Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires

Autores
Romero, José Rodolfo; Roncallo, Pablo Federico; Akkiraju, Pavan C.; Ponzoni, Ignacio; Echenique, Carmen Viviana; Carballido, Jessica Andrea
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Wheat is one of the most important cereals worldwide for human nutrition. Tetraploid wheat (Triticum turgidum L. ssp. durum, 2n = 28, genomes AABB) is mainly used to produce pasta. The main objective of durum wheat breeding programs is to develop varieties with good quality and high yields. Yield is a very complex trait, and depends on different yield components that are genetically controlled and affected by environmental constraints. In this context, machine learning constitutes an excellent alternative for the analysis of a high number of traits in order to extract the most relevant ones as confident predictors of the performance of this crop, allowing a better agricultural planning. Thus, we propose the use of machine learning algorithms for the classification of yield components and for the search of new rules to infer high yields at harvest of durum wheat. The main objective of this work was to obtain rules for predicting durum wheat yield through different machine learning algorithms, and compare them to detect the one that best fits the model. In order to achieve this goal, One-R, J48, Ibk and A priori algorithms were run with data collected by our research group of a RIL (recombinant inbreed lines) population growing in six different environments from the Province of Buenos Aires in Argentina. The results indicate that the A priori method obtains the best performance for all locations, and the classificators generated using the different algorithms share a common set of selected traits. Moreover, comparing these results with the previous ones obtained using different techniques, mainly QTL mapping, the traits indicated to be the most significant ones were the same. The analysis of the resulting rules shows the soundness in the agronomic relevance of the extracted knowledge.
Fil: Romero, José Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina
Fil: Roncallo, Pablo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Akkiraju, Pavan C.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Ponzoni, Ignacio. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina
Fil: Echenique, Carmen Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina
Materia
Machine Learning
Expert System
Classification Algorithm
Yield
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/12720
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/12720
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Using classification algorithms for predicting durum wheat yield in the province of Buenos AiresRomero, José RodolfoRoncallo, Pablo FedericoAkkiraju, Pavan C.Ponzoni, IgnacioEchenique, Carmen VivianaCarballido, Jessica AndreaMachine LearningExpert SystemClassification AlgorithmYieldhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Wheat is one of the most important cereals worldwide for human nutrition. Tetraploid wheat (Triticum turgidum L. ssp. durum, 2n = 28, genomes AABB) is mainly used to produce pasta. The main objective of durum wheat breeding programs is to develop varieties with good quality and high yields. Yield is a very complex trait, and depends on different yield components that are genetically controlled and affected by environmental constraints. In this context, machine learning constitutes an excellent alternative for the analysis of a high number of traits in order to extract the most relevant ones as confident predictors of the performance of this crop, allowing a better agricultural planning. Thus, we propose the use of machine learning algorithms for the classification of yield components and for the search of new rules to infer high yields at harvest of durum wheat. The main objective of this work was to obtain rules for predicting durum wheat yield through different machine learning algorithms, and compare them to detect the one that best fits the model. In order to achieve this goal, One-R, J48, Ibk and A priori algorithms were run with data collected by our research group of a RIL (recombinant inbreed lines) population growing in six different environments from the Province of Buenos Aires in Argentina. The results indicate that the A priori method obtains the best performance for all locations, and the classificators generated using the different algorithms share a common set of selected traits. Moreover, comparing these results with the previous ones obtained using different techniques, mainly QTL mapping, the traits indicated to be the most significant ones were the same. The analysis of the resulting rules shows the soundness in the agronomic relevance of the extracted knowledge.Fil: Romero, José Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); ArgentinaFil: Roncallo, Pablo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur; ArgentinaFil: Akkiraju, Pavan C.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur; ArgentinaFil: Ponzoni, Ignacio. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); ArgentinaFil: Echenique, Carmen Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur; ArgentinaFil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); ArgentinaElsevier2013-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/12720Romero, José Rodolfo; Roncallo, Pablo Federico; Akkiraju, Pavan C.; Ponzoni, Ignacio; Echenique, Carmen Viviana; et al.; Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires; Elsevier; Computers And Eletronics In Agriculture; 96; 5-2013; 173-1790168-1699enginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0168169913001257info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2013.05.006info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:19:42Zoai:ri.conicet.gov.ar:11336/12720instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-15 15:19:43.118CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires
title Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires
spellingShingle Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires
Romero, José Rodolfo
Machine Learning
Expert System
Classification Algorithm
Yield
title_short Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires
title_full Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires
title_fullStr Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires
title_full_unstemmed Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires
title_sort Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires
dc.creator.none.fl_str_mv Romero, José Rodolfo
Roncallo, Pablo Federico
Akkiraju, Pavan C.
