Federated learning for crop yield prediction: A comprehensive review of techniques and applications
Vani Hiremani, Raghavendra M Devadas, Preethi Preethi, R. Sapna, T Sowmya, Praveen Gujjar, N. Shobha Rani, K R Bhavya
Abstract
The demand for food all over the world requires the implementation of advanced technologies to improve agricultural productivity. Federated Learning (FL) as a decentralized approach to machine learning facilitates collaborative model training on different data sources while maintaining privacy-making it highly applicable technology for sensitive agricultural data. This paper offers a systematic overview of the recent knowledge on the application of FL towards the prediction of crop yield and other agricultural uses. We discussed the mathematical basis of FL, the variety of machine learning models used, the types of used agricultural data, and the major performance metrics. The paper presents real-world applications and lists the current limitations, including communication overhead, data heterogeneity, and interpretability issues. Lastly, we introduce open research directions to inform the development of FL in precision agriculture.