Data mining aproach for prediction of fruit color properties

Color is an important feature that dictates the quality and consumer preferences of many fresh fruits and vegetables. In color measurement of fruits, the CIE L*a*b* color space is widely used since it is a uniform color scale. In this study, raw data for the color features of apple varieties were divided into two parts as test and train data in the first stage, analyses were performed on train data and tests were performed on test data. The rules obtained by applying the Find laws algorithm were used to estimate the color index (CI), hue angle (h *) and Chroma (C *) values. In the second stage, raw data were classified by Strict and Liberal options of cluster analysis. Find Laws algorithm was applied to each cluster and 7 different prediction rules were obtained for CI, h*and C* parameters. R2 values of the rules were compared and the rules with the most accurate outcomes were identified.