Maize Disease Recognition via Fuzzy Least Square Support Vector Machine

摘要

In this paper, we propose a new approach to recognize the maize disease, which is based on fuzzy least square vector machine (FLSVM) algorithm. According to the texture characteristics of Maize diseases, it uses YCbCr color space technology to segment disease spot, and uses the co-occurrence matrix spatial gray level layer to extract disease spot texture feature, and uses FLSVM to class the maize disease. In this method, the sample mean is calculated, and the center of each class is got; then the distance between the sample and the center is calculated, according to the distance sample's initial membership is got; by finding K neighbors for each sample point, the sample membership degree is calculated according to the fuzzy K nearest neighbor method. Extensive experiments on public datasets show that the algorithm can effectively identify the disease image, the accuracy was as high as 98% or more.