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Deep Reinforcement Learning-Based Irrigation Scheduling

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Transactions of the ASABE. 63(3): 549-556. (doi: 10.13031/trans.13633) @2020
Authors:   Yanxiang Yang, Jiang Hu, Dana Porter, Thomas Marek, Kevin Heflin, Hongxin Kong
Keywords:   Automated irrigation scheduling, Deep reinforcement learning, Machine learning.


Deep reinforcement learning-based irrigation scheduling is proposed to determine the amount of irrigation required at each time step considering soil moisture level, evapotranspiration, forecast precipitation, and crop growth stage.

The proposed methodology was compared with traditional irrigation scheduling approaches and some machine learning based scheduling approaches based on simulation.

Abstract. Machine learning has been widely applied in many areas, with promising results and large potential. In this article, deep reinforcement learning-based irrigation scheduling is proposed. This approach can automate the irrigation process and can achieve highly precise water application that results in higher simulated net return. Using this approach, the irrigation controller can automatically determine the optimal or near-optimal water application amount. Traditional reinforcement learning can be superior to traditional periodic and threshold-based irrigation scheduling. However, traditional reinforcement learning fails to accurately represent a real-world irrigation environment due to its limited state space. Compared with traditional reinforcement learning, the deep reinforcement learning method can better model a real-world environment based on multi-dimensional observations. Simulations for various weather conditions and crop types show that the proposed deep reinforcement learning irrigation scheduling can increase net return.

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