae , and Uromyces betae causing Cercospora leaf spot , powdery mildew and rust , respectively , were recorded repeatedly during pathogenesis with a spectro - radiometer and analyzed for disease - specific spectral signatures . Calculating the spectral difference and reflectance sensitivity for each wavelength emphasized regions of high interest in the visible and near infrared region of the spectral signatures . The best correlating spectral bands differed depending on the diseases . Spectral vegetation indices related to physiological parameters were calculated and correlated to the severity of diseases . The spectral vegetation indices Normalised Difference Vegetation Index ( NDVI ) , Anthocyanin Reflectance Index ( ARI ) and modified Chlorophyll Absorption Integral ( mCAI ) differed in their ability to assess the different diseases at an early stage of disease development , or even before first symptoms became visible . Results suggested that a distinctive differentiation of the three sugar beet diseases using spectral vegetation indices is possible using two or more indices in combination .
author: A.-K. Mahlein, U. Steiner, H.-W. Dehne, E.-C. Oerke
publish year: 2010
creator: A.-K. Mahlein, U. Steiner, H.-W. Dehne, E.-C. Oerke
reference: A.-K. Mahlein, U. Steiner, H.-W. Dehne, E.-C. Oerke, "Spectral signatures of sugar beet leaves for the detection and differentiation of diseases", Precision Agriculture volume 11, pages413–431(2010) , Springer
Source: Precision Agriculture volume 11, pages413–431(2010) , Springer
label: Article 2339

Prediction of yield and the contribution of legumes in legume - grass mixtures using field spectrometry
type: NamedIndividual
type: Article
has knowledge model: Regressor 2345
abstract: Productivity and botanical composition of legume - grass swards in rotation systems are important factors for successful arable farming in both organic and conventional farming systems . As these attributes vary considerably within a field , a non - destructive method of detection while doing other tasks would facilitate more targeted management of crops and nutrients in the soil – plant – animal system . Two pot experiments were conducted to examine the potential of field spectroscopy to assess total biomass and the proportions of legume , using binary mixtures and pure swards of grass and legumes . The spectral reflectance of swards was measured under artificial light conditions at a sward age ranging from 21 to 70 days . Total biomass was determined by modified partial least squares ( MPLS ) regression , stepwise multiple linear regression ( SMLR ) and the vegetation indices ( VIs ) simple ratio ( SR ) , normalized difference vegetation index ( NDVI ) , enhanced vegetation index ( EVI ) and red edge position ( REP ) . Modified partial least squares and SMLR gave the largest R 2 values ranging from 0.85 to 0.99 . Total biomass prediction by VIs resulted in R 2 values of 0.87–0.90 for swards with large leaf to stem ratios ; the greatest accuracy was for EVI . For more mature and open swards VI - based detection of biomass was not possible . The contribution of legumes to the sward could be determined at a constant biomass level by the VIs , but this was not possible when the level of biomass varied .
author: Sonja Biewer, Stefan Erasmi, Thomas Fricke, Michael Wachendorf
publish year: 2009
creator: Sonja Biewer, Stefan Erasmi, Thomas Fricke, Michael Wachendorf
reference: Sonja Biewer, Stefan Erasmi, Thomas Fricke, Michael Wachendorf, "Prediction of yield and the contribution of legumes in legume - grass mixtures using field spectrometry", Precision Agriculture volume 10, pages128–144(2009) , Springer
Source: Precision Agriculture volume 10, pages128–144(2009) , Springer
label: Article 2345

A broad - band leaf chlorophyll vegetation index at the canopy scale
type: NamedIndividual
type: Article
has knowledge model: Regressor 2349
abstract: An assessment of the sensitivity at the canopy scale to leaf chlorophyll concentration of the broad - band chlorophyll vegetation index ( CVI ) is carried out for a wide range of soils and crops conditions and for different sun zenith angles by the analysis of a large synthetic dataset obtained by using in the direct mode the coupled PROSPECT + SAILH leaf and canopy reflectance model . An optimized version ( OCVI ) of the CVI is proposed . A single correction factor is incorporated in the OCVI algorithm to take into account the different spectral behaviors due to crop and soil types , sensor spectral resolution and scene sun zenith angle . An estimate of the value of the correction factor and of the minimum leaf area index ( LAI ) value of applicability are given for each considered condition . The results of the analysis of the synthetic dataset indicated that the broad - band CVI index could be used as a leaf chlorophyll estimator for planophile crops in most soil conditions . Results indicated as well that , in principle , a single correction factor incorporated in the OCVI could take into account the different spectral behaviors due to crop and soil types , sensor spectral resolution and scene sun zenith angle .
author: M. Vincini, E. Frazzi, P. D’Alessio
publish year: 2008
creator: M. Vincini, E. Frazzi, P. D’Alessio
reference: M. Vincini, E. Frazzi, P. D’Alessio, "A broad - band leaf chlorophyll vegetation index at the canopy scale", Precision Agriculture volume 9, pages303–319(2008) , Springer
Source: Precision Agriculture volume 9, pages303–319(2008) , Springer
label: Article 2349

Multi - temporal wheat disease detection by multi - spectral remote sensing
type: NamedIndividual
type: Article
has knowledge model: Classifier 2356
abstract: For the implementation of site - specific fungicide applications , the spatio - temporal dynamics of crop diseases must be well known . Remote sensing can be a useful tool to monitor the heterogeneity of crop vitality within agricultural sites . However , the identification of fungal infections at an early growth stage is essential . This study examines the potential of multi - spectral remote sensing for a multi - temporal analysis of crop diseases . Within an experimental field , a 6 ha plot of winter wheat was grown , containing all possible infective stages of the powdery mildew ( Blumeria graminis ) and leaf rust ( Puccinia recondita ) pathogens . Three high - resolution remote sensing images were used to execute a spatio - temporal analysis of the infection dynamics . A decision tree , using mixture tuned matched filtering ( MTMF ) results and the Normalized Difference Vegetation Index ( NDVI ) , was applied to classify the data into areas showing different levels of disease severity . Classification results were compared to ground truth data . The classification accuracy of the first scene was only 56.8 % , whereas the scenes from May 28th and June 20th achieved considerably higher accuracies of 65.9 % and 88.6 % respectively . The results showed that high - resolution multi - spectral data are generally suitable to detect in - field heterogeneities of crop vigour but are only moderately suitable for early detection of crop infections .
author: Jonas Franke, Gunter Menz
publish year: 2007
creator: Jonas Franke, Gunter Menz
reference: Jonas Franke, Gunter Menz, "Multi - temporal wheat disease detection by multi - spectral remote sensing", Precision Agriculture volume 8, pages161–172(2007) , Springer
Source: Precision Agriculture volume 8, pages161–172(2007) , Springer
label: Article 2356

Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density
type: NamedIndividual
type: Article
has knowledge model: Classifier 2357
abstract: Using spectral reflectance to estimate crop status is a method suitable for developing sensors for site - specific agricultural applications . When developing spectral analysis methods , it is important to know the influence of different crop parameters on the spectral reflectance profile . The objective of this report was to present and evaluate a multivariate method for objective hyperspectral analysis in the examination of how different parts of the reflectance spectrum are affected by disease severity and above ground plant density . Data from two field experiments were used ; fungal disease severity assessments in wheat 1998 and above ground plant density measurements 2003 . The analysis method consisted of two steps : a pre - processing step where the data was normalized and a classification step for estimating the crop variable . Using only 12 % of the data as training data , the method resulted in coefficients of determination ( R 2 ) of 94.3 % for the disease severity data and 96.9 % for the plant density data . The hyperspectral analysis method presented could also be used to extract spectral signatures of disease severity and plant density using the experimental data . In general , two types of spectral signatures for both data sets , with respect to increasing disease severity and decreasing plant density , were observed ( 1 ) a flattening of the green reflectance peak together with a general decrease in reflectance in the near infrared region and , ( 2 ) a decrease of the shoulder of the near infrared reflectance plateau together with a general increase in the visible region between 550 and 750 nm .
author: A. Larsolle, H. Hamid Muhammed
publish year: 2007
creator: A. Larsolle, H. Hamid Muhammed
reference: A. Larsolle, H. Hamid Muhammed, "Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density", Precision Agriculture volume 8, pages37–47(2007) , Springer
Source: Precision Agriculture volume 8, pages37–47(2007) , Springer
label: Article 2357

Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self - organising maps
type: NamedIndividual
type: Article
has knowledge model: Classifier 2364
abstract: The objective of this research was to detect plant stress caused by disease infestation and to discriminate this type of stress from nutrient deficiency stress in field conditions using spectral reflectance information . Yellow Rust infected winter wheat plants were compared to nutrient stressed and healthy plants . In - field hyperspectral reflectance images were taken with an imaging spectrograph . A normalisation method based on reflectance and light intensity adjustments was applied . For achieving high performance stress identification , Self - Organising Maps ( SOMs ) and Quadratic Discriminant Analysis ( QDA ) were introduced . Winter wheat infected with Yellow Rust was successfully recognised from nutrient stressed and healthy plants . Overall performance using five wavebands was more than 99 % .
author: D. Moshou, C. Bravo, S. Wahlen, J. West, A. McCartney, J. De Baerdemaeker, H. Ramon
publish year: 2006
creator: D. Moshou, C. Bravo, S. Wahlen, J. West, A. McCartney, J. De Baerdemaeker, H. Ramon
reference: D. Moshou, C. Bravo, S. Wahlen, J. West, A. McCartney, J. De Baerdemaeker, H. Ramon, "Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self - organising maps", Precision Agriculture volume 7, pages149–164(2006) , Springer
Source: Precision Agriculture volume 7, pages149–164(2006) , Springer
label: Article 2364

Relationship Between Six Years of Corn Yields and Terrain Attributes
type: NamedIndividual
type: Article
has knowledge model: Regressor 2381
abstract: Crop yield , soil properties , and erosion are strongly related to terrain attributes . The objectives of our study were to examine the relationship between six years of corn ( Zea mays L. ) yield data and relative elevation , slope , and curvature , and to develop a linear regression model to describe the spatial patterns of corn yield for a 16 ha field in central Iowa , USA . Corn grain yield was measured in six crop years , and relative elevation was measured using a kinematic global positioning system . Slope and curvature were then determined using digital terrain analysis . Our data showed that in the four years with less than normal growing season precipitation , corn yield was negatively correlated with relative elevation , slope , and curvature . In the two years with greater than normal precipitation , yield was positively correlated with relative elevation and slope . A multiple linear regression model based on relative elevation , slope , and curvature was developed that predicted 78 % of the spatial variability of the average yield of the transect plots for the four dry years . This model also adequately identified the spatial patterns within the entire field for yield monitor data from 1997 , which was one of the dry years . The relationship between terrain attributes and corn yield spatial patterns may provide opportunities for implementing site - specific management .
author: Thomas C. Kaspar, Thomas S. Colvin, Daniel B. Jaynes, Douglas L. Karlen, David E. James, David W. Meek, Daniel Pulido, Howard Butler
publish year: 2003
creator: Thomas C. Kaspar, Thomas S. Colvin, Daniel B. Jaynes, Douglas L. Karlen, David E. James, David W. Meek, Daniel Pulido, Howard Butler
reference: Thomas C. Kaspar, Thomas S. Colvin, Daniel B. Jaynes, Douglas L. Karlen, David E. James, David W. Meek, Daniel Pulido, Howard Butler, "Relationship Between Six Years of Corn Yields and Terrain Attributes", Precision Agriculture volume 4, pages87–101(2003) , Springer
Source: Precision Agriculture volume 4, pages87–101(2003) , Springer
label: Article 2381

Mapping Grain Sorghum Yield Variability Using Airborne Digital Videography
type: NamedIndividual
type: Article
has knowledge model: Classifier 2387
abstract: Mapping crop yield variability is one important aspect of precision agriculture . Combine - mounted yield monitors are becoming widely available for measuring and mapping yields for different crops . This study was designed to assess airborne digital videography as a tool for mapping grain sorghum yields for precision farming . Color - infrared ( CIR ) imagery was acquired with a three - camera digital video imaging system from two grain sorghum fields in south Texas over the 1995 and 1996 growing seasons . The multispectral video data obtained during the bloom to soft dough stages of plant development were related to hand - harvested grain yields at sampling sites determined from unsupervised image classification maps of the two fields . Significant correlations were found between grain yields and the red band , the green band , and the normalized difference vegetation index ( NDVI ) . Regression equations were developed to describe the relations between grain yields and each of the three significant spectral variables using an exponential model and two segmented models . Multiple linear regression equations were also determined to relate grain yields to the three bands and NDVI . These equations were then used to estimate grain yields at each video image pixel within each field and to generate grain yield maps . Comparisons of the estimated average yields from the regression equations with the actual yields indicated that yield estimation errors from the equations ranged from 0.0 to 10.0 % in 1995 and from 0.2 to 7.3 % in 1996 for field 1 , and from 4.0 to 11.2 % in 1995 and 6.3 to 12.5 % in 1996 for field 2 . Although the equations developed for one field in a given year may not apply to the same field in any other year , the practical value of these relationships is for mapping within - field grain yield variations . The results from this study showed that airborne digital videography , combined with ground sampling , regression analysis , and image processing ,…
author: Chenghai Yang, Gerald L. Anderson
publish year: 2000
creator: Chenghai Yang, Gerald L. Anderson
reference: Chenghai Yang, Gerald L. Anderson, "Mapping Grain Sorghum Yield Variability Using Airborne Digital Videography", Precision Agriculture volume 2, pages7–23(2000) , Springer
Source: Precision Agriculture volume 2, pages7–23(2000) , Springer
label: Article 2387

Grain yield , evapotranspiration , and water - use efficiency of maize hybrids differing in drought tolerance
type: NamedIndividual
type: Article
has knowledge model: Regressor 2408
abstract: Adoption of drought - tolerant ( DT ) hybrids is a viable strategy for maize production in drought - prone environments . We conducted four - year field studies ( 2011–2014 ) to investigate yield , crop evapotranspiration ( ETc ) , and water - use efficiency ( WUE ) in one conventional ( N58L ) and one DT hybrid ( N59B - DT ) under three water regimes ( I100 , I75 , and I50 , where the subscripts were the percentage of irrigation applied relative to meeting full ETc ) and three plant densities . At I100 and I75 , N59B - DT did not show advantage in yield and WUE relative to N58L , however , at I50 it showed an advantage of 8.5 % and 10.5 % , respectively . At I100 and I75 , high plant density treatment had greater grain yield ( 9.1 % ) and WUE ( 9.4 % ) than low plant density . Comparing hybrids , N59B - DT had greater yield ( 5.9 % ) and WUE ( 7.3 % ) than N58L at high plant density . N59B - DT had large advantage over N58L in yield ( 18.0 % ) and WUE ( 26.2 % ) when the hybrids were grown under severe water deficit ( I50 ) and high plant density ( 9.9 plants m−2 ) . At I50 , increasing plant density reduced yield ( 14.1 % ) for N58L but did not affect yield for N59B - DT . On average , plant density had no effect on seasonal ETc but N59B - DT had more seasonal ETc than N58L at I100 and I75 . The results of this study indicate that DT hybrid was tolerant to high panting density . Planting a DT hybrid with a higher plant density may provide greater yield stability under water - limited conditions while also maintaining maximum yield potential when moisture is sufficient .
author: Baozhen Hao, Qingwu Xue, Thomas H. Marek, Kirk E. Jessup, Jacob D. Becker, Xiaobo Hou, Wenwei Xu, Edsel D. Bynum, Brent W. Bean, Paul D. Colaizzi, Terry A. Howell
publish year: 2019
creator: Baozhen Hao, Qingwu Xue, Thomas H. Marek, Kirk E. Jessup, Jacob D. Becker, Xiaobo Hou, Wenwei Xu, Edsel D. Bynum, Brent W. Bean, Paul D. Colaizzi, Terry A. Howell
reference: Baozhen Hao, Qingwu Xue, Thomas H. Marek, Kirk E. Jessup, Jacob D. Becker, Xiaobo Hou, Wenwei Xu, Edsel D. Bynum, Brent W. Bean, Paul D. Colaizzi, Terry A. Howell, "Grain yield , evapotranspiration , and water - use efficiency of maize hybrids differing in drought tolerance", Irrigation Science volume 37, pages25–34(2019) , Springer
Source: Irrigation Science volume 37, pages25–34(2019) , Springer
label: Article 2408

Statistical Analysis of Grain Yield in Iranian Cultivars of Barley ( Hordeum vulgare )
type: NamedIndividual
type: Article
has knowledge model: Regressor 2434
abstract: To determine the most important traits influencing grain yield of barley , 10 cultivars were evaluated in three field experiments in randomized block design with three replicates during 2013–2014 growing season . In each experiment , 10 yield component traits were evaluated . Following a combined analysis of variance , multivariate statistical analyses such as simple correlation coefficients , factor analysis , stepwise analysis , path analysis and cluster analysis were administered on combined means . The correlation coefficient analysis showed significant positive correlation of grain yield with grain filling period , biological yield , harvest index , number of seed / spike , number of seed / m2 , 1000-seed weight and number of spikes / m2 . Factor analysis divided the 11 measured variables into five factors . Factor 1 was affected by grain yield and grain filling period . Factor 2 was affected by number of spikes / m2 and number of seed . Factor 3 was associated with days to hardening and days to maturity . Factor 4 was influenced by number of seed / spike , number of spikes / m2 and 1000-seed weight . Fifth factor was strongly affected by biological yield and plant height . In stepwise regression analysis for grain yield as independent variable , two traits include biological yield and harvest index entered to regression model in four steps with R - square = 0.98 . Regression coefficient of both entered variables was positive and significant at 1 % probability level . The path coefficient analysis based on grain yield , as a dependent variable , implicated that biological yield had the highest positive direct effect on grain yield . Highest positive indirect effect on grain yield was related to harvest index through biological yield . Cluster analysis using Ward ’s method divide 10 investigated cultivars into three clusters . Cluster 1 specified to cultivars with the highest plant height , number of seed / spike and 1000-seed weight . Cluster 2 was specified to…
author: Seyyed Hamid Reza Ramazani, Moslem Abdipour
publish year: 2019
creator: Seyyed Hamid Reza Ramazani, Moslem Abdipour
reference: Seyyed Hamid Reza Ramazani, Moslem Abdipour, "Statistical Analysis of Grain Yield in Iranian Cultivars of Barley ( Hordeum vulgare )", Agricultural Research volume 8, pages239–246(2019) , Springer
Source: Agricultural Research volume 8, pages239–246(2019) , Springer
label: Article 2434

Empirical Analysis for Crop Yield Forecasting in India
type: NamedIndividual
type: Article
has knowledge model: Regressor 2438
abstract: Several factors , including weather vagaries , possess a serious threat to agricultural crop production in India and also are noteworthy risks to the economy . Crop yield depends on nutrition level of soils , fertilizer availability and cost , pest control , agro - meteorological input parameters like temperature , rainfall and other factors . Further , each particular crop needs specific growing weather conditions . Therefore , prognosticating crop yield is a challenging task for every nation . Statistical models are the most commonly used tools to forecast the crop yield , whereas statistical forecasting model for predicting dynamic behavior of crop yield should be able to take advantage not only of historical data of crop yield , but also the impact of various driving forces of the external environment . This paper describes both the linear regression and time - series models to predict crop yield efficiently and precisely . In particular , Bajra yield data for Alwar district of Rajasthan have been considered for empirical fitting of the models . Additionally , the selection of auxiliary variables , based on the knowledge of crop growth stages , has mediated the outperformance of time - series model .
author: S. Dharmaraja, Vidyottama Jain, Priyanka Anjoy, Hukum Chandra
publish year: 2020
creator: S. Dharmaraja, Vidyottama Jain, Priyanka Anjoy, Hukum Chandra
reference: S. Dharmaraja, Vidyottama Jain, Priyanka Anjoy, Hukum Chandra, "Empirical Analysis for Crop Yield Forecasting in India", Agricultural Research volume 9, pages132–138(2020) , Springer
Source: Agricultural Research volume 9, pages132–138(2020) , Springer
label: Article 2438

Expression of Tcchitinase - I gene in transgenic peanut ( Arachis hypogaea L. ) confers enhanced resistance against leaf spot and rust diseases
type: NamedIndividual
type: Article
has knowledge model: Classifier 2452
abstract: The disease - resistant transgenic peanut cv ICG 13942 plants were developed by using Tcchitinase - I gene . Agrobacterium tumefaciens strain LBA4404 harboring the binary vector ( pBinAR ) contains the chitinase ( Tcchitinase - I ) gene and neomycin phosphotransferase resistance ( nptII ) gene . The transformed shoots were developed on selection medium ( MMS + 0.5 mg / L IAA + 15 mg / L TDZ + 100 mg / L Kan + 250 mg / L Cefotaxime ) from deembryonated cotyledon ( DC ) explants . Established plantlets were screened for the presence of Tcchitinase - I and nptII genes . Stable integration and expression of the transgenes ( T0 ) were confirmed by using PCR , RT - PCR and Southern blot analyses . The transformation frequency 63.34 % was recorded . All the transformed ( T0 ) plants were found normal , flowered and set seeds . After selfing the T0 plants , a Mendelian inheritance pattern ( 3:1 ) for the transgene in T1 progeny is revealed . T1 transgenic peanut plants were evaluated for resistance against Cercospora arachidicola , C. personatum and Puccinia arachidis by infection with the microspores using detached leaf assay . These T1 plants have shown longer incubation , latent period and lower infection frequencies in comparison to non - transformed ( WT ) plants . The Tcchitinase - I gene expression in resistant transgenic plants was compared to that of a susceptible control . A significant negative correlation was recorded between chitinase activity and the frequency of infection to the three tested disease causing agents .
author: Rajinikanth Marka, Rama Swamy Nanna
publish year: 2021
creator: Rajinikanth Marka, Rama Swamy Nanna
reference: Rajinikanth Marka, Rama Swamy Nanna, "Expression of Tcchitinase - I gene in transgenic peanut ( Arachis hypogaea L. ) confers enhanced resistance against leaf spot and rust diseases", Plant Growth Regulation volume 93, pages53–63(2021) , Springer
Source: Plant Growth Regulation volume 93, pages53–63(2021) , Springer
label: Article 2452

Insights into the salt tolerance mechanism in barley ( Hordeum vulgare ) from comparisons of cultivars that differ in salt sensitivity
type: NamedIndividual
type: Article
has knowledge model: Regressor 2672
abstract: Although barley ( Hordeum vulgare L. ) is a salt - tolerant crop , the underlying physiological and molecular mechanisms of salt tolerance remain to be elucidated . Therefore , we investigated the response of salt - tolerant ( K305 ) and salt - sensitive ( I743 ) cultivars to salt stress at both physiological and molecular levels . Salt treatment increased xylem sap osmolarity , which was attributed primarily to a rise in Na+ and Cl− concentration ; enhanced accumulation of the ions in shoots ; and reduced plant growth more severely in I743 than K305 . The concentration of K+ in roots and shoots decreased during 8 h of salt treatment in both cultivars but with no marked difference between cultivars . Hence , the severe growth reduction in I743 is attributed to the elevated levels of ( mainly ) Na+ in shoots . Analysis of gene expression using quantitative RT - PCR showed that transcripts of K+-transporters ( HvHAK1 and HvAKT1 ) , vacuolar H+-ATPase and inorganic pyrophosphatase ( HvHVA/68 and HvHVP1 ) were more abundant in shoots of K305 than in shoots of I743 . Expression of HvHAK1 and Na+/H+ antiporters ( HvNHX1 , HvNHX3 and HvNHX4 ) was higher in roots of K305 than in I743 with prolonged exposure to salt . Taken together , these results suggest that the better performance of K305 compared to I743 during salt stress may be related to its greater ability to sequester Na+ into sub - cellular compartments and/or maintain K+ homeostasis .
author: Ayalew Ligaba, Maki Katsuhara
publish year: 2010
creator: Ayalew Ligaba, Maki Katsuhara
reference: Ayalew Ligaba, Maki Katsuhara, "Insights into the salt tolerance mechanism in barley ( Hordeum vulgare ) from comparisons of cultivars that differ in salt sensitivity", Journal of Plant Research volume 123, pages105–118(2010) , Springer
Source: Journal of Plant Research volume 123, pages105–118(2010) , Springer
label: Article 2672

Predicting vegetation water content in wheat using normalized difference water indices derived from ground measurements
type: NamedIndividual
type: Article
has knowledge model: Regressor 2673
abstract: Vegetation water content ( VWC ) is an important variable for both agriculture and forest fire management . Remote sensing technology offers an instantaneous and non - destructive method for VWC assessment provided we can relate in situ measurements of VWC to spectral reflectance in a reliable way . In this paper , based on radiative transfer models , three new normalized difference water indices ( NDWI ) are proposed for VWC [ fuel moisture content ( FMC ) , and equivalent water thickness ( EWT ) ] estimation , taking both leaf internal structure and dry matter content into account . Reflectance at 1,200 , 1,450 and 1,940 nm were selected and normalized with reflectance at 860 nm to establish three water indices , NDWI1200 , NDWI1450 and NDWI1940 . Good correlations were observed between FMC ( R 2 = 0.65–0.80 ) and EWT ( both at the leaf scale , R 2 = 0.75–0.81 for EWTL and at the canopy scale , R 2 = 0.80–0.83 for EWTC ) at various stages of wheat crop development .
author: Chaoyang Wu, Zheng Niu, Quan Tang, Wenjiang Huang
publish year: 2009
creator: Chaoyang Wu, Zheng Niu, Quan Tang, Wenjiang Huang
reference: Chaoyang Wu, Zheng Niu, Quan Tang, Wenjiang Huang, "Predicting vegetation water content in wheat using normalized difference water indices derived from ground measurements", Journal of Plant Research volume 122, pages317–326(2009) , Springer
Source: Journal of Plant Research volume 122, pages317–326(2009) , Springer
label: Article 2673

Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale
type: NamedIndividual
type: Article
has knowledge model: AssociationRule 2759
abstract: Herbicide resistance is a major weed control issue that threatens the sustainability of rice cropping systems . Its epidemiology at large scale is largely unknown . Several rice weed species have evolved resistant populations in Italy , including multiple resistant ones . The study objectives were to analyze the impact in Italian rice fields of major agronomic factors on the epidemiology of herbicide resistance and to generate a large - scale resistance risk map . The Italian Herbicide Resistance Working Group database was used to generate herbicide resistance maps . The distribution of resistant weed populations resulted as not homogeneous in the area studied , with two pockets where resistance had not been detected . To verify the situation , random sampling was done in the pockets where resistance had never been reported . Based on data from 230 Italian municipalities , three different statistics , stepwise discriminant analysis , stepwise logistic regression , and neural network , were used to correlate resistance distribution in the main Italian rice growing area with seeding type , rotation rate , and soil texture . Through the integration of complaint monitoring , mapping , and neural network analyses , we prove that a high risk of resistance evolution is associated with traditional rice cropping systems with intense monoculture rates and where water - seeding is widespread . This is the first study that determines the degree of association between herbicide resistance and a few important predictors at large scale . It also demonstrates that resistance is present in areas where it had never been reported through extensive complaint monitoring . However , these resistant populations cause medium - low density infestations , likely not alarming rice farmers . This highlights the importance of integrated agronomic techniques at cropping system level to prevent the diffusion and impact of herbicide resistance or limit it to an acceptable level . The identificatio…
author: Elisa Mascanzoni, Alessia Perego, Niccolò Marchi, Laura Scarabel, Silvia Panozzo, Aldo Ferrero, Marco Acutis, Maurizio Sattin
publish year: 2018
creator: Elisa Mascanzoni, Alessia Perego, Niccolò Marchi, Laura Scarabel, Silvia Panozzo, Aldo Ferrero, Marco Acutis, Maurizio Sattin
reference: Elisa Mascanzoni, Alessia Perego, Niccolò Marchi, Laura Scarabel, Silvia Panozzo, Aldo Ferrero, Marco Acutis, Maurizio Sattin, "Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale", Agronomy for Sustainable Development volume 38, Article number: 68 (2018) , Springer
Source: Agronomy for Sustainable Development volume 38, Article number: 68 (2018) , Springer
label: Article 2759

Advanced methods of plant disease detection . A review
type: NamedIndividual
type: Article
has knowledge model: Classifier 2771
abstract: Plant diseases are responsible for major economic losses in the agricultural industry worldwide . Monitoring plant health and detecting pathogen early are essential to reduce disease spread and facilitate effective management practices . DNA - based and serological methods now provide essential tools for accurate plant disease diagnosis , in addition to the traditional visual scouting for symptoms . Although DNA - based and serological methods have revolutionized plant disease detection , they are not very reliable at asymptomatic stage , especially in case of pathogen with systemic diffusion . They need at least 1–2 days for sample harvest , processing , and analysis . Here , we describe modern methods based on nucleic acid and protein analysis . Then , we review innovative approaches currently under development . Our main findings are the following : ( 1 ) novel sensors based on the analysis of host responses , e.g. , differential mobility spectrometer and lateral flow devices , deliver instantaneous results and can effectively detect early infections directly in the field ; ( 2 ) biosensors based on phage display and biophotonics can also detect instantaneously infections although they can be integrated with other systems ; and ( 3 ) remote sensing techniques coupled with spectroscopy - based methods allow high spatialization of results , these techniques may be very useful as a rapid preliminary identification of primary infections . We explain how these tools will help plant disease management and complement serological and DNA - based methods . While serological and PCR - based methods are the most available and effective to confirm disease diagnosis , volatile and biophotonic sensors provide instantaneous results and may be used to identify infections at asymptomatic stages . Remote sensing technologies will be extremely helpful to greatly spatialize diagnostic results . These innovative techniques represent unprecedented tools to render agriculture more sust…
author: Federico Martinelli, Riccardo Scalenghe, Salvatore Davino, Stefano Panno, Giuseppe Scuderi, Paolo Ruisi, Paolo Villa, Daniela Stroppiana, Mirco Boschetti, Luiz R. Goulart, Cristina E. Davis, Abhaya M. Dandekar
publish year: 2015
creator: Federico Martinelli, Riccardo Scalenghe, Salvatore Davino, Stefano Panno, Giuseppe Scuderi, Paolo Ruisi, Paolo Villa, Daniela Stroppiana, Mirco Boschetti, Luiz R. Goulart, Cristina E. Davis, Abhaya M. Dandekar
reference: Federico Martinelli, Riccardo Scalenghe, Salvatore Davino, Stefano Panno, Giuseppe Scuderi, Paolo Ruisi, Paolo Villa, Daniela Stroppiana, Mirco Boschetti, Luiz R. Goulart, Cristina E. Davis, Abhaya M. Dandekar, "Advanced methods of plant disease detection . A review", Agronomy for Sustainable Development volume 35, pages1–25(2015) , Springer
Source: Agronomy for Sustainable Development volume 35, pages1–25(2015) , Springer
label: Article 2771

Molecular detection and first report of Cucumber mosaic virus infecting ‘ Cavendish ’ banana plants in Ethiopia
type: NamedIndividual
type: Article
has knowledge model: Classifier 2799
abstract: Cucumber mosaic virus ( CMV ) is one of the most important viral pathogen limiting banana production globally . The aim of the present study was to test the presence of CMV infection in banana plants from Ethiopia . Dot immune - binding assay and RT - PCR of symptomatic leaves could detect CMV in 92 % and 100 % of the samples , respectively . Direct sequencing of the PCR product produced 292 bp long nucleotide sequence showing 96.23–96.58 % sequence identity at nucleotide level with isolates from Serbia ( KT270566 ) , Italy ( KU976469 ) , and others confirming CMV infection . The phylogenetic tree constructed at nucleotide level with MEGA 7.0 program revealed that the isolate belongs to subgroup IA . To the best of our knowledge , this is the first report of CMV infecting banana plants from Ethiopia .
author: Yohanis Kebede, S. Majumder
publish year: 2020
creator: Yohanis Kebede, S. Majumder
reference: Yohanis Kebede, S. Majumder, "Molecular detection and first report of Cucumber mosaic virus infecting ‘ Cavendish ’ banana plants in Ethiopia", Journal of Plant Diseases and Protection volume 127, pages417–420(2020) , Springer
Source: Journal of Plant Diseases and Protection volume 127, pages417–420(2020) , Springer
label: Article 2799

Occurrence and frequency of spot form and net form of net blotch disease of barley in Algeria
type: NamedIndividual
type: Article
has knowledge model: Classifier 2800
abstract: In Algeria , barley ( Hordeum vulgare L. ) is the second most cultivated cereal after wheat ; however , this crop is challenged by several foliar diseases . Net blotch is one of the most common diseases of barley in Algeria and is caused by the fungus Pyrenophora teres ( Died . ) Drechsler , which occurs in two forms : P. teres f. teres ( Ptt ) , causing the net form ( NFNB ) , and P.teres f. maculata ( Ptm ) , causing the spot form ( SFNB ) . Since the two forms are morphologically similar but genetically distinct , several PCR primer sets have been developed for their differentiation in the past . In this study , net blotch symptom types were monitored in 58 fields , through almost all cereal - growing areas in Algeria , then 212 P. teres mono - spore isolates were analyzed with respect to the occurrence of Ptt and Ptm by using two type - specific primer pairs . The results indicate that Ptt is the dominating type of net blotch in Algeria and was prevalent in almost all provinces surveyed , while Ptm was found less frequently . This is the first characterization of the Algerian net blotch population with the aim to distinguish both forms and , therefore , could contribute to a wider knowledge of the epidemics of this important plant pathogen .
author: Hamama-Iméne Lammari, Alexandra Rehfus, Gerd Stammler, Zine El Abidine Fellahi, Abdelkader Benbelkacem, Hamida Benslimane
publish year: 2020
creator: Hamama-Iméne Lammari, Alexandra Rehfus, Gerd Stammler, Zine El Abidine Fellahi, Abdelkader Benbelkacem, Hamida Benslimane
reference: Hamama-Iméne Lammari, Alexandra Rehfus, Gerd Stammler, Zine El Abidine Fellahi, Abdelkader Benbelkacem, Hamida Benslimane, "Occurrence and frequency of spot form and net form of net blotch disease of barley in Algeria", Journal of Plant Diseases and Protection volume 127, pages35–42(2020) , Springer
Source: Journal of Plant Diseases and Protection volume 127, pages35–42(2020) , Springer
label: Article 2800

Benefits of hyperspectral imaging for plant disease detection and plant protection : a technical perspective
type: NamedIndividual
type: Article
has knowledge model: Classifier 2815
abstract: The detection and identification of plant diseases is crucial for an appropriate and targeted application of plant protection measures in crop production . Recently , intensive research has been conducted to develop innovative and technology - based optical methods for plant disease detection . In contrast to common visual rating and detection methods , optical sensors are able to measure pathogen - induced changes in the plant physiology non - invasively and objectively . Several studies showed that especially hyperspectral sensors are valuable tools for disease detection , identification and quantification on different scales from the tissue to the canopy level . This review describes the basic principles of hyperspectral measurements and different types of available hyperspectral sensors . Possible applications of hyperspectral sensors on different scales for disease detection and plant protection are discussed and evaluated . The advantages and disadvantages on each particular scale , as well as the impact of external factors , such as : light , wind , viewing angle , for measurements in laboratories , greenhouses and fields , are critically assessed in order to support researchers and agriculture technicians . Additionally , a comprehensive literature review about the use of hyperspectral sensors on these different scales for plant disease detection reflects the possibilities of non - invasive measurement systems . This highlights advantages of hyperspectral sensors when investigating plant – pathogen interactions through multiple examples . By some approaches , detection before visible symptoms appear is feasible . The potential of hyperspectral sensors as a tool for disease identification and quantification , based on disease characteristic changes in the plants spectral signature , is discussed as well . The review is concluded with an overview on different data analysis methods , which are required to extract key information from gathered hyperspectral data…
author: Stefan Thomas, Matheus Thomas Kuska, David Bohnenkamp, Anna Brugger, Elias Alisaac, Mirwaes Wahabzada, Jan Behmann, Anne-Katrin Mahlein
publish year: 2018
creator: Stefan Thomas, Matheus Thomas Kuska, David Bohnenkamp, Anna Brugger, Elias Alisaac, Mirwaes Wahabzada, Jan Behmann, Anne-Katrin Mahlein
reference: Stefan Thomas, Matheus Thomas Kuska, David Bohnenkamp, Anna Brugger, Elias Alisaac, Mirwaes Wahabzada, Jan Behmann, Anne-Katrin Mahlein, "Benefits of hyperspectral imaging for plant disease detection and plant protection : a technical perspective", Journal of Plant Diseases and Protection volume 125, pages5–20(2018) , Springer
Source: Journal of Plant Diseases and Protection volume 125, pages5–20(2018) , Springer
label: Article 2815

Plant pest and disease diagnosis using electronic nose and support vector machine approach
type: NamedIndividual
type: Article
has knowledge model: Classifier 2829
abstract: In this paper , we evaluate the use of an electronic nose ( EN ) containing 13 conducting polymer gas sensors to discriminate between patterns of volatile organic compounds ( VOCs ) emitted by plants . The VOC patterns examined were produced by tomato , cucumber and pepper plants under both healthy and infected or infested conditions . Leaves from the plants were subjected to mechanical damage or pest and disease attacks ( i.e. spider mites infested or mildew infected ) and others were judged against undamaged healthy leaves . Support vector machines ( SVMs ) with linear , polynomial and Gaussian radial basis function ( RBF ) kernels were used to process and classify the raw data collected . The SVM illustrated an ability to discriminate between different VOC patterns and hence was able to classify correctly the infected leaves using the EN data . The results indicate that the array of 13 EN gas sensors can discriminate among VOC patterns from undamaged and artificially damaged leaves of the three plant species . This study demonstrates the potential application of such an EN technology coupled with suitable pattern recognition and signal processing methods to be used as a real time pest and disease detection system in the greenhouse environment .
author: Reza Ghaffari, Jullada Laothawornkitkul, Daciana Iliescu, Evor Hines, Mark Leeson, Richard Napier, Jason P Moore, Nigel D Paul, C Nicholas Hewitt, Jane E Taylor
publish year: 2012
creator: Reza Ghaffari, Jullada Laothawornkitkul, Daciana Iliescu, Evor Hines, Mark Leeson, Richard Napier, Jason P Moore, Nigel D Paul, C Nicholas Hewitt, Jane E Taylor
reference: Reza Ghaffari, Jullada Laothawornkitkul, Daciana Iliescu, Evor Hines, Mark Leeson, Richard Napier, Jason P Moore, Nigel D Paul, C Nicholas Hewitt, Jane E Taylor, "Plant pest and disease diagnosis using electronic nose and support vector machine approach", Journal of Plant Diseases and Protection volume 119, pages200–207(2012) , Springer
Source: Journal of Plant Diseases and Protection volume 119, pages200–207(2012) , Springer
label: Article 2829

Screening banana plants for banana bunchy top virus with primers specific to Indian isolates
type: NamedIndividual
type: Article
has knowledge model: Classifier 2845
abstract: The banana bunchy top disease is a serious threat to banana production . Caused by a DNA virus , its detection using specific primers is the only sensitive method to certify mother plants for tissue culture . Newer strains and variants of this virus were reported based on severity of symptoms and difference in the sizes of amplicons in a PCR assay . A search for newer strains or variants of this virus was therefore attempted in this study . We designed primers unique to the stem - loop region of DNA 2 from clones of this virus occurring in Indian Musa spp . These and other primers obtained from the literature were used to screen plants in the field and tissue cultured propagules . The results presented here report the occurrence of at least two distinct strains showing variation in sequences of DNA 2 . The likely origin of this variant sequence is discussed here .
author: J. Anandhi, C. Vijila, G. S. Viswanath, T. S. Lokeswari
publish year: 2007
creator: J. Anandhi, C. Vijila, G. S. Viswanath, T. S. Lokeswari
reference: J. Anandhi, C. Vijila, G. S. Viswanath, T. S. Lokeswari, "Screening banana plants for banana bunchy top virus with primers specific to Indian isolates", Journal of Plant Diseases and Protection volume 114, pages101–107(2007) , Springer
Source: Journal of Plant Diseases and Protection volume 114, pages101–107(2007) , Springer
label: Article 2845

Article 2927
type: NamedIndividual
type: Article
has knowledge model: Regressor 2927
abstract: Effects of Pseudomonas putida and Bacillus subtilis alone , and in combinations for the management of Meloidogyne incognita , Pectobacterium betavasculorum , and Rhizoctonia solani disease complex of beetroot ( Beta vulgaris L. ) , were studied . Application of P. putida or B. subtilis to plants with M. incognita or P. betavasculorum or R. solani singly or in combinations caused a significant increase in plant growth parameters and the activities of defense enzymes . A significant increase in chlorophyll fluorescence attributes , viz . , Fv / Fm , ɸPSII , qP , NPQ , and ETR were recorded in plants treated with P. putida or B. subtilis over pathogen - inoculated plants . Inoculation of P. putida results in a higher reduction in galling and nematode multiplication than B. subtilis . Maximum reduction in nematode multiplication and galling occurred when a mixture of P. putida and B. subtilis was used . Soft rot and root rot indices were 3 when Pectobacterium betavasculorum and Rhizoctonia solani were inoculated alone . The disease indices were rated 5 when these pathogens and M. incognita were inoculated in combinations . Inoculation of P. putida / B. subtilis with P. betavasculorum or R. solani reduced soft rot and root rot indices to 2 out of 3 , while the use of P. putida + B. subtilis reduced indices to 1 . Disease indices were reduced to 2–3 out of 5 , when P. putida + B. subtilis were used to plants inoculated with two or three pathogens . The principal component analysis showed significant correlations among the various studied attributes . Two principal components explained a total of 86.1 and 93.4 % of the overall data variability . Therefore , the use of P. putida together with B. subtilis had the potential for successful management of disease complex of beetroot .
author: Manzoor R. Khan, Zaki A. Siddiqui
publish year: 2019
creator: Manzoor R. Khan, Zaki A. Siddiqui
reference: Manzoor R. Khan, Zaki A. Siddiqui, "Potential of Pseudomonas putida , Bacillus subtilis , and their mixture on the management of Meloidogyne incognita , Pectobacterium betavasculorum , and Rhizoctonia solani disease complex of beetroot ( Beta vulgaris L. )", Egyptian Journal of Biological Pest Control volume 29, Article number: 73 (2019) , Springer
Source: Egyptian Journal of Biological Pest Control volume 29, Article number: 73 (2019) , Springer
title: Potential of Pseudomonas putida , Bacillus subtilis , and their mixture on the management of Meloidogyne incognita , Pectobacterium betavasculorum , and Rhizoctonia solani disease complex of beetroot ( Beta vulgaris L. )

A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model , Images and Vegetation Indices Captured by a Drone
type: NamedIndividual
type: Article
has knowledge model: Regressor 2947
abstract: Silage is the main feed in milk and ruminant meat production in Northern Europe . Novel drone - based remote sensing technology could be utilized in many phases of silage production , but advanced methods of utilizing these data are still developing . Grass swards are harvested three times in season , and fertilizer is applied similarly three times — once for each harvest when aiming at maximum yields . Timely information of the yield is thus necessary several times in a season for making decisions on harvesting time and rate of fertilizer application . Our objective was to develop and assess a novel machine learning technique for the estimation of canopy height and biomass of grass swards utilizing multispectral photogrammetric camera data . Variation in the studied crop stand was generated using six different nitrogen fertilizer levels and four harvesting dates . The sward was a timothy - meadow fescue mixture dominated by timothy . We extracted various features from the remote sensing data by combining an ultra - high resolution photogrammetric canopy height model ( CHM ) with a pixel size of 1.0 cm and red , green , blue ( RGB ) and near - infrared range intensity values and different vegetation indices ( VI ) extracted from orthophoto mosaics . We compared the performance of multiple linear regression ( MLR ) and a Random Forest estimator ( RF ) with different combinations of the CHM , RGB and VI features . The best estimation results with both methods were obtained by combining CHM and VI features and all three feature classes ( CHM , RGB and VI features ) . Both estimators provided equally accurate results . The Pearson correlation coefficients ( PCC ) and Root Mean Square Errors ( RMSEs ) of the estimations were at best 0.98 and 0.34 t / ha ( 12.70 % ) , respectively , for the dry matter yield ( DMY ) and 0.98 and 1.22 t / ha ( 11.05 % ) , respectively , for the fresh yield ( FY ) estimations . Our assessment of the sensitivity of the method with respect to …
author: Niko Viljanen, Eija Honkavaara, Roope Näsi, Teemu Hakala, Oiva Niemeläinen, Jere Kaivosoja
publish year: 2018
creator: Niko Viljanen, Eija Honkavaara, Roope Näsi, Teemu Hakala, Oiva Niemeläinen, Jere Kaivosoja
reference: Niko Viljanen, Eija Honkavaara, Roope Näsi, Teemu Hakala, Oiva Niemeläinen, Jere Kaivosoja, "A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model , Images and Vegetation Indices Captured by a Drone", Agriculture 2018, 8(5), 70; https://doi.org/10.3390/agriculture8050070
Source: Agriculture 2018, 8(5), 70; https://doi.org/10.3390/agriculture8050070
label: Article 2947

Soybean Yield Estimation and Its Components : A Linear Regression Approach
type: NamedIndividual
type: Article
has knowledge model: Regressor 2982
abstract: Soybean yield estimation is either based on yield monitors or agro - meteorological and satellite imagery data , but they present several limiting factors regarding on - farm decision level . Aware that machine learning approaches have been largely applied to estimate soybean yield and the availability of data regarding soybean yield and its components ( number of grains ( NG ) and thousand grains weight ( TGW ) ) , there is an opportunity to study their relationships . The objective was to explore the relationships between soybean yield and its components , generate equations to estimate yield and evaluate its prediction accuracy . The training dataset was composed of soybean yield and its components ’ data from 2010 to 2019 . Linear regression models based on NG , TGW and yield were fitted on the training dataset and applied to a validation dataset composed of 58 on - field collected samples . It was found that globally TGW and NG presented weak ( r = 0.50 ) and strong ( r = 0.92 ) linear relationships with yield , respectively . In addition to that , applying the fitted models to the validation dataset , model based on NG presented the highest accuracy , coefficient of determination ( R2 ) of 0.70 , mean absolute error ( MAE ) of 639.99 kg ha−1 and root mean squared error ( RMSE ) of 726.67 kg ha−1 .
author: Marcelo Chan Fu Wei, José Paulo Molin
publish year: 2020
creator: Marcelo Chan Fu Wei, José Paulo Molin
reference: Marcelo Chan Fu Wei, José Paulo Molin, "Soybean Yield Estimation and Its Components : A Linear Regression Approach", Agriculture 2020, 10(8), 348; https://doi.org/10.3390/agriculture10080348
Source: Agriculture 2020, 10(8), 348; https://doi.org/10.3390/agriculture10080348
label: Article 2982

Monitoring and Modelling Analysis of Maize ( Zea mays L. ) Yield Gap in Smallholder Farming in Ghana
type: NamedIndividual
type: Article
has knowledge model: Regressor 2984
abstract: Modelling and multiple linear regression were used to explore the reason for low maize yield in the Atebubu - Amantin and West Mamprusi Districts of Ghana , West Africa . The study evaluated maize yields on twenty farms against measures of soil fertility , agronomic attributes and soil water availability . Correlations between yield , soil fertility , rain , crop density , and weed biomass , were low , and no single factor could explain the low yields . A 50-year virtual experiment was then set up using the Agricultural Production Systems Simulator ( APSIM ) to explore the interactions between climate , crop management ( sowing date and nitrogen fertilization ) and rooting depth on grain yield and nitrate ( NO3-N ) dynamics . The analysis showed that a lack of optimal sowing dates that synchronize radiation , rainfall events and nitrogen ( N ) management with critical growth stages explained the low farm yields .
author: Eric Owusu Danquah, Yacob Beletse, Richard Stirzaker, Christopher Smith, Stephen Yeboah, Patricia Oteng-Darko, Felix Frimpong, Stella Ama Ennin
publish year: 2020
creator: Eric Owusu Danquah, Yacob Beletse, Richard Stirzaker, Christopher Smith, Stephen Yeboah, Patricia Oteng-Darko, Felix Frimpong, Stella Ama Ennin
reference: Eric Owusu Danquah, Yacob Beletse, Richard Stirzaker, Christopher Smith, Stephen Yeboah, Patricia Oteng-Darko, Felix Frimpong, Stella Ama Ennin, "Monitoring and Modelling Analysis of Maize ( Zea mays L. ) Yield Gap in Smallholder Farming in Ghana", Agriculture 2020, 10(9), 420; https://doi.org/10.3390/agriculture10090420
Source: Agriculture 2020, 10(9), 420; https://doi.org/10.3390/agriculture10090420
label: Article 2984

Site - Specific Forage Management of Sericea Lespedeza : Geospatial Technology - Based Forage Quality and Yield Enhancement Model Development
type: NamedIndividual
type: Article
has knowledge model: Regressor 2985
abstract: Site - specific forage management ( SSFM ) , comprising growth observation , impact assessment , and timely strategic response to small variations in sericea lespedeza ( SL ; Lespedeza cuneata ( Dum - Cours . ) G. Don ) production , has been envisioned as a life - changing approach for resource - poor ( R - P ) farmers in developing countries , assisting in the effective rearing of their small ruminants . The application of geospatial technologies , including geographic information systems ( GIS ) , remote sensing , global navigation satellite system , and information technology , can support SSFM but has not been widely used for site - specific forage management . From our previous studies , it appears that the entire range of condensed tannins of lespedeza , namely extractable condensed tannin ( ECT ) , fiber - bound condensed tannin ( FBCT ) , and protein - bound condensed tannin ( PBCT ) , as well as crude protein ( CP ) , are excellent for promoting small ruminant digestion and overall health . The goal of this study was to develop an SSFM strategy for SL to enhance animal production in areas of drought - prone , low pH , marginally infertile soils . To achieve this goal , study objectives were to : ( i ) develop statistical and artificial neural networks - based ( ANN ) models to identify if a sound correlation exists among forage growth environmental features and SL - ECT content ; ( ii ) determine suitability criteria , including climate , soil , and land use / land cover ( LULC ) , for mass scale production of SL and collect supporting environmental geospatial data ; and ( iii ) develop an automated geospatial model for SL growth suitability analysis in relation to optimal areas for its production in a case - study location . Telemetric data and individual climatologic parameters ( including minimum , maximum , and average temperature , humidity , dewpoint , soil temperatures at three depths , soil moisture , evapotranspiration , total solar radiation , and…
author: Sudhanshu S. Panda, Thomas H. Terrill, Ajit K. Mahapatra, Brian Kelly, Eric R. Morgan, Jan A. van Wyk
publish year: 2020
creator: Sudhanshu S. Panda, Thomas H. Terrill, Ajit K. Mahapatra, Brian Kelly, Eric R. Morgan, Jan A. van Wyk
reference: Sudhanshu S. Panda, Thomas H. Terrill, Ajit K. Mahapatra, Brian Kelly, Eric R. Morgan, Jan A. van Wyk, "Site - Specific Forage Management of Sericea Lespedeza : Geospatial Technology - Based Forage Quality and Yield Enhancement Model Development", Agriculture 2020, 10(9), 419; https://doi.org/10.3390/agriculture10090419
Source: Agriculture 2020, 10(9), 419; https://doi.org/10.3390/agriculture10090419
label: Article 2985

Time Series Prediction with Artificial Neural Networks : An Analysis Using Brazilian Soybean Production
type: NamedIndividual
type: Article
has knowledge model: Regressor 2991
abstract: Food production to meet human demand has been a challenge to society . Nowadays , one of the main sources of feeding is soybean . Considering agriculture food crops , soybean is sixth by production volume and the fourth by both production area and economic value . The grain can be used directly to human consumption , but it is highly used as a source of protein for animal production that corresponds 75 % of the total , or as oil and derived food products . Brazil and the US are the most important players responsible for more than 70 % of world production . Therefore , a reliable forecasting is essential for decision - makers to plan adequate policies to this important commodity and to establish the necessary logistical resources . In this sense , this study aims to predict soybean harvest area , yield , and production using Artificial Neural Networks ( ANN ) and compare with classical methods of Time Series Analysis . To this end , we collected data from a time series ( 1961–2016 ) regarding soybean production in Brazil . The results reveal that ANN is the best approach to predict soybean harvest area and production while classical linear function remains more effective to predict soybean yield . Moreover , ANN presents as a reliable model to predict time series and can help the stakeholders to anticipate the world soybean offer .
author: Emerson Rodolfo Abraham, João Gilberto Mendes dos Reis, Oduvaldo Vendrametto, Pedro Luiz de Oliveira Costa Neto, Rodrigo Carlo Toloi, Aguinaldo Eduardo de Souza, Marcos de Oliveira Morais
publish year: 2020
creator: Emerson Rodolfo Abraham, João Gilberto Mendes dos Reis, Oduvaldo Vendrametto, Pedro Luiz de Oliveira Costa Neto, Rodrigo Carlo Toloi, Aguinaldo Eduardo de Souza, Marcos de Oliveira Morais
reference: Emerson Rodolfo Abraham, João Gilberto Mendes dos Reis, Oduvaldo Vendrametto, Pedro Luiz de Oliveira Costa Neto, Rodrigo Carlo Toloi, Aguinaldo Eduardo de Souza, Marcos de Oliveira Morais, "Time Series Prediction with Artificial Neural Networks : An Analysis Using Brazilian Soybean Production", Agriculture 2020, 10(10), 475; https://doi.org/10.3390/agriculture10100475
Source: Agriculture 2020, 10(10), 475; https://doi.org/10.3390/agriculture10100475
label: Article 2991

Assessing the Influence of Soil Quality on Rainfed Wheat Yield
type: NamedIndividual
type: Article
has knowledge model: Classifier 2992
abstract: Soil quality assessment based on crop yields and identification of key indicators of it can be used for better management of agricultural production . In the current research , the weighted additive soil quality index ( SQIw ) , factor analysis ( FA ) , and multiple linear regression ( MLR ) are used to assess the soil quality of rainfed winter wheat fields with two soil orders on 53.20 km2 of agricultural land in western Iran . A total of 18 soil quality indicators were determined for 100 soil samples ( 0–20 cm depth ) from two soil orders ( Inceptisols and Entisols ) . The soil properties measured were : pH , soil texture , organic carbon ( OC ) , cation exchange capacity ( CEC ) , electrical conductivity ( EC ) , soil microbial respiration ( SMR ) , carbonate calcium equivalent ( CCE ) , soil porosity ( SP ) , bulk density ( BD ) , exchangeable sodium percentage ( ESP ) , mean weight diameter ( MWD ) , available potassium ( AK ) , total nitrogen ( TN ) , available phosphorus ( AP ) , available Fe ( AFe ) , available Zn ( AZn ) , available Mn ( AMn ) , and available Cu ( ACu ) . Wheat grain yield for all of the 100 sampling sites was also gathered . The SQIw was calculated using two weighting methods ( FA and MLR ) and maps were created using a digital soil mapping framework . The soil indicators determined for the minimum data set ( MDS ) were AK , clay , CEC , AP , SMR , and sand . The correlation between the MLR weighting technique ( SQIw - M ) and the rainfed wheat yield ( r = 0.62 ) was slightly larger than that the correlation of yield with the FA weighted technique ( SQIw - F ) ( r = 0.58 ) . Results showed that the means of both SQIw - M and SQIw - F and rainfed wheat yield for Inceptisols were higher than for Entisols , although these differences were not statistically significant . Both SQIw - M and SQIw - F showed that areas with Entisols had lower proportions of good soil quality grades ( Grades I and II ) , and higher proportions of poor soil quality …
author: Kamal Nabiollahi, Eskandari Heshmat, Amir Mosavi, Ruth Kerry, Mojtaba Zeraatpisheh, Ruhollah Taghizadeh-Mehrjardi
publish year: 2020
creator: Kamal Nabiollahi, Eskandari Heshmat, Amir Mosavi, Ruth Kerry, Mojtaba Zeraatpisheh, Ruhollah Taghizadeh-Mehrjardi
reference: Kamal Nabiollahi, Eskandari Heshmat, Amir Mosavi, Ruth Kerry, Mojtaba Zeraatpisheh, Ruhollah Taghizadeh-Mehrjardi, "Assessing the Influence of Soil Quality on Rainfed Wheat Yield", Agriculture 2020, 10(10), 469; https://doi.org/10.3390/agriculture10100469
Source: Agriculture 2020, 10(10), 469; https://doi.org/10.3390/agriculture10100469
label: Article 2992

A Memory - Based Learning Approach as Compared to Other Data Mining Algorithms for the Prediction of Soil Texture Using Diffuse Reflectance Spectra
type: NamedIndividual
type: Article
has knowledge model: Regressor 3002
abstract: Successful determination of soil texture using reflectance spectroscopy across Visible and Near - Infrared ( VNIR , 400–1200 nm ) and Short - Wave - Infrared ( SWIR , 1200–2500 nm ) ranges depends largely on the selection of a suitable data mining algorithm . The objective of this research was to explore whether the new Memory - Based Learning ( MBL ) method performs better than the other methods , namely : Partial Least Squares Regression ( PLSR ) , Support Vector Machine Regression ( SVMR ) and Boosted Regression Trees ( BRT ) . For this purpose , we chose soil texture ( contents of clay , silt and sand ) as testing attributes . A selected set of soil samples , classified as Technosols , were collected from brown coal mining dumpsites in the Czech Republic ( a total of 264 samples ) . Spectral readings were taken in the laboratory with a fiber optic ASD FieldSpec III Pro FR spectroradiometer . Leave - one - out cross - validation was used to optimize and validate the models . Comparisons were made in terms of the coefficient of determination ( R2cv ) and the Root Mean Square Error of Prediction of Cross - Validation ( RMSEPcv ) . Predictions of the three soil properties by MBL outperformed the accuracy of the remaining algorithms . We found that the MBL performs better than the other three methods by about 10 % ( largest R2cv and smallest RMSEPcv ) , followed by the SVMR . It should be pointed out that the other methods ( PLSR and BRT ) still provided reliable results . The study concluded that in this examined dataset , reflectance spectroscopy combined with the MBL algorithm is rapid and accurate , offers major efficiency and cost - saving possibilities in other datasets and can lead to better targeting of management interventions .
author: Asa Gholizadeh, Luboš Borůvka, Mohammadmehdi Saberioon, Radim Vašát
publish year: 2016
creator: Asa Gholizadeh, Luboš Borůvka, Mohammadmehdi Saberioon, Radim Vašát
reference: Asa Gholizadeh, Luboš Borůvka, Mohammadmehdi Saberioon, Radim Vašát, "A Memory - Based Learning Approach as Compared to Other Data Mining Algorithms for the Prediction of Soil Texture Using Diffuse Reflectance Spectra", Remote Sens. 2016, 8(4), 341; https://doi.org/10.3390/rs8040341
Source: Remote Sens. 2016, 8(4), 341; https://doi.org/10.3390/rs8040341
label: Article 3002

Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery : A Case Study in South Korea
type: NamedIndividual
type: Article
has knowledge model: Classifier 3048
abstract: The Geostationary Ocean Color Imager ( GOCI ) of the Communication , Ocean , and Meteorological Satellite ( COMS ) increases the chance of acquiring images with greater clarity eight times a day and is equipped with spectral bands suitable for monitoring crop yield in the national scale with a spatial resolution of 500 m. The objectives of this study were to classify nationwide paddy fields and to project rice ( Oryza sativa ) yield and production using the grid - based GRAMI - rice model and GOCI satellite products over South Korea from 2011 to 2014 . Solar insolation and temperatures were obtained from COMS and the Korea local analysis and prediction systems for model inputs , respectively . The paddy fields and transplanting dates were estimated by using Moderate Resolution Imaging Spectroradiometer ( MODIS ) reflectance and land cover products . The crop model was calibrated using observed yield data in 11 counties and was applied to 62 counties in South Korea . The overall accuracies of the estimated paddy fields using MODIS data ranged from 89.5 % to 90.2 % . The simulated rice yields statistically agreed with the observed yields with mean errors of −0.07 to +0.10 ton ha−1 , root - mean - square errors of 0.219 to 0.451 ton ha−1 , and Nash – Sutcliffe efficiencies of 0.241 to 0.733 in four years , respectively . According to paired t - tests ( α = 0.05 ) , the simulated and observed rice yields were not significantly different . These results demonstrate the possible development of a crop information delivery system that can classify land cover , simulate crop yield , and monitor regional crop production on a national scale .
author: Seungtaek Jeong, Jonghan Ko, Jong-Min Yeom
publish year: 2018
creator: Seungtaek Jeong, Jonghan Ko, Jong-Min Yeom
reference: Seungtaek Jeong, Jonghan Ko, Jong-Min Yeom, "Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery : A Case Study in South Korea", Remote Sens. 2018, 10(10), 1665; https://doi.org/10.3390/rs10101665
Source: Remote Sens. 2018, 10(10), 1665; https://doi.org/10.3390/rs10101665
label: Article 3048

UAV - Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning
type: NamedIndividual
type: Article
has knowledge model: Classifier 3067
abstract: A remote sensing technique was developed to detect citrus canker in laboratory conditions and was verified in the grove by utilizing an unmanned aerial vehicle ( UAV ) . In the laboratory , a hyperspectral ( 400–1000 nm ) imaging system was utilized for the detection of citrus canker in several disease development stages ( i.e. , asymptomatic , early , and late symptoms ) on Sugar Belle leaves and immature ( green ) fruit by using two classification methods : ( i ) radial basis function ( RBF ) and ( ii ) K nearest neighbor ( KNN ) . The same imaging system mounted on an UAV was used to detect citrus canker on tree canopies in the orchard . The overall classification accuracy of the RBF was higher ( 94 % , 96 % , and 100 % ) than the KNN method ( 94 % , 95 % , and 96 % ) for detecting canker in leaves . Among the 31 studied vegetation indices , the water index ( WI ) and the Modified Chlorophyll Absorption in Reflectance Index ( ARI and TCARI 1 ) more accurately detected canker in laboratory and in orchard conditions , respectively . Immature fruit was not a reliable tissue for early detection of canker . However , the proposed technique successfully distinguished the late stage canker - infected fruit with 92 % classification accuracy . The UAV - based technique achieved 100 % classification accuracy for identifying healthy and canker - infected trees .
author: Jaafar Abdulridha, Ozgur Batuman, Yiannis Ampatzidis
publish year: 2019
creator: Jaafar Abdulridha, Ozgur Batuman, Yiannis Ampatzidis
reference: Jaafar Abdulridha, Ozgur Batuman, Yiannis Ampatzidis, "UAV - Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning", Remote Sens. 2019, 11(11), 1373; https://doi.org/10.3390/rs11111373
Source: Remote Sens. 2019, 11(11), 1373; https://doi.org/10.3390/rs11111373
label: Article 3067

Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation
type: NamedIndividual
type: Article
has knowledge model: Regressor 3070
abstract: Crop yield estimation at a regional scale over a long period of time is of great significance to food security . In past decades , the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation . Optical remote sensing data are susceptible to cloud and rain , while synthetic aperture radar ( SAR ) can penetrate through clouds and has all - weather capabilities . This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data . The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images , which are retrieved by a water cloud model using SAR and optical data as input , into the crop model . In this study , SAR images were acquired by C - band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi - spectral instrument ( MSI ) for Hengshui city of Hebei province in China . Remote sensing data and ground data were all collected during the main growing season of winter wheat . Both the normalized difference vegetation index ( NDVI ) , derived from Sentinel-2 , and backscattering coefficients and polarimetric indicators , computed from Sentinel-1 , were used in the water cloud model to derive time series soil moisture ( SM ) images . To improve the prediction of crop yields at the field scale , we incorporated remotely sensed soil moisture into the World Food Studies ( WOFOST ) model using the Ensemble Kalman Filter ( EnKF ) algorithm . In general , the trend of soil moisture inversion was consistent with the ground measurements , with the coefficient of determination ( R2 ) equal to 0.45 , 0.53 , and 0.49 , respectively , and RMSE was 9.16 % , 7.43 % , and 8.53 % , respectively , for three observation dates . The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moistur…
author: Wen Zhuo, Jianxi Huang, Li Li, Xiaodong Zhang, Hongyuan Ma, Xinran Gao, Hai Huang, Baodong Xu, Xiangming Xiao
publish year: 2019
creator: Wen Zhuo, Jianxi Huang, Li Li, Xiaodong Zhang, Hongyuan Ma, Xinran Gao, Hai Huang, Baodong Xu, Xiangming Xiao
reference: Wen Zhuo, Jianxi Huang, Li Li, Xiaodong Zhang, Hongyuan Ma, Xinran Gao, Hai Huang, Baodong Xu, Xiangming Xiao, "Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation", Remote Sens. 2019, 11(13), 1618; https://doi.org/10.3390/rs11131618
Source: Remote Sens. 2019, 11(13), 1618; https://doi.org/10.3390/rs11131618
label: Article 3070

Monitoring Within - Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
type: NamedIndividual
type: Article
has knowledge model: Regressor 3089
abstract: Monitoring and prediction of within - field crop variability can support farmers to make the right decisions in different situations . The current advances in remote sensing and the availability of high resolution , high frequency , and free Sentinel-2 images improve the implementation of Precision Agriculture ( PA ) for a wider range of farmers . This study investigated the possibility of using vegetation indices ( VIs ) derived from Sentinel-2 images and machine learning techniques to assess corn ( Zea mays ) grain yield spatial variability within the field scale . A 22-ha study field in North Italy was monitored between 2016 and 2018 ; corn yield was measured and recorded by a grain yield monitor mounted on the harvester machine recording more than 20,000 georeferenced yield observation points from the study field for each season . VIs from a total of 34 Sentinel-2 images at different crop ages were analyzed for correlation with the measured yield observations . Multiple regression and two different machine learning approaches were also tested to model corn grain yield . The three main results were the following : ( i ) the Green Normalized Difference Vegetation Index ( GNDVI ) provided the highest R2 value of 0.48 for monitoring within - field variability of corn grain yield ; ( ii ) the most suitable period for corn yield monitoring was a crop age between 105 and 135 days from the planting date ( R4–R6 ) ; ( iii ) Random Forests was the most accurate machine learning approach for predicting within - field variability of corn yield , with an R2 value of almost 0.6 over an independent validation set of half of the total observations . Based on the results , within - field variability of corn yield for previous seasons could be investigated from archived Sentinel-2 data with GNDVI at crop stage ( R4–R6 ) .
author: Ahmed Kayad, Marco Sozzi, Simone Gatto, Francesco Marinello, Francesco Pirotti
publish year: 2019
creator: Ahmed Kayad, Marco Sozzi, Simone Gatto, Francesco Marinello, Francesco Pirotti
reference: Ahmed Kayad, Marco Sozzi, Simone Gatto, Francesco Marinello, Francesco Pirotti, "Monitoring Within - Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques", Remote Sens. 2019, 11(23), 2873; https://doi.org/10.3390/rs11232873
Source: Remote Sens. 2019, 11(23), 2873; https://doi.org/10.3390/rs11232873
label: Article 3089

Estimation of Leaf Chlorophyll a , b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance
type: NamedIndividual
type: Article
has knowledge model: Classifier 3134
abstract: Japanese horseradish ( wasabi ) grows in very specific conditions , and recent environmental climate changes have damaged wasabi production . In addition , the optimal culture methods are not well known , and it is becoming increasingly difficult for incipient farmers to cultivate it . Chlorophyll a , b and carotenoid contents , as well as their allocation , could be an adequate indicator in evaluating its production and environmental stress ; thus , developing an in situ method to monitor photosynthetic pigments based on reflectance could be useful for agricultural management . Besides original reflectance ( OR ) , five pre - processing techniques , namely , first derivative reflectance ( FDR ) , continuum - removed ( CR ) , de - trending ( DT ) , multiplicative scatter correction ( MSC ) , and standard normal variate transformation ( SNV ) , were compared to assess the accuracy of the estimation . Furthermore , five machine learning algorithms — random forest ( RF ) , support vector machine ( SVM ) , kernel - based extreme learning machine ( KELM ) , Cubist , and Stochastic Gradient Boosting ( SGB ) — were considered . To classify the samples under different pH or sulphur ion concentration conditions , the end of the red edge bands was effective for OR , FDR , DT , MSC , and SNV , while a green - peak band was effective for CR . Overall , KELM and Cubist showed high performance and incorporating pre - processing techniques was effective for obtaining estimated values with high accuracy . The best combinations were found to be DT – KELM for chl a ( RPD = 1.511–5.17 , RMSE = 1.23–3.62 μg cm−2 ) and chl a : b ( RPD = 0.73–3.17 , RMSE = 0.13–0.60 ) ; CR – KELM for chl b ( RPD = 1.92–5.06 , RMSE = 0.41–1.03 μg cm−2 ) and chl a : car ( RPD = 1.31–3.23 , RMSE = 0.26–0.50 ) ; SNV – Cubist for car ( RPD = 1.63–3.32 , RMSE = 0.31–1.89 μg cm−2 ) ; and DT – Cubist for chl : car ( RPD = 1.53–3.96 , RMSE = 0.27–0.74 ) .
author: Rei Sonobe, Hiroto Yamashita, Harumi Mihara, Akio Morita, Takashi Ikka
publish year: 2020
creator: Rei Sonobe, Hiroto Yamashita, Harumi Mihara, Akio Morita, Takashi Ikka
reference: Rei Sonobe, Hiroto Yamashita, Harumi Mihara, Akio Morita, Takashi Ikka, "Estimation of Leaf Chlorophyll a , b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance", Remote Sens. 2020, 12(19), 3265; https://doi.org/10.3390/rs12193265
Source: Remote Sens. 2020, 12(19), 3265; https://doi.org/10.3390/rs12193265
label: Article 3134

Deep Learning in Hyperspectral Image Reconstruction from Single RGB images — A Case Study on Tomato Quality Parameters
type: NamedIndividual
type: Article
has knowledge model: Regressor 3135
abstract: Hyperspectral imaging has many applications . However , the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields . Hyperspectral image reconstruction from a single RGB image fully addresses these two problems . The robust HSCNN - R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics , using tomato as a case study . Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera . The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index . Both predicted parameters showed very good agreement with corresponding “ ground truth ” values and high significance in an F test . This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes , underpinning the potential of the technology — recovering hyperspectral properties in high resolution — for real - world , real time monitoring applications in agriculture any beyond .
author: Jiangsan Zhao, Dmitry Kechasov, Boris Rewald, Gernot Bodner, Michel Verheul, Nicholas Clarke, Jihong Liu Clarke
publish year: 2020
creator: Jiangsan Zhao, Dmitry Kechasov, Boris Rewald, Gernot Bodner, Michel Verheul, Nicholas Clarke, Jihong Liu Clarke
reference: Jiangsan Zhao, Dmitry Kechasov, Boris Rewald, Gernot Bodner, Michel Verheul, Nicholas Clarke, Jihong Liu Clarke, "Deep Learning in Hyperspectral Image Reconstruction from Single RGB images — A Case Study on Tomato Quality Parameters", Remote Sens. 2020, 12(19), 3258; https://doi.org/10.3390/rs12193258
Source: Remote Sens. 2020, 12(19), 3258; https://doi.org/10.3390/rs12193258
label: Article 3135

Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf - Tissue Nutrients in Fields Managed with Straw Removal
type: NamedIndividual
type: Article
has knowledge model: AssociationRule 3158
abstract: The total or partial removal of sugarcane ( Saccharum spp . L. ) straw for bioenergy production may deplete soil quality and consequently affect negatively crop yield . Plants with lower yield potential may present lower concentration of leaf - tissue nutrients , which in turn changes light reflectance of canopy in different wavelengths . Therefore , vegetation indexes , such as the normalized difference vegetation index ( NDVI ) associated with concentration of leaf - tissue nutrients could be a useful tool for monitoring potential sugarcane yield changes under straw management . Two sites in São Paulo state , Brazil were utilized to evaluate the potential of NDVI for monitoring sugarcane yield changes imposed by different straw removal rates . The treatments were established with 0 % , 25 % , 50 % , and 100 % straw removal . The data used for the NDVI calculation was obtained using satellite images ( CBERS-4 ) and hyperspectral sensor ( FieldSpec Spectroradiometer , Malvern Panalytical , Almelo , Netherlands ) . Besides sugarcane yield , the concentration of the leaf - tissue nutrients ( N , P , K , Ca , and S ) were also determined . The NDVI efficiently predicted sugarcane yield under different rates of straw removal , with the highest performance achieved with NDVI derived from satellite images than hyperspectral sensor . In addition , leaf - tissue N and P concentrations were also important parameters to compose the prediction models of sugarcane yield . A prediction model approach based on data of NDVI and leaf - tissue nutrient concentrations may help the Brazilian sugarcane sector to monitor crop yield changes in areas intensively managed for bioenergy production .
author: Izaias Pinheiro Lisboa, Júnior Melo Damian, Maurício Roberto Cherubin, Pedro Paulo Silva Barros, Peterson Ricardo Fiorio, Carlos Clemente Cerri, Carlos Eduardo Pellegrino Cerri
publish year: 2018
creator: Izaias Pinheiro Lisboa, Júnior Melo Damian, Maurício Roberto Cherubin, Pedro Paulo Silva Barros, Peterson Ricardo Fiorio, Carlos Clemente Cerri, Carlos Eduardo Pellegrino Cerri
reference: Izaias Pinheiro Lisboa, Júnior Melo Damian, Maurício Roberto Cherubin, Pedro Paulo Silva Barros, Peterson Ricardo Fiorio, Carlos Clemente Cerri, Carlos Eduardo Pellegrino Cerri, "Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf - Tissue Nutrients in Fields Managed with Straw Removal", Agronomy 2018, 8(9), 196; https://doi.org/10.3390/agronomy8090196
Source: Agronomy 2018, 8(9), 196; https://doi.org/10.3390/agronomy8090196
label: Article 3158

Mapping the Depth - to - Soil pH Constraint , and the Relationship with Cotton and Grain Yield at the Within - Field Scale
type: NamedIndividual
type: Article
has knowledge model: Classifier 3176
abstract: Subsoil alkalinity is a common issue in the alluvial cotton - growing valleys of northern New South Wales ( NSW ) , Australia . Soil alkalinity can cause nutrient deficiencies and toxic effects , and inhibit rooting depth , which can have a detrimental impact on crop production . The depth at which a soil constraint is reached is important information for land managers , but it is difficult to measure or predict spatially . This study predicted the depth in which a pH ( H2O ) constraint ( > 9 ) was reached to a 1-cm vertical resolution to a 100-cm depth , on a 1070-hectare dryland cropping farm . Equal - area quadratic smoothing splines were used to resample vertical soil profile data , and a random forest ( RF ) model was used to produce the depth - to - soil pH constraint map . The RF model was accurate , with a Lin ’s Concordance Correlation Coefficient ( LCCC ) of 0.63–0.66 , and a Root Mean Square Error ( RMSE ) of 0.47–0.51 when testing with leave - one - site - out cross - validation . Approximately 77 % of the farm was found to be constrained by a strongly alkaline pH greater than 9 ( H2O ) somewhere within the top 100 cm of the soil profile . The relationship between the predicted depth - to - soil pH constraint map and cotton and grain ( wheat , canola , and chickpea ) yield monitor data was analyzed for individual fields . Results showed that yield increased when a soil pH constraint was deeper in the profile , with a good relationship for wheat , canola , and chickpea , and a weaker relationship for cotton . The overall results from this study suggest that the modelling approach is valuable in identifying the depth - to - soil pH constraint , and could be adopted for other important subsoil constraints , such as sodicity . The outputs are also a promising opportunity to understand crop yield variability , which could lead to improvements in management practices .
author: Patrick Filippi, Edward J. Jones, Bradley J. Ginns, Brett M. Whelan, Guy W. Roth, Thomas F.A. Bishop
publish year: 2019
creator: Patrick Filippi, Edward J. Jones, Bradley J. Ginns, Brett M. Whelan, Guy W. Roth, Thomas F.A. Bishop
reference: Patrick Filippi, Edward J. Jones, Bradley J. Ginns, Brett M. Whelan, Guy W. Roth, Thomas F.A. Bishop, "Mapping the Depth - to - Soil pH Constraint , and the Relationship with Cotton and Grain Yield at the Within - Field Scale", Agronomy 2019, 9(5), 251; https://doi.org/10.3390/agronomy9050251
Source: Agronomy 2019, 9(5), 251; https://doi.org/10.3390/agronomy9050251
label: Article 3176

Real - Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System
type: NamedIndividual
type: Article
has knowledge model: Classifier 3207
abstract: Strawberry cropping system relies heavily on proper disease management to maintain high crop yield . Powdery mildew , caused by Sphaerotheca macularis ( Wall . Ex Fries ) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70 % . Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time - consuming endeavor . The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real - time machine vision system . A global positioning system , two cameras , a custom image processing program , and a ruggedized laptop computer were utilized for development of the disease detection system . The custom image processing program was developed using color co - occurrence matrix - based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously . Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system . A total of 36 strawberry rows ( ~1.06 ha ) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system . The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system . Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00 , 3.42 , and 2.83 per row and root mean square error values of 4.12 , 3.71 , and 3.00 per row in field site - I , field site - II , and field site - III , respectively . The slight deviation in performance was likely caused by high wind speeds ( > 8 km h−1 ) , leaf overlapping , leaf angle , and presence of spider mite disease during field t…
author: Md Sultan Mahmud, Qamar U. Zaman, Travis J. Esau, Young K. Chang, G. W. Price, Balakrishnan Prithiviraj
publish year: 2020
creator: Md Sultan Mahmud, Qamar U. Zaman, Travis J. Esau, Young K. Chang, G. W. Price, Balakrishnan Prithiviraj
reference: Md Sultan Mahmud, Qamar U. Zaman, Travis J. Esau, Young K. Chang, G. W. Price, Balakrishnan Prithiviraj, "Real - Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System", Agronomy 2020, 10(7), 1027; https://doi.org/10.3390/agronomy10071027
Source: Agronomy 2020, 10(7), 1027; https://doi.org/10.3390/agronomy10071027
label: Article 3207

Dissecting the Contribution of Environmental Influences , Plant Phenology , and Disease Resistance to Improving Genomic Predictions for Fusarium Head Blight Resistance in Wheat
type: NamedIndividual
type: Article
has knowledge model: Regressor 3212
abstract: Environmental factors like temperature and humidity are presumed to greatly influence Fusarium head blight FHB infections in wheat . Anther retention AR , on the other hand , is a morphologically neutral trait that shares a common genetic basis with FHB resistance . In this study , our aims were to : ( i ) Evaluate two types of corrections of FHB severity scores , namely method-1 via linear regression on flowering time ( FT ) , and method-2 via a best - subset multiple linear regression analysis comprising FT plus accumulated thermal time variables ; and ( ii ) assess the performance of multi - trait genomic selection ( MT.GS ) models for FHB severity assisted by AR . The forward prediction scenarios where GS models were trained with data from the previous years revealed average prediction accuracies ( PA ) of 0.28 , 0.33 , and 0.36 for FHB severity scores that were uncorrected or corrected by method-1 and method-2 , respectively . FHB severity scores free from the influences of both environment and phenology seemed to be the most efficient trait to be predicted across different seasons . Average PA increments up to 1.9-fold were furthermore obtained for the MT.GS models , evidencing the feasibility of using AR as an assisting trait to improve the genomic selection of FHB resistance breeding lines .
author: Jose Moreno-Amores, Sebastian Michel, Franziska Löschenberger, Hermann Buerstmayr
publish year: 2020
creator: Jose Moreno-Amores, Sebastian Michel, Franziska Löschenberger, Hermann Buerstmayr
reference: Jose Moreno-Amores, Sebastian Michel, Franziska Löschenberger, Hermann Buerstmayr, "Dissecting the Contribution of Environmental Influences , Plant Phenology , and Disease Resistance to Improving Genomic Predictions for Fusarium Head Blight Resistance in Wheat", Agronomy 2020, 10(12), 2008; https://doi.org/10.3390/agronomy10122008
Source: Agronomy 2020, 10(12), 2008; https://doi.org/10.3390/agronomy10122008
label: Article 3212

Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf - Based Hyperspectral Reflectance
type: NamedIndividual
type: Article
has knowledge model: Classifier 3293
abstract: Monitoring of soybean genotypes is important because of intellectual property over seed technology , better management over seed genetics , and more efficient strategies for its agricultural production process . This paper aims at spectrally classifying soybean genotypes submitted to diverse water availability levels at different phenological stages using leaf - based hyperspectral reflectance . Leaf reflectance spectra were collected using a hyperspectral proximal sensor . Two experiments were conducted as field trials : one experiment was at Embrapa Soja in the 2016/2017 , 2017/2018 , and 2018/2019 cropping seasons , where ten soybean genotypes were grown under four water conditions ; and another experiment was in the experimental farm of Unoeste University in the 2018/2019 cropping season , where nine soybean genotypes were evaluated . The spectral data collected was divided into nine spectral datasets , comprising single and multiple cropping seasons ( from 2016 to 2019 ) , and two contrasting crop - growing environments . Principal component analysis , applied as an indicator of the explained variance of the reflectance spectra among genotypes within each spectral dataset , explained over 94 % of the spectral variance in the first three principal components . Linear discriminant analysis , used to obtain a model of classification of each reflectance spectra of soybean leaves into each soybean genotype , achieved accuracy between 61 % and 100 % in the calibration procedure and between 50 % and 100 % in the validation procedure . Misclassification was observed only between genotypes from the same genetic background . The results demonstrated the great potential of the spectral classification of soybean genotypes at leaf - scale , regardless of the phenological stages or water status to which plants were submitted .
author: Luis Guilherme Teixeira Crusiol, Marcos Rafael Nanni, Renato Herrig Furlanetto, Rubson Natal Ribeiro Sibaldelli, Everson Cezar, Liang Sun, José Salvador Simonetto Foloni, Liliane Marcia Mertz-Henning, Alexandre Lima Nepomuceno, Norman Neumaier, José Renato Bouças Farias
publish year: 2021
creator: Luis Guilherme Teixeira Crusiol, Marcos Rafael Nanni, Renato Herrig Furlanetto, Rubson Natal Ribeiro Sibaldelli, Everson Cezar, Liang Sun, José Salvador Simonetto Foloni, Liliane Marcia Mertz-Henning, Alexandre Lima Nepomuceno, Norman Neumaier, José Renato Bouças Farias
reference: Luis Guilherme Teixeira Crusiol, Marcos Rafael Nanni, Renato Herrig Furlanetto, Rubson Natal Ribeiro Sibaldelli, Everson Cezar, Liang Sun, José Salvador Simonetto Foloni, Liliane Marcia Mertz-Henning, Alexandre Lima Nepomuceno, Norman Neumaier, José Renato Bouças Farias, "Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf - Based Hyperspectral Reflectance", Remote Sens. 2021, 13(2), 172; https://doi.org/10.3390/rs13020172
Source: Remote Sens. 2021, 13(2), 172; https://doi.org/10.3390/rs13020172
label: Article 3293

High - Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data
type: NamedIndividual
type: Article
has knowledge model: Regressor 3303
abstract: Cloud computing and freely available , high - resolution satellite data have enabled recent progress in crop yield mapping at fine scales . However , extensive validation data at a matching resolution remain uncommon or infeasible due to data availability . This has limited the ability to evaluate different yield estimation models and improve understanding of key features useful for yield estimation in both data - rich and data - poor contexts . Here , we assess machine learning models ’ capacity for soybean yield prediction using a unique ground - truth dataset of high - resolution ( 5 m ) yield maps generated from combine harvester yield monitor data for over a million field - year observations across the Midwestern United States from 2008 to 2018 . First , we compare random forest ( RF ) implementations , testing a range of feature engineering approaches using Sentinel-2 and Landsat spectral data for 20- and 30-m scale yield prediction . We find that Sentinel-2-based models can explain up to 45 % of out - of - sample yield variability from 2017 to 2018 ( r2 = 0.45 ) , while Landsat models explain up to 43 % across the longer 2008–2018 period . Using discrete Fourier transforms , or harmonic regressions , to capture soybean phenology improved the Landsat - based model considerably . Second , we compare RF models trained using this ground - truth data to models trained on available county - level statistics . We find that county - level models rely more heavily on just a few predictors , namely August weather covariates ( vapor pressure deficit , rainfall , temperature ) and July and August near - infrared observations . As a result , county - scale models perform relatively poorly on field - scale validation ( r2 = 0.32 ) , especially for high - yielding fields , but perform similarly to field - scale models when evaluated at the county scale ( r2 = 0.82 ) . Finally , we test whether our findings on variable importance can inform a simple , generalizable framework…
author: Walter T. Dado, Jillian M. Deines, Rinkal Patel, Sang-Zi Liang, David B. Lobell
publish year: 2020
creator: Walter T. Dado, Jillian M. Deines, Rinkal Patel, Sang-Zi Liang, David B. Lobell
reference: Walter T. Dado, Jillian M. Deines, Rinkal Patel, Sang-Zi Liang, David B. Lobell, "High - Resolution Soybean Yield Mapping Across the US Midwest Using Subfield Harvester Data", Remote Sens. 2020, 12(21), 3471; https://doi.org/10.3390/rs12213471
Source: Remote Sens. 2020, 12(21), 3471; https://doi.org/10.3390/rs12213471
label: Article 3303

Prediction of End - Of - Season Tuber Yield and Tuber Set in Potatoes Using In - Season UAV - Based Hyperspectral Imagery and Machine Learning
type: NamedIndividual
type: Article
has knowledge model: Regressor 3311
abstract: Potato is the largest non - cereal food crop in the world . Timely estimation of end - of - season tuber production using in - season information can inform sustainable agricultural management decisions that increase productivity while reducing impacts on the environment . Recently , unmanned aerial vehicles ( UAVs ) have become increasingly popular in precision agriculture due to their flexibility in data acquisition and improved spatial and spectral resolutions . In addition , compared with natural color and multispectral imagery , hyperspectral data can provide higher spectral fidelity which is important for modelling crop traits . In this study , we conducted end - of - season potato tuber yield and tuber set predictions using in - season UAV - based hyperspectral images and machine learning . Specifically , six mainstream machine learning models , i.e. , ordinary least square ( OLS ) , ridge regression , partial least square regression ( PLSR ) , support vector regression ( SVR ) , random forest ( RF ) , and adaptive boosting ( AdaBoost ) , were developed and compared across potato research plots with different irrigation rates at the University of Wisconsin Hancock Agricultural Research Station . Our results showed that the tuber set could be better predicted than the tuber yield , and using the multi - temporal hyperspectral data improved the model performance . Ridge achieved the best performance for predicting tuber yield ( R2 = 0.63 ) while Ridge and PLSR had similar performance for predicting tuber set ( R2 = 0.69 ) . Our study demonstrated that hyperspectral imagery and machine learning have good potential to help potato growers efficiently manage their irrigation practices .
author: Chen Sun, Luwei Feng, Zhou Zhang, Yuchi Ma, Trevor Crosby, Mack Naber, Yi Wang
publish year: 2020
creator: Chen Sun, Luwei Feng, Zhou Zhang, Yuchi Ma, Trevor Crosby, Mack Naber, Yi Wang
reference: Chen Sun, Luwei Feng, Zhou Zhang, Yuchi Ma, Trevor Crosby, Mack Naber, Yi Wang, "Prediction of End - Of - Season Tuber Yield and Tuber Set in Potatoes Using In - Season UAV - Based Hyperspectral Imagery and Machine Learning", Sensors 2020, 20(18), 5293; https://doi.org/10.3390/s20185293
Source: Sensors 2020, 20(18), 5293; https://doi.org/10.3390/s20185293
label: Article 3311

Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre , Spain
type: NamedIndividual
type: Article
has knowledge model: Regressor 3320
abstract: Reliable methods for estimating wheat grain yield before harvest could help improve farm management and , if applied on a regional level , also help identify spatial factors that influence yield . Regional grain yield can be estimated using conventional methods , but the typical process is complex and labor - intensive . Here we describe the development of a streamlined approach using publicly accessible agricultural data , field - level yield , and remote sensing data from Sentinel-2 satellite to estimate regional wheat grain yield . We validated our method on wheat croplands in Navarre in northern Spain , which features heterogeneous topography and rainfall . First , this study developed stepwise multilinear equations to estimate grain yield based on various vegetation indices , which were measured at various phenological stages in order to determine the optimal timings . Second , the most suitable model was used to estimate grain yield in wheat parcels mapped from Sentinel-2 satellite images . We used a supervised pixel - based random forest classification and the estimates were compared to government - published post - harvest yield statistics . When tested , the model achieved an R2 of 0.83 in predicting grain yield at field level . The wheat parcels were mapped with an accuracy close to 86 % for both overall accuracy and compared to official statistics . Third , the validated model was used to explore potential relationships of the mapped per - parcel grain yield estimation with topographic features and rainfall by using geographically weighted regressions . Topographic features and rainfall together accounted for an average for 11 to 20 % of the observed spatial variation in grain yield in Navarre . These results highlight the ability of our method for estimating wheat grain yield before harvest and determining spatial factors that influence yield at the regional scale .
author: Joel Segarra, Jon González-Torralba, Íker Aranjuelo, Jose Luis Araus, Shawn C. Kefauver
publish year: 2020
creator: Joel Segarra, Jon González-Torralba, Íker Aranjuelo, Jose Luis Araus, Shawn C. Kefauver
reference: Joel Segarra, Jon González-Torralba, Íker Aranjuelo, Jose Luis Araus, Shawn C. Kefauver, "Estimating Wheat Grain Yield Using Sentinel-2 Imagery and Exploring Topographic Features and Rainfall Effects on Wheat Performance in Navarre , Spain", Remote Sens. 2020, 12(14), 2278; https://doi.org/10.3390/rs12142278
Source: Remote Sens. 2020, 12(14), 2278; https://doi.org/10.3390/rs12142278
label: Article 3320

An Integrated Approach to Unravelling Smallholder Yield Levels : The Case of Small Family Farms , Eastern Region , Ghana
type: NamedIndividual
type: Article
has knowledge model: Regressor 3326
abstract: Yield levels and the factors determining crop yields is an important strand of research on rainfed family farms . This is particularly true for Sub - Saharan Africa ( SSA ) , which reports some of the lowest crop yields . This also holds for Ghana , where actual yields of maize , the most important staple crop , are currently about only a third of achievable yields . Developing a comprehensive understanding of the factors underpinning these yield levels is key to improving them . Previous research endeavours on this frontier have been incumbered by the mono - disciplinary focus and/or limitations relating to spatial scales , which do not allow the actual interactions at the farm level to be explored . Using the sustainable livelihoods framework and , to a lesser extent , the induced innovation theory as inspiring theoretical frames , the present study employs an integrated approach of multiple data sources and methods to unravel the sources of current maize yield levels on smallholder farms in two farming villages in the Eastern region of Ghana . The study relies on farm and household survey data , remotely - sensed aerial photographs of maize fields and photo - elicitation interviews ( PEIs ) with farmers . These data cover the 2016 major farming season that spanned the period March – August . We found that the factors that contributed to current yield levels are not consistent across yield measures and farming villages . From principal component analysis ( PCA ) and multiple linear regression ( MLR ) , the timing of maize planting is the most important determinant of yield levels , explaining 25 % of the variance in crop cut yields in Akatawia , and together with household income level , explaining 32 % of the variance . Other statistically significant yield determinants include level of inorganic fertiliser applied , soil penetrability and phosphorus content , weed control and labour availability . However , this model only explains a third of the yields , which …
author: Ibrahim Wahab, Magnus Jirström, Ola Hall
publish year: 2020
creator: Ibrahim Wahab, Magnus Jirström, Ola Hall
reference: Ibrahim Wahab, Magnus Jirström, Ola Hall, "An Integrated Approach to Unravelling Smallholder Yield Levels : The Case of Small Family Farms , Eastern Region , Ghana", Agriculture 2020, 10(6), 206; https://doi.org/10.3390/agriculture10060206
Source: Agriculture 2020, 10(6), 206; https://doi.org/10.3390/agriculture10060206
label: Article 3326

Prediction of Winter Wheat Yield Based on Multi - Source Data and Machine Learning in China
type: NamedIndividual
type: Article
has knowledge model: Regressor 3340
abstract: Wheat is one of the main crops in China , and crop yield prediction is important for regional trade and national food security . There are increasing concerns with respect to how to integrate multi - source data and employ machine learning techniques to establish a simple , timely , and accurate crop yield prediction model at an administrative unit . Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys , remote sensing , or climate data . However , the effect of selecting different time window on yield prediction was still unknown . Thus , we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study . Firstly we developed a modeling framework to integrate climate data , remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine ( GEE ) platform . The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10 % . Support vector machine ( SVM ) , Gaussian process regression ( GPR ) , and random forest ( RF ) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study . In addition , we also found that different agricultural zones and temporal training settings affect prediction accuracy . The three models perform better as more winter wheat growing season information becomes available . Our findings highlight a potentially powerful tool to predict yield using multiple - source data and machine learning in other regions and for crops .
author: Jichong Han, Zhao Zhang, Juan Cao, Yuchuan Luo, Liangliang Zhang, Ziyue Li, Jing Zhang
publish year: 2020
creator: Jichong Han, Zhao Zhang, Juan Cao, Yuchuan Luo, Liangliang Zhang, Ziyue Li, Jing Zhang
reference: Jichong Han, Zhao Zhang, Juan Cao, Yuchuan Luo, Liangliang Zhang, Ziyue Li, Jing Zhang, "Prediction of Winter Wheat Yield Based on Multi - Source Data and Machine Learning in China", Remote Sens. 2020, 12(2), 236; https://doi.org/10.3390/rs12020236
Source: Remote Sens. 2020, 12(2), 236; https://doi.org/10.3390/rs12020236
label: Article 3340

Predicting Rice Pest Population Occurrence with Satellite - Derived Crop Phenology , Ground Meteorological Observation , and Machine Learning : A Case Study for the Central Plain of Thailand
type: NamedIndividual
type: Article
has knowledge model: Regressor 3347
abstract: The brown planthopper Nilaparvata lugens ( BPH ) is one of the most harmful insect pests in rice paddy fields , which causes considerable yield loss and consequent economic problems , particularly in the central plain of Thailand . Accurate and timely forecasting of pest population incidence would support farmers in planning effective mitigation . In this study , artificial neural network ( ANN ) , random forest ( RF ) and classic linear multiple regression ( MLR ) analyses were applied and compared to forecast the BPH population using weather and host - plant phenology factors during the crop dry season from 2006 to 2016 in the central plain of Thailand . Data from satellite earth observation was used to monitor crop phenology factors affecting BPH population density . An ANN model with integrated ground - based meteorological variables and satellite - derived host plant variables was more accurate for short - term forecasting of the peak abundance of BPH when compared with RF and MLR , according to a reasonably validating dataset ( RMSE of natural log - transformed ( ln ) BPH light trap catches = 1.686 , 1.737 , and 2.015 , respectively ) . This finding indicates that the utilization of ground meteorological observations , satellite - derived NDVI time series , and ANN have the potential to predict BPH population density in support of integrated pest management programs . We expect the results from this study can be applied in conjunction with the satellite - based rice monitoring system developed by the Geo - Informatic and Space Technology Development Agency of Thailand ( GISTDA ) to support an effective pest early warning system .
author: Sukij Skawsang, Masahiko Nagai, Nitin K. Tripathi, Peeyush Soni
publish year: 2019
creator: Sukij Skawsang, Masahiko Nagai, Nitin K. Tripathi, Peeyush Soni
reference: Sukij Skawsang, Masahiko Nagai, Nitin K. Tripathi, Peeyush Soni, "Predicting Rice Pest Population Occurrence with Satellite - Derived Crop Phenology , Ground Meteorological Observation , and Machine Learning : A Case Study for the Central Plain of Thailand", Appl. Sci. 2019, 9(22), 4846; https://doi.org/10.3390/app9224846
Source: Appl. Sci. 2019, 9(22), 4846; https://doi.org/10.3390/app9224846
label: Article 3347

Rapid Measurement of Soybean Seed Viability Using Kernel - Based Multispectral Image Analysis
type: NamedIndividual
type: Article
has knowledge model: Classifier 3362
abstract: Viability is an important quality factor influencing seed germination and crop yield . Current seed - viability testing methods rely on conventional manual inspections , which use destructive , labor - intensive and time - consuming measurements . The aim of this study is to distinguish between viable and nonviable soybean seeds , using a near - infrared ( NIR ) hyperspectral imaging ( HSI ) technique in a rapid and nondestructive manner . The data extracted from the NIR – HSI of viable and nonviable soybean seeds were analyzed using a partial least - squares discrimination analysis ( PLS - DA ) technique for classifying the viable and nonviable soybean seeds . Variable importance in projection ( VIP ) was used as a waveband selection method to develop a multispectral imaging model . Initially , the spectral profile of each pixel in the soybean seed images was subjected to PLS - DA analysis , which yielded a reasonable classification accuracy ; however , the pixel - based classification method was not successful for high accuracy detection for nonviable seeds . Another viability detection method was then investigated : a kernel image threshold method with an optimum - detection - rate strategy . The kernel - based classification of seeds showed over 95 % accuracy even when using only seven optimal wavebands selected through VIP . The results show that the proposed multispectral NIR imaging method is an effective and accurate nondestructive technique for the discrimination of soybean seed viability .
author: Insuck Baek, Dewi Kusumaningrum, Lalit Mohan Kandpal, Santosh Lohumi, Changyeun Mo, Moon S. Kim, Byoung-Kwan Cho
publish year: 2019
creator: Insuck Baek, Dewi Kusumaningrum, Lalit Mohan Kandpal, Santosh Lohumi, Changyeun Mo, Moon S. Kim, Byoung-Kwan Cho
reference: Insuck Baek, Dewi Kusumaningrum, Lalit Mohan Kandpal, Santosh Lohumi, Changyeun Mo, Moon S. Kim, Byoung-Kwan Cho, "Rapid Measurement of Soybean Seed Viability Using Kernel - Based Multispectral Image Analysis", Sensors 2019, 19(2), 271; https://doi.org/10.3390/s19020271
Source: Sensors 2019, 19(2), 271; https://doi.org/10.3390/s19020271
label: Article 3362

Potential of Visible and Near - Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves
type: NamedIndividual
type: Article
has knowledge model: Classifier 3375
abstract: Mulberry trees are an important crop for sericulture . Pests can affect the yield and quality of mulberry leaves . This study aims to develop a hyperspectral imaging system in visible and near - infrared ( NIR ) region ( 400–1700 nm ) for the rapid identification of Diaphania pyloalis larvae and its damage . The extracted spectra of five region of interests ( ROI ) , namely leaf vein , healthy mesophyll , slight damage , serious damage , and Diaphania pyloalis larva at 400–1000 nm ( visible range ) and 900–1700 nm ( NIR range ) , were used to establish a partial least squares discriminant analysis ( PLS - DA ) and least - squares support vector machines ( LS - SVM ) models . Successive projections algorithm ( SPA ) , uninformation variable elimination ( UVE ) , UVE - SPA , and competitive adaptive reweighted sampling were used for variable selection . The best models in distinguishing between leaf vein , healthy mesophyll , slight damage and serious damage , leaf vein , healthy mesophyll , and larva , slight damage , serious damage , and larva were all the SPA - LS - SVM models , based on the NIR range data , and their correct rate of prediction ( CRP ) were all 100.00 % . The best model for the identification of all five ROIs was the UVE - SPA - LS - SVM model , based on visible range data , which had the CRP value of 97.30 % . In summary , visible and near infrared hyperspectral imaging could distinguish Diaphania pyloalis larvae and their damage from leaf vein and healthy mesophyll in a rapid and non - destructive way .
author: Lingxia Huang, Liang Yang, Liuwei Meng, Jingyu Wang, Shaojia Li, Xiaping Fu, Xiaoqiang Du, Di Wu
publish year: 2018
creator: Lingxia Huang, Liang Yang, Liuwei Meng, Jingyu Wang, Shaojia Li, Xiaping Fu, Xiaoqiang Du, Di Wu
reference: Lingxia Huang, Liang Yang, Liuwei Meng, Jingyu Wang, Shaojia Li, Xiaping Fu, Xiaoqiang Du, Di Wu, "Potential of Visible and Near - Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves", Sensors 2018, 18(7), 2077; https://doi.org/10.3390/s18072077
Source: Sensors 2018, 18(7), 2077; https://doi.org/10.3390/s18072077
label: Article 3375

Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning
type: NamedIndividual
type: Article
has knowledge model: Classifier 3381
abstract: The detection of water stress in vineyards plays an integral role in the sustainability of high - quality grapes and prevention of devastating crop loses . Hyperspectral remote sensing technologies combined with machine learning provides a practical means for modelling vineyard water stress . In this study , we applied two ensemble learners , i.e. , random forest ( RF ) and extreme gradient boosting ( XGBoost ) , for discriminating stressed and non - stressed Shiraz vines using terrestrial hyperspectral imaging . Additionally , we evaluated the utility of a spectral subset of wavebands , derived using RF mean decrease accuracy ( MDA ) and XGBoost gain . Our results show that both ensemble learners can effectively analyse the hyperspectral data . When using all wavebands ( p = 176 ) , RF produced a test accuracy of 83.3 % ( KHAT ( kappa analysis ) = 0.67 ) , and XGBoost a test accuracy of 80.0 % ( KHAT = 0.6 ) . Using the subset of wavebands ( p = 18 ) produced slight increases in accuracy ranging from 1.7 % to 5.5 % for both RF and XGBoost . We further investigated the effect of smoothing the spectral data using the Savitzky - Golay filter . The results indicated that the Savitzky - Golay filter reduced model accuracies ( ranging from 0.7 % to 3.3 % ) . The results demonstrate the feasibility of terrestrial hyperspectral imagery and machine learning to create a semi - automated framework for vineyard water stress modelling .
author: Kyle Loggenberg, Albert Strever, Berno Greyling, Nitesh Poona
publish year: 2018
creator: Kyle Loggenberg, Albert Strever, Berno Greyling, Nitesh Poona
reference: Kyle Loggenberg, Albert Strever, Berno Greyling, Nitesh Poona, "Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning", Remote Sens. 2018, 10(2), 202; https://doi.org/10.3390/rs10020202
Source: Remote Sens. 2018, 10(2), 202; https://doi.org/10.3390/rs10020202
label: Article 3381

Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing : Analysis of RF , ANN , and SVM Regression Models
type: NamedIndividual
type: Article
has knowledge model: Regressor 3387
abstract: Leaf area index ( LAI ) is an important indicator of plant growth and yield that can be monitored by remote sensing . Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean ( multiple strains ) LAI inversion for the whole crop growth period and a single growth period . Random forest ( RF ) , artificial neural network ( ANN ) , and support vector machine ( SVM ) regression models were compared with a partial least - squares regression ( PLS ) model . The RF model yielded the highest precision , accuracy , and stability with V - R2 , SDR2 , V - RMSE , and SDRMSE values of 0.741 , 0.031 , 0.106 , and 0.005 , respectively , over the whole growth period based on STR sampling . The ANN model had the highest precision , accuracy , and stability ( 0.452 , 0.132 , 0.086 , and 0.009 , respectively ) over a single growth phase based on STR sampling . The precision , accuracy , and stability of the RF , ANN , and SVM models were improved by inclusion of STR sampling . The RF model is suitable for estimating LAI when sample plots and variation are relatively large ( i.e. , the whole growth period or more than one growth period ) . The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low ( i.e. , a single growth period ) .
author: Huanhuan Yuan, Guijun Yang, Changchun Li, Yanjie Wang, Jiangang Liu, Haiyang Yu, Haikuan Feng, Bo Xu, Xiaoqing Zhao, Xiaodong Yang
publish year: 2017
creator: Huanhuan Yuan, Guijun Yang, Changchun Li, Yanjie Wang, Jiangang Liu, Haiyang Yu, Haikuan Feng, Bo Xu, Xiaoqing Zhao, Xiaodong Yang
reference: Huanhuan Yuan, Guijun Yang, Changchun Li, Yanjie Wang, Jiangang Liu, Haiyang Yu, Haikuan Feng, Bo Xu, Xiaoqing Zhao, Xiaodong Yang, "Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing : Analysis of RF , ANN , and SVM Regression Models", Remote Sens. 2017, 9(4), 309; https://doi.org/10.3390/rs9040309
Source: Remote Sens. 2017, 9(4), 309; https://doi.org/10.3390/rs9040309
label: Article 3387

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features
type: NamedIndividual
type: Article
has knowledge model: Regressor 3450
abstract: The timely estimation of crop biomass and nitrogen content is a crucial step in various tasks in precision agriculture , for example in fertilization optimization . Remote sensing using drones and aircrafts offers a feasible tool to carry out this task . Our objective was to develop and assess a methodology for crop biomass and nitrogen estimation , integrating spectral and 3D features that can be extracted using airborne miniaturized multispectral , hyperspectral and colour ( RGB ) cameras . We used the Random Forest ( RF ) as the estimator , and in addition Simple Linear Regression ( SLR ) was used to validate the consistency of the RF results . The method was assessed with empirical datasets captured of a barley field and a grass silage trial site using a hyperspectral camera based on the Fabry - Pérot interferometer ( FPI ) and a regular RGB camera onboard a drone and an aircraft . Agricultural reference measurements included fresh yield ( FY ) , dry matter yield ( DMY ) and amount of nitrogen . In DMY estimation of barley , the Pearson Correlation Coefficient ( PCC ) and the normalized Root Mean Square Error ( RMSE% ) were at best 0.95 % and 33.2 % , respectively ; and in the grass DMY estimation , the best results were 0.79 % and 1.9 % , respectively . In the nitrogen amount estimations of barley , the PCC and RMSE% were at best 0.97 % and 21.6 % , respectively . In the biomass estimation , the best results were obtained when integrating hyperspectral and 3D features , but the integration of RGB images and 3D features also provided results that were almost as good . In nitrogen content estimation , the hyperspectral camera gave the best results . We concluded that the integration of spectral and high spatial resolution 3D features and radiometric calibration was necessary to optimize the accuracy .
author: Roope Näsi, Niko Viljanen, Jere Kaivosoja, Katja Alhonoja, Teemu Hakala, Lauri Markelin, Eija Honkavaara
publish year: 2018
creator: Roope Näsi, Niko Viljanen, Jere Kaivosoja, Katja Alhonoja, Teemu Hakala, Lauri Markelin, Eija Honkavaara
reference: Roope Näsi, Niko Viljanen, Jere Kaivosoja, Katja Alhonoja, Teemu Hakala, Lauri Markelin, Eija Honkavaara, "Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features", Remote Sens. 2018, 10(7), 1082; https://doi.org/10.3390/rs10071082
Source: Remote Sens. 2018, 10(7), 1082; https://doi.org/10.3390/rs10071082
label: Article 3450

Improving Unmanned Aerial Vehicle Remote Sensing - Based Rice Nitrogen Nutrition Index Prediction with Machine Learning
type: NamedIndividual
type: Article
has knowledge model: Regressor 3465
abstract: Optimizing nitrogen ( N ) management in rice is crucial for China ’s food security and sustainable agricultural development . Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status , which may be used to guide the in - season site - specific N recommendations . The fixed - wing unmanned aerial vehicle ( UAV ) -based remote sensing is a low - cost , easy - to - operate technology for collecting spectral reflectance imagery , an important data source for precision N management . The relationships between many vegetation indices ( VIs ) derived from spectral reflectance data and crop parameters are known to be nonlinear . As a result , nonlinear machine learning methods have the potential to improve the estimation accuracy . The objective of this study was to evaluate five different approaches for estimating rice ( Oryza sativa L. ) aboveground biomass ( AGB ) , plant N uptake ( PNU ) , and N nutrition index ( NNI ) at stem elongation ( SE ) and heading ( HD ) stages in Northeast China : ( 1 ) single VI ( SVI ) ; ( 2 ) stepwise multiple linear regression ( SMLR ) ; ( 3 ) random forest ( RF ) ; ( 4 ) support vector machine ( SVM ) ; and ( 5 ) artificial neural networks ( ANN ) regression . The results indicated that machine learning methods improved the NNI estimation compared to VI - SLR and SMLR methods . The RF algorithm performed the best for estimating NNI ( R2 = 0.94 ( SE ) and 0.96 ( HD ) for calibration and 0.61 ( SE ) and 0.79 ( HD ) for validation ) . The root mean square errors ( RMSEs ) were 0.09 , and the relative errors were < 10 % in all the models . It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing . The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management . More studies are needed to further im…
author: Hainie Zha, Yuxin Miao, Tiantian Wang, Yue Li, Jing Zhang, Weichao Sun, Zhengqi Feng, Krzysztof Kusnierek
publish year: 2020
creator: Hainie Zha, Yuxin Miao, Tiantian Wang, Yue Li, Jing Zhang, Weichao Sun, Zhengqi Feng, Krzysztof Kusnierek
reference: Hainie Zha, Yuxin Miao, Tiantian Wang, Yue Li, Jing Zhang, Weichao Sun, Zhengqi Feng, Krzysztof Kusnierek, "Improving Unmanned Aerial Vehicle Remote Sensing - Based Rice Nitrogen Nutrition Index Prediction with Machine Learning", Remote Sens. 2020, 12(2), 215; https://doi.org/10.3390/rs12020215
Source: Remote Sens. 2020, 12(2), 215; https://doi.org/10.3390/rs12020215
label: Article 3465

Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid - Growth Stages
type: NamedIndividual
type: Article
has knowledge model: Regressor 3473
abstract: Predicting the grain yield during early to mid - growth stages is important for initial diagnosis of rice and quantitative regulation of topdressing . In this study , we conducted four experiments using different nitrogen ( N ) application rates ( 0–400 kg N∙ha−1 ) in three Japonica rice cultivars ( Wuyunjing24 , Ningjing4 , and Lianjing7 ) grown in Jiangsu province , Eastern China , from 2015–2016 . Spectral reflectance data were collected multiple times during early to mid - growth stages using an active mounted sensor ( RapidScan CS-45 , Holland Scientific Inc. , Lincoln , NE , USA ) . Data were then used to calculate optimal vegetation indexes ( normalized difference red edge , NDRE ; normalized difference vegetation index , NDVI ; ratio vegetation index , RVI ; red - edge ratio vegetation index , RERVI ) , which were used to develop a dynamic change model and in - season grain yield prediction model . The NDRE index was more stable than other indexes ( NDVI , RVI , RERVI ) , showing less standard deviation at the same N fertilizer rate . The R2 of the relationships between leaf area index ( LAI ) , plant nitrogen accumulation ( PNA ) , and NDRE also increased compared to other indexes . These findings suggest that NDRE is suitable for analysis of paddy rice N nutrition . According to real - time series changes in NDRE , the resulting dynamic model followed a sigmoid curve , with a coefficient of determination ( R2 ) > 0.9 and relative root - mean - square error < 5 % . Moreover , the feature platform value ( saturation value , SV ) of the NDRE - based model accurately predicted the differences between treatments and the final grain yield levels . R2 values of the relationship between SV and yield were > 0.7 . For every 0.1 increase in SV , grain yield increased by 3608.1 kg·ha−1 . Overall , our new dynamic model effectively predicted grain yield at stem elongation and booting stages , providing real - time crop N nutrition data for management of N fertilizer to…
author: Ke Zhang, Xiaokang Ge, Pengcheng Shen, Wanyu Li, Xiaojun Liu, Qiang Cao, Yan Zhu, Weixing Cao, Yongchao Tian
publish year: 2019
creator: Ke Zhang, Xiaokang Ge, Pengcheng Shen, Wanyu Li, Xiaojun Liu, Qiang Cao, Yan Zhu, Weixing Cao, Yongchao Tian
reference: Ke Zhang, Xiaokang Ge, Pengcheng Shen, Wanyu Li, Xiaojun Liu, Qiang Cao, Yan Zhu, Weixing Cao, Yongchao Tian, "Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid - Growth Stages", Remote Sens. 2019, 11(4), 387; https://doi.org/10.3390/rs11040387
Source: Remote Sens. 2019, 11(4), 387; https://doi.org/10.3390/rs11040387
label: Article 3473

Partial Least Square Discriminant Analysis Based on Normalized Two - Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets
type: NamedIndividual
type: Article
has knowledge model: Classifier 3475
abstract: In recent decades , rice disease co - epidemics have caused tremendous damage to crop production in both China and Southeast Asia . A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate - resolution imagery . However , the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data . The aims of this study were ( 1 ) to develop a set of normalized two - stage vegetation indices ( VIs ) for characterizing the progressive development of different diseases with rice ; ( 2 ) to explore the performance of combined normalized two - stage VIs in partial least square discriminant analysis ( PLS - DA ) ; and ( 3 ) to map and evaluate the damage caused by rice diseases at fine spatial scales , for the first time using bi - temporal , high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution . Our findings suggest that the primary biophysical parameters caused by different disease ( e.g. , changes in leaf area , pigment contents , or canopy morphology ) can be captured using combined normalized two - stage VIs . PLS - DA was able to classify rice diseases at a sub - field scale , with an overall accuracy of 75.62 % and a Kappa value of 0.47 . The approach was successfully applied during a typical co - epidemic outbreak of rice dwarf ( Rice dwarf virus , RDV ) , rice blast ( Magnaporthe oryzae ) , and glume blight ( Phyllosticta glumarum ) in Guangxi Province , China . Furthermore , our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents .
author: Yue Shi, Wenjiang Huang, Huichun Ye, Chao Ruan, Naichen Xing, Yun Geng, Yingying Dong, Dailiang Peng
publish year: 2018
creator: Yue Shi, Wenjiang Huang, Huichun Ye, Chao Ruan, Naichen Xing, Yun Geng, Yingying Dong, Dailiang Peng
reference: Yue Shi, Wenjiang Huang, Huichun Ye, Chao Ruan, Naichen Xing, Yun Geng, Yingying Dong, Dailiang Peng, "Partial Least Square Discriminant Analysis Based on Normalized Two - Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets", Sensors 2018, 18(6), 1901; https://doi.org/10.3390/s18061901
Source: Sensors 2018, 18(6), 1901; https://doi.org/10.3390/s18061901
label: Article 3475

Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods
type: NamedIndividual
type: Article
has knowledge model: Cluster 3494
abstract: Leaf area index ( LAI ) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure . As the only in - operation hyperspectral satellite launched by China , GF-5 is potentially useful for accurate LAI estimation . However , there is no research focus on evaluating GF-5 data for LAI estimation . Hyperspectral remote sensing data contains abundant information about the reflective characteristics of vegetation canopies , but these abound data also easily result in a dimensionality curse . Therefore , feature selection ( FS ) is necessary to reduce data redundancy to achieve more reliable estimations . Currently , machine learning ( ML ) algorithms have been widely used for FS . Moreover , the same ML algorithm is usually conducted for both FS and regression in LAI estimation . However , no evidence suggests that this is the optimal solution . Therefore , this study focuses on evaluating the capacity of GF-5 spectral reflectance for estimating LAI and the performances of different combination of FS and ML algorithms . Firstly , the PROSAIL model , which coupled leaf optical properties model PROSPECT and the scattering by arbitrarily inclined leaves ( SAIL ) model , was used to generate simulated GF-5 reflectance data under different vegetation and soil conditions , and then three FS methods , including random forest ( RF ) , K - means clustering ( K - means ) and mean impact value ( MIV ) , and three ML algorithms , including random forest regression ( RFR ) , back propagation neural network ( BPNN ) and K - nearest neighbor ( KNN ) were used to develop nine LAI estimation models . The FS process was conducted twice using different strategies : Firstly , three FS methods were conducted to search the lowest dimension number , which maintained the estimation accuracy of all bands . Then , the sequential backward selection ( SBS ) method was used to eliminate the bands having minimal impact on LAI estimation accuracy . …
author: Zhulin Chen, Kun Jia, Chenchao Xiao, Dandan Wei, Xiang Zhao, Jinhui Lan, Xiangqin Wei, Yunjun Yao, Bing Wang, Yuan Sun, Lei Wang
publish year: 2020
creator: Zhulin Chen, Kun Jia, Chenchao Xiao, Dandan Wei, Xiang Zhao, Jinhui Lan, Xiangqin Wei, Yunjun Yao, Bing Wang, Yuan Sun, Lei Wang
reference: Zhulin Chen, Kun Jia, Chenchao Xiao, Dandan Wei, Xiang Zhao, Jinhui Lan, Xiangqin Wei, Yunjun Yao, Bing Wang, Yuan Sun, Lei Wang, "Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods", Remote Sens. 2020, 12(13), 2110; https://doi.org/10.3390/rs12132110
Source: Remote Sens. 2020, 12(13), 2110; https://doi.org/10.3390/rs12132110
label: Article 3494

Prediction of Antioxidant Activity of Cherry Fruits from UAS Multispectral Imagery Using Machine Learning
type: NamedIndividual
type: Article
has knowledge model: Regressor 3510
abstract: In this research , a model for the estimation of antioxidant content in cherry fruits from multispectral imagery acquired from drones was developed , based on machine learning methods . For two consecutive cultivation years , the trees were sampled on different dates and then analysed for their fruits ’ radical scavenging activity ( DPPH ) and Folin – Ciocalteu ( FCR ) reducing capacity . Multispectral images from unmanned aerial vehicles were acquired on the same dates with fruit sampling . Soil samples were collected throughout the study fields at the end of the season . Topographic , hydrographic and weather data also were included in modelling . First - year data were used for model - fitting , whereas second - year data for testing . Spatial autocorrelation tests indicated unbiased sampling and , moreover , allowed restriction of modelling input parameters to a smaller group . The optimum model employs 24 input variables resulting in a 6.74 root mean square error . Provided that soil profiles and other ancillary data are known in advance of the cultivation season , capturing drone images in critical growth phases , together with contemporary weather data , can support site- and time - specific harvesting . It could also support site - specific treatments ( precision farming ) for improving fruit quality in the long - term , with analogous marketing perspectives .
author: Christos Karydas, Miltiadis Iatrou, Dimitrios Kouretas, Anastasia Patouna, George Iatrou, Nikolaos Lazos, Sandra Gewehr, Xanthi Tseni, Fotis Tekos, Zois Zartaloudis, Evangelos Mainos, Spiros Mourelatos
publish year: 2020
creator: Christos Karydas, Miltiadis Iatrou, Dimitrios Kouretas, Anastasia Patouna, George Iatrou, Nikolaos Lazos, Sandra Gewehr, Xanthi Tseni, Fotis Tekos, Zois Zartaloudis, Evangelos Mainos, Spiros Mourelatos
reference: Christos Karydas, Miltiadis Iatrou, Dimitrios Kouretas, Anastasia Patouna, George Iatrou, Nikolaos Lazos, Sandra Gewehr, Xanthi Tseni, Fotis Tekos, Zois Zartaloudis, Evangelos Mainos, Spiros Mourelatos, "Prediction of Antioxidant Activity of Cherry Fruits from UAS Multispectral Imagery Using Machine Learning", Antioxidants 2020, 9(2), 156; https://doi.org/10.3390/antiox9020156
Source: Antioxidants 2020, 9(2), 156; https://doi.org/10.3390/antiox9020156
label: Article 3510

Repaid Identification and Prediction of Cadmium – Lead Cross - Stress of Different Stress Levels in Rice Canopy Based on Visible and Near - Infrared Spectroscopy
type: NamedIndividual
type: Article
has knowledge model: Classifier 3511
abstract: Accurate detection of cadmium ( Cd ) and lead ( Pb ) -induced cross - stress on crops is essential for agricultural , ecological environment , and food security . The feasibility to diagnose and predict Cd – Pb cross - stress in agricultural soil was explored by measuring the visible and near - infrared reflectance of rice leaves . In this study , two models were developed — namely a diagnostic model and a prediction model . The diagnostic model was established based on visible and near - infrared reflectance spectroscopy ( VNIRS ) datasets with Support Vector Machine ( SVM ) , followed by leave - one - out cross - validation ( LOOCV ) . A partial least - squares ( PLS ) regression , as the prediction model was employed to predict the foliar concentration of Cd and Pb contents . To accurately calibrate the two models , a rigorous greenhouse experiment was designed and implemented , with 4 levels of treatments on each of the Cd and Pb stress on rice . Results show that with the appropriate pre - processing , the diagnostic model can identify 79 % of Cd and 85 % of Pb stress of any levels . The significant bands that have been used mainly distributed between 681–776 nm and 1224–1349 nm for Cd stress and 712–784 nm for Pb stress . The prediction model can estimate Cd with coefficient of determination of 0.7 , but failed to predict Pb accurately . The results illustrated the feasibility to diagnose Cd stress accurately by measuring the visible and near - infrared reflectance of rice canopy in a cross - contamination soil environment . This study serves as one step forward to heavy metal pollutant detection in a farmland environment .
author: Shuangyin Zhang, Jun Li, Siying Wang, Yingjing Huang, Yizhuo Li, Yiyun Chen, Teng Fei
publish year: 2020
creator: Shuangyin Zhang, Jun Li, Siying Wang, Yingjing Huang, Yizhuo Li, Yiyun Chen, Teng Fei
reference: Shuangyin Zhang, Jun Li, Siying Wang, Yingjing Huang, Yizhuo Li, Yiyun Chen, Teng Fei, "Repaid Identification and Prediction of Cadmium – Lead Cross - Stress of Different Stress Levels in Rice Canopy Based on Visible and Near - Infrared Spectroscopy", Remote Sens. 2020, 12(3), 469; https://doi.org/10.3390/rs12030469
Source: Remote Sens. 2020, 12(3), 469; https://doi.org/10.3390/rs12030469
label: Article 3511

Combining Optical , Fluorescence , Thermal Satellite , and Environmental Data to Predict County - Level Maize Yield in China Using Machine Learning Approaches
type: NamedIndividual
type: Article
has knowledge model: Regressor 3513
abstract: Maize is an extremely important grain crop , and the demand has increased sharply throughout the world . China contributes nearly one - fifth of the total production alone with its decreasing arable land . Timely and accurate prediction of maize yield in China is critical for ensuring global food security . Previous studies primarily used either visible or near - infrared ( NIR ) based vegetation indices ( VIs ) , or climate data , or both to predict crop yield . However , other satellite data from different spectral bands have been underutilized , which contain unique information on crop growth and yield . In addition , although a joint application of multi - source data significantly improves crop yield prediction , the combinations of input variables that could achieve the best results have not been well investigated . Here we integrated optical , fluorescence , thermal satellite , and environmental data to predict county - level maize yield across four agro - ecological zones ( AEZs ) in China using a regression - based method ( LASSO ) , two machine learning ( ML ) methods ( RF and XGBoost ) , and deep learning ( DL ) network ( LSTM ) . The results showed that combining multi - source data explained more than 75 % of yield variation . Satellite data at the silking stage contributed more information than other variables , and solar - induced chlorophyll fluorescence ( SIF ) had an almost equivalent performance with the enhanced vegetation index ( EVI ) largely due to the low signal to noise ratio and coarse spatial resolution . The extremely high temperature and vapor pressure deficit during the reproductive period were the most important climate variables affecting maize production in China . Soil properties and management factors contained extra information on crop growth conditions that can not be fully captured by satellite and climate data . We found that ML and DL approaches definitely outperformed regression - based methods , and ML had more computational…
author: Liangliang Zhang, Zhao Zhang, Yuchuan Luo, Juan Cao, Fulu Tao
publish year: 2019
creator: Liangliang Zhang, Zhao Zhang, Yuchuan Luo, Juan Cao, Fulu Tao
reference: Liangliang Zhang, Zhao Zhang, Yuchuan Luo, Juan Cao, Fulu Tao, "Combining Optical , Fluorescence , Thermal Satellite , and Environmental Data to Predict County - Level Maize Yield in China Using Machine Learning Approaches", Remote Sens. 2020, 12(1), 21; https://doi.org/10.3390/rs12010021
Source: Remote Sens. 2020, 12(1), 21; https://doi.org/10.3390/rs12010021
label: Article 3513

Long - Term Yield Variability of Triticale ( ×Triticosecale Wittmack ) Tested Using a CART Model
type: NamedIndividual
type: Article
has knowledge model: Classifier 3571
abstract: Triticale is a promising food crop that combines the yield potential and grain quality of wheat with the disease and environmental tolerance of rye . The objective of this study was to evaluate the impact of genotype , environment and crop management on spring and winter triticale yield variability , using data from 31 locations across the whole of Poland , from 2009 to 2017 , with the Classification and Regression Tree ( CART ) analysis . It was found that CART is able to detect differences in spring and winter triticale successful growth . The yield variability of spring triticale was more dependent on the soil quality than winter triticale because of a shorter cycle duration , which increases sensitivity to nutrient supply and weather conditions . Spring triticale also needs to be sown as soon as possible to ensure a successful establishment . A strong dependence of yield variability on the availability of water for the winter triticale was observed . When growing winter triticale in Poland , with periodic excess water especially during autumn and early spring , the use of fungicides and growth regulators should be taken into account .
author: Elżbieta Wójcik-Gront, Marcin Studnicki
publish year: 2021
creator: Elżbieta Wójcik-Gront, Marcin Studnicki
reference: Elżbieta Wójcik-Gront, Marcin Studnicki, "Long - Term Yield Variability of Triticale ( ×Triticosecale Wittmack ) Tested Using a CART Model", Agriculture 2021, 11(2), 92; https://doi.org/10.3390/agriculture11020092
Source: Agriculture 2021, 11(2), 92; https://doi.org/10.3390/agriculture11020092
label: Article 3571

