Poster Abstract: Smart Irrigation Control Using Deep Reinforcement Learning

Xianzhong Ding, Wan Du

Abstract: Agriculture is a major user of ground and surface water in the United States. Improving the efficiency of agricultural irrigation systems is critical for sustainable agriculture. We propose an IoT-based irrigation system, which includes two major components, i.e., an IoT wireless network of sensing and actuation nodes, and a DRL-based control algorithm. Given the collected soil moisture data and weather data, the DRL-based algorithm finds an optimal irrigation schedule, which uses the minimum amount of water to guarantee the soil-water content above the required level before the next irrigation cycle. We deploy the system in our testbed composed of six almond trees. Through a 12-day in-field experiment, we find that our proposed system can save up to 7.8% of water over a widely-used irrigation scheme.