Efficient Deep Learning Framework for Leaf Disease Classification in Agriculture

Crop threats arise from plant diseases. It produces worldwide food stability, creating prompt identification essential for sustainable agriculture. This project creates an automated detection system for leaf diseases by employing deep learning, incorporating CNNs for extracting features and RNNs for analyzing patterns. The system utilizes OpenCV for processing images and has been trained on the PlantVillage dataset to improve resilience. Learning by transferring enhances precision with limited labeled information. A significant advancement is the key implementation of explainable AI to guarantee clarity in categorization and using live updates and forecasts available via an easy-to-use interface. Findings indicate that the combined CNN-RNN model exceeds conventional approaches in precision and effectiveness. Upcoming tasks involve integrating hyperspectral imaging and expanding datasets for wider applicability.