Cotton is one of the most important cash crops, playing a vital role in the agricultural economy. However, cotton plants are highly susceptible to various leaf diseases such as bacterial blight, leaf spot, and aphid infestations, which significantly reduce crop yield and quality. Traditional disease detection methods rely on manual inspection by farmers and experts, which is time-consuming, labor-intensive, and prone to human error. To address these challenges, this project proposes an automated cotton leaf disease detection system using deep learning techniques. A Convolutional Neural Network (CNN)-based model is trained on a labeled dataset of cotton leaf images to accurately classify different disease categories. The system is integrated into a user-friendly web application that allows users to upload leaf images and obtain real-time predictions along with disease classification and affected percentage.
R et al. (Thu,) studied this question.