Image recognition of wheat stripe rust and wheat leaf rust based on support vector machine

It is very important to discriminate wheat stripe rust and wheat leaf rust quickly and accurately for forecast and integrated management of the diseases.In this study,a new method based on supporting vector machine(SVM) and multiple feature parameters of their images was proposed for recognition of two kinds of wheat rusts.Sub-images of visual symptoms were acquired using image cutting.The image de-noising was performed with median filtering.The diseased region was then segmented by K_means clustering algorithm.Fifty feature parameters from shape-related,color-related and texture-related features were extracted as inputs of the SVMs to identify the best classification model.The results showed that,using the SVMs with radial basis function(RBF) kernel based on the selected twenty-six features,the recognition rates of wheat stripe rust and wheat leaf rust were both 96.67% for the training sets,and 100% for the tested sets,It was thus evident that RBF kernel function was the most suitable method for image recognition of these two kinds of wheat rusts.The image recognition method based on SVM and multiple features could successfully discriminate wheat stripe rust from wheat leaf rust.