Please use this identifier to cite or link to this item:
http://hdl.handle.net/1843/59465| Type: | Artigo de Periódico |
| Title: | Area estimation of soybean leaves of different shapes with artificial neural networks |
| Authors: | Ludimila Geiciane de Sá Carlos Juliano Brant Albuquerque Alcinei Mistico Azevedo Orlando Gonçalves Brito Nermy Ribeiro Valadares Amara Nunes Mota Ana Clara Gonçalves Fernandes |
| Abstract: | 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. |
| Subject: | Soja Perceptrons Inteligência artificial |
| language: | eng |
| metadata.dc.publisher.country: | Brasil |
| Publisher: | Universidade Federal de Minas Gerais |
| Publisher Initials: | UFMG |
| metadata.dc.publisher.department: | ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS |
| Rights: | Acesso Aberto |
| metadata.dc.identifier.doi: | https://doi.org/10.4025/actasciagron.v44i1.54787 |
| URI: | http://hdl.handle.net/1843/59465 |
| Issue Date: | 2022 |
| metadata.dc.url.externa: | https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/54787 |
| metadata.dc.relation.ispartof: | Acta Scientiarum. Agronomy |
| Appears in Collections: | Artigo de Periódico |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Area estimation of soybean leaves of different shapes with artificial neural networks.pdf | 526.66 kB | Adobe PDF | View/Open |
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