Cotton Plant Disease Detection: A Deep Learning Approach

Cotton plays a crucial role in the global economy and is a primary raw material for the textile sector. Despite its importance, cotton crops are prone to various diseases that can severely impact yield and quality. Early and accurate detection of these diseases is vital for effective crop management and sustainable farming practices. This research introduces a machine learning-driven system designed to detect cotton plant diseases. Utilizing a curated dataset of cotton leaf images-comprising healthy leaves and those infected by bacterial blight, leaf curl virus, and fungal pathogens-the study employs advanced algorithms, such as Convolutional Neural Networks (CNN) and Random Forest, for classification and prediction. Image preprocessing methods are integrated to boost feature detection and enhance overall model performance. Results indicate that the system delivers reliable and prompt disease identification, enabling farmers to implement timely interventions. This work highlights the significant potential of Artificial Intelligence (AI) in transforming agricultural practices and improving crop outcomes.