UAV - Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions
type: NamedIndividual
type: Article
has knowledge model: Classifier 3573
abstract: Accelerating crop improvement for increased yield and better adaptation to changing climatic conditions is an issue of increasing urgency in order to satisfy the ever - increasing global food demand . However , the major bottleneck is the absence of high - throughput plant phenotyping methods for rapid and cost - effective data - driven variety selection and release in plant breeding . Traditional phenotyping methods that rely on trained experts are slow , costly , labor - intensive , subjective , and often require destructive sampling . We explore ways to improve the efficiency of crop phenotyping through the use of unmanned aerial vehicle ( UAV ) -based multispectral remotely sensed data in maize ( Zea mays L. ) varietal response to maize streak virus ( MSV ) disease . Twenty - five maize varieties grown in a trial with three replications were evaluated under artificial MSV inoculation . Ground scoring for MSV infection was carried out at mid - vegetative , flowering , and mid - grain filling on a scale of 1 ( resistant ) to 9 ( susceptible ) . UAV - derived spectral data were acquired at these three different phenological stages in multispectral bands corresponding to Green ( 0.53–0.57 μm ) , Red ( 0.64–0.68 μm ) , Rededge ( 0.73–0.74 μm ) , and Near - Infrared ( 0.77–0.81 μm ) . The imagery captured was stitched together in Pix4Dmapper , which generates two types of multispectral orthomosaics : the NoAlpha and the transparent mosaics for each band . The NoAlpha imagery was used as input into QGIS to extract reflectance data . Six vegetation indices were derived for each variety : normalized difference vegetation index ( NDVI ) , green normalized difference vegetation index ( GNDVI ) , Rededge NDVI ( NDVIrededge ) , Simple Ratio ( SR ) , green Chlorophyll Index ( CIgreen ) , and Rededge Chlorophyll Index ( CIrededge ) . The Random Forest ( RF ) classifier was used to evaluate UAV - derived spectral and VIs with and without variable optimization . Correlations bet…
author: Walter Chivasa, Onisimo Mutanga, Chandrashekhar Biradar
publish year: 2020
creator: Walter Chivasa, Onisimo Mutanga, Chandrashekhar Biradar
reference: Walter Chivasa, Onisimo Mutanga, Chandrashekhar Biradar, "UAV - Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions", Remote Sens. 2020, 12(15), 2445; https://doi.org/10.3390/rs12152445
Source: Remote Sens. 2020, 12(15), 2445; https://doi.org/10.3390/rs12152445
label: Article 3573

A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging
type: NamedIndividual
type: Article
has knowledge model: Regressor 3622
abstract: This study attempts to estimate spatial soil moisture in South Korea ( 99,000 km2 ) from January 2013 to December 2015 using a multiple linear regression ( MLR ) model and the Terra moderate - resolution imaging spectroradiometer ( MODIS ) land surface temperature ( LST ) and normalized distribution vegetation index ( NDVI ) data . The MODIS NDVI was used to reflect vegetation variations . Observed precipitation was measured using the automatic weather stations ( AWSs ) of the Korea Meteorological Administration ( KMA ) , and soil moisture data were recorded at 58 stations operated by various institutions . Prior to MLR analysis , satellite LST data were corrected by applying the conditional merging ( CM ) technique and observed LST data from 71 KMA stations . The coefficient of determination ( R2 ) of the original LST and observed LST was 0.71 , and the R2 of corrected LST and observed LST was 0.95 for 3 selected LST stations . The R2 values of all corrected LSTs were greater than 0.83 for total 71 LST stations . The regression coefficients of the MLR model were estimated seasonally considering the five - day antecedent precipitation . The p - values of all the regression coefficients were less than 0.05 , and the R2 values were between 0.28 and 0.67 . The reason for R2 values less than 0.5 is that the soil classification at each observation site was not completely accurate . Additionally , the observations at most of the soil moisture monitoring stations used in this study started in December 2014 , and the soil moisture measurements did not stabilize . Notably , R2 and root mean square error ( RMSE ) in winter were poor , as reflected by the many missing values , and uncertainty existed in observations due to freezing and mechanical errors in the soil . Thus , the prediction accuracy is low in winter due to the difficulty of establishing an appropriate regression model . Specifically , the estimated map of the soil moisture index ( SMI ) can be used to better und…
author: Chunggil Jung, Yonggwan Lee, Younghyun Cho, Seongjoon Kim
publish year: 2017
creator: Chunggil Jung, Yonggwan Lee, Younghyun Cho, Seongjoon Kim
reference: Chunggil Jung, Yonggwan Lee, Younghyun Cho, Seongjoon Kim, "A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging", Remote Sens. 2017, 9(8), 870; https://doi.org/10.3390/rs9080870
Source: Remote Sens. 2017, 9(8), 870; https://doi.org/10.3390/rs9080870
label: Article 3622

A Multi - Modal Approach for Crop Health Mapping Using Low Altitude Remote Sensing , Internet of Things ( IoT ) and Machine Learning
type: NamedIndividual
type: Article
has knowledge model: Classifier 3632
abstract: The agriculture sector holds paramount importance in Pakistan due to the intrinsic agrarian nature of the economy . Pakistan has its GDP based on agriculture , however it relies on manual monitoring of crops , which is a labour intensive and ineffective method . In contrast to this , several cutting edge technology - based solutions are being employed in the developed countries to enhance the crop yield with the optimal use of resources . To this end , we have proposed an integrated approach for monitoring crop health using IoT , machine learning and drone technology . The integration of these sensing modalities generate heterogeneous data which not only varies in nature ( i.e. observed parameter ) but also has different temporal fidelity . The spatial resolution of these methods is also different , hence , the optimal integration of these sensing modalities and their implementation in practice are addressed in the proposed system . In our proposed solution , the IoT sensors provide the real - time status of environmental parameters impacting the crop , and the drone platform provide the multispectral data used for generating Vegetation Indices ( VIs ) such as Normalized Difference vegetation Index ( NDVI ) for analyzing the crop health . The NDVI provides information about the crop based on the chlorophyll content , which offers limited information regarding the crop health . In order to obtain a rich and detailed knowledge about crop health , the variable length time series data of IoT sensors and multispectral images were converted to a fixed - sized representation to generate crop health maps . A number of machine and deep learning algorithms were applied on the collected data wherein deep neural network with two hidden layers was found to be the most optimal model among all the selected models , providing an accuracy of ( 98.4 % ) . Further , the health maps were validated through ground surveys and by agriculture experts due to the absence of reference data . …
author: Uferah Shafi, Rafia Mumtaz, Naveed Iqbal, Syed Mohammad Hassan Zaidi, Syed Ali Raza Zaidi, Imtiaz Hussain, Zahid Mahmood
publish year: 2020
creator: Uferah Shafi, Rafia Mumtaz, Naveed Iqbal, Syed Mohammad Hassan Zaidi, Syed Ali Raza Zaidi, Imtiaz Hussain, Zahid Mahmood
reference: Uferah Shafi, Rafia Mumtaz, Naveed Iqbal, Syed Mohammad Hassan Zaidi, Syed Ali Raza Zaidi, Imtiaz Hussain, Zahid Mahmood, "A Multi - Modal Approach for Crop Health Mapping Using Low Altitude Remote Sensing , Internet of Things ( IoT ) and Machine Learning", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 3632

A Deep Learning Approach for Multi - Depth Soil Water Content Prediction in Summer Maize Growth Period
type: NamedIndividual
type: Article
has knowledge model: Regressor 3643
abstract: Advance knowledge of soil water content ( SWC ) in the soil wetting layer of crop irrigation can help develop more reasonable irrigation plans and improve the efficiency of agricultural irrigation water use . To improve the accuracy of predicting SWC at multiple depths , the ResBiLSTM model was proposed , in which continuous meteorological and SWC data were gridded and transformed as model inputs , and then high - dimensional spatial and time series features were extracted by ResNet and BiLSTM , respectively , and integrated by a meta - learner . Meteorological , SWC and growth stage records data from seven typical maize monitoring stations in Hebei Province , China , during the 2016 - 2018 summer maize planting process were utilized for the training , evaluation and testing of the ResBiLSTM model , with model prediction targets set at 20 cm , 30 cm , 40 cm and 50 cm depths . Experimental results showed that : 1 ) ResBiLSTM model could achieve better model fit and prediction of meteorological and SWC data at all growth stages , with R 2 within [ 0.818 , 0.991 ] , average MAE within [ 0.79 % , 2.00 % ] , and the overall prediction accuracy ranked as follows : anthesis maturity stage > seedling stage > tassel stage ; 2 ) The average MSE of the ResBiLSTM model for the prediction of SWC in the next 1 - 6 days was within [ 3.91 % , 15.82 % ] , and the prediction accuracy decreased with the extension of the prediction time ; 3 ) Compared with the classical machine learning model and related deep learning models , the ResBiLSTM model was able to obtain better prediction accuracy performance .
author: Jingxin Yu, Song Tang, Lili Zhangzhong, Wengang Zheng, Long Wang, Alexander Wong, Linlin Xu
publish year: 2020
creator: Jingxin Yu, Song Tang, Lili Zhangzhong, Wengang Zheng, Long Wang, Alexander Wong, Linlin Xu
reference: Jingxin Yu, Song Tang, Lili Zhangzhong, Wengang Zheng, Long Wang, Alexander Wong, Linlin Xu, "A Deep Learning Approach for Multi - Depth Soil Water Content Prediction in Summer Maize Growth Period", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 3643

Automatic Detection of Field - Grown Cucumbers for Robotic Harvesting
type: NamedIndividual
type: Article
has knowledge model: Classifier 3648
abstract: The objective of the research presented in this paper was to design an accurate and robust algorithm for the challenging task of detecting field - grown cucumbers for robotic harvesting automation in precision agriculture applications . The proposed algorithm is based on a combination of several processing and data mining techniques to achieve a classification system capable of segmenting cucumbers from the different elements of the scene , such as leaves , stems , the ground , stones , and irrigation pipes . The algorithm includes a support vector machine pixel classifier that provides the initial regions of interest for further processing , a Euclidean distance transform for eliminating less compact blobs that usually correspond to flowers and young leaves , an image category classifier based on a bag - of - visual - words model that increases the detection reliability , and a segmentation procedure based on the watershed transform and the minima imposition technique . Several experimental campaigns were carried out in field conditions to acquire data for training the classifiers and for validating the designed algorithm . Detection performance was evaluated at both the pixel and cucumber levels by comparing the results provided by the proposed algorithm with the ground truth data generated from hand - labeled images . The high hit rate and the low false - positive rate obtained at the pixel level and the high recall and precision at the cucumber level demonstrated the satisfactory performance of the proposed solution and highlight its potential benefits for automatic cucumber - harvesting applications .
author: Roemi Fernández, Héctor Montes, Jelena Surdilovic, Dragojlub Surdilovic, Pablo Gonzalez-De-Santos, Manuel Armada
publish year: 2018
creator: Roemi Fernández, Héctor Montes, Jelena Surdilovic, Dragojlub Surdilovic, Pablo Gonzalez-De-Santos, Manuel Armada
reference: Roemi Fernández, Héctor Montes, Jelena Surdilovic, Dragojlub Surdilovic, Pablo Gonzalez-De-Santos, Manuel Armada, "Automatic Detection of Field - Grown Cucumbers for Robotic Harvesting", IEEE Access ( Volume: 6)
Source: IEEE Access ( Volume: 6)
label: Article 3648

Early Disease Classification of Mango Leaves Using Feed - Forward Neural Network and Hybrid Metaheuristic Feature Selection
type: NamedIndividual
type: Article
has knowledge model: Classifier 3649
abstract: Plant disease , especially crop plants , is a major threat to global food security since many diseases directly affect the quality of the fruits , grains , and so on , leading to a decrease in agricultural productivity . Farmers have to observe and determine whether a leaf was infected by naked eyes . This process is unreliable , inconsistent , and error prone . Several works on deep learning techniques for detecting leaf diseases had been proposed . Most of them built their models based on limited resolution images using convolutional neural networks ( CNNs ) . In this research , we aim at detecting early disease on plant leaves with small disease blobs , which can only be detected with higher resolution images , by an artificial neural network ( ANN ) approach . After a pre - processing step using a contrast enhancement method , all the infested blobs are segmented for the whole dataset . A list of several measurement - based features that represents the blobs are chosen and then selected based on their influences on the model 's performance using a wrapper - based feature selection algorithm , which is built based on a hybrid metaheuristic . The chosen features are used as inputs for an ANN . We compare the results obtained using our methods with another approach using popular CNN models ( AlexNet , VGG16 , ResNet-50 ) enhanced with transfer learning . The ANN 's results are better than those of CNNs using a simpler network structure ( 89.41 % vs 78.64 % , 79.92 % , and 84.88 % , respectively ) . This shows that our approach can be implemented on low - end devices such as smartphones , which will be of great assistance to farmers on the field .
author: Tan Nhat Pham, Ly Van Tran, Son Vu Truong Dao
publish year: 2020
creator: Tan Nhat Pham, Ly Van Tran, Son Vu Truong Dao
reference: Tan Nhat Pham, Ly Van Tran, Son Vu Truong Dao, "Early Disease Classification of Mango Leaves Using Feed - Forward Neural Network and Hybrid Metaheuristic Feature Selection", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 3649

Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease
type: NamedIndividual
type: Article
has knowledge model: Classifier 3650
abstract: Fungal diseases not only influence the economic importance of the plants and its products but also abate their ecological prominence . Mango tree , specifically the fruits and the leaves are highly affected by the fungal disease named as Anthracnose . The main aim of this paper is to develop an appropriate and effective method for diagnosis of the disease and its symptoms , therefore espousing a suitable system for an early and cost - effective solution of this problem . Over the last few years , due to their higher performance capability in terms of computation and accuracy , computer vision , and deep learning methodologies have gained popularity in assorted fungal diseases classification . Therefore , for this paper , a multilayer convolutional neural network ( MCNN ) is proposed for the classification of the Mango leaves infected by the Anthracnose fungal disease . This paper is validated on a real - time dataset captured at the Shri Mata Vaishno Devi University , Katra , J&K , India consists of 1070 images of the Mango tree leaves . The dataset contains both healthy and infected leaf images . The results envisage the higher classification accuracy of the proposed MCNN model when compared to the other state - of - the - art approaches .
author: Uday Pratap Singh, Siddharth Singh Chouhan, Sukirty Jain, Sanjeev Jain
publish year: 2019
creator: Uday Pratap Singh, Siddharth Singh Chouhan, Sukirty Jain, Sanjeev Jain
reference: Uday Pratap Singh, Siddharth Singh Chouhan, Sukirty Jain, Sanjeev Jain, "Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease", IEEE Access ( Volume: 7)
Source: IEEE Access ( Volume: 7)
label: Article 3650

Deep - Learning - Based Approach for Estimation of Fractional Abundance of Nitrogen in Soil From Hyperspectral Data
type: NamedIndividual
type: Article
has knowledge model: Regressor 3652
abstract: One of the vital growth nutrient parameters of crops is soil Nitrogen ( N ) content . The ability to accurately grasp soil nutrient information is a prerequisite for scientific fertilization within the field of precision agriculture . Information pertaining to soil macronutrients , such as N , may be obtained quickly through hyperspectral imaging techniques . Objective of this research is to explore the use of a deep learning ( DL ) network to estimate the abundance of urea fertilizer mixed soils for spectroradiometer data . The proposed approach was tested for silt clay and loamy types of soils . Spectral regions of 1899.2 nm for urea and 2195.1 nm for soils were identified as optimum spectral absorption features . The accuracy evaluation was performed using a linear regression model between actual and estimated abundances . At 1899.2 nm , the coefficient of determination ( R 2 ) for mixed samples of urea and silt clay soil was found to be 0.945 , while R 2 for urea mixed loamy soil were 0.954 . Similarly , at 2195.1 nm , R 2 obtained 0.953 for urea mixed silt clay soil and 0.944 for urea mixed loamy soil . The results show that the estimated abundances obtained through the derivative analysis for spectral unmixing ( DASU ) -based DL network facilitated a greater accuracy in comparison to the sole use of DASU . These results were then verified through conventional chemical analysis methods . The outcome of this article determines the abundance of urea mixed soils . Therefore , it is inferred that the hyperspectral imaging technique may be utilized in - situ to assess the agricultural land 's soil fertility status .
author: Ajay Kumar Patel, Jayanta Kumar Ghosh, Shivam Pande, Sameer Usmangani Sayyad
publish year: 2020
creator: Ajay Kumar Patel, Jayanta Kumar Ghosh, Shivam Pande, Sameer Usmangani Sayyad
reference: Ajay Kumar Patel, Jayanta Kumar Ghosh, Shivam Pande, Sameer Usmangani Sayyad, "Deep - Learning - Based Approach for Estimation of Fractional Abundance of Nitrogen in Soil From Hyperspectral Data", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 13)
Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 13)
label: Article 3652

An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection
type: NamedIndividual
type: Article
has knowledge model: Classifier 3654
abstract: Agriculture is a major part of the world economy as it provides food safety . However , in recent years , it has been noted that plants are extensively infected by different diseases . This causes enormous economic losses in agriculture industry around the world . The manual inspection of fruit diseases is a difficult process which can be minimized by using automated methods for detection of plant diseases at the earlier stage . In this paper , a new method is implemented for apple diseases identification and recognition . Three pipeline procedures are followed by preprocessing , spot segmentation , and features extraction , and classification . In the first step , the apple leaf spots are enhanced by a hybrid method which is the conjunction of 3D box filtering , de - correlation , 3D - Gaussian filter , and 3D - median filter . After that , the lesion spots are segmented by the strong correlation - based method and optimized their results by fusion of expectation maximization ( EM ) segmentation . Finally , the color , color histogram , and local binary pattern ( LBP ) features are fused by comparison - based parallel fusion . The extracted features are optimized by genetic algorithm and classified by One - vs - All M - SVM . The experimental results are performed on plant village dataset . The proposed methodology is tested for four types of apple disease classes including healthy leaves , Blackrot , Rust , and Scab . The classification accuracy shows the improvement of our method on selected apple diseases . Moreover , the good preprocessing step always produced prominent features which later achieved significant classification accuracy .
author: Muhammad Attique Khan, M Ikram Ullah Lali, Muhammad Sharif, Kashif Javed, Khursheed Aurangzeb, Syed Irtaza Haider, Abdulaziz Saud Altamrah, Talha Akram
publish year: 2019
creator: Muhammad Attique Khan, M Ikram Ullah Lali, Muhammad Sharif, Kashif Javed, Khursheed Aurangzeb, Syed Irtaza Haider, Abdulaziz Saud Altamrah, Talha Akram
reference: Muhammad Attique Khan, M Ikram Ullah Lali, Muhammad Sharif, Kashif Javed, Khursheed Aurangzeb, Syed Irtaza Haider, Abdulaziz Saud Altamrah, Talha Akram, "An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection", IEEE Access ( Volume: 7)
Source: IEEE Access ( Volume: 7)
label: Article 3654

Accurate Prediction Scheme of Water Quality in Smart Mariculture With Deep Bi - S - SRU Learning Network
type: NamedIndividual
type: Article
has knowledge model: Regressor 3656
abstract: In the smart mariculture , the timely and accurate predictions of water quality can help farmers take countermeasures before the ecological environment deteriorates seriously . However , the openness of the mariculture environment makes the variation of water quality nonlinear , dynamic and complex . Traditional methods face challenges in prediction accuracy and generalization performance . To address these problems , an accurate water quality prediction scheme is proposed for pH , water temperature and dissolved oxygen . First , we construct a new huge raw data set collected in time series consisting of 23,204 groups of data . Then , the water quality parameters are preprocessed for data cleaning successively through threshold processing , mean proximity method , wavelet filter , and improved smoothing method . Next , the correlation between the water quality to be predicted and other dynamics parameters is revealed by the Pearson correlation coefficient method . Meanwhile , the data for training is weighted by the discovered correlation coefficients . Finally , by adding a backward SRU node to the training sequence , which can be integrated into the future context information , the deep Bi - S - SRU ( Bi - directional Stacked Simple Recurrent Unit ) learning network is proposed . After training , the prediction model can be obtained . The experimental results demonstrate that our proposed prediction method achieve higher prediction accuracy than the method based on RNN ( Recurrent Neural Network ) or LSTM ( Long Short - Term Memory ) with similar or less time computing complexity . In our experiments , the proposed method takes 12.5ms to predict data on average , and the prediction accuracy can reach 94.42 % in the next 3~8 days .
author: Juntao Liu, Chuang Yu, Zhuhua Hu, Yaochi Zhao, Yong Bai, Mingshan Xie, Jian Luo
publish year: 2020
creator: Juntao Liu, Chuang Yu, Zhuhua Hu, Yaochi Zhao, Yong Bai, Mingshan Xie, Jian Luo
reference: Juntao Liu, Chuang Yu, Zhuhua Hu, Yaochi Zhao, Yong Bai, Mingshan Xie, Jian Luo, "Accurate Prediction Scheme of Water Quality in Smart Mariculture With Deep Bi - S - SRU Learning Network", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 3656

MDFC – ResNet : An Agricultural IoT System to Accurately Recognize Crop Diseases
type: NamedIndividual
type: Article
has knowledge model: Classifier 3666
abstract: Crop disease diagnosis is an essential step in crop disease treatment and is a hot issue in agricultural research . However , in agricultural production , identifying only coarse - grained diseases of crops is insufficient because treatment methods are different in different grades of even the same disease . Inappropriate treatments are not only ineffective in treating diseases but also affect crop yield and food safety . We combine IoT technology with deep learning to build an IoT system for crop fine - grained disease identification . This system can automatically detect crop diseases and send diagnostic results to farmers . We propose a multidimensional feature compensation residual neural network ( MDFC - ResNet ) model for fine - grained disease identification in the system . MDFC - ResNet identifies from three dimensions , namely , species , coarse - grained disease , and fine - grained disease and sets up a compensation layer that uses a compensation algorithm to fuse multidimensional recognition results . Experiments show that the MDFC - ResNet neural network has better recognition effect and is more instructive in actual agricultural production activities than other popular deep learning models .
author: Wei-Jian Hu, Jie Fan, Yong-Xing Du, Bao-Shan Li, Naixue Xiong, Ernst Bekkering
publish year: 2020
creator: Wei-Jian Hu, Jie Fan, Yong-Xing Du, Bao-Shan Li, Naixue Xiong, Ernst Bekkering
reference: Wei-Jian Hu, Jie Fan, Yong-Xing Du, Bao-Shan Li, Naixue Xiong, Ernst Bekkering, "MDFC – ResNet : An Agricultural IoT System to Accurately Recognize Crop Diseases", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 3666

Multilevel Deep Learning Network for County - Level Corn Yield Estimation in the U.S. Corn Belt
type: NamedIndividual
type: Article
has knowledge model: Regressor 3669
abstract: Accurate and timely estimation of crop yield at a small scale is of great significance to food security and harvest management . Recent studies have proven remote sensing is an efficient method for yield estimation and machine learning , especially deep learning , can infer a good prediction by integrating multisource datasets such as satellite data , climate data , soil data , and so on . However , there are some bottleneck challenges to improve accuracy . First , the popular remote sensing data used for yield prediction fall into two major groups - time - series data and constant data . Surprisingly little attention has been devoted to deep learning networks which can integrate the two kinds of data effectively ; second , both temporal and spatial features play a role in affecting the yields . But most of the existing approaches employed either convolutional neural network ( CNN ) or recurrent neural network ( RNN ) . CNN can not learn temporal patterns , while RNN barely can learn spatial characteristics . This work proposed a novel multilevel deep learning model coupling RNN and CNN to extract both spatial and temporal features . The inputs include both time - series remote sensing data , soil property data , and the model outputs yield . We experimented with the model in U.S. Corn Belt states , and used it to predict corn yield from 2013 to 2016 at the county - level . The results approve the effectiveness and advantages of the proposed approach over the other methods . In the future , the model will be used on other crops such as soybean and winter wheat to assist agricultural decision - making .
author: Jie Sun, Zulong Lai, Liping Di, Ziheng Sun, Jianbin Tao, Yonglin Shen
publish year: 2020
creator: Jie Sun, Zulong Lai, Liping Di, Ziheng Sun, Jianbin Tao, Yonglin Shen
reference: Jie Sun, Zulong Lai, Liping Di, Ziheng Sun, Jianbin Tao, Yonglin Shen, "Multilevel Deep Learning Network for County - Level Corn Yield Estimation in the U.S. Corn Belt", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 13)
Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 13)
label: Article 3669

Real - Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks
type: NamedIndividual
type: Article
has knowledge model: Classifier 3671
abstract: Alternaria leaf spot , Brown spot , Mosaic , Grey spot , and Rust are five common types of apple leaf diseases that severely affect apple yield . However , the existing research lacks an accurate and fast detector of apple diseases for ensuring the healthy development of the apple industry . This paper proposes a deep learning approach that is based on improved convolutional neural networks ( CNNs ) for the real - time detection of apple leaf diseases . In this paper , the apple leaf disease dataset ( ALDD ) , which is composed of laboratory images and complex images under real field conditions , is first constructed via data augmentation and image annotation technologies . Based on this , a new apple leaf disease detection model that uses deep - CNNs is proposed by introducing the GoogLeNet Inception structure and Rainbow concatenation . Finally , under the hold - out testing dataset , using a dataset of 26,377 images of diseased apple leaves , the proposed INAR - SSD ( SSD with Inception module and Rainbow concatenation ) model is trained to detect these five common apple leaf diseases . The experimental results show that the INAR - SSD model realizes a detection performance of 78.80 % mAP on ALDD , with a high - detection speed of 23.13 FPS . The results demonstrate that the novel INAR - SSD model provides a high - performance solution for the early diagnosis of apple leaf diseases that can perform real - time detection of these diseases with higher accuracy and faster detection speed than previous methods .
author: Peng Jiang, Yuehan Chen, Bin Liu, Dongjian He, Chunquan Liang
publish year: 2019
creator: Peng Jiang, Yuehan Chen, Bin Liu, Dongjian He, Chunquan Liang
reference: Peng Jiang, Yuehan Chen, Bin Liu, Dongjian He, Chunquan Liang, "Real - Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks", IEEE Access ( Volume: 7)
Source: IEEE Access ( Volume: 7)
label: Article 3671

Deep Learning and Computer Vision for Estimating Date Fruits Type , Maturity Level , and Weight
type: NamedIndividual
type: Article
has knowledge model: Regressor 3676
abstract: According to the Food and Agriculture Organization , the world production of date fruits is 8,526,218 tons and around 1,302,859 tons in the Kingdom of Saudi Arabia ( KSA ) in 2018 . There are several types of date fruits , and the most common in KSA are Barhi , Khalas , Meneifi , Naboot Saif , and Sullaj . Moreover , there are around five main maturity levels : Immature , Khalal , Khalal with Rutab , Pre - Tamar , and Tamar . Harvesting date fruits is performed according to its maturity level and type , which is a critical decision that significantly affects profit . In this paper , we propose a smart harvesting decision system to estimate date fruits type , maturity level , and weight using computer vision ( CV ) and deep learning ( DL ) techniques . The proposed system consists of three sub - systems : Dates maturity estimation system ( DMES ) , type estimation system ( DTES ) , and dates weight estimation system ( DWES ) . We utilized four DL architectures , including ResNet , VGG-19 , Inception - V3 , and NASNet for both DMES and DTES and support vector machine ( SVM ) ( regression and linear ) for DWES . We evaluated the performance of the proposed system using the dataset collected by the Center of Smart Robotics Research . Using multiple performance metrics , DTES achieved maximum performance of 99.175 % accuracy , an F1 score of 99.225 % , 99.8 % average precision , and 99.05 % average recall . The maximum performance of DMES was 99.058 % accuracy , F1 score of 99.34 % , 99.64 % average precision , and 99.08 % average Recall . DWES achieved a maximum performance of 84.27 % using SVM - Linear .
author: Mohammed Faisal, Fahad Albogamy, Hebah Elgibreen, Mohammed Algabri, Fattoh Abdu Alqershi
publish year: 2020
creator: Mohammed Faisal, Fahad Albogamy, Hebah Elgibreen, Mohammed Algabri, Fattoh Abdu Alqershi
reference: Mohammed Faisal, Fahad Albogamy, Hebah Elgibreen, Mohammed Algabri, Fattoh Abdu Alqershi, "Deep Learning and Computer Vision for Estimating Date Fruits Type , Maturity Level , and Weight", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 3676

Rapid Detection of Rice Disease Based on FCM - KM and Faster R - CNN Fusion
type: NamedIndividual
type: Article
has knowledge model: Classifier 3679
abstract: In this paper , a method for detecting rapid rice disease based on FCM - KM and Faster R - CNN fusion is proposed to address various problems with the rice disease images , such as noise , blurred image edge , large background interference and low detection accuracy . Firstly , the method uses a two - dimensional filtering mask combined with a weighted multilevel median filter ( 2DFM - AMMF ) for noise reduction , and uses a faster two - dimensional Otsu threshold segmentation algorithm ( Faster 2D - Otsu ) to reduce the interference of complex background with the detection of target blade in the image . Then the dynamic population firefly algorithm based on the chaos theory as well as the maximum and minimum distance algorithm is applied for optimization of the K - Means clustering algorithm ( FCM - KM ) to determine the optimal clustering class k value while addressing the tendency of the algorithm to fall into the local optimum problem . Combined with the R - CNN algorithm for the identification of rice diseases , FCM - KM analysis is conducted to determine the different sizes of the Faster R - CNN target frame . As revealed by the application results of 3010 images , the accuracy and time required for detection of rice blast , bacterial blight and blight were 96.71%/0.65s , 97.53%/0.82s and 98.26%/0.53s , respectively , indicating clearly that the method is more capable of detecting rice diseases and improving the identification accuracy of Faster R - CNN algorithm , while reducing the time required for identification .
author: Guoxiong Zhou, Wenzhuo Zhang, Aibin Chen, Mingfang He, Xueshuo Ma
publish year: 2019
creator: Guoxiong Zhou, Wenzhuo Zhang, Aibin Chen, Mingfang He, Xueshuo Ma
reference: Guoxiong Zhou, Wenzhuo Zhang, Aibin Chen, Mingfang He, Xueshuo Ma, "Rapid Detection of Rice Disease Based on FCM - KM and Faster R - CNN Fusion", IEEE Access ( Volume: 7)
Source: IEEE Access ( Volume: 7)
label: Article 3679

A Low - Complexity Machine Learning Nitrate Loss Predictive Model – Towards Proactive Farm Management in a Networked Catchment
type: NamedIndividual
type: Article
has knowledge model: Regressor 3684
abstract: With the advent of wireless sensor networks , the ability to predict nutrient - rich discharges , using on - node prediction models , offers huge potential for enabling real - time water reuse and management within an agriculturally dominated catchment . Existing discharge models use multiple parameters and large historical data which are difficult to extract and this , coupled with constraints on network nodes ( battery life , computing power , and sensor availability ) , makes it necessary to develop low - dimensional models . This paper investigates a data - driven model for predicting daily total oxidized nitrate fluxes and reduces the number of model parameters used to 5-a reduction of at least 50 % . Trained on only a 12-month training dataset derived from the published measured data , results for the model generated using an M5 decision tree , giving an R 2 of 0.92 and a relative root - mean - square error of 26 % . The 80 % of the residuals for test data falls within + /-0.05 Kgůha -1 ůday -1 error range , which is minimal , offering an improvement over results obtained by the contemporary research .
author: Huma Zia, Nick R. Harris, Geoff V. Merrett, Mark Rivers
publish year: 2019
creator: Huma Zia, Nick R. Harris, Geoff V. Merrett, Mark Rivers
reference: Huma Zia, Nick R. Harris, Geoff V. Merrett, Mark Rivers, "A Low - Complexity Machine Learning Nitrate Loss Predictive Model – Towards Proactive Farm Management in a Networked Catchment", IEEE Access ( Volume: 7)
Source: IEEE Access ( Volume: 7)
label: Article 3684

A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery
type: NamedIndividual
type: Article
has knowledge model: Regressor 3693
abstract: Rapid , non - destructive detection of wheat Fusarium head blight ( FHB ) is an important tool for disease control . Red - edge ( RE ) is a prominent spectral feature for determining crop conditions with the potential to enhance the accuracy of monitoring FHB regionally . This study explored the potential of RE for FHB monitoring based on Sentinel-2 Multispectral Instrument ( MSI ) data . The novel red - edge head blight index ( REHBI ) was developed to detect FHB at a regional scale . Hyperspectral data at the canopy scale was integrated to simulate Sentinel-2 multispectral reflectance using the relative spectral response ( RSR ) function of the sensor . Then , many differential and ratio combinations of Sentinel-2 bands that were sensitive to FHB severity were selected . REHBI was established based on these basic vegetation indexes ( VIs ) , and the model developed from REHBI performed best in monitoring FHB severity ( R 2 = 0.82 , RMSE = 10.1 ) . Additionally , the infected canopies with disease index ( DI ) values between 10 and 50 were classified as slightly diseased canopies . Ordinary least square ( OLS ) was used to test the performance of REHBI and two conventional VIs , i.e. , OSAVI and RDVI , in monitoring slightly diseased canopies ; REHBI outperformed these alternatives ( R 2 = 0.69 , RMSE = 3.6 ) . To approximate real agricultural conditions , Poisson noise was added to the simulated Sentinel-2 multispectral data and generalized performance of VIs was evaluated again ; REHBI still had the highest R 2 and lowest RMSE values ( 0.74 and 12.6 , respectively ) . Finally , to validate REHBI 's ability to detect FHB infection in agricultural production , it was applied to monitoring FHB in the wheat planting areas of Changfeng and Dingyuan counties from Sentinel-2 imagery . Generally , REHBI performed better in disease monitoring than OSAVI and RDVI . The overall accuracy was up to 78.6 % , and the kappa coefficient was 0.51 . Experimental results demonstrate…
author: Linyi Liu, Yingying Dong, Wenjiang Huang, Xiaoping Du, Binyuan Ren, Linsheng Huang, Qiong Zheng, Huiqin Ma
publish year: 2020
creator: Linyi Liu, Yingying Dong, Wenjiang Huang, Xiaoping Du, Binyuan Ren, Linsheng Huang, Qiong Zheng, Huiqin Ma
reference: Linyi Liu, Yingying Dong, Wenjiang Huang, Xiaoping Du, Binyuan Ren, Linsheng Huang, Qiong Zheng, Huiqin Ma, "A Disease Index for Efficiently Detecting Wheat Fusarium Head Blight Using Sentinel-2 Multispectral Imagery", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 3693

Estimating Total Leaf Chlorophyll Content of Gannan Navel Orange Leaves Using Hyperspectral Data Based on Partial Least Squares Regression
type: NamedIndividual
type: Article
has knowledge model: Regressor 3698
abstract: The goal of this study was to model the total leaf chlorophyll content ( LCCtot ) of Gannan navel orange leaves using a field imaging spectroscopy system in the visible and near - infrared domain . The spectral range from 400 to 1000 nm with 176 wavebands ( a wavelength interval of 3.41 nm ) or 360 wavebands ( a wavelength interval of 1.67 nm ) , labeled as “ Datasets_1.67 ” and “ Datasets_3.41 ” , respectively , were used . Although different spectral data types were used , better prediction results for LCCtot were based on Datasets_1.67 for LCCtot prediction . Several prediction models of LCCtot were built based on partial least squares regression ( PLSR ) , artificial neural networks ( ANN ) , ordinary least squares regression ( OLSR ) , and stepwise linear regression ( SLR ) using full spectral and effective wavelength ( EW ) data ( raw spectral ( RS ) , first derivative spectral ( FDS ) and second derivative spectral ( SDS ) data ) . The determination coefficient ( R 2 ) , the root mean square error ( RMSE ) and the residual predictive deviation ( RPD ) were used to evaluate the reliability and accuracy of the predicted LCCtot values . As a result , 14 ( 7 obtained from Datasets_1.67 , 7 obtained from Datasets_3.41 ) , 39 ( 21 obtained from Datasets_1.67 , 18 obtained from Datasets_3.41 ) and 50 ( 27 obtained from Datasets_1.67 , 23 obtained from Datasets_3.41 ) wavebands were selected from the RS data , FDS data and SDS data , respectively , as the EWs for LCCtot prediction of navel orange leaves . After that , PLSR and ANN predictive models were established using full spectra , and OLSR and SLR predictive models were built using the selected EWs . The experimental results demonstrated that these various regression methods were useful for estimating LCCtot in the order of PLSR models established using full spectra from RS data ( F - RS - PLSR ) > PLSR models established using full spectra from SDS data ( F - SDS - PLSR ) > PLSR models established using full sp…
author: Zhongzheng Peng, Lixin Guan, Yubo Liao, Suyun Lian
publish year: 2019
creator: Zhongzheng Peng, Lixin Guan, Yubo Liao, Suyun Lian
reference: Zhongzheng Peng, Lixin Guan, Yubo Liao, Suyun Lian, "Estimating Total Leaf Chlorophyll Content of Gannan Navel Orange Leaves Using Hyperspectral Data Based on Partial Least Squares Regression", IEEE Access ( Volume: 7)
Source: IEEE Access ( Volume: 7)
label: Article 3698

DCGAN - Based Data Augmentation for Tomato Leaf Disease Identification
type: NamedIndividual
type: Article
has knowledge model: Classifier 3702
abstract: Tomato leaf disease seriously affects the yield of tomato . It is extremely vital for agricultural economy to identify agricultural diseases . The traditional data augmentation methods , such as rotation , flip and translation , are severely limited , which can not achieve good generalization results . To improve the recognition accuracy of tomato leaf diseases , a new method of data augmentation by generative adversarial networks ( GANs ) is proposed for leaf disease recognition in this work . Generated images augmented by deep convolutional generative adversarial networks ( DCGAN ) and original images as the input of GoogLeNet , this model can achieve a top-1 average identification accuracy of 94.33 % . By adjusting the hyper - parameters , modifying the architecture of the convolutional neural networks , and selecting different generative adversarial networks , an improved model for training and testing 5 classes of tomato leaf images was obtained . Meanwhile , images generated by DCGAN not only enlarge the size of the data set , but also have the characteristics of diversity , which makes the model have a good generalization effect . We have also visually confirmed that the images generated by DCGAN have much better quality and are more convincing through the t - Distributed Stochastic Neighbor Embedding ( t - SNE ) and Visual Turing Test . Experiments with tomato leaf disease identification show that DCGAN can generate data that approximate to real images , which can be used to ( 1 ) provide a larger data set for the training of large neural networks , and improve the performance of the recognition model through highly discriminating image generation technology ; ( 2 ) reduce the cost of data collection ; ( 3 ) enhance the diversity of data and the generalization ability of the recognition models .
author: Qiufeng Wu, Yiping Chen, Jun Meng
publish year: 2020
creator: Qiufeng Wu, Yiping Chen, Jun Meng
reference: Qiufeng Wu, Yiping Chen, Jun Meng, "DCGAN - Based Data Augmentation for Tomato Leaf Disease Identification", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 3702

Deep Metric Learning Based Citrus Disease Classification With Sparse Data
type: NamedIndividual
type: Article
has knowledge model: Classifier 3722
abstract: Early recognition of citrus diseases is important for preventing crop losses and employing timely disease control measures in farms . Employing machine learning - based approaches , such as deep learning for accurate detection of multiple citrus diseases is challenging due to the limited availability of labeled diseased samples . Further , a lightweight architecture with low computational complexity is required to perform citrus disease classification on resource - constrained devices , such as mobile phones . This enables practical utility of the architecture to perform effective monitoring of diseases by farmers using their own mobile devices in the farms . Hence , we propose a lightweight , fast , and accurate deep metric learning - based architecture for citrus disease detection from sparse data . In particular , we propose a patch - based classification network that comprises an embedding module , a cluster prototype module , and a simple neural network classifier , to detect the citrus diseases accurately . Evaluation of our proposed approach using publicly available citrus fruits and leaves dataset reveals its efficiency in accurately detecting the various diseases from leaf images . Further , the generalization capability of our approach is demonstrated using another dataset , namely the tea leaves dataset . Comparison analysis of our approach with existing state - of - the - art algorithms demonstrate its superiority in terms of detection accuracy ( 95.04 % ) , the number of parameters required for tuning ( less than 2.3 M ) , and the time efficiency in detecting the citrus diseases ( less than 10 ms ) using the trained model . Moreover , the ability to learn with fewer resources and without compromising accuracy empowers the practical utility of the proposed scheme on resource - constrained devices , such as mobile phones .
author: Sivasubramaniam Janarthan, Selvarajah Thuseethan, Sutharshan Rajasegarar, Qiang Lyu, Yongqiang Zheng, John Yearwood
publish year: 2020
creator: Sivasubramaniam Janarthan, Selvarajah Thuseethan, Sutharshan Rajasegarar, Qiang Lyu, Yongqiang Zheng, John Yearwood
reference: Sivasubramaniam Janarthan, Selvarajah Thuseethan, Sutharshan Rajasegarar, Qiang Lyu, Yongqiang Zheng, John Yearwood, "Deep Metric Learning Based Citrus Disease Classification With Sparse Data", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 3722

A Machine Learning - Based Geostatistical Downscaling Method for Coarse - Resolution Soil Moisture Products
type: NamedIndividual
type: Article
has knowledge model: Regressor 3730
abstract: The surface soil moisture ( SSM ) products derived from microwave remote sensing have a coarse spatial resolution ; therefore , downscaling is required to obtain accurate SSM at high spatial resolution . An effective way to handle the stratified heterogeneity is to model for various stratifications ; however , the number of samples is often limited under each stratification , influencing the downscaling accuracy . In this study , a machine learning - based geostatistical model , which combines various kinds of ancillary information at fine spatial scale , is developed for spatial downscaling . The proposed support vector area - to - area regression kriging ( SVATARK ) model incorporates support vector regression and area - to - area kriging by considering the nonlinear relationships among variables for various stratifications . SVATARK also considers the change of support problem in the downscaling interpolation process as well as for solving the small sample size in trend prediction . The SVATARK method is evaluated in the Naqu region on the Tibetan Plateau , China , to downscale the European Space Agency 's ( ESA ) 25-km - resolution SSM product . The 1-km - resolution SSM predictions have been produced every eight days over a six - year period ( 2010–2015 ) . Compared with five other downscaling methods , the downscaled predictions from the SVATARK method performs the best with in situ observations , resulting in a 24.4 % reduction in root - mean - square error with 0.08 m 3 · m −3 and a 8.2 % increase in correlation coefficient with 0.72 , on average . Additionally , anomalously low SSM values , an indicator of drought , had a record low anomaly in mid - July for 2015 , as noted by previous studies , indicating that SVATARK could be utilized for drought monitoring .
author: Yan Jin, Yong Ge, Yaojie Liu, Yuehong Chen, Haitao Zhang, Gerard B. M. Heuvelink
publish year: 2020
creator: Yan Jin, Yong Ge, Yaojie Liu, Yuehong Chen, Haitao Zhang, Gerard B. M. Heuvelink
reference: Yan Jin, Yong Ge, Yaojie Liu, Yuehong Chen, Haitao Zhang, Gerard B. M. Heuvelink, "A Machine Learning - Based Geostatistical Downscaling Method for Coarse - Resolution Soil Moisture Products", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 14)
Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( Volume: 14)
label: Article 3730

Bacterial Foraging Optimization Based Radial Basis Function Neural Network ( BRBFNN ) for Identification and Classification of Plant Leaf Diseases : An Automatic Approach Towards Plant Pathology
type: NamedIndividual
type: Article
has knowledge model: Classifier 3737
abstract: The contribution of a plant is highly important for both human life and environment . Plants do suffer from diseases , like human beings and animals . There is the number of plant diseases that occur and affects the normal growth of a plant . These diseases affect complete plant including leaf , stem , fruit , root , and flower . Most of the time when the disease of a plant has not been taken care of , the plant dies or may cause leaves drop , flowers , and fruits drop . Appropriate diagnosis of such diseases is required for accurate identification and treatment of plant diseases . Plant pathology is the study of plant diseases , their causes , procedures for controlling and managing them . But , the existing method encompasses human involvement for classification and identification of diseases . This procedure is time - consuming and costly . Automatic segmentation of diseases from plant leaf images using soft computing approach can be reasonably useful than the existing one . In this paper , we have introduced a method named as bacterial foraging optimization based radial basis function neural network ( BRBFNN ) for identification and classification of plant leaf diseases automatically . For assigning optimal weight to radial basis function neural network we use bacterial foraging optimization that further increases the speed and accuracy of the network to identify and classify the regions infected of different diseases on the plant leafs . The region growing algorithm increases the efficiency of the network by searching and grouping of seed points having common attributes for feature extraction process . We worked on fungal diseases like common rust , cedar apple rust , late blight , leaf curl , leaf spot , and early blight . The proposed method attains higher accuracy in identification and classification of diseases .
author: Siddharth Singh Chouhan, Ajay Kaul, Uday Pratap Singh, Sanjeev Jain
publish year: 2018
creator: Siddharth Singh Chouhan, Ajay Kaul, Uday Pratap Singh, Sanjeev Jain
reference: Siddharth Singh Chouhan, Ajay Kaul, Uday Pratap Singh, Sanjeev Jain, "Bacterial Foraging Optimization Based Radial Basis Function Neural Network ( BRBFNN ) for Identification and Classification of Plant Leaf Diseases : An Automatic Approach Towards Plant Pathology", IEEE Access ( Volume: 6)
Source: IEEE Access ( Volume: 6)
label: Article 3737

A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification
type: NamedIndividual
type: Article
has knowledge model: Classifier 3740
abstract: The identification of grape leaf diseases based on deep learning is critical to controlling the spread of diseases and ensuring the healthy development of the grape industry . Focusing on the lack of training images of grape leaf diseases , this paper proposes a novel model named Leaf GAN , which is based on generative adversarial networks ( GANs ) , to generate images of four different grape leaf diseases for training identification models . A generator model with degressive channels is first designed to generate grape leaf disease images ; then , the dense connectivity strategy and instance normalization are fused into an efficient discriminator to identify real and fake disease images by utilizing their excellent feature extraction capability on grape leaf lesions . Finally , the deep regret gradient penalty method is applied to stabilize the training process of the model . Using a total of 4,062 grape leaf disease images , the Leaf GAN model ultimately generates 8,124 grape leaf disease images . The generated grape leaf disease images based on Leaf GAN model can obtain better performance than DCGAN and WGAN in terms of the Fréchet inception distance . The experimental results show that the proposed Leaf GAN model generates sufficient grape leaf disease images with prominent lesions , providing a feasible solution for the data augmentation of grape leaf disease images . For the eight prevailing classification models with the expanded dataset , the identification performance based on CNNs indicated higher accuracies , whereby all the accuracies were better than those of the initial dataset with other data augmentation methods . Among them , Xception achieves a recognition accuracy of 98.70 % on the testing set . The results demonstrate that the proposed data augmentation method represents a new approach to overcoming the overfitting problem in disease identification and can effectively improve the identification accuracy .
author: Bin Liu, Cheng Tan, Shuqin Li, Jinrong He, Hongyan Wang
publish year: 2020
creator: Bin Liu, Cheng Tan, Shuqin Li, Jinrong He, Hongyan Wang
reference: Bin Liu, Cheng Tan, Shuqin Li, Jinrong He, Hongyan Wang, "A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification", IEEE Access ( Volume: 8)
Source: IEEE Access ( Volume: 8)
label: Article 3740

Molecular Diagnostic and Using Deep Learning Techniques for Predict Functional Recovery of Patients Treated of Cardiovascular Disease
type: NamedIndividual
type: Article
has knowledge model: Regressor 3745
abstract: Today , with the development of industry and mechanized life style , the prevalence of the disease is rising steadily as well . Observing at the trend and lifecycle style , its predict that after ten years around 23.6 million people die because of Cardiovascular Disease ( CVD ) . For that reason , aim to use Deep Learning Techniques ( DLTs ) , to analysis stable CVD that would give valuable awareness to decrease misdiagnosis in the Robust Healthcare Industry ( RHI ) . An objective of this paper is first , Molecular diagnosis ( MD ) , and second using Deep Learning Techniques DLTs , to synthesis and characterize to accumulate ( raw information ) from CVD patients , those who admitted the emergency section between January ( 2018 to December 2019 ) . We are using Artificial Neural Network ( ANN ) , model characterize to predict CVD patients and configuration , Feature selection ( FS ) , Mean Square Error ( MSE ) , accuracy , sensitivity . The ANN accuracy is 98.4 , K - nearest neighbor ( KNN ) accuracy is 98.01 % , Naïve Bayes ( NB ) , accuracy is 96.99 % . Decision tree ( DT ) , accuracy is 87.81 % . Our robust data driven model explore the efficient accuracy rate to predict CVD patients . The ANN model in term of their efficient in disease analysis , and prognosis of the RHI .
author: Ar Junejo, Yin Shen, Asif Ali Laghari, Xiaobo Zhang, Hao Luo
publish year: 2019
creator: Ar Junejo, Yin Shen, Asif Ali Laghari, Xiaobo Zhang, Hao Luo
reference: Ar Junejo, Yin Shen, Asif Ali Laghari, Xiaobo Zhang, Hao Luo, "Molecular Diagnostic and Using Deep Learning Techniques for Predict Functional Recovery of Patients Treated of Cardiovascular Disease", IEEE Access ( Volume: 7)
Source: IEEE Access ( Volume: 7)
label: Article 3745

A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices
type: NamedIndividual
type: Article
has knowledge model: Regressor 3774
abstract: Random Forest (RF) is a machine learning technique that has been proved to be highly accurate in several agricultural applications. However, to yield prediction, how much this technique may be improved with the adoption of a ranking-based strategy is still an unknown issue. Here we propose a ranking-based approach to potentialize the RF method for maize yield prediction. This approach is based on the correlation parameter of individual vegetation indices (VIs). The VIs were individually ranked based on a merit metric that measures the improvement on the Pearson’s correlation coefficient by using RF against a baseline method. As a result, only the most relevant VIs were considered as input features to the RF model. We used 33 VIs extracted from multispectral UAV-based (unmanned aerial vehicle) imagery. The multispectral data were generated with two different sensors: Sequoia and MicaSense; during the 2017/2018 and 2018/2019 crop seasons, respectively. Amongst all the evaluated indices, NDVI, NDRE, and GNDVI were the top three in the ranking-based analysis, and their combination with RF increased the maize yield prediction. Our approach also outperformed other known machine learning methods, like support vector machine and artificial neural network. Additive regression, using the RF as the base weak learner, provided a higher accuracy with a correlation coefficient and MAE (Mean Absolute Error) of 0.78 and 853.11 kg ha−1, respectively. We conclude that the ranking-based strategy of VIs is appropriate to predict maize yield using machine learning methods and data derived from multispectral images. We demonstrated that our approach reduces the number of VIs needed to determine a high accuracy and relative low MAE, and the approach may contribute to decision-making actions, resulting in accurate management of maize fields.
author: Ramos, Ana Paula Marques, et al.
publish year: 2020
creator: Ramos, Ana Paula Marques, et al.
reference: Ramos, Ana Paula Marques, et al., "A random forest ranking approach to predict yield in maize with uav-based vegetation spectral indices", Computers and Electronics in Agriculture, Volume 178, November 2020, 105791
Source: Computers and Electronics in Agriculture, Volume 178, November 2020, 105791
label: Article 3774

Crop yield prediction using deep neural networks
type: NamedIndividual
type: Article
has knowledge model: Regressor 3775
abstract: Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12% of the average yield and 50% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11% of the average yield and 46% of the standard deviation. We also performed feature selection based on the trained DNN model, which successfully decreased the dimension of the input space without significant drop in the prediction accuracy. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT). The results also revealed that environmental factors had a greater effect on the crop yield than genotype.
author: Saeed Khaki, Lizhi Wang
publish year: 2019
creator: Saeed Khaki, Lizhi Wang
reference: Saeed Khaki, Lizhi Wang, "Crop yield prediction using deep neural networks", Front. Plant Sci., 22 May 2019
Source: Front. Plant Sci., 22 May 2019
label: Article 3775

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China
type: NamedIndividual
type: Article
has knowledge model: Regressor 3776
abstract: Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.
author: Han, Jichong, et al.
publish year: 2020
creator: Han, Jichong, et al.
reference: Han, Jichong, et al., "Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China", Journals Remote Sensing , Volume 12 , Issue 2
Source: Journals Remote Sensing , Volume 12 , Issue 2
label: Article 3776

Damage mapping of powdery mildew in winter wheat with high-resolution satellite image
type: NamedIndividual
type: Article
has knowledge model: Classifier 3777
abstract: Powdery mildew, caused by the fungus Blumeria graminis, is a major winter wheat disease in China. Accurate delineation of powdery mildew infestations is necessary for site-specific disease management. In this study, high-resolution multispectral imagery of a 25 km2 typical outbreak site in Shaanxi, China, taken by a newly-launched satellite, SPOT-6, was analyzed for mapping powdery mildew disease. Two regions with high representation were selected for conducting a field survey of powdery mildew. Three supervised classification methods—artificial neural network, mahalanobis distance, and maximum likelihood classifier—were implemented and compared for their performance on disease detection. The accuracy assessment showed that the ANN has the highest overall accuracy of 89%, following by MD and MLC with overall accuracies of 84% and 79%, respectively. These results indicated that the high-resolution multispectral imagery with proper classification techniques incorporated with the field investigation can be a useful tool for mapping powdery mildew in winter wheat.
author: L. Yuan, J. Zhang, Y. Shi, C. Nie, L. Wei, J. Wang
publish year: 2014
creator: L. Yuan, J. Zhang, Y. Shi, C. Nie, L. Wei, J. Wang
reference: L. Yuan, J. Zhang, Y. Shi, C. Nie, L. Wei, J. Wang, "Damage mapping of powdery mildew in winter wheat with high-resolution satellite image", Remote Sens., 6 (2014), pp. 3611-3623,
Source: Remote Sens., 6 (2014), pp. 3611-3623,
label: Article 3777

Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops
type: NamedIndividual
type: Article
has knowledge model: Classifier 3778
abstract: The development of a ground-based real-time remote sensing system that can be carried by tractors or robotic platforms is described. This prototype system makes possible the detection of plant diseases in arable crops automatically at an early stage of disease development and during field operations. The methodology uses differences in reflectance between healthy and diseased plants. Hyperspectral reflectance and multi-spectral imaging techniques were developed for simultaneous acquisition in the same canopy. Experimental platforms were constructed, and the advantage of using sensor fusion was demonstrated. An intelligent multi-sensor fusion decision system based on neural networks was developed to predict the presence of diseases or plant stresses, in order to treat the diseases in a spatially variable way. A robust multi-sensor platform integrating optical sensing, GPS (Geostationary Positioning System) and a data processing unit was constructed and calibrated. The functionality of automatic disease sensing and detection devices is crucial in order to conceive a site-specific spraying strategy against fungal foliar diseases. Field tests were carried out to optimise the functioning of the multi-sensor disease detection device. An overview is provided on how disease presence data are processed in order to enable an automatic site-specific spraying strategy in winter wheat. Furthermore, mapping of diseases based on automated optical sensing and intelligent prediction provide a spatially variable recommendation for spraying.
author: D. Moshou, C. Bravo, R. Oberti, J.S. West, H. Ramon, S. Vougioukas, D. Bochtis
publish year: 2011
creator: D. Moshou, C. Bravo, R. Oberti, J.S. West, H. Ramon, S. Vougioukas, D. Bochtis
reference: D. Moshou, C. Bravo, R. Oberti, J.S. West, H. Ramon, S. Vougioukas, D. Bochtis, "Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops", Biosyst. Eng., 108 (2011), pp. 311-321,
Source: Biosyst. Eng., 108 (2011), pp. 311-321,
label: Article 3778

Remote detection of rhizomania in sugar beets
type: NamedIndividual
type: Article
has knowledge model: Classifier 3779
abstract: As a prelude to remote sensing of rhizomania, hyper-spectral leaf reflectance and multi-spectral canopy reflectance were used to study the physiological differences between healthy sugar beets and beets infested with Beet necrotic yellow vein virus. This study was conducted over time in the presence of declining nitrogen levels. Total leaf nitrogen was significantly lower in symptomatic beets than in healthy beets. Chlorophyll and carotenoid levels were reduced in symptomatic beets. Vegetative indices calculated from leaf spectra showed reductions in chlorophyll and carotenoids in symptomatic beets. Betacyanin levels estimated from leaf spectra were decreased at the end of the 2000 season and not in 2001. The ratio of betacyanins to chlorophyll, estimated from canopy spectra, was increased in symptomatic beets at four of seven sampling dates. Differences in betacyanin and carotenoid levels appeared to be related to disease and not nitrogen content. Vegetative indices calculated from multi-spectral canopy spectra supported results from leaf spectra. Logistic regression models that incorporate vegetative indices and reflectance correctly predicted 88.8% of the observations from leaf spectra and 87.9% of the observations for canopy reflectance into healthy or symptomatic classes. Classification was best in August with a gradual decrease in accuracy until harvest. These results indicate that remote sensing technologies can facilitate detection of rhizomania.
author: K. Steddom, G. Heidel, D. Jones, C.M. Rush
publish year: 2003
creator: K. Steddom, G. Heidel, D. Jones, C.M. Rush
reference: K. Steddom, G. Heidel, D. Jones, C.M. Rush, "Remote detection of rhizomania in sugar beets", Phytopathology., 93 (2003), pp. 720-726,
Source: Phytopathology., 93 (2003), pp. 720-726,
label: Article 3779

Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging
type: NamedIndividual
type: Article
has knowledge model: Classifier 3780
abstract: The aim of this study was to evaluate the accuracy of the spectro-optical, photochemical reflectance index (PRI) for quantifying the disease index (DI) of yellow rust (Biotroph Puccinia striiformis) in wheat (Triticum aestivum L.), and its applicability in the detection of the disease using hyperspectral imagery. Over two successive seasons, canopy reflectance spectra and disease index (DI) were measured five times during the growth of wheat plants (3 varieties) infected with varying amounts of yellow rust. Airborne hyperspectral images of the field site were also acquired in the second season. The PRI exhibited a significant, negative, linear, relationship with DI in the first season (r 2 = 0.91, n = 64), which was insensitive to both variety and stage of crop development from Zadoks stage 3–9. Application of the PRI regression equation to measured spectral data in the second season yielded a coefficient of determination of r 2 = 0.97 (n = 80). Application of the same PRI regression equation to airborne hyperspectral imagery in the second season also yielded a coefficient of determination of DI of r 2 = 0.91 (n = 120). The results show clearly the potential of PRI for quantifying yellow rust levels in winter wheat, and as the basis for developing a proximal, or airborne/spaceborne imaging sensor of yellow rust in fields of winter wheat.
author: W. Huang, D.W. Lamb, Z. Niu, Y. Zhang, L. Liu, J. Wang
publish year: 2007
creator: W. Huang, D.W. Lamb, Z. Niu, Y. Zhang, L. Liu, J. Wang
reference: W. Huang, D.W. Lamb, Z. Niu, Y. Zhang, L. Liu, J. Wang, "Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging", Precis. Agric., 8 (2007), pp. 187-197
Source: Precis. Agric., 8 (2007), pp. 187-197
label: Article 3780

Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis)
type: NamedIndividual
type: Article
has knowledge model: Regressor 3781
abstract: The reflectance from rice (Oryza sativa L.) leaves and canopy damaged by rice leaf folder (RLF), Cnaphalocrocis medinalis (Guenée) was studied at the booting stage in order to establish a monitoring method for RLF based on hyperspectral data. The results showed that reflectance from rice leaves significantly decreased in the green (530–570 nm) and near infrared (700–1000 nm) regions, and significantly increased in the blue (450–520 nm) and red (580–700 nm) regions as the leaf-roll rate of rice increased. Reflectance from rice canopy significantly decreased in the spectral regions from 737 to 1000 nm as the infestation scale of RLF increased, and the most correlation appeared at 938 nm. Seven spectral regions 503–521, 526–545, 550–568, 581–606, 688–699, 703–715, and 722–770 nm at leaf-level, and one region 747–754 nm at canopy-level were found to be sensitive bands to exhibit the damage severity in rice by RLF. The position of the red edge peak remarkably moved to blue region, and the amplitude and area of the red edge significantly decreased when rice leaves were severely infected by RLF. Thirty-eight spectral indices at leaf-level and 29 indices at canopy-level were found to be sensitive to leaf-roll rate and infestation scale in rice, respectively. The linear regression models were built to detect the leaf-roll rate (0.0–1.0) and infestation scale (0–5) in rice using leaf- and canopy-level reflectance data. The root mean square error of the model was only 0.059 and 0.22 for the leaf-roll rate and infestation scale, respectively. These results suggested that the hyperspectral reflectance was potential to detect RLF damage severity in rice.
author: J. Huang, H. Liao, Y. Zhu, J. Sun, Q. Sun, X. Liu
publish year: 2012
creator: J. Huang, H. Liao, Y. Zhu, J. Sun, Q. Sun, X. Liu
reference: J. Huang, H. Liao, Y. Zhu, J. Sun, Q. Sun, X. Liu, "Hyperspectral detection of rice damaged by rice leaf folder (Cnaphalocrocis medinalis)", Comput. Electron. Agr., 82 (2012), pp. 100-107
Source: Comput. Electron. Agr., 82 (2012), pp. 100-107
label: Article 3781

Diagnosis of bacterial spot of tomato using spectral signatures
type: NamedIndividual
type: Article
has knowledge model: Regressor 3782
abstract: Ultraviolet, visible, and near-infrared reflectance spectroscopy was used to determine the disease severity of tomato (Lycopersicon esculentum) leaves infected with Xanthomonas perforans, the causal agent of bacterial leaf spot of tomato. Chemometric methods were used to identify significant wavelengths and create spectral-based prediction models. Significant wavelengths were identified through analysis of the B-matrix from partial least squares (PLS) regression, analysis of a correlation coefficient spectrum, and through the use of a stepwise multiple linear regression (SMLR) procedure. These analysis methods revealed several significant regions wavelengths and produced predictive models of disease severity based on absorbance spectra. The best model predicted the disease severity of the validation data set with a root mean square difference (RMSD) of 4.9% and a coefficient of determination (R2) of 0.82. The results of this initial study indicate the potential for the use of spectral technology to detect bacterial leaf spot of tomato in the field.
author: C.D. Jones, J.B. Jones, W.S. Lee
publish year: 2010
creator: C.D. Jones, J.B. Jones, W.S. Lee
reference: C.D. Jones, J.B. Jones, W.S. Lee, "Diagnosis of bacterial spot of tomato using spectral signatures", Comput. Electron. Agr., 74 (2010), pp. 329-335
Source: Comput. Electron. Agr., 74 (2010), pp. 329-335
label: Article 3782

