AI Powered Plant Disease Detection and Diagnosis

Certain plant diseases cause extensive damage in agriculture; this leads results in a huge loss, and poses a major risk to food security. In general, conventional techniques of diagnosing these diseases are time- consuming, expensive and depend solely on human operator’s experience. As part of this study, we suggest an entirely new architecture for disease detection, diagnostics, and remedies using Ensemble Learning, Generative AI, and, Explainable AI (XAI). It combines deep learning method Convolutional Neural Networks (CNNs), Random Forest classifiers and transformer-based generative models to give diagnosis and suggest treatment processes. LIME and heat plots of attention make the behavior of the system clear to users and contribute to the perfect implementation of artificial intelligence technology in agriculture. This framework is also both timely and suitable for the development and practice of sustainable agriculture especially in the developing world where professional input on agriculture is hard to come by.