Federated Learning for Predicting Irrigation Requirements in Multi-farm Irrigation Scheduling Systems

The irrigation scheduling system can play an important role in an environment with multiple farms and shared and limited available water. An accurate prediction of required water for every farm can help the irrigation scheduling system to manage better and optimize the usage of water. However, many farmers are reluctant to share their farms data with others. As a solution, this paper proposes to use the Federated Learning (FL) technique for predicting irrigation requirements where the farmers do not need to share their farm data. To our knowledge, this paper is the first one that proposes using FL for solving the problem mentioned above. We propose a use case to show the feasibility of the idea and how FL can be adapted and utilized for predicting irrigation requirements in an environment with multiple farms and a shared water source.