Artificial intelligence applied to estimate soybean yield

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.creatorDos Santos, Wesley Prado L.-
Autor(es): dc.creatorSilva, Mariana Bonini-
Autor(es): dc.creatorBonini Neto, Alfredo-
Autor(es): dc.creatorBonini, Carolina S. B.-
Autor(es): dc.creatorMoreira, Adônis-
Data de aceite: dc.date.accessioned2025-08-21T23:02:53Z-
Data de disponibilização: dc.date.available2025-08-21T23:02:53Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-02-20-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.18011/bioeng.2024.v18.1211-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/301228-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/301228-
Descrição: dc.descriptionThe application of mathematical models using biotic and abiotic factors for the efficient use of fertilizers to obtain maximum economic productivity can be an important tool to minimize the cost of soybean (Glycine max (L.) Merr.) grain yield. In this sense, using Artificial Neural Networks (ANN) is an important tool in studies involving optimization. This study aimed to estimate soybean yield in Luiziana, Paraná state, Brazil, by considering two growing seasons and an Artificial Neural Network (ANN) as a function of the morphological and nutritional parameters of the plants. Results reveal a well-trained network, with a margin of error of approximately 10-5, thus acting as a tool to estimate soybean data. For the phases, model validation and network test, i.e., data that were not part of the training (validation), the errors averaged 10-3. These results indicate that our approach is adequate for optimizing soybean yield estimates in the area studied.-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Sciences and Engineering, São Paulo State-
Descrição: dc.descriptionSão Paulo State University (UNESP) College of Agricultural and Technological Sciences, São Paulo State-
Descrição: dc.descriptionDepartment of Soil Science Embrapa Soja, Paraná State-
Descrição: dc.descriptionSão Paulo State University (UNESP) School of Sciences and Engineering, São Paulo State-
Descrição: dc.descriptionSão Paulo State University (UNESP) College of Agricultural and Technological Sciences, São Paulo State-
Idioma: dc.languageen-
Relação: dc.relationBrazilian Journal of Biosystems Engineering-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial Neural Network-
Palavras-chave: dc.subjectForecast-
Palavras-chave: dc.subjectIntelligent systems-
Palavras-chave: dc.subjectMathematical modelling-
Palavras-chave: dc.subjectSoy-
Título: dc.titleArtificial intelligence applied to estimate soybean yield-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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