International Journal of Engineering and Technology

This work exposes the automatic computation system to analyse the cotton leaf spot diseases. First to initialize the images from the database (Image features) that are highly related to the test image (new image), where test image is given by the user. Three features are used for matching the train image features in database images, namely color feature variance, shape and texture feature variance. These features are extracted by PSO. The feature selection method which helps to identify the injured leaf spot of cotton and at the same time improve the accuracy of the system and reduce the error rate also. These features are calculated by different techniques. The proposed Skew divergence color variance feature is calculated by color histogram and color descriptor. The shape Skew divergence feature is calculated by Sobel and Canny through the find out edge variance, edge location using Edge detection method. The skew divergence texture feature is calculated by Gober filter and texture descriptor. This investigation is based on six types of diseases like Bacterial Blight, Fusarium wilt, Leaf Blight, Root rot, Micro Nutrient, Verticillium wilt. This work utilizes these three features and combined the classifier of proposed Cross Information Gain Deep forward Neural Network (CIGDFNN) which helps to recognize and identify cotton leaf spot diseases. The forceful feature vector set is a combination of three features to obtain the higher accuracy rate and sensitivity, specificity when tested with the cotton leaf dataset.