Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/342163
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampCosta, Daniel dos Santos-
dc.contributor.authorunicampTeruel Mederos, Barbara Janet-
dc.typeArtigopt_BR
dc.titleIRIS-GRAPE: an approach for prediction of quality attributes in vineyard grapes inspired by iris biometric recognitionpt_BR
dc.contributor.authordos Santos, Costa D.-
dc.contributor.authorde Oliveira, Neto R.F.-
dc.contributor.authorRamos, R.P.-
dc.contributor.authorda Silva, Oliveira V.G.-
dc.contributor.authorTeruel, B.-
dc.subjectBiometriapt_BR
dc.subjectSegmentação de imagenspt_BR
dc.subject.otherlanguageBiometrypt_BR
dc.subject.otherlanguageImage segmentationpt_BR
dc.description.abstractThe determination of the period of harvest is essential in the vineyard. For that, attributes of quality such as Total Soluble Solids (TSS) and phenolic compounds are constantly monitored along the maturation. This work proposes a new non-destructive approach for prediction of TSS, total anthocyanins and yellow flavonoids using RGB images, called IRIS-GRAPE. It is inspired by the process of biometric recognition using the iris. In order to validate the proposed approach, a study comparing its performance with the traditional method was performed, using the average of RGB pixel values of the region of interest (ROI) as input variables for a multiple linear regression. The study used two performance evaluation metrics: the correlation coefficient (rP) and the mean square error (MSE). In order to compare the performance differences between the proposed approach, IRIS-GRAPE, and the traditional approach, hypothesis tests were done. The results show that the proposed approach has a superior performance than the traditional method with a confidence level of 95%pt_BR
dc.relation.ispartofComputers and electronics in agriculturept_BR
dc.relation.ispartofabbreviationComput. electron. agric.pt_BR
dc.publisher.cityAmsterdampt_BR
dc.publisher.countryPaíses Baixospt_BR
dc.publisherElsevierpt_BR
dc.date.issued2020-
dc.date.monthofcirculationJan.pt_BR
dc.language.isoengpt_BR
dc.description.volume168pt_BR
dc.rightsFechadopt_BR
dc.sourceSCOPUSpt_BR
dc.identifier.issn0168-1699pt_BR
dc.identifier.eissn1872-7107pt_BR
dc.identifier.doi10.1016/j.compag.2019.105140pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0168169918315436pt_BR
dc.date.available2020-05-28T16:28:03Z-
dc.date.accessioned2020-05-28T16:28:03Z-
dc.description.provenanceSubmitted by Cintia Oliveira de Moura (cintiaom@unicamp.br) on 2020-05-28T16:28:03Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-08-27T19:15:30Z : No. of bitstreams: 1 2-s2.0-85076251346.pdf: 1527920 bytes, checksum: 083dafcdcb9ad5584aae732a990bf7b4 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-05-28T16:28:03Z (GMT). No. of bitstreams: 0 Previous issue date: 2020en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/342163-
dc.contributor.unidadeFaculdade de Engenharia Agrícolapt_BR
dc.contributor.unidadeFaculdade de Engenharia Agrícolapt_BR
dc.subject.keywordLinear regressionpt_BR
dc.subject.keywordMean square errorpt_BR
dc.subject.keywordNondestructive examinationpt_BR
dc.subject.keywordIris recognitionpt_BR
dc.identifier.source2-s2.0-85076251346pt_BR
dc.creator.orcid0000-0001-7703-3183pt_BR
dc.creator.orcid0000-0002-5102-6716pt_BR
dc.type.formArtigopt_BR
dc.identifier.articleid105140pt_BR
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