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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/61681
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dc.creatorPaula Patrícia Oliveira da Silvapt_BR
dc.creatorFrankley Gustavo Fernandes Mesquitapt_BR
dc.creatorGuilherme Barbosa Vilelapt_BR
dc.creatorRodinei Facco Pegoraropt_BR
dc.creatorVictor Martins Maiapt_BR
dc.creatorMarcos Koiti Kondopt_BR
dc.date.accessioned2023-12-04T16:45:03Z-
dc.date.available2023-12-04T16:45:03Z-
dc.date.issued2022-08-06-
dc.citation.volume13pt_BR
dc.citation.spagee3719pt_BR
dc.identifier.doihttps://doi.org/10.14295/cs.v13.3719pt_BR
dc.identifier.issn2177-5133pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/61681-
dc.description.resumoHybrid 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.pt_BR
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Geraispt_BR
dc.description.sponsorshipOutra Agênciapt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIASpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofComunicata Scientiae-
dc.rightsAcesso Abertopt_BR
dc.subjectAgriculture 4.0pt_BR
dc.subjectAnanas comosus var. comosuspt_BR
dc.subjectArtificial intelligencept_BR
dc.subjectFruit growingpt_BR
dc.subject.otherFrutas - Cultivopt_BR
dc.subject.otherAbacaxipt_BR
dc.subject.otherInteligência artificialpt_BR
dc.subject.otherRedes neurais (Computação)pt_BR
dc.subject.otherLógica difusapt_BR
dc.titleVitoria pineapple yield predictions by neuro-fuzzy modeling and linear regressionpt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.comunicatascientiae.com.br/comunicata/article/view/3719pt_BR
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