In this study, the quality assessment of dried figs was performed using a machine vision system and data mining techniques. Images from five different classes were obtained using a color CCD camera. After preprocessing and segmentation of the images, 52 features including 6 from size and shape, 4 from texture and 42 from color information were extracted. To find and select the best features for figs grading, the correlation-based feature selection was utilized. It was found that five features (Mouth Area, Homogeneity, Variance value for R, Variance value for B and Kurtosis value for B/R 1 G1B) surpassed the other features in the attribute selection process. Afterwards, a combined decision tree-fuzzy logic (DT-FL) technique was developed to classify the dried figs based on the superior features. Comparison of validation stage of the utilized DT classifiers indicated that the DT with REP algorithm was the best classifier with an accuracy of 91.74%.
