Area estimation of soybean leaves of different shapes with artificial neural networks

Detalhes bibliográficos
Autor(a) principal: Sá, Ludimila Geiciane de
Data de Publicação: 2022
Outros Autores: Albuquerque, Carlos Juliano Brant, Valadares, Nermy Ribeiro, Brito, Orlando Gonçalves, Mota, Amara Nunes, Fernandes, Ana Clara Gonçalves, Azevedo, Alcinei Mistico
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/54787
Resumo: Leaf area is one of the most commonly used physiological parameters in plant growth analysis because it facilitates the interpretation of factors associated with yield. The different leaf formats related to soybean genotypes can influence the quality of the model fit for the estimation of leaf area. Direct leaf area measurement is difficult and inaccurate, requires expensive equipment, and is labor intensive. This study developed methodologies to estimate soybean leaf area using neural networks and considering different leaf shapes. A field experiment was carried out from February to July 2017. Data were collected from thirty-six cultivars separated into three groups according to the leaf shape. Multilayer perceptrons were developed using 300 leaves per group, of which 70% were used for training and 30% for validation. The most important morphological measures were also tested with Garson’s method. The artificial neural networks were efficient in estimating the soybean leaf area, with coefficients of determination close to 0.90. The left leaflet width and right leaflet length are sufficient to estimate the leaf area. Network 4, trained with leaves from all groups, was the most general and suitable for the prediction of soybean leaf area.
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spelling Area estimation of soybean leaves of different shapes with artificial neural networksArea estimation of soybean leaves of different shapes with artificial neural networksGlycine max; multilayer perceptrons; computational intelligence.Glycine max; multilayer perceptrons; computational intelligence.Leaf area is one of the most commonly used physiological parameters in plant growth analysis because it facilitates the interpretation of factors associated with yield. The different leaf formats related to soybean genotypes can influence the quality of the model fit for the estimation of leaf area. Direct leaf area measurement is difficult and inaccurate, requires expensive equipment, and is labor intensive. This study developed methodologies to estimate soybean leaf area using neural networks and considering different leaf shapes. A field experiment was carried out from February to July 2017. Data were collected from thirty-six cultivars separated into three groups according to the leaf shape. Multilayer perceptrons were developed using 300 leaves per group, of which 70% were used for training and 30% for validation. The most important morphological measures were also tested with Garson’s method. The artificial neural networks were efficient in estimating the soybean leaf area, with coefficients of determination close to 0.90. The left leaflet width and right leaflet length are sufficient to estimate the leaf area. Network 4, trained with leaves from all groups, was the most general and suitable for the prediction of soybean leaf area.Leaf area is one of the most commonly used physiological parameters in plant growth analysis because it facilitates the interpretation of factors associated with yield. The different leaf formats related to soybean genotypes can influence the quality of the model fit for the estimation of leaf area. Direct leaf area measurement is difficult and inaccurate, requires expensive equipment, and is labor intensive. This study developed methodologies to estimate soybean leaf area using neural networks and considering different leaf shapes. A field experiment was carried out from February to July 2017. Data were collected from thirty-six cultivars separated into three groups according to the leaf shape. Multilayer perceptrons were developed using 300 leaves per group, of which 70% were used for training and 30% for validation. The most important morphological measures were also tested with Garson’s method. The artificial neural networks were efficient in estimating the soybean leaf area, with coefficients of determination close to 0.90. The left leaflet width and right leaflet length are sufficient to estimate the leaf area. Network 4, trained with leaves from all groups, was the most general and suitable for the prediction of soybean leaf area.Universidade Estadual de Maringá2022-05-24info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/5478710.4025/actasciagron.v44i1.54787Acta Scientiarum. Agronomy; Vol 44 (2022): Publicação contínua; e54787Acta Scientiarum. Agronomy; v. 44 (2022): Publicação contínua; e547871807-86211679-9275reponame:Acta Scientiarum. Agronomy (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/54787/751375154244Copyright (c) 2022 Acta Scientiarum. Agronomyhttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessSá, Ludimila Geiciane de Albuquerque, Carlos Juliano Brant Valadares, Nermy RibeiroBrito, Orlando Gonçalves Mota, Amara NunesFernandes, Ana Clara GonçalvesAzevedo, Alcinei Mistico2022-06-22T14:15:53Zoai:periodicos.uem.br/ojs:article/54787Revistahttp://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:2022-06-22T14:15:53Acta Scientiarum. Agronomy (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Area estimation of soybean leaves of different shapes with artificial neural networks
Area estimation of soybean leaves of different shapes with artificial neural networks
title Area estimation of soybean leaves of different shapes with artificial neural networks
spellingShingle Area estimation of soybean leaves of different shapes with artificial neural networks
Sá, Ludimila Geiciane de
Glycine max; multilayer perceptrons; computational intelligence.
