| Revista: | Acta scientiarum. Agronomy |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000459810 |
| ISSN: | 1679-9275 |
| Autores: | Sá, Ludimila Geiciane de1 Albuquerque, Carlos Juliano Brant1 Valadares, Nermy Ribeiro1 Brito, Orlando Gonçalves2 Fernandes, Ana Clara Gonçalves1 Azevedo, Alcinei Místico de1 |
| Instituciones: | 1Universidade Federal de Minas Gerais, Instituto de Ciencias Agrarias, Montes Claros, Minas Gerais. Brasil 2Universidade Federal de Lavras, Lavras, Minas Gerais. Brasil |
| Año: | 2022 |
| Volumen: | 44 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, analítico |
| Resumen en inglés | 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 |
| Disciplinas: | Agrociencias, Ciencias de la computación |
| Palabras clave: | Leguminosas, Inteligencia artificial, Soya, Glycine max, Cultivos, Hojas, Perceptrón multicapa |
| Keyword: | Legumes, Artificial intelligence, Soybean, Glycine max, Crops, Leaves, Multilayer perceptron |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
