CLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERS

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA)-
Autor(es): dc.creatorNeto, Alfredo Bonini-
Autor(es): dc.creatorde Souza, Angela V.-
Autor(es): dc.creatorBonini, Carolina dos S. B.-
Autor(es): dc.creatorde Mello, Jéssica M.-
Autor(es): dc.creatorMoreira, Adonis-
Data de aceite: dc.date.accessioned2025-08-21T15:11:38Z-
Data de disponibilização: dc.date.available2025-08-21T15:11:38Z-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2023-03-02-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42n3e20210197/2022-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241916-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241916-
Descrição: dc.descriptionBrazil is currently the 4th world’s largest banana producer, producing around 7 million tons. In this scenario, several studies have been developed with a large amount of data, such as climatic, morphological, and nutritional data, in an attempt to improve these numbers even further. This study aims to classify banana ripening stages by artificial neural networks (ANN) as a function of plant physical, physicochemical, and biochemical parameters. The used ANN consisted of a three-layer feedforward backpropagation network, with eight neurons in the input layer (physical, physicochemical, and biochemical parameters), ten neurons in the intermediate layer, and two neurons in the output layer (classification of banana ripening stages). The results showed three configurations. ANN presented an excellent result for the training phase, with 100% accuracy in the sample classification for the three configurations. The validation and testing phases, that is, the classification of samples that were not part of the training, showed 91.6% and 94.4% accuracy in the first and second configurations, respectively, and 89.5% accuracy in the third configuration.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
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, Paraná State, Embrapa Soja-
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.relationEngenharia Agricola-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial intelligence-
Palavras-chave: dc.subjectBanana stages-
Palavras-chave: dc.subjectEstimation-
Palavras-chave: dc.subjectMathematical modeling-
Título: dc.titleCLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERS-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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