Ponzoni, Ignacio
Echenique, Carmen Viviana
Carballido, Jessica Andrea
author Romero, José Rodolfo
author_facet Romero, José Rodolfo
Roncallo, Pablo Federico
Akkiraju, Pavan C.
Ponzoni, Ignacio
Echenique, Carmen Viviana
Carballido, Jessica Andrea
author_role author
author2 Roncallo, Pablo Federico
Akkiraju, Pavan C.
Ponzoni, Ignacio
Echenique, Carmen Viviana
Carballido, Jessica Andrea
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Machine Learning
Expert System
Classification Algorithm
Yield
topic Machine Learning
Expert System
Classification Algorithm
Yield
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Wheat is one of the most important cereals worldwide for human nutrition. Tetraploid wheat (Triticum turgidum L. ssp. durum, 2n = 28, genomes AABB) is mainly used to produce pasta. The main objective of durum wheat breeding programs is to develop varieties with good quality and high yields. Yield is a very complex trait, and depends on different yield components that are genetically controlled and affected by environmental constraints. In this context, machine learning constitutes an excellent alternative for the analysis of a high number of traits in order to extract the most relevant ones as confident predictors of the performance of this crop, allowing a better agricultural planning. Thus, we propose the use of machine learning algorithms for the classification of yield components and for the search of new rules to infer high yields at harvest of durum wheat. The main objective of this work was to obtain rules for predicting durum wheat yield through different machine learning algorithms, and compare them to detect the one that best fits the model. In order to achieve this goal, One-R, J48, Ibk and A priori algorithms were run with data collected by our research group of a RIL (recombinant inbreed lines) population growing in six different environments from the Province of Buenos Aires in Argentina. The results indicate that the A priori method obtains the best performance for all locations, and the classificators generated using the different algorithms share a common set of selected traits. Moreover, comparing these results with the previous ones obtained using different techniques, mainly QTL mapping, the traits indicated to be the most significant ones were the same. The analysis of the resulting rules shows the soundness in the agronomic relevance of the extracted knowledge.
Fil: Romero, José Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina
Fil: Roncallo, Pablo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Akkiraju, Pavan C.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Ponzoni, Ignacio. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina
Fil: Echenique, Carmen Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina. Universidad Nacional del Sur; Argentina
Fil: Carballido, Jessica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Centro de Recursos Naturales Renovables de la Zona Semiárida(i); Argentina
description Wheat is one of the most important cereals worldwide for human nutrition. Tetraploid wheat (Triticum turgidum L. ssp. durum, 2n = 28, genomes AABB) is mainly used to produce pasta. The main objective of durum wheat breeding programs is to develop varieties with good quality and high yields. Yield is a very complex trait, and depends on different yield components that are genetically controlled and affected by environmental constraints. In this context, machine learning constitutes an excellent alternative for the analysis of a high number of traits in order to extract the most relevant ones as confident predictors of the performance of this crop, allowing a better agricultural planning. Thus, we propose the use of machine learning algorithms for the classification of yield components and for the search of new rules to infer high yields at harvest of durum wheat. The main objective of this work was to obtain rules for predicting durum wheat yield through different machine learning algorithms, and compare them to detect the one that best fits the model. In order to achieve this goal, One-R, J48, Ibk and A priori algorithms were run with data collected by our research group of a RIL (recombinant inbreed lines) population growing in six different environments from the Province of Buenos Aires in Argentina. The results indicate that the A priori method obtains the best performance for all locations, and the classificators generated using the different algorithms share a common set of selected traits. Moreover, comparing these results with the previous ones obtained using different techniques, mainly QTL mapping, the traits indicated to be the most significant ones were the same. The analysis of the resulting rules shows the soundness in the agronomic relevance of the extracted knowledge.
publishDate 2013
dc.date.none.fl_str_mv 2013-05
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/12720
Romero, José Rodolfo; Roncallo, Pablo Federico; Akkiraju, Pavan C.; Ponzoni, Ignacio; Echenique, Carmen Viviana; et al.; Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires; Elsevier; Computers And Eletronics In Agriculture; 96; 5-2013; 173-179
0168-1699
url http://hdl.handle.net/11336/12720
identifier_str_mv Romero, José Rodolfo; Roncallo, Pablo Federico; Akkiraju, Pavan C.; Ponzoni, Ignacio; Echenique, Carmen Viviana; et al.; Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires; Elsevier; Computers And Eletronics In Agriculture; 96; 5-2013; 173-179
0168-1699
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0168169913001257
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compag.2013.05.006
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.22299

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