Yield prediction of ‘Prata Anã’ and ‘BRS Platina’ banana plants by artificial neural networks

• Autor(a) principal: Guimarães, Bruno Vinícius Castro
• Data de Publicação: 2021
• Outros Autores: Donato, Sérgio Luiz Rodrigues, Aspiazú, Ignacio, Azevedo, Alcinei Mistico
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Pesquisa Agropecuária Tropical (Online)
• Texto Completo: https://revistas.ufg.br/pat/article/view/66008

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. KEYWORDS: Musa spp., mathematical models, rural planning.