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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/40465
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dc.creatorBruno Vinícius Castro Guimarãespt_BR
dc.creatorSérgio Luiz Rodrigues Donatopt_BR
dc.creatorIgnacio Aspiazúpt_BR
dc.creatorAlcinei Mistico Azevedopt_BR
dc.creatorAbner José de Carvalhopt_BR
dc.date.accessioned2022-03-25T12:28:25Z-
dc.date.available2022-03-25T12:28:25Z-
dc.date.issued2019-
dc.citation.volume11pt_BR
dc.citation.issue14pt_BR
dc.citation.spage216pt_BR
dc.citation.epage224pt_BR
dc.identifier.doihttps://doi.org/10.5539/jas.v11n14p216pt_BR
dc.identifier.issn19169760pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/40465-
dc.description.resumoBehavior analysis and plant expression are the answers the researcher needs to construct predictive models that minimize the effects of the uncertainties of field production. The objective of this study was to compare the simple and multiple linear regression methods and the artificial neural networks to allow the maximum security in the prediction of harvest in ‘Gigante’ cactus pear. The uniformity test was conducted at the Federal Institute of Bahia, Campus Guanambi, Bahia, Brazil, coordinates 14°13′30″ S, 42°46′53″ W and altitude of 525 m. At 930 days after planting, we evaluated 384 basic units, in which were measured the following variables: plant height (PH); cladode length (CL), width (CW) and thickness (CT); cladode number (CN); total cladode area (TCA); cladode area (CA) and cladode yield (Y). For the comparison between the artificial neural networks (ANN) and regression models (single and multiple-SLR and MLR), we considered the mean prediction error (MPE), the mean quadratic error (MQE), the mean square of deviation (MSD) and the coefficient of determination (R2).The values estimated by the ANN 7-5-1 showed the best proximity to the data obtained in field conditions, followed by ANN 6-2-1, MLR (TCA and CT), SLR (TCA) and SLR (CN). In this way, the ANN models with the topologies 7-2-1 and 6-2-1, MLR with the variables total cladode area and cladode thickness and SLR with the isolated descriptors total cladode area and cladode number, explain 85.1; 81.5; 76.3; 74.09 and 65.87%, respectively, of the yield variation. The ANNs were more efficient at predicting the yield of the ‘Gigante’ cactus pear when compared to the simple and multiple linear regression models.pt_BR
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_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.ispartofJournal of agricultural sciencept_BR
dc.rightsAcesso Abertopt_BR
dc.subject.otherCactopt_BR
dc.subject.otherPalma forrageirapt_BR
dc.subject.otherÉpoca de colheitapt_BR
dc.subject.otherRedes neurais (Computação)pt_BR
dc.subject.otherAnálise de variânciapt_BR
dc.titleComparison of methods for harvest prediction in 'gigante' cactus pearpt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.ccsenet.org/journal/index.php/jas/article/view/0/40370?msclkid=02cff214ac3411eca17dec86d5f1f48fpt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-5196-0851pt_BR
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