Cotton Leaf Disease Prediction and Diagnosis Using Deep Learning

This project aims to investigate the application of deep learning techniques for the identification and diagnosis of diseases affecting cotton leaves, with the ultimate goal of increasing the efficacy and precision of crop health management. A sizable dataset, comprising labeled images of both healthy and diseased cotton leaves, reflecting a variety of disease stages and environmental conditions, is used in the study. During the implementation stage, preprocessing, data collecting, and model training are done using contemporary deep learning architectures. The trained model can discriminate between healthy and sick instances.