基于颜色分割与GA-SVM的花生表皮破损识别

The traditional methods of crop color selection mainly set the color threshold, which featureslow classification accuracy, subjective judgment and poor generalization ability, and other disadvantages. In this paper, a peanut cuticle damage recognition algorithm based on color segmentation pre-processing and Genetic Algorithm to optimize Support Vector Machine parameters was proposed. According to the damage of peanut's cuticle, peanuts were divided into two types: perfect peanuts and cuticle damaged peanuts. The image data sets of different varieties of peanuts were constructed under different light conditions. The Histogram of Oriented Gradient feature of peanut image was extracted, and the data set of peanut image was classified by Support Vector Machine. In this paper, in order to improve the classification accuracy, color peanut image was pre-processed based on Support Vector Machine in RGB color space, and a color peanut image was transformed into a black-and-white image. This method would be able to improve the classification accuracy more effectively than directly graying the color image. At the same time, this paper used the soft-margin and nonlinear Support Vector Machine model, based on Genetic Algorithm to optimize the model parameters. The accuracy of the algorithm was 96.88% in the training set, 100% in the test set, and the average time of each peanut image was 5.6 ms. The simulation results showed that the algorithm has commendable robustness to the interference of peanut varieties and light changes, and the algorithm did not depend on human experience, had strong generalization ability, and had promisingapplication prospects.