Deep Reinforcement Learning for irrigation optimization: Advantages, opportunities, and challenges

Jiamei Liu, Fangle Chang, Jiahong Yang, Xinyi Jie, Caiyun Lu, Chao Wang, Lei Xie, Longhua Ma, Hongye Su,
Deep Reinforcement Learning for irrigation optimization: Advantages, opportunities, and challenges,
Agricultural Water Management,
Volume 322,
2025,
110030,
ISSN 0378-3774,
https://doi.org/10.1016/j.agwat.2025.110030.
(https://www.sciencedirect.com/science/article/pii/S0378377425007449)
Abstract: Irrigation decision-making using Reinforcement Learning (RL) performs well in changing environment, but easily falls into sub-optimal solutions with high-dimensional data. Deep Reinforcement Learning (DRL) has fused RL with Deep Learning (DL) and excels at learning adaptive and long-term irrigation strategies directly from high-dimensional environment data. This paper systematically reviews the applications of DRL in irrigation optimization, covering both pre-trained environments based on crop growth simulators and dynamic environments driven by real-time sensors. We discussed the strengths of classic DRL algorithms, including their ability to handle dynamic and non-linear environments, and reviewed their performance in irrigation multi-objective optimization and decision-making. In addition, we identified constraints in applying DRL in irrigation decision making, which include data scarcity, poor model interpretability, and difficulties in field deployment. It shows DRL can provide a powerful framework for adaptive irrigation, but is constrained by the gap between simulation and real-world complexity. To address these limitations, we discussed approaches in future work, such as developing multi-objective DRL algorithms. These approaches will improve DRL modeling outcomes and provide a technological foundation for smart agriculture and sustainable resource management.
Keywords: Deep Reinforcement Learning; Irrigation; Multi-objective optimization; Crop growth simulation