Data/hora: 01/06/2024 16:34:54

Provedor de dados: | Acta Scientiarum: Agronomy
País: | Brazil
Título: | Estimating soybean yields with artificial neural networks
Autores: | Alves, Guiliano Rangel; Teixeira, Itamar Rosa; Melo, Francisco Ramos; Souza, Raniele Tadeu Guimarães; Silva, Alessandro Guerra.
Data: | 2018-03-01
Ano: | 2018
Palavras-chave: | Agronomia; Biometria  modelagem e estatística Glycine max (L.) Merrill; Agronomic characteristics; Modeling; MLP; Perceptron. Produção vegetal.
Resumo: | The complexity of the statistical models used to estimate the productivity of many crops, including soybeans, restricts the use of this practice, but an alternative is the use of artificial neural networks (ANNs). This study aimed to estimate soybean productivity based on growth habit, sowing density and agronomic characteristics using an ANN multilayer perceptron (MLP). Agronomic data from experiments conducted during the 2013/2014 soybean harvest in Anápolis, Goiás State, B razil, were used to conduct this study after being normalized to an ANN-compatible range. Then, several ANNs were trained to choose the best-performing one. After training the network, a performance analysis was conducted to select the ANN with a performance most appropriate for the problem, and the selected network had a 98% success rate with training data and a 72% data validation accuracy. The application of the MLP to the data used in the experiment shows that it is possible to estimate soybean productivity based on agronomic characteristics, growth habit and population density through AI.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês

Identificador:

http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/35250

10.4025/actasciagron.v40i1.35250

Editor: | Universidade Estadual de Maringá
Relação: | http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/35250/pdf
Formato: | application/pdf

Fonte:

Acta Scientiarum. Agronomy; v. 40 (2018): Publicação Contínua; e35250

Acta Scientiarum. Agronomy; v. 40 (2018): Publicação Contínua; e35250

1807-8621

1679-9275

Fonte: | Direitos autorais 2018 Acta Scientiarum. Agronomy