Early detection of Fusarium infection in wheat using hyper-spectral imaging
type: NamedIndividual
type: Article
has knowledge model: Classifier 3783
abstract: Infections of wheat, rye, oat and barley by Fusarium ssp. are serious problems worldwide due to the mycotoxins, potentially produced by the fungi. In 2005, limit values were issued by the EU commission to avoid health risks by mycotoxins, both for humans and animals. This increased the need to develop tools for early detection of infections. Occurrence of Fusarium-caused head blight disease can be detected by spectral analysis (400–1000 nm) before harvest. With this information, farmers could recognize Fusarium contaminations. They could, therefore, harvest the grains separately and supply it to other utilizations, if applicable. In the present study, wheat plants were analyzed using a hyper-spectral imaging system under laboratory conditions. Principal component analysis (PCA) was applied to differentiate spectra of diseased and healthy ear tissues in the wavelength ranges of 500–533 nm, 560–675 nm, 682–733 nm and 927–931 nm, respectively. Head blight could be successfully recognized during the development stages (BBCH-stages) 71–85. However, the best time for disease determination was at the beginning of medium milk stage (BBCH 75). Just after start of flowering (BBCH 65) and, again, in the fully ripe stage (BBCH 89), distinction by spectral analysis is impossible. With the imaging analysis method ‘Spectral Angle Mapper’ (SAM) the degree of disease was correctly classified (87%) considering an error of visual rating of 10%. However, SAM is time-consuming. It involves both the analysis of all spectral bands and the setup of reference spectra for classification. The application of specific spectral sub-ranges is a very promising alternative. The derived head blight index (HBI), which uses spectral differences in the ranges of 665–675 nm and 550–560 nm, can be a suitable outdoor classification method for the recognition of head blight. In these experiments, mean hit rates were 67% during the whole study period (BBCH 65–89). However, if only the optimal classification…
author: E. Bauriegel, A. Giebel, M. Geyer, U. Schmidt, W.B. Herppich
publish year: 2011
creator: E. Bauriegel, A. Giebel, M. Geyer, U. Schmidt, W.B. Herppich
reference: E. Bauriegel, A. Giebel, M. Geyer, U. Schmidt, W.B. Herppich, "Early detection of Fusarium infection in wheat using hyper-spectral imaging", Comput. Electron. Agr., 75 (2011), pp. 304-312
Source: Comput. Electron. Agr., 75 (2011), pp. 304-312
label: Article 3783

Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery
type: NamedIndividual
type: Article
has knowledge model: Classifier 3784
abstract: Insect outbreaks cause significant tree mortality across western North America, including in high-elevation whitebark pine forests. These forests are under several threats, which include attack by insects and white pine blister rust, as well as conversion to other tree species as a result of fire suppression. Mapping tree mortality is critical to determining the status of whitebark pine as a species. Satellite remote sensing builds upon existing aerial surveys by using objective, repeatable methods that can result in high spatial resolution monitoring. Past studies concentrated on level terrain and only forest vegetation type. The objective of this study was to develop a means of classifying whitebark pine mortality caused by a mountain pine beetle infestation in rugged, remote terrain using high spatial resolution satellite imagery. We overcame three challenges of mapping mortality in this mountainous region: (1) separating non-vegetated cover types, green and brown herbaceous cover, green (live) tree cover, and red-attack (dead) tree cover; (2) variations in illumination as a result of variations in slope and aspect related to the mountainous terrain of the study site; and (3) the difficulty of georegistering the imagery for use in comparing field measurements. Quickbird multi-spectral imagery (2.4 m spatial resolution) was used, together with a maximum likelihood classification method, to classify vegetation cover types over a 6400 ha area. To train the classifier, we selected pixels in each cover class from the imagery guided by our knowledge of the study site. Variables used in the maximum likelihood classifier included the ratio of red reflectance to green reflectance as well as green reflectance. These variables were stratified by solar incidence angle to account for illumination variability. We evaluated the results of the classified image using a reserved set of image-derived class members and field measurements of live and dead trees. Classification result…
author: J.A. Hicke, J. Logan
publish year: 2009
creator: J.A. Hicke, J. Logan
reference: J.A. Hicke, J. Logan, "Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery", Int. J. Remote Sens., 30 (2009), pp. 4427-4441
Source: Int. J. Remote Sens., 30 (2009), pp. 4427-4441
label: Article 3784

Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels
type: NamedIndividual
type: Article
has knowledge model: Classifier 3785
abstract: The prospect of regular assessments of insect defoliation using remote sensing technologies has increased in recent years through advances in the understanding of the spectral reflectance properties of vegetation. The aim of the present study was to evaluate the ability of the red edge channel of Rapideye imagery to discriminate different levels of insect defoliation in an African savanna by comparing the results of obtained from two classifiers. Random Forest and Support vector machine classification algorithms were applied using different sets of spectral analysis involving the red edge band. Results show that the integration of information from red edge increases classification accuracy of insect defoliation levels in all analysis performed in the study. For instance, when all the 5 bands of Rapideye imagery were used for classification, the overall accuracies increases about 19% and 21% for SVM and RF, respectively, as opposed to when the red edge channel was excluded. We also found out that the normalized difference red-edge index yielded a better accuracy result than normalized difference vegetation index. We conclude that the red-edge channel of relatively affordable and readily available high-resolution multispectral satellite data such as Rapideye has the potential to considerably improve insect defoliation classification especially in sub-Saharan Africa where data availability is limited.
author: S. Adelabu, O. Mutanga, E. Adam
publish year: 2014
creator: S. Adelabu, O. Mutanga, E. Adam
reference: S. Adelabu, O. Mutanga, E. Adam, "Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels", ISPRS J. Photogramm. Remote Sens., 95 (2014), pp. 34-41
Source: ISPRS J. Photogramm. Remote Sens., 95 (2014), pp. 34-41
label: Article 3785

Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot
type: NamedIndividual
type: Article
has knowledge model: Classifier 3786
abstract: Cotton root rot, caused by the soilborne fungus Phymatotrichum omnivorum, is a major cotton disease affecting cotton production in the southwestern and south central U.S. Accurate delineation of root rot infestations is necessary for cost-effective management of the disease. The objective of this study was to compare airborne multispectral and hyperspectral imagery for detecting and mapping root rot areas in cotton fields. Two center-pivot irrigated fields in south Texas were selected for this study. Airborne 3-band multispectral and 128-band hyperspectral imagery was taken from the two fields shortly before harvest in 2002 when the infested areas were fully expressed for the season. Both types of imagery and the principal component images derived from the hyperspectral imagery were classified into 2-10 spectral classes using unsupervised classification techniques. The individual spectral classes were then grouped into infested and non-infested zones. Accuracy assessment on the two-zone classification maps showed that both types of imagery as well as the principal component imagery equally accurately identified root rot areas within the fields. These results indicate that both airborne multispectral and hyperspectral imagery can be used for assessing root rot infestations within cotton fields.
author: C. Yang, J.H. Everitt, C.J. Fernandez
creator: C. Yang, J.H. Everitt, C.J. Fernandez
reference: C. Yang, J.H. Everitt, C.J. Fernandez, "Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot", Biosyst. Eng., 107 (2010), pp. 131-139
Source: Biosyst. Eng., 107 (2010), pp. 131-139
label: Article 3786

Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with Support Vector Machines
type: NamedIndividual
type: Article
has knowledge model: Classifier 3787
abstract: Early recognition of pathogen infection is of great relevance in precision plant protection. Pre-symptomatic disease detection is of particular interest. By use of a laserfluoroscope, UV-light induced fluorescence data were collected from healthy and with leaf rust inoculated wheat leaves of the susceptible cultivar Ritmo 2–4 days after inoculation under controlled conditions. In order to evaluate pathogen impact on fluorescence spectra 215 wavelengths in the range of 370–800 nm were recorded. The medians of fluorescence signatures suggest that inoculated leaves may be separated from healthy ones, but high-frequency oscillations and individual reactions of leaves indicate that separability is difficult to achieve. The misbalance between the high number of measured wavelengths and the low number of training examples induces a high overfitting risk. For a pre-symptomatic pathogen identification a small number of robust features was desired which comprise most of the information relevant for the given classification task. Instead of choosing only the most relevant wavelengths, the coefficients of polynomials fitting the spectra were used for classification. They specify the global curve characteristics. Piecewise fitting by polynomials of fourth order led to high classification accuracy. Support Vector Machines were used for classification. Cross validation demonstrated that the achieved classification accuracy reached 93%. This result could be attained on the second day after inoculation, before any visible symptoms appeared. The described method is of general interest for pre-symptomatic pathogen detection based on fluorescence spectra.
author: C. Römer, K. Bürling, M. Hunsche, T. Rumpf, G. Noga, L. Plümer
publish year: 2011
creator: C. Römer, K. Bürling, M. Hunsche, T. Rumpf, G. Noga, L. Plümer
reference: C. Römer, K. Bürling, M. Hunsche, T. Rumpf, G. Noga, L. Plümer, "Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with Support Vector Machines", Comput. Electron. Agr., 79 (2011), pp. 180-188
Source: Comput. Electron. Agr., 79 (2011), pp. 180-188
label: Article 3787

Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado
type: NamedIndividual
type: Article
has knowledge model: Classifier 3788
abstract: Avocado (Persea americana) is a crop that is second in importance in Florida behind citrus with a wholesale value of $35 million and represents approximately 60% of the tropical fruit crop acreage. Laurel wilt (LW) is a lethal disease that has spread rapidly along the southeastern seaboard of the United States affecting commercial avocado production. This article evaluates the spatial and spectral requirements for quick and accurate detection of LW. Spectral data from healthy (H), Phytophthora root rot (PPR) and LW leaves were analyzed using ANOVA and two neural networks, multilayer perceptron (MLP) and radial basis function (RBF). The most effective wavelengths were identified and the filters were updated to a MCA-6 Tetracam camera (580–10 nm, 650–10 nm, 740–10 nm, 750–10 nm, 760–10 nm and 850–40 nm). Then, the MCA camera was used to take multispectral aerial images from a helicopter at three altitudes (180, 250 and 300 m) in an avocado field with trees at different stages of LW development, early, intermediate and late. The analyses were conducted based upon 2-class and 4-class systems. The 2-class system was designed to differentiate H and LW trees sufficient to identify trees for removal and the 4-class system was used to differentiate H plants and the three stages of LW development. Aerial image analysis proved the utility of the selected filters for successful identification of LW, even for trees in early stage of disease development with minimal symptoms. The ideal flight altitude of 250 m (15.3 cm pixel size) was selected according to the M-values and biological parameters such as canopy size and orchard size. The optimum VIs determined by higher M-values were TCARI760–650 as well as GNDVI, NIR/G, Redge/G and VIGreen using any of the bands related to Redge (740 and 750 nm) or NIR regions (760 and 850 nm). Results reported on the utility of the 2-class and 4-class systems using the above VIs to discriminate LW; however it would be more convenient to develop t…
author: A.I.D. Castro, R. Ehsani, R. Ploetz, J.H. Crane, J. Abdulridha
publish year: 2015
creator: A.I.D. Castro, R. Ehsani, R. Ploetz, J.H. Crane, J. Abdulridha
reference: A.I.D. Castro, R. Ehsani, R. Ploetz, J.H. Crane, J. Abdulridha, "Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado", Remote Sens. Environ., 171 (2015), pp. 33-44
Source: Remote Sens. Environ., 171 (2015), pp. 33-44
label: Article 3788

Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysis
type: NamedIndividual
type: Article
has knowledge model: Classifier 3789
abstract: Spectral mixture analysis and hyperspectral remote sensing are analytical and hardware tools new to precision agriculture. They can allow detection and identification of various crop stresses and other plant and canopy characteristics through analysis of their spectral signatures. One stressor in cotton, the strawberry spider mite (Tetranychus turkestani U.N.), feeds on plants causing leaf puckering and reddish discoloration in early stages of infestation and leaf drop later. To determine the feasibility of detecting the damage caused by this pest at the field level, AVIRIS imagery was collected from USDA-ARS cotton research fields at Shafter, CA on 4 dates in 1999. Additionally, cotton plants and soil were imaged in situ in 10 nm increments from 450 to 1050 nm with a liquid-crystal tunable-filter camera system. Mite-damaged areas on leaves, healthy leaves, tilled shaded soil, and tilled sunlit soil were chosen as reference endmembers and used in a constrained linear spectral mixture analysis to unmix the AVIRIS data producing fractional abundance maps. The procedure successfully distinguished between adjacent mite-free and mite-infested cotton fields although shading due to sun angle differences between dates was a complicating factor. The resulting healthy plant, soil, mite-damaged, and shade fraction maps showed the distribution and relative abundance of these endmembers in the fields. These hardware and software technologies can identify the location, spatial extent, and severity of crop stresses for use in precision agriculture.
author: G.J. Fitzgerald, S.J. Maas, W.R. Detar
publish year: 2004
creator: G.J. Fitzgerald, S.J. Maas, W.R. Detar
reference: G.J. Fitzgerald, S.J. Maas, W.R. Detar, "Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysis", Precis. Agric., 5 (2004), pp. 275-289
Source: Precis. Agric., 5 (2004), pp. 275-289
label: Article 3789

Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat
type: NamedIndividual
type: Article
has knowledge model: Classifier 3790
abstract: Detection of yellow rust is of great importance in disease control and reducing the use of fungicide. Spectral analysis is an important method for disease detection in terms of remote sensing. In this study, an emerging spectral analysis method known as continuous wavelet analysis (CWA) was examined and compared with several conventional spectral features for the detection of yellow rust disease at a leaf level. The leaf spectral measurements were made by a spectroradiometer at both Zodaks 37 and 70 stages with a large sample size. The results showed that the wavelet features were able to capture the major spectral signatures of yellow rust, and exhibited considerable potential for disease detection at both growth stages. Both the accuracies of the univariate and multivariate models suggested that wavelet features outperformed conventional spectral features in quantifying disease severity at leaf level. Optimal accuracies returned a coefficient of determination (R2) of 0.81 and a root mean square error (RMSE) of 0.110 for pooled data at both stages. Furthermore, wavelet features showed a stronger response to the yellow rust at Zodaks 70 stage than at Zodaks 37 stage, indicating reliable estimation of disease severity can be made until the Zodaks 70 stage.
author: J. Zhang, R. Pu, R.W. Loraamm, G. Yang, J. Wang
publish year: 2014
creator: J. Zhang, R. Pu, R.W. Loraamm, G. Yang, J. Wang
reference: J. Zhang, R. Pu, R.W. Loraamm, G. Yang, J. Wang, "Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat", Comput. Electron. Agr., 100 (2014), pp. 79-87
Source: Comput. Electron. Agr., 100 (2014), pp. 79-87
label: Article 3790

Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects
type: NamedIndividual
type: Article
has knowledge model: Classifier 3791
abstract: Yellow rust (Puccinia striiformis f. sp. Tritici), powdery mildew (Blumeria graminis) and wheat aphid (Sitobion avenae F.) infestation are three serious conditions that have a severe impact on yield and grain quality of winter wheat worldwide. Discrimination among these three stressors is of practical importance, given that specific procedures (i.e. adoption of fungicide and insecticide) are needed to treat different diseases and insects. This study examines the potential of hyperspectral sensor systems in discriminating these three stressors at leaf level. Reflectance spectra of leaves infected with yellow rust, powdery mildew and aphids were measured at the early grain filling stage. Normalization was performed prior to spectral analysis on all three groups of samples for removing differences in the spectral baseline among different cultivars. To obtain appropriate bands and spectral features (SFs) for stressor discrimination and damage intensity estimation, a correlation analysis and an independent t-test were used jointly. Based on the most efficient bands/SFs, models for discriminating stressors and estimating stressor intensity were established by Fisher's linear discriminant analysis (FLDA) and partial least square regression (PLSR), respectively. The results showed that the performance of the discrimination model was satisfactory in general, with an overall accuracy of 0.75. However, the discrimination model produced varied classification accuracies among different types of diseases and insects. The regression model produced reasonable estimates of stress intensity, with an R2 of 0.73 and a RMSE of 0.148. This study illustrates the potential use of hyperspectral information in discriminating yellow rust, powdery mildew and wheat aphid infestation in winter wheat. In practice, it is important to extend the discriminative analysis from leaf level to canopy level.
author: Yuan L, Huang Y, Loraamm RW, Nie C, Wang J, Zhang J
publish year: 2014
creator: Yuan L, Huang Y, Loraamm RW, Nie C, Wang J, Zhang J
reference: Yuan L, Huang Y, Loraamm RW, Nie C, Wang J, Zhang J, "Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects ", Field Crop Res 156:199–207.
Source: Field Crop Res 156:199–207.
label: Article 3791

Proximal sensing to detect symptoms associated with wheat curl mite-vectored viruses.
type: NamedIndividual
type: Article
has knowledge model: Classifier 3792
author: Stilwell AR, Hein GL, Zygielbaum AI, Rundquist DC
publish year: 2013
creator: Stilwell AR, Hein GL, Zygielbaum AI, Rundquist DC
reference: Stilwell AR, Hein GL, Zygielbaum AI, Rundquist DC, "Proximal sensing to detect symptoms associated with wheat curl mite-vectored viruses.", Intern J Remote Sens 34:4951–4966.
Source: Intern J Remote Sens 34:4951–4966.
label: Article 3792

Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance.
type: NamedIndividual
type: Article
has knowledge model: Classifier 3793
abstract: Automatic methods for an early detection of plant diseases are vital for precision crop protection. The main contribution of this paper is a procedure for the early detection and differentiation of sugar beet diseases based on Support Vector Machines and spectral vegetation indices. The aim was (I) to discriminate diseased from non-diseased sugar beet leaves, (II) to differentiate between the diseases Cercospora leaf spot, leaf rust and powdery mildew, and (III) to identify diseases even before specific symptoms became visible. Hyperspectral data were recorded from healthy leaves and leaves inoculated with the pathogens Cercospora beticola, Uromyces betae or Erysiphe betae causing Cercospora leaf spot, sugar beet rust and powdery mildew, respectively for a period of 21 days after inoculation. Nine spectral vegetation indices, related to physiological parameters were used as features for an automatic classification. Early differentiation between healthy and inoculated plants as well as among specific diseases can be achieved by a Support Vector Machine with a radial basis function as kernel. The discrimination between healthy sugar beet leaves and diseased leaves resulted in classification accuracies up to 97%. The multiple classification between healthy leaves and leaves with symptoms of the three diseases still achieved an accuracy higher than 86%. Furthermore the potential of presymptomatic detection of the plant diseases was demonstrated. Depending on the type and stage of disease the classification accuracy was between 65% and 90%.
author: Rumpf T, Mahlein AK, Steiner U, Oerke EC, Dehne HW, Plümer L
publish year: 2010
creator: Rumpf T, Mahlein AK, Steiner U, Oerke EC, Dehne HW, Plümer L
reference: Rumpf T, Mahlein AK, Steiner U, Oerke EC, Dehne HW, Plümer L, "Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance.", Comput Electron Agric 74:91–99
Source: Comput Electron Agric 74:91–99
label: Article 3793

Detecting Sugarcane yellow leaf virus infection in asymptomatic leaves with hyperspectral remote sensing and associated leaf pigment changes
type: NamedIndividual
type: Article
has knowledge model: Regressor 3794
abstract: Sugarcane infected with Sugarcane yellow leaf virus (SCYLV) rarely produces visual symptoms until late in the growing season. High-resolution, hyperspectral reflectance data from SCYLV-infected and non-infected leaves of two cultivars, LCP 85-384 and Ho 95-988, were measured and analyzed on 13 July, 12 October, and 4 November 2005. All plants were asymptomatic. Infection was determined by reverse transcriptase-polymerase chain reaction (RT-PCR) analysis. Results from discriminant analysis showed that leaf reflectance was effective at predicting SCYLV infection in 73% of the cases in both cultivars using resubstitution and 63% and 62% in LCP 85-384 and Ho 95-988, respectively, using cross-validation. Predictive equations were improved when data from sampling dates were analyzed individually. SCYLV infection influenced the concentration of several leaf pigments including violaxanthin, β-carotene, neoxanthin, and chlorophyll a. Pigment data were effective at predicting SCYLV infection in 80% of the samples in the combined data set using the derived discriminant function with resubstitution, and 71% with cross-validation. Although further research is needed to improve the accuracy of the predictive equations, the results of this study demonstrate the potential application of hyperspectral remote sensing as a rapid, field-based method of identifying SCYLV-infected sugarcane plants prior to symptom expression.
author: Grisham MP, Johnson RM, Zimba PV
publish year: 2010
creator: Grisham MP, Johnson RM, Zimba PV
reference: Grisham MP, Johnson RM, Zimba PV, "Detecting Sugarcane yellow leaf virus infection in asymptomatic leaves with hyperspectral remote sensing and associated leaf pigment changes", J Virol Method 167:140–145.
Source: J Virol Method 167:140–145.
label: Article 3794

A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT.
type: NamedIndividual
type: Article
has knowledge model: Regressor 3795
abstract: This study compares two machine learning algorithms to predict regional winter wheat yields. The models, based on Boosted Regression Trees (BRT) and Support Vector Machines (SVM), are constructed of Normalized Difference Vegetation Indices (NDVI) derived from low resolution SPOT VEGETATION imagery. Three types of NDVI-related predictors were used: Single NDVI, Incremental NDVI and Targeted NDVI. BRT and SVM were first used to select features with high relevance for predicting the yield. Periods of high influence spanning from March to June were detected by both machine learning methods. After feature selection, BRT and SVM models were applied to the subset of selected features for yield forecasting. BRT seems to consistently outperform SVM.
author: Stas, M., Orshoven, J.V., Dong, Q., Heremans, S., Zhang, B.
publish year: 2016
creator: Stas, M., Orshoven, J.V., Dong, Q., Heremans, S., Zhang, B.
reference: Stas, M., Orshoven, J.V., Dong, Q., Heremans, S., Zhang, B., "A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT.", In: 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pp. 1–5.
Source: In: 2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics), pp. 1–5.
label: Article 3795

Potential of ensemble tree methods for early-season prediction of winter wheat yield from short time series of remotely sensed normalized difference vegetation index and in situ meteorological data
type: NamedIndividual
type: Article
has knowledge model: Regressor 3796
abstract: In this study, we aimed at analyzing the potential of two ensemble tree machine learning methods – Boosted Regression Trees and Random Forests – for (early) prediction of winter wheat yield from short time series of remotely sensed vegetation indices (NDVI) at low spatial resolution in combination with meteorological data and data on fertilization levels. The study area was the Huaibei Plain in eastern China and all models were calibrated and validated for five separate prefectures. To this end, a cross-validation process was developed that integrates model meta-parameterization and simple forward feature selection. The resulting models guarantee the delivery of early estimates that are accurate enough to support decision making in the agricultural sector and to allow an operational use of the forecast model. To attain maximum prediction accuracy, incorporating predictors from the end of the growing season is however recommended.
author: S. Heremans, Q. Dong, B. Zhang, L. Bydekerke, J. Van Orshoven
publish year: 2015
creator: S. Heremans, Q. Dong, B. Zhang, L. Bydekerke, J. Van Orshoven
reference: S. Heremans, Q. Dong, B. Zhang, L. Bydekerke, J. Van Orshoven, "Potential of ensemble tree methods for early-season prediction of winter wheat yield from short time series of remotely sensed normalized difference vegetation index and in situ meteorological data", APPRES, 9 (2015), p. 097095
Source: APPRES, 9 (2015), p. 097095
label: Article 3796

Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method
type: NamedIndividual
type: Article
has knowledge model: Regressor 3797
abstract: Leaf area index (LAI) is an important indicator of crop growth. In this paper, a hybrid inversion method was developed to estimate the LAI values of crops. Based on PROSAIL simulation datasets, 43 hyperspectral vegetation indices (VIs), including the optimized soil-adjusted vegetation index (OSVAI) and modified triangular vegetation index (MTVI2), were analyzed to identify optimal VIs for estimating LAI values. Hybrid inversion models were then built to determine the relationships between optimal simulated VIs and the LAI values (generated by the PROSAIL model) using modeling methods, including curve fitting, artificial neural network (ANN) and random forest regression (RFR) algorithms. Finally, remote sensing mapping of a Compact High Resolution Imaging Spectrometer (CHRIS) image was completed using the inversion model to verify the LAI estimation accuracy. The remote sensing mapping of the CHRIS image yielded an accuracy of R2 = 0.928 and RMSE = 0.485 for OSAVI and R2 = 0.910 and RMSE = 0.554 for MTVI2, demonstrating the feasibility of high-accuracy estimation of crop LAI using hyperspectral VIs and a hybrid inversion method. The estimation results of various VIs suggested that the identification of the appropriate VIs is critical to improve the inversion accuracy. In addition, to obtain the appropriate VIs, the factors must be evaluated with respect to two aspects, i.e., the sensitivity to target parameters and the insensitivity to interference. In this study, OSVAI and MTVI2 were sensitive to LAI and relatively insensitive to the effects of interference factors, such as chlorophyll, soil background, sky scattered light and observed geometry. Therefore, these indices could be primarily used as VIs for an LAI estimation. The inversion results of different datasets demonstrated that prior information is critical for improving the inversion accuracy and identifying the optimal VIs. Additionally, based on the comparison of the curve fitting, ANN, and RFR algorithms, …
author: L. Liang, L. Di, L. Zhang, M. Deng, Z. Qin, S. Zhao, H. Lin
publish year: 2015
creator: L. Liang, L. Di, L. Zhang, M. Deng, Z. Qin, S. Zhao, H. Lin
reference: L. Liang, L. Di, L. Zhang, M. Deng, Z. Qin, S. Zhao, H. Lin, "Estimation of crop LAI using hyperspectral vegetation indices and a hybrid inversion method", Remote Sens. Environ., 165 (2015), pp. 123-134
Source: Remote Sens. Environ., 165 (2015), pp. 123-134
label: Article 3797

Multilocation Straw Removal Effects on Sugarcane Yield in South-Central Brazil
type: NamedIndividual
type: Article
has knowledge model: Regressor 3804
abstract: Sugarcane straw is a crop residue that has a dual purpose and can be maintained on the fields or used to produce bioenergy. The straw retention in the field provides multiple ecosystem services, and the complex interactions between straw and yield responses are hard to predict by local studies. The aim of this study was to investigate the straw removal effects on sugarcane yield in south-central Brazil. To achieve the objective, a set of 21 field studies was conducted in contrasting edaphoclimatic conditions. In addition, data of seven studies from the literature were included to build a more robust dataset. Since straw removal treatments were not homogeneous in all experiments, they were grouped as follows: NR, no removal (baseline treatment); LR, low removal; MR, moderate removal; and TR, total removal. To facilitate the data analysis, the experiments were gathered in four macroregions: southern Goiás, western São Paulo, central-eastern São Paulo, and northeastern São Paulo. The site location was the most effective individual factor to explain the straw removal effects on sugarcane yields. Compared with NR treatment, the average yield losses induced by LR, MR, and TR were 2, 10, and 13 Mg ha−1 in southern Goiás and 2, 4, and 6 Mg ha−1 in western São Paulo states, respectively. In other regions, no clear pattern was observed, and only site-specific effects were observed. Straw removal affected sugarcane yields in all soil types, but higher responses were observed under best conditions for sugarcane growth (i.e., clayey soils in southern Goiás). Our findings indicated harvesting season has a relevant role on determining yield responses to straw removal and younger ratoons are more responsive to straw removal. Conclusions from this study suggest straw removal recommendations cannot be designed based on isolated factors but require holistic and integrated knowledge to ensure that the straw amount left on field is enough to sustain multiple soil ecosystem services and …
author: João Luís Nunes Carvalho, Lauren Maine Santos Menandro, Sérgio Gustavo Quassi de Castro, Maurício Roberto Cherubin, Ricardo de Oliveira Bordonal, Leandro Carneiro Barbosa, Leandro Carolino Gonzaga, Sarah Tenelli, Henrique Coutinho Junqueira Franco, Oriel Tiago Kolln, Guilherme Adalberto Ferreira Castioni
publish year: 2019
creator: João Luís Nunes Carvalho, Lauren Maine Santos Menandro, Sérgio Gustavo Quassi de Castro, Maurício Roberto Cherubin, Ricardo de Oliveira Bordonal, Leandro Carneiro Barbosa, Leandro Carolino Gonzaga, Sarah Tenelli, Henrique Coutinho Junqueira Franco, Oriel Tiago Kolln, Guilherme Adalberto Ferreira Castioni
reference: João Luís Nunes Carvalho, Lauren Maine Santos Menandro, Sérgio Gustavo Quassi de Castro, Maurício Roberto Cherubin, Ricardo de Oliveira Bordonal, Leandro Carneiro Barbosa, Leandro Carolino Gonzaga, Sarah Tenelli, Henrique Coutinho Junqueira Franco, Oriel Tiago Kolln, Guilherme Adalberto Ferreira Castioni, "Multilocation Straw Removal Effects on Sugarcane Yield in South-Central Brazil", BioEnergy Research volume 12, pages 813–829 (2019) , Springer
Source: BioEnergy Research volume 12, pages 813–829 (2019) , Springer
label: Article 3804

In-season weather data provide reliable yield estimates of maize and soybean in the US central Corn Belt
type: NamedIndividual
type: Article
has knowledge model: Regressor 3805
abstract: Weather conditions regulate the growth and yield of crops, especially in rain-fed agricultural systems. This study evaluated the use and relative importance of readily available weather data to develop yield estimation models for maize and soybean in the US central Corn Belt. Total rainfall (Rain), average air temperature (Tavg), and the difference between maximum and minimum air temperature (Tdiff) at weekly, biweekly, and monthly timescales from May to August were used to estimate county-level maize and soybean grain yields for Iowa, Illinois, Indiana, and Minnesota. Step-wise multiple linear regression (MLR), general additive (GAM), and support vector machine (SVM) models were trained with Rain, Tavg, and with/without Tdiff. For the total study area and at individual state level, SVM outperformed other models at all temporal levels for both maize and soybean. For maize, Tavg and Tdiff during July and August, and Rain during June and July, were relatively more important whereas for soybean, Tavg in June and Tdiff and Rain during August were more important. The SVM model with weekly Rain and Tavg estimated the overall maize yield with a root mean square error (RMSE) of 591 kg ha−1 (4.9% nRMSE) and soybean yield with a RMSE of 205 kg ha−1 (5.5% nRMSE). Inclusion of Tdiff in the model considerably improved yield estimation for both crops; however, the magnitude of improvement varied with the model and temporal level of weather data. This study shows the relative importance of weather variables and reliable yield estimation of maize and soybean from readily available weather data to develop a decision support tool in the US central Corn Belt.
author: Vijaya R. Joshi, Maciej J. Kazula, Jeffrey A. Coulter, Seth L. Naeve, Axel Garcia y Garcia
publish year: 2021
creator: Vijaya R. Joshi, Maciej J. Kazula, Jeffrey A. Coulter, Seth L. Naeve, Axel Garcia y Garcia
reference: Vijaya R. Joshi, Maciej J. Kazula, Jeffrey A. Coulter, Seth L. Naeve, Axel Garcia y Garcia, "In-season weather data provide reliable yield estimates of maize and soybean in the US central Corn Belt", International Journal of Biometeorology volume 65, pages 489–502 (2021) , Springer
Source: International Journal of Biometeorology volume 65, pages 489–502 (2021) , Springer
label: Article 3805

Machine learning techniques in disease forecasting: a case study on rice blast prediction
type: NamedIndividual
type: Article
has knowledge model: Regressor 3808
abstract: Diverse modeling approaches viz. neural networks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases.ResultsSix significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year) were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG) approach achieved an average correlation coefficient (r) of 0.50, which increased to 0.60 and percent mean absolute error (%MAE) decreased from 65.42 to 52.24 when back-propagation neural network (BPNN) was used. With generalized regression neural network (GRNN), the r increased to 0.70 and %MAE also improved to 46.30, which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM) based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding %MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in %MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops.ConclusionOur case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also developed a SVM-based web server for r…
author: Rakesh Kaundal, Amar S Kapoor, Gajendra PS Raghava
publish year: 2006
creator: Rakesh Kaundal, Amar S Kapoor, Gajendra PS Raghava
reference: Rakesh Kaundal, Amar S Kapoor, Gajendra PS Raghava, "Machine learning techniques in disease forecasting: a case study on rice blast prediction", BMC Bioinformatics volume 7, Article number: 485 (2006) , Springer
Source: BMC Bioinformatics volume 7, Article number: 485 (2006) , Springer
label: Article 3808

Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis
type: NamedIndividual
type: Article
has knowledge model: Regressor 3817
abstract: The accurate quantification of yield in rapeseed is important for evaluating the supply of vegetable oil, especially at regional scales.MethodsThis study developed an approach to estimate rapeseed yield with remotely sensed canopy spectra and abundance data by spectral mixture analysis. A six-band image of the studied rapeseed plots was obtained by an unmanned aerial vehicle (UAV) system during the rapeseed flowering stage. Several widely used vegetation indices (VIs) were calculated from canopy reflectance derived from the UAV image. And the plot-level abundance of flower, leaf and soil, indicating the fraction of different components within the plot, was retrieved based on spectral mixture analysis on the six-band image and endmember spectra collected in situ for different components. ResultsThe results showed that for all tested indices VI multiplied by leaf-related abundance closely related to rapeseed yield. The product of Normalized Difference Vegetation Index and short-stalk-leaf abundance was the most accurate for estimating yield in rapeseed under different nitrogen treatments with the estimation errors below 13%.ConclusionThis study gives an important indication that spectral mixture analysis needs to be considered when estimating yield by remotely sensed VI, especially for the image containing obviously spectral different components or for crops which have conspicuous flowers or fruits with significantly different spectra from their leave.
author: Yan Gong, Bo Duan, Shenghui Fang, Renshan Zhu, Xianting Wu, Yi Ma, Yi Peng
publish year: 2018
creator: Yan Gong, Bo Duan, Shenghui Fang, Renshan Zhu, Xianting Wu, Yi Ma, Yi Peng
reference: Yan Gong, Bo Duan, Shenghui Fang, Renshan Zhu, Xianting Wu, Yi Ma, Yi Peng, "Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis", Plant Methods volume 14, Article number: 70 (2018) , Springer
Source: Plant Methods volume 14, Article number: 70 (2018) , Springer
label: Article 3817

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
type: NamedIndividual
type: Article
has knowledge model: Regressor 3914
abstract: Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at tillering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha−1 with an R2 of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.
author: Tatiana Fernanda Canata, Marcelo Chan Fu Wei, Leonardo Felipe Maldaner, José Paulo Molin
publish year: 2021
creator: Tatiana Fernanda Canata, Marcelo Chan Fu Wei, Leonardo Felipe Maldaner, José Paulo Molin
reference: Tatiana Fernanda Canata, Marcelo Chan Fu Wei, Leonardo Felipe Maldaner, José Paulo Molin, "Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique", Remote Sens. 2021, 13(2), 232; https://doi.org/10.3390/rs13020232
Source: Remote Sens. 2021, 13(2), 232; https://doi.org/10.3390/rs13020232
label: Article 3914

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield
type: NamedIndividual
type: Article
has knowledge model: Regressor 3926
abstract: A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision a…
author: Nahuel R. Peralta, Yared Assefa, Juan Du, Charles J. Barden, Ignacio A. Ciampitti
publish year: 2016
creator: Nahuel R. Peralta, Yared Assefa, Juan Du, Charles J. Barden, Ignacio A. Ciampitti
reference: Nahuel R. Peralta, Yared Assefa, Juan Du, Charles J. Barden, Ignacio A. Ciampitti, "Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield", Remote Sens. 2016, 8(10), 848; https://doi.org/10.3390/rs8100848
Source: Remote Sens. 2016, 8(10), 848; https://doi.org/10.3390/rs8100848
label: Article 3926

Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States
type: NamedIndividual
type: Article
has knowledge model: Regressor 3928
abstract: Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R2 = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the f…
author: Yumiao Wang, Zhou Zhang, Luwei Feng, Qingyun Du, Troy Runge
publish year: 2020
creator: Yumiao Wang, Zhou Zhang, Luwei Feng, Qingyun Du, Troy Runge
reference: Yumiao Wang, Zhou Zhang, Luwei Feng, Qingyun Du, Troy Runge, "Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States", Remote Sens. 2020, 12(8), 1232; https://doi.org/10.3390/rs12081232
Source: Remote Sens. 2020, 12(8), 1232; https://doi.org/10.3390/rs12081232
label: Article 3928

Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques
type: NamedIndividual
type: Article
has knowledge model: Regressor 3931
abstract: Spatial variability in a crop field creates a need for precision agriculture. Economical and rapid means of identifying spatial variability is obtained through the use of geotechnology (remotely sensed images of the crop field, image processing, GIS modeling approach, and GPS usage) and data mining techniques for model development. Higher-end image processing techniques are followed to establish more precision. The goal of this paper was to investigate the strength of key spectral vegetation indices for agricultural crop yield prediction using neural network techniques. Four widely used spectral indices were investigated in a study of irrigated corn crop yields in the Oakes Irrigation Test Area research site of North Dakota, USA. These indices were: (a) red and near-infrared (NIR) based normalized difference vegetation index (NDVI), (b) green and NIR based green vegetation index (GVI), (c) red and NIR based soil adjusted vegetation index (SAVI), and (d) red and NIR based perpendicular vegetation index (PVI). These four indices were investigated for corn yield during 3 years (1998, 1999, and 2001) and for the pooled data of these 3 years. Initially, Back-propagation Neural Network (BPNN) models were developed, including 16 models (4 indices * 4 years including the data from the pooled years) to test for the efficiency determination of those four vegetation indices in corn crop yield prediction. The corn yield was best predicted using BPNN models that used the means and standard deviations of PVI grid images. In all three years, it provided higher prediction accuracies, coefficient of determination (r2), and lower standard error of prediction than the models involving GVI, NDVI, and SAVI image information. The GVI, NDVI, and SAVI models for all three years provided average testing prediction accuracies of 24.26% to 94.85%, 19.36% to 95.04%, and 19.24% to 95.04%, respectively while the PVI models for all three years provided average testing prediction accuracies of 83.…
author: Sudhanshu Sekhar Panda, Daniel P. Ames, Suranjan Panigrahi
publish year: 2010
creator: Sudhanshu Sekhar Panda, Daniel P. Ames, Suranjan Panigrahi
reference: Sudhanshu Sekhar Panda, Daniel P. Ames, Suranjan Panigrahi, "Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques", Remote Sens. 2010, 2(3), 673-696; https://doi.org/10.3390/rs2030673
Source: Remote Sens. 2010, 2(3), 673-696; https://doi.org/10.3390/rs2030673
label: Article 3931

Prediction of Maize Yield at the City Level in China Using Multi-Source Data
type: NamedIndividual
type: Article
has knowledge model: Regressor 3935
abstract: Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices f…
author: Xinxin Chen, Lan Feng, Rui Yao, Xiaojun Wu, Jia Sun, Wei Gong
publish year: 2021
creator: Xinxin Chen, Lan Feng, Rui Yao, Xiaojun Wu, Jia Sun, Wei Gong
reference: Xinxin Chen, Lan Feng, Rui Yao, Xiaojun Wu, Jia Sun, Wei Gong, "Prediction of Maize Yield at the City Level in China Using Multi-Source Data", Remote Sens. 2021, 13(1), 146; https://doi.org/10.3390/rs13010146
Source: Remote Sens. 2021, 13(1), 146; https://doi.org/10.3390/rs13010146
label: Article 3935

Aspect 0702
type: NamedIndividual
type: Aspect
is condition of: Regressor_0702

Aspect 3730
type: NamedIndividual
type: Aspect
is condition of: Regressor 3730

AssociationRule 0013
type: NamedIndividual
type: Association Rule
type: Knowledge Model
defined in: Analysis of green manure decomposition parameters in northeast Brazil using association rule networks
has algorithm: Association Rule Networks (ARNs)
has algorithm: Apriori (Apriori)
has algorithm: Decision Tree (DT)
has condition: Biomass 0013
has condition: Flower_0013
has condition: Seed_0013
has condition: Stem_0013
has location: Brazil
grade: 60.0
reference: Dario Brito Calcada, Solange Oliveira Rezende, Mauro Sergio Teodoro, "Analysis of green manure decomposition parameters in northeast Brazil using association rule networks", Computers and Electronics in Agriculture, Volume 159, April 2019, Pages 34-41, Elsevier B.V.
comment: Analysis of green manure decomposition parameters in northeast Brazil using association rule networks

AssociationRule 0102
type: NamedIndividual
type: Association Rule
type: Knowledge Model
defined in: A spectral correction method for multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application to nitrogen content assessment in wheat
has algorithm: Partial Least Squares (PLS)
has algorithm: Algorithm PLS-R
has condition: SpectralImage_0102
has location: France
has location: Norway
predicts: Nitrogen_0102
related to: Canopy
related to: Nitrogen (N)
related to: Wheat
grade: 70.0
reference: Nathalie Al Makdessi, Martin Ecarnot, Pierre Roumet, Gilles Rabatel, "A spectral correction method for multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application to nitrogen content assessment in wheat", Precision Agriculture, Volume 20, pages 237–259 (2019) , Springer
comment: A spectral correction method for multi-scattering effects in close range hyperspectral imagery of vegetation scenes: application to nitrogen content assessment in wheat

AssociationRule 1227
type: NamedIndividual
type: Association Rule
type: Knowledge Model
defined in: An effective neural network and fuzzy time series - based hybridized model to handle forecasting problems of two factors
has algorithm: Undefine (Undefine)
has condition: Boron 1227
has condition: Calcium 1227
has condition: Copper 1227
has condition: Iron 1227
has condition: Magnesium 1227
has condition: Manganese 1227
has condition: Nitrogen 1227
has condition: Phosphorus 1227
has condition: Potassium 1227
has condition: Sodium 1227
has condition: Sulphur 1227
has condition: Zinc 1227
has location: United States
predicts: Yield 1227
related to: Basil
grade: 65.0
reference: Subhrajit Saha, Amber Monroe, Martin R. Day, "Growth , yield , plant quality and nutrition of basil ( Ocimum basilicum L. ) under soilless agricultural systems", Annals of Agricultural SciencesVolume 61, Issue 2, December 2016, Elsevier B.V.
comment: Growth , yield , plant quality and nutrition of basil ( Ocimum basilicum L. ) under soilless agricultural systems

AssociationRule 1631
type: NamedIndividual
type: Association Rule
type: Knowledge Model
defined in: Fifty years of semi - dwarf spring wheat breeding at CIMMYT : Grain yield progress in optimum , drought and heat stress environments
has algorithm: Least Absolute Shrinkage and Selection Operator (LASSO)
has condition: GrowthStage 1631
has condition: Irrigation 1631
has condition: Yield 1631
has location: Mexico
predicts: Yield 1631x
related to: Wheat
grade: 68.33333333333334
reference: Suchismita Mondal, Somak Dutta, Leonardo Crespo-Herrera, Julio Huerta-Espino, Hans J. Braun, Ravi P. Singh, "Fifty years of semi - dwarf spring wheat breeding at CIMMYT : Grain yield progress in optimum , drought and heat stress environments", Field Crops ResearchVolume 250, 1 May 2020, Elsevier B.V.
comment: Fifty years of semi - dwarf spring wheat breeding at CIMMYT : Grain yield progress in optimum , drought and heat stress environments

AssociationRule 1986
type: NamedIndividual
type: Association Rule
type: Knowledge Model
defined in: Evaluation of resistance to cassava mosaic disease in selected African cassava cultivars using combined molecular and greenhouse grafting tools
has algorithm: Principal Component Regression (PCR)
has condition: Cassava 1986
has condition: Ear 1986
has condition: Farmer 1986
has dataset: Dataset 1986a
has location: Benin
has location: Cameroon
has location: Nigeria
predicts: CropDisease 1986
related to: Cassava
related to: Greenhouse
grade: 75.0
reference: Jerome Anani Houngue, Martine Zandjanakou-Tachin, Hermine Bille Ngalle, Justin S. Pita, Gilles Habib Todjro Cacaï, Sergine E. Ngatat, Joseph Martin Bell, Corneille Ahanhanzo, "Evaluation of resistance to cassava mosaic disease in selected African cassava cultivars using combined molecular and greenhouse grafting tools", Physiological and Molecular Plant PathologyVolume 105, January 2019, Elsevier B.V.
comment: Evaluation of resistance to cassava mosaic disease in selected African cassava cultivars using combined molecular and greenhouse grafting tools

AssociationRule 2759
type: NamedIndividual
type: Association Rule
type: Knowledge Model
defined in: Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale
has algorithm: Discriminant Analysis (DA)
has algorithm: Stepwise Discriminant Analysis (SDA)
has condition: FieldImage 2759
has location: Italy
predicts: Herbicide 2759
related to: Rice
grade: 65.0
reference: Elisa Mascanzoni, Alessia Perego, Niccolò Marchi, Laura Scarabel, Silvia Panozzo, Aldo Ferrero, Marco Acutis, Maurizio Sattin, "Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale", Agronomy for Sustainable Development volume 38, Article number: 68 (2018) , Springer
comment: Epidemiology and agronomic predictors of herbicide resistance in rice at a large scale

AssociationRule 3158
type: NamedIndividual
type: Association Rule
type: Knowledge Model
defined in: Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf - Tissue Nutrients in Fields Managed with Straw Removal
has algorithm: Vegetation Indices (VI)
has condition: Calcium 3158
has condition: Magnesium 3158
has condition: Nitrogen 3158
has condition: Phosphorus 3158
has condition: Potassium 3158
has condition: SoilPH 3158
has condition: SpectralImage 3158
has condition: Sulphur 3158
has location: Brazil
predicts: Yield 3158
related to: Sugarcane
grade: 65.625
reference: Izaias Pinheiro Lisboa, Júnior Melo Damian, Maurício Roberto Cherubin, Pedro Paulo Silva Barros, Peterson Ricardo Fiorio, Carlos Clemente Cerri, Carlos Eduardo Pellegrino Cerri, "Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf - Tissue Nutrients in Fields Managed with Straw Removal", Agronomy 2018, 8(9), 196; https://doi.org/10.3390/agronomy8090196
comment: Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf - Tissue Nutrients in Fields Managed with Straw Removal

AtmosphericPressure 1222
type: NamedIndividual
type: Atmospheric Pressure
is condition of: Regressor 1222

BiologicalDay 0474
type: NamedIndividual
type: Biological Day
is condition of: Regressor_0474

Biomass 0013
type: NamedIndividual
type: Biomass
has transformation: Stems Fresh Biomass (MFPA)
has transformation: Root Fresh Biomass (MFR)
has transformation: Stems Dry Biomass (MSPA)
is condition of: AssociationRule 0013

Biomass 0015
type: NamedIndividual
type: Biomass
has transformation: Biomass by Dried
has transformation: Biomass by Fresh

Biomass 0055
type: NamedIndividual
type: Biomass

Biomass 0144
type: NamedIndividual
type: Biomass

Biomass 0264
type: NamedIndividual
type: Biomass

Biomass 0265
type: NamedIndividual
type: Biomass
has state: Corn
has state: Waste
has state: Wheat
has state: Wood

Biomass 0314
type: NamedIndividual
type: Biomass
has transformation: Aboveground Dry Biomass (AGDB)
has transformation: Aboveground Fresh Biomass (AGFB)

Biomass 0318
type: NamedIndividual
type: Biomass

Biomass 0426
type: NamedIndividual
type: Biomass
has transformation: Dry Matter Content (DMC)

Biomass 0525
type: NamedIndividual
type: Biomass
has transformation: Above Ground Biomass (AGB)

Biomass 0566
type: NamedIndividual
type: Biomass

Biomass 0677
type: NamedIndividual
type: Biomass

Biomass 1207
type: NamedIndividual
type: Biomass

Biomass 1208
type: NamedIndividual
type: Biomass

Biomass 1223
type: NamedIndividual
type: Biomass
has transformation: Water Limited Biomass (WLB)
is condition of: Regressor 1223

Biomass 1224
type: NamedIndividual
type: Biomass
has transformation: Dry Matter Content (DMC)

Biomass 2313
type: NamedIndividual
type: Biomass

Biomass 2673
type: NamedIndividual
type: Biomass
has transformation: Biomass by Dried
has transformation: Biomass by Fresh
is condition of: Regressor 2673

Biomass 3450
type: NamedIndividual
type: Biomass
has transformation: Dry Matter Yield (DMY)
has transformation: Fresh Yield (FY)

Biomass 3465
type: NamedIndividual
type: Biomass
has transformation: Above Ground Biomass (AGB)

Boron 0001
type: NamedIndividual
type: Boron (B)
is condition of: Regressor_0001

Boron 0203
type: NamedIndividual
type: Boron (B)
is condition of: Regressor_0203

Boron 0254
type: NamedIndividual
type: Boron (B)
is condition of: Regressor_0254

Boron 0254x
type: NamedIndividual
type: Boron (B)

Boron 0633
type: NamedIndividual
type: Boron (B)
has transformation: Boron by Tier3
is condition of: Classifier 0633

Boron 1209
type: NamedIndividual
type: Boron (B)

Boron 1227
type: NamedIndividual
type: Boron (B)
is condition of: AssociationRule 1227

Boundary 0053
type: NamedIndividual
type: Boundary

Boundary 0249
type: NamedIndividual
type: Boundary

Boundary 0688
type: NamedIndividual
type: Boundary

Boundary 1427
type: NamedIndividual
type: Boundary

BulkDensity 0005
type: NamedIndividual
type: Bulk Density
is condition of: Regressor_0005

BulkDensity 0309
type: NamedIndividual
type: Bulk Density
is condition of: Regressor_0309

BulkDensity 0633
type: NamedIndividual
type: Bulk Density
has transformation: Bulk Density by Tier3
is condition of: Classifier 0633

BulkDensity 0701
type: NamedIndividual
type: Bulk Density
is condition of: Cluster 0701

BulkDensity 1073
type: NamedIndividual
type: Bulk Density
is condition of: Regressor 1073

BulkDensity 1210
type: NamedIndividual
type: Bulk Density
is condition of: Regressor 1210

BulkDensity 1232
type: NamedIndividual
type: Bulk Density
is condition of: Regressor 1232

BulkDensity 1236
type: NamedIndividual
type: Bulk Density
is condition of: Regressor 1236

BulkDensity 1250
type: NamedIndividual
type: Bulk Density
is condition of: Regressor 1250

BulkDensity 1369
type: NamedIndividual
type: Bulk Density
is condition of: Regressor 1369

BulkDensity 1648
type: NamedIndividual
type: Bulk Density
is condition of: Regressor 1648

BulkDensity 2992
type: NamedIndividual
type: Bulk Density
is condition of: Classifier 2992

BulkDensity 3669
type: NamedIndividual
type: Bulk Density
is condition of: Regressor 3669

BulkDensity 3684
type: NamedIndividual
type: Bulk Density
is condition of: Regressor 3684

BulkDensity 3776
type: NamedIndividual
type: Bulk Density
is condition of: Regressor 3776

BulkDensity 3928
type: NamedIndividual
type: Bulk Density
is condition of: Regressor 3928

Cadmium 1209
type: NamedIndividual
type: Cadmium (Cd)

Cadmium 2166
type: NamedIndividual
type: Cadmium (Cd)
is condition of: Classifier 2166

Cadmium 3511
type: NamedIndividual
type: Cadmium (Cd)
is condition of: Classifier 3511

Calcium 0006
type: NamedIndividual
type: Calcium (Ca)
is condition of: Classifier 0006

Calcium 0905
type: NamedIndividual
type: Calcium (Ca)

Calcium 1073
type: NamedIndividual
type: Calcium (Ca)
is condition of: Regressor 1073

Calcium 1209
type: NamedIndividual
type: Calcium (Ca)

Calcium 1227
type: NamedIndividual
type: Calcium (Ca)
is condition of: AssociationRule 1227

Calcium 1250
type: NamedIndividual
type: Calcium (Ca)

Calcium 1369
type: NamedIndividual
type: Calcium (Ca)
is condition of: Regressor 1369

Calcium 3158
type: NamedIndividual
type: Calcium (Ca)
is condition of: AssociationRule 3158

Calcium 3804
type: NamedIndividual
type: Calcium (Ca)
is condition of: Regressor 3804

Canal 0076
type: NamedIndividual
type: Canal
has transformation: Canal by Length
is condition of: Regressor_0076

Canopy 0012
type: NamedIndividual
type: Canopy
has transformation: Leaf Area Index (LAI)

Canopy 0149
type: NamedIndividual
type: Canopy
has transformation: Leaf Area Index (LAI)
is condition of: Regressor_0149

Canopy 0182
type: NamedIndividual
type: Canopy
is condition of: Cluster 0182

Canopy 0198
type: NamedIndividual
type: Canopy
has transformation: Leaf by LAITier3
is condition of: Regressor_0198

Canopy 0559
type: NamedIndividual
type: Canopy
has transformation: Leaf Area Index (LAI)
is condition of: Regressor_0559

Canopy 0636
type: NamedIndividual
type: Canopy
has transformation: Canopy Weight (CWT)
is condition of: Regressor_0636

Canopy 0725
type: NamedIndividual
type: Canopy
has transformation: Leaf Area Index (LAI)

Canopy 0919
type: NamedIndividual
type: Canopy
has transformation: Leaf Area Index (LAI)

Canopy 1498
type: NamedIndividual
type: Canopy
has transformation: Vegetation Optical Depth (VOD)
is condition of: Regressor 1498

Canopy 2298
type: NamedIndividual
type: Canopy
has transformation: Leaf Area Index (LAI)

Canopy 2673
type: NamedIndividual
type: Canopy
has transformation: Leaf Area Index (LAI)
is condition of: Regressor 2673

Canopy 3494
type: NamedIndividual
type: Canopy
has transformation: Leaf Area Index (LAI)

Carbohydrate 1939
type: NamedIndividual
type: Carbohydrate

Carbon 1049
type: NamedIndividual
type: Carbon (C)
has transformation: Total Carbon (TC)
is condition of: Regressor 1049

Carbon 1209
type: NamedIndividual
type: Carbon (C)

Carbon 1210
type: NamedIndividual
type: Carbon (C)
has transformation: Total Carbon (TC)
is condition of: Regressor 1210

Carbon 3669
type: NamedIndividual
type: Carbon (C)
is condition of: Regressor 3669

Carbon 3684
type: NamedIndividual
type: Carbon (C)
has transformation: Carbon Content (CC)
is condition of: Regressor 3684

Carbon 3804
type: NamedIndividual
type: Carbon (C)
is condition of: Regressor 3804

Cassava 1986
type: NamedIndividual
type: Cassava
is condition of: AssociationRule 1986

CationExchangeCapacity 0005
type: NamedIndividual
type: Cation Exchange Capacity
is condition of: Regressor_0005

CationExchangeCapacity 0006
type: NamedIndividual
type: Cation Exchange Capacity
is condition of: Classifier 0006

CationExchangeCapacity 0010
type: NamedIndividual
type: Cation Exchange Capacity
is condition of: Regressor_0010

CationExchangeCapacity 0313
type: NamedIndividual
type: Cation Exchange Capacity

CationExchangeCapacity 0702
type: NamedIndividual
type: Cation Exchange Capacity
is condition of: Regressor_0702

CationExchangeCapacity 1049
type: NamedIndividual
type: Cation Exchange Capacity
is condition of: Regressor 1049

CationExchangeCapacity 1073
type: NamedIndividual
type: Cation Exchange Capacity
is condition of: Regressor 1073

CationExchangeCapacity 1232
type: NamedIndividual
type: Cation Exchange Capacity
is condition of: Regressor 1232

CationExchangeCapacity 2992
type: NamedIndividual
type: Cation Exchange Capacity
is condition of: Classifier 2992

CationExchangeCapacity 3775
type: NamedIndividual
type: Cation Exchange Capacity
is condition of: Regressor 3775

Chlorophyll 0317
type: NamedIndividual
type: Chlorophyll

Chlorophyll 0321
type: NamedIndividual
type: Chlorophyll

Chlorophyll 0550
type: NamedIndividual
type: Chlorophyll

Chlorophyll 0559
type: NamedIndividual
type: Chlorophyll
has transformation: Chlorophyll by Default
is condition of: Regressor_0559

Chlorophyll 1211
type: NamedIndividual
type: Chlorophyll

Chlorophyll 1212
type: NamedIndividual
type: Chlorophyll

Chlorophyll 2349
type: NamedIndividual
type: Chlorophyll

Chlorophyll 2673
type: NamedIndividual
type: Chlorophyll
has transformation: Chlorophyll content (Cab)
is condition of: Regressor 2673

Chlorophyll 3134
type: NamedIndividual
type: Chlorophyll

Chlorophyll 3698
type: NamedIndividual
type: Chlorophyll

Chromium 0905
type: NamedIndividual
type: Chromium (Cr)

Citrus 3722
type: NamedIndividual
type: Citrus
is condition of: Classifier 3722

Classifier 0006
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Wheat yield prediction using machine learning and advanced sensing techniques
evaluated by: Evaluation 0006
has algorithm: Counter-propagation Artificial Neural Network (CPANN)
has algorithm: Supervised Kohonen Network (SKN)
has algorithm: Vegetation Indices (VI)
has algorithm: XY-fusion network (XYF)
has condition: Calcium 0006
has condition: CationExchangeCapacity 0006
has condition: Magnesium_0006
has condition: OrganicCarbon_0006
has condition: Phosphorus_0006
has condition: SatelliteImage_0006
has condition: SoilMoisture_0006
has condition: SoilN_0006
has condition: SoilPH_0006
has condition: Yield 0006a
has condition: Yield 0006b
has dataset: Dataset 0006a
has location: United Kingdom
predicts: Yield 0006x
related to: Remote Sensing
related to: Soil
related to: Wheat
grade: 81.36363636363637
reference: X.E. Pantazi, D. Moshou, T. Alexandridis, R.L. Whetton, A.M. Mouazen, "Wheat yield prediction using machine learning and advanced sensing techniques", Computers and Electronics in Agriculture, Volume 121, February 2016, pages 57-65, Elsevier B.V.
comment: Wheat yield prediction using machine learning and advanced sensing techniques

Classifier 0016
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Rice diseases classification using feature selection and rule generation techniques.
evaluated by: Evaluation 0016
has algorithm: Rule Generation (RG)
has condition: CropImage 0016
predicts: CropDisease 0016
related to: Rice
grade: 70.0
reference: Phadikar, Santanu, Jaya Sil, and Asit Kumar Das, "Rice diseases classification using feature selection and rule generation techniques.", Computers and Electronics in Agriculture, Volume 90, January 2013, Pages 76-85, Elsevier B.V.
comment: Rice diseases classification using feature selection and rule generation techniques.

Classifier 0017
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Crop leaf disease recognition based on Self-Attention convolutional neural network.
evaluated by: Evaluation 0017
has algorithm: Self-Attention Convolutional Neural Network (SACNN)
has condition: CropImage 0017
has dataset: Dataset AES-CD9214
has dataset: Dataset MK-D2
predicts: CropDisease 0017
related to: Leaf Disease
grade: 80.0
reference: Weihui Zenga, Miao Li, "Crop leaf disease recognition based on Self-Attention convolutional neural network.", Computers and Electronics in Agriculture 172 (2020): 105341., Elsevier B.V.
comment: Crop leaf disease recognition based on Self-Attention convolutional neural network.

