Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/39545
metadata.artigo.dc.title: Recognition of coffee roasting degree using a computer vision system
metadata.artigo.dc.creator: Leme, Dimas Samid
Silva, Sabrina Alves da
Barbosa, Bruno Henrique Groenner
Borém, Flávio Meira
Pereira, Rosemary Gualberto Fonseca Alvarenga
metadata.artigo.dc.subject: Artificial neural networks
Coffee roasting
Color
Regression model
Computer vision
metadata.artigo.dc.publisher: Elsevier
metadata.artigo.dc.date.issued: Jan-2019
metadata.artigo.dc.identifier.citation: LEMES, S. L. et al. Recognition of coffee roasting degree using a computer vision system. Computers and Electronics in Agriculture, [S.l.], v. 156, p. 312-317, Jan. 2019.
metadata.artigo.dc.description.abstract: The definition of the coffee roasting degree is mainly based on the coloring of beans and is directly related to the beverage quality. This bean color reading usually occurs by visual inspection process or by using traditional instruments with scope limitations. Thus, the aim of this study was to construct a computational vision model that compares color patterns in CIE L*a*b* and grayscale with the numerical scale of roasting defined by Specialty Coffee Association of America. Artificial neural networks were used as a color transformation model and quadratic/cubic polynomial regression models and neural models for roasting index approximation. For whole beans, the applied Tukey test (95% of confidence level) showed that the neural model outperformed the polynomial ones for roasting index approximation, getting a R2 factor of 0.99. For ground beans, the quadratic polynomial grayscale model was the best predictor, showing an average error of 0.93. Therefore, the proposed system is considered as effective in the identification and approximation of coffee bean color allowing greater automation and reliability in roasting degree analysis.
metadata.artigo.dc.identifier.uri: https://www.sciencedirect.com/science/article/pii/S0168169918310305
http://repositorio.ufla.br/jspui/handle/1/39545
metadata.artigo.dc.language: en_US
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