Estimating soybean yields with artificial neural networks
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Acta Scientiarum. Agronomy (Online) |
Texto Completo: | http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/35250 |
Resumo: | The complexity of the statistical models used to estimate the productivity of many crops, including soybeans, restricts the use of this practice, but an alternative is the use of artificial neural networks (ANNs). This study aimed to estimate soybean productivity based on growth habit, sowing density and agronomic characteristics using an ANN multilayer perceptron (MLP). Agronomic data from experiments conducted during the 2013/2014 soybean harvest in Anápolis, Goiás State, B razil, were used to conduct this study after being normalized to an ANN-compatible range. Then, several ANNs were trained to choose the best-performing one. After training the network, a performance analysis was conducted to select the ANN with a performance most appropriate for the problem, and the selected network had a 98% success rate with training data and a 72% data validation accuracy. The application of the MLP to the data used in the experiment shows that it is possible to estimate soybean productivity based on agronomic characteristics, growth habit and population density through AI. |
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Acta Scientiarum. Agronomy (Online) |
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Estimating soybean yields with artificial neural networksGlycine max (L.) Merrillagronomic characteristicsmodelingMLPperceptron.Produção vegetal The complexity of the statistical models used to estimate the productivity of many crops, including soybeans, restricts the use of this practice, but an alternative is the use of artificial neural networks (ANNs). This study aimed to estimate soybean productivity based on growth habit, sowing density and agronomic characteristics using an ANN multilayer perceptron (MLP). Agronomic data from experiments conducted during the 2013/2014 soybean harvest in Anápolis, Goiás State, B razil, were used to conduct this study after being normalized to an ANN-compatible range. Then, several ANNs were trained to choose the best-performing one. After training the network, a performance analysis was conducted to select the ANN with a performance most appropriate for the problem, and the selected network had a 98% success rate with training data and a 72% data validation accuracy. The application of the MLP to the data used in the experiment shows that it is possible to estimate soybean productivity based on agronomic characteristics, growth habit and population density through AI. Universidade Estadual de Maringá2018-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPesquisa de campo e simulaçãoapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/3525010.4025/actasciagron.v40i1.35250Acta Scientiarum. Agronomy; Vol 40 (2018): Publicação Contínua; e35250Acta Scientiarum. Agronomy; v. 40 (2018): Publicação Contínua; e352501807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/35250/pdfCopyright (c) 2018 Acta Scientiarum. Agronomyinfo:eu-repo/semantics/openAccessAlves, Guiliano RangelTeixeira, Itamar RosaMelo, Francisco RamosSouza, Raniele Tadeu GuimarãesSilva, Alessandro Guerra2019-09-24T12:26:47Zoai:periodicos.uem.br/ojs:article/35250Revistahttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgronPUBhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/oaiactaagron@uem.br||actaagron@uem.br|| edamasio@uem.br1807-86211679-9275opendoar:2019-09-24T12:26:47Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false |
dc.title.none.fl_str_mv |
Estimating soybean yields with artificial neural networks |
title |
Estimating soybean yields with artificial neural networks |
spellingShingle |
Estimating soybean yields with artificial neural networks Alves, Guiliano Rangel Glycine max (L.) Merrill agronomic characteristics modeling MLP perceptron. Produção vegetal |
title_short |
Estimating soybean yields with artificial neural networks |
title_full |
Estimating soybean yields with artificial neural networks |
title_fullStr |
Estimating soybean yields with artificial neural networks |
title_full_unstemmed |
Estimating soybean yields with artificial neural networks |
title_sort |
Estimating soybean yields with artificial neural networks |
author |
Alves, Guiliano Rangel |
author_facet |
Alves, Guiliano Rangel Teixeira, Itamar Rosa Melo, Francisco Ramos Souza, Raniele Tadeu Guimarães Silva, Alessandro Guerra |
author_role |
author |
author2 |
Teixeira, Itamar Rosa Melo, Francisco Ramos Souza, Raniele Tadeu Guimarães Silva, Alessandro Guerra |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Alves, Guiliano Rangel Teixeira, Itamar Rosa Melo, Francisco Ramos Souza, Raniele Tadeu Guimarães Silva, Alessandro Guerra |
dc.subject.por.fl_str_mv |
Glycine max (L.) Merrill agronomic characteristics modeling MLP perceptron. Produção vegetal |
topic |
Glycine max (L.) Merrill agronomic characteristics modeling MLP perceptron. Produção vegetal |
description |
The complexity of the statistical models used to estimate the productivity of many crops, including soybeans, restricts the use of this practice, but an alternative is the use of artificial neural networks (ANNs). This study aimed to estimate soybean productivity based on growth habit, sowing density and agronomic characteristics using an ANN multilayer perceptron (MLP). Agronomic data from experiments conducted during the 2013/2014 soybean harvest in Anápolis, Goiás State, B razil, were used to conduct this study after being normalized to an ANN-compatible range. Then, several ANNs were trained to choose the best-performing one. After training the network, a performance analysis was conducted to select the ANN with a performance most appropriate for the problem, and the selected network had a 98% success rate with training data and a 72% data validation accuracy. The application of the MLP to the data used in the experiment shows that it is possible to estimate soybean productivity based on agronomic characteristics, growth habit and population density through AI. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-03-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Pesquisa de campo e simulação |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/35250 10.4025/actasciagron.v40i1.35250 |
url |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/35250 |
identifier_str_mv |
10.4025/actasciagron.v40i1.35250 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/35250/pdf |
dc.rights.driver.fl_str_mv |
Copyright (c) 2018 Acta Scientiarum. Agronomy info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2018 Acta Scientiarum. Agronomy |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
publisher.none.fl_str_mv |
Universidade Estadual de Maringá |
dc.source.none.fl_str_mv |
Acta Scientiarum. Agronomy; Vol 40 (2018): Publicação Contínua; e35250 Acta Scientiarum. Agronomy; v. 40 (2018): Publicação Contínua; e35250 1807-8621 1679-9275 reponame:Acta Scientiarum. Agronomy (Online) instname:Universidade Estadual de Maringá (UEM) instacron:UEM |
instname_str |
Universidade Estadual de Maringá (UEM) |
instacron_str |
UEM |
institution |
UEM |
reponame_str |
Acta Scientiarum. Agronomy (Online) |
collection |
Acta Scientiarum. Agronomy (Online) |
repository.name.fl_str_mv |
Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM) |
repository.mail.fl_str_mv |
actaagron@uem.br||actaagron@uem.br|| edamasio@uem.br |
_version_ |
1799305909997928448 |