Classifier 0022
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A Band Math-ROC operation for early differentiation between Sclerotinia sclerotiorum and Botrytis cinerea in oilseed rape
evaluated by: Evaluation 0022
has algorithm: Least Squares Support Vector Machine (LSSVM)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0022
has dataset: Dataset 0022a
has location: China
predicts: CropDisease 0022
related to: Grape
grade: 80.0
reference: Xing Xu, Yun Zhao, "A Band Math-ROC operation for early differentiation between Sclerotinia sclerotiorum and Botrytis cinerea in oilseed rape", Computers and Electronics in Agriculture, Volume 118, October 2015, pages 116-123, Elsevier B.V.
comment: A Band Math-ROC operation for early differentiation between Sclerotinia sclerotiorum and Botrytis cinerea in oilseed rape

Classifier 0024
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A deep-level region-based visual representation architecture for detecting strawberry flowers in an outdoor field
evaluated by: Evaluation 0024
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Regional Convolutional Neural Network (R-CNN)
has condition: CropImage 0024
predicts: ObjectBoundary_0024
related to: Flower
related to: Fruit
grade: 70.0
reference: P. Lin, W. S. Lee, Y. M. Chen, N. Peres, C. Fraisse, "A deep-level region-based visual representation architecture for detecting strawberry flowers in an outdoor field", Precision Agriculture, Volume 21, pages 387–402 (2020) , Springer
comment: A deep-level region-based visual representation architecture for detecting strawberry flowers in an outdoor field

Classifier 0028
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A comparative study of fine-tuning deep learning models for plant disease identification
evaluated by: Evaluation 0028
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Deep Convolutional Neural Network (DCNN)
has algorithm: Vegetation Indices (VI)
has condition: CropImage 0028a
has condition: CropImage 0028b
has condition: CropImage 0028c
has condition: CropImage 0028d
has dataset: Dataset PlantVillage
predicts: CropDisease 0028
grade: 80.0
reference: Edna Chebet Too, Li Yujian, Sam Njuki, Liu Yingchun, "A comparative study of fine-tuning deep learning models for plant disease identification", Computers and Electronics in Agriculture, Volume 161, June 2019, pages 272-279, Elsevier B.V.
comment: A comparative study of fine-tuning deep learning models for plant disease identification

Classifier 0031
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A framework for the management of agricultural resources with automated aerial imagery detection
evaluated by: Evaluation 0031
has algorithm: Convolutional Neural Network (CNN)
has condition: SatelliteImage_0031
has dataset: Dataset 0031a
has location: Germany
has location: Tonga
predicts: Fruit_0031
related to: Banana
related to: Coconut
related to: Fruit
related to: Mango
related to: Papaya
related to: Remote Sensing
grade: 80.0
reference: Karla Saldana Ochoa, Zifeng Guo, "A framework for the management of agricultural resources with automated aerial imagery detection", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 53-69, Elsevier B.V.
comment: A framework for the management of agricultural resources with automated aerial imagery detection

Classifier 0033
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images
evaluated by: Evaluation 0033
has algorithm: Fuzzy C-means (FCM)
has condition: CropImage 0033
has dataset: Dataset 0033a
predicts: CropDisease 0033
related to: Cucumber
grade: 80.0
reference: Xuebing Bai, Xinxing Li, Zetian Fu, Xiongjie Lv, Lingxian Zhang, "A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images", Computers and Electronics in Agriculture, Volume 136, 15 April 2017, pages 157-165, Elsevier B.V.
comment: A fuzzy clustering segmentation method based on neighborhood grayscale information for defining cucumber leaf spot disease images

Classifier 0034
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A fuzzy inference system to model grape quality in vineyards
has algorithm: Decision Support System
has algorithm: Fuzzy Inference System
has condition: Anthocyanin 0034
has condition: Weight_0034
has location: Greece
predicts: Quality_0034
related to: Grape
related to: Soil
grade: 65.0
reference: A. Tagarakis, S. Koundouras, E. I. Papageorgiou, Z. Dikopoulou, S. Fountas, T. A. Gemtos, "A fuzzy inference system to model grape quality in vineyards", Precision Agriculture, Volume 15, pages 555–578 (2014) , Springer
comment: A fuzzy inference system to model grape quality in vineyards

Classifier 0036
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: ‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging
evaluated by: Evaluation 0036
has algorithm: Support Vector Machine (SVM)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 0036a
has condition: SpectralImage 0036b
has condition: SpectralImage 0036c
has dataset: Dataset 0036a
has location: United States
predicts: CropDisease 0036
related to: Citrus
grade: 80.0
reference: Han Li, Won Suk Lee, Ku Wang, Reza Ehsani, Chenghai Yang, "‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging", Precision Agriculture, Volume 15, pages 162–183 (2014) , Springer
comment: ‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging

Classifier 0037
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm
evaluated by: Evaluation 0037
has algorithm: Principal Component Analysis (PCA)
has algorithm: Quadratic Discriminant Analysis (QDA)
has algorithm: Support Vector Machine (SVM)
has algorithm: Support Vector Machine-Feature Selection (SVM-FS)
has condition: SpectralImage_0037
has dataset: Dataset 0037a
has location: Malaysia
predicts: CropDisease 0037
related to: Basal Stem Rot
related to: Oil palm
grade: 85.0
reference: Alfadhl Yahya Khaled, Samsuzana Abd Aziz, Siti Khairunniza Bejo, Nazmi Mat Nawi, Diyana Jamaludin, Nur Ul Atikah Ibrahim, "A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm", Computers and Electronics in Agriculture, Volume 170, March 2020, 105288, Elsevier B.V.
comment: A comparative study on dimensionality reduction of dielectric spectral data for the classification of basal stem rot (BSR) disease in oil palm

Classifier 0039
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A leaf-based back propagation neural network for oleander (Nerium oleander L.) cultivar identification
evaluated by: Evaluation 0039
has algorithm: Artificial Neural Network (ANN)
has algorithm: Back Propagation Neural Network (BPNN)
has condition: CropImage 0039
has condition: Leaf_0039
has dataset: Dataset 0039a
has location: Italy
predicts: Variety_0039
related to: Leaf
related to: Oleander
grade: 80.0
reference: Ada Baldi, Camilla Pandolfi, Stefano Mancuso, Anna Lenzi, "A leaf-based back propagation neural network for oleander (Nerium oleander L.) cultivar identification", Computers and Electronics in Agriculture, Volume 142, Part B, November 2017, pages 515-520, Elsevier B.V.
comment: A leaf-based back propagation neural network for oleander (Nerium oleander L.) cultivar identification

Classifier 0040
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.)
evaluated by: Evaluation 0040
has algorithm: Least Squares Support Vector Machine (LSSVM)
has condition: CropImage 0040
has dataset: Dataset 0040a
has location: China
predicts: Quality_0040
related to: Mango
grade: 80.0
reference: Hong Zheng, Hongfei Lu, "A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.)", Computers and Electronics in Agriculture, Volume 83, April 2012, pages 47-51, Elsevier B.V.
comment: A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.)

Classifier 0043
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A low shot learning method for tea leaf’s disease identification
evaluated by: Evaluation 0043
has algorithm: Algorithm C-DCGAN
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0043
has location: China
predicts: CropDisease 0043
related to: Tea
related to: Yield
grade: 70.0
reference: Gensheng Hu, Haoyu Wu, Yan Zhang, Mingzhu Wan, "A low shot learning method for tea leaf’s disease identification", Computers and Electronics in Agriculture, Volume 163, August 2019, 104852, Elsevier B.V.
comment: A low shot learning method for tea leaf’s disease identification

Classifier 0051
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A methodology for fresh tomato maturity detection using computer vision
evaluated by: Evaluation 0051
has algorithm: Back Propagation Neural Network (BPNN)
has condition: CropImage 0051
has dataset: Dataset 0051a
has location: United States
predicts: Maturity_0051
related to: Tomato
grade: 85.0
reference: Peng Wan, Arash Toudeshki, Hequn Tan, Reza Ehsani, "A methodology for fresh tomato maturity detection using computer vision", Computers and Electronics in Agriculture, Volume 146, March 2018, pages 43-50, Elsevier B.V.
comment: A methodology for fresh tomato maturity detection using computer vision

Classifier 0053
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A comparison of object-based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery
evaluated by: Evaluation 0053
has algorithm: Multi-resolution Segmentation (MRS)
has algorithm: Multi-threshold segmentation (MTS)
has algorithm: Vegetation Indices (VI)
has condition: SatelliteImage_0053
has dataset: Dataset 0053a
has location: South Africa
predicts: Boundary 0053
related to: Field
grade: 80.0
reference: Barry Watkins, Adriaan van Niekerk, "A comparison of object-based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery", Computers and Electronics in Agriculture, Volume 158, March 2019, pages 294-302, Elsevier B.V.
comment: A comparison of object-based image analysis approaches for field boundary delineation using multi-temporal Sentinel-2 imagery

Classifier 0057
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A multispectral machine vision system for invertebrate detection on green leaves
evaluated by: Evaluation 0057
has algorithm: Algorithm NH
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0057
has location: Australia
has location: Japan
predicts: CropPest 0057
related to: Crop Pest
grade: 65.0
reference: Huajian Liu, Javaan Singh Chahl, "A multispectral machine vision system for invertebrate detection on green leaves", Computers and Electronics in Agriculture, Volume 150, July 2018, pages 279-288, Elsevier B.V.
comment: A multispectral machine vision system for invertebrate detection on green leaves

Classifier 0062
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A new low-cost portable multispectral optical device for precise plant status assessment
evaluated by: Evaluation 0062
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 0062a
has condition: SpectralImage 0062b
has location: Serbia
predicts: Health_0062
grade: 70.0
reference: Goran Kitić, Aristotelis Tagarakis, Norbert Cselyuszka, Marko Panić, Slobodan Birgermajer, Dušan Sakulski, Jovan Matović, "A new low-cost portable multispectral optical device for precise plant status assessment", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 300-308, Elsevier B.V.
comment: A new low-cost portable multispectral optical device for precise plant status assessment

Classifier 0068
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A new proposal for automatic identification of multiple soybean diseases
evaluated by: Evaluation 0068
has algorithm: K-Means (KMeans)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0068
has dataset: Dataset 0068a
has location: Brazil
predicts: CropDisease 0068
related to: Soybean
grade: 75.0
reference: Juliana Mariana Macedo Araujo, Zelia Myriam Assis Peixoto, "A new proposal for automatic identification of multiple soybean diseases", Computers and Electronics in Agriculture, Volume 167, December 2019, 105060, Elsevier B.V.
comment: A new proposal for automatic identification of multiple soybean diseases

Classifier 0077
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A novel approach for vegetation classification using UAV-based hyperspectral imaging
evaluated by: Evaluation 0077
has algorithm: Radial Basis Function (RBF)
has algorithm: Support Vector Machine (SVM)
has condition: SpectralImage_0077
has dataset: Dataset 0077a
has location: Philippines
predicts: Field_0077
related to: Remote Sensing
grade: 75.0
reference: Tetsuro Ishida, Junichi Kurihara, Fra Angelico Viray, Shielo Baes Namuco, Enrico C. Paringit, Gay Jane Perez, Yukihiro Takahashi, Joel Joseph Marciano, "A novel approach for vegetation classification using UAV-based hyperspectral imaging", Computers and Electronics in Agriculture, Volume 144, January 2018, pages 80-85, Elsevier B.V.
comment: A novel approach for vegetation classification using UAV-based hyperspectral imaging

Classifier 0083
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A polarized hyperspectral imaging system for in vivo detection: Multiple applications in sunflower leaf analysis
evaluated by: Evaluation 0083
has algorithm: Discriminant Analysis (DA)
has algorithm: Partial Least Squares (PLS)
has condition: SpectralImage_0083
has location: France
has location: Norway
predicts: CropDisease 0083
related to: Leaf
grade: 70.0
reference: Jun-Li Xu, Alexia Gobrecht, Daphne Heran, Nathalie Gorretta, Marie Coque, Aoife A. Gowen, Ryad Bendoula, Da-Wen Sun, "A polarized hyperspectral imaging system for in vivo detection: Multiple applications in sunflower leaf analysis", Computers and Electronics in Agriculture, Volume 158, March 2019, pages 258-270, Elsevier B.V.
comment: A polarized hyperspectral imaging system for in vivo detection: Multiple applications in sunflower leaf analysis

Classifier 0091
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network
evaluated by: Evaluation 0091
has algorithm: Deep Convolutional Neural Network (DCNN)
has condition: CropImage 0091
has dataset: Dataset PlantVillage
has location: China
predicts: CropDisease 0091
related to: Cucumber
related to: Leaf
grade: 80.0
reference: Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhihong Gong, Zhongfu Sun, "A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network", Computers and Electronics in Agriculture, Volume 154, November 2018, pages 18-24, Elsevier B.V.
comment: A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network

Classifier 0093
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses
evaluated by: Evaluation 0093
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Multi-layer Perceptron (MLP)
has condition: SpectralImage_0093
predicts: CropDisease 0093
related to: Avocado
related to: Crop Disease
related to: Crop Stress
related to: Iron (Fe)
related to: Nitrogen (N)
related to: Remote Sensing
grade: 65.0
reference: Jaafar Abdulridha, Reza Ehsani, Amr Abd-Elrahman, Yiannis Ampatzidis, "A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses", Computers and Electronics in Agriculture, Volume 156, January 2019, pages 549-557, Elsevier B.V.
comment: A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses

Classifier 0094
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A review of advanced machine learning methods for the detection of biotic stress in precision crop protection
has algorithm: Artificial Neural Network (ANN)
has algorithm: Support Vector Machine (SVM)
has condition: SpectralImage_0094
predicts: CropStress 0094
related to: Crop Disease
related to: Crop Pest
related to: Weeds
grade: 65.0
reference: Jan Behmann, Anne-Katrin Mahlein, Till Rumpf, Christoph Romer, Lutz Plümer, "A review of advanced machine learning methods for the detection of biotic stress in precision crop protection", Precision Agriculture, Volume 16, pages 239–260 (2015) , Springer
comment: A review of advanced machine learning methods for the detection of biotic stress in precision crop protection

Classifier 0098
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A segmentation method for disease spot images incorporating chrominance in Comprehensive Color Feature and Region Growing
evaluated by: Evaluation 0098
has algorithm: Color Content Fidelity Ratio (CCFR)
has algorithm: Color Contrast Preservation Ratio (CCPR)
has algorithm: Singular Value Decomposition (SVD)
has condition: CropImage 0098
has dataset: Dataset 0098a
predicts: CropDisease 0098
related to: Cucumber
related to: Lemon
grade: 75.0
reference: N. Jothiaruna, K. Joseph Abraham Sundar, B. Karthikeyan, "A segmentation method for disease spot images incorporating chrominance in Comprehensive Color Feature and Region Growing", Computers and Electronics in Agriculture, Volume 165, October 2019, 104934, Elsevier B.V.
comment: A segmentation method for disease spot images incorporating chrominance in Comprehensive Color Feature and Region Growing

Classifier 0111
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Wheat landraces identification through glumes image analysis
evaluated by: Evaluation 0111
has algorithm: Linear Discriminant Analysis (LDA)
has condition: CropImage 0111a
has condition: CropImage 0111b
has condition: CropImage 0111c
has dataset: Dataset 0111a
has location: Italy
predicts: Crop 0111
related to: Glume
related to: Wheat
grade: 85.0
reference: Oscar Grillo, Sebastiano Blangiforti, Gianfranco Venora, "Wheat landraces identification through glumes image analysis", Computers and Electronics in Agriculture, Volume 141, September 2017, pages 223-231, Elsevier B.V.
comment: Wheat landraces identification through glumes image analysis

Classifier 0114
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China
evaluated by: Evaluation 0114
has algorithm: Artificial Neural Network (ANN)
has algorithm: Support Vector Machine (SVM)
has condition: Elevation 0114
has condition: Slope 0114a
has condition: Slope 0114b
has condition: Slope 0114c
has condition: SoilTexture_0114
has condition: Terrain 0114a
has condition: Terrain 0114b
has condition: Terrain 0114c
has condition: Terrain 0114d
has condition: Terrain 0114e
has condition: Terrain 0114f
has condition: Terrain 0114g
has dataset: Dataset 0114a
has location: China
predicts: SoilTexture 0114x
related to: Soil
grade: 79.16666666666666
reference: Wei Wu, Ai-Di Li, Xin-Hua He, Ran Ma, Hong-Bin Liu, Jia-Ke Lv, "A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China", Computers and Electronics in Agriculture, Volume 144, January 2018, pages 86-93, Elsevier B.V.
comment: A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China

Classifier 0115
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A computer vision system for early stage grape yield estimation based on shoot detection
evaluated by: Evaluation 0115
has algorithm: Computer Vision System
has condition: CropImage 0115
has dataset: Dataset 0115a
has location: Australia
predicts: Yield_0115
related to: Grape
grade: 80.0
reference: Scarlett Liu, Steve Cossell, Julie Tang, Gregory Dunn, Mark Whitty, "A computer vision system for early stage grape yield estimation based on shoot detection", Computers and Electronics in Agriculture, Volume 137, May 2017, pages 88-101, Elsevier B.V.
comment: A computer vision system for early stage grape yield estimation based on shoot detection

Classifier 0140
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Soil clustering by fuzzy c-means algorithm
evaluated by: Evaluation 0140
has algorithm: Fuzzy C-means (FCM)
has condition: SoilType_0140
has dataset: Dataset 0140a
has location: Turkey
predicts: SoilType 0140x
related to: Soil
related to: Soil Texture
grade: 85.0
reference: A.B.Goktepe, S.Altun, A.Sezer, "Soil clustering by fuzzy c-means algorithm", Advances in Engineering Software, Volume 36, Issue 10, October 2005, Pages 691-698
comment: Soil clustering by fuzzy c-means algorithm

Classifier 0158
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: An intelligent mobile application for diagnosis of crop diseases in Pakistan using fuzzy inference system
evaluated by: Evaluation 0158
has algorithm: Fuzzy Inference System
has condition: CropImage 0158
has condition: CropPart 0158
has dataset: Dataset 0158a
has location: Pakistan
predicts: CropDisease 0158
related to: Cotton
related to: Smartphone
related to: Soil
related to: Wheat
grade: 77.5
reference: Muhammad Toseef, Malik Jahan Khan, "An intelligent mobile application for diagnosis of crop diseases in Pakistan using fuzzy inference system", Computers and Electronics in Agriculture, Volume 153, October 2018, pages 1-11, Elsevier B.V.
comment: An intelligent mobile application for diagnosis of crop diseases in Pakistan using fuzzy inference system

Classifier 0167
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: An yield estimation in citrus orchards via fruit detection and counting using image processing
evaluated by: Evaluation 0167
has algorithm: Undefine (Undefine)
has condition: CropImage 0167
predicts: Yield_0167
related to: Fruit
grade: 75.0
reference: Ulzii-Orshikh Dorj, Malrey Lee, Sang-seok Yun, "An yield estimation in citrus orchards via fruit detection and counting using image processing", Computers and Electronics in Agriculture, Volume 140, August 2017, pages 103-112, Elsevier B.V.
comment: An yield estimation in citrus orchards via fruit detection and counting using image processing

Classifier 0172
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat
evaluated by: Evaluation 0172
has algorithm: Partial Least Squares (PLS)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0172
has dataset: Dataset 0172a
has dataset: Dataset 0172b
has location: China
predicts: CropDisease 0172
related to: Wheat
grade: 80.0
reference: Lin Yuan, Jing-Cheng Zhang, Ke Wang, Rebecca-W. Loraamm, Wen-Jiang Huang, Ji-Hua Wang, Jin-Ling Zhao, "Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat", Precision Agriculture, Volume 14, pages 495–511 (2013) , Springer
comment: Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat

Classifier 0175
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Analysis of transfer learning for deep neural network based plant classification models
evaluated by: Evaluation 0175
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 0175
has dataset: Dataset PlantVillage
has dataset: Dataset 0175a
has dataset: Dataset 0175b
has dataset: Dataset 0175c
predicts: CropName 0175
related to: Leaf
grade: 80.0
reference: Aydin Kaya, Ali Seydi Keceli, Cagatay Catal, Hamdi Yalin Yalic, Huseyin Temucin, Bedir Tekinerdogan, "Analysis of transfer learning for deep neural network based plant classification models", Computers and Electronics in Agriculture, Volume 158, March 2019, pages 20-29, Elsevier B.V.
comment: Analysis of transfer learning for deep neural network based plant classification models

Classifier 0180
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Apple detection during different growth stages in orchards using the improved YOLO-V3 model
evaluated by: Evaluation 0180
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 0180
has location: China
predicts: GrowthStage_0180
related to: Apple
related to: Fruit
related to: Yield
grade: 70.0
reference: Yunong Tian, Guodong Yang, Zhe Wang, Hao Wang, En Li, Zize Liang, "Apple detection during different growth stages in orchards using the improved YOLO-V3 model", Computers and Electronics in Agriculture, Volume 157, February 2019, pages 417-426, Elsevier B.V.
comment: Apple detection during different growth stages in orchards using the improved YOLO-V3 model

Classifier 0185
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Application of aerial remote sensing technology for detection of fire blight infected pear trees
evaluated by: Evaluation 0185
has algorithm: Support Vector Machine (SVM)
has condition: SpectralImage_0185
has location: Iran
predicts: CropDisease 0185
related to: Apple
related to: Crop Disease
related to: Pear
related to: Remote Sensing
grade: 70.0
reference: Nikrooz Bagheri, "Application of aerial remote sensing technology for detection of fire blight infected pear trees", Computers and Electronics in Agriculture, Volume 168, January 2020, 105147, Elsevier B.V.
comment: Application of aerial remote sensing technology for detection of fire blight infected pear trees

Classifier 0193
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis
evaluated by: Evaluation 0193
has algorithm: Artificial Neural Network (ANN)
has algorithm: Principal Component Analysis (PCA)
has condition: SpectralImage_0193
has location: China
predicts: CropDisease 0193
related to: Remote Sensing
related to: Rice
grade: 70.0
reference: Zhan-Yu Liu, Hong-Feng Wu, Jing-Feng Huang, "Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis", Computers and Electronics in Agriculture, Volume 72, Issue 2, July 2010, pages 99-106, Elsevier B.V.
comment: Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis

Classifier 0212
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Weed detection in soybean crops using ConvNets
evaluated by: Evaluation 0212
has algorithm: Adaptive Boosting (AdaBoost)
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Random Forest (RF)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0212
has dataset: Dataset 0212a
has location: Brazil
predicts: Weeds_0212
related to: Drone
related to: Soil
related to: Soybean
grade: 80.0
reference: Alessandro dos Santos Ferreira, Daniel Matte Freitas, Gercina Gonçalves da Silva, Hemerson Pistori, Marcelo Theophilo Folhes, "Weed detection in soybean crops using ConvNets", Computers and Electronics in Agriculture, Volume 143, December 2017, pages 314-324, Elsevier B.V.
comment: Weed detection in soybean crops using ConvNets

Classifier 0220
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Assessment of the optimal spectral bands for designing a sensor for vineyard disease detection: the case of ‘Flavescence dorée’
evaluated by: Evaluation 0220
has algorithm: Discriminant Analysis (DA)
has algorithm: Support Vector Machine (SVM)
has algorithm: Vegetation Indices (VI)
has condition: CropImage 0220
has condition: SpectralImage_0220
has dataset: Dataset 0220a
has dataset: Dataset 0220b
has dataset: Dataset 0220c
has location: France
predicts: CropDisease 0220
related to: Grape
related to: Yield
grade: 82.5
reference: H. Al-Saddik, J. C. Simon, F. Cointault, "Assessment of the optimal spectral bands for designing a sensor for vineyard disease detection: the case of ‘Flavescence dorée’", Precision Agriculture, Volume 20, pages 398–422 (2019) , Springer
comment: Assessment of the optimal spectral bands for designing a sensor for vineyard disease detection: the case of ‘Flavescence dorée’

Classifier 0238
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Automatic classification of non-touching cereal grains in digital images using limited morphological and color features
evaluated by: Evaluation 0238
has algorithm: Undefine (Undefine)
has condition: CropImage 0238a
has condition: CropImage 0238b
has dataset: Dataset 0238a
has location: Canada
predicts: Crop 0238
related to: Barley
related to: Oats
related to: Rye
related to: Wheat
grade: 85.0
reference: H.K. Mebatsion, J. Paliwal, D.S. Jayas, "Automatic classification of non-touching cereal grains in digital images using limited morphological and color features", Computers and Electronics in Agriculture, Volume 90, January 2013, pages 99-105, Elsevier B.V.
comment: Automatic classification of non-touching cereal grains in digital images using limited morphological and color features

Classifier 0239
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic
evaluated by: Evaluation 0239
has algorithm: Artificial Neural Network (ANN)
has algorithm: Convolutional Neural Network (CNN)
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Optimum-Path Forest (OPF)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0239
has dataset: Dataset 0239a
predicts: CropStress 0239
grade: 75.0
reference: Danillo Roberto Pereira, Joao Paulo Papa, Gustavo Francisco Rosalin Saraiva, Gustavo Maia Souza, "Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic", Computers and Electronics in Agriculture, Volume 145, February 2018, pages 35-42, Elsevier B.V.
comment: Automatic classification of plant electrophysiological responses to environmental stimuli using machine learning and interval arithmetic

Classifier 0243
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Automatic cucumber recognition algorithm for harvesting robots in the natural environment using deep learning and multi-feature fusion
evaluated by: Evaluation 0243
has algorithm: Artificial Neural Network (ANN)
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0243
has dataset: Dataset 0243a
has location: China
predicts: ObjectBoundary_0243
related to: Cucumber
grade: 80.0
reference: Shihan Mao, Yuhua Li, You Ma, Baohua Zhang, Jun Zhou, "Automatic cucumber recognition algorithm for harvesting robots in the natural environment using deep learning and multi-feature fusion", Computers and Electronics in Agriculture, Volume 170, March 2020, 105254, Elsevier B.V.
comment: Automatic cucumber recognition algorithm for harvesting robots in the natural environment using deep learning and multi-feature fusion

Classifier 0245
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Automatic detection and identification of brown stink bug, Euschistus servus, and southern green stink bug, Nezara viridula, (Heteroptera: Pentatomidae)
evaluated by: Evaluation 0245
has algorithm: Artificial Neural Network (ANN)
has algorithm: Algorithm Gaussian
has condition: SpectralImage_0245
has location: United States
predicts: CropPest 0245
related to: Cotton
related to: Crop Pest
grade: 65.0
reference: B.D. Lampson, Y.J. Han, A. Khalilian, J. Greene, R.W. Mankin, E.G. Foreman, "Automatic detection and identification of brown stink bug, Euschistus servus, and southern green stink bug, Nezara viridula, (Heteroptera: Pentatomidae)", Computers and Electronics in Agriculture, Volume 91, February 2013, pages 154-159, Elsevier B.V.
comment: Automatic detection and identification of brown stink bug, Euschistus servus, and southern green stink bug, Nezara viridula, (Heteroptera: Pentatomidae)

Classifier 0246
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Automatic detection of cereal rows by means of pattern recognition techniques
evaluated by: Evaluation 0246
has algorithm: K-Means (KMeans)
has algorithm: Linear Regression (LR)
has algorithm: Principal Component Analysis (PCA)
has condition: FieldImage_0246
has dataset: Dataset 0246a
has location: Finland
predicts: CropRow 0246
related to: Rye
grade: 75.0
reference: Henri Tenhunen, Tapio Pahikkala, Olli Nevalainen, Jukka Teuhola, Heta Mattila, Esa Tyystjarvi, "Automatic detection of cereal rows by means of pattern recognition techniques", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 677-688, Elsevier B.V.
comment: Automatic detection of cereal rows by means of pattern recognition techniques

Classifier 0247
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach
evaluated by: Evaluation 0247
has algorithm: Principal Component Analysis (PCA)
has condition: CropImage 0247
has dataset: Dataset 0247a
has location: Spain
predicts: CropInjure 0247
related to: Citrus
related to: Mandarin orange
related to: Orange
grade: 75.0
reference: Fernando Lopez-Garcia, Gabriela Andreu-Garcia, Jose Blasco, Nuria Aleixos, Jose-Miguel Valiente, "Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach", Computers and Electronics in Agriculture, Volume 71, Issue 2, May 2010, pages 189-197, Elsevier B.V.
comment: Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach

Classifier 0249
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Automatic fruit count on coffee branches using computer vision
evaluated by: Evaluation 0249
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Naive Bayes (NB)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0249
has dataset: Dataset 0249a
has location: Brazil
predicts: Boundary 0249
related to: Coffee
related to: Smartphone
grade: 75.0
reference: P.J. Ramos, F.A. Prieto, E.C. Montoya, C.E. Oliveros, "Automatic fruit count on coffee branches using computer vision", Computers and Electronics in Agriculture, Volume 137, May 2017, pages 9-22, Elsevier B.V.
comment: Automatic fruit count on coffee branches using computer vision

Classifier 0258
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.)
evaluated by: Evaluation 0258
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 0258a
has condition: SpectralImage 0258b
has dataset: Dataset 0258a
has location: India
has location: Israel
predicts: Nitrogen_0258
predicts: Phosphorus_0258
predicts: Potassium_0258
predicts: Sulphur_0258
related to: Biomass
related to: Canopy
related to: Fertilizer
related to: Remote Sensing
related to: Wheat
grade: 80.0
reference: G. R. Mahajan, R. N. Sahoo, R. N. Pandey, V. K. Gupta, Dinesh Kumar, "Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.)", Precision Agriculture, Volume 15, pages 499–522 (2014) , Springer
comment: Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.)

Classifier 0259
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Banana detection based on color and texture features in the natural environment
evaluated by: Evaluation 0259
has algorithm: Adaptive Boosting (AdaBoost)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0259
has dataset: Dataset 0259a
predicts: Fruit_0259
related to: Banana
grade: 85.0
reference: Lanhui Fu, Jieli Duan, Xiangjun Zou, Guichao Lin, Shuaishuai Song, Bang Ji, Zhou Yang, "Banana detection based on color and texture features in the natural environment", Computers and Electronics in Agriculture, Volume 167, December 2019, 105057, Elsevier B.V.
comment: Banana detection based on color and texture features in the natural environment

Classifier 0264
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Biomass estimation from canopy measurements for leafy vegetables based on ultrasonic and laser sensors
evaluated by: Evaluation 0264
has algorithm: Vegetation Indices (VI)
has algorithm: Wavelength (WL)
has condition: SpectralImage_0264
has location: Canada
has location: Germany
has location: India
predicts: Biomass 0264
related to: Kale
related to: Lettuce
related to: Spinach
grade: 70.0
reference: Roberto M. Buelvas, Viacheslav I. Adamchuk, Eko Leksono, Peter Tikasz, Mark Lefsrud, Jarek Holoszkiewicz, "Biomass estimation from canopy measurements for leafy vegetables based on ultrasonic and laser sensors", Computers and Electronics in Agriculture, Volume 164, September 2019, 104896, Elsevier B.V.
comment: Biomass estimation from canopy measurements for leafy vegetables based on ultrasonic and laser sensors

Classifier 0265
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: BiomassID: A biomass type identification system for mobile devices
evaluated by: Evaluation 0265
has algorithm: Kernel Ridge Regression (KRR)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0265
has dataset: Dataset 0265a
predicts: Biomass 0265
related to: Corn
related to: Waste
related to: Wheat
related to: Wood
grade: 75.0
reference: Radu Tudor Ionescu, Andreea-Lavinia Popescu, Marius Popescu, Dan Popescu, "BiomassID: A biomass type identification system for mobile devices", Computers and Electronics in Agriculture, Volume 113, April 2015, pages 244-253, Elsevier B.V.
comment: BiomassID: A biomass type identification system for mobile devices

Classifier 0282
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features
evaluated by: Evaluation 0282
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Decision Tree (DT)
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Linear Regression (LR)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0282
has dataset: Dataset PlantVillage
predicts: CropDisease 0282
related to: Apple
related to: Banana
related to: Crop Disease
related to: Fruit
grade: 80.0
reference: Muhammad Attique Khan, Tallha Akram, Muhammad Sharif, Muhammad Awais, Kashif Javed, Hashim Ali, Tanzila Saba, "CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features", Computers and Electronics in Agriculture, Volume 155, December 2018, pages 220-236, Elsevier B.V.
comment: CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features

Classifier 0285
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Changes in yield classification in a soybean-rice rotation
evaluated by: Evaluation 0285
has algorithm: Discriminant Analysis (DA)
has condition: Elevation 0285
has condition: SoilK_0285
has condition: SoilMG_0285
has condition: SoilPH_0285
has condition: SoilP_0285
has condition: SoilTexture_0285
has dataset: Dataset 0285a
has location: United States
predicts: Yield_0285
related to: Rice
related to: Soybean
grade: 80.0
reference: M. S. Cox, P. D. Gerard, "Changes in yield classification in a soybean-rice rotation", Precision Agriculture, Volume 11, pages 507–519 (2010) , Springer
comment: Changes in yield classification in a soybean-rice rotation

Classifier 0290
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Citrus disease recognition based on weighted scalable vocabulary tree
evaluated by: Evaluation 0290
has algorithm: Gaussian Mixture Model (GMM)
has algorithm: K-Means (KMeans)
has condition: CropImage 0290
has dataset: Dataset 0290a
has location: China
predicts: CropDisease 0290
related to: Citrus
grade: 75.0
reference: Xiao-Ling Deng, Zhen Li, Xiao-Ling Deng, Tian-Sheng Hong, "Citrus disease recognition based on weighted scalable vocabulary tree", Precision Agriculture, Volume 15, pages 321–330 (2014) , Springer
comment: Citrus disease recognition based on weighted scalable vocabulary tree

Classifier 0300
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Classification of maize seeds of different years based on hyperspectral imaging and model updating
evaluated by: Evaluation 0300
has algorithm: Least Squares Support Vector Machine (LSSVM)
has algorithm: Support Vector Machine (SVM)
has condition: SpectralImage_0300
has location: China
predicts: Seed_0300
related to: Maize
related to: Yield
grade: 65.0
reference: Min Huang, Jinya Tang, Bao Yang, Qibing Zhu, "Classification of maize seeds of different years based on hyperspectral imaging and model updating", Computers and Electronics in Agriculture, Volume 122, March 2016, pages 139-145, Elsevier B.V.
comment: Classification of maize seeds of different years based on hyperspectral imaging and model updating

Classifier 0301
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation
evaluated by: Evaluation 0301
has algorithm: Principal Component Analysis (PCA)
has algorithm: Vegetation Indices (VI)
has condition: FieldImage 0301a
has condition: FieldImage 0301b
has location: Spain
predicts: Yield_0301
related to: Elevation
related to: Sunflower
related to: Weeds
grade: 75.0
reference: Jose M. Pena-Barragan, Francisca Lopez-Granados, Montserrat Jurado-Exposito, Luis Garcia-Torres, "Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation", Precision Agriculture, Volume 11, pages 568–585 (2010) , Springer
comment: Sunflower yield related to multi-temporal aerial photography, land elevation and weed infestation

Classifier 0306
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Classifying rapeseed varieties using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS)
evaluated by: Evaluation 0306
has algorithm: Principal Component Analysis (PCA)
has algorithm: Partial Least Squares Discriminant Analysis (PLSDA)
has algorithm: Successive Projections Algorithm (SPA)
has algorithm: Support Vector Machine (SVM)
has condition: SpectralImage_0306
has dataset: Dataset 0306a
predicts: CropName 0306
related to: Rapeseed
grade: 75.0
reference: Yuzhen Lu, Changwen Du, Changbing Yu, Jianmin Zhou, "Classifying rapeseed varieties using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS)", Computers and Electronics in Agriculture, Volume 107, September 2014, pages 58-63, Elsevier B.V.
comment: Classifying rapeseed varieties using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS)

Classifier 0311
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture
evaluated by: Evaluation 0311
has algorithm: Artificial Neural Network (ANN)
has condition: CropImage 0311
has dataset: Dataset 0311a
has location: Spain
predicts: CropPest 0311
related to: Crop Pest
related to: Tomato
grade: 80.0
reference: Karlos Espinoza, Diego L. Valera, Jose A. Torres, Alejandro Lopez, Francisco D. Molina-Aiz, "Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture", Computers and Electronics in Agriculture, Volume 127, September 2016, pages 495-505, Elsevier B.V.
comment: Combination of image processing and artificial neural networks as a novel approach for the identification of Bemisia tabaci and Frankliniella occidentalis on sticky traps in greenhouse agriculture

Classifier 0315
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Combining discriminant analysis and neural networks for corn variety identification
evaluated by: Evaluation 0315
has algorithm: Artificial Neural Network (ANN)
has algorithm: Back Propagation Neural Network (BPNN)
has algorithm: Discriminant Analysis (DA)
has algorithm: Stepwise Discriminant Analysis (SDA)
has condition: CropImage 0315
has dataset: Dataset 0315a
has location: China
predicts: Variety_0315
related to: Corn
related to: Seed
grade: 80.0
reference: Xiao Chen, Yi Xun, Wei Li, Junxiong Zhang, "Combining discriminant analysis and neural networks for corn variety identification", Computers and Electronics in Agriculture, Volume 71, Supplement 1, April 2010, pages S48-S53, Elsevier B.V.
comment: Combining discriminant analysis and neural networks for corn variety identification

Classifier 0326
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat
evaluated by: Evaluation 0326
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0326
has location: China
predicts: CropDisease 0326
related to: Remote Sensing
related to: Wheat
grade: 70.0
reference: Jingcheng Zhang, Lin yuan, Ruiliang Pu, Rebecca W. Loraamm, Guijun Yang, Jihua Wang, "Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat", Computers and Electronics in Agriculture, Volume 100, January 2014, pages 79-87, Elsevier B.V.
comment: Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat

Classifier 0351
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions
evaluated by: Evaluation 0351
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 0351
has dataset: Dataset 0351a
predicts: CropDisease 0351
related to: Barley
related to: Corn
related to: Rapeseed
related to: Rice
related to: Wheat
grade: 80.0
reference: Artzai Picon, Maximiliam Seitz, Aitor Alvarez-Gila, Patrick Mohnke, Amaia Ortiz-Barredo, Jone Echazarra, "Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions", Computers and Electronics in Agriculture, Volume 167, December 2019, 105093, Elsevier B.V.
comment: Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions

Classifier 0353
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Crop pest classification based on deep convolutional neural network and transfer learning
evaluated by: Evaluation 0353
has algorithm: Convolutional Neural Network (CNN)
has condition: ObjectImage_0353
has dataset: Dataset 0353a
predicts: CropPest 0353
related to: Bean
related to: Crop Pest
related to: Maize
related to: Rice
related to: Soybean
related to: Wheat
grade: 80.0
reference: K. Thenmozhi, U. Srinivasulu Reddy, "Crop pest classification based on deep convolutional neural network and transfer learning", Computers and Electronics in Agriculture, Volume 164, September 2019, 104906, Elsevier B.V.
comment: Crop pest classification based on deep convolutional neural network and transfer learning

Classifier 0354
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Crop pest recognition in natural scenes using convolutional neural networks
has algorithm: Artificial Neural Network (ANN)
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Deep Convolutional Neural Network (DCNN)
has condition: ObjectImage_0354
has dataset: Dataset 0354a
has location: Montserrat
predicts: CropPest 0354
related to: Crop Pest
grade: 80.0
reference: Yanfen Li, Hanxiang Wang, L. Minh Dang, Abolghasem Sadeghi-Niaraki, Hyeonjoon Moon, "Crop pest recognition in natural scenes using convolutional neural networks", Computers and Electronics in Agriculture, Volume 169, February 2020, 105174, Elsevier B.V.
comment: Crop pest recognition in natural scenes using convolutional neural networks

Classifier 0356
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Crop type mapping using spectral–temporal profiles and phenological information
evaluated by: Evaluation 0356
has algorithm: Maximum Likelihood Classification (MLC)
has condition: SpectralImage_0356
has dataset: Dataset 0356a
has location: Germany
predicts: CropName 0356
grade: 80.0
reference: Saskia Foerster, Klaus Kaden, Michael Foerster, Sibylle Itzerott, "Crop type mapping using spectral–temporal profiles and phenological information", Computers and Electronics in Agriculture, Volume 89, November 2012, pages 30-40, Elsevier B.V.
comment: Crop type mapping using spectral–temporal profiles and phenological information

Classifier 0357
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Crop water status assessment in controlled environment using crop reflectance and temperature measurements
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0357
has condition: Temperature_0357
has location: Greece
has location: Italy
has location: Netherlands
predicts: Water_0357
related to: Remote Sensing
related to: Temperature
grade: 72.5
reference: A. Elvanidi, N. Katsoulas, T. Bartzanas, K. P. Ferentinos, C. Kittas, "Crop water status assessment in controlled environment using crop reflectance and temperature measurements", Precision Agriculture, Volume 18, pages 332–349 (2017) , Springer
comment: Crop water status assessment in controlled environment using crop reflectance and temperature measurements

Classifier 0367
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild
evaluated by: Evaluation 0367
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 0367
has dataset: Dataset 0367a
has location: Germany
has location: Spain
predicts: CropDisease 0367
related to: Crop Disease
related to: Smartphone
related to: Wheat
related to: Yield
grade: 80.0
reference: Artzai Picon, Aitor Alvarez-Gila, Maximiliam Seitz, Amaia Ortiz-Barredo, Jone Echazarra, Alexander Johannes, "Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild", Computers and Electronics in Agriculture, Volume 161, June 2019, pages 280-290, Elsevier B.V.
comment: Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild

Classifier 0371
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Deep learning for classification and severity estimation of coffee leaf biotic stress
evaluated by: Evaluation 0371
has algorithm: Artificial Neural Network (ANN)
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 0371
has dataset: Dataset 0371a
has location: Brazil
predicts: CropStress 0371
related to: Coffee
related to: Crop Disease
grade: 80.0
reference: Jose G.M. Esgario, Renato A. Krohling, Jose A. Ventura, "Deep learning for classification and severity estimation of coffee leaf biotic stress", Computers and Electronics in Agriculture, Volume 169, February 2020, 105162, Elsevier B.V.
comment: Deep learning for classification and severity estimation of coffee leaf biotic stress

Classifier 0375
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Deep learning models for plant disease detection and diagnosis
evaluated by: Evaluation 0375
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 0375
has dataset: Dataset 0375a
predicts: CropDisease 0375
related to: Apple
related to: Banana
related to: Blueberry
related to: Cabbage
related to: Cantaloupe
related to: Cassava
related to: Celery
related to: Cherry
related to: Corn
related to: Crop Disease
related to: Cucumber
related to: Eggplant
related to: Gourd
related to: Grape
related to: Onion
related to: Orange
related to: Peach
related to: Pepper
related to: Potato
related to: Pumpkin
related to: Raspberry
related to: Soybean
related to: Squash
related to: Strawberry
related to: Tomato
related to: Watermelon
grade: 80.0
reference: Konstantinos P. Ferentinos, "Deep learning models for plant disease detection and diagnosis", Computers and Electronics in Agriculture, Volume 145, February 2018, pages 311-318, Elsevier B.V.
comment: Deep learning models for plant disease detection and diagnosis

Classifier 0383
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Depthwise separable convolution architectures for plant disease classification
evaluated by: Evaluation 0383
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 0383
has dataset: Dataset PlantVillage
predicts: CropDisease 0383
related to: Crop Disease
grade: 80.0
reference: Kamal KC, Zhendong Yin, Mingyang Wu, Zhilu Wu, "Depthwise separable convolution architectures for plant disease classification", Computers and Electronics in Agriculture, Volume 165, October 2019, 104948, Elsevier B.V.
comment: Depthwise separable convolution architectures for plant disease classification

Classifier 0393
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detecting Bakanae disease in rice seedlings by machine vision
evaluated by: Evaluation 0393
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0393a
has condition: CropImage 0393b
has location: Taiwan
predicts: CropDisease 0393
related to: Rice
grade: 70.0
reference: Chia-Lin Chung, Kai-Jyun Huang, Szu-Yu Chen, Ming-Hsing Lai, Yu-Chia Chen, Yan-Fu Kuo, "Detecting Bakanae disease in rice seedlings by machine vision", Computers and Electronics in Agriculture, Volume 121, February 2016, pages 404-411, Elsevier B.V.
comment: Detecting Bakanae disease in rice seedlings by machine vision

Classifier 0394
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detecting creeping thistle in sugar beet fields using vegetation indices
evaluated by: Evaluation 0394
has algorithm: Linear Discriminant Analysis (LDA)
has algorithm: Mahalanobis Distance (MD)
has algorithm: Principal Component Analysis (PCA)
has algorithm: Vegetation Indices (VI)
has condition: CropImage 0394
has dataset: Dataset 0394a
has location: Denmark
predicts: CropName 0394
related to: Weeds
grade: 80.0
reference: Wajahat Kazmi, Francisco Jose Garcia-Ruiz, Jon Nielsen, Jesper Rasmussen, Hans Jorgen Andersen, "Detecting creeping thistle in sugar beet fields using vegetation indices", Computers and Electronics in Agriculture, Volume 112, March 2015, pages 10-19, Elsevier B.V.
comment: Detecting creeping thistle in sugar beet fields using vegetation indices

Classifier 0396
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detecting fruit surface wetness using a custom-built low-resolution thermal-RGB imager
has algorithm: Computer Vision (CV)
has condition: CropImage 0396
predicts: Temperature_0396
related to: Cherry
grade: 65.0
reference: Yasin Osroosh, R. Troy Peters, "Detecting fruit surface wetness using a custom-built low-resolution thermal-RGB imager", Computers and Electronics in Agriculture, Volume 157, February 2019, pages 509-517, Elsevier B.V.
comment: Detecting fruit surface wetness using a custom-built low-resolution thermal-RGB imager

Classifier 0398
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection
evaluated by: Evaluation 0398
has algorithm: Principal Component Analysis (PCA)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0398
has dataset: Dataset PlantVillage
has dataset: Dataset 0398a
predicts: CropDisease 0398
related to: Citrus
grade: 75.0
reference: Muhammad Sharif, Muhammad Attique Khan, Zahid Iqbal, Muhammad Faisal Azam, M. Ikram Ullah Lali, Muhammad Younus Javed, "Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection", Computers and Electronics in Agriculture, Volume 150, July 2018, pages 220-234, Elsevier B.V.
comment: Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection

Classifier 0399
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection and counting of flowers on apple trees for better chemical thinning decisions
evaluated by: Evaluation 0399
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Linear Regression (LR)
has condition: CropImage 0399
has location: Israel
predicts: ObjectBoundary_0399
related to: Apple
related to: Flower
grade: 65.0
reference: Guy Farjon, Omri Krikeb, Aharon Bar Hillel, Victor Alchanatis, "Detection and counting of flowers on apple trees for better chemical thinning decisions", Precision Agriculture, Volume 21, pages 503–521 (2020) , Springer
comment: Detection and counting of flowers on apple trees for better chemical thinning decisions

Classifier 0401
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection and differentiation between potato (Solanum tuberosum) diseases using calibration models trained with non-imaging spectrometry data
evaluated by: Evaluation 0401
has algorithm: Back Propagation Neural Network (BPNN)
has algorithm: Partial Least Squares (PLS)
has condition: CropImage 0401
predicts: CropDisease 0401
related to: Crop Disease
related to: Crop Pest
related to: Potato
grade: 65.0
reference: Damian Bienkowski, Matt J. Aitkenhead, Alison K. Lees, Christopher Gallagher, Roy Neilson, "Detection and differentiation between potato (Solanum tuberosum) diseases using calibration models trained with non-imaging spectrometry data", Computers and Electronics in Agriculture, Volume 167, December 2019, 105056, Elsevier B.V.
comment: Detection and differentiation between potato (Solanum tuberosum) diseases using calibration models trained with non-imaging spectrometry data

Classifier 0402
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis
evaluated by: Evaluation 0402
has algorithm: Discriminant Analysis (DA)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0402
predicts: CropDisease 0402
related to: Crop Pest
related to: Wheat
grade: 70.0
reference: Yue Shi, Wenjiang Huang, Juhua Luo, Linsheng Huang, Xianfeng Zhou, "Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis", Computers and Electronics in Agriculture, Volume 141, September 2017, pages 171-180, Elsevier B.V.
comment: Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis

Classifier 0405
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection of anthracnose in tea plants based on hyperspectral imaging
evaluated by: Evaluation 0405
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 0405a
has condition: SpectralImage 0405b
has dataset: Dataset 0405a
has location: China
predicts: CropDisease 0405
related to: Leaf
related to: Tea
grade: 80.0
reference: Lin Yuan, Peng Yan, Wenyan Han, Yanbo Huang, Bin Wang, Jingcheng Zhang, Haibo Zhang, Zhiyan Bao, "Detection of anthracnose in tea plants based on hyperspectral imaging", Computers and Electronics in Agriculture, Volume 167, December 2019, 105039, Elsevier B.V.
comment: Detection of anthracnose in tea plants based on hyperspectral imaging

Classifier 0407
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers
has algorithm: Artificial Neural Network (ANN)
has algorithm: Supervised Kohonen Network (SKN)
has condition: CropImage 0407
has location: Finland
predicts: CropStress 0407
related to: Fertilizer
related to: Nitrogen (N)
grade: 65.0
reference: Xanthoula Eirini Pantazi, Dimitrios Moshou, Roberto Oberti, Jon West, Abdul Mounem Mouazen, Dionysios Bochtis, "Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers", Precision Agriculture, Volume 18, pages 383–393 (2017) , Springer
comment: Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers

Classifier 0412
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection of grapevine leafroll disease based on 11-index imagery and ant colony clustering algorithm
evaluated by: Evaluation 0412
has algorithm: Ant Colony Clustering Algorithm (ACCA)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0412
has dataset: Dataset 0412a
has location: China
predicts: CropDisease 0412
related to: Grape
grade: 80.0
reference: Jingwei Hou, Longtang Li, Jie He, "Detection of grapevine leafroll disease based on 11-index imagery and ant colony clustering algorithm", Precision Agriculture, Volume 17, pages 488–505 (2016) , Springer
comment: Detection of grapevine leafroll disease based on 11-index imagery and ant colony clustering algorithm

Classifier 0416
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection of orchard citrus fruits using a monocular machine vision-based method for automatic fruit picking applications
evaluated by: Evaluation 0416
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0416
has dataset: Dataset 0416a
has location: China
predicts: ObjectBoundary_0416
related to: Citrus
grade: 80.0
reference: J.J. Zhuang, S.M. Luo, C.J. Hou, Y. Tang, Y. He, X.Y. Xue, "Detection of orchard citrus fruits using a monocular machine vision-based method for automatic fruit picking applications", Computers and Electronics in Agriculture, Volume 152, September 2018, pages 64-73, Elsevier B.V.
comment: Detection of orchard citrus fruits using a monocular machine vision-based method for automatic fruit picking applications

Classifier 0417
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection of peanut leaf spots disease using canopy hyperspectral reflectance
evaluated by: Evaluation 0417
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0417
has location: China
predicts: CropDisease 0417
related to: Peanut
grade: 70.0
reference: Tingting Chen, Jialei Zhang, Yong Chen, Shubo Wan, Lei Zhang, "Detection of peanut leaf spots disease using canopy hyperspectral reflectance", Computers and Electronics in Agriculture, Volume 156, January 2019, pages 677-683, Elsevier B.V.
comment: Detection of peanut leaf spots disease using canopy hyperspectral reflectance

Classifier 0421
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques
evaluated by: Evaluation 0421
has algorithm: Multi-layer Perceptron (MLP)
has algorithm: Stepwise Discriminant Analysis (SDA)
has condition: SpectralImage_0421
has location: Canada
has location: China
has location: France
predicts: CropDisease 0421
related to: Drone
related to: Tomato
related to: Yield
grade: 70.0
reference: Jaafar Abdulridha, Yiannis Ampatzidis, Sri Charan Kakarla, Pamela Roberts, "Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques", Precision Agriculture (2019) , Springer
comment: Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques

Classifier 0423
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection of waterlogging stress based on hyperspectral images of oilseed rape leaves (Brassica napus L.)
evaluated by: Evaluation 0423
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Quadratic Discriminant Analysis (QDA)
has algorithm: Successive Projections Algorithm (SPA)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0423
has condition: SpectralImage_0423
has dataset: Dataset 0423a
has location: China
predicts: CropStress 0423
related to: Grape
related to: Leaf
grade: 80.0
reference: Ji'An Xia, HongXin Cao, YuWang Yang, WeiXin Zhang, Qian Wan, Lei Xu, DaoKuo Ge, WenYu Zhang, YaQi Ke, Bo Huang, "Detection of waterlogging stress based on hyperspectral images of oilseed rape leaves (Brassica napus L.)", Computers and Electronics in Agriculture, Volume 159, April 2019, pages 59-68, Elsevier B.V.
comment: Detection of waterlogging stress based on hyperspectral images of oilseed rape leaves (Brassica napus L.)

Classifier 0467
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Development of an artificial cloud lighting condition system using machine vision for strawberry powdery mildew disease detection
evaluated by: Evaluation 0467
has algorithm: Discriminant Analysis (DA)
has condition: CropImage 0467
has dataset: Dataset 0467a
has location: Canada
predicts: CropDisease 0467
related to: Strawberry
grade: 80.0
reference: Md. Sultan Mahmud, Qamar U. Zaman, Travis J. Esau, Gordon W. Price, Balakrishnan Prithiviraj, "Development of an artificial cloud lighting condition system using machine vision for strawberry powdery mildew disease detection", Computers and Electronics in Agriculture, Volume 158, March 2019, pages 219-225, Elsevier B.V.
comment: Development of an artificial cloud lighting condition system using machine vision for strawberry powdery mildew disease detection

Classifier 0478
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Development of spectral indices for identifying glyphosate-resistant weeds
evaluated by: Evaluation 0478
has algorithm: Random Forest (RF)
has algorithm: Vegetation Indices (VI)
has condition: CropImage 0478
predicts: Weeds_0478
related to: Weeds
grade: 70.0
reference: Alimohammad Shirzadifar, Sreekala Bajwa, John Nowatzki, Jamileh Shojaeiarani, "Development of spectral indices for identifying glyphosate-resistant weeds", Computers and Electronics in Agriculture, Volume 170, March 2020, 105276, Elsevier B.V.
comment: Development of spectral indices for identifying glyphosate-resistant weeds

Classifier 0491
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching
evaluated by: Evaluation 0491
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0491
has location: Japan
predicts: CropDisease 0491
related to: Crop Disease
related to: Sugar beet
grade: 65.0
reference: Rong Zhou, Shun’ichi Kaneko, Fumio Tanaka, Miyuki Kayamori, Motoshige Shimizu, "Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching", Computers and Electronics in Agriculture, Volume 108, October 2014, pages 58-70, Elsevier B.V.
comment: Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching

Classifier 0497
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Early cherry fruit pathogen disease detection based on data mining prediction
evaluated by: Evaluation 0497
has algorithm: Linear Discriminant Analysis (LDA)
has algorithm: Principal Component Analysis (PCA)
has algorithm: Quadratic Discriminant Analysis (QDA)
has condition: Humidity_0497
has condition: Rainfall_0497
has condition: Temperature_0497
has condition: WindSpeed_0497
has location: Serbia
predicts: CropDisease 0497
related to: Cherry
grade: 67.5
reference: Milos Ilic, Sinisa Ilic, Srdjan Jovic, Stefan Panic, "Early cherry fruit pathogen disease detection based on data mining prediction", Computers and Electronics in Agriculture, Volume 150, July 2018, pages 418-425, Elsevier B.V.
comment: Early cherry fruit pathogen disease detection based on data mining prediction

Classifier 0499
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms
evaluated by: Evaluation 0499
has algorithm: Boosted Regression Trees (BRT)
has algorithm: Classification and Regression Trees (CART)
has algorithm: Genetic Algorithm (GA)
has algorithm: Successive Projections Algorithm (SPA)
has algorithm: Support Vector Machine (SVM)
has condition: SpectralImage_0499
predicts: CropDisease 0499
related to: Tobacco
grade: 65.0
reference: Qing Gu, Li Sheng, Tianhao Zhang, Yuwen Lu, Zhijun Zhang, Kefeng Zheng, Hao Hu, Hongkui Zhou, "Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms", Computers and Electronics in Agriculture, Volume 167, December 2019, 105066, Elsevier B.V.
comment: Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms

Classifier 0500
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Early detection of water stress in maize based on digital images
evaluated by: Evaluation 0500
has algorithm: Decision Tree (DT)
has algorithm: Gradient Boosting Decision Tree (GBDT)
has condition: CropImage 0500
has location: China
predicts: CropStress 0500
related to: Maize
grade: 65.0
reference: Shuo Zhuang, Ping Wang, Boran Jiang, Maosong Li, Zhihong Gong, "Early detection of water stress in maize based on digital images", Computers and Electronics in Agriculture, Volume 140, August 2017, pages 461-468, Elsevier B.V.
comment: Early detection of water stress in maize based on digital images

Classifier 0501
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Early powdery mildew detection system for application in greenhouse automation
evaluated by: Evaluation 0501
has algorithm: Receiver Operating Characteristic (ROC)
has condition: CropImage 0501
has dataset: Dataset 0501a
predicts: CropDisease 0501
related to: Greenhouse
related to: Tomato
grade: 75.0
reference: Patrick Wspanialy, Medhat Moussa, "Early powdery mildew detection system for application in greenhouse automation", Computers and Electronics in Agriculture, Volume 127, September 2016, pages 487-494, Elsevier B.V.
comment: Early powdery mildew detection system for application in greenhouse automation

Classifier 0503
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Early season detection of rice plants using RGB, NIR-G-B and multispectral images from unmanned aerial vehicle (UAV)
evaluated by: Evaluation 0503
has algorithm: Decision Tree (DT)
has algorithm: Support Vector Machine (SVM)
has algorithm: Vegetation Indices (VI)
has condition: FieldImage_0503
has condition: SpectralImage 0503a
has condition: SpectralImage 0503b
has location: China
predicts: GrowthStage_0503
related to: Canopy
related to: Rice
related to: Soil
grade: 70.0
reference: Hengbiao Zheng, Xiang Zhou, Jiaoyang He, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, Yongchao Tian, "Early season detection of rice plants using RGB, NIR-G-B and multispectral images from unmanned aerial vehicle (UAV)", Computers and Electronics in Agriculture, Volume 169, February 2020, 105223, Elsevier B.V.
comment: Early season detection of rice plants using RGB, NIR-G-B and multispectral images from unmanned aerial vehicle (UAV)

Classifier 0550
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Evaluating chlorophyll density in winter oilseed rape (Brassica napus L.) using canopy hyperspectral red-edge parameters
evaluated by: Evaluation 0550
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0550
has location: China
predicts: Chlorophyll 0550
related to: OilSeeds
grade: 70.0
reference: Lantao Li, Tao Ren, Yi Ma, Quanquan Wei, Shanqin Wang, Xiaokun Li, Rihuan Cong, Shishi Liu, Jianwei Lu, "Evaluating chlorophyll density in winter oilseed rape (Brassica napus L.) using canopy hyperspectral red-edge parameters", Computers and Electronics in Agriculture, Volume 126, August 2016, pages 21-31, Elsevier B.V.
comment: Evaluating chlorophyll density in winter oilseed rape (Brassica napus L.) using canopy hyperspectral red-edge parameters

Classifier 0553
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado
evaluated by: Evaluation 0553
has algorithm: Decision Tree (DT)
has algorithm: Multi-layer Perceptron (MLP)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0553
has location: Jordan
predicts: CropDisease 0553
related to: Avocado
related to: Nitrogen (N)
related to: Remote Sensing
grade: 70.0
reference: Jaafar Abdulridha, Yiannis Ampatzidis, Reza Ehsani, Ana I. de Castro, "Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado", Computers and Electronics in Agriculture, Volume 155, December 2018, pages 203-211, Elsevier B.V.
comment: Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado

Classifier 0554
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Evaluating the performance of xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in five fruit tree species
evaluated by: Evaluation 0554
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0554
has location: Spain
predicts: CropStress 0554
related to: Almond
grade: 70.0
reference: C. Ballester, P. J. Zarco-Tejada, E. Nicolas, J. J. Alarcon, E. Fereres, D. S. Intrigliolo, V. Gonzalez-Dugo, "Evaluating the performance of xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in five fruit tree species", Precision Agriculture, Volume 19, pages 178–193 (2018) , Springer
comment: Evaluating the performance of xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in five fruit tree species

Classifier 0619
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Grading of ripening stages of red banana using dielectric properties changes and image processing approach
has algorithm: Fuzzy C-means (FCM)
has algorithm: Algorithm NRLBP
has condition: CropImage 0619
has location: India
predicts: GrowthStage_0619
related to: Banana
grade: 65.0
reference: Asutosh Mohapatra, S. Shanmugasundaram, R. Malmathanraj, "Grading of ripening stages of red banana using dielectric properties changes and image processing approach", Computers and Electronics in Agriculture, Volume 143, December 2017, pages 100-110, Elsevier B.V.
comment: Grading of ripening stages of red banana using dielectric properties changes and image processing approach

Classifier 0620
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association
evaluated by: Evaluation 0620
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 0620
has dataset: Dataset 0620a
has location: Brazil
predicts: ObjectBoundary_0620
related to: Grape
related to: Yield
grade: 80.0
reference: Thiago T. Santos, Leonardo L. de Souza, Andreza A. dos Santos, Sandra Avila, "Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association", Computers and Electronics in Agriculture, Volume 170, March 2020, 105247, Elsevier B.V.
comment: Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association

Classifier 0622
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Grapevine variety identification using “Big Data” collected with miniaturized spectrometer combined with support vector machines and convolutional neural networks
evaluated by: Evaluation 0622
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Support Vector Machine (SVM)
has condition: SpectralImage_0622
has location: Portugal
predicts: Variety_0622
related to: Rape
grade: 70.0
reference: Armando M. Fernandes, Andrei B. Utkin, Jose Eiras-Dias, Jorge Cunha, Jose Silvestre, Pedro Melo-Pinto, "Grapevine variety identification using “Big Data” collected with miniaturized spectrometer combined with support vector machines and convolutional neural networks", Computers and Electronics in Agriculture, Volume 163, August 2019, 104855, Elsevier B.V.
comment: Grapevine variety identification using “Big Data” collected with miniaturized spectrometer combined with support vector machines and convolutional neural networks

Classifier 0623
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Ground-level hyperspectral imagery for detecting weeds in wheat fields
evaluated by: Evaluation 0623
has algorithm: Discriminant Analysis (DA)
has algorithm: Partial Least Squares (PLS)
has algorithm: Partial Least Squares Discriminant Analysis (PLSDA)
has condition: SpectralImage_0623
has location: Finland
has location: Israel
predicts: Weeds_0623
related to: Wheat
grade: 65.0
reference: I. Herrmann, U. Shapira, S. Kinast, A. Karnieli, D. J. Bonfil, "Ground-level hyperspectral imagery for detecting weeds in wheat fields", Precision Agriculture, Volume 14, pages 637–659 (2013) , Springer
comment: Ground-level hyperspectral imagery for detecting weeds in wheat fields

Classifier 0633
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Hybrid learning of fuzzy cognitive maps for sugarcane yield classification
evaluated by: Evaluation 0633
has algorithm: Fuzzy C-means (FCM)
has algorithm: Genetic Algorithm (GA)
has condition: Boron 0633
has condition: BulkDensity 0633
has condition: Copper 0633
has condition: CropPest 0633
has condition: ElectricalConductivity 0633
has condition: Humidity_0633
has condition: Iron_0633
has condition: Irrigation_0633
has condition: Manganese_0633
has condition: Nitrogen_0633
has condition: OrganicMatter_0633
has condition: Phosphorus_0633
has condition: Potassium_0633
has condition: Rainfall_0633
has condition: SoilMoisture_0633
has condition: SoilPH_0633
has condition: SolarRadiation_0633
has condition: Sulphur_0633
has condition: Temperature_0633
has condition: Zinc_0633
has dataset: Dataset 0633a
has location: India
predicts: Yield_0633
related to: Crop Pest
related to: Nutrient
related to: Soil
related to: Sugarcane
related to: Weather
grade: 85.0
reference: Rajathi Natarajan, Jayashree Subramanian, Elpiniki I. Papageorgiou, "Hybrid learning of fuzzy cognitive maps for sugarcane yield classification", Computers and Electronics in Agriculture, Volume 127, September 2016, pages 147-157, Elsevier B.V.
comment: Hybrid learning of fuzzy cognitive maps for sugarcane yield classification

Classifier 0634
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars
evaluated by: Evaluation 0634
has algorithm: Partial Least Squares (PLS)
has condition: SpectralImage_0634
has location: Finland
predicts: CropStress 0634
related to: Biomass
related to: Canopy
related to: Fertilizer
related to: Nitrogen (N)
related to: Nutrient Stress
related to: Potato
related to: Remote Sensing
grade: 65.0
reference: Tyler J. Nigon, David J. Mulla, Carl J. Rosen, Yafit Cohen, Victor Alchanatis, Joseph Knight, Ronit Rud, "Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars", Computers and Electronics in Agriculture, Volume 112, March 2015, pages 36-46, Elsevier B.V.
comment: Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars

Classifier 0644
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Hyperspectral remote sensing of grapevine drought stress
evaluated by: Evaluation 0644
has algorithm: Discriminant Analysis (DA)
has algorithm: Partial Least Squares (PLS)
has algorithm: Support Vector Machine (SVM)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0644
has location: Croatia
predicts: CropStress 0644
related to: Drought Stress
related to: Grape
related to: Irrigation
related to: Remote Sensing
grade: 70.0
reference: M. Zovko, U. Žibrat, M. Knapič, M. Bubalo Kovačić, D. Romić, "Hyperspectral remote sensing of grapevine drought stress", Precision Agriculture, Volume 20, pages 335–347 (2019) , Springer
comment: Hyperspectral remote sensing of grapevine drought stress

