Yield prediction of ‘Prata Anã’ and ‘BRS Platina’ banana plants by artificial neural networks1
| dc.creator | Bruno Vinícius Castro Guimarães | |
| dc.creator | Sérgio Luiz Rodrigues Donato | |
| dc.creator | Ignacio Aspiazú | |
| dc.creator | Alcinei Mistico Azevedo | |
| dc.date | 2021 | |
| dc.date.accessioned | 2026-07-07T04:31:51Z | |
| dc.description | Prediction models may contribute to data analysis and decision-making in the management of a crop. This study aimed to evaluate the feasibility of predicting the yield of ‘Prata-Anã’ and ‘BRS Platina’ banana plants by means of artificial neural networks, as well as to determine the most important morphological descriptors for this purpose. The following characteristics were measured: plant height; perimeter of the pseudostem at the ground level, at 30 cm and 100 cm; number of live leaves at harvest; stalk mass, length and diameter; number of hands and fruits; bunches and hands masses; hands average mass; and ratio between the stalk and bunch masses. The data were submitted to artificial neural networks analysis using the R software. The best adjustments were obtained with two and three neurons at the intermediate layer, respectively for ‘Prata-Anã’ and ‘BRS Platina’. These models presented the lowest mean square errors, which correspond to the higher proximity between the predicted and the real data, and, therefore, a higher efficiency of the networks in the yield prediction. By the coefficient of determination, the best adjustments were found for ‘Prata-Anã’ (R² = 0.99 for all the network compositions), while, for ‘BRS Platina’, the data adjustment enabled an R² with values between 0.97 and 1.00, approximately. Yield predictions for ‘Prata-Anã’ and ‘BRS Platina’ were obtained with high efficiency by using artificial neural networks. | |
| dc.format | application/pdf | |
| dc.identifier | 1517-6398 | |
| dc.identifier | https://www.redalyc.org/articulo.oa?id=253068585014 | |
| dc.identifier | https://www.redalyc.org/journal/2530/253068585014/ | |
| dc.identifier | https://www.redalyc.org/journal/2530/253068585014/html/ | |
| dc.identifier | https://www.redalyc.org/journal/2530/253068585014/253068585014.epub | |
| dc.identifier | https://www.redalyc.org/journal/2530/253068585014/movil | |
| dc.identifier | 10.1590/1983-40632021v5166008 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/464205 | |
| dc.language | en | |
| dc.publisher | Universidade Federal de Goiás | |
| dc.relation | http://www.redalyc.org/revista.oa?id=2530 | |
| dc.rights | Pesquisa Agropecuária Tropical | |
| dc.source | Pesquisa Agropecuária Tropical (Brasil) Vol.51 | |
| dc.subject | Agrociencias | |
| dc.title | Yield prediction of ‘Prata Anã’ and ‘BRS Platina’ banana plants by artificial neural networks1 | |
| dc.type | artículo científico |