COTTON LEAF DISEASE CLASSIFICATION USING GREY WOLF OPTIMIZATION BASED DEEP NEURAL NETWORK

Abstract

One of the most current study issues in the field of agriculture is the classification of disease from a plant's leaf images. The use of image processing techniques to identify plant diseases in agricultural plants will reduce the need for farmers to protect their crops. For the image acquisition, pictures of cotton plant leaves for normal, bacterial blight, anthracnose, cercospora leaf spot, and alternaria disease datasets are collected from www.kaggle.com.          A Gath-Geva (G-G) Fuzzy clustering is used to separate the diseased area from the normal component. The classification of cotton leaf diseases are carried out by using Grey Wolf Optimization (GWO) based Deep Neural Network (DNN).

Keywords: Grey wolf optimization, G-G Fuzzy Clustering, Cotton leaf, Image processing and Neural Network