Classifier 0651
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging
evaluated by: Evaluation 0651
has algorithm: Back Propagation Neural Network (BPNN)
has algorithm: Linear Discriminant Analysis (LDA)
has algorithm: Quadratic Discriminant Analysis (QDA)
has condition: CropImage 0651a
has condition: CropImage 0651b
has dataset: Dataset 0651a
has location: Canada
predicts: CropPest 0651
related to: Cereal
related to: Crop Disease
related to: Wheat
grade: 80.0
reference: Chandra B. Singh, Digvir S. Jayas, Jitendra Paliwal, Noel D.G. White, "Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging", Computers and Electronics in Agriculture, Volume 73, Issue 2, August 2010, pages 118-125, Elsevier B.V.
comment: Identification of insect-damaged wheat kernels using short-wave near-infrared hyperspectral and digital colour imaging

Classifier 0656
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Identification of purple spot disease on asparagus crops across spatial and spectral scales
evaluated by: Evaluation 0656
has algorithm: Vegetation Indices (VI)
has condition: FieldImage_0656
has location: Macedonia
predicts: CropDisease 0656
related to: Alfalfa
related to: Fruit Tree
related to: Landsat-8
related to: Maize
related to: Remote Sensing
related to: Rice
grade: 70.0
reference: I. Navrozidis, T.K. Alexandridis, A. Dimitrakos, A.L. Lagopodi, D. Moshou, G. Zalidis, "Identification of purple spot disease on asparagus crops across spatial and spectral scales", Computers and Electronics in Agriculture, Volume 148, May 2018, pages 322-329, Elsevier B.V.
comment: Identification of purple spot disease on asparagus crops across spatial and spectral scales

Classifier 0660
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Identifying barley varieties by computer vision
evaluated by: Evaluation 0660
has algorithm: Artificial Neural Network (ANN)
has algorithm: Discriminant Analysis (DA)
has condition: CropImage 0660
has location: Mali
has location: Poland
predicts: Crop 0660
related to: Barley
grade: 65.0
reference: Piotr M. Szczypinski, Artur Klepaczko, Piotr Zapotoczny, "Identifying barley varieties by computer vision", Computers and Electronics in Agriculture, Volume 110, January 2015, pages 1-8, Elsevier B.V.
comment: Identifying barley varieties by computer vision

Classifier 0661
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Identifying blueberry fruit of different growth stages using natural outdoor color images
evaluated by: Evaluation 0661
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Naive Bayes (NB)
has condition: CropImage 0661
has location: Canada
has location: Japan
predicts: GrowthStage_0661
related to: Blueberry
related to: Fruit
related to: Yield
grade: 65.0
reference: Han Li, Won Suk Lee, Ku Wang, "Identifying blueberry fruit of different growth stages using natural outdoor color images", Computers and Electronics in Agriculture, Volume 106, August 2014, pages 91-101, Elsevier B.V.
comment: Identifying blueberry fruit of different growth stages using natural outdoor color images

Classifier 0673
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove
evaluated by: Evaluation 0673
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 0673
predicts: ObjectBoundary_0673
related to: Citrus
related to: Yield
grade: 70.0
reference: Chuanyuan Zhao, Won Suk Lee, Dongjian He, "Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove", Computers and Electronics in Agriculture, Volume 124, June 2016, pages 243-253, Elsevier B.V.
comment: Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove

Classifier 0674
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Immature green citrus fruit detection using color and thermal images
evaluated by: Evaluation 0674
has algorithm: Faster Regional Convolutional Neural Network (FasterR-CNN)
has condition: CropImage 0674
has location: Japan
predicts: Maturity_0674
related to: Citrus
related to: Yield
grade: 70.0
reference: H. Gan, W.S. Lee, V. Alchanatis, R. Ehsani, J.K. Schueller, "Immature green citrus fruit detection using color and thermal images", Computers and Electronics in Agriculture, Volume 152, September 2018, pages 117-125, Elsevier B.V.
comment: Immature green citrus fruit detection using color and thermal images

Classifier 0676
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification
evaluated by: Evaluation 0676
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 0676
has dataset: Dataset 0676a
predicts: CropDisease 0676
related to: Bean
related to: Cassava
related to: Citrus
related to: Coconut
related to: Coffee
related to: Corn
related to: Cotton
related to: Crop Disease
related to: Grapevines
related to: Maize
related to: Soybean
related to: Sugarcane
related to: Wheat
grade: 80.0
reference: Jayme Garcia Arnal Barbedo, "Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification", Computers and Electronics in Agriculture, Volume 153, October 2018, pages 46-53, Elsevier B.V.
comment: Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification

Classifier 0680
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices
evaluated by: Evaluation 0680
has algorithm: Vegetation Indices (VI)
has condition: CropImage 0680
has location: China
predicts: CropDisease 0680
related to: Remote Sensing
related to: Wheat
grade: 70.0
reference: Wei Feng, Wenying Shen, Li He, Jianzhao Duan, Binbin Guo, Yingxue Li, Chenyang Wang, Tiancai Guo, "Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices", Precision Agriculture, Volume 17, pages 608–627 (2016) , Springer
comment: Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices

Classifier 0684
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor
evaluated by: Evaluation 0684
has algorithm: Vegetation Indices (VI)
has condition: Nitrogen_0684
has condition: SpectralImage 0684a
has condition: SpectralImage 0684b
has condition: SpectralImage 0684c
has condition: SpectralImage 0684d
has location: China
predicts: GrowthStage_0684
predicts: Yield_0684
related to: Canopy
related to: Nitrogen (N)
related to: Rice
related to: Soil
related to: Yield
grade: 69.0
reference: Qiang Cao, Yuxin Miao, Jianning Shen, Weifeng Yu, Fei Yuan, Shanshan Cheng, Shanyu Huang, Hongye Wang, Wen Yang, Fengyan Liu, "Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor", Precision Agriculture, Volume 17, pages 136–154 (2016) , Springer
comment: Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor

Classifier 0688
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: In-field cotton detection via region-based semantic image segmentation
evaluated by: Evaluation 0688
has algorithm: Density-based Spatial Clustering Of Applications With Noise (DBSCAN)
has algorithm: Simple Linear Iterative Clustering (SLIC)
has condition: FieldImage_0688
has dataset: Dataset 0688a
has location: China
predicts: Boundary 0688
related to: Cotton
grade: 75.0
reference: Yanan Li, Zhiguo Cao, Hao Lu, Yang Xiao, Yanjun Zhu, Armin B. Cremers, "In-field cotton detection via region-based semantic image segmentation", Computers and Electronics in Agriculture, Volume 127, September 2016, pages 475-486, Elsevier B.V.
comment: In-field cotton detection via region-based semantic image segmentation

Classifier 0689
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: In-field detection of Alternaria solani in potato crops using hyperspectral imaging
evaluated by: Evaluation 0689
has algorithm: Principal Component Analysis (PCA)
has algorithm: Partial Least Squares Discriminant Analysis (PLSDA)
has algorithm: Support Vector Machine (SVM)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0689
has dataset: Dataset 0689a
has location: Belgium
predicts: CropDisease 0689
related to: Potato
grade: 80.0
reference: Ruben Van De Vijver, Koen Mertens, Kurt Heungens, Ben Somers, David Nuyttens, Irene Borra-Serrano, Peter Lootens, Isabel Roldan-Ruiz, Jürgen Vangeyte, Wouter Saeys, "In-field detection of Alternaria solani in potato crops using hyperspectral imaging", Computers and Electronics in Agriculture, Volume 168, January 2020, 105106, Elsevier B.V.
comment: In-field detection of Alternaria solani in potato crops using hyperspectral imaging

Classifier 0743
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Machine vision-based automatic disease symptom detection of onion downy mildew
evaluated by: Evaluation 0743
has algorithm: Artificial Neural Network (ANN)
has condition: CropImage 0743
has dataset: Dataset 0743a
has location: Korea
predicts: CropDisease 0743
related to: Onion
grade: 75.0
reference: Wan-Soo Kim, Dae-Hyun Lee, Yong-Joo Kim, "Machine vision-based automatic disease symptom detection of onion downy mildew", Computers and Electronics in Agriculture, Volume 168, January 2020, 105099, Elsevier B.V.
comment: Machine vision-based automatic disease symptom detection of onion downy mildew

Classifier 0747
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery
evaluated by: Evaluation 0747
has algorithm: Vegetation Indices (VI)
has condition: SatelliteImage_0747
has dataset: Dataset 0747a
has location: Indonesia
predicts: CropDisease 0747
related to: Oil palm
related to: Remote Sensing
grade: 80.0
reference: Heri Santoso, Totok Gunawan, Retnadi Heru Jatmiko, Witjaksana Darmosarkoro, Budiman Minasny, "Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery", Precision Agriculture, Volume 12, pages 233–248 (2011) , Springer
comment: Mapping and identifying basal stem rot disease in oil palms in North Sumatra with QuickBird imagery

Classifier 0748
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle
has algorithm: Vegetation Indices (VI)
has condition: FieldImage_0748
has location: Spain
predicts: CropStress 0748
related to: Grape
related to: Irrigation
related to: Remote Sensing
related to: Water Stress
grade: 70.0
reference: J. Bellvert, P. J. Zarco-Tejada, J. Girona, E. Fereres, "Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle", Precision Agriculture, Volume 15, pages 361–376 (2014) , Springer
comment: Mapping crop water stress index in a ‘Pinot-noir’ vineyard: comparing ground measurements with thermal remote sensing imagery from an unmanned aerial vehicle

Classifier 0772
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm
evaluated by: Evaluation 0772
has algorithm: Discriminant Analysis (DA)
has algorithm: Naive Bayes (NB)
has algorithm: Principal Component Analysis (PCA)
has algorithm: Quadratic Discriminant Analysis (QDA)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0772
has location: Malaysia
predicts: CropDisease 0772
related to: Basal Stem Rot
related to: Oil palm
grade: 75.0
reference: Shohreh Liaghat, Shattri Mansor, Reza Ehsani, Helmi Zulhaidi Mohd Shafri, Sariah Meon, Sindhuja Sankaran, "Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm", Computers and Electronics in Agriculture, Volume 101, February 2014, pages 48-54, Elsevier B.V.
comment: Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm

Classifier 0782
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Using RapidEye imagery to identify within-field variability of crop growth and yield in Ontario, Canada
has algorithm: Undefine (Undefine)
has algorithm: Vegetation Indices (VI)
has condition: SoilClay_0782
has condition: SoilPH_0782
has condition: SoilSand_0782
has condition: SoilSilt_0782
has condition: SoilTexture_0782
has condition: SpectralImage_0782
has dataset: Dataset 0782a
has location: Canada
predicts: GrowthStage_0782
predicts: Yield_0782
related to: Nitrogen (N)
related to: Remote Sensing
related to: Soil
related to: Wheat
related to: Yield
grade: 75.83333333333334
reference: Taifeng Dong, Jiali Shang, Jiangui Liu, Budong Qian, Qi Jing, Baoluo Ma, Ted Huffman, Xiaoyuan Geng, Abdoul Sow, Yichao Shi, Francis Canisius, Xianfeng Jiao, John M. Kovacs, Dan Walters, Jeff Cable, Jeff Wilson, "Using RapidEye imagery to identify within-field variability of crop growth and yield in Ontario, Canada", Precision Agriculture, Volume 20, pages 1231–1250 (2019) , Springer
comment: Using RapidEye imagery to identify within-field variability of crop growth and yield in Ontario, Canada

Classifier 0801
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing
evaluated by: Evaluation 0801
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage_0801
has dataset: Dataset 0801a
has location: India
predicts: Nitrogen_0801
predicts: Phosphorus_0801
predicts: Sulphur_0801
related to: Remote Sensing
related to: Soil
grade: 80.0
reference: G. R. Mahajan, R. N. Pandey, R. N. Sahoo, V. K. Gupta, S. C. Datta, Dinesh Kumar, "Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing", Precision Agriculture, Volume 18, pages 736–761 (2017) , Springer
comment: Monitoring nitrogen, phosphorus and sulphur in hybrid rice (Oryza sativa L.) using hyperspectral remote sensing

Classifier 0803
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Monitoring plant diseases and pests through remote sensing technology: A review
has algorithm: Machine Learning (ML)
has condition: CropImage 0803
predicts: CropDisease 0803
related to: Crop Disease
related to: Crop Pest
related to: Remote Sensing
grade: 65.0
reference: Jingcheng Zhang, Yanbo Huang, Ruiliang Pu, Pablo Gonzalez-Moreno, Lin Yuan, Kaihua Wu, Wenjiang Huang, "Monitoring plant diseases and pests through remote sensing technology: A review", Computers and Electronics in Agriculture, Volume 165, October 2019, 104943, Elsevier B.V.
comment: Monitoring plant diseases and pests through remote sensing technology: A review

Classifier 0815
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Multiple view image analysis of freefalling U.S. wheat grains for damage assessment
evaluated by: Evaluation 0815
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Linear Discriminant Analysis (LDA)
has condition: CropImage 0815
has dataset: Dataset 0815a
predicts: CropDisease 0815
related to: Seed
related to: Wheat
grade: 80.0
reference: Stephen R. Delwiche, I-Chang Yang, Robert A. Graybosch, "Multiple view image analysis of freefalling U.S. wheat grains for damage assessment", Computers and Electronics in Agriculture, Volume 98, October 2013, pages 62-73, Elsevier B.V.
comment: Multiple view image analysis of freefalling U.S. wheat grains for damage assessment

Classifier 0832
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: New perspectives on plant disease characterization based on deep learning
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 0832
has dataset: Dataset PlantVillage
predicts: CropDisease 0832
related to: Apple
related to: Blueberry
related to: Cherry
related to: Grape
related to: Tomato
grade: 80.0
reference: Sue Han Lee, Herve Goëau, Pierre Bonnet, Alexis Joly, "New perspectives on plant disease characterization based on deep learning", Computers and Electronics in Agriculture, Volume 170, March 2020, 105220, Elsevier B.V.
comment: New perspectives on plant disease characterization based on deep learning

Classifier 0855
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Optical sensing for early spring freeze related blueberry bud damage detection: Hyperspectral imaging for salient spectral wavelengths identification
evaluated by: Evaluation 0855
has algorithm: Principal Component Analysis (PCA)
has algorithm: Partial Least Squares Discriminant Analysis (PLSDA)
has algorithm: Successive Projections Algorithm (SPA)
has condition: SpectralImage_0855
has dataset: Dataset 0855a
has location: Norway
predicts: CropDisease 0855
related to: Blueberry
related to: Bud
related to: Temperature
grade: 80.0
reference: Zongmei Gao, Yanru Zhao, Lav R. Khot, Gwen-Alyn Hoheisel, Qin Zhang, "Optical sensing for early spring freeze related blueberry bud damage detection: Hyperspectral imaging for salient spectral wavelengths identification", Computers and Electronics in Agriculture, Volume 167, December 2019, 105025, Elsevier B.V.
comment: Optical sensing for early spring freeze related blueberry bud damage detection: Hyperspectral imaging for salient spectral wavelengths identification

Classifier 0874
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Particle Swarm Optimization based incremental classifier design for rice disease prediction
evaluated by: Evaluation 0874
has algorithm: Particle Swarm Optimization (PSO)
has condition: CropImage 0874
has dataset: Dataset 0874a
predicts: CropDisease 0874
related to: Rice
grade: 75.0
reference: Shampa Sengupta, Asit K. Das, "Particle Swarm Optimization based incremental classifier design for rice disease prediction", Computers and Electronics in Agriculture, Volume 140, August 2017, pages 443-451, Elsevier B.V.
comment: Particle Swarm Optimization based incremental classifier design for rice disease prediction

Classifier 0877
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network
evaluated by: Evaluation 0877
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 0877
has dataset: Dataset 0877a
predicts: CropDisease 0877
related to: Apple
related to: Corn
related to: Crop Disease
related to: Grape
related to: Pepper
related to: Potato
related to: Strawberry
related to: Tomato
grade: 80.0
reference: Qiaokang Liang, Shao Xiang, Yucheng Hu, Gianmarc Coppola, Dan Zhang, Wei Sun, "PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network", Computers and Electronics in Agriculture, Volume 157, February 2019, pages 518-529, Elsevier B.V.
comment: PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network

Classifier 0880
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Pest identification via deep residual learning in complex background
evaluated by: Evaluation 0880
has algorithm: Deep Residual Network (ResNet)
has condition: CropImage 0880
has dataset: Dataset 0880a
predicts: CropPest 0880
related to: Crop Pest
grade: 80.0
reference: Xi Cheng, Youhua Zhang, Yiqiong Chen, Yunzhi Wu, Yi Yue, "Pest identification via deep residual learning in complex background", Computers and Electronics in Agriculture, Volume 141, September 2017, pages 351-356, Elsevier B.V.
comment: Pest identification via deep residual learning in complex background

Classifier 0971
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Research on deep learning in apple leaf disease recognition
evaluated by: Evaluation_0971
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 0971
has dataset: Dataset 0971a
predicts: CropDisease 0971
related to: Apple
grade: 80.0
reference: Yong Zhong, Ming Zhao, "Research on deep learning in apple leaf disease recognition", Computers and Electronics in Agriculture, Volume 168, January 2020, 105146, Elsevier B.V.
comment: Research on deep learning in apple leaf disease recognition

Classifier 0993
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Segmenting ears of winter wheat at flowering stage using digital images and deep learning
evaluated by: Evaluation_0993
has algorithm: Deep Convolutional Neural Network (DCNN)
has algorithm: Fully Convolutional Network (FCN)
has condition: CropImage 0993
has dataset: Dataset 0993a
predicts: Ear 0993
related to: Wheat
grade: 80.0
reference: Juncheng Ma, Yunxia Li, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhihong Gong, Weihua Jiao, "Segmenting ears of winter wheat at flowering stage using digital images and deep learning", Computers and Electronics in Agriculture, Volume 168, January 2020, 105159, Elsevier B.V.
comment: Segmenting ears of winter wheat at flowering stage using digital images and deep learning

Classifier 1015
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: SLIC_SVM based leaf diseases saliency map extraction of tea plant
evaluated by: Evaluation 1015
has algorithm: Simple Linear Iterative Clustering (SLIC)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 1015
has dataset: Dataset 1015a
predicts: CropDisease 1015
related to: Leaf
related to: Tea
grade: 75.0
reference: Yunyun Sun, Zhaohui Jiang, Liping Zhang, Wei Dong, Yuan Rao, "SLIC_SVM based leaf diseases saliency map extraction of tea plant", Computers and Electronics in Agriculture, Volume 157, February 2019, pages 102-109, Elsevier B.V.
comment: SLIC_SVM based leaf diseases saliency map extraction of tea plant

Classifier 1040
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors
has algorithm: Discriminant Analysis (DA)
has condition: Elevation 1040
has condition: Slope 1040
has condition: SoilType 1040
has condition: SpectralImage 1040
has dataset: Dataset 1040a
has location: United States
predicts: CropStress 1040
related to: Remote Sensing
related to: Wheat
grade: 75.0
reference: Georges F. Backoulou, Norman C. Elliott, Kristopher Giles, Mpho Phoofolo, Vasile Catana, Mustafa Mirik, Jerry Michels, "Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors", Computers and Electronics in Agriculture, Volume 78, Issue 2, September 2011, pages 123-129, Elsevier B.V.
comment: Spatially discriminating Russian wheat aphid induced plant stress from other wheat stressing factors

Classifier 1044
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy
evaluated by: Evaluation 1044
has algorithm: Support Vector Machine (SVM)
has algorithm: Support Vector Machine-Feature Selection (SVM-FS)
has condition: SpectralImage 1044
has dataset: Dataset 1044a
has location: Malaysia
predicts: CropDisease 1044
related to: Basal Stem Rot
related to: Oil palm
grade: 85.0
reference: Alfadhl Yahya Khaled, Samsuzana Abd Aziz, Siti Khairunniza Bejo, Nazmi Mat Nawi, Idris Abu Seman, "Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy", Computers and Electronics in Agriculture, Volume 144, January 2018, pages 297-309, Elsevier B.V.
comment: Spectral features selection and classification of oil palm leaves infected by Basal stem rot (BSR) disease using dielectric spectroscopy

Classifier 1047
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection
evaluated by: Evaluation 1047
has algorithm: Spectral Angle Mapper (SAM)
has algorithm: Support Vector Machine (SVM)
has condition: SpectralImage 1047
has location: Germany
predicts: CropDisease 1047
related to: Remote Sensing
related to: Wheat
grade: 65.0
reference: Thorsten Mewes, Jonas Franke, Gunter Menz, "Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection", Precision Agriculture, Volume 12, Article number: 795 (2011) , Springer
comment: Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection

Classifier 1077
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: The potential of automatic methods of classification to identify leaf diseases from multispectral images
has algorithm: Condition Random Fields (CRF)
has algorithm: Gaussian Mixture Model (GMM)
has algorithm: K-Nearest Neighbours (KNN)
has condition: CropImage 1077
has condition: SpectralImage 1077
has dataset: Dataset 1077a
predicts: CropDisease 1077
related to: Sugar beet
grade: 75.0
reference: Sabine D. Bauer, Filip Korč, Wolfgang Forstner, "The potential of automatic methods of classification to identify leaf diseases from multispectral images", Precision Agriculture, Volume 12, pages 361–377 (2011) , Springer
comment: The potential of automatic methods of classification to identify leaf diseases from multispectral images

Classifier 1111
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale
evaluated by: Evaluation 1111
has algorithm: Spectral Angle Mapper (SAM)
has algorithm: Vegetation Indices (VI)
has condition: FieldImage 1111
has location: China
predicts: CropDisease 1111
related to: Remote Sensing
related to: Wheat
grade: 70.0
reference: Lin Yuan, Ruiliang Pu, Jingcheng Zhang, Jihua Wang, Hao Yang, "Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale", Precision Agriculture, Volume 17, pages 332–348 (2016) , Springer
comment: Using high spatial resolution satellite imagery for mapping powdery mildew at a regional scale

Classifier 1124
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate
evaluated by: Evaluation 1124
has algorithm: Vegetation Indices (VI)
has condition: CropImage 1124
has location: Finland
has location: Jordan
predicts: CropDisease 1124
related to: Remote Sensing
related to: Soybean
grade: 70.0
reference: Haibo Yao, Yanbo Huang, Zuzana Hruska, Steven J. Thomson, Krishna N. Reddy, "Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate", Computers and Electronics in Agriculture, Volume 89, November 2012, pages 145-157, Elsevier B.V.
comment: Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate

Classifier 1149
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Weed identification based on K-means feature learning combined with convolutional neural network
evaluated by: Evaluation 1149
has algorithm: Convolutional Neural Network (CNN)
has algorithm: K-Means (KMeans)
has condition: CropImage 1149
has dataset: Dataset 1149a
has location: United States
predicts: CropDisease 1149
related to: Soybean
related to: Weeds
grade: 80.0
reference: JingLei Tang, Dong Wang, ZhiGuang Zhang, LiJun He, Jing Xin, Yang Xu, "Weed identification based on K-means feature learning combined with convolutional neural network", Computers and Electronics in Agriculture, Volume 135, 1 April 2017, pages 63-70, Elsevier B.V.
comment: Weed identification based on K-means feature learning combined with convolutional neural network

Classifier 1154
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery
evaluated by: Evaluation 1154
has algorithm: Random Forest (RF)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 1154
has location: China
has location: Switzerland
predicts: CropDisease 1154
related to: Remote Sensing
related to: Wheat
grade: 70.0
reference: Jinya Su, Cunjia Liu, Matthew Coombes, Xiaoping Hu, Conghao Wang, Xiangming Xu, Qingdong Li, Lei Guo, Wen-Hua Chen, "Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery", Computers and Electronics in Agriculture, Volume 155, December 2018, pages 157-166, Elsevier B.V.
comment: Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery

Classifier 1205
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images
evaluated by: Evaluation 1205
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 1205
has dataset: Dataset 1205a
predicts: CropDisease 1205
related to: Crop Stress
grade: 80.0
reference: Basavaraj S. Anami, Naveen N. Malvade, Surendra Palaiah, "Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images", Artificial Intelligence in AgricultureVolume 4, 2020, Elsevier B.V.
comment: Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images

Classifier 1214
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Mapping Maize Cropping Patterns in Dak Lak , Vietnam Through MODIS EVI Time Series
evaluated by: Evaluation 1214
has algorithm: Support Vector Machine (SVM)
has algorithm: Vegetation Indices (VI)
has condition: SatelliteImage 1214
has dataset: Dataset 1214a
has location: Vietnam
predicts: Yield 1214
related to: Maize
grade: 80.0
reference: Ha Thi Thu Nguyen, Loc Van Nguyen, C.A.J.M (Kees) de Bie, Ignacio A. Ciampitti, Duc Anh Nguyen, Minh Van Nguyen, Luciana Nieto, Rai Schwalbert, Long Viet Nguyen, "Mapping Maize Cropping Patterns in Dak Lak , Vietnam Through MODIS EVI Time Series", Agronomy 2020, 10(4), 478; https://doi.org/10.3390/agronomy10040478
comment: Mapping Maize Cropping Patterns in Dak Lak , Vietnam Through MODIS EVI Time Series

Classifier 1216
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Insect classification and detection in field crops using modern machine learning techniques
evaluated by: Evaluation 1216
has algorithm: Artificial Neural Network (ANN)
has algorithm: Convolutional Neural Network (CNN)
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Naive Bayes (NB)
has algorithm: Support Vector Machine (SVM)
has condition: ObjectImage 1216
has dataset: Dataset 1216a
predicts: CropPest 1216
grade: 75.0
reference: Thenmozhi Kasinathan, Dakshayani Singaraju, Srinivasulu Reddy Uyyala, "Insect classification and detection in field crops using modern machine learning techniques", Information Processing in AgricultureAvailable online 8 October 2020, Elsevier B.V.
comment: Insect classification and detection in field crops using modern machine learning techniques

Classifier 1217
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Research on insect pest image detection and recognition based on bio-inspired methods
evaluated by: Evaluation 1217
has algorithm: Radial Basis Function (RBF)
has algorithm: Scale Invariant Feature Transform (SIFT)
has algorithm: Support Vector Machine (SVM)
has condition: ObjectImage 1217
has dataset: Dataset 1217a
predicts: CropPest 1217
grade: 75.0
reference: Limiao Denga, Yanjiang Wang, Zhongzhi Han, Renshi Yu, "Research on insect pest image detection and recognition based on bio-inspired methods", Biosystems Engineering, Volume 169, May 2018, Pages 139-148, Elsevier B.V.
comment: Research on insect pest image detection and recognition based on bio-inspired methods

Classifier 1218
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Ip102: A large-scale benchmark dataset for insect pest recognition
evaluated by: Evaluation 1218
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Support Vector Machine (SVM)
has condition: ObjectImage 1218
has dataset: Dataset 1218a
predicts: CropPest 1218
grade: 75.0
reference: Wu, Xiaoping, Chi Zhan, Yu-Kun Lai, Ming-Ming Cheng, and Jufeng Yang., "Ip102: A large-scale benchmark dataset for insect pest recognition", Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8787-8796. 2019.
comment: Ip102: A large-scale benchmark dataset for insect pest recognition

Classifier 1219
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Classification of yield affecting biotic and abiotic paddy crop stresses using field images
evaluated by: Evaluation 1219
has algorithm: Back Propagation Neural Network (BPNN)
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Sequential Forward Floating Selection (SFFS)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 1219
has dataset: Dataset 1219a
predicts: CropStress 1219
related to: Rice
grade: 80.0
reference: Basavaraj S. Anami, Naveen N. Malvade, Surendra Palaiah, "Classification of yield affecting biotic and abiotic paddy crop stresses using field images", Information Processing in AgricultureVolume 7, Issue 2, June 2020, Elsevier B.V.
comment: Classification of yield affecting biotic and abiotic paddy crop stresses using field images

Classifier 1220
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Maturity status classification of papaya fruits based on machine learning and transfer learning approach
evaluated by: Evaluation 1220
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Naive Bayes (NB)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 1220
predicts: Maturity 1220
related to: Fruit
related to: Papaya
grade: 65.0
reference: Santi Kumari Behera, Amiya Kumar Rath, Prabira Kumar Sethy, "Maturity status classification of papaya fruits based on machine learning and transfer learning approach", Information Processing in AgricultureAvailable online 20 May 2020, Elsevier B.V.
comment: Maturity status classification of papaya fruits based on machine learning and transfer learning approach

Classifier 1221
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection of plant leaf diseases using image segmentation and soft computing techniques
evaluated by: Evaluation 1221
has algorithm: Genetic Algorithm (GA)
has condition: CropImage 1221
predicts: CropDisease 1221
related to: Banana
related to: Bean
related to: Lemon
related to: Rose
grade: 65.0
reference: Vijai Singh, A.K. Misra, "Detection of plant leaf diseases using image segmentation and soft computing techniques", Information Processing in AgricultureVolume 4, Issue 1, March 2017, Elsevier B.V.
comment: Detection of plant leaf diseases using image segmentation and soft computing techniques

Classifier 1231
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification
evaluated by: Evaluation 1231
has algorithm: Support Vector Machine (SVM)
has algorithm: Vegetation Indices (VI)
has condition: CropImage 1231
has location: China
predicts: CropDisease 1231
related to: Corn
grade: 70.0
reference: Ran Meng, Zhengang Lv, Jianbing Yan, Gengshen Chen, Feng Zhao, Linglin Zeng, Binyuan Xu, "Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification", Remote Sens. 2020, 12(19), 3233; https://doi.org/10.3390/rs12193233
comment: Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification

Classifier 1233
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks , Remote Sensing , and Deep Learning : Case Study of the Caturra Variety in Colombia
has algorithm: Artificial Neural Network (ANN)
has condition: CropImage 1233
has location: Colombia
predicts: CropDisease 1233
related to: Coffee
related to: Remote Sensing
grade: 70.0
reference: David Velásquez, Alejandro Sánchez , Sebastian Sarmiento , Mauricio Toro , Mikel Maiza, Basilio Sierra , "A Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks , Remote Sensing , and Deep Learning : Case Study of the Caturra Variety in Colombia", Appl. Sci. 2020, 10(2), 697; https://doi.org/10.3390/app10020697
comment: A Method for Detecting Coffee Leaf Rust through Wireless Sensor Networks , Remote Sensing , and Deep Learning : Case Study of the Caturra Variety in Colombia

Classifier 1234
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Assessing Correlation of High - Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs
evaluated by: Evaluation 1234
has algorithm: Vegetation Indices (VI)
has condition: SatelliteImage 1234
has dataset: Dataset 1234a
has location: Japan
predicts: Yield 1234
related to: Rice
related to: Wheat
grade: 80.0
reference: Senlin Guan, Koichiro Fukami, Hitoshi Matsunaka, Midori Okami, Ryo Tanaka, Hiroshi Nakano, Tetsufumi Sakai, Keiko Nakano, Hideki Ohdan, Kimiyasu Takahashi, "Assessing Correlation of High - Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs", Remote Sens. 2019, 11(2), 112; https://doi.org/10.3390/rs11020112
comment: Assessing Correlation of High - Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops Using Small UAVs

Classifier 1237
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques
evaluated by: Evaluation 1237
has algorithm: Back Propagation Neural Network (BPNN)
has algorithm: Vegetation Indices (VI)
has condition: SatelliteImage 1237
has dataset: Dataset 1237a
has location: United States
predicts: Yield 1237
related to: Corn
grade: 80.0
reference: Sudhanshu Sekhar Panda, Daniel P. Ames, Suranjan Panigrahi, "Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques", Remote Sens. 2010, 2(3), 673-696; https://doi.org/10.3390/rs2030673
comment: Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

Classifier 1239
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Drought Stress Detection in Juvenile Oilseed Rape Using Hyperspectral Imaging with a Focus on Spectra Variability
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 1239
has location: Czech
has location: Finland
has location: France
has location: Germany
has location: Hungary
predicts: CropStress 1239
related to: Grape
grade: 75.0
reference: Wiktor R. Żelazny, Jan Lukáš, "Drought Stress Detection in Juvenile Oilseed Rape Using Hyperspectral Imaging with a Focus on Spectra Variability", Remote Sens. 2020, 12(20), 3462; https://doi.org/10.3390/rs12203462
comment: Drought Stress Detection in Juvenile Oilseed Rape Using Hyperspectral Imaging with a Focus on Spectra Variability

Classifier 1242
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A Model for Yellow Tea Polyphenols Content Estimation Based on Multi - Feature Fusion
evaluated by: Evaluation 1242
has algorithm: Partial Least Squares Regression (PLSR)
has algorithm: Support Vector Regression (SVR)
has condition: CropImage 1242
has location: China
predicts: Polyphenol 1242
related to: Tea
grade: 70.0
reference: Baohua Yang, Yue Zhu, Mengxuan Wang, Jingming Ning, "A Model for Yellow Tea Polyphenols Content Estimation Based on Multi - Feature Fusion", IEEE Access ( Volume: 7)
comment: A Model for Yellow Tea Polyphenols Content Estimation Based on Multi - Feature Fusion

Classifier 1243
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection of Peach Disease Image Based on Asymptotic Non - Local Means and PCNN - IPELM
evaluated by: Evaluation 1243
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Extreme Learning Machine (ELM)
has algorithm: Improved Particle Swarm Optimization (IPSO)
has condition: CropImage 1243
has dataset: Dataset 1243a
predicts: CropDisease 1243
related to: Peach
related to: Tea
grade: 80.0
reference: Shuangjie Huang, Guoxiong Zhou, Mingfang He, Aibin Chen, Wenzhuo Zhang, Yahui Hu, "Detection of Peach Disease Image Based on Asymptotic Non - Local Means and PCNN - IPELM", IEEE Access ( Volume: 8)
comment: Detection of Peach Disease Image Based on Asymptotic Non - Local Means and PCNN - IPELM

Classifier 1246
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent - Convolutional Neural Network ( R - CNN )
evaluated by: Evaluation 1246
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Random Forest (RF)
has algorithm: Recurrent neural network (RNN)
has algorithm: Support Vector Machine (SVM)
has algorithm: XGBoost
has condition: SpectralImage 1246
has location: Italy
predicts: CropName 1246
related to: Remote Sensing
grade: 70.0
reference: Vittorio Mazzia, Aleem Khaliq, Marcello Chiaberge, "Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent - Convolutional Neural Network ( R - CNN )", Appl. Sci. 2020, 10(1), 238; https://doi.org/10.3390/app10010238
comment: Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent - Convolutional Neural Network ( R - CNN )

Classifier 1262
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Can just one - second measurement of chlorophyll a fluorescence be used to predict sulphur deficiency in radish ( Raphanus sativus L. sativus ) plants ?
has algorithm: Principal Component Analysis (PCA)
has condition: Iron 1262
has condition: Magnesium 1262
has condition: Nitrogen 1262
has condition: Phosphorus 1262
has condition: Potassium 1262
has condition: Sulphur 1262
predicts: CropStress 1262
related to: Radish
related to: SulphurDeficiency
grade: 65.0
reference: Izabela A. Samborska, Hazem M. Kalaji, Leszek Sieczko, Wojciech Borucki, Radosław Mazur, Margarita Kouzmanova, Vasilij Goltsev, "Can just one - second measurement of chlorophyll a fluorescence be used to predict sulphur deficiency in radish ( Raphanus sativus L. sativus ) plants ?", Current Plant BiologyVolume 19, September 2019, Elsevier B.V.
comment: Can just one - second measurement of chlorophyll a fluorescence be used to predict sulphur deficiency in radish ( Raphanus sativus L. sativus ) plants ?

Classifier 1318
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A predictive machine learning application in agriculture : Cassava disease detection and classification with imbalanced dataset using convolutional neural networks
evaluated by: Evaluation 1318
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 1318
has dataset: Dataset 1318a
has location: Uganda
predicts: CropDisease 1318
related to: Cassava
grade: 75.0
reference: G. Sambasivam, Geoffrey Duncan Opiyo, "A predictive machine learning application in agriculture : Cassava disease detection and classification with imbalanced dataset using convolutional neural networks", Egyptian Informatics JournalAvailable online 9 March 2020, Elsevier B.V.
comment: A predictive machine learning application in agriculture : Cassava disease detection and classification with imbalanced dataset using convolutional neural networks

Classifier 1371
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network
has algorithm: Maximum Likelihood Classification (MLC)
has condition: FieldImage 1371
predicts: CropDisease 1371
related to: Canola
grade: 70.0
reference: Muhammad Hamza Asad, Abdul Bais, "Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network", Information Processing in AgricultureVolume 7, Issue 4, December 2020, Elsevier B.V.
comment: Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network

Classifier 1373
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Can just one - second measurement of chlorophyll a fluorescence be used to predict sulphur deficiency in radish ( Raphanus sativus L. sativus ) plants ?
has algorithm: Principal Component Analysis (PCA)
has condition: Iron 1373
has condition: Magnesium 1373
has condition: Nitrogen 1373
has condition: Phosphorus 1373
has condition: Potassium 1373
has condition: Sulphur 1373
predicts: CropStress 1373
related to: Radish
grade: 65.0
reference: Izabela A. Samborska, Hazem M. Kalaji, Leszek Sieczko, Wojciech Borucki, Radosław Mazur, Margarita Kouzmanova, Vasilij Goltsev, "Can just one - second measurement of chlorophyll a fluorescence be used to predict sulphur deficiency in radish ( Raphanus sativus L. sativus ) plants ?", Current Plant BiologyVolume 19, September 2019, Elsevier B.V.
comment: Can just one - second measurement of chlorophyll a fluorescence be used to predict sulphur deficiency in radish ( Raphanus sativus L. sativus ) plants ?

Classifier 1405
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Grading method of soybean mosaic disease based on hyperspectral imaging technology
evaluated by: Evaluation 1405
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Support Vector Machine (SVM)
has condition: SpectralImage 1405
has dataset: Dataset 1405a
predicts: CropDisease 1405
related to: Soybean
grade: 80.0
reference: Jiangsheng Gui, Jingyi Fei, Zixian Wu, Xiaping Fu, Alou Diakite, "Grading method of soybean mosaic disease based on hyperspectral imaging technology", Information Processing in AgricultureAvailable online 10 November 2020, Elsevier B.V.
comment: Grading method of soybean mosaic disease based on hyperspectral imaging technology

Classifier 1407
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection of banana plants and their major diseases through aerial images and machine learning methods : A case study in DR Congo and Republic of Benin
evaluated by: Evaluation 1407
has algorithm: Principal Component Analysis (PCA)
has algorithm: Random Forest (RF)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 1407a
has condition: CropImage 1407b
has location: Benin
has location: Congo
predicts: CropDisease 1407
related to: Banana
related to: PlanetScope
related to: Sentinel-2
related to: WorldView-2
grade: 70.0
reference: Michael Gomez Selvaraj, Alejandro Vergara, Frank Montenegro, Henry Alonso Ruiz, Nancy Safari, Dries Raymaekers, Walter Ocimati, Jules Ntamwira, Laurent Tits, Aman Bonaventure Omondi, Guy Blomme, "Detection of banana plants and their major diseases through aerial images and machine learning methods : A case study in DR Congo and Republic of Benin", ISPRS Journal of Photogrammetry and Remote SensingVolume 169, November 2020, Elsevier B.V.
comment: Detection of banana plants and their major diseases through aerial images and machine learning methods : A case study in DR Congo and Republic of Benin

Classifier 1410
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques
evaluated by: Evaluation 1410
has algorithm: Artificial Neural Network (ANN)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 1410
has location: Korea
predicts: CropDisease 1410
grade: 65.0
reference: Mutiara Syifa, Sung-Jae Park, Chang-Wook Lee, "Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques", EngineeringVolume 6, Issue 8, August 2020, Elsevier B.V.
comment: Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques

Classifier 1422
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: An evaluation of Landsat , Sentinel-2 , Sentinel-1 and MODIS data for crop type mapping
evaluated by: Evaluation 1422
has algorithm: Decision Tree (DT)
has algorithm: Vegetation Indices (VI)
has condition: SatelliteImage 1422
has location: United States
predicts: CropName 1422
related to: Bean
related to: Corn
related to: Soybean
grade: 65.0
reference: Xiao-Peng Song, Wenli Huang, Matthew C. Hansen, Peter Potapov, "An evaluation of Landsat , Sentinel-2 , Sentinel-1 and MODIS data for crop type mapping", Science of Remote SensingVolume 3, June 2021, Elsevier B.V.
comment: An evaluation of Landsat , Sentinel-2 , Sentinel-1 and MODIS data for crop type mapping

Classifier 1423
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Automated mapping of soybean and corn using phenology
evaluated by: Evaluation 1423
has algorithm: Decision Tree (DT)
has algorithm: Vegetation Indices (VI)
has condition: SatelliteImage 1423
has location: Brazil
predicts: Crop 1423
related to: Bean
related to: Corn
related to: MODIS
related to: Soybean
grade: 70.0
reference: Liheng Zhong, Lina Hu, Le Yu, Peng Gong, Gregory S. Biging, "Automated mapping of soybean and corn using phenology", ISPRS Journal of Photogrammetry and Remote SensingVolume 119, September 2016, Elsevier B.V.
comment: Automated mapping of soybean and corn using phenology

Classifier 1427
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Automated detection of individual clove trees for yield quantification in northeastern Madagascar based on multi - spectral satellite data
evaluated by: Evaluation 1427
has algorithm: Random Forest (RF)
has condition: FieldImage 1427
has location: Madagascar
predicts: Boundary 1427
predicts: Yield 1427
related to: Clove
grade: 65.0
reference: Sandra I.B. Roth, Reik Leiterer, Michele Volpi, Enrico Celio, Michael E. Schaepman, Philip C. Joerg, "Automated detection of individual clove trees for yield quantification in northeastern Madagascar based on multi - spectral satellite data", Remote Sensing of EnvironmentVolume 221, February 2019, Elsevier B.V.
comment: Automated detection of individual clove trees for yield quantification in northeastern Madagascar based on multi - spectral satellite data

Classifier 1430
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Classification of some strategic crops in Egypt using multi remotely sensing sensors and time series analysis
evaluated by: Evaluation 1430
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 1430
predicts: GrowthStage 1430
grade: 70.0
reference: Eslam Farg, Mohsen Nabil Ramadan, Sayed Medany Arafat, "Classification of some strategic crops in Egypt using multi remotely sensing sensors and time series analysis", The Egyptian Journal of Remote Sensing and Space ScienceVolume 22, Issue 3, December 2019, Elsevier B.V.
comment: Classification of some strategic crops in Egypt using multi remotely sensing sensors and time series analysis

Classifier 1438
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Computer vision - based apple grading for golden delicious apples based on surface features
evaluated by: Evaluation 1438
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Multi-layer Perceptron (MLP)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 1438
predicts: Quality 1438
related to: Apple
grade: 65.0
reference: Payman Moallem, Alireza Serajoddin, Hossein Pourghassem, "Computer vision - based apple grading for golden delicious apples based on surface features", Information Processing in AgricultureVolume 4, Issue 1, March 2017, Elsevier B.V.
comment: Computer vision - based apple grading for golden delicious apples based on surface features

Classifier 1457
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks
evaluated by: Evaluation 1457
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 1457
predicts: CropDisease 1457
related to: Grape
related to: Rape
grade: 70.0
reference: Miaomiao Ji, Lei Zhang, Qiufeng Wu, "Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks", Information Processing in AgricultureVolume 7, Issue 3, September 2020, Elsevier B.V.
comment: Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks

Classifier 1460
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A segmentation method for processing greenhouse vegetable foliar disease symptom images
evaluated by: Evaluation 1460
has algorithm: Classification and Regression Trees (CART)
has algorithm: Decision Tree (DT)
has condition: CropImage 1460
has location: China
predicts: CropDisease 1460
related to: Cucumber
related to: Greenhouse
grade: 70.0
reference: Juncheng Ma, Keming Du, Feixiang Zheng, Lingxian Zhang, Zhongfu Sun, "A segmentation method for processing greenhouse vegetable foliar disease symptom images", Information Processing in AgricultureVolume 6, Issue 2, June 2019, Elsevier B.V.
comment: A segmentation method for processing greenhouse vegetable foliar disease symptom images

Classifier 1585
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Performance analysis of deep learning CNN models for disease detection in plants using image segmentation
evaluated by: Evaluation 1585
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 1585
has dataset: Dataset PlantVillage
predicts: CropDisease 1585
related to: Tomato
grade: 75.0
reference: Parul Sharma, Yash Paul Singh Berwal, Wiqas Ghai, "Performance analysis of deep learning CNN models for disease detection in plants using image segmentation", Information Processing in AgricultureVolume 7, Issue 4, December 2020, Elsevier B.V.
comment: Performance analysis of deep learning CNN models for disease detection in plants using image segmentation

Classifier 1693
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Transcriptome Comparison of Susceptible and Resistant Wheat in Response to Powdery Mildew Infection
has algorithm: Principal Component Regression (PCR)
has condition: SpectralImage 1693
has location: China
predicts: CropDisease 1693
related to: Wheat
grade: 65.0
reference: Mingming Xin, Xiangfeng Wang, Huiru Peng, Yingyin Yao, Chaojie Xie, Yao Han, Zhongfu Ni, Qixin Sun, "Transcriptome Comparison of Susceptible and Resistant Wheat in Response to Powdery Mildew Infection", Genomics, Proteomics & BioinformaticsVolume 10, Issue 2, April 2012, Elsevier B.V.
comment: Transcriptome Comparison of Susceptible and Resistant Wheat in Response to Powdery Mildew Infection

Classifier 1923
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Early detection of forest stress from European spruce bark beetle attack , and a new vegetation index : Normalized distance red & SWIR ( NDRS )
has algorithm: Linear Discriminant Analysis (LDA)
has algorithm: Random Forest (RF)
has algorithm: Vegetation Indices (VI)
has condition: SatelliteImage 1923
has location: Sweden
predicts: CropDisease 1923
related to: Beet
related to: Sentinel-2
grade: 70.0
reference: Langning Huo, Henrik Jan Persson, Eva Lindberg, "Early detection of forest stress from European spruce bark beetle attack , and a new vegetation index : Normalized distance red & SWIR ( NDRS )", Remote Sensing of EnvironmentVolume 255, 15 March 2021, Elsevier B.V.
comment: Early detection of forest stress from European spruce bark beetle attack , and a new vegetation index : Normalized distance red & SWIR ( NDRS )

Classifier 2129
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Automatic drought stress detection in grapevines without using conventional threshold values
has algorithm: Principal Component Analysis (PCA)
has algorithm: Unfold Principal Component Analysis (UPCA)
has condition: Drought 2129
has location: Belgium
predicts: CropStress 2129
related to: Drought Stress
related to: Grape
grade: 65.0
reference: Annelies Baert, Kris Villez, Kathy Steppe, "Automatic drought stress detection in grapevines without using conventional threshold values", Plant and Soil volume 369, pages439–452(2013) , Springer
comment: Automatic drought stress detection in grapevines without using conventional threshold values

Classifier 2166
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Effects of cadmium and salicylic acid on growth , spectral reflectance and photosynthesis of castor bean seedlings
has algorithm: Vegetation Indices (VI)
has condition: Cadmium 2166
has condition: SalicylicAcid 2166
has condition: SpectralImage 2166
has condition: Water 2166
predicts: GrowthStage 2166
related to: Bean
grade: 67.5
reference: Caifeng Liu, Jiali Guo, Yanlan Cui, Tianfeng Lü, Xiaohuan Zhang, Gangrong Shi, "Effects of cadmium and salicylic acid on growth , spectral reflectance and photosynthesis of castor bean seedlings", Plant and Soil volume 344, pages131–141(2011) , Springer
comment: Effects of cadmium and salicylic acid on growth , spectral reflectance and photosynthesis of castor bean seedlings

Classifier 2173
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Analysis of bacterial communities in rhizosphere soil of healthy and diseased cotton ( Gossypium sp . ) at different plant growth stages
has algorithm: Principal Component Analysis (PCA)
has condition: DNA 2173
has condition: RNA 2173
predicts: CropDisease 2173
related to: Cotton
related to: Flower
grade: 65.0
reference: Yan Zhang, Bing-Hai Du, Zhi-gang Jin, Zheng-hua Li, Hong-ning Song, Yan-Qin Ding, "Analysis of bacterial communities in rhizosphere soil of healthy and diseased cotton ( Gossypium sp . ) at different plant growth stages", Plant and Soil volume 339, pages447–455(2011) , Springer
comment: Analysis of bacterial communities in rhizosphere soil of healthy and diseased cotton ( Gossypium sp . ) at different plant growth stages

Classifier 2297
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Vegetation indices and NIR - SWIR spectral bands as a phenotyping tool for water status determination in soybean
evaluated by: Evaluation 2297
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 2297
predicts: Water 2297
related to: Soybean
grade: 75.0
reference: P. Braga, L. G. T. Crusiol, M. R. Nanni, A. L. H. Caranhato, M. B. Fuhrmann, A. L. Nepomuceno, N. Neumaier, J. R. B. Farias, A. Koltun, L. S. A. Gonçalves, L. M. Mertz-Henning, "Vegetation indices and NIR - SWIR spectral bands as a phenotyping tool for water status determination in soybean", Precision Agriculture volume 22, pages249–266(2021) , Springer
comment: Vegetation indices and NIR - SWIR spectral bands as a phenotyping tool for water status determination in soybean

Classifier 2308
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves
has algorithm: Wavelength (WL)
has condition: CropImage 2308
predicts: CropDisease 2308
related to: Tomato
grade: 70.0
reference: Jinzhu Lu, Mingchuan Zhou, Yingwang Gao, Huanyu Jiang, "Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves", Precision Agriculture volume 19, pages379–394(2018) , Springer
comment: Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves

Classifier 2339
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Spectral signatures of sugar beet leaves for the detection and differentiation of diseases
evaluated by: Evaluation 2339
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 2339
has dataset: Dataset 2339a
has location: Germany
predicts: CropDisease 2339
related to: Sugar beet
grade: 80.0
reference: A.-K. Mahlein, U. Steiner, H.-W. Dehne, E.-C. Oerke, "Spectral signatures of sugar beet leaves for the detection and differentiation of diseases", Precision Agriculture volume 11, pages413–431(2010) , Springer
comment: Spectral signatures of sugar beet leaves for the detection and differentiation of diseases

Classifier 2356
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Multi - temporal wheat disease detection by multi - spectral remote sensing
evaluated by: Evaluation 2356
has algorithm: Decision Tree (DT)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 2356
has location: Germany
predicts: CropDisease 2356
related to: Wheat
grade: 70.0
reference: Jonas Franke, Gunter Menz, "Multi - temporal wheat disease detection by multi - spectral remote sensing", Precision Agriculture volume 8, pages161–172(2007) , Springer
comment: Multi - temporal wheat disease detection by multi - spectral remote sensing

Classifier 2357
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density
has algorithm: Vegetation Indices (VI)
has condition: CropImage 2357
has dataset: Dataset 2357a
predicts: CropDisease 2357
predicts: Density 2357
related to: Tea
related to: Wheat
grade: 75.0
reference: A. Larsolle, H. Hamid Muhammed, "Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density", Precision Agriculture volume 8, pages37–47(2007) , Springer
comment: Measuring crop status using multivariate analysis of hyperspectral field reflectance with application to disease severity and plant density

Classifier 2364
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self - organising maps
has algorithm: Discriminant Analysis (DA)
has algorithm: Quadratic Discriminant Analysis (QDA)
has condition: Ear 2364
has condition: Wheat 2364
predicts: CropDisease 2364
related to: Wheat
grade: 65.0
reference: D. Moshou, C. Bravo, S. Wahlen, J. West, A. McCartney, J. De Baerdemaeker, H. Ramon, "Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self - organising maps", Precision Agriculture volume 7, pages149–164(2006) , Springer
comment: Simultaneous identification of plant stresses and diseases in arable crops using proximal optical sensing and self - organising maps

Classifier 2387
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Mapping Grain Sorghum Yield Variability Using Airborne Digital Videography
evaluated by: Evaluation 2387
has algorithm: Linear Regression (LR)
has algorithm: Multinomial Logistic Regression (MLR)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 2387
has location: United States
predicts: Yield 2387
related to: Sorghum
grade: 70.0
reference: Chenghai Yang, Gerald L. Anderson, "Mapping Grain Sorghum Yield Variability Using Airborne Digital Videography", Precision Agriculture volume 2, pages7–23(2000) , Springer
comment: Mapping Grain Sorghum Yield Variability Using Airborne Digital Videography

Classifier 2452
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Expression of Tcchitinase - I gene in transgenic peanut ( Arachis hypogaea L. ) confers enhanced resistance against leaf spot and rust diseases
has condition: DNA 2452
predicts: CropDisease 2452
related to: Peanut
grade: 35.0
reference: Rajinikanth Marka, Rama Swamy Nanna, "Expression of Tcchitinase - I gene in transgenic peanut ( Arachis hypogaea L. ) confers enhanced resistance against leaf spot and rust diseases", Plant Growth Regulation volume 93, pages53–63(2021) , Springer
comment: Expression of Tcchitinase - I gene in transgenic peanut ( Arachis hypogaea L. ) confers enhanced resistance against leaf spot and rust diseases

Classifier 2771
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Advanced methods of plant disease detection . A review
has algorithm: Principal Component Regression (PCR)
has condition: CropImage 2771
predicts: CropDisease 2771
grade: 65.0
reference: Federico Martinelli, Riccardo Scalenghe, Salvatore Davino, Stefano Panno, Giuseppe Scuderi, Paolo Ruisi, Paolo Villa, Daniela Stroppiana, Mirco Boschetti, Luiz R. Goulart, Cristina E. Davis, Abhaya M. Dandekar, "Advanced methods of plant disease detection . A review", Agronomy for Sustainable Development volume 35, pages1–25(2015) , Springer
comment: Advanced methods of plant disease detection . A review

Classifier 2799
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Molecular detection and first report of Cucumber mosaic virus infecting ‘ Cavendish ’ banana plants in Ethiopia
has algorithm: Principal Component Regression (PCR)
has condition: CropImage 2799
has location: Ethiopia
predicts: CropDisease 2799
related to: Banana
grade: 65.0
reference: Yohanis Kebede, S. Majumder, "Molecular detection and first report of Cucumber mosaic virus infecting ‘ Cavendish ’ banana plants in Ethiopia", Journal of Plant Diseases and Protection volume 127, pages417–420(2020) , Springer
comment: Molecular detection and first report of Cucumber mosaic virus infecting ‘ Cavendish ’ banana plants in Ethiopia

Classifier 2800
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Occurrence and frequency of spot form and net form of net blotch disease of barley in Algeria
has algorithm: Principal Component Regression (PCR)
has condition: CropImage 2800
has location: Algeria
predicts: CropDisease 2800
related to: Barley
related to: Cereal
related to: Wheat
grade: 65.0
reference: Hamama-Iméne Lammari, Alexandra Rehfus, Gerd Stammler, Zine El Abidine Fellahi, Abdelkader Benbelkacem, Hamida Benslimane, "Occurrence and frequency of spot form and net form of net blotch disease of barley in Algeria", Journal of Plant Diseases and Protection volume 127, pages35–42(2020) , Springer
comment: Occurrence and frequency of spot form and net form of net blotch disease of barley in Algeria

Classifier 2815
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Benefits of hyperspectral imaging for plant disease detection and plant protection : a technical perspective
has algorithm: Principal Component Analysis (PCA)
has algorithm: Partial Least Squares Regression (PLSR)
has algorithm: Spectral Angle Mapper (SAM)
has algorithm: Support Vector Machine (SVM)
has condition: SpectralImage 2815
predicts: CropDisease 2815
related to: Pear
grade: 65.0
reference: Stefan Thomas, Matheus Thomas Kuska, David Bohnenkamp, Anna Brugger, Elias Alisaac, Mirwaes Wahabzada, Jan Behmann, Anne-Katrin Mahlein, "Benefits of hyperspectral imaging for plant disease detection and plant protection : a technical perspective", Journal of Plant Diseases and Protection volume 125, pages5–20(2018) , Springer
comment: Benefits of hyperspectral imaging for plant disease detection and plant protection : a technical perspective

Classifier 2829
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Plant pest and disease diagnosis using electronic nose and support vector machine approach
has algorithm: Radial Basis Function (RBF)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 2829
predicts: CropDisease 2829
related to: Cucumber
related to: Pepper
related to: Tomato
grade: 65.0
reference: Reza Ghaffari, Jullada Laothawornkitkul, Daciana Iliescu, Evor Hines, Mark Leeson, Richard Napier, Jason P Moore, Nigel D Paul, C Nicholas Hewitt, Jane E Taylor, "Plant pest and disease diagnosis using electronic nose and support vector machine approach", Journal of Plant Diseases and Protection volume 119, pages200–207(2012) , Springer
comment: Plant pest and disease diagnosis using electronic nose and support vector machine approach

Classifier 2845
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Screening banana plants for banana bunchy top virus with primers specific to Indian isolates
has algorithm: Principal Component Regression (PCR)
has condition: CropImage 2845
has condition: DNA 2845
has location: India
predicts: CropDisease 2845
related to: Banana
grade: 65.0
reference: J. Anandhi, C. Vijila, G. S. Viswanath, T. S. Lokeswari, "Screening banana plants for banana bunchy top virus with primers specific to Indian isolates", Journal of Plant Diseases and Protection volume 114, pages101–107(2007) , Springer
comment: Screening banana plants for banana bunchy top virus with primers specific to Indian isolates

Classifier 2992
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Assessing the Influence of Soil Quality on Rainfed Wheat Yield
has algorithm: Factor Analysis (FA)
has algorithm: Linear Regression (LR)
has algorithm: Multinomial Logistic Regression (MLR)
has condition: BulkDensity 2992
has condition: CationExchangeCapacity 2992
has condition: Copper 2992
has condition: ElectricalConductivity 2992
has condition: Iron 2992
has condition: Manganese 2992
has condition: Nitrogen 2992
has condition: OrganicCarbon 2992
has condition: Phosphorus 2992
has condition: Potassium 2992
has condition: SoilPH 2992
has condition: SoilTexture 2992
has condition: Zinc 2992
has location: Iran
predicts: Yield 2992
related to: Wheat
grade: 67.6923076923077
reference: Kamal Nabiollahi, Eskandari Heshmat, Amir Mosavi, Ruth Kerry, Mojtaba Zeraatpisheh, Ruhollah Taghizadeh-Mehrjardi, "Assessing the Influence of Soil Quality on Rainfed Wheat Yield", Agriculture 2020, 10(10), 469; https://doi.org/10.3390/agriculture10100469
comment: Assessing the Influence of Soil Quality on Rainfed Wheat Yield

Classifier 3048
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery : A Case Study in South Korea
evaluated by: Evaluation 3048
has algorithm: Vegetation Indices (VI)
has condition: Radiation 3048
has condition: SatelliteImage 3048
has condition: Temperature 3048
has dataset: Dataset 3048a
has location: South Korea
predicts: Yield 3048
related to: Remote Sensing
related to: Rice
grade: 80.0
reference: Seungtaek Jeong, Jonghan Ko, Jong-Min Yeom, "Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery : A Case Study in South Korea", Remote Sens. 2018, 10(10), 1665; https://doi.org/10.3390/rs10101665
comment: Nationwide Projection of Rice Yield Using a Crop Model Integrated with Geostationary Satellite Imagery : A Case Study in South Korea

Classifier 3067
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: UAV - Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning
evaluated by: Evaluation 3067
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Radial Basis Function (RBF)
has condition: CropImage 3067
predicts: CropDisease 3067
related to: Citrus
related to: Fruit
grade: 65.0
reference: Jaafar Abdulridha, Ozgur Batuman, Yiannis Ampatzidis, "UAV - Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning", Remote Sens. 2019, 11(11), 1373; https://doi.org/10.3390/rs11111373
comment: UAV - Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning

Classifier 3134
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Estimation of Leaf Chlorophyll a , b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance
evaluated by: Evaluation 3134
has algorithm: Decision Tree (DT)
has algorithm: Extreme Learning Machine (ELM)
has algorithm: Random Forest (RF)
has algorithm: Stochastic Gradient Boosting (SGB)
has algorithm: Support Vector Machine (SVM)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 3134
has location: Japan
predicts: Chlorophyll 3134
related to: Wasabi
grade: 70.0
reference: Rei Sonobe, Hiroto Yamashita, Harumi Mihara, Akio Morita, Takashi Ikka, "Estimation of Leaf Chlorophyll a , b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance", Remote Sens. 2020, 12(19), 3265; https://doi.org/10.3390/rs12193265
comment: Estimation of Leaf Chlorophyll a , b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

Classifier 3176
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Mapping the Depth - to - Soil pH Constraint , and the Relationship with Cotton and Grain Yield at the Within - Field Scale
has algorithm: Random Forest (RF)
has condition: Area 3176
has condition: Evapotranspiration 3176
has condition: Humidity 3176
has condition: Precipitation 3176
has condition: Slope 3176
has condition: SoilK 3176
has condition: SoilN 3176
has condition: SoilP 3176
has condition: SoilType 3176
has condition: Temperature 3176
has condition: TopSoil 3176
has condition: WindSpeed 3176
predicts: Yield 3176
related to: Canola
related to: Chickpea
related to: Cotton
related to: Wheat
grade: 65.41666666666666
reference: Patrick Filippi, Edward J. Jones, Bradley J. Ginns, Brett M. Whelan, Guy W. Roth, Thomas F.A. Bishop, "Mapping the Depth - to - Soil pH Constraint , and the Relationship with Cotton and Grain Yield at the Within - Field Scale", Agronomy 2019, 9(5), 251; https://doi.org/10.3390/agronomy9050251
comment: Mapping the Depth - to - Soil pH Constraint , and the Relationship with Cotton and Grain Yield at the Within - Field Scale

Classifier 3207
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Real - Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System
evaluated by: Evaluation 3207
has algorithm: Artificial Neural Network (ANN)
has condition: CropImage 3207
has location: Canada
has location: Japan
has location: Sweden
predicts: CropDisease 3207
related to: Smartphone
related to: Strawberry
grade: 65.0
reference: Md Sultan Mahmud, Qamar U. Zaman, Travis J. Esau, Young K. Chang, G. W. Price, Balakrishnan Prithiviraj, "Real - Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System", Agronomy 2020, 10(7), 1027; https://doi.org/10.3390/agronomy10071027
comment: Real - Time Detection of Strawberry Powdery Mildew Disease Using a Mobile Machine Vision System

