The design of flexible fruit classification system based on machine vision

A flexible fruit classification system based on machine vision was designed to cope with the issues of low manual classification efficiency, skin damage-prone from machine grading, and fixed classification standards and types in the conventional classification methods for cherry tomato. Firstly, the principle of cherry tomatoes flexible classification system was determine and the technical roadmap was determined including offline training and online detection. Secondly, the standards was determined for the classification experiment of the cherry tomato based on the classification theoretical model and market demands, while machine vision was applied to extract the image information by preprocessing; Thirdly, the range of feature threshold including shape, size, and color were obtained by feature recognition research using roundness value method, two-dimensional feature method, and color space threshold method respectively; Finally, experiments was conducted after camera calibration. The single feature recognition experiment shows that three types of features can be effectively recognized separately, and the threshold is adjustable, which verifies the flexibility of the recognition system; The comprehensive feature recognition experiment has verified that the similarity between machine recognition and manual recognition is 93.3%, which is close to manual recognition and has practical and promotional significance.