Machine learning algorithms for soybean yield forecasting in the Brazilian Cerrado
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1002/jsfa.11713 http://hdl.handle.net/11449/230112 |
Resumo: | BACKGROUND: We evaluated different machine learning (ML) models for predicting soybean productivity up to 1 month in advance for the Matopiba agricultural frontier (States of Maranhão, Tocantins, Piauí, and Bahia). We collected meteorological data on the NASA-POWER platform and soybean yield on the SIDRA/IBGE base between 2008 and 2017. The ML models evaluated were random forest (RF), artificial neural networks, radial base support vector machines (SVM_RBF), linear model and polynomial regression. To assess the performance of the models, cross-validation was used, obtaining the value of precision by R2, accuracy by root mean square error (RMSE), and trend by the mean error of the estimate (EME). RESULTS: The results showed that the RF algorithm achieves the highest precision and accuracy, with R2 of 0.81, RMSE of 176.93 kg ha−1 and trend (EME) of 1.99 kg ha−1. On the other hand, the SVM_RBF algorithm showed the lowest performance, with R2 of 0.74, RMSE of 213.58 kg ha−1 and EME of −15.06 kg ha−1. The average yield values predicted by the models were within the expected range for the region, which has a historical average value of 2.730 kg ha−1. CONCLUSION: All models had acceptable precision, accuracy and trend indices, which makes it possible to use all algorithms to be applied in the prediction of soybean crop yield, observing the particularities of the region to be studied, in addition to being a useful tool for agricultural planning and decision making in soy-producing regions such as the Brazilian Cerrado. © 2021 Society of Chemical Industry. |
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Repositório Institucional da UNESP |
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Machine learning algorithms for soybean yield forecasting in the Brazilian CerradoBACKGROUND: We evaluated different machine learning (ML) models for predicting soybean productivity up to 1 month in advance for the Matopiba agricultural frontier (States of Maranhão, Tocantins, Piauí, and Bahia). We collected meteorological data on the NASA-POWER platform and soybean yield on the SIDRA/IBGE base between 2008 and 2017. The ML models evaluated were random forest (RF), artificial neural networks, radial base support vector machines (SVM_RBF), linear model and polynomial regression. To assess the performance of the models, cross-validation was used, obtaining the value of precision by R2, accuracy by root mean square error (RMSE), and trend by the mean error of the estimate (EME). RESULTS: The results showed that the RF algorithm achieves the highest precision and accuracy, with R2 of 0.81, RMSE of 176.93 kg ha−1 and trend (EME) of 1.99 kg ha−1. On the other hand, the SVM_RBF algorithm showed the lowest performance, with R2 of 0.74, RMSE of 213.58 kg ha−1 and EME of −15.06 kg ha−1. The average yield values predicted by the models were within the expected range for the region, which has a historical average value of 2.730 kg ha−1. CONCLUSION: All models had acceptable precision, accuracy and trend indices, which makes it possible to use all algorithms to be applied in the prediction of soybean crop yield, observing the particularities of the region to be studied, in addition to being a useful tool for agricultural planning and decision making in soy-producing regions such as the Brazilian Cerrado. © 2021 Society of Chemical Industry.Graduate Program in Agronomy (Soil Science) State University of Sao Paulo (FCAV/UNESP)School of Agricultural and Veterinarian Sciences São Paulo State University (UNESP)Graduate Program in Agronomy (Soil Science) State University of Sao Paulo (FCAV/UNESP)School of Agricultural and Veterinarian Sciences São Paulo State University (UNESP)Universidade Estadual Paulista (UNESP)Barbosa dos Santos, Valter [UNESP]Moreno Ferreira dos Santos, Aline [UNESP]da Silva Cabral de Moraes, José Reinaldo [UNESP]de Oliveira Vieira, Igor Cristian [UNESP]de Souza Rolim, Glauco [UNESP]2022-04-29T08:38:00Z2022-04-29T08:38:00Z2021-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1002/jsfa.11713Journal of the Science of Food and Agriculture.1097-00100022-5142http://hdl.handle.net/11449/23011210.1002/jsfa.117132-s2.0-85121704767Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal of the Science of Food and Agricultureinfo:eu-repo/semantics/openAccess2022-04-29T08:38:00Zoai:repositorio.unesp.br:11449/230112Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462022-04-29T08:38Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Machine learning algorithms for soybean yield forecasting in the Brazilian Cerrado |
