Image recognition of grape downy mildew and grape powdery mildew based on support vector machine

Abstract

In order to realize automatic disease diagnosis and provide related information for disease prediction and control timely and accurately, the identification and diagnosis of grape downy mildew and grape powdery mildew was conducted based on image recognition technologies. The method based on K-means clustering algorithm was used to implement unsupervised segmentation of the disease images. Fifty shape, color and texture features were extracted from the images of the diseases. Support vector machine (SVM) classifier for the diseases was designed based on thirty-one effective selected features. The training recognition rates of these two kinds of grape diseases were both 100%, and the testing recognition rates of grape downy mildew and grape powdery mildew were 90% and 93.33%, respectively. The recognition results using the SVMs with different kernels indicated that the SVM with linear kernel was the most suitable for image recognition of the diseases. This study provided an effective way for rapid and accurate identification and diagnosis of plant diseases, and also provided a basis and reference for further development of automatic diagnosis system for plant diseases. © 2012 IFIP Federation for Information Processing.