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.creatorNermy Ribeiro Valadares
dc.creatorOrlando Gonçalves Brito
dc.creatorAna Clara Gonçalves Fernandes
dc.creatorAlcinei Místico de Azevedo
dc.date2022
dc.date.accessioned2026-07-07T03:53:57Z
dc.descriptionLeaf 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.
dc.formatapplication/pdf
dc.identifier1679-9275
dc.identifierhttps://www.redalyc.org/articulo.oa?id=303071489033
dc.identifierhttps://www.redalyc.org/journal/3030/303071489033/
dc.identifierhttps://www.redalyc.org/journal/3030/303071489033/html/
dc.identifierhttps://www.redalyc.org/journal/3030/303071489033/303071489033.epub
dc.identifierhttps://www.redalyc.org/journal/3030/303071489033/movil
dc.identifier10.4025/actasciagron.v44i1.54787
dc.identifier.urihttp://hdl.handle.net/123456789/446639
dc.languageen
dc.publisherUniversidade Estadual de Maringá
dc.relationhttp://www.redalyc.org/revista.oa?id=3030
dc.rightsActa Scientiarum. Agronomy
dc.sourceActa Scientiarum. Agronomy (Brasil) Vol.44
dc.subjectAgrociencias
dc.titleArea estimation of soybean leaves of different shapes with artificial neural networks
dc.typeartículo científico

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Acta Scientiarum. Agronomy - Universidade Estadual de Maringá