Vitoria pineapple yield predictions by neuro-fuzzy modeling and linear regression

Detalhes bibliográficos
Autor(a) principal: Paula Patrícia Oliveira da Silva
Data de Publicação: 2022
Outros Autores: Frankley Gustavo Fernandes Mesquita, Guilherme Barbosa Vilela, Rodinei Facco Pegoraro, Victor Martins Maia, Marcos Koiti Kondo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: https://doi.org/10.14295/cs.v13.3719
http://hdl.handle.net/1843/61681
Resumo: CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
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spelling 2023-12-04T16:45:03Z2023-12-04T16:45:03Z2022-08-0613e3719https://doi.org/10.14295/cs.v13.37192177-5133http://hdl.handle.net/1843/61681CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas GeraisOutra AgênciaHybrid intelligent systems that combine artificial intelligence techniques, such as neural networks and fuzzy logic, have become common for the development of complex models to predict and estimate variable parameters. The objective of this study was to compare predictions of Vitoria pineapple yields by Adaptive-Network-Based Fuzzy Inference Systems (ANFIS) and linear or quadratic regression models. The prediction models developed calculate the fruit fresh weight based on the D leaf fresh weight (DLFW) and stem diameter (SD), measured at the time of floral induction. ANFIS were developed using the genfisOptions function of the Neuro Fuzzy Designer toolbox of the Matlab program (Mathworks®- Neuro Fuzzy Designer, R2018a), considering DLFW and SD as the entry parameters, single and combined. The yield prediction error was calculated using the root mean square error (RMSE). The RMSE found for all ANFIS developed were lower than that predicted by linear or quadratic regression models. The lowest RMSE was obtained when the parameters DLFW and SD were combined for the development of the ANFIS. Therefore, the results showed that the use of neuro-fuzzy modeling (ANFIS) for predicting Vitoria pineapple yield presents better results than the use of linear or quadratic regression models.engUniversidade Federal de Minas GeraisUFMGBrasilICA - INSTITUTO DE CIÊNCIAS AGRÁRIASComunicata ScientiaeFrutas - CultivoAbacaxiInteligência artificialRedes neurais (Computação)Lógica difusaAgriculture 4.0Ananas comosus var. comosusArtificial intelligenceFruit growingVitoria pineapple yield predictions by neuro-fuzzy modeling and linear regressioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://www.comunicatascientiae.com.br/comunicata/article/view/3719Paula Patrícia Oliveira da SilvaFrankley Gustavo Fernandes MesquitaGuilherme Barbosa VilelaRodinei Facco PegoraroVictor Martins MaiaMarcos Koiti Kondoinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGLICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/61681/1/License.txtfa505098d172de0bc8864fc1287ffe22MD51ORIGINALVitoria pineapple yield predictions by neuro-fuzzy modeling and linear regression.pdfVitoria pineapple yield predictions by neuro-fuzzy modeling and linear regression.pdfapplication/pdf718300https://repositorio.ufmg.br/bitstream/1843/61681/2/Vitoria%20pineapple%20yield%20predictions%20by%20neuro-fuzzy%20modeling%20and%20linear%20regression.pdffc008780b6830b3e08ccd8c7894029b0MD521843/616812023-12-04 17:57:54.868oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2023-12-04T20:57:54Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.pt_BR.fl_str_mv Vitoria pineapple yield predictions by neuro-fuzzy modeling and linear regression
title Vitoria pineapple yield predictions by neuro-fuzzy modeling and linear regression
spellingShingle Vitoria pineapple yield predictions by neuro-fuzzy modeling and linear regression
Paula Patrícia Oliveira da Silva
Agriculture 4.0
Ananas comosus var. comosus
Artificial intelligence
Fruit growing
Frutas - Cultivo
Abacaxi
Inteligência artificial
Redes neurais (Computação)
Lógica difusa
title_short Vitoria pineapple yield predictions by neuro-fuzzy modeling and linear regression
title_full Vitoria pineapple yield predictions by neuro-fuzzy modeling and linear regression
title_fullStr Vitoria pineapple yield predictions by neuro-fuzzy modeling and linear regression
title_full_unstemmed Vitoria pineapple yield predictions by neuro-fuzzy modeling and linear regression
title_sort Vitoria pineapple yield predictions by neuro-fuzzy modeling and linear regression
author Paula Patrícia Oliveira da Silva
author_facet Paula Patrícia Oliveira da Silva
Frankley Gustavo Fernandes Mesquita
Guilherme Barbosa Vilela
Rodinei Facco Pegoraro
Victor Martins Maia
Marcos Koiti Kondo
author_role author
author2 Frankley Gustavo Fernandes Mesquita
Guilherme Barbosa Vilela
Rodinei Facco Pegoraro
Victor Martins Maia
Marcos Koiti Kondo
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Paula Patrícia Oliveira da Silva
Frankley Gustavo Fernandes Mesquita
Guilherme Barbosa Vilela
Rodinei Facco Pegoraro
Victor Martins Maia
Marcos Koiti Kondo
dc.subject.por.fl_str_mv Agriculture 4.0
Ananas comosus var. comosus
Artificial intelligence
Fruit growing
topic Agriculture 4.0
Ananas comosus var. comosus
Artificial intelligence
Fruit growing
Frutas - Cultivo
Abacaxi
Inteligência artificial
Redes neurais (Computação)
Lógica difusa
dc.subject.other.pt_BR.fl_str_mv Frutas - Cultivo
Abacaxi
Inteligência artificial
Redes neurais (Computação)
Lógica difusa
description CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
publishDate 2022
dc.date.issued.fl_str_mv 2022-08-06
dc.date.accessioned.fl_str_mv 2023-12-04T16:45:03Z
dc.date.available.fl_str_mv 2023-12-04T16:45:03Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1843/61681
dc.identifier.doi.pt_BR.fl_str_mv https://doi.org/10.14295/cs.v13.3719
dc.identifier.issn.pt_BR.fl_str_mv 2177-5133
url https://doi.org/10.14295/cs.v13.3719
http://hdl.handle.net/1843/61681
identifier_str_mv 2177-5133
dc.language.iso.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Comunicata Scientiae
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.publisher.initials.fl_str_mv UFMG
dc.publisher.country.fl_str_mv Brasil
dc.publisher.department.fl_str_mv ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
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bitstream.url.fl_str_mv https://repositorio.ufmg.br/bitstream/1843/61681/1/License.txt
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