Federated Learning Systems for Enhancing AI in Precision Agriculture

Résumé

Federated learning (FL) has emerged as a promising solution for enhancing AI applications in precision agriculture, particularly in data-sensitive environments. By enabling machine learning models to be trained on decentralized data across multiple devices or farms, FL addresses concerns related to data privacy, security, and the need for large, centralized datasets. This study explores the potential of federated learning systems in optimizing agricultural processes, such as crop yield prediction, soil health monitoring, and pest detection, by leveraging diverse, real-time data from sensors and IoT devices spread across various farms. The key advantage of federated learning is its ability to train robust AI models without the need for raw data to leave the local farms, thus ensuring data privacy while still benefiting from collaborative learning. Our analysis demonstrates that FL can improve the accuracy of predictions and decisions in precision agriculture while reducing the communication overhead and increasing the scalability of AI solutions. Results from simulated farm scenarios show that FL-based models perform comparably to traditional centralized models, with the added benefit of greater data privacy. This work highlights the potential of federated learning as a scalable and secure framework for advancing AI-driven innovations in precision agriculture, offering a new pathway for sustainable and data-efficient farming practices.