Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/13129
metadata.artigo.dc.title: A computer vision system for coffee beans classification based on computational intelligence techniques
metadata.artigo.dc.creator: Oliveira, Emanuelle Morais de
Leme, Dimas Samid
Barbosa, Bruno Henrique Groenner
Rodarte, Mirian Pereira
Pereira, Rosemary Gualberto Fonseca Alvarenga
metadata.artigo.dc.subject: Coffee bean
Computer vision system
Bayes classifier
Artificial neural network
Pattern recognition
Grãos de café
Sistema de visão por computador
Classificação Bayesiana
Redes neurais artificiais
Reconhecimento de padrões
metadata.artigo.dc.publisher: Elsevier
metadata.artigo.dc.date.issued: Feb-2016
metadata.artigo.dc.identifier.citation: OLIVEIRA, E. M. de et al. A computer vision system for coffee beans classification based on computational intelligence techniques. Journal of Food Engineering, Essex, v. 171, p. 22-27, Feb. 2016.
metadata.artigo.dc.description.abstract: Evaluating the color of green coffee beans is an important process in defining their quality and market price. This evaluation is normally carried out by visual inspection or using traditional instruments which have some limitations. Thus, the objective of this study was to construct a computer vision system that yields CIE (Commission Internationale d'Eclairage) L*a*b* measurements of green coffee beans and classifies them according to their color. Artificial Neural Networks (ANN) were used as the transformation model and the Bayes classifier was used to classify the coffee beans into four groups: whitish, cane green, green, and bluish-green. The neural networks models achieved a generalization error of 1.15% and the Bayesian classifier was able to classify all samples into their expected classes (100% accuracy). Therefore, the proposed system is effective in classifying variations in the color of green coffee beans and can be used to help growers classify their beans.
metadata.artigo.dc.identifier.uri: http://www.sciencedirect.com/science/article/pii/S0260877415300108
http://repositorio.ufla.br/jspui/handle/1/13129
metadata.artigo.dc.language: en_US
Appears in Collections:DCA - Artigos publicados em periódicos
DEG - Artigos publicados em periódicos

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