Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Artículo científico |
| Lingua: | en |
| Pubblicazione: |
Universidade Estadual de Maringá
2022
|
| Soggetti: | |
| Accesso online: | https://www.redalyc.org/articulo.oa?id=303071489033 https://www.redalyc.org/journal/3030/303071489033/ https://www.redalyc.org/journal/3030/303071489033/html/ https://www.redalyc.org/journal/3030/303071489033/303071489033.epub https://www.redalyc.org/journal/3030/303071489033/movil |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
Sommario:
- Area estimation of soybean leaves of different shapes with artificial neural networks Ludimila Geiciane de Sá Carlos Juliano Brant Albuquerque Nermy Ribeiro Valadares Orlando Gonçalves Brito Ana Clara Gonçalves Fernandes Alcinei Místico de Azevedo Agrociencias Glycine max multilayer perceptrons computational intelligence 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. 2022 artículo científico 1679-9275 https://www.redalyc.org/articulo.oa?id=303071489033 https://www.redalyc.org/journal/3030/303071489033/ https://www.redalyc.org/journal/3030/303071489033/html/ https://www.redalyc.org/journal/3030/303071489033/303071489033.epub https://www.redalyc.org/journal/3030/303071489033/movil 10.4025/actasciagron.v44i1.54787 en http://www.redalyc.org/revista.oa?id=3030 Acta Scientiarum. Agronomy application/pdf Universidade Estadual de Maringá Acta Scientiarum. Agronomy (Brasil) Vol.44