Reinforcement Learning Control for Water-Efficient Agricultural Irrigation

Modern sensor technologies, internet and advanced irrigation equipment allow a relative precise control of agricultural irrigation that leads to high water-use efficiency. However, the core control algorithms that make use of these technologies have not been well studied. In this work, a reinforcement learning based irrigation control technique is investigated. The delayed reward of crop yield is handled by the temporal difference technique. The learning process can be based on both off-line simulation and real data from sensors and crop yield. Neural network based fast models for soil water level and crop yield are developed to improve the scalability of learning. Simulations for various geographic locations and crop types show that the proposed method can significantly increase net return considering both crop yield and water expense.