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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/165462
Ver los metadatos del registro completo
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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 Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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http://sedici.unlp.edu.ar/handle/10915/165462 |
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http://sedici.unlp.edu.ar/handle/10915/165462 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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openAccess |
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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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