Identification and characterization of crops through the analysis of spectral data with machine learning algorithms

Autores
Rigalli, Nicolás Francisco; Montero Bulacio, Enrique; Romagnoli, Martín; Terissi, Lucas D.; Portapila, Margarita Isabel
Año de publicación
2018
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This paper assesses the capability of an spectrometer used in field experiments of soybean, maize and wheat. The objective of this work is to select different wavelengths intervals of the spectral reflectance curve, within the range 632-1125 nm, as features for classification using machine learning methods. Two different classifications are presented, species selection and growth stage identification. For species classification accuracy of 92% is reached, while 99% is obtained for stage classification. In addition we propose a new index that outperforms analyzed established vegetation indices, which shows the potential advantage of using this type of devices.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
remote sensing
NIR
spectral feature selection
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/71510
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/71510
network_acronym_str SEDICI
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network_name_str SEDICI (UNLP)
spelling Identification and characterization of crops through the analysis of spectral data with machine learning algorithmsRigalli, Nicolás FranciscoMontero Bulacio, EnriqueRomagnoli, MartínTerissi, Lucas D.Portapila, Margarita IsabelCiencias Informáticasremote sensingNIRspectral feature selectionThis paper assesses the capability of an spectrometer used in field experiments of soybean, maize and wheat. The objective of this work is to select different wavelengths intervals of the spectral reflectance curve, within the range 632-1125 nm, as features for classification using machine learning methods. Two different classifications are presented, species selection and growth stage identification. For species classification accuracy of 92% is reached, while 99% is obtained for stage classification. In addition we propose a new index that outperforms analyzed established vegetation indices, which shows the potential advantage of using this type of devices.Sociedad Argentina de Informática e Investigación Operativa2018-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf374-387http://sedici.unlp.edu.ar/handle/10915/71510enginfo:eu-repo/semantics/altIdentifier/url/http://47jaiio.sadio.org.ar/sites/default/files/CAI-50.pdfinfo:eu-repo/semantics/altIdentifier/issn/2525-0949info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/3.0/Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2026-06-23T10:34:12Zoai:sedici.unlp.edu.ar:10915/71510Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292026-06-23 10:34:12.3SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Identification and characterization of crops through the analysis of spectral data with machine learning algorithms
title Identification and characterization of crops through the analysis of spectral data with machine learning algorithms
spellingShingle Identification and characterization of crops through the analysis of spectral data with machine learning algorithms
Rigalli, Nicolás Francisco
Ciencias Informáticas
remote sensing
NIR
spectral feature selection
title_short Identification and characterization of crops through the analysis of spectral data with machine learning algorithms
title_full Identification and characterization of crops through the analysis of spectral data with machine learning algorithms
title_fullStr Identification and characterization of crops through the analysis of spectral data with machine learning algorithms
title_full_unstemmed Identification and characterization of crops through the analysis of spectral data with machine learning algorithms
title_sort Identification and characterization of crops through the analysis of spectral data with machine learning algorithms
dc.creator.none.fl_str_mv Rigalli, Nicolás Francisco
Montero Bulacio, Enrique
Romagnoli, Martín
Terissi, Lucas D.
Portapila, Margarita Isabel
author Rigalli, Nicolás Francisco
author_facet Rigalli, Nicolás Francisco
Montero Bulacio, Enrique
Romagnoli, Martín
Terissi, Lucas D.
Portapila, Margarita Isabel
author_role author
author2 Montero Bulacio, Enrique
Romagnoli, Martín
Terissi, Lucas D.
Portapila, Margarita Isabel
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
remote sensing
NIR
spectral feature selection
topic Ciencias Informáticas
remote sensing
NIR
spectral feature selection
dc.description.none.fl_txt_mv This paper assesses the capability of an spectrometer used in field experiments of soybean, maize and wheat. The objective of this work is to select different wavelengths intervals of the spectral reflectance curve, within the range 632-1125 nm, as features for classification using machine learning methods. Two different classifications are presented, species selection and growth stage identification. For species classification accuracy of 92% is reached, while 99% is obtained for stage classification. In addition we propose a new index that outperforms analyzed established vegetation indices, which shows the potential advantage of using this type of devices.
Sociedad Argentina de Informática e Investigación Operativa
description This paper assesses the capability of an spectrometer used in field experiments of soybean, maize and wheat. The objective of this work is to select different wavelengths intervals of the spectral reflectance curve, within the range 632-1125 nm, as features for classification using machine learning methods. Two different classifications are presented, species selection and growth stage identification. For species classification accuracy of 92% is reached, while 99% is obtained for stage classification. In addition we propose a new index that outperforms analyzed established vegetation indices, which shows the potential advantage of using this type of devices.
publishDate 2018
dc.date.none.fl_str_mv 2018-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/71510
url http://sedici.unlp.edu.ar/handle/10915/71510
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://47jaiio.sadio.org.ar/sites/default/files/CAI-50.pdf
info:eu-repo/semantics/altIdentifier/issn/2525-0949
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-sa/3.0/
Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-sa/3.0/
Creative Commons Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
dc.format.none.fl_str_mv application/pdf
374-387
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.365483

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