SECURE AND SCALABLE BLOCK CHAIN-INTEGRATED AI MODEL FOR PLANT DISEASE DETECTION

The occurrence of crop diseases creates substantial danger for both agricultural production outputs and system stability. Accurate and early detection is essential for mitigating crop losses, but existing AI-based methods often suffer from challenges in noise sensitivity, data integrity, and computational inefficiency. This paper proposes an advanced plant leaf disease detection system integrating Vision Transformers (ViT) for feature extraction, Reinforcement Learning (RL) for feature optimization, and block chain technology for secure and decentralized data management. Experimental results on the Plant Village dataset demonstrate a 97.8% accuracy with a reduced processing time of 68.2 seconds. Block chain integration further ensures data transparency and immutability, setting a new benchmark for scalable and trustworthy plant leaf disease detection.