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

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dc.creatorLudimila Geiciane de Sá
dc.creatorCarlos Juliano Brant Albuquerque
dc.creatorAlcinei Mistico Azevedo
dc.creatorOrlando Gonçalves Brito
dc.creatorNermy Ribeiro Valadares
dc.creatorAmara Nunes Mota
dc.creatorAna Clara Gonçalves Fernandes
dc.date.accessioned2023-10-16T19:09:54Z
dc.date.accessioned2025-09-09T01:02:00Z
dc.date.available2023-10-16T19:09:54Z
dc.date.issued2022
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.identifier.doihttps://doi.org/10.4025/actasciagron.v44i1.54787
dc.identifier.issn1807-8621
dc.identifier.urihttps://hdl.handle.net/1843/59465
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofActa Scientiarum. Agronomy
dc.rightsAcesso Aberto
dc.subjectSoja
dc.subjectPerceptrons
dc.subjectInteligência artificial
dc.titleArea estimation of soybean leaves of different shapes with artificial neural networks
dc.typeArtigo de periódico
local.citation.epage9
local.citation.spage1
local.citation.volume44
local.description.resumoLeaf 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.
local.identifier.orcidhttps://orcid.org/0000-0001-5196-0851
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/54787

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