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

The article presents "CottonLeafVision," a deep learning framework for classifying cotton leaf diseases, achieving a peak classification accuracy of 98% using the DenseNet201 architecture on a dataset comprising seven classes. The framework incorporates techniques like Gradient-weighted Class Activation Mapping (Grad-CAM) and adversarial training to enhance model interpretability and robustness against noise. This development is significant for practitioners in agriculture AI, as it offers a reliable tool for disease detection, potentially improving crop management and economic outcomes in the cotton industry.