AI-Driven Early Detection of Crop Diseases Using Computer Vision and Federated Learning

Early detection of crop diseases is extremely important for lowering agricultural losses and guaranteeing global food security. Traditional techniques of detecting crops diseases are frequently time-consuming, labour-intensive, and prone to human error. This paper presents an AI-driven system that employs computer vision and federated learning to detect agricultural diseases early while maintaining data privacy. The method is offered to help farmers by allowing them to detect diseases in real time without the need for centralized data storage. The proposed methodology includes collecting and preparing crop image dataset and using advanced computer vision models like EfficientNet combining it with federated learning through a decentralized training architecture. Besides that, a federated averaging approach was used to train the model on five simulated clients. Model performance is measured using evaluation measures such as accuracy, precision, recall, and F1-score, whereas federated learning efficiency is to be evaluated using convergence time, which showed improvements within the first 3 rounds following it with minimal improvements detected after the 3rd round. The outcome of the paper is an AI-powered system capable of identifying crops diseases with 93% accuracy and scalability while maintaining data privacy. Farmers, agricultural organizations, and policymakers will profit from the suggested approach, which allows for privacy-preserving disease detection while supporting data-driven agricultural management and sustainable farming practices. This paper will help to advance AI applications in smart agriculture by supporting innovation in crop disease control using cuttingedge machine learning technology.