A computer vision method to characterize the types of coffee beans based on color and texture analysis

Several types of local Indonesian coffee have been recognized in the international market. The gap in coffee prices leads to the counterfeiting of favorite coffee products. Therefore, it is necessary to develop a non-destructive system that can recognize the external appearance characteristics of each type of coffee bean. This study aimed to develop a computer vision system to characterize three types of Indonesian Arabica coffee beans i.e., Gayo Aceh, Kintamani Bali, and Toraja Tongkonan, based on the external appearances. Each type of coffee bean was analyzed for its characteristics based on color and textural features. Color features included the average value of red, green, blue, grey, hue, saturation(HSL), saturation(HSV), lightness, value, X(XYZ), Y(XYZ), Z(XYZ), C(CMY), M(CMY), Y(CMY), C(CMYK), M(CMYK), Y(CMYK), K(CMYK), L(Lab), a(Lab), b(Lab), C(LCH), H(LCH), U(LUV), and V(LUV). Textural features included energy, entropy, contrast, homogeneity, inverse difference moment, correlation, sum-mean, variance, cluster tendency, and maximum probability on every type of color space. From the features extraction, a total of 286 types of image features were analyzed. The results showed that 19 features of 26 color features and 185 features of 260 textural features could characterize and classify three types of coffee beans. From 286 image features, the three image features which were recommended to have the best performance with the smallest and most stable standard deviations were X(XYZ) sum-mean (average standard deviation 0.01186), Y(XYZ) sum-mean (average standard deviation 0.01187), and Z(XYZ) sum-mean (standard deviation 0.01419).