EFFINET-CL: A FRAMEWORK FOR LEAF DISEASE DETECTION AND CLASSIFICATION USING CONTINUAL LEARNING

This paper presents an innovative approach to detect and classify leaf diseases using EfficientNetB3 combined with continual learning strategies. The proposed model leverages data augmentation techniques for improved generalization and integrates continual learning through Elastic Weight Consolidation (EWC) and Replay Memory to prevent catastrophic forgetting. This approach keeps the model's knowledge of formerly learnt diseases intact while permitting it to learn continually from new disease classes. The integration of an Out-of-Distribution (OOD) detection mechanism further improves the ability of the model to recognize unknown diseases, making it a reliable tool for real-time agricultural applications. The model demonstrated excellent performance with an accuracy of 98.44%, highlighting the potential of deep learning combined with continual learning for plant disease diagnosis, and outperformed ResNet50, DenseNet121, InceptionV3, and MobileNet-V2 algorithms.