Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina

Autores
Sahajpal, Ritvik; Fontana, Lucas; Lafluf, Pedro; Leale, Guillermo; Puricelli, Estefania; O’Neill, Dan; Hosseini, Mehdi; Varela, Mauricio; Reshef, Inbal
Año de publicación
2020
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Accurately determining crop growth progress and crop yields at field-scale can help farmers estimate their net profit, enable insurance companies to ascertain payouts, and help in ensuring food security. At field scales, the troika of management, soil and weather combine to impact crop growth progress, and this progress can be monitored in-season using satellite data. Here, we use satellite derived metrics, from both optical and radar satellites, and machine learning models to model field-scale crop yields for over 3,000 Soybean and Wheat in Argentina. We compare several machine learning models and our results show the promise of combining mixed effect models with non-parametric models in improving yield modeling capabilities. We also demonstrate the utility of specific satellite derived metrics and extracted features in improving model performance and show that our approach can explain greater than 70% of the variation in yields while remaining generalizable across crops and agro-ecological zones.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Crop Yield Forecasting
Machine Learning
Mixed Effect Models
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/115530

id SEDICI_c39608a75c43b2a2ddbef1182d009461
oai_identifier_str oai:sedici.unlp.edu.ar:10915/115530
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in ArgentinaSahajpal, RitvikFontana, LucasLafluf, PedroLeale, GuillermoPuricelli, EstefaniaO’Neill, DanHosseini, MehdiVarela, MauricioReshef, InbalCiencias InformáticasCrop Yield ForecastingMachine LearningMixed Effect ModelsAccurately determining crop growth progress and crop yields at field-scale can help farmers estimate their net profit, enable insurance companies to ascertain payouts, and help in ensuring food security. At field scales, the troika of management, soil and weather combine to impact crop growth progress, and this progress can be monitored in-season using satellite data. Here, we use satellite derived metrics, from both optical and radar satellites, and machine learning models to model field-scale crop yields for over 3,000 Soybean and Wheat in Argentina. We compare several machine learning models and our results show the promise of combining mixed effect models with non-parametric models in improving yield modeling capabilities. We also demonstrate the utility of specific satellite derived metrics and extracted features in improving model performance and show that our approach can explain greater than 70% of the variation in yields while remaining generalizable across crops and agro-ecological zones.Sociedad Argentina de Informática e Investigación Operativa2020-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf238-241http://sedici.unlp.edu.ar/handle/10915/115530enginfo:eu-repo/semantics/altIdentifier/issn/2525-0949info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:26:54Zoai:sedici.unlp.edu.ar:10915/115530Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:26:54.844SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
title Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
spellingShingle Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
Sahajpal, Ritvik
Ciencias Informáticas
Crop Yield Forecasting
Machine Learning
Mixed Effect Models
title_short Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
title_full Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
title_fullStr Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
title_full_unstemmed Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
title_sort Using Machine-Learning Models for Field-Scale Crop Yield and Condition Modeling in Argentina
dc.creator.none.fl_str_mv Sahajpal, Ritvik
Fontana, Lucas
Lafluf, Pedro
Leale, Guillermo
Puricelli, Estefania
O’Neill, Dan
Hosseini, Mehdi
Varela, Mauricio
Reshef, Inbal
author Sahajpal, Ritvik
author_facet Sahajpal, Ritvik
Fontana, Lucas
Lafluf, Pedro
Leale, Guillermo
Puricelli, Estefania
O’Neill, Dan
Hosseini, Mehdi
Varela, Mauricio
Reshef, Inbal
author_role author
author2 Fontana, Lucas
Lafluf, Pedro
Leale, Guillermo
Puricelli, Estefania
O’Neill, Dan
Hosseini, Mehdi
Varela, Mauricio
Reshef, Inbal
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Crop Yield Forecasting
Machine Learning
Mixed Effect Models
topic Ciencias Informáticas
Crop Yield Forecasting
Machine Learning
Mixed Effect Models
dc.description.none.fl_txt_mv Accurately determining crop growth progress and crop yields at field-scale can help farmers estimate their net profit, enable insurance companies to ascertain payouts, and help in ensuring food security. At field scales, the troika of management, soil and weather combine to impact crop growth progress, and this progress can be monitored in-season using satellite data. Here, we use satellite derived metrics, from both optical and radar satellites, and machine learning models to model field-scale crop yields for over 3,000 Soybean and Wheat in Argentina. We compare several machine learning models and our results show the promise of combining mixed effect models with non-parametric models in improving yield modeling capabilities. We also demonstrate the utility of specific satellite derived metrics and extracted features in improving model performance and show that our approach can explain greater than 70% of the variation in yields while remaining generalizable across crops and agro-ecological zones.
Sociedad Argentina de Informática e Investigación Operativa
description Accurately determining crop growth progress and crop yields at field-scale can help farmers estimate their net profit, enable insurance companies to ascertain payouts, and help in ensuring food security. At field scales, the troika of management, soil and weather combine to impact crop growth progress, and this progress can be monitored in-season using satellite data. Here, we use satellite derived metrics, from both optical and radar satellites, and machine learning models to model field-scale crop yields for over 3,000 Soybean and Wheat in Argentina. We compare several machine learning models and our results show the promise of combining mixed effect models with non-parametric models in improving yield modeling capabilities. We also demonstrate the utility of specific satellite derived metrics and extracted features in improving model performance and show that our approach can explain greater than 70% of the variation in yields while remaining generalizable across crops and agro-ecological zones.
publishDate 2020
dc.date.none.fl_str_mv 2020-10
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/10915/115530
url http://sedici.unlp.edu.ar/handle/10915/115530
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/2525-0949
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
dc.format.none.fl_str_mv application/pdf
238-241
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
_version_ 1844616147186483200
score 13.070432