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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/115530
Ver los metadatos del registro completo
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/3.0/ Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) |
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application/pdf 238-241 |
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