DRLIC: Deep Reinforcement Learning for Irrigation Control

Agricultural irrigation is a major consumer of freshwater. Current irrigation systems used in the field are not efficient, since they are mainly based on soil moisture sensors' measurement and growers' experience, but not future soil moisture loss. It is hard to predict soil moisture loss, as it depends on a variety of factors, such as soil texture, weather and plants' characteristics. To improve irrigation efficiency, this paper presents DRLIC, a deep reinforcement learning (DRL)-based irrigation system. DRLIC uses a neural network (DRL control agent) to learn an optimal control policy that takes both current soil moisture measurement and future soil moisture loss into account. We define an irrigation reward function that facilitates the control agent to learn from past experience. Sometimes, our DRL control agent may output an unsafe action (e.g., irrigating too much water or too little). To prevent any possible damage to plants' health, we adopt a safe mechanism that leverages a soil moisture predictor to estimate each action's performance. If it is unsafe, we will perform a relatively-conservative action instead. Finally, we develop a real-world irrigation system that is composed of sprinklers, sensing and control nodes, and a wireless network. We deploy DRLIC in our testbed composed of six almond trees. Through a 15-day in-field experiment, we find that DRLIC can save up to 9.52% of water over a widely-used irrigation scheme.