Apple Leaf Disease Identification Using Genetic Algorithm and Correlation Based Feature Selection Method
Apple leaf disease is one of the main factors to constrain the apple production and quality. It takes a long time to detect the diseases by using the traditional diagnostic approach, thus farmers often miss the best time to prevent and treat the diseases. Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision, where the key task is to find an effective way to represent the diseased leaf images. In this research, based on image processing techniques and pattern recognition methods, an apple leaf disease recognition method was proposed. A color transformation structure for the input RGB (Red, Green and Blue) image was designed firstly and then RGB model was converted to HSI (Hue, Saturation and Intensity), YUV and gray models. The background was removed based on a specific threshold value, and then the disease spot image was segmented with region growing algorithm (RGA). Thirty-eight classifying features of color, texture and shape were extracted from each spot image. To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification, the most valuable features were selected by combining genetic algorithm (GA) and correlation based feature selection (CFS). Finally, the diseases were recognized by SVM classifier. In the proposed method, the selected feature subset was globally optimum. The experimental results of more than 90% correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases, powdery mildew, mosaic and rust, demonstrate that the proposed method is feasible and effective.