Automatic grading of apples based on multi-features and weighted K-means clustering algorithm

Abstract In this paper, a fast and effective method based on multiple image features and a weighted K-means clustering algorithm is proposed to achieve the automatic grading of apples. The method provides a novel way of using four images (top, bottom and two sides) and average gray values for each apple to distinguish between the apple defects, stem and calyx. Furthermore, weighted features (MCSAD (maximum cross-sectional average diameter), circularity, PRA (proportion of red area) and defect regions) were carefully selected according to the requirements of the national apple grading standard, which improves the practicality of the proposed method. Finally, qualitative and quantitative evaluation results demonstrate that the total accuracy of the proposed multi-feature grading method is greater than 96%, which provides encouragement for the additional research and implementation of multi-feature automatic grading for the fruit industry.