Glycine max; multilayer perceptrons; computational intelligence.
title_short Area estimation of soybean leaves of different shapes with artificial neural networks
title_full Area estimation of soybean leaves of different shapes with artificial neural networks
title_fullStr Area estimation of soybean leaves of different shapes with artificial neural networks
title_full_unstemmed Area estimation of soybean leaves of different shapes with artificial neural networks
title_sort Area estimation of soybean leaves of different shapes with artificial neural networks
author Sá, Ludimila Geiciane de
author_facet Sá, Ludimila Geiciane de
Albuquerque, Carlos Juliano Brant
Valadares, Nermy Ribeiro
Brito, Orlando Gonçalves
Mota, Amara Nunes
Fernandes, Ana Clara Gonçalves
Azevedo, Alcinei Mistico
author_role author
author2 Albuquerque, Carlos Juliano Brant
Valadares, Nermy Ribeiro
Brito, Orlando Gonçalves
Mota, Amara Nunes
Fernandes, Ana Clara Gonçalves
Azevedo, Alcinei Mistico
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Sá, Ludimila Geiciane de
Albuquerque, Carlos Juliano Brant
Valadares, Nermy Ribeiro
Brito, Orlando Gonçalves
Mota, Amara Nunes
Fernandes, Ana Clara Gonçalves
Azevedo, Alcinei Mistico
dc.subject.por.fl_str_mv Glycine max; multilayer perceptrons; computational intelligence.
Glycine max; multilayer perceptrons; computational intelligence.
topic Glycine max; multilayer perceptrons; computational intelligence.
Glycine max; multilayer perceptrons; computational intelligence.
description Leaf area is one of the most commonly used physiological parameters in plant growth analysis because it facilitates the interpretation of factors associated with yield. The different leaf formats related to soybean genotypes can influence the quality of the model fit for the estimation of leaf area. Direct leaf area measurement is difficult and inaccurate, requires expensive equipment, and is labor intensive. This study developed methodologies to estimate soybean leaf area using neural networks and considering different leaf shapes. A field experiment was carried out from February to July 2017. Data were collected from thirty-six cultivars separated into three groups according to the leaf shape. Multilayer perceptrons were developed using 300 leaves per group, of which 70% were used for training and 30% for validation. The most important morphological measures were also tested with Garson’s method. The artificial neural networks were efficient in estimating the soybean leaf area, with coefficients of determination close to 0.90. The left leaflet width and right leaflet length are sufficient to estimate the leaf area. Network 4, trained with leaves from all groups, was the most general and suitable for the prediction of soybean leaf area.
publishDate 2022
dc.date.none.fl_str_mv 2022-05-24
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/54787
10.4025/actasciagron.v44i1.54787
url http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/54787
identifier_str_mv 10.4025/actasciagron.v44i1.54787
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/54787/751375154244
dc.rights.driver.fl_str_mv Copyright (c) 2022 Acta Scientiarum. Agronomy
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2022 Acta Scientiarum. Agronomy
https://creativecommons.org/licenses/by/4.0
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 44 (2022): Publicação contínua; e54787
Acta Scientiarum. Agronomy; v. 44 (2022): Publicação contínua; e54787
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
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