Classification of Cotton Leaf Diseases using Whales Optimization Algorithm based Deep Neural Network

Abstract

In the realm of agriculture, a current focal point of research revolves around the identification of plant diseases through leaf imagery. Employing image processing techniques for the recognition of agricultural plant diseases holds promise for reducing farmers' reliance on safeguarding their crops. This paper introduces a novel approach to classify cotton leaf diseases, utilizing a Deep Neural Network enhanced by the Whales Optimization Algorithm (WOA). The dataset comprises 10,000 cotton images sourced from Kaggle.com, directly captured from farm fields, covering healthy leaves, bacterial blight, Anthracnose, Cercospora leaf spot, and Alternaria diseases. Preprocessing involves the application of a median filter to eliminate image noise, and for segmenting diseased and normal regions, the Gustaffson-Kessel (G-K) fuzzy clustering method is employed. The WOA-augmented DNN demonstrates its effectiveness in classifying cotton images.