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2023-05-16T15:10:05Z2023-05-16T15:10:05Z202151111https://doi.org/10.1590/1983-40632021v51660081983-4063http://hdl.handle.net/1843/53444Prediction 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.Modelos de predição podem contribuir para a análise de dados e tomada de decisões no manejo de uma cultura. Objetivou-se avaliar a viabilidade da predição de produtividade de bananeiras ‘Prata-Anã’ e ‘BRS Platina’, por meio de redes neurais artificiais, bem como determinar os descritores morfológicos mais importantes para este fim. Foram mensurados a altura de planta; perímetro do pseudocaule ao nível do solo, a 30 e 100 cm de altura; número de folhas vivas na colheita; massa, comprimento e diâmetro do engaço; número de pencas e de frutos; massa do cacho e das pencas; massa média das pencas; e relação entre a massa do engaço e do cacho. Os dados foram submetidos a análise por redes neurais artificiais, utilizando-se o software R. Os melhores ajustes foram obtidos com dois e três neurônios na camada intermediária, respectivamente, para ‘Prata-Anã’ e ‘BRS Platina’. Esses modelos apresentaram os menores erros quadráticos médios, o que corresponde a maior proximidade entre os dados preditos e os reais, e, por conseguinte, maior eficiência das redes na predição da produtividade. Pelo coeficiente de determinação, verificaram-se os melhores ajustes para ‘Prata-Anã’ (R2 = 0,99 para todas as composições de rede), enquanto, para ‘BRS Platina’, a adequação dos dados possibilitou R² com valores entre 0,97 e 1,00, aproximadamente. Previsões de produtividade para ‘Prata-Anã’ e ‘BRS Platina’ foram obtidas com alta eficiência por meio de redes neurais artificiais.engUniversidade Federal de Minas GeraisUFMGBrasilICA - INSTITUTO DE CIÊNCIAS AGRÁRIASPesquisa Agropecuária TropicalBananaModelos matemáticosProdutividade agrícolaRedes neurais (Computação)Yield prediction of 'Prata Anã' and 'BRS Platina' banana plants by artificial neural networksPredição da produtividade de bananeiras ‘Prata-Anã’ e ‘BRS Platina’ por redes neurais artificiaisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://www.scielo.br/j/pat/a/KF6KdHsHBhkT6LQ3FkvkCHJ/?format=pdf&lang=enBruno Vinícius Castro GuimarãesSérgio Luiz Rodrigues DonatoIgnacio AspiazúAlcinei Mistico Azevedoinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/53444/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALYield prediction of ‘Prata Anã’ and 'BRS Platina' banana plants by artificial neural networks.pdfYield prediction of ‘Prata Anã’ and 'BRS Platina' banana plants by artificial neural networks.pdfapplication/pdf1049651https://repositorio.ufmg.br/bitstream/1843/53444/2/Yield%20prediction%20of%20%e2%80%98Prata%20An%c3%a3%e2%80%99%20and%20%27BRS%20Platina%27%20banana%20plants%20by%20artificial%20neural%20networks.pdff668cda7c38b4500f7e0b5ef96d7fde6MD521843/534442023-05-16 17:11:09.218oai:repositorio.ufmg.br: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Repositório InstitucionalPUBhttps://repositorio.ufmg.br/oaiopendoar:2023-05-16T20:11:09Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
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