Predicting crop phenology: a simple logistic regression model approach

Autores
Leale, Guillermo; Cocitto, Bruno; Cardoso, Ana Laura; Lafluf, Pedro; Tantucci, Ligia; Mendez, Fernanda
Año de publicación
2023
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Crop yield prediction plays a central role in the agricultural planning and decision-making processes. In this paper, we analyze the phenology as a crucial aspect of this topic. We propose a simple model to predict phenology groups on maize and wheat crops at the field-level in Argentina. Our model uses logistic regression and includes photoperiod as an explanatory variable, which is very simple to calculate taking into account latitude and date as input. A large number of data records are used to obtain accurate results. Our model has been tested with over 77% accuracy for both crops. It was also benchmarked with Random Forest, which gives comparable results. However, our study shows that a very simple approach could be used with logistic regression, with very little loss of performance. Our model obtains phenology groups and also performs well with certain critical phenology stages for both crops. Our study aims to provide a simple and effective method for predicting phenology, which can be an aid to crop prediction and for farmers to make accurate decisions. Our work emphasizes the simplicity of the model, the use of a large number of data records, and the inclusion of the photoperiod as an input variable.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
phenology prediction
logistic regression
photoperiod
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/165462

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spelling Predicting crop phenology: a simple logistic regression model approachLeale, GuillermoCocitto, BrunoCardoso, Ana LauraLafluf, PedroTantucci, LigiaMendez, FernandaCiencias Informáticasphenology predictionlogistic regressionphotoperiodCrop yield prediction plays a central role in the agricultural planning and decision-making processes. In this paper, we analyze the phenology as a crucial aspect of this topic. We propose a simple model to predict phenology groups on maize and wheat crops at the field-level in Argentina. Our model uses logistic regression and includes photoperiod as an explanatory variable, which is very simple to calculate taking into account latitude and date as input. A large number of data records are used to obtain accurate results. Our model has been tested with over 77% accuracy for both crops. It was also benchmarked with Random Forest, which gives comparable results. However, our study shows that a very simple approach could be used with logistic regression, with very little loss of performance. Our model obtains phenology groups and also performs well with certain critical phenology stages for both crops. Our study aims to provide a simple and effective method for predicting phenology, which can be an aid to crop prediction and for farmers to make accurate decisions. Our work emphasizes the simplicity of the model, the use of a large number of data records, and the inclusion of the photoperiod as an input variable.Sociedad Argentina de Informática e Investigación Operativa2023-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf111-124http://sedici.unlp.edu.ar/handle/10915/165462enginfo:eu-repo/semantics/altIdentifier/url/https://publicaciones.sadio.org.ar/index.php/JAIIO/article/view/713info:eu-repo/semantics/altIdentifier/issn/2451-7496info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:43:55Zoai:sedici.unlp.edu.ar:10915/165462Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:43:55.898SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Predicting crop phenology: a simple logistic regression model approach
title Predicting crop phenology: a simple logistic regression model approach
spellingShingle Predicting crop phenology: a simple logistic regression model approach
Leale, Guillermo
Ciencias Informáticas
phenology prediction
logistic regression
photoperiod
title_short Predicting crop phenology: a simple logistic regression model approach
title_full Predicting crop phenology: a simple logistic regression model approach
title_fullStr Predicting crop phenology: a simple logistic regression model approach
title_full_unstemmed Predicting crop phenology: a simple logistic regression model approach
title_sort Predicting crop phenology: a simple logistic regression model approach
dc.creator.none.fl_str_mv Leale, Guillermo
Cocitto, Bruno
Cardoso, Ana Laura
Lafluf, Pedro
Tantucci, Ligia
Mendez, Fernanda
author Leale, Guillermo
author_facet Leale, Guillermo
Cocitto, Bruno
Cardoso, Ana Laura
Lafluf, Pedro
Tantucci, Ligia
Mendez, Fernanda
author_role author
author2 Cocitto, Bruno
Cardoso, Ana Laura
Lafluf, Pedro
Tantucci, Ligia
Mendez, Fernanda
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
phenology prediction
logistic regression
photoperiod
topic Ciencias Informáticas
phenology prediction
logistic regression
photoperiod
dc.description.none.fl_txt_mv Crop yield prediction plays a central role in the agricultural planning and decision-making processes. In this paper, we analyze the phenology as a crucial aspect of this topic. We propose a simple model to predict phenology groups on maize and wheat crops at the field-level in Argentina. Our model uses logistic regression and includes photoperiod as an explanatory variable, which is very simple to calculate taking into account latitude and date as input. A large number of data records are used to obtain accurate results. Our model has been tested with over 77% accuracy for both crops. It was also benchmarked with Random Forest, which gives comparable results. However, our study shows that a very simple approach could be used with logistic regression, with very little loss of performance. Our model obtains phenology groups and also performs well with certain critical phenology stages for both crops. Our study aims to provide a simple and effective method for predicting phenology, which can be an aid to crop prediction and for farmers to make accurate decisions. Our work emphasizes the simplicity of the model, the use of a large number of data records, and the inclusion of the photoperiod as an input variable.
Sociedad Argentina de Informática e Investigación Operativa
description Crop yield prediction plays a central role in the agricultural planning and decision-making processes. In this paper, we analyze the phenology as a crucial aspect of this topic. We propose a simple model to predict phenology groups on maize and wheat crops at the field-level in Argentina. Our model uses logistic regression and includes photoperiod as an explanatory variable, which is very simple to calculate taking into account latitude and date as input. A large number of data records are used to obtain accurate results. Our model has been tested with over 77% accuracy for both crops. It was also benchmarked with Random Forest, which gives comparable results. However, our study shows that a very simple approach could be used with logistic regression, with very little loss of performance. Our model obtains phenology groups and also performs well with certain critical phenology stages for both crops. Our study aims to provide a simple and effective method for predicting phenology, which can be an aid to crop prediction and for farmers to make accurate decisions. Our work emphasizes the simplicity of the model, the use of a large number of data records, and the inclusion of the photoperiod as an input variable.
publishDate 2023
dc.date.none.fl_str_mv 2023-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/165462
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info:eu-repo/semantics/altIdentifier/issn/2451-7496
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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