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Classification of robusta coffee fruits at different maturation stages using colorimetric characteristics

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dc.contributor.author Costa, Anderson G.
dc.contributor.author Sousa, Daniela A. G. de
dc.contributor.author Paes, Juliana L.
dc.contributor.author Cunha, João P. B.
dc.contributor.author Oliveira, Marcus V. M. de
dc.date.accessioned 2021-12-06T13:00:11Z
dc.date.available 2021-12-06T13:00:11Z
dc.date.issued 2020
dc.identifier.citation COSTA, A. G. et al. Classification of robusta coffee fruits at different maturation stages using colorimetric characteristics. Engenharia Agrícola, Jaboticabal, v. 40, n. 4, p. 518-525, jul./ago. 2020. pt_BR
dc.identifier.issn 1809-4430
dc.identifier.uri http://dx.doi.org/10.1590/1809-4430-Eng.Agric.v40n4p518-525/2020 pt_BR
dc.identifier.uri http://www.sbicafe.ufv.br/handle/123456789/12907
dc.description.abstract Coffee growers who produce the robusta species (Conilon) have sought to increase productivity and drink quality by improving production techniques. Artificial vision systems can assist in increasing the efficiency of operations associated with crop management. This study aimed to obtain colorimetric characteristics of robusta coffee fruits at different stages of maturity and use them for classifying fruits from digital images. A digital camera with spectral resolution in the visible was used to acquire images from 60 samples of coffee fruits at the green, cherry, and over-ripe stages of maturity. Colorimetric variables were extracted from the RGB, HIS, and L*a*b* color models and correlated with the physicochemical attributes of the fruits. The principal componente analysis associated with the k-means technique was applied to the colorimetric variables that showed a significant correlation with the physical-chemical attributes. The colorimetric variables were reduced to a principal component, which presented na explanatory percentage of the variance of 82.33%. The clustering obtained by the application of the k-means technique showed the feasibility of using images for the automatic classification of robusta coffee fruits, with an overall accuracy of 100%. pt_BR
dc.format pdf pt_BR
dc.language.iso en pt_BR
dc.publisher Associação Brasileira de Engenharia Agrícola pt_BR
dc.relation.ispartofseries Engenharia Agrícola;v.40, n.4, 2020
dc.rights Open Access pt_BR
dc.subject Artificial vision systems pt_BR
dc.subject Coffea canephora pt_BR
dc.subject Principal component analysis pt_BR
dc.subject.classification Cafeicultura::Qualidade de bebida pt_BR
dc.title Classification of robusta coffee fruits at different maturation stages using colorimetric characteristics pt_BR
dc.type Artigo pt_BR

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