Abstract
The post-harvest grading of apples is the key to improve the competitiveness of apples in high-end market. A grading method based on machine vision and image processing technology is proposed in this paper, which is different from traditional manual grading technology. In this paper, the original image collected by machine vision technology is used to extract the apple area based on common morphological operations and hole filling, and the apple outline is extracted by Canny edge detection. Secondly, the red component, roundness, fruit diameter, defect area and color distribution are extracted. Finally, the optimal penalty factor and kernel function parameters are obtained by particle swarm optimization, and a support vector machine classification model is constructed to classify apples. The experimental results show that the classification accuracy of support vector machine is 92.9%, which verifies the feasibility of the method.
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