The apple color grading based on PSO and SVM

In order to eliminate the shortcomings in apple color grading, such as the slow speed, the large error, a novel fast intelligent grading method is presented, which is based on the improved particle swarm optimization (PSO) algorithm and Support Vector Machine. The main process is to acquire the colority of apple surface by the computer vision technology and extract its features which used as the samples to train SVM. In this method the intelligent PSO algorithm is used to select the optimal parameters of kernel function in support vector machine to improve the classifier´s performance, finally grade the apple color with the trained SVM. The actual application shows that the method can achieves high precision, and get very fast grading speed. In apple color grading, the application effect is very notable.