| Registro completo |
| Provedor de dados: |
Agronomy
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| País: |
Brazil
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| Título: |
Estimating soybean yields with artificial neural networks
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| Autores: |
Alves, Guiliano Rangel
Teixeira, Itamar Rosa
Melo, Francisco Ramos
Souza, Raniele Tadeu Guimarães
Silva, Alessandro Guerra
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| Data: |
2018-03-01
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| Ano: |
2018
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| Palavras-chave: |
Agronomia
Biometria modelagem e estatística Glycine max (L.) Merrill
Agronomic characteristics
Modeling
MLP
Perceptron. Produção vegetal
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| 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.
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| Tipo: |
Info:eu-repo/semantics/article
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| Idioma: |
Inglês
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| Identificador: |
http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/35250
10.4025/actasciagron.v40i1.35250
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| Editor: |
Universidade Estadual de Maringá
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| Relação: |
http://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/35250/pdf
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| Formato: |
application/pdf
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| Fonte: |
Acta Scientiarum. Agronomy; v. 40 (2018): Publicação Contínua; e35250
Acta Scientiarum. Agronomy; v. 40 (2018): Publicação Contínua; e35250
1807-8621
1679-9275
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| Direitos: |
Direitos autorais 2018 Acta Scientiarum. Agronomy
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