CottonLeafVision: An Explainable and Robust Deep Learning Framework for Cotton Leaf Disease Classification

Cotton underpins a massive share of global textile production, yet crop diseases routinely devastate yields in farming communities with limited diagnostic infrastructure. CottonLeafVision applies deep learning — specifically DenseNet201 — to classify seven categories of cotton leaf conditions from field photographs, achieving 98% accuracy. Crucially, the framework goes beyond raw accuracy: it uses Grad-CAM visual explanations and adversarial training to make predictions interpretable and resistant to noise. A working prototype demonstrates real-world deployment potential. Applications include mobile field tools for smallholder farmers, integration with drone-based crop monitoring systems, and broader frameworks for agricultural disease surveillance across other economically critical crops. Authors: Rafi Ahamed, Md. Abir Rahman, Tasnia Tarannum Roza, Munaia Jannat Easha, Md. Asif Khan, Sudeepta Mandal Paper: https://arxiv.org/abs/2606.14686v1