Cotton Leaf Spot Diseases Detection Utilizing Feature Selection with Skew Divergence Method

This research work exposes the novel approach of analysis at existing works based on machine vision system for the identification of the visual symptoms of Cotton crop diseases, from RGB images. Diseases regions of cotton crops are revealed in digital pictures, Which were amended and segmented. In this work Proposed Enhanced PSO feature selection method adopts Skew divergence method and user features like Edge, Color, Texture variances to extract the features. Set of features was extracted from each of them. The extracted feature was input to the SVM, Back propagation neural network (BPN), Fuzzy with Edge CYMK color feature and GA feature selection. Tests were performed to identify the best classification model. It has been hypothesized that from the given characteristics of the images, there should be a subset of features more informative of the image domain. To test this hypothesis, three classification models were assessed via cross validation. To Evaluate its efficiency of six types of diseases have been accurately classified like Bacterial Blight, Fusariumwilt, Leaf Blight, Root rot, Micro Nurient, Verticilium Wilt. The Experimental results obtained show that the robust feature vector set which is an Enhancement of a feature extraction method (EPSO) has been afforded the performance assessment of this system.