CLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERS
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42n3e20210197/2022 http://hdl.handle.net/11449/241916 |
Resumo: | Brazil 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. |
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Repositório Institucional da UNESP |
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CLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERSArtificial intelligenceBanana stagesEstimationMathematical modelingBrazil 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.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)São Paulo State University (UNESP) School of Sciences and Engineering, São Paulo StateSão Paulo State University (UNESP) College of Agricultural and Technological Sciences, São Paulo StateDepartment of Soil Science, Paraná State, Embrapa SojaSão Paulo State University (UNESP) School of Sciences and Engineering, São Paulo StateSão Paulo State University (UNESP) College of Agricultural and Technological Sciences, São Paulo StateUniversidade Estadual Paulista (UNESP)Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA)Neto, Alfredo Bonini [UNESP]de Souza, Angela V. [UNESP]Bonini, Carolina dos S. B. [UNESP]de Mello, Jéssica M. [UNESP]Moreira, Adonis2023-03-02T04:20:33Z2023-03-02T04:20:33Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42n3e20210197/2022Engenharia Agricola, v. 42, n. 3, 2022.1809-44300100-6916http://hdl.handle.net/11449/24191610.1590/1809-4430-Eng.Agric.v42n3e20210197/20222-s2.0-85131406321Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengEngenharia Agricolainfo:eu-repo/semantics/openAccess2024-05-07T13:47:02Zoai:repositorio.unesp.br:11449/241916Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T13:34:16.661879Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
CLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERS |
title |
CLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERS |
spellingShingle |
CLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERS Neto, Alfredo Bonini [UNESP] Artificial intelligence Banana stages Estimation Mathematical modeling |
title_short |
CLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERS |
title_full |
CLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERS |
title_fullStr |
CLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERS |
title_full_unstemmed |
CLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERS |
title_sort |
CLASSIFICATION OF BANANA RIPENING STAGES BY ARTIFICIAL NEURAL NETWORKS AS A FUNCTION OF PLANT PHYSICAL, PHYSICOCHEMICAL, AND BIOCHEMICAL PARAMETERS |
author |
Neto, Alfredo Bonini [UNESP] |
author_facet |
Neto, Alfredo Bonini [UNESP] de Souza, Angela V. [UNESP] Bonini, Carolina dos S. B. [UNESP] de Mello, Jéssica M. [UNESP] Moreira, Adonis |
author_role |
author |
author2 |
de Souza, Angela V. [UNESP] Bonini, Carolina dos S. B. [UNESP] de Mello, Jéssica M. [UNESP] Moreira, Adonis |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (UNESP) Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA) |
dc.contributor.author.fl_str_mv |
Neto, Alfredo Bonini [UNESP] de Souza, Angela V. [UNESP] Bonini, Carolina dos S. B. [UNESP] de Mello, Jéssica M. [UNESP] Moreira, Adonis |
dc.subject.por.fl_str_mv |
Artificial intelligence Banana stages Estimation Mathematical modeling |
topic |
Artificial intelligence Banana stages Estimation Mathematical modeling |
description |
Brazil 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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 2023-03-02T04:20:33Z 2023-03-02T04:20:33Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42n3e20210197/2022 Engenharia Agricola, v. 42, n. 3, 2022. 1809-4430 0100-6916 http://hdl.handle.net/11449/241916 10.1590/1809-4430-Eng.Agric.v42n3e20210197/2022 2-s2.0-85131406321 |
url |
http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v42n3e20210197/2022 http://hdl.handle.net/11449/241916 |
identifier_str_mv |
Engenharia Agricola, v. 42, n. 3, 2022. 1809-4430 0100-6916 10.1590/1809-4430-Eng.Agric.v42n3e20210197/2022 2-s2.0-85131406321 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Engenharia Agricola |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
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1808128217174769664 |