title |
Machine learning algorithms for soybean yield forecasting in the Brazilian Cerrado |
spellingShingle |
Machine learning algorithms for soybean yield forecasting in the Brazilian Cerrado Barbosa dos Santos, Valter [UNESP] |
title_short |
Machine learning algorithms for soybean yield forecasting in the Brazilian Cerrado |
title_full |
Machine learning algorithms for soybean yield forecasting in the Brazilian Cerrado |
title_fullStr |
Machine learning algorithms for soybean yield forecasting in the Brazilian Cerrado |
title_full_unstemmed |
Machine learning algorithms for soybean yield forecasting in the Brazilian Cerrado |
title_sort |
Machine learning algorithms for soybean yield forecasting in the Brazilian Cerrado |
author |
Barbosa dos Santos, Valter [UNESP] |
author_facet |
Barbosa dos Santos, Valter [UNESP] Moreno Ferreira dos Santos, Aline [UNESP] da Silva Cabral de Moraes, José Reinaldo [UNESP] de Oliveira Vieira, Igor Cristian [UNESP] de Souza Rolim, Glauco [UNESP] |
author_role |
author |
author2 |
Moreno Ferreira dos Santos, Aline [UNESP] da Silva Cabral de Moraes, José Reinaldo [UNESP] de Oliveira Vieira, Igor Cristian [UNESP] de Souza Rolim, Glauco [UNESP] |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Barbosa dos Santos, Valter [UNESP] Moreno Ferreira dos Santos, Aline [UNESP] da Silva Cabral de Moraes, José Reinaldo [UNESP] de Oliveira Vieira, Igor Cristian [UNESP] de Souza Rolim, Glauco [UNESP] |
description |
BACKGROUND: We evaluated different machine learning (ML) models for predicting soybean productivity up to 1 month in advance for the Matopiba agricultural frontier (States of Maranhão, Tocantins, Piauí, and Bahia). We collected meteorological data on the NASA-POWER platform and soybean yield on the SIDRA/IBGE base between 2008 and 2017. The ML models evaluated were random forest (RF), artificial neural networks, radial base support vector machines (SVM_RBF), linear model and polynomial regression. To assess the performance of the models, cross-validation was used, obtaining the value of precision by R2, accuracy by root mean square error (RMSE), and trend by the mean error of the estimate (EME). RESULTS: The results showed that the RF algorithm achieves the highest precision and accuracy, with R2 of 0.81, RMSE of 176.93 kg ha−1 and trend (EME) of 1.99 kg ha−1. On the other hand, the SVM_RBF algorithm showed the lowest performance, with R2 of 0.74, RMSE of 213.58 kg ha−1 and EME of −15.06 kg ha−1. The average yield values predicted by the models were within the expected range for the region, which has a historical average value of 2.730 kg ha−1. CONCLUSION: All models had acceptable precision, accuracy and trend indices, which makes it possible to use all algorithms to be applied in the prediction of soybean crop yield, observing the particularities of the region to be studied, in addition to being a useful tool for agricultural planning and decision making in soy-producing regions such as the Brazilian Cerrado. © 2021 Society of Chemical Industry. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-01-01 2022-04-29T08:38:00Z 2022-04-29T08:38:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1002/jsfa.11713 Journal of the Science of Food and Agriculture. 1097-0010 0022-5142 http://hdl.handle.net/11449/230112 10.1002/jsfa.11713 2-s2.0-85121704767 |
url |
http://dx.doi.org/10.1002/jsfa.11713 http://hdl.handle.net/11449/230112 |
identifier_str_mv |
Journal of the Science of Food and Agriculture. 1097-0010 0022-5142 10.1002/jsfa.11713 2-s2.0-85121704767 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal of the Science of Food and Agriculture |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1799965637315919872 |