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

Detalhes bibliográficos
Autor(a) principal: Ludimila Geiciane de Sá
Data de Publicação: 2022
Outros Autores: Carlos Juliano Brant Albuquerque, Alcinei Mistico Azevedo, Orlando Gonçalves Brito, Nermy Ribeiro Valadares, Amara Nunes Mota, Ana Clara Gonçalves Fernandes
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: https://doi.org/10.4025/actasciagron.v44i1.54787
http://hdl.handle.net/1843/59465
https://orcid.org/0000-0001-5196-0851
Resumo: CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
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spelling 2023-10-16T19:09:54Z2023-10-16T19:09:54Z20224419https://doi.org/10.4025/actasciagron.v44i1.547871807-8621http://hdl.handle.net/1843/59465https://orcid.org/0000-0001-5196-0851CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas GeraisCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorLeaf 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.engUniversidade Federal de Minas GeraisUFMGBrasilICA - INSTITUTO DE CIÊNCIAS AGRÁRIASActa Scientiarum. AgronomySojaPerceptronsInteligência artificialArea estimation of soybean leaves of different shapes with artificial neural networksinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/54787Ludimila Geiciane de SáCarlos Juliano Brant AlbuquerqueAlcinei Mistico AzevedoOrlando Gonçalves BritoNermy Ribeiro ValadaresAmara Nunes MotaAna Clara Gonçalves Fernandesinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/59465/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALArea estimation of soybean leaves of different shapes with artificial neural networks.pdfArea estimation of soybean leaves of different shapes with artificial neural networks.pdfapplication/pdf539297https://repositorio.ufmg.br/bitstream/1843/59465/2/Area%20estimation%20of%20soybean%20leaves%20of%20different%20shapes%20with%20artificial%20neural%20networks.pdfe075ddbadaf0254febd9443d0f9a8a13MD521843/594652023-10-16 16:26:00.257oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2023-10-16T19:26Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.pt_BR.fl_str_mv 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
Ludimila Geiciane de Sá
Soja
Perceptrons
Inteligência artificial
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 Ludimila Geiciane de Sá
author_facet Ludimila Geiciane de Sá
Carlos Juliano Brant Albuquerque
Alcinei Mistico Azevedo
Orlando Gonçalves Brito
Nermy Ribeiro Valadares
Amara Nunes Mota
Ana Clara Gonçalves Fernandes
author_role author
author2 Carlos Juliano Brant Albuquerque
Alcinei Mistico Azevedo
Orlando Gonçalves Brito
Nermy Ribeiro Valadares
Amara Nunes Mota
Ana Clara Gonçalves Fernandes
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Ludimila Geiciane de Sá
Carlos Juliano Brant Albuquerque
Alcinei Mistico Azevedo
Orlando Gonçalves Brito
Nermy Ribeiro Valadares
Amara Nunes Mota
Ana Clara Gonçalves Fernandes
dc.subject.other.pt_BR.fl_str_mv Soja
Perceptrons
Inteligência artificial
topic Soja
Perceptrons
Inteligência artificial
description CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
publishDate 2022
dc.date.issued.fl_str_mv 2022
dc.date.accessioned.fl_str_mv 2023-10-16T19:09:54Z
dc.date.available.fl_str_mv 2023-10-16T19:09:54Z
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://hdl.handle.net/1843/59465
dc.identifier.doi.pt_BR.fl_str_mv https://doi.org/10.4025/actasciagron.v44i1.54787
dc.identifier.issn.pt_BR.fl_str_mv 1807-8621
dc.identifier.orcid.pt_BR.fl_str_mv https://orcid.org/0000-0001-5196-0851
url https://doi.org/10.4025/actasciagron.v44i1.54787
http://hdl.handle.net/1843/59465
https://orcid.org/0000-0001-5196-0851
identifier_str_mv 1807-8621
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Acta Scientiarum. Agronomy
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.publisher.initials.fl_str_mv UFMG
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
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https://repositorio.ufmg.br/bitstream/1843/59465/2/Area%20estimation%20of%20soybean%20leaves%20of%20different%20shapes%20with%20artificial%20neural%20networks.pdf
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