Classifier 3293
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf - Based Hyperspectral Reflectance
evaluated by: Evaluation 3293
has algorithm: Discriminant Analysis (DA)
has algorithm: Linear Discriminant Analysis (LDA)
has algorithm: Principal Component Analysis (PCA)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 3293
has location: Brazil
predicts: Genotype 3293
related to: Soybean
grade: 70.0
reference: Luis Guilherme Teixeira Crusiol, Marcos Rafael Nanni, Renato Herrig Furlanetto, Rubson Natal Ribeiro Sibaldelli, Everson Cezar, Liang Sun, José Salvador Simonetto Foloni, Liliane Marcia Mertz-Henning, Alexandre Lima Nepomuceno, Norman Neumaier, José Renato Bouças Farias, "Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf - Based Hyperspectral Reflectance", Remote Sens. 2021, 13(2), 172; https://doi.org/10.3390/rs13020172
comment: Classification of Soybean Genotypes Assessed Under Different Water Availability and at Different Phenological Stages Using Leaf - Based Hyperspectral Reflectance

Classifier 3362
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Rapid Measurement of Soybean Seed Viability Using Kernel - Based Multispectral Image Analysis
evaluated by: Evaluation 3362
has algorithm: Discriminant Analysis (DA)
has algorithm: Partial Least Squares (PLS)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 3362
predicts: Seeds 3362
related to: Soybean
grade: 65.0
reference: Insuck Baek, Dewi Kusumaningrum, Lalit Mohan Kandpal, Santosh Lohumi, Changyeun Mo, Moon S. Kim, Byoung-Kwan Cho, "Rapid Measurement of Soybean Seed Viability Using Kernel - Based Multispectral Image Analysis", Sensors 2019, 19(2), 271; https://doi.org/10.3390/s19020271
comment: Rapid Measurement of Soybean Seed Viability Using Kernel - Based Multispectral Image Analysis

Classifier 3375
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Potential of Visible and Near - Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves
has algorithm: Least Squares Support Vector Machine (LSSVM)
has algorithm: Partial Least Squares (PLS)
has algorithm: Partial Least Squares Discriminant Analysis (PLSDA)
has algorithm: Successive Projections Algorithm (SPA)
has condition: SpectralImage 3375
has location: China
has location: Finland
predicts: CropDisease 3375
grade: 70.0
reference: Lingxia Huang, Liang Yang, Liuwei Meng, Jingyu Wang, Shaojia Li, Xiaping Fu, Xiaoqiang Du, Di Wu, "Potential of Visible and Near - Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves", Sensors 2018, 18(7), 2077; https://doi.org/10.3390/s18072077
comment: Potential of Visible and Near - Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves

Classifier 3381
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning
evaluated by: Evaluation 3381
has algorithm: Random Forest (RF)
has algorithm: XGBoost
has condition: SpectralImage 3381
has location: South Africa
predicts: CropStress 3381
related to: Grape
related to: Rape
grade: 70.0
reference: Kyle Loggenberg, Albert Strever, Berno Greyling, Nitesh Poona, "Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning", Remote Sens. 2018, 10(2), 202; https://doi.org/10.3390/rs10020202
comment: Modelling Water Stress in a Shiraz Vineyard Using Hyperspectral Imaging and Machine Learning

Classifier 3475
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Partial Least Square Discriminant Analysis Based on Normalized Two - Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets
evaluated by: Evaluation 3475
has algorithm: Discriminant Analysis (DA)
has algorithm: Partial Least Squares (PLS)
has condition: SatelliteImage 3475
has location: China
predicts: CropDisease 3475
related to: PlanetScope
related to: Rice
grade: 70.0
reference: Yue Shi, Wenjiang Huang, Huichun Ye, Chao Ruan, Naichen Xing, Yun Geng, Yingying Dong, Dailiang Peng, "Partial Least Square Discriminant Analysis Based on Normalized Two - Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets", Sensors 2018, 18(6), 1901; https://doi.org/10.3390/s18061901
comment: Partial Least Square Discriminant Analysis Based on Normalized Two - Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets

Classifier 3511
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Repaid Identification and Prediction of Cadmium – Lead Cross - Stress of Different Stress Levels in Rice Canopy Based on Visible and Near - Infrared Spectroscopy
has algorithm: Partial Least Squares (PLS)
has algorithm: Support Vector Machine (SVM)
has condition: Cadmium 3511
has condition: Lead 3511
has condition: SpectralImage 3511
has location: China
has location: India
predicts: CropStress 3511
related to: Rice
grade: 66.66666666666666
reference: Shuangyin Zhang, Jun Li, Siying Wang, Yingjing Huang, Yizhuo Li, Yiyun Chen, Teng Fei, "Repaid Identification and Prediction of Cadmium – Lead Cross - Stress of Different Stress Levels in Rice Canopy Based on Visible and Near - Infrared Spectroscopy", Remote Sens. 2020, 12(3), 469; https://doi.org/10.3390/rs12030469
comment: Repaid Identification and Prediction of Cadmium – Lead Cross - Stress of Different Stress Levels in Rice Canopy Based on Visible and Near - Infrared Spectroscopy

Classifier 3571
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Long - Term Yield Variability of Triticale ( ×Triticosecale Wittmack ) Tested Using a CART Model
has algorithm: Classification and Regression Trees (CART)
has condition: FoliarFertilizer 3571
has condition: Fungicide 3571
has condition: Herbicide 3571
has condition: Insecticide 3571
has condition: Nitrogen 3571
has condition: SeedRate 3571
has location: Bangladesh
has location: France
has location: Pakistan
has location: Poland
predicts: Yield 3571
related to: Triticale
grade: 65.0
reference: Elżbieta Wójcik-Gront, Marcin Studnicki, "Long - Term Yield Variability of Triticale ( ×Triticosecale Wittmack ) Tested Using a CART Model", Agriculture 2021, 11(2), 92; https://doi.org/10.3390/agriculture11020092
comment: Long - Term Yield Variability of Triticale ( ×Triticosecale Wittmack ) Tested Using a CART Model

Classifier 3573
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: UAV - Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions
evaluated by: Evaluation 3573
has algorithm: Random Forest (RF)
has algorithm: Vegetation Indices (VI)
has condition: SatelliteImage 3573
has location: Netherlands
has location: Switzerland
predicts: CropDisease 3573
related to: Maize
grade: 70.0
reference: Walter Chivasa, Onisimo Mutanga, Chandrashekhar Biradar, "UAV - Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions", Remote Sens. 2020, 12(15), 2445; https://doi.org/10.3390/rs12152445
comment: UAV - Based Multispectral Phenotyping for Disease Resistance to Accelerate Crop Improvement under Changing Climate Conditions

Classifier 3632
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A Multi - Modal Approach for Crop Health Mapping Using Low Altitude Remote Sensing , Internet of Things ( IoT ) and Machine Learning
evaluated by: Evaluation 3632
has algorithm: Artificial Neural Network (ANN)
has algorithm: Naive Bayes (NB)
has algorithm: Support Vector Machine (SVM)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 3632
predicts: Health 3632
related to: Growth Stage
related to: Remote Sensing
grade: 70.0
reference: Uferah Shafi, Rafia Mumtaz, Naveed Iqbal, Syed Mohammad Hassan Zaidi, Syed Ali Raza Zaidi, Imtiaz Hussain, Zahid Mahmood, "A Multi - Modal Approach for Crop Health Mapping Using Low Altitude Remote Sensing , Internet of Things ( IoT ) and Machine Learning", IEEE Access ( Volume: 8)
comment: A Multi - Modal Approach for Crop Health Mapping Using Low Altitude Remote Sensing , Internet of Things ( IoT ) and Machine Learning

Classifier 3648
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Automatic Detection of Field - Grown Cucumbers for Robotic Harvesting
evaluated by: Evaluation 3648
has algorithm: Speeded-Up Robust Features (SURF)
has algorithm: Support Vector Machine (SVM)
has condition: FieldImage 3648
has location: Spain
predicts: ObjectBoundary 3648
related to: Cucumber
related to: Flower
grade: 70.0
reference: Roemi Fernández, Héctor Montes, Jelena Surdilovic, Dragojlub Surdilovic, Pablo Gonzalez-De-Santos, Manuel Armada, "Automatic Detection of Field - Grown Cucumbers for Robotic Harvesting", IEEE Access ( Volume: 6)
comment: Automatic Detection of Field - Grown Cucumbers for Robotic Harvesting

Classifier 3649
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Early Disease Classification of Mango Leaves Using Feed - Forward Neural Network and Hybrid Metaheuristic Feature Selection
has algorithm: Artificial Neural Network (ANN)
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 3649
has dataset: Dataset 3649a
predicts: CropDisease 3649
related to: Mango
grade: 80.0
reference: Tan Nhat Pham, Ly Van Tran, Son Vu Truong Dao, "Early Disease Classification of Mango Leaves Using Feed - Forward Neural Network and Hybrid Metaheuristic Feature Selection", IEEE Access ( Volume: 8)
comment: Early Disease Classification of Mango Leaves Using Feed - Forward Neural Network and Hybrid Metaheuristic Feature Selection

Classifier 3650
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease
evaluated by: Evaluation 3650
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 3650
has dataset: Dataset 3650a
predicts: CropDisease 3650
related to: Mango
grade: 75.0
reference: Uday Pratap Singh, Siddharth Singh Chouhan, Sukirty Jain, Sanjeev Jain, "Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease", IEEE Access ( Volume: 7)
comment: Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease

Classifier 3654
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection
evaluated by: Evaluation 3654
has algorithm: Expectation-Maximization (EM)
has algorithm: Genetic Algorithm (GA)
has algorithm: Support Vector Machine (SVM)
has condition: CropImage 3654
has dataset: Dataset 3654a
predicts: CropDisease 3654
related to: Apple
related to: Fruit
grade: 80.0
reference: Muhammad Attique Khan, M Ikram Ullah Lali, Muhammad Sharif, Kashif Javed, Khursheed Aurangzeb, Syed Irtaza Haider, Abdulaziz Saud Altamrah, Talha Akram, "An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection", IEEE Access ( Volume: 7)
comment: An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection

Classifier 3666
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: MDFC – ResNet : An Agricultural IoT System to Accurately Recognize Crop Diseases
evaluated by: Evaluation 3666
has algorithm: Deep Learning (DL)
has condition: CropImage 3666
has dataset: Dataset 3666a
predicts: CropDisease 3666
grade: 80.0
reference: Wei-Jian Hu, Jie Fan, Yong-Xing Du, Bao-Shan Li, Naixue Xiong, Ernst Bekkering, "MDFC – ResNet : An Agricultural IoT System to Accurately Recognize Crop Diseases", IEEE Access ( Volume: 8)
comment: MDFC – ResNet : An Agricultural IoT System to Accurately Recognize Crop Diseases

Classifier 3671
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Real - Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks
evaluated by: Evaluation 3671
has algorithm: Convolutional Neural Network (CNN)
has condition: CropImage 3671
has dataset: Dataset 3671a
has location: China
predicts: CropDisease 3671
related to: Apple
grade: 80.0
reference: Peng Jiang, Yuehan Chen, Bin Liu, Dongjian He, Chunquan Liang, "Real - Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks", IEEE Access ( Volume: 7)
comment: Real - Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks

Classifier 3679
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Rapid Detection of Rice Disease Based on FCM - KM and Faster R - CNN Fusion
evaluated by: Evaluation 3679
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Fuzzy C-means (FCM)
has condition: CropImage 3679
has location: Japan
predicts: CropDisease 3679
related to: Rice
grade: 65.0
reference: Guoxiong Zhou, Wenzhuo Zhang, Aibin Chen, Mingfang He, Xueshuo Ma, "Rapid Detection of Rice Disease Based on FCM - KM and Faster R - CNN Fusion", IEEE Access ( Volume: 7)
comment: Rapid Detection of Rice Disease Based on FCM - KM and Faster R - CNN Fusion

Classifier 3702
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: DCGAN - Based Data Augmentation for Tomato Leaf Disease Identification
evaluated by: Evaluation 3702
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Deep Convolutional Generative Adversarial Network (DCGAN)
has condition: CropImage 3702
has dataset: Dataset 3702a
predicts: CropDisease 3702
related to: Tomato
grade: 80.0
reference: Qiufeng Wu, Yiping Chen, Jun Meng, "DCGAN - Based Data Augmentation for Tomato Leaf Disease Identification", IEEE Access ( Volume: 8)
comment: DCGAN - Based Data Augmentation for Tomato Leaf Disease Identification

Classifier 3722
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Deep Metric Learning Based Citrus Disease Classification With Sparse Data
evaluated by: Evaluation 3722
has algorithm: Machine Learning (ML)
has condition: Citrus 3722
has condition: Crop 3722
has condition: Ear 3722
has condition: Farmer 3722
has condition: Fruit 3722
has condition: Leaf 3722
has condition: Tea 3722
has location: Pakistan
predicts: CropDisease 3722
related to: Citrus
related to: Fruit
related to: Tea
grade: 65.0
reference: Sivasubramaniam Janarthan, Selvarajah Thuseethan, Sutharshan Rajasegarar, Qiang Lyu, Yongqiang Zheng, John Yearwood, "Deep Metric Learning Based Citrus Disease Classification With Sparse Data", IEEE Access ( Volume: 8)
comment: Deep Metric Learning Based Citrus Disease Classification With Sparse Data

Classifier 3737
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Bacterial Foraging Optimization Based Radial Basis Function Neural Network ( BRBFNN ) for Identification and Classification of Plant Leaf Diseases : An Automatic Approach Towards Plant Pathology
evaluated by: Evaluation 3737
has algorithm: Bacterial Foraging Optimization (BFO)
has algorithm: Radial Basis Function (RBF)
has algorithm: Algorithm RBFNN
has condition: CropImage 3737
predicts: CropDisease 3737
related to: Apple
related to: Fruit
grade: 65.0
reference: Siddharth Singh Chouhan, Ajay Kaul, Uday Pratap Singh, Sanjeev Jain, "Bacterial Foraging Optimization Based Radial Basis Function Neural Network ( BRBFNN ) for Identification and Classification of Plant Leaf Diseases : An Automatic Approach Towards Plant Pathology", IEEE Access ( Volume: 6)
comment: Bacterial Foraging Optimization Based Radial Basis Function Neural Network ( BRBFNN ) for Identification and Classification of Plant Leaf Diseases : An Automatic Approach Towards Plant Pathology

Classifier 3740
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification
evaluated by: Evaluation 3740
has algorithm: Generative Adversarial Networks (GANs)
has condition: CropImage 3740
has dataset: Dataset 3740a
predicts: CropDisease 3740
related to: Grape
related to: Rape
grade: 75.0
reference: Bin Liu, Cheng Tan, Shuqin Li, Jinrong He, Hongyan Wang, "A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification", IEEE Access ( Volume: 8)
comment: A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification

Classifier 3777
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Damage mapping of powdery mildew in winter wheat with high-resolution satellite image
evaluated by: Evaluation 3777
has algorithm: Artificial Neural Network (ANN)
has algorithm: Mahalanobis Distance (MD)
has algorithm: Maximum Likelihood Classification (MLC)
has condition: SatelliteImage 3777
has location: China
predicts: CropDisease 3777
related to: Remote Sensing
related to: Wheat
grade: 70.0
reference: L. Yuan, J. Zhang, Y. Shi, C. Nie, L. Wei, J. Wang, "Damage mapping of powdery mildew in winter wheat with high-resolution satellite image", Remote Sens., 6 (2014), pp. 3611-3623,
comment: Damage mapping of powdery mildew in winter wheat with high-resolution satellite image

Classifier 3778
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops
evaluated by: Evaluation 3778
has algorithm: Quadratic Discriminant Analysis (QDA)
has condition: SpectralImage 3778
predicts: CropDisease 3778
related to: Remote Sensing
related to: Wheat
grade: 70.0
reference: D. Moshou, C. Bravo, R. Oberti, J.S. West, H. Ramon, S. Vougioukas, D. Bochtis, "Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops", Biosyst. Eng., 108 (2011), pp. 311-321,
comment: Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops

Classifier 3779
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Remote detection of rhizomania in sugar beets
has algorithm: Linear Regression (LR)
has condition: SpectralImage 3779
has location: United States
predicts: CropDisease 3779
related to: Rhizomania
grade: 70.0
reference: K. Steddom, G. Heidel, D. Jones, C.M. Rush, "Remote detection of rhizomania in sugar beets", Phytopathology., 93 (2003), pp. 720-726,
comment: Remote detection of rhizomania in sugar beets

Classifier 3780
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging
has algorithm: Linear Regression (LR)
has algorithm: Univariate linear regression (ULR)
has condition: Leaf 3780
has condition: SpectralImage 3780
has location: China
predicts: CropDisease 3780
related to: Wheat
grade: 70.0
reference: W. Huang, D.W. Lamb, Z. Niu, Y. Zhang, L. Liu, J. Wang, "Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging", Precis. Agric., 8 (2007), pp. 187-197
comment: Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging

Classifier 3783
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Early detection of Fusarium infection in wheat using hyper-spectral imaging
evaluated by: Evaluation 3783
has algorithm: Spectral Angle Mapper (SAM)
has condition: CropImage 3783
has location: Germany
predicts: CropDisease 3783
related to: Wheat
grade: 70.0
reference: E. Bauriegel, A. Giebel, M. Geyer, U. Schmidt, W.B. Herppich, "Early detection of Fusarium infection in wheat using hyper-spectral imaging", Comput. Electron. Agr., 75 (2011), pp. 304-312
comment: Early detection of Fusarium infection in wheat using hyper-spectral imaging

Classifier 3784
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery
evaluated by: Evaluation 3784
has algorithm: Maximum Likelihood Classification (MLC)
has condition: SatelliteImage 3784
has location: United States
predicts: CropPest 3784
related to: Beetle
grade: 70.0
reference: J.A. Hicke, J. Logan, "Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery", Int. J. Remote Sens., 30 (2009), pp. 4427-4441
comment: Mapping whitebark pine mortality caused by a mountain pine beetle outbreak with high spatial resolution satellite imagery

Classifier 3785
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels
has algorithm: Random Forest (RF)
has algorithm: Support Vector Machine (SVM)
has condition: SatelliteImage 3785
has location: United States
predicts: CropPest 3785
related to: ENVISAT
related to: Remote Sensing
grade: 70.0
reference: S. Adelabu, O. Mutanga, E. Adam, "Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels", ISPRS J. Photogramm. Remote Sens., 95 (2014), pp. 34-41
comment: Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels

Classifier 3786
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot
has algorithm: Iterative Self-Organizing Data Analysis (ISODATA)
has condition: SpectralImage 3786
has dataset: Dataset 3786a
has location: United States
predicts: CropDisease 3786
related to: Cotton
grade: 75.0
reference: C. Yang, J.H. Everitt, C.J. Fernandez, "Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot", Biosyst. Eng., 107 (2010), pp. 131-139
comment: Comparison of airborne multispectral and hyperspectral imagery for mapping cotton root rot

Classifier 3787
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with Support Vector Machines
has algorithm: Support Vector Machine (SVM)
has condition: SpectralImage 3787
has location: Germany
predicts: CropDisease 3787
related to: Wheat
grade: 70.0
reference: C. Römer, K. Bürling, M. Hunsche, T. Rumpf, G. Noga, L. Plümer, "Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with Support Vector Machines", Comput. Electron. Agr., 79 (2011), pp. 180-188
comment: Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with Support Vector Machines

Classifier 3788
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado
evaluated by: Evaluation 3788
has algorithm: Artificial Neural Network (ANN)
has algorithm: Multi-layer Perceptron (MLP)
has algorithm: Radial Basis Function (RBF)
has condition: FieldImage 3788
has condition: SpectralImage 3788
has location: United States
predicts: CropDisease 3788
related to: Avocado
grade: 67.5
reference: A.I.D. Castro, R. Ehsani, R. Ploetz, J.H. Crane, J. Abdulridha, "Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado", Remote Sens. Environ., 171 (2015), pp. 33-44
comment: Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado

Classifier 3789
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysis
evaluated by: Evaluation 3789
has algorithm: Linear spectral mixture analysis (LSMA)
has condition: SpectralImage 3789
has location: United States
predicts: CropDisease 3789
related to: Cotton
grade: 65.0
reference: G.J. Fitzgerald, S.J. Maas, W.R. Detar, "Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysis", Precis. Agric., 5 (2004), pp. 275-289
comment: Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysis

Classifier 3790
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat
evaluated by: Evaluation 3790
has algorithm: Mixture Tuned Matched Filtering (MTMF)
has condition: Leaf 3790
has condition: SpectralImage 3790
has location: China
predicts: CropDisease 3790
related to: Wheat
grade: 70.0
reference: J. Zhang, R. Pu, R.W. Loraamm, G. Yang, J. Wang, "Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat", Comput. Electron. Agr., 100 (2014), pp. 79-87
comment: Comparison between wavelet spectral features and conventional spectral features in detecting yellow rust for winter wheat

Classifier 3791
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects
evaluated by: Evaluation 3791
has algorithm: Fisher's linear discriminant analysis (FLDA)
has algorithm: Partial Least Squares Regression (PLSR)
has condition: CropImage 3791
has location: China
predicts: CropDisease 3791
related to: Wheat
grade: 70.0
reference: Yuan L, Huang Y, Loraamm RW, Nie C, Wang J, Zhang J, "Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects ", Field Crop Res 156:199–207.
comment: Spectral analysis of winter wheat leaves for detection and differentiation of diseases and insects

Classifier 3792
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Proximal sensing to detect symptoms associated with wheat curl mite-vectored viruses.
has algorithm: Vegetation Indices (VI)
has condition: CropImage 3792
has location: United States
predicts: CropDisease 3792
related to: Wheat
grade: 70.0
reference: Stilwell AR, Hein GL, Zygielbaum AI, Rundquist DC, "Proximal sensing to detect symptoms associated with wheat curl mite-vectored viruses.", Intern J Remote Sens 34:4951–4966.
comment: Proximal sensing to detect symptoms associated with wheat curl mite-vectored viruses.

Classifier 3793
type: NamedIndividual
type: Classifier
type: Knowledge Model
defined in: Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance.
evaluated by: Evaluation 3793
has algorithm: Artificial Neural Network (ANN)
has algorithm: Decision Tree (DT)
has algorithm: Support Vector Machine (SVM)
has algorithm: Vegetation Indices (VI)
has condition: SpectralImage 3793
has location: Germany
predicts: CropDisease 3793
related to: Sugar beet
grade: 70.0
reference: Rumpf T, Mahlein AK, Steiner U, Oerke EC, Dehne HW, Plümer L, "Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance.", Comput Electron Agric 74:91–99
comment: Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance.

Climate 0336
type: NamedIndividual
type: Climate
is condition of: Regressor_0336

Climate 0905
type: NamedIndividual
type: Climate
has transformation: Climate by Tier6
is condition of: Regressor_0905

Cloud 1227
type: NamedIndividual
type: Cloud
has transformation: Agronomic CD
is condition of: Cluster 1227

Cluster 0113
type: NamedIndividual
type: Cluster
type: Knowledge Model
defined in: A weighted multivariate spatial clustering model to determine irrigation management zones
has algorithm: Boosted Regression Trees (BRT)
has algorithm: K-Means (KMeans)
has condition: ElectricalConductivity 0113
has condition: Elevation 0113
has condition: Slope_0113
has condition: SoilPH_0113
has condition: SpectralImage_0113
has condition: Terrain_0113
predicts: Irrigation_0113
related to: Grape
related to: Irrigation
related to: Remote Sensing
related to: Soil
grade: 66.66666666666666
reference: Noa Ohana-Levi, Idan Bahat, Aviva Peeters, Alexandra Shtein, Yishai Netzer, Yafit Cohen, Alon Ben-Gal, "A weighted multivariate spatial clustering model to determine irrigation management zones", Computers and Electronics in Agriculture, Volume 162, July 2019, pages 719-731, Elsevier B.V.
comment: A weighted multivariate spatial clustering model to determine irrigation management zones

Cluster 0182
type: NamedIndividual
type: Cluster
type: Knowledge Model
defined in: Apple tree canopy leaf spatial location automated extraction based on point cloud data
evaluated by: Evaluation 0182
has algorithm: Density-based Spatial Clustering Of Applications With Noise (DBSCAN)
has condition: Canopy 0182
predicts: Leaf_0182
related to: Apple
related to: Canopy
related to: Fruit
related to: Yield
grade: 70.0
reference: Cailing Guo, Gang Liu, Weijie Zhang, Juan Feng, "Apple tree canopy leaf spatial location automated extraction based on point cloud data", Computers and Electronics in Agriculture, Volume 166, November 2019, 104975, Elsevier B.V.
comment: Apple tree canopy leaf spatial location automated extraction based on point cloud data

Cluster 0571
type: NamedIndividual
type: Cluster
type: Knowledge Model
defined in: Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems
evaluated by: Evaluation 0571
has algorithm: Artificial Neural Network (ANN)
has algorithm: Extreme Learning Machine (ELM)
has algorithm: Fuzzy C-means (FCM)
has algorithm: Support Vector Machine (SVM)
has algorithm: Adaptive Neuro-Fuzzy Inference System
has condition: Humidity_0571
has condition: Radiation_0571
has condition: SoilTemperature_0571
has condition: Temperature_0571
has location: Belgium
has location: Germany
has location: Sweden
predicts: Evapotranspiration_0571
related to: Root
grade: 66.25
reference: Xianming Dou, Yongguo Yang, "Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems", Computers and Electronics in Agriculture, Volume 148, May 2018, pages 95-106, Elsevier B.V.
comment: Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems

Cluster 0701
type: NamedIndividual
type: Cluster
type: Knowledge Model
defined in: Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning
evaluated by: Evaluation 0701
has algorithm: Particle Swarm Optimization (PSO)
has condition: BulkDensity 0701
has condition: FieldCapacity_0701
has condition: HydraulicConductivity 0701
has condition: Nitrogen_0701
has condition: OrganicMatter_0701
has condition: Potassium_0701
has condition: Precipitation_0701
has condition: SpectralImage_0701
has condition: Sunshine_0701
has condition: Temperature_0701
has location: China
predicts: GrowthStage_0701
predicts: Yield_0701
related to: Nitrogen (N)
related to: Remote Sensing
related to: Soil
related to: Wheat
related to: Yield
grade: 68.0
reference: Caili Guo, Ling Zhang, Xiang Zhou, Yan Zhu, Weixing Cao, Xiaolei Qiu, Tao Cheng, Yongchao Tian, "Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning", Precision Agriculture, Volume 19, pages 55–78 (2018) , Springer
comment: Integrating remote sensing information with crop model to monitor wheat growth and yield based on simulation zone partitioning

Cluster 1227
type: NamedIndividual
type: Cluster
type: Knowledge Model
defined in: An effective neural network and fuzzy time series - based hybridized model to handle forecasting problems of two factors
evaluated by: Evaluation 1227
has algorithm: Artificial Neural Network (ANN)
has condition: Cloud 1227
has condition: Temperature 1227
predicts: Temperature 1227x
related to: Weather
grade: 67.5
reference: Pritpal Singh, Bhogeswar Borah, "An effective neural network and fuzzy time series - based hybridized model to handle forecasting problems of two factors", Knowledge and Information Systems volume 38, pages669–690(2014) , Springer
comment: An effective neural network and fuzzy time series - based hybridized model to handle forecasting problems of two factors

Cluster 1241
type: NamedIndividual
type: Cluster
type: Knowledge Model
defined in: Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R - CNN
evaluated by: Evaluation 1241
has algorithm: Agglomerative Hierarchical Clustering (AHC)
has algorithm: Convolutional Neural Network (CNN)
has algorithm: Faster Regional Convolutional Neural Network (FasterR-CNN)
has condition: FieldImage 1241
has dataset: Dataset 1241a
has location: China
predicts: CropRow 1241
related to: Rice
grade: 80.0
reference: Shaomin Lin, Yu Jiang, Xueshen Chen, Asim Biswas, Shuai Li, Zihao Yuan, Hailin Wang, Long Qi, "Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R - CNN", IEEE Access ( Volume: 8)
comment: Automatic Detection of Plant Rows for a Transplanter in Paddy Field Using Faster R - CNN

Cluster 1399
type: NamedIndividual
type: Cluster
type: Knowledge Model
defined in: Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment
evaluated by: Evaluation 1399
has algorithm: Gray level co-occurrence matrix (GLCM)
has algorithm: K-Means (KMeans)
has algorithm: Pyramid of histograms of orientation gradients (PHOG)
has algorithm: Simple Linear Iterative Clustering (SLIC)
has condition: CropImage 1399
has location: India
predicts: CropDisease 1399
related to: Tomato
grade: 70.0
reference: Saiqa Khan, Meera Narvekar, "Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment", Journal of King Saud University - Computer and Information SciencesAvailable online 15 September 2020, Elsevier B.V.
comment: Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment

Cluster 1412
type: NamedIndividual
type: Cluster
type: Knowledge Model
defined in: Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm
evaluated by: Evaluation 1412
has algorithm: Artificial Neural Network (ANN)
has condition: Ear 1412
has condition: Farmer 1412
has condition: Leaf 1412
has condition: Rice 1412
predicts: CropDisease 1412
related to: Rice
grade: 65.0
reference: S. Ramesh, D. Vydeki, "Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm", Information Processing in AgricultureVolume 7, Issue 2, June 2020, Elsevier B.V.
comment: Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm

Cluster 1425
type: NamedIndividual
type: Cluster
type: Knowledge Model
defined in: Machine vision based papaya disease recognition
evaluated by: Evaluation 1425
has algorithm: Support Vector Machine (SVM)
has condition: Ear 1425
has condition: Farmer 1425
has condition: Papaya 1425
has condition: Pear 1425
has condition: Stem 1425
predicts: CropDisease 1425
related to: Papaya
related to: Pear
grade: 65.0
reference: Md. Tarek Habib, Anup Majumder, A.Z.M. Jakaria, Morium Akter, Mohammad Shorif Uddin, Farruk Ahmed, "Machine vision based papaya disease recognition", Journal of King Saud University - Computer and Information SciencesVolume 32, Issue 3, March 2020, Elsevier B.V.
comment: Machine vision based papaya disease recognition

Cluster 3494
type: NamedIndividual
type: Cluster
type: Knowledge Model
defined in: Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods
evaluated by: Evaluation 3494
has algorithm: Back Propagation Neural Network (BPNN)
has algorithm: K-Nearest Neighbours (KNN)
has algorithm: Random Forest (RF)
has condition: SpectralImage 3494
has location: China
predicts: Canopy 3494
grade: 70.0
reference: Zhulin Chen, Kun Jia, Chenchao Xiao, Dandan Wei, Xiang Zhao, Jinhui Lan, Xiangqin Wei, Yunjun Yao, Bing Wang, Yuan Sun, Lei Wang, "Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods", Remote Sens. 2020, 12(13), 2110; https://doi.org/10.3390/rs12132110
comment: Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods

Copper 0633
type: NamedIndividual
type: Copper (Cu)
has transformation: Copper by Tier5
is condition of: Classifier 0633

Copper 0905
type: NamedIndividual
type: Copper (Cu)

Copper 1209
type: NamedIndividual
type: Copper (Cu)

Copper 1227
type: NamedIndividual
type: Copper (Cu)
is condition of: AssociationRule 1227

Copper 1250
type: NamedIndividual
type: Copper (Cu)

Copper 1369
type: NamedIndividual
type: Copper (Cu)
is condition of: Regressor 1369

Copper 2992
type: NamedIndividual
type: Copper (Cu)
has transformation: Available Copper (ACu)
is condition of: Classifier 2992

Copper 3510
type: NamedIndividual
type: Copper (Cu)
is condition of: Regressor 3510

CropDisease 0016
type: NamedIndividual
type: Crop Disease
has state: Bacterial Blight
has state: Leaf Brown spot
has state: Rice blast
has state: Sheath rot

CropDisease 0017
type: NamedIndividual
type: Crop Disease
has state: Leaf Disease
has state: None

CropDisease 0019
type: NamedIndividual
type: Crop Disease
has state: Downy mildew

CropDisease 0022
type: NamedIndividual
type: Crop Disease
has state: Botrytis Cinerea
has state: None
has state: Sclerotinia sclerotiorum

CropDisease 0028
type: NamedIndividual
type: Crop Disease

CropDisease 0033
type: NamedIndividual
type: Crop Disease
has state: Leaf Spot

CropDisease 0036
type: NamedIndividual
type: Crop Disease
has state: Citrus Greening Disease
has state: None

CropDisease 0037
type: NamedIndividual
type: Crop Disease
has transformation: Crop Disease by Tier3

CropDisease 0043
type: NamedIndividual
type: Crop Disease

CropDisease 0058
type: NamedIndividual
type: Crop Disease
has state: Alternaria Blight

CropDisease 0068
type: NamedIndividual
type: Crop Disease
has state: Bacterial Blight
has state: Copper phytotoxicity
has state: Downy mildew
has state: Powdery Mildew
has state: Septoria Brown Spot
has state: Soybean Mosaic
has state: Soybean rust
has state: Target spot

CropDisease 0083
type: NamedIndividual
type: Crop Disease

CropDisease 0091
type: NamedIndividual
type: Crop Disease
has state: Anthracnose
has state: Downy mildew
has state: Powdery Mildew
has state: Target Leaf Spot

CropDisease 0093
type: NamedIndividual
type: Crop Disease
has state: Healthy
has state: Iron Deficiency
has state: Laurel wilt
has state: Nitrogen Deficiency
has state: Phytophthora Root Rot

CropDisease 0098
type: NamedIndividual
type: Crop Disease

CropDisease 0158
type: NamedIndividual
type: Crop Disease
has state: Alternaria Leaf Spot
has state: Anthracnose
has state: Bacterial Blight
has state: Black Stem Rust
has state: Complete Bunt
has state: Ear Cockle
has state: Flag Smut
has state: Fusarium Wilt
has state: Leaf Curl
has state: Leaf Rust
has state: Loose Smut
has state: Karnal Bunt
has state: Root Rot
has state: Seed Rot
has state: Stripe Rust
has state: Tobacco Streak Virus
has state: Tropical Rust
has state: Tundo
has state: Verticillium Wilt

CropDisease 0172
type: NamedIndividual
type: Crop Disease
has state: Healthy
has state: Stripe Rust

CropDisease 0185
type: NamedIndividual
type: Crop Disease
has state: Fire Blight
has state: Healthy
has state: Nutrient Deficiency

CropDisease 0193
type: NamedIndividual
type: Crop Disease

CropDisease 0198
type: NamedIndividual
type: Crop Disease
has transformation: Crop Disease by Disease Severity

CropDisease 0220
type: NamedIndividual
type: Crop Disease
has state: Healthy
has state: Highly Infested
has state: Slightly Infested
has transformation: Crop Disease by Tier3

CropDisease 0222
type: NamedIndividual
type: Crop Disease
has state: Bacterial Leaf Blight

CropDisease 0282
type: NamedIndividual
type: Crop Disease
has state: Apple Rot
has state: Apple Scab
has state: Banana Sigotka

CropDisease 0290
type: NamedIndividual
type: Crop Disease
has state: Huanglongbing

CropDisease 0326
type: NamedIndividual
type: Crop Disease
has state: Stripe Rust

CropDisease 0351
type: NamedIndividual
type: Crop Disease
has state: Blumeria Graminis
has state: Drechslera Tritici-repentis
has state: Gibberella Zeae
has state: Helminthosporium Turcicum
has state: Phoma Lingam
has state: Puccinia Hordei
has state: Puccinia Recondita
has state: Puccinia Striiformis
has state: Pyrenophora Teres
has state: Ramularia Collo-cygni
has state: Rhynchosporium Secalis
has state: Leptosphaeria nodorum
has state: Mycosphaerella graminicola
has state: Thanatephorus Cucumeris

CropDisease 0367
type: NamedIndividual
type: Crop Disease
has state: Rust
has state: Tan Spot

CropDisease 0375
type: NamedIndividual
type: Crop Disease
has state: Apple Scab
has state: Bacterial Spot
has state: Banana Speckle
has state: Black Measles
has state: Black Rot
has state: Black Sigatoka
has state: Brown Leaf Spot
has state: Cassava Green Spider Mite
has state: Cedar Apple Rust
has state: Cercospora Leaf Spot
has state: Common Rust
has state: Cucumber Mosaic
has state: Downy mildew
has state: Early Blight
has state: FrogeyeLeafSpot
has state: Huanglongbing
has state: Leaf Blight
has state: Leaf Mold
has state: Leaf Scorch
has state: Northern Leaf Blight
has state: Powdery Mildew
has state: Septoria Leaf Blight
has state: Septoria Leaf Spot
has state: Spider Mites
has state: Target spot
has state: Tomato Mosaic Virus

CropDisease 0383
type: NamedIndividual
type: Crop Disease

CropDisease 0393
type: NamedIndividual
type: Crop Disease
has state: Bakanae disease
has state: None

CropDisease 0397
type: NamedIndividual
type: Crop Disease
has state: Healthy
has state: Powdery Mildew

CropDisease 0398
type: NamedIndividual
type: Crop Disease
has state: Anthracnose
has state: Black Spot
has state: Canker
has state: Greening
has state: Healthy
has state: Melanose
has state: Scab

CropDisease 0401
type: NamedIndividual
type: Crop Disease

CropDisease 0402
type: NamedIndividual
type: Crop Disease
has state: Aphids
has state: Healthy
has state: Powdery Mildew
has state: Stripe Rust

CropDisease 0405
type: NamedIndividual
type: Crop Disease
has state: Anthracnose
has state: None

CropDisease 0412
type: NamedIndividual
type: Crop Disease
has state: Grapevine Leafroll

CropDisease 0417
type: NamedIndividual
type: Crop Disease
has state: Leaf Spot

CropDisease 0421
type: NamedIndividual
type: Crop Disease
has state: Bacterial Spot
has state: Healthy
has state: Target Leaf Spot

CropDisease 0467
type: NamedIndividual
type: Crop Disease
has state: None
has state: Powdery Mildew

CropDisease 0481
type: NamedIndividual
type: Crop Disease
has transformation: Crop Disease by Percentage

CropDisease 0491
type: NamedIndividual
type: Crop Disease
has state: Cercospora Leaf Spot

CropDisease 0497
type: NamedIndividual
type: Crop Disease

CropDisease 0498
type: NamedIndividual
type: Crop Disease

CropDisease 0499
type: NamedIndividual
type: Crop Disease

CropDisease 0501
type: NamedIndividual
type: Crop Disease
has state: Powdery Mildew

CropDisease 0553
type: NamedIndividual
type: Crop Disease
has state: Healthy
has state: Laurel wilt

CropDisease 0559
type: NamedIndividual
type: Crop Disease
has state: Gummosis Disease
has state: Healthy

CropDisease 0637
type: NamedIndividual
type: Crop Disease
has state: Rice Leaf Folder

CropDisease 0656
type: NamedIndividual
type: Crop Disease

CropDisease 0676
type: NamedIndividual
type: Crop Disease
has state: Algal spot
has state: Angular Leaf spot
has state: Anthracnose
has state: Areolate Mildew
has state: Bacterial Blight
has state: Bacterial Canker
has state: Bacterial Spot
has state: Black Mould
has state: Blister Spot
has state: Brown Eye Spot
has state: Brown Leaf Spot
has state: Brown spot
has state: Canker
has state: Chlorosis
has state: Cylindrocladium Leaf Spot
has state: Diplodia Leaf Streak
has state: Downy mildew
has state: Greasy spot
has state: Omiodes indicata
has state: Leaf Miner
has state: Leaf Rust
has state: Citrus leprosis
has state: Lixa Pequena
has state: Mites
has state: Mosaic of Citrus
has state: Myrothecium Leaf Spot
has state: Northern Corn Leaf Blight
has state: Orange rust
has state: Phaeosphaeria Leaf Spot
has state: Phytotoxicity
has state: Powdery Mildew
has state: Red Rot
has state: Ring Spot
has state: Rust
has state: Seedling Disease Complex
has state: Southern Corn Leaf Blight
has state: Southern Corn Rust
has state: Soybean Mosaic
has state: Target Leaf Spot
has state: Target spot
has state: Tropical Corn Rust
has state: Wheat Blast
has state: White leaf spot

CropDisease 0680
type: NamedIndividual
type: Crop Disease
has state: Powdery Mildew

CropDisease 0689
type: NamedIndividual
type: Crop Disease
has state: Alternaria Solani
has state: Healthy

CropDisease 0743
type: NamedIndividual
type: Crop Disease
has state: Downy mildew

CropDisease 0747
type: NamedIndividual
type: Crop Disease
has state: Basal Stem Rot

CropDisease 0772
type: NamedIndividual
type: Crop Disease
has state: Healthy Trees
has state: Mildly-infected Trees
has state: Moderately-infected Trees
has state: Severely-infected Trees
has transformation: Crop Disease by Tier4

CropDisease 0803
type: NamedIndividual
type: Crop Disease

CropDisease 0815
type: NamedIndividual
type: Crop Disease
has state: Crop Injure
has state: Healthy

CropDisease 0832
type: NamedIndividual
type: Crop Disease

CropDisease 0855
type: NamedIndividual
type: Crop Disease
has state: Crop Injure
has state: Healthy

CropDisease 0874
type: NamedIndividual
type: Crop Disease
has state: Bacterial Blight
has state: Leaf Brown spot
has state: Rice blast
has state: Sheath rot

CropDisease 0877
type: NamedIndividual
type: Crop Disease

CropDisease 0971
type: NamedIndividual
type: Crop Disease

CropDisease 1015
type: NamedIndividual
type: Crop Disease
has state: Pestalotiopsis theae
has state: Tea anthracnose
has state: Tea brown blight

CropDisease 1044
type: NamedIndividual
type: Crop Disease
has transformation: Crop Disease by Tier4

CropDisease 1047
type: NamedIndividual
type: Crop Disease

CropDisease 1077
type: NamedIndividual
type: Crop Disease

CropDisease 1111
type: NamedIndividual
type: Crop Disease
has state: Powdery Mildew

CropDisease 1124
type: NamedIndividual
type: Crop Disease

CropDisease 1149
type: NamedIndividual
type: Crop Disease
has state: Weeds

CropDisease 1154
type: NamedIndividual
type: Crop Disease
has state: Stripe Rust

CropDisease 1205
type: NamedIndividual
type: Crop Disease
has state: Bacterial Blight
has state: Boron Deficiency
has state: Drought Stress
has state: Fungal Blast
has state: Healthy
has state: Iron Deficiency
has state: Nitrogen Deficiency
has state: Phosphorus Deficiency
has state: Potassium Deficiency
has state: Waterlogging Stress
has state: Zinc Deficiency

CropDisease 1221
type: NamedIndividual
type: Crop Disease
has state: Bacterial Leaf Spot
has state: Early Scorch
has state: Frog Eye Leaf Spot
has state: Fungal Disease

CropDisease 1231
type: NamedIndividual
type: Crop Disease

CropDisease 1233
type: NamedIndividual
type: Crop Disease
has state: Coffee Leaf Rust
has state: Healthy

CropDisease 1243
type: NamedIndividual
type: Crop Disease

CropDisease 1318
type: NamedIndividual
type: Crop Disease
has state: Cassava Brown Streak Virus Disease
has state: Cassava Green Mite
has state: Cassava Mosaic Disease

CropDisease 1371
type: NamedIndividual
type: Crop Disease
has state: Weeds

CropDisease 1399
type: NamedIndividual
type: Crop Disease

CropDisease 1405
type: NamedIndividual
type: Crop Disease
has state: Soybean Mosaic Virus

CropDisease 1407
type: NamedIndividual
type: Crop Disease
has state: BananaXanthomonasWilt
has state: Healthy

CropDisease 1410
type: NamedIndividual
type: Crop Disease
has state: Pine Wilt Disease

CropDisease 1412
type: NamedIndividual
type: Crop Disease

CropDisease 1425
type: NamedIndividual
type: Crop Disease

CropDisease 1457
type: NamedIndividual
type: Crop Disease

CropDisease 1460
type: NamedIndividual
type: Crop Disease

CropDisease 1585
type: NamedIndividual
type: Crop Disease
has state: Bacterial Spot
has state: Early Blight
has state: Healthy
has state: Leaf Mold
has state: Septoria Leaf Spot
has state: Spider Mite
has state: Target spot
has state: Tomato Mosaic Virus
has state: Yellow Leaf Curl

CropDisease 1693
type: NamedIndividual
type: Crop Disease
has state: Powdery Mildew

CropDisease 1923
type: NamedIndividual
type: Crop Disease

CropDisease 1986
type: NamedIndividual
type: Crop Disease
has state: Cassava Mosaic Disease

CropDisease 2173
type: NamedIndividual
type: Crop Disease

CropDisease 2308
type: NamedIndividual
type: Crop Disease

CropDisease 2339
type: NamedIndividual
type: Crop Disease
has state: Leaf Spot
has state: Powdery Mildew
has state: Rust

CropDisease 2356
type: NamedIndividual
type: Crop Disease

CropDisease 2357
type: NamedIndividual
type: Crop Disease

CropDisease 2364
type: NamedIndividual
type: Crop Disease
has state: Stripe Rust

CropDisease 2452
type: NamedIndividual
type: Crop Disease

CropDisease 2771
type: NamedIndividual
type: Crop Disease

CropDisease 2799
type: NamedIndividual
type: Crop Disease
has state: Cucumber Mosaic

CropDisease 2800
type: NamedIndividual
type: Crop Disease
has state: Net Blotch

CropDisease 2815
type: NamedIndividual
type: Crop Disease

CropDisease 2829
type: NamedIndividual
type: Crop Disease

CropDisease 2845
type: NamedIndividual
type: Crop Disease

CropDisease 3067
type: NamedIndividual
type: Crop Disease
has state: Canker

CropDisease 3207
type: NamedIndividual
type: Crop Disease
has state: Powdery Mildew

CropDisease 3212
type: NamedIndividual
type: Crop Disease
has state: Fusarium Head Blight

CropDisease 3375
type: NamedIndividual
type: Crop Disease
has state: Diaphania Pyloalis Larva
has state: Healthy
has state: LeafVein
has state: Serious Damage
has state: Slight Damage

CropDisease 3475
type: NamedIndividual
type: Crop Disease
has state: Rice blast

CropDisease 3510
type: NamedIndividual
type: Crop Disease

CropDisease 3573
type: NamedIndividual
type: Crop Disease

CropDisease 3649
type: NamedIndividual
type: Crop Disease
has state: Anthracnose
has state: Gall Midge
has state: Healthy
has state: Powdery Mildew

CropDisease 3650
type: NamedIndividual
type: Crop Disease
has state: Anthracnose

CropDisease 3654
type: NamedIndividual
type: Crop Disease

CropDisease 3666
type: NamedIndividual
type: Crop Disease

CropDisease 3671
type: NamedIndividual
type: Crop Disease
has state: Alternaria Leaf Spot
has state: Brown spot
has state: Grey Spot
has state: Mosaic
has state: Rust

CropDisease 3679
type: NamedIndividual
type: Crop Disease

CropDisease 3693
type: NamedIndividual
type: Crop Disease
has state: Fusarium Head Blight

CropDisease 3702
type: NamedIndividual
type: Crop Disease

CropDisease 3722
type: NamedIndividual
type: Crop Disease

CropDisease 3737
type: NamedIndividual
type: Crop Disease

CropDisease 3740
type: NamedIndividual
type: Crop Disease
has state: Black Rot
has state: Healthy
has state: Leaf Spot

CropDisease 3745
type: NamedIndividual
type: Crop Disease

CropDisease 3777
type: NamedIndividual
type: Crop Disease
has state: Powdery Mildew

CropDisease 3778
type: NamedIndividual
type: Crop Disease
has state: SeptoriaLeafBlotch
has state: Stripe Rust

CropDisease 3779
type: NamedIndividual
type: Crop Disease
has state: Healthy

CropDisease 3780
type: NamedIndividual
type: Crop Disease
has state: Stripe Rust

CropDisease 3781
type: NamedIndividual
type: Crop Disease
has state: Rice Leaf Folder

CropDisease 3782
type: NamedIndividual
type: Crop Disease
has state: Bacterial Spot

CropDisease 3783
type: NamedIndividual
type: Crop Disease
has state: Fusarium Head Blight

CropDisease 3786
type: NamedIndividual
type: Crop Disease

CropDisease 3787
type: NamedIndividual
type: Crop Disease
has state: Leaf Rust

CropDisease 3788
type: NamedIndividual
type: Crop Disease
has state: Laurel wilt

CropDisease 3789
type: NamedIndividual
type: Crop Disease

CropDisease 3790
type: NamedIndividual
type: Crop Disease
has state: Stripe Rust

CropDisease 3791
type: NamedIndividual
type: Crop Disease
has state: Aphids
has state: Powdery Mildew
has state: Stripe Rust

CropDisease 3792
type: NamedIndividual
type: Crop Disease
has state: Wheat Curl Mite

CropDisease 3793
type: NamedIndividual
type: Crop Disease
has state: Cercospora Leaf Spot
has state: Leaf Rust
has state: Powdery Mildew

CropDisease 3794
type: NamedIndividual
type: Crop Disease
has state: Rice Leaf Folder

CropDisease 3808
type: NamedIndividual
type: Crop Disease

CropHeight 0015
type: NamedIndividual
type: CropHeight
is condition of: Regressor_0015

CropHeight 0543
type: NamedIndividual
type: CropHeight
has transformation: Crop by Height
is condition of: Regressor_0543

CropHeight 0659
type: NamedIndividual
type: CropHeight
has transformation: Crop Height by Default

CropHeight 0799
type: NamedIndividual
type: CropHeight
has transformation: Crop Height by Default

CropHeight 2313
type: NamedIndividual
type: CropHeight
is condition of: Regressor 2313

CropImage 0016
type: NamedIndividual
type: Crop Imagery
has transformation: Image by Color
has transformation: Image by Position
has transformation: Image by Shape
is condition of: Classifier 0016

CropImage 0017
type: NamedIndividual
type: Crop Imagery
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 0017

CropImage 0024
type: NamedIndividual
type: Crop Imagery
has transformation: Gray Color Image (Gray)
is condition of: Classifier 0024

CropImage 0028a
type: NamedIndividual
type: Crop Imagery
has transformation: DenseNet
is condition of: Classifier 0028

CropImage 0028b
type: NamedIndividual
type: Crop Imagery
has transformation: InceptionV4
is condition of: Classifier 0028

CropImage 0028c
type: NamedIndividual
type: Crop Imagery
has transformation: VGG
is condition of: Classifier 0028

CropImage 0028d
type: NamedIndividual
type: Crop Imagery
has transformation: ResNet
is condition of: Classifier 0028

CropImage 0033
type: NamedIndividual
type: Crop Imagery
has transformation: Hue, Saturation and Intensity (HSI)
is condition of: Classifier 0033

CropImage 0039
type: NamedIndividual
type: Crop Imagery
has transformation: Image by Color
has transformation: Image by Shape
is condition of: Classifier 0039

CropImage 0040
type: NamedIndividual
type: Crop Imagery
has transformation: Transformation Image BCD
has transformation: Transformation Image CD
has transformation: Transformation Image FD
is condition of: Classifier 0040

CropImage 0043
type: NamedIndividual
type: Crop Imagery
has transformation: VGG16
is condition of: Classifier 0043

CropImage 0051
type: NamedIndividual
type: Crop Imagery
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 0051

CropImage 0068
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0068

CropImage 0075
type: NamedIndividual
type: Crop Imagery
is condition of: Regressor_0075

CropImage 0091
type: NamedIndividual
type: Crop Imagery
has transformation: Comprehensive Color Feature (CCF)
is condition of: Classifier 0091

CropImage 0098
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0098

CropImage 0111a
type: NamedIndividual
type: Crop Imagery
has transformation: Image by Shape
is condition of: Classifier 0111

CropImage 0111b
type: NamedIndividual
type: Crop Imagery
has transformation: Image by Size
is condition of: Classifier 0111

CropImage 0111c
type: NamedIndividual
type: Crop Imagery
has transformation: Image by Color
is condition of: Classifier 0111

CropImage 0115
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0115

CropImage 0133
type: NamedIndividual
type: Crop Imagery
is condition of: Regressor_0133

CropImage 0158
type: NamedIndividual
type: Crop Imagery
has transformation: Image by Colorname
is condition of: Classifier 0158

CropImage 0167
type: NamedIndividual
type: Crop Imagery
has transformation: Image by HSV
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 0167

CropImage 0175
type: NamedIndividual
type: Crop Imagery
has transformation: AlexNet
has transformation: VGG
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 0175

CropImage 0180
type: NamedIndividual
type: Crop Imagery
has transformation: YOLOv3
is condition of: Classifier 0180

CropImage 0198
type: NamedIndividual
type: Crop Imagery
has transformation: Red Green Blue Color (RGB)
is condition of: Regressor_0198

CropImage 0209
type: NamedIndividual
type: Crop Imagery
has transformation: Canopy Chlorophyll Content Index (CCCI)
has transformation: Normalized Difference Red-edge Index (NDRE)
has transformation: Normalized Difference Vegetation Index (NDVI)
has transformation: Red-Edge Infection Point (REIP)
is condition of: Regressor_0209

CropImage 0212
type: NamedIndividual
type: Crop Imagery
has transformation: Image by ConvNets
is condition of: Classifier 0212

CropImage 0220
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0220

CropImage 0226
type: NamedIndividual
type: Crop Imagery
has transformation: Image by Morphological
has transformation: Red Green Blue Color (RGB)
is condition of: Regressor_0226

CropImage 0238a
type: NamedIndividual
type: Crop Imagery
has transformation: Image by Morphological
is condition of: Classifier 0238

CropImage 0238b
type: NamedIndividual
type: Crop Imagery
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 0238

CropImage 0239
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0239

CropImage 0243
type: NamedIndividual
type: Crop Imagery
has transformation: VGG
has transformation: Hue, Saturation and Intensity (HSI)
has transformation: Transformation Image Lab
has transformation: Red Green Blue Color (RGB)
has transformation: YCbCr Color
has transformation: Image by YIQ
is condition of: Classifier 0243

CropImage 0247
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0247

CropImage 0249
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0249

CropImage 0259
type: NamedIndividual
type: Crop Imagery
has transformation: Image by HSV
is condition of: Classifier 0259

CropImage 0265
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0265

CropImage 0282
type: NamedIndividual
type: Crop Imagery
has transformation: AlexNet
has transformation: VGG
is condition of: Classifier 0282

CropImage 0290
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0290

CropImage 0310
type: NamedIndividual
type: Crop Imagery
is condition of: Regressor_0310

CropImage 0311
type: NamedIndividual
type: Crop Imagery
has transformation: Image by HSV
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 0311

CropImage 0315
type: NamedIndividual
type: Crop Imagery
has transformation: Transformation Crop Area
has transformation: Crop by Length
has transformation: Transformation Crop Perimeter
has transformation: Crop by Radius
has transformation: Crop by Shape
has transformation: Transformation Crop Width
is condition of: Classifier 0315

CropImage 0351
type: NamedIndividual
type: Crop Imagery
has transformation: ResNet50
is condition of: Classifier 0351

CropImage 0367
type: NamedIndividual
type: Crop Imagery
has transformation: ResNet50
is condition of: Classifier 0367

CropImage 0369
type: NamedIndividual
type: Crop Imagery
has transformation: VGG
has transformation: Red Green Blue Color (RGB)
is condition of: Regressor_0369

CropImage 0371
type: NamedIndividual
type: Crop Imagery
has transformation: LeNet
is condition of: Classifier 0371

CropImage 0375
type: NamedIndividual
type: Crop Imagery
has transformation: AlexNet
has transformation: AlexNetOWTBn
has transformation: GoogLeNet
has transformation: OverFeat
has transformation: VGG
is condition of: Classifier 0375

CropImage 0383
type: NamedIndividual
type: Crop Imagery
has transformation: AlexNet
has transformation: MobileNet
has transformation: VGG
is condition of: Classifier 0383

CropImage 0393a
type: NamedIndividual
type: Crop Imagery
has transformation: Image by Morphological
is condition of: Classifier 0393

CropImage 0393b
type: NamedIndividual
type: Crop Imagery
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 0393

CropImage 0394
type: NamedIndividual
type: Crop Imagery
has transformation: Blue Band (B/Blue)
has transformation: Excess-Blue Index (EBI)
has transformation: Excess-Green Index (EGI)
has transformation: Excess-Red Index (ERI)
has transformation: Excess Green (ExG)
has transformation: Excess Green Red (ExGR)
has transformation: Excess Red (ExR)
has transformation: Green minus Blue (GB)
has transformation: Green Band (G/Green)
has transformation: Gray Light (Gray)
has transformation: Normalised Difference Index (NDI)
has transformation: Red Blue Index (RBI)
has transformation: Red Band (R/Red)
is condition of: Classifier 0394

CropImage 0396
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0396

CropImage 0398
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0398

CropImage 0399
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0399

CropImage 0401
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0401

CropImage 0407
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0407

CropImage 0416
type: NamedIndividual
type: Crop Imagery
has transformation: Image by HSV
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 0416

CropImage 0423
type: NamedIndividual
type: Crop Imagery
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 0423

CropImage 0467
type: NamedIndividual
type: Crop Imagery
has transformation: Green Ratio, Hue and Intensity (GHI)
has transformation: Hue, Saturation and Intensity (HSI)
has transformation: Hue, Saturation and Luminance (HSL)
has transformation: Image by SIL
is condition of: Classifier 0467

CropImage 0478
type: NamedIndividual
type: Crop Imagery
has transformation: Modified Chlorophyll Absorption Ratio Index 2 (MCARI2)
has transformation: Spectral MNDVI
has transformation: Normalized Difference Vegetation Index (NDVI)
has transformation: Normalized Pigment Chlorophyll Index 2 (NPCI2)
has transformation: Optimized Soil Adjusted Vegetation Index (OSAVI)
has transformation: Optimized Soil Adjusted Vegetation Index 2 (OSAVI2)
has transformation: Pigment Specific Normalized Difference-b (PSNDb)
has transformation: Structure Insensitive Pigment Index (SIPI)
has transformation: Simple Ratio 1 (SR1)
has transformation: Transformed Chlorophyll Absorption and Reflectance Index (TCARI)
has transformation: modified Normalized Difference index (mND)
is condition of: Classifier 0478

CropImage 0491
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0491

CropImage 0500
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0500

CropImage 0501
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0501

CropImage 0526
type: NamedIndividual
type: Crop Imagery
has transformation: Red Green Blue Color (RGB)
has transformation: YCbCr Color
is condition of: Regressor_0526

CropImage 0539a
type: NamedIndividual
type: Crop Imagery
has transformation: Normalized Difference Vegetation Index (NDVI)
is condition of: Regressor_0539

CropImage 0539b
type: NamedIndividual
type: Crop Imagery
has transformation: Red Green Blue Color (RGB)
is condition of: Regressor_0539

CropImage 0619
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0619

CropImage 0620
type: NamedIndividual
type: Crop Imagery
has transformation: Tretrained YOLOv3
is condition of: Classifier 0620

CropImage 0651a
type: NamedIndividual
type: Crop Imagery
has transformation: Image by Morphological
has transformation: Red Green Blue Color (RGB)
has transformation: Image by Texture
is condition of: Classifier 0651

CropImage 0651b
type: NamedIndividual
type: Crop Imagery
has transformation: Image by NIR
is condition of: Classifier 0651

CropImage 0660
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0660

CropImage 0661
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0661

CropImage 0668
type: NamedIndividual
type: Crop Imagery
has transformation: Image by ObjDetect
is condition of: Regressor_0668

CropImage 0673
type: NamedIndividual
type: Crop Imagery
has transformation: VGG
is condition of: Classifier 0673

CropImage 0674
type: NamedIndividual
type: Crop Imagery
has transformation: VGG
is condition of: Classifier 0674

CropImage 0676
type: NamedIndividual
type: Crop Imagery
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 0676

CropImage 0680
type: NamedIndividual
type: Crop Imagery
has transformation: Anthocyanin Reflectance Index (ARI)
has transformation: Carter Index 2 (CTR2)
has transformation: Green Normalized Difference Vegetation Index (GNDVI)
has transformation: Modified Chlorophyll Absorption Ratio Index (MCARI)
has transformation: Modified Soil Adjusted Vegetation Index (MSAVI)
has transformation: Spectral MSR
has transformation: NIR Shoulder Region Index (NSRI)
has transformation: Powdery Mildew Index (PMI)
has transformation: Photochemical Reflectance Index (PRI)
has transformation: Plant Senescence Reflectance Index (PSRI)
has transformation: Ratio Vegetation Index (RVI)
has transformation: Soil Adjusted Vegetation Index (SAVI)
has transformation: Sum of 1st Derivative values within Blue edge (SDb)
has transformation: Sum of 1st Derivative values within Red edge (SDr)
has transformation: Sum of 1st Derivative values within Yellow edge (SDy)
has transformation: Structure Insensitive Pigment Index (SIPI)
has transformation: Transformed soil adjusted vegetation index (TSAVI)
has transformation: Water Index (WI)
has transformation: modified Normalized Difference index (mND)
is condition of: Classifier 0680

CropImage 0726
type: NamedIndividual
type: Crop Imagery
has transformation: Transformed Chlorophyll Absorption and Reflectance Index (TCARI)
is condition of: Regressor_0726

CropImage 0743
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0743

CropImage 0803
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0803

CropImage 0813
type: NamedIndividual
type: Crop Imagery
is condition of: Regressor_0813

CropImage 0815
type: NamedIndividual
type: Crop Imagery
has transformation: Image by Morphological
has transformation: Image by Texture
is condition of: Classifier 0815

CropImage 0832
type: NamedIndividual
type: Crop Imagery
has transformation: GoogLeNet
has transformation: BERT
has transformation: VGG16
is condition of: Classifier 0832

CropImage 0874
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 0874

CropImage 0877
type: NamedIndividual
type: Crop Imagery
has transformation: ResNet
is condition of: Classifier 0877

CropImage 0880
type: NamedIndividual
type: Crop Imagery
has transformation: Image by ConvNets
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 0880

CropImage 0971
type: NamedIndividual
type: Crop Imagery
has transformation: DenseNet
is condition of: Classifier 0971

CropImage 0993
type: NamedIndividual
type: Crop Imagery
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 0993

CropImage 1015
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 1015

CropImage 1077
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 1077

CropImage 1124
type: NamedIndividual
type: Crop Imagery
has transformation: Difference Vegetation Index (DVI)
has transformation: Normalized Difference Vegetation Index (NDVI)
has transformation: Redness Index (RI)
has transformation: Ratio Vegetation Index (RVI)
has transformation: Soil Adjusted Vegetation Index (SAVI)
has transformation: Triangular Vegetation Index (TVI)
has transformation: Spectral VNNIR
has transformation: VNR
is condition of: Classifier 1124

CropImage 1149
type: NamedIndividual
type: Crop Imagery
has transformation: LeNet
is condition of: Classifier 1149

