Potato grading method of mass and shapes based on machine vision

In the machine vision technique of potato grading process, mass and shape are two important characteristics. In order to achieve potato grading with these two factors, a grading method of mass and shapes based on image characteristic parameters was put forward in this paper. After extracting parameters of top view area and side view perimeter, a potato mass grading model was constructed with stepwise regression analysis, and then four rounds of mass classification were completed with machine vision. To implement potato shape classification, six invariant moment parameters of vertical view were input the trained neural network. The potato grading experimental results showed that the precision ratio of potato mass grading was 95.3%, and the accuracy of potato shape grading was 96%. Therefore the potato grading results indicate that this kind of classification method can detect different mass of potatoes and distinguish 3 classes of potato shapes effectively, which meet the practical application requirement.