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Surface Defect Detection and Grading of Apples

This paper presents a novel method to detect surface defects of an apple using RGB images and apples are graded based on these identified defects. We only consider the outer surface to grade the apples. The method starts with background removal and region of interest (ROI) selection using grow-cut method; this is followed by multi-threshold segmentation. We fuse the results of grow-cut and multi-threshold segmentation to detect the defects of an apple. In order to represent the defected area, we extract color statistical, color texture features in addition to shape features, which are further used for apple grading. Sequential Forward Selection algorithm is used for selecting useful discriminant features. Based on the selected features, we grade the apples into different grades using KNearest Neighbor (K-NN) classifier. The experimental results show that our technique achieves highest accuracy..