CropImage 1205
type: NamedIndividual
type: Crop Imagery
has transformation: VGG
is condition of: Classifier 1205

CropImage 1219
type: NamedIndividual
type: Crop Imagery
has transformation: Image by HSV
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 1219

CropImage 1220
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 1220

CropImage 1221
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 1221

CropImage 1231
type: NamedIndividual
type: Crop Imagery
has transformation: Disease-Water Stress Index (DWSI)
has transformation: Photochemical Reflectance Index (PRI)
is condition of: Classifier 1231

CropImage 1233
type: NamedIndividual
type: Crop Imagery
has transformation: Red Green Blue Color (RGB)
has transformation: Red Green NIR Color (RGN)
is condition of: Classifier 1233

CropImage 1242
type: NamedIndividual
type: Crop Imagery
has transformation: Image by Texture
has transformation: Hyperspectral Image by Wavelength
is condition of: Classifier 1242

CropImage 1243
type: NamedIndividual
type: Crop Imagery
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 1243

CropImage 1318
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 1318

CropImage 1399
type: NamedIndividual
type: Crop Imagery
has transformation: Image by HSV
has transformation: Red Green Blue Color (RGB)
is condition of: Cluster 1399

CropImage 1407a
type: NamedIndividual
type: Crop Imagery
has transformation: Atmospherically Resistant Vegetation Index (ARVI)
has transformation: ChlorophyII Index-Green (CIG)
has transformation: Enhanced Vegetation Index (EVI)
has transformation: Infrared Percentage Vegetation Index (IPVI)
has transformation: Optimized Soil Adjusted Vegetation Index (OSAVI)
has transformation: Ratio Vegetation Index (RVI)
has transformation: Triangular Greenness Index (TGI)
is condition of: Classifier 1407

CropImage 1407b
type: NamedIndividual
type: Crop Imagery
has transformation: RetinaNet
has transformation: VGG
is condition of: Classifier 1407

CropImage 1410
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 1410

CropImage 1438
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 1438

CropImage 1457
type: NamedIndividual
type: Crop Imagery
has transformation: GoogLeNet
has transformation: ResNet
is condition of: Classifier 1457

CropImage 1460
type: NamedIndividual
type: Crop Imagery
has transformation: Image by HSV
has transformation: Red Green Blue Color (RGB)
is condition of: Classifier 1460

CropImage 1585
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 1585

CropImage 2308
type: NamedIndividual
type: Crop Imagery
has transformation: Wavelength (WL)
is condition of: Classifier 2308

CropImage 2357
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 2357

CropImage 2771
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 2771

CropImage 2799
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 2799

CropImage 2800
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 2800

CropImage 2829
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 2829

CropImage 2845
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 2845

CropImage 2982
type: NamedIndividual
type: Crop Imagery
is condition of: Regressor 2982

CropImage 3067
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 3067

CropImage 3207
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 3207

CropImage 3649
type: NamedIndividual
type: Crop Imagery
has transformation: AlexNet
has transformation: ResNet50
has transformation: VGG16
is condition of: Classifier 3649

CropImage 3650
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 3650

CropImage 3654
type: NamedIndividual
type: Crop Imagery
has transformation: AlexNet
has transformation: VGG16
is condition of: Classifier 3654

CropImage 3666
type: NamedIndividual
type: Crop Imagery
has transformation: ResNet
is condition of: Classifier 3666

CropImage 3671
type: NamedIndividual
type: Crop Imagery
has transformation: GoogLeNet
has transformation: VGG
is condition of: Classifier 3671

CropImage 3676
type: NamedIndividual
type: Crop Imagery
is condition of: Regressor 3676

CropImage 3679
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 3679

CropImage 3702
type: NamedIndividual
type: Crop Imagery
has transformation: GoogLeNet
is condition of: Classifier 3702

CropImage 3737
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 3737

CropImage 3740
type: NamedIndividual
type: Crop Imagery
is condition of: Classifier 3740

CropImage 3782
type: NamedIndividual
type: Crop Imagery
has transformation: Wavelength (WL)
is condition of: Regressor 3782

CropImage 3783
type: NamedIndividual
type: Crop Imagery
has transformation: Wavelength (WL)
is condition of: Classifier 3783

CropImage 3791
type: NamedIndividual
type: Crop Imagery
has transformation: Aphid-index (AI)
has transformation: Chlorophyll Absorption Ratio Index (CARI)
has transformation: Damage sensitive Spectral Index-2 (DSSI2)
has transformation: Greenness Index (GI)
has transformation: Normalized Difference Vegetation Index (NDVI)
has transformation: Normalized Difference Water Index (NDWI)
has transformation: Normalized Pigment Chlorophyll Index (NPCI)
has transformation: Photochemical Reflectance Index (PRI)
has transformation: Ratio Vegetation Structure Index (RVSI)
has transformation: Structure Insensitive Pigment Index (SIPI)
has transformation: Triangular Vegetation Index (TVI)
has transformation: Water Index (WI)
is condition of: Classifier 3791

CropImage 3792
type: NamedIndividual
type: Crop Imagery
has transformation: Chlorophyll Index RedEdge 710 (CIrededge710)
has transformation: Chlorophyll Index (ChI)
has transformation: Green Normalized Difference Vegetation Index (GNDVI)
has transformation: Normalized Difference Vegetation Index (NDVI)
has transformation: Photochemical Reflectance Index (PRI)
has transformation: Red-Edge Eosition (REP)
has transformation: Stress Index (SI)
has transformation: Visible Atmospherically Resistant Index (VARI)
has transformation: Vegetation Fraction (VF)
is condition of: Classifier 3792

CropInjure 0247
type: NamedIndividual
type: Crop Injure
has state: Anthracnose
has state: Oleocellosis
has state: Phytotoxicity
has state: Sooty Mould
has state: Stem-End Injury
has state: Thrips Scarring
has state: Wind Scarring

CropName 0175
type: NamedIndividual
type: Crop Name

CropName 0306
type: NamedIndividual
type: Crop Name

CropName 0356
type: NamedIndividual
type: Crop Name
has state: Barley
has state: Grass
has state: Maize
has state: OilSeeds
has state: Rape
has state: Rye
has state: Sugar beet
has state: Wheat

CropName 0394
type: NamedIndividual
type: Crop Name
has state: Sugar beet
has state: Thistle

CropName 1246
type: NamedIndividual
type: Crop Name

CropName 1422
type: NamedIndividual
type: Crop Name

CropPart 0158
type: NamedIndividual
type: Crop Part
is condition of: Classifier 0158

CropPest 0057
type: NamedIndividual
type: Crop Pest
has state: Australian Plague Locusts
has state: Brown Coloured Larva Of Looper Moth
has state: Brown Garden Snail
has state: Earwig
has state: Greenhouse Slug
has state: Larva Of Diamondback Moth
has state: Larva Of Green Fruit Worm
has state: Larva Of Orange-Striped Oakworm
has state: Portuguese Millipede
has state: Vegetable Grasshopper
has state: Vineyard Snail
has state: White Garden Snails

CropPest 0075
type: NamedIndividual
type: Crop Pest

CropPest 0245
type: NamedIndividual
type: Crop Pest
has state: Brown Stink Bug
has state: Euschistus Servus
has state: Nezara Viridula
has state: Southern Green Stink Bug

CropPest 0311
type: NamedIndividual
type: Crop Pest
has state: Thrips
has state: Whiteflies

CropPest 0353
type: NamedIndividual
type: Crop Pest
has state: Aelia Sibirica
has state: Atractomorpha Sinensis
has state: Aulacophora Indica
has state: Bemisia Tabaci
has state: Callitettix Versicolor
has state: Ceroplastes Ceriferus
has state: Ceutorhynchus Asper Roelofs
has state: Chauliops Fallax Scott
has state: Chilo Suppressalis
has state: Chromatomyia Horticola
has state: Cicadella Viridis
has state: Cifuna Locuples
has state: Cletus Punctiger
has state: Cnaphalocrocis Medinalis
has state: Colaphellus Bowvingi
has state: Colposcelis Signata
has state: Corythucha Ciliata
has state: Corythucha Marmorata
has state: Dicladispa Armigera
has state: Diostrombus Politus Uhler
has state: Dolerus Tritici
has state: Dolerus Tritici Chu
has state: Dolycoris Baccarum
has state: Dryocosmus Kuriphilus Yasumatsu
has state: Empoasca Flavescens
has state: Erthesina Fullo
has state: Eurydema Dominulus
has state: Eurydema Gebleri
has state: Eysacoris Guttiger
has state: Graphosoma Rubrolineata
has state: Halyomorpha Halys
has state: Iscadia Inexacta
has state: Laodelphax Striatellua
has state: Laodelphax Striatellus
has state: Leptocorisa Acuta
has state: Luperomorpha Suturalis Chen
has state: Lycorma Delicatula
has state: Maruca Testulalis
has state: Maruca Testulalis Gryer
has state: Mythimna Separta
has state: Nephotettix Bipunctatus
has state: Nezara Viridula
has state: NilaparvataLugens
has state: Pentfaleus Major
has state: Phyllotreta Striolata
has state: PierisRapae
has state: Plutella Xylostella
has state: Porthesia Taiwana Shiraki
has state: Riptortus Pedestris
has state: Scotinophara Lurida
has state: SesamiaInferens
has state: Sitobion Avenae
has state: Sogatella Furcifera
has state: Spilosoma Obliqua
has state: Spodoptera Litura
has state: Stollia Ventralis
has state: Strongyloides Variegatus
has state: Sympiezomias Velatus
has state: Tettigella Viridis

CropPest 0354
type: NamedIndividual
type: Crop Pest
has state: Cydia pomonella
has state: Gryllotalpa
has state: Leafhopper
has state: Locust
has state: Oriental fruit fly
has state: Pieris rapae Linnaeus
has state: Snail
has state: Spodoptera Litura
has state: Stinkbug
has state: Weevil

CropPest 0633
type: NamedIndividual
type: Crop Pest
has transformation: Pest Incidence by Tier5
is condition of: Classifier 0633

CropPest 0642
type: NamedIndividual
type: Crop Pest
has state: Healthy
has state: Serious Damage
has state: Slight Damage

CropPest 0651
type: NamedIndividual
type: Crop Pest
has state: Healthy
has state: Lesser Grain Borer
has state: Red Flour Beetle
has state: Rice Weevil
has state: Rusty Grain Beetle

CropPest 0720
type: NamedIndividual
type: Crop Pest
has transformation: Crop Pest by Count

CropPest 0880
type: NamedIndividual
type: Crop Pest

CropPest 1216
type: NamedIndividual
type: Crop Pest
has state: Asiatic Rice Borer
has state: Atractomorpha Sinensis
has state: Brown Plant Hopper
has state: Chrysocus Chinensis
has state: Empoasca Flavescens
has state: Gypsy Moth Larva
has state: Laspeyresia Pomonella
has state: Locusta Migratoria
has state: Chlorops oryzae
has state: Parasa Lepida
has state: Rice Gall Midge
has state: Rice Leaf Caterpillar
has state: Rice Leaf Roller
has state: Rice Leafhopper
has state: Rice Stemfly
has state: Rice Water Weevil
has state: Small Brown Plant Hopper
has state: Spodoptera Exigua
has state: White Backed Plant Hopper
has state: Yellow Rice Borer

CropPest 1217
type: NamedIndividual
type: Crop Pest
has state: Atractomorpha Sinensis
has state: Chrysocus Chinensis
has state: Empoasca Flavescens
has state: Gypsy Moth Larva
has state: Laspeyresia Pomonella
has state: Locusta Migratoria
has state: Parasa Lepida
has state: Spodoptera Exigua

CropPest 1218
type: NamedIndividual
type: Crop Pest
has state: Asiatic Rice Borer
has state: Brown Plant Hopper
has state: Chlorops oryzae
has state: Rice Gall Midge
has state: Rice Leaf Caterpillar
has state: Rice Leaf Roller
has state: Rice Leafhopper
has state: Rice Stemfly
has state: Rice Water Weevil
has state: Small Brown Plant Hopper
has state: White Backed Plant Hopper
has state: Yellow Rice Borer

CropPest 3347
type: NamedIndividual
type: Crop Pest

CropPest 3784
type: NamedIndividual
type: Crop Pest

CropPest 3785
type: NamedIndividual
type: Crop Pest

CropRotation 1208
type: NamedIndividual
type: Crop Rotation
is condition of: Regressor 1208

CropRotation 3684
type: NamedIndividual
type: Crop Rotation
is condition of: Regressor 3684

CropRow 0246
type: NamedIndividual
type: Crop Row

CropRow 1241
type: NamedIndividual
type: Crop Row

CropStress 0094
type: NamedIndividual
type: Crop Stress

CropStress 0239
type: NamedIndividual
type: Crop Stress
has state: Cold Stress
has state: Light Stress

CropStress 0252
type: NamedIndividual
type: Crop Stress
has state: Water Stress
has transformation: Crop Water Stress Index (CWSI)

CropStress 0371
type: NamedIndividual
type: Crop Stress
has state: Biotic Stress

CropStress 0407
type: NamedIndividual
type: Crop Stress
has state: Abiotic Stress
has state: Biotic Stress

CropStress 0423
type: NamedIndividual
type: Crop Stress
has state: Healthy
has state: Waterlogging Stress

CropStress 0449
type: NamedIndividual
type: Crop Stress
has state: Water Stress
has transformation: Crop Water Stress Index (CWSI)

CropStress 0500
type: NamedIndividual
type: Crop Stress
has state: Water Stress

CropStress 0554
type: NamedIndividual
type: Crop Stress
has state: Water Stress

CropStress 0634
type: NamedIndividual
type: Crop Stress
has state: Nutrient Stress

CropStress 0644
type: NamedIndividual
type: Crop Stress
has state: Drought Stress

CropStress 0748
type: NamedIndividual
type: Crop Stress
has state: Water Stress
has transformation: Crop Water Stress Index (CWSI)

CropStress 0813
type: NamedIndividual
type: Crop Stress
has transformation: Crop Water Stress Index (CWSI)

CropStress 0969
type: NamedIndividual
type: Crop Stress
has state: Water Stress
has transformation: Crop Water Stress Index (CWSI)

CropStress 1040
type: NamedIndividual
type: Crop Stress

CropStress 1156
type: NamedIndividual
type: Crop Stress
has transformation: Crop Water Stress Index (CWSI)

CropStress 1202
type: NamedIndividual
type: Crop Stress
has state: Water Stress
is condition of: Regressor 1202

CropStress 1219
type: NamedIndividual
type: Crop Stress
has state: Bacterial Blight
has state: Boron Deficiency
has state: Chemical Injury
has state: Drought Stress
has state: Fungal Blast
has state: Healthy
has state: Iron Deficiency
has state: Nitrogen Deficiency
has state: Phosphorus Deficiency
has state: Potassium Deficiency
has state: Submergence
has state: Zinc Deficiency

CropStress 1239
type: NamedIndividual
type: Crop Stress
has transformation: Drought Stress

CropStress 1262
type: NamedIndividual
type: Crop Stress
has state: None
has state: SulphurDeficiency

CropStress 1373
type: NamedIndividual
type: Crop Stress
has state: SulphurDeficiency

CropStress 2129
type: NamedIndividual
type: Crop Stress
has state: Drought Stress

CropStress 2672
type: NamedIndividual
type: Crop Stress

CropStress 3381
type: NamedIndividual
type: Crop Stress
has state: Water Stress

CropStress 3511
type: NamedIndividual
type: Crop Stress

Crop 0111
type: NamedIndividual
type: Crop
has transformation: Crop by Variety

Crop 0238
type: NamedIndividual
type: Crop
has state: Barley
has state: Oats
has state: Rye
has state: Wheat
has transformation: Crop by Name

Crop 0660
type: NamedIndividual
type: Crop

Crop 1423
type: NamedIndividual
type: Crop
has state: Corn
has state: Soybean

Crop 3722
type: NamedIndividual
type: Crop
is condition of: Classifier 3722

CrudeOil 1633
type: NamedIndividual
type: Crude Oil Content

DNA 2173
type: NamedIndividual
type: DNA
is condition of: Classifier 2173

DNA 2452
type: NamedIndividual
type: DNA
is condition of: Classifier 2452

DNA 2845
type: NamedIndividual
type: DNA
is condition of: Classifier 2845

Dataset 0006a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0006
state: 8798 samples
comment: 8798 samples

Dataset 0012a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0012
state: 123 observations recorded in the 2004 growing season
comment: 123 observations recorded in the 2004 growing season

Dataset 0015a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0015
state: 180 sampling points with images, measurements for crop height, fresh biomass, and dry biomass
comment: 180 sampling points with images, measurements for crop height, fresh biomass, and dry biomass

Dataset 0018a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0018
state: Data from a twenty-four year period between 1993 and 2016, Every year in the dataset consisted of 26 to 28 fields, with 24 fields being present in all years
comment: Data from a twenty-four year period between 1993 and 2016, Every year in the dataset consisted of 26 to 28 fields, with 24 fields being present in all years

Dataset 0019a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0019
state: The disease data between 2006 and 2009 that we investigated included 41 (of 60) points at two sites in Beijing, China that exhibited occurrence of cucumber downy mildew.
comment: The disease data between 2006 and 2009 that we investigated included 41 (of 60) points at two sites in Beijing, China that exhibited occurrence of cucumber downy mildew.

Dataset 0022a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0022
state: 359 single-band images, 60 leaves were selected and divided into 2 groups of 30, each inoculated with S. sclerotiorum and B. cinerea, respectively, in vitro.
comment: 359 single-band images, 60 leaves were selected and divided into 2 groups of 30, each inoculated with S. sclerotiorum and B. cinerea, respectively, in vitro.

Dataset 0031a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0031
state: 27,293 labeled images
comment: 27,293 labeled images

Dataset 0033a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0033
state: 129 cucumber disease images.
comment: 129 cucumber disease images.

Dataset 0036a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0036
state: Spectral data from 51 healthy and 45 Citrus greening disease infected samples
comment: Spectral data from 51 healthy and 45 Citrus greening disease infected samples

Dataset 0037a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0037
state: 240 samples for 4 classes
comment: 240 samples for 4 classes

Dataset 0039a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0039
state: 880 oleander leaves for 22 different varieties.
comment: 880 oleander leaves for 22 different varieties.

Dataset 0040a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0040
state: 90 fruits with different degrees of browning were selected.
comment: 90 fruits with different degrees of browning were selected.

Dataset 0041a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0041
state: All the 56 cases were used for the training of FCM model based on 20 soil samples, 5-ha field in 2005.
comment: All the 56 cases were used for the training of FCM model based on 20 soil samples, 5-ha field in 2005.

Dataset 0051a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0051
state: 150 samples, 50 images were captured for each variety of tomato.
comment: 150 samples, 50 images were captured for each variety of tomato.

Dataset 0053a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0053
state: The area is 369.5 km2. Seven cloud-free Sentinel-2 images were acquired for the months of November 2016 to April 2017
comment: The area is 369.5 km2. Seven cloud-free Sentinel-2 images were acquired for the months of November 2016 to April 2017

Dataset 0068a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0068
state: Digipathos consists of 354 images of 8 different soybean diseases.
comment: Digipathos consists of 354 images of 8 different soybean diseases.

Dataset 0074a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0074
state: The data sample consisted of five years (2010–2014) of daily records of air temperature (T), wind speed (W), sunshine hours (H), relative humidity (R) and pan evaporation (E).
comment: The data sample consisted of five years (2010–2014) of daily records of air temperature (T), wind speed (W), sunshine hours (H), relative humidity (R) and pan evaporation (E).

Dataset 0076a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0076
state: 745 instances with 16 features including area, canal length, tanks, tube wells, open wells, production, rainfall, maximum temperature, minimum temperature, average temperature, solar radiation, seed quantity, nitrogen, phosphorus, potassium applied to the soil and yield.
comment: 745 instances with 16 features including area, canal length, tanks, tube wells, open wells, production, rainfall, maximum temperature, minimum temperature, average temperature, solar radiation, seed quantity, nitrogen, phosphorus, potassium applied to the soil and yield.

Dataset 0077a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0077
state: 420 sunlit spectral reflectance with 33-bands of each spectral reflectance.
comment: 420 sunlit spectral reflectance with 33-bands of each spectral reflectance.

Dataset 0084a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0084
state: The cleaned dataset remained with 560 instances and 22 attributes.
comment: The cleaned dataset remained with 560 instances and 22 attributes.

Dataset 0098a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0098
state: APS Image Dataset
comment: APS Image Dataset

Dataset 0111a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0111
state: 4253 wheat glumes
comment: 4253 wheat glumes

Dataset 0114a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0114
state: 1032 soil samples at a depth of 20 cm.
comment: 1032 soil samples at a depth of 20 cm.

Dataset 0115a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0115
state: Data was collected from two different locations, four blocks. Shoot detection has 80 videos of TLs collected during V2015, with 1281 images were sampled from this footage and 20,046 shoot patches were manual labeled from those 1281 images as ground truth. Shoot counting 240 videos of TLs collected during V2015 and V2016.
comment: Data was collected from two different locations, four blocks. Shoot detection has 80 videos of TLs collected during V2015, with 1281 images were sampled from this footage and 20,046 shoot patches were manual labeled from those 1281 images as ground truth. Shoot counting 240 videos of TLs collected during V2015 and V2016.

Dataset 0117a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0117
state: 414 sample sites in 24 felds in Western Australia (WA), Queensland (Qld) and Tasmania (Tas), Australia.
comment: 414 sample sites in 24 felds in Western Australia (WA), Queensland (Qld) and Tasmania (Tas), Australia.

Dataset 0134a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0134
state: 50 soil samples, 0.30-m depth.
comment: 50 soil samples, 0.30-m depth.

Dataset 0135a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0135
state: The data from the 2013, 2014 and 2015 growing seasons, with notable variation in the amount of total rainfall received between each year. Soil physical test results from 72 point samples.
comment: The data from the 2013, 2014 and 2015 growing seasons, with notable variation in the amount of total rainfall received between each year. Soil physical test results from 72 point samples.

Dataset 0140a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0140
state: 110 data patterns are obtained from fine grained soils of Antalya region as well as 20 data patterns are collected from different parts of Turkey.
comment: 110 data patterns are obtained from fine grained soils of Antalya region as well as 20 data patterns are collected from different parts of Turkey.

Dataset 0144a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0144
state: A total of 108 plots and 102 plots were sampled in 2008 and 2009, respectively.
comment: A total of 108 plots and 102 plots were sampled in 2008 and 2009, respectively.

Dataset 0158a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0158
state: The raw data gathered from different sources has been stored in a plain database. It contains 160 rows (diseases) and 340 columns (symptoms). A real data set has 100 instances for the 21 diseases of cotton .
comment: The raw data gathered from different sources has been stored in a plain database. It contains 160 rows (diseases) and 340 columns (symptoms). A real data set has 100 instances for the 21 diseases of cotton .

Dataset 0172a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0172
state: Stage S1: 107 samples (29 healthy, 78 diseased leaves)
comment: Stage S1: 107 samples (29 healthy, 78 diseased leaves)

Dataset 0172b
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0172
state: Stage S2: 91 leaf samples (26 healthy, 65 diseased leaves)
comment: Stage S2: 91 leaf samples (26 healthy, 65 diseased leaves)

Dataset 0175a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0175
state: Flavia Dataset (contains 32 species and 1900 images)
comment: Flavia Dataset (contains 32 species and 1900 images)

Dataset 0175b
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0175
state: Swedish Leaf Dataset (leaf images which belong to 15 tree classes)
comment: Swedish Leaf Dataset (leaf images which belong to 15 tree classes)

Dataset 0175c
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0175
state: Dataset UCI Leaf (40 different species of plants included in this dataset with a total number of 443 images)
comment: Dataset UCI Leaf (40 different species of plants included in this dataset with a total number of 443 images)

Dataset 0186a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0186
state: Data of essential oil collected from 131 sites covering 8 agro climatic regions of Odisha and analysis of their soil and environmental factors
comment: Data of essential oil collected from 131 sites covering 8 agro climatic regions of Odisha and analysis of their soil and environmental factors

Dataset 0198a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0198
state: A total of 284 samples were collected from different arrangements of healthy and diseased leaves within the canopy, where the LAI values were measured using the LAI-2000
comment: A total of 284 samples were collected from different arrangements of healthy and diseased leaves within the canopy, where the LAI values were measured using the LAI-2000

Dataset 0203a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0203
state: Growing season, soil fertility parameters and coffee bean yield data were collected from 44 randomly-selected smallholder farmers during the 2013–2014. Measured data were partitioned into training = 25, validation = 9 and testing = 10.
comment: Growing season, soil fertility parameters and coffee bean yield data were collected from 44 randomly-selected smallholder farmers during the 2013–2014. Measured data were partitioned into training = 25, validation = 9 and testing = 10.

Dataset 0212a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0212
state: 400 images, 15,336 segments, 4 classes
comment: 400 images, 15,336 segments, 4 classes

Dataset 0213a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0213
state: The data of 35 and 15 stations were utilized to train and test the models, respectively
comment: The data of 35 and 15 stations were utilized to train and test the models, respectively

Dataset 0218a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0218
state: A total of 151 plots for the superspectral image analyses.
comment: A total of 151 plots for the superspectral image analyses.

Dataset 0220a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0220
state: In total, 213 diseased and 201 healthy leaves were assessed (63 Diseased Grenache and 64 Healthy Grenache
comment: In total, 213 diseased and 201 healthy leaves were assessed (63 Diseased Grenache and 64 Healthy Grenache

Dataset 0220b
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0220
state: 63 Diseased Marselan and 64 Healthy Marselan
comment: 63 Diseased Marselan and 64 Healthy Marselan

Dataset 0220c
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0220
state: 47 Diseased Vermentino and 40 Healthy Vermentino, 42 Diseased Chardonnay and 34 Healthy Chardonnay).
comment: 47 Diseased Vermentino and 40 Healthy Vermentino, 42 Diseased Chardonnay and 34 Healthy Chardonnay).

Dataset 0238a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0238
state: Digital images consisting of 7440, 6918, 7200, 5340, 7200 kernels of barley, CWAD, CWRS, oats, and rye, respectively were used. The testing set consisted of 3716, 3453, 2667, 3599, 3598 kernel images for the respective grains.
comment: Digital images consisting of 7440, 6918, 7200, 5340, 7200 kernels of barley, CWAD, CWRS, oats, and rye, respectively were used. The testing set consisted of 3716, 3453, 2667, 3599, 3598 kernel images for the respective grains.

Dataset 0239a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0239
state: 342 signals obtained in ideal conditions (without stress), 76 signals after cold stress, 118 signals after low light stress, and 145 signals obtained after osmotic stress.
comment: 342 signals obtained in ideal conditions (without stress), 76 signals after cold stress, 118 signals after low light stress, and 145 signals obtained after osmotic stress.

Dataset 0243a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0243
state: 225 images are taken and 218 effective samples.
comment: 225 images are taken and 218 effective samples.

Dataset 0246a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0246
state: A set of 48 aerial photographs taken by a drone from an altitude of approximately 10 meters above a rye field.
comment: A set of 48 aerial photographs taken by a drone from an altitude of approximately 10 meters above a rye field.

Dataset 0247a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0247
state: 120 citrus from four different cultivars, 30 samples per variety. Six different types of surface defects: wind scarring, thrips scarring, sooty mould, stem-end injury, scale infestation and green mould.
comment: 120 citrus from four different cultivars, 30 samples per variety. Six different types of surface defects: wind scarring, thrips scarring, sooty mould, stem-end injury, scale infestation and green mould.

Dataset 0249a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0249
state: A dataset of 1,018 coffee branches, with 4,300 total acquired images.
comment: A dataset of 1,018 coffee branches, with 4,300 total acquired images.

Dataset 0254a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0254
state: Data include 372 geo-referenced patterns / soil profiles.
comment: Data include 372 geo-referenced patterns / soil profiles.

Dataset 0257a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0257
state: 38 fields were sown between 15 and 30 May 2010.
comment: 38 fields were sown between 15 and 30 May 2010.

Dataset 0258a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0258
state: 144 wheat leaf and canopy spectral data were recorded over two seasons of the experiment.
comment: 144 wheat leaf and canopy spectral data were recorded over two seasons of the experiment.

Dataset 0259a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0259
state: A total of 135 banana clusters in 120 banana images (21 banana clusters in sunny front light, 88 banana clusters under sunny backlight, and 26 banana clusters under cloudy conditions) were selected for testing.
comment: A total of 135 banana clusters in 120 banana images (21 banana clusters in sunny front light, 88 banana clusters under sunny backlight, and 26 banana clusters under cloudy conditions) were selected for testing.

Dataset 0265a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0265
state: There are 360 images of four types of biomass. There are 90 images per class.
comment: There are 360 images of four types of biomass. There are 90 images per class.

Dataset 0285a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0285
state: There are 42 soil samples from Field 1 and 48 soil samples from Field 2.
comment: There are 42 soil samples from Field 1 and 48 soil samples from Field 2.

Dataset 0290a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0290
state: 216 images from the campus in South China Agricultural University
comment: 216 images from the campus in South China Agricultural University

Dataset 0299a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0299
state: 735 and 736 data points for Voulund1 and Voulund2.
comment: 735 and 736 data points for Voulund1 and Voulund2.

Dataset 0306a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0306
state: Three common varieties of rapeseeds with a total of 120 samples
comment: Three common varieties of rapeseeds with a total of 120 samples

Dataset 0309a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0309
state: 148 soil samples from North West of Iran
comment: 148 soil samples from North West of Iran

Dataset 0311a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0311
state: a total of 3185 digital images were acquired, of which 1806 were yellow sticky traps images and 1380 were blue sticky traps images.
comment: a total of 3185 digital images were acquired, of which 1806 were yellow sticky traps images and 1380 were blue sticky traps images.

Dataset 0315a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0315
state: All 750 corn kernels were investigated, and 58 features were extracted from each kernel using image processing techniques.
comment: All 750 corn kernels were investigated, and 58 features were extracted from each kernel using image processing techniques.

Dataset 0351a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0351
state: 5 crops, 17 diseases and 121955 images dataset over field conditions.
comment: 5 crops, 17 diseases and 121955 images dataset over field conditions.

Dataset 0353a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0353
state: Dataset_NBAIR, Dataset_Xie1, Dataset_Xie2
comment: Dataset_NBAIR, Dataset_Xie1, Dataset_Xie2

Dataset 0354a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0354
state: A manually collected and validated crop pests dataset with 10 classes/5629images.
comment: A manually collected and validated crop pests dataset with 10 classes/5629images.

Dataset 0356a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0356
state: 3745 reference plots from the years 1987 to 2002 of 424 agricultural plots with a total area of 9021 ha.
comment: 3745 reference plots from the years 1987 to 2002 of 424 agricultural plots with a total area of 9021 ha.

Dataset 0359a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0359
state: Details of crops and their varieties sown in each of the 9 fields in 2017.
comment: Details of crops and their varieties sown in each of the 9 fields in 2017.

Dataset 0367a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0367
state: 8178 images were captured in two pilot sites in Spain and Germany during and 2016.
comment: 8178 images were captured in two pilot sites in Spain and Germany during and 2016.

Dataset 0371a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0371
state: 1747 images of arabica coffee leaves were collected, including healthy leaves and diseased leaves, affected by one or more of the following biotic stresses: leaf miner, rust, brown leaf spot, and cercospora leaf spot.
comment: 1747 images of arabica coffee leaves were collected, including healthy leaves and diseased leaves, affected by one or more of the following biotic stresses: leaf miner, rust, brown leaf spot, and cercospora leaf spot.

Dataset 0375a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0375
state: An open database containing 87,848 images of leaves of healthy and infected plants, 58 different classes.
comment: An open database containing 87,848 images of leaves of healthy and infected plants, 58 different classes.

Dataset 0394a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0394
state: A total of six image sets were acquired in June and July 2013 with 474 samples.
comment: A total of six image sets were acquired in June and July 2013 with 474 samples.

Dataset 0398a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0398
state: Dataset_CitrusDiseases (5,632 images of four diseases)
comment: Dataset_CitrusDiseases (5,632 images of four diseases)

Dataset 0405a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0405
state: A total of 100 tea leaves (50 healthy and 50 with anthracnose) were tested. Leaves with bruises or that received abnormal illumination were removed, leaving 78 leaves (39 healthy and 39 with anthracnose) as samples for subsequent studies. For calibration and validation, the leaves were randomly split into subgroups of 26 (13 healthy leaves and 13 leaves with anthracnose) and 52 (26 healthy leaves and 26 leaves with anthracnose).
comment: A total of 100 tea leaves (50 healthy and 50 with anthracnose) were tested. Leaves with bruises or that received abnormal illumination were removed, leaving 78 leaves (39 healthy and 39 with anthracnose) as samples for subsequent studies. For calibration and validation, the leaves were randomly split into subgroups of 26 (13 healthy leaves and 13 leaves with anthracnose) and 52 (26 healthy leaves and 26 leaves with anthracnose).

Dataset 0412a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0412
state: 49 leaf samples were collected from the four blocks on the day of the multi-spectral images.
comment: 49 leaf samples were collected from the four blocks on the day of the multi-spectral images.

Dataset 0416a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0416
state: A total of 778 samples (including 458 citrus samples and 320 non-citrus samples) with an average size of 95 × 103 pixels.
comment: A total of 778 samples (including 458 citrus samples and 320 non-citrus samples) with an average size of 95 × 103 pixels.

Dataset 0423a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0423
state: 36 experimental plots, 360 leaf samples
comment: 36 experimental plots, 360 leaf samples

Dataset 0426a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0426
state: Total 576 samples including fresh tea leaf
comment: Total 576 samples including fresh tea leaf

Dataset 0451a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0451
state: A set of 2701 full frame (1280 × 1024) images.
comment: A set of 2701 full frame (1280 × 1024) images.

Dataset 0467a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0467
state: Around 12,000 strawberry plants,
comment: Around 12,000 strawberry plants,

Dataset 0474a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0474
state: 22 site-years of data
comment: 22 site-years of data

Dataset 0481a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0481
state: Samples were collected 8 and 11 days after inoculation for the first trial and after 9, 14, and 20 days for the second trial, with 30 and 32 samples collected on the respective days for the first trial and 25, 22, and 47 samples collected on the respective days for the second trial. A total of 156 spectra were obtained from the measurement of 156 leaf samples.
comment: Samples were collected 8 and 11 days after inoculation for the first trial and after 9, 14, and 20 days for the second trial, with 30 and 32 samples collected on the respective days for the first trial and 25, 22, and 47 samples collected on the respective days for the second trial. A total of 156 spectra were obtained from the measurement of 156 leaf samples.

Dataset 0501a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0501
state: 856 mildew image samples and 2039 background images samples were extracted from 60 images at 5184 × 3456 pixels/images.
comment: 856 mildew image samples and 2039 background images samples were extracted from 60 images at 5184 × 3456 pixels/images.

Dataset 0526a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0526
state: Four sets of images in 2011, a set of 100 mango images was collected from 50 trees as a calibration set.
comment: Four sets of images in 2011, a set of 100 mango images was collected from 50 trees as a calibration set.

Dataset 0533a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0533
state: 132 wavelength samples
comment: 132 wavelength samples

Dataset 0536a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0536
state: 469 trees using count of a sample of trees, in-field machine vision, and correlation to a tree spectral index
comment: 469 trees using count of a sample of trees, in-field machine vision, and correlation to a tree spectral index

Dataset 0539a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0539
state: 555 images
comment: 555 images

Dataset 0540a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0540
state: The data used in the study consist of the mean monthly values of maximum, minimum, and average temperatures, recorded at 275 stations in Turkey having at least a 20 year length over the period: 1974–2010.
comment: The data used in the study consist of the mean monthly values of maximum, minimum, and average temperatures, recorded at 275 stations in Turkey having at least a 20 year length over the period: 1974–2010.

Dataset 0543a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0543
state: 36 sampling sites. At each site, some rice parameters were directly measured in the field: (1) length, width and thickness of leaves, rice canopy height (m), (2) leaf number per rice plant, ear density (ear/m2), (3) leaf insertion angles (degree), i.e. the angle between single leaf and rice stalk, and (4) soil volumetric moisture (mv) at upper 0–7 cm depth.
comment: 36 sampling sites. At each site, some rice parameters were directly measured in the field: (1) length, width and thickness of leaves, rice canopy height (m), (2) leaf number per rice plant, ear density (ear/m2), (3) leaf insertion angles (degree), i.e. the angle between single leaf and rice stalk, and (4) soil volumetric moisture (mv) at upper 0–7 cm depth.

Dataset 0559a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0559
state: There are 25 samples, 240 points of data as per sample.
comment: There are 25 samples, 240 points of data as per sample.

Dataset 0561a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0561
state: Data were collected from 34 mid-season N fertilizer response trials.
comment: Data were collected from 34 mid-season N fertilizer response trials.

Dataset 0576a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0576
state: 386 field samples
comment: 386 field samples

Dataset 0577a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0577
state: the 2555 days in the data sets: first set of five years from 1998–2002, approximately 70% of the whole data set, while the second set of two years from 2003–2004, approximately 30% of the whole data set.
comment: the 2555 days in the data sets: first set of five years from 1998–2002, approximately 70% of the whole data set, while the second set of two years from 2003–2004, approximately 30% of the whole data set.

Dataset 0620a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0620
state: The Embrapa Wine Grape Instance Segmentation Dataset, WGISD, is composed by 300 RGB images showing 4,432 grape clusters from five different grape varieties.
comment: The Embrapa Wine Grape Instance Segmentation Dataset, WGISD, is composed by 300 RGB images showing 4,432 grape clusters from five different grape varieties.

Dataset 0633a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0633
state: 76 records have been used to evaluate the FCM tool.
comment: 76 records have been used to evaluate the FCM tool.

Dataset 0651a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0651
state: A total of 300 healthy kernels and 300 kernels damaged by each insect species were selected for imaging.
comment: A total of 300 healthy kernels and 300 kernels damaged by each insect species were selected for imaging.

Dataset 0659a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0659
state: 16 historical yield maps, representing each crop season from 2004 to 2011.
comment: 16 historical yield maps, representing each crop season from 2004 to 2011.

Dataset 0668a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0668
state: 210 images
comment: 210 images

Dataset 0676a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0676
state: 1,383 images
comment: 1,383 images

Dataset 0677a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0677
state: A two-year rice experiment was conducted in 2015 and 2016,
comment: A two-year rice experiment was conducted in 2015 and 2016,

Dataset 0688a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0688
state: 76 RGB color images (3648 × 2736) of the cotton plants
comment: 76 RGB color images (3648 × 2736) of the cotton plants

Dataset 0689a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0689
state: High resolution canopy reflectance images were obtained for 32 potato plants that had been infected with A. solani and 32 healthy reference plants.
comment: High resolution canopy reflectance images were obtained for 32 potato plants that had been infected with A. solani and 32 healthy reference plants.

Dataset 0720a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0720
state: The rat trapping location data, n = 128.
comment: The rat trapping location data, n = 128.

Dataset 0725a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0725
state: The leaf area of 704 vines was manually measured by the inclined point quadrant approach and six UAV flights were contextually performed to acquire the aerial images.
comment: The leaf area of 704 vines was manually measured by the inclined point quadrant approach and six UAV flights were contextually performed to acquire the aerial images.

Dataset 0743a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0743
state: A total of 12,813 cropped images were obtained and each image was annotated with an image-level and classified into training, validation, and test datasets.
comment: A total of 12,813 cropped images were obtained and each image was annotated with an image-level and classified into training, validation, and test datasets.

Dataset 0747a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0747
state: 53 samples in the first field, 53 in the second, 55 samples in the third and 45 in the fourth field.
comment: 53 samples in the first field, 53 in the second, 55 samples in the third and 45 in the fourth field.

Dataset 0782a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0782
state: Seven cloud-free images covering the whole growth cycle were successfully acquired
comment: Seven cloud-free images covering the whole growth cycle were successfully acquired

Dataset 0799a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0799
state: The VTCIs and LAIs during the five stages at 44 cities from 2010 to 2014 are calculated as the training, and 44 cities in 2015 are normalized as the testing samples.
comment: The VTCIs and LAIs during the five stages at 44 cities from 2010 to 2014 are calculated as the training, and 44 cities in 2015 are normalized as the testing samples.

Dataset 0801a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0801
state: A total of 25 flag leaves were collected per treatment and grouped in five samples. A total of 144 leaf and 144 canopy spectral signatures (72 during each year of experimentation) were captured.
comment: A total of 25 flag leaves were collected per treatment and grouped in five samples. A total of 144 leaf and 144 canopy spectral signatures (72 during each year of experimentation) were captured.

Dataset 0815a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0815
state: 3144 samples (1574 sound samples, 1571 damaged samples).
comment: 3144 samples (1574 sound samples, 1571 damaged samples).

Dataset 0834a
type: NamedIndividual
type: Dataset
is dataset of: Regressor_0834
state: There were 8–10 sampling points in each field, resulting in 34, 33, and 32 total sampling points for each year. Four fields were used over a 3-year period (2008–2010).
comment: There were 8–10 sampling points in each field, resulting in 34, 33, and 32 total sampling points for each year. Four fields were used over a 3-year period (2008–2010).

Dataset 0855a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0855
state: Data was collected from early to late spring seasons (March-May 2017 and March-April 2018) for 450 bud swell (254 normal + 196 injured) and early pink stages of 300 blueberry buds (129 normal + 171 injured).
comment: Data was collected from early to late spring seasons (March-May 2017 and March-April 2018) for 450 bud swell (254 normal + 196 injured) and early pink stages of 300 blueberry buds (129 normal + 171 injured).

Dataset 0874a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0874
state: Rice disease dataset is prepared from 500 infected rice plant images having 25 features with four disease classes.
comment: Rice disease dataset is prepared from 500 infected rice plant images having 25 features with four disease classes.

Dataset 0877a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0877
state: Synthetic dataset that was retrieved from AI Challenger Global AI Contes: 9 different plant species, each species consists of a healthy class and several classes of diseases, and every disease class contains two categories of disease severity (the general and serious types). Finally, we get 45 classes for disease severity estimation, 27 different healthy or diseased classes for the plant diseases classification, and 9 plant species classes for recognition, respectively.
comment: Synthetic dataset that was retrieved from AI Challenger Global AI Contes: 9 different plant species, each species consists of a healthy class and several classes of diseases, and every disease class contains two categories of disease severity (the general and serious types). Finally, we get 45 classes for disease severity estimation, 27 different healthy or diseased classes for the plant diseases classification, and 9 plant species classes for recognition, respectively.

Dataset 0880a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0880
state: 550 images for 10 pest groups
comment: 550 images for 10 pest groups

Dataset 0971a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0971
state: 2462 images of six apple leaf diseases.
comment: 2462 images of six apple leaf diseases.

Dataset 0993a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 0993
state: 29,719 images
comment: 29,719 images

Dataset 1015a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1015
state: Tea plant leaf disease images were collected 1308 samples with five common diseases.
comment: Tea plant leaf disease images were collected 1308 samples with five common diseases.

Dataset 1040a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1040
state: 4943 verified stressed patches, 1582 were caused by Russian wheat aphid infestation, 1749 were caused by drought, and 1612 were caused by agronomic conditions.
comment: 4943 verified stressed patches, 1582 were caused by Russian wheat aphid infestation, 1749 were caused by drought, and 1612 were caused by agronomic conditions.

Dataset 1044a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1044
state: 180 samples were gathered from 32 oil palm trees in total.
comment: 180 samples were gathered from 32 oil palm trees in total.

Dataset 1073a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 1073
state: 1028 plots for 2011 and 1071 plots for 2012, with a total area of about 25,000 ha in Brazil
comment: 1028 plots for 2011 and 1071 plots for 2012, with a total area of about 25,000 ha in Brazil

Dataset 1077a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1077
state: 55 rectified images from the Cercospora dataset and 73 from the Uromyces dataset
comment: 55 rectified images from the Cercospora dataset and 73 from the Uromyces dataset

Dataset 1107a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 1107
state: Meteorological data were collected for 1980–2011, while upland rice yield data (ton/ha) were collected for 1985–2010.
comment: Meteorological data were collected for 1980–2011, while upland rice yield data (ton/ha) were collected for 1985–2010.

Dataset 1149a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1149
state: The acquired experimental data sets are 820 images of 256x256x3 dimensions, which include 210 soybean, 210 cephalanoplos, 200 digitaria, and 200 bindweed, 40 samples of each type are selected as training sets, and the rest as the testing sets.
comment: The acquired experimental data sets are 820 images of 256x256x3 dimensions, which include 210 soybean, 210 cephalanoplos, 200 digitaria, and 200 bindweed, 40 samples of each type are selected as training sets, and the rest as the testing sets.

Dataset 1201a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 1201
state: Barley yield data from 2015 to 2019 were collected in 24 plots
comment: Barley yield data from 2015 to 2019 were collected in 24 plots

Dataset 1202a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 1202
state: Maize yield statistics from the National Agricultural Statistics Service (NASS) of USDA for 2001 to 2016 in 12 states in U.S.: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin.
comment: Maize yield statistics from the National Agricultural Statistics Service (NASS) of USDA for 2001 to 2016 in 12 states in U.S.: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin.

Dataset 1205a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1205
state: Over 30,000 labeled images are collected to create the experimental dataset of paddy field images from healthy crops and the crops exhibiting different stresses.
comment: Over 30,000 labeled images are collected to create the experimental dataset of paddy field images from healthy crops and the crops exhibiting different stresses.

Dataset 1210a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 1210
state: 18 distinct environments, which were randomly sampled at 120 locations.
comment: 18 distinct environments, which were randomly sampled at 120 locations.

Dataset 1214a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1214
state: 1544 crop fields
comment: 1544 crop fields

Dataset 1215a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 1215
state: Data from 260 wheat farms in the 2011–2012 production year.
comment: Data from 260 wheat farms in the 2011–2012 production year.

Dataset 1216a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1216
state: IP102 dataset
comment: IP102 dataset

Dataset 1217a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1217
state: A collection of ten categories of insect pests, each category contained about 40–70 sample images.
comment: A collection of ten categories of insect pests, each category contained about 40–70 sample images.

Dataset 1218a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1218
state: IP102 dataset has 75, 222 images and an average size of 737 samples per class.
comment: IP102 dataset has 75, 222 images and an average size of 737 samples per class.

Dataset 1219a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1219
state: Over 12,000 labeled images are collected to create the experimental dataset of paddy field images from healthy crops and crops exhibiting different stresses. As a part of the dataset preparation, the acquired images of size 1920 × 1080 pixels are manually cropped to size 400 × 400 pixels
comment: Over 12,000 labeled images are collected to create the experimental dataset of paddy field images from healthy crops and crops exhibiting different stresses. As a part of the dataset preparation, the acquired images of size 1920 × 1080 pixels are manually cropped to size 400 × 400 pixels

Dataset 1222a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 1222
state: The monthly climatological data utilized in the present study were recorded between 2000 and 2007, which acquired from Adana meteorological station
comment: The monthly climatological data utilized in the present study were recorded between 2000 and 2007, which acquired from Adana meteorological station

Dataset 1229a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 1229
state: 9 fields
comment: 9 fields

Dataset 1234a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1234
state: 11 sites comprising 120 fields
comment: 11 sites comprising 120 fields

Dataset 1237a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1237
state: 75 testing data points
comment: 75 testing data points

Dataset 1241a
type: NamedIndividual
type: Dataset
is dataset of: Cluster 1241
state: A total of 2,904 images containing 43,721 seedlings were obtained.
comment: A total of 2,904 images containing 43,721 seedlings were obtained.

Dataset 1243a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1243
state: 30659 peach pictures
comment: 30659 peach pictures

Dataset 1289a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 1289
state: 42 soil samples
comment: 42 soil samples

Dataset 1318a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1318
state: 10,000 labeled images collected during a regular survey in Uganda
comment: 10,000 labeled images collected during a regular survey in Uganda

Dataset 1405a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 1405
state: 60 soybean plants were collected from the experimental samples.
comment: 60 soybean plants were collected from the experimental samples.

Dataset 1633a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 1633
state: 244 faba bean samples originating from 12 producing regions in China and collected from 1980 to 2010
comment: 244 faba bean samples originating from 12 producing regions in China and collected from 1980 to 2010

Dataset 1939a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 1939
state: 95 soybean flour samples
comment: 95 soybean flour samples

Dataset 1986a
type: NamedIndividual
type: Dataset
is dataset of: AssociationRule 1986
state: A total of 216 plants of 24 cassava cultivars from Nigeria, Cameroon and Benin, including 13 local and 11 improved cassava cultivars
comment: A total of 216 plants of 24 cassava cultivars from Nigeria, Cameroon and Benin, including 13 local and 11 improved cassava cultivars

Dataset 2339a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 2339
state: Spectra from 15 plants and 2 leaves per plant from the adaxial leaf surface were taken
comment: Spectra from 15 plants and 2 leaves per plant from the adaxial leaf surface were taken

Dataset 2357a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 2357
state: The two data sets consisted of 120 and 823 hyperspectral crop reflectance data vectors of 164 spectral bands, with the corresponding measurements of disease severity and plant density, respectively
comment: The two data sets consisted of 120 and 823 hyperspectral crop reflectance data vectors of 164 spectral bands, with the corresponding measurements of disease severity and plant density, respectively

Dataset 2381a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 2381
state: 224 yield transect plots in 16 ha field in central Iowa
comment: 224 yield transect plots in 16 ha field in central Iowa

Dataset 3048a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 3048
state: Of the total 97 counties with reported rice data from KOSIS, the data consisting of 73 counties with more than 5000 ha of paddy areas were used for the evaluation of the model performance from 2011 to 2014.
comment: Of the total 97 counties with reported rice data from KOSIS, the data consisting of 73 counties with more than 5000 ha of paddy areas were used for the evaluation of the model performance from 2011 to 2014.

Dataset 3089a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 3089
state: A total of 34 Sentinel-2 images between 2016 and 2018 were available during the corn growing season at different crop ages
comment: A total of 34 Sentinel-2 images between 2016 and 2018 were available during the corn growing season at different crop ages

Dataset 3643a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 3643
state: Soil water content and meteorological time series data
comment: Soil water content and meteorological time series data

Dataset 3649a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 3649
state: Data set contains 450 images of mango leaves
comment: Data set contains 450 images of mango leaves

Dataset 3650a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 3650
state: 1070 images of the Mango tree leaves.
comment: 1070 images of the Mango tree leaves.

Dataset 3654a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 3654
state: For each disease class, 80 images are collected from Google
comment: For each disease class, 80 images are collected from Google

Dataset 3656a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 3656
state: A new huge raw data set collected in time series consisting of 23,204 groups of data.
comment: A new huge raw data set collected in time series consisting of 23,204 groups of data.

Dataset 3666a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 3666
state: A total of 59 categories, including 10 species, 49 detailed disease categories, and 10 health categories with a total of 36258 pictures.
comment: A total of 59 categories, including 10 species, 49 detailed disease categories, and 10 health categories with a total of 36258 pictures.

Dataset 3671a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 3671
state: A total of 2029 images of diseased apple leaves are obtained, which correspond to five classes: Alternaria leaf spot, Brown spot, Mosaic, Grey spot, Rust.
comment: A total of 2029 images of diseased apple leaves are obtained, which correspond to five classes: Alternaria leaf spot, Brown spot, Mosaic, Grey spot, Rust.

Dataset 3698a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 3698
state: The 80 samples were washed with clean water to clean the dust and other contaminants on the surface, 8 leaves of each tree.
comment: The 80 samples were washed with clean water to clean the dust and other contaminants on the surface, 8 leaves of each tree.

Dataset 3702a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 3702
state: Dataset_PlantVillage (1500 tomato leaf images)
comment: Dataset_PlantVillage (1500 tomato leaf images)

Dataset 3740a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 3740
state: A total of 4,062 images of grape leaves with typical symptoms were collected from Plant-Village
comment: A total of 4,062 images of grape leaves with typical symptoms were collected from Plant-Village

Dataset 3775a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 3775
state: 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016, from 2018 Syngenta Crop Challenge, Syngenta
comment: 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016, from 2018 Syngenta Crop Challenge, Syngenta

Dataset 3782a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 3782
state: A total of 156 samples were collected. 104 samples, or two thirds of the total samples, were used as a calibration dataset to develop prediction models for disease severity. The remaining 52 samples, or one third of the total samples, were used as a validation data set.
comment: A total of 156 samples were collected. 104 samples, or two thirds of the total samples, were used as a calibration dataset to develop prediction models for disease severity. The remaining 52 samples, or one third of the total samples, were used as a validation data set.

Dataset 3786a
type: NamedIndividual
type: Dataset
is dataset of: Classifier 3786
state: Cotton yield data were collected from 20 of the 50 points used for accuracy assessment, with 10 points from the infested zone and 10 from the non-infested zone for each field. Cotton bolls were handpicked from a 1.93 m 2 area at each sampling site.
comment: Cotton yield data were collected from 20 of the 50 points used for accuracy assessment, with 10 points from the infested zone and 10 from the non-infested zone for each field. Cotton bolls were handpicked from a 1.93 m 2 area at each sampling site.

Dataset 3817a
type: NamedIndividual
type: Dataset
is dataset of: Regressor 3817
state: 24 rapeseed plots
comment: 24 rapeseed plots

Dataset CONSUS 001
type: NamedIndividual
type: Dataset
has location: United Kingdom
related to: Soil pH
state: CONSUS 001 Dataset contains over 4,000 records for soil profiles of 4,000 fields.

Daylight 0328
type: NamedIndividual
type: Daylight
is condition of: Regressor_0328

DeepElectricalConductivity 0299
type: NamedIndividual
type: Deep Electrical Conductivity (ECdp)
is condition of: Regressor_0299

Density 0725
type: NamedIndividual
type: Density
is condition of: Regressor_0725

Density 2357
type: NamedIndividual
type: Density

Diameter 0187
type: NamedIndividual
type: Diameter
is condition of: Regressor_0187

DieselFuel 1215
type: NamedIndividual
type: Diesel Fuel
is condition of: Regressor 1215

Drought 0336
type: NamedIndividual
type: Drought

Drought 0439
type: NamedIndividual
type: Drought

Drought 1201
type: NamedIndividual
type: Drought
is condition of: Regressor 1201

Drought 1202
type: NamedIndividual
type: Drought
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Drought 2129
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Drought 3340
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Drought 3513
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Drought 3776
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Ear 0218
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Ear 0543a
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Ear 0543b
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Ear 0543c
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Ear 0543d
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Ear 0993
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Ear 1412
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Ear 1425
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Ear 1986
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Ear 2364
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Ear 3722
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Ear 3745
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ElectricalConductivity 0113
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ElectricalConductivity 0135
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ElectricalConductivity 0254
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ElectricalConductivity 0313
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ElectricalConductivity 0633
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ElectricalConductivity 1240
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ElectricalConductivity 1289
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ElectricalConductivity 1369
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ElectricalConductivity 2992
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ElectricalConductivity 3510
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Electricity 1215
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Elevation 0113
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Elevation 0114
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Elevation 0285
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Elevation 0576
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Elevation 0702
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has transformation: Roughness
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Elevation 0721
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Elevation 0905
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Elevation 1040
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Elevation 2381
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Elevation 3510
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Elevation 3730
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Elevation 3804
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Evaluation 0001
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Evaluation 0003
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Evaluation 0004
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Evaluation 0005
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Evaluation 0006
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Evaluation 0012
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Evaluation 0015
type: NamedIndividual
type: Evaluation
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is evaluation of: Regressor_0015
comments: NIR image clustering yielded more accurate estimates of fresh and dry biomass (R2 = 0.79/0.68)
state: 0.79/0.68

Evaluation 0016
type: NamedIndividual
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Evaluation 0017
type: NamedIndividual
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Evaluation 0019
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Evaluation 0022
type: NamedIndividual
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Evaluation 0024
type: NamedIndividual
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Evaluation 0028
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Evaluation 0030
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Evaluation 0031
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Evaluation 0033
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Evaluation 0036
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Evaluation 0037
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Evaluation 0039
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Evaluation 0040
type: NamedIndividual
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Evaluation 0041
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Evaluation 0043
type: NamedIndividual
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Evaluation 0048
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Evaluation 0051
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Evaluation 0053
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Evaluation 0055
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Evaluation 0057
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Evaluation 0062
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Evaluation 0068
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Evaluation 0074
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Evaluation 0075
type: NamedIndividual
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Evaluation 0076
type: NamedIndividual
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Evaluation 0077
type: NamedIndividual
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Evaluation 0082
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Evaluation 0083
type: NamedIndividual
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Evaluation 0084
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Evaluation 0091
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Evaluation 0093
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Evaluation 0098
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Evaluation 0111
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Evaluation 0114
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Evaluation 0115
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Evaluation 0117
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Evaluation 0133
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Evaluation 0135
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Evaluation 0136
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Evaluation 0137
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Evaluation 0140
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Evaluation 0144
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Evaluation 0149
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Evaluation 0158
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Evaluation 0161
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Evaluation 0167
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Evaluation 0172
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Evaluation 0175
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Evaluation 0180
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Evaluation 0182
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Evaluation 0183
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Evaluation 0185
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Evaluation 0186
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Evaluation 0187
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Evaluation 0193
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Evaluation 0198
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Evaluation 0203
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Evaluation 0209
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Evaluation 0212
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Evaluation 0213
type: NamedIndividual
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Evaluation 0218
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Evaluation 0220
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Evaluation 0222
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Evaluation 0226
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Evaluation 0238
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Evaluation 0239
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Evaluation 0243
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Evaluation 0245
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Evaluation 0246
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Evaluation 0247
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Evaluation 0249
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Evaluation 0252
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Evaluation 0254
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Evaluation 0257
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Evaluation 0258
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Evaluation 0259
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Evaluation 0264
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Evaluation 0265
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Evaluation 0280
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Evaluation 0282
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Evaluation 0285
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Evaluation 0290
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Evaluation 0299
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Evaluation 0300
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Evaluation 0301
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Evaluation 0306
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Evaluation 0309
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Evaluation 0310
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Evaluation 0311
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Evaluation 0313
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Evaluation 0314
type: NamedIndividual
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Evaluation 0315
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Evaluation 0316
type: NamedIndividual
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Evaluation 0317
type: NamedIndividual
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Evaluation 0318
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Evaluation 0321
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Evaluation 0326
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Evaluation 0336
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Evaluation 0351
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Evaluation 0353
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Evaluation 0356
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Evaluation 0359
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Evaluation 0367
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Evaluation 0369
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Evaluation 0371
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Evaluation 0375
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Evaluation 0383
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Evaluation 0389
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Evaluation 0393
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Evaluation 0394
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Evaluation 0397
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Evaluation 0398
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Evaluation 0399
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Evaluation 0401
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Evaluation 0402
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Evaluation 0405
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Evaluation 0412
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Evaluation 0416
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Evaluation 0417
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Evaluation 0421
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Evaluation 0423
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Evaluation 0426
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Evaluation 0427
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Evaluation 0439
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Evaluation 0449
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Evaluation 0451
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Evaluation 0467
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Evaluation 0474
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Evaluation 0475
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Evaluation 0478
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Evaluation 0481
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Evaluation 0491
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Evaluation 0497
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Evaluation 0498
type: NamedIndividual
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Evaluation 0499
type: NamedIndividual
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Evaluation 0500
type: NamedIndividual
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Evaluation 0501
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Evaluation 0503
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Evaluation 0510
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Evaluation 0525
type: NamedIndividual
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Evaluation 0526
type: NamedIndividual
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Evaluation 0527
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Evaluation 0533
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Evaluation 0534
type: NamedIndividual
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Evaluation 0536
type: NamedIndividual
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Evaluation 0537
type: NamedIndividual
type: Evaluation
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Evaluation 0538
type: NamedIndividual
type: Evaluation
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Evaluation 0539
type: NamedIndividual
type: Evaluation
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Evaluation 0540
type: NamedIndividual
type: Evaluation
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Evaluation 0543
type: NamedIndividual
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Evaluation 0545
type: NamedIndividual
type: Evaluation
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Evaluation 0549
type: NamedIndividual
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Evaluation 0550
type: NamedIndividual
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Evaluation 0553
type: NamedIndividual
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Evaluation 0554
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Evaluation 0559
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Evaluation 0561
type: NamedIndividual
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Evaluation 0571
type: NamedIndividual
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Evaluation 0576
type: NamedIndividual
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Evaluation 0577
type: NamedIndividual
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Evaluation 0620
type: NamedIndividual
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Evaluation 0622
type: NamedIndividual
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Evaluation 0623
type: NamedIndividual
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Evaluation 0633
type: NamedIndividual
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Evaluation 0634
type: NamedIndividual
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Evaluation 0636
type: NamedIndividual
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Evaluation 0637
type: NamedIndividual
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Evaluation 0642
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Evaluation 0644
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Evaluation 0651
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Evaluation 0656
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Evaluation 0660
type: NamedIndividual
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Evaluation 0661
type: NamedIndividual
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Evaluation 0668
type: NamedIndividual
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Evaluation 0673
type: NamedIndividual
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Evaluation 0674
type: NamedIndividual
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Evaluation 0676
type: NamedIndividual
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Evaluation 0677
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Evaluation 0680
type: NamedIndividual
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Evaluation 0684
type: NamedIndividual
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Evaluation 0688
type: NamedIndividual
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Evaluation 0689
type: NamedIndividual
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Evaluation 0701
type: NamedIndividual
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Evaluation 0702
type: NamedIndividual
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Evaluation 0716
type: NamedIndividual
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Evaluation 0720
type: NamedIndividual
type: Evaluation
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Evaluation 0721
type: NamedIndividual
type: Evaluation
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Evaluation 0725
type: NamedIndividual
type: Evaluation
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Evaluation 0726
type: NamedIndividual
type: Evaluation
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Evaluation 0743
type: NamedIndividual
type: Evaluation
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Evaluation 0747
type: NamedIndividual
type: Evaluation
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Evaluation 0765
type: NamedIndividual
type: Evaluation
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Evaluation 0772
type: NamedIndividual
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Evaluation 0790
type: NamedIndividual
type: Evaluation
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Evaluation 0799
type: NamedIndividual
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Evaluation 0801
type: NamedIndividual
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Evaluation 0813
type: NamedIndividual
type: Evaluation
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Evaluation 0815
type: NamedIndividual
type: Evaluation
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Evaluation 0834
type: NamedIndividual
type: Evaluation
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Evaluation 0855
type: NamedIndividual
type: Evaluation
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Evaluation 0874
type: NamedIndividual
type: Evaluation
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Evaluation 0877
type: NamedIndividual
type: Evaluation
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Evaluation 0880
type: NamedIndividual
type: Evaluation
is evaluation of: Classifier 0880