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Agriculture
Volume 15
Issue 24
10.3390/agriculture15242569
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Smart Irrigation Scheduling for Crop Production Using a Crop Model and Improved Deep Reinforcement Learning

by
Jiamei Liu
1,2,
Fangle Chang
2,3,*,
Xiujuan Wang
4,
Mengzhen Kang
4,
Caiyun Lu
5,
Chao Wang
5,
Shaopeng Hu
6,
Yangyang Li
2,3,
Longhua Ma
2,3 and
Hongye Su
2,3
1
School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
2
School of Information Science and Engineering, NingboTech University, Ningbo 315100, China
3
Ningbo Global Innovation Center, Zhejiang University, Ningbo 315100, China
4
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
5
College of Engineering, China Agricultural University, Beijing 100091, China
6
AeroDigital Research Institute, Hangzhou 310015, China
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(24), 2569; https://doi.org/10.3390/agriculture15242569
Submission received: 17 November 2025 / Revised: 8 December 2025 / Accepted: 9 December 2025 / Published: 11 December 2025
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
Versions Notes

Abstract

In arid regions characterized by extreme water scarcity, it is important to synergistically optimize both crop yield and water use. Irrigation strategies based on empirical knowledge overlook crops’ dynamic water needs and may cause water waste and yield loss. To address this issue, this paper proposes an intelligent irrigation scheduling method based on a crop growth model and an improved deep reinforcement learning (DRL) agent. We construct a high-fidelity cotton growth environment using the Decision Support System for Agrotechnology Transfer (DSSAT) model. The model was calibrated with local data from the Shihezi region, Xinjiang, to provide a reliable simulation platform for DRL agent training. We developed a temporal state representation module based on a Bidirectional Long Short-Term Memory (BiLSTM) network and an attention mechanism. This module captures dynamic trends in historical environmental information to focus on critical decision factors. The Soft Actor–Critic (SAC) algorithm was improved by integrating a feature attention mechanism to enhance decision-making precision. A dynamic reward function was designed based on the critical growth stages of cotton to incorporate agronomic prior knowledge into the optimization objective. Simulation results demonstrate that our proposed method can improve water use efficiency (WUE) by 39.0% (with an 8.4% increase in yield and a 22.1% reduction in water consumption) compared to fixed-schedule irrigation strategies. An ablation study further confirms that each of our proposed modules—BiLSTM, the attention mechanism, and the dynamic reward—makes a significant contribution to the final performance.
Keywords: smart irrigation; deep reinforcement learning; DSSAT; long short-term memory; cotton; water resource management smart irrigation; deep reinforcement learning; DSSAT; long short-term memory; cotton; water resource management

Share and Cite

MDPI and ACS Style

Liu, J.; Chang, F.; Wang, X.; Kang, M.; Lu, C.; Wang, C.; Hu, S.; Li, Y.; Ma, L.; Su, H. Smart Irrigation Scheduling for Crop Production Using a Crop Model and Improved Deep Reinforcement Learning. Agriculture 2025, 15, 2569. https://doi.org/10.3390/agriculture15242569

AMA Style

Liu J, Chang F, Wang X, Kang M, Lu C, Wang C, Hu S, Li Y, Ma L, Su H. Smart Irrigation Scheduling for Crop Production Using a Crop Model and Improved Deep Reinforcement Learning. Agriculture. 2025; 15(24):2569. https://doi.org/10.3390/agriculture15242569

Chicago/Turabian Style

Liu, Jiamei, Fangle Chang, Xiujuan Wang, Mengzhen Kang, Caiyun Lu, Chao Wang, Shaopeng Hu, Yangyang Li, Longhua Ma, and Hongye Su. 2025. "Smart Irrigation Scheduling for Crop Production Using a Crop Model and Improved Deep Reinforcement Learning" Agriculture 15, no. 24: 2569. https://doi.org/10.3390/agriculture15242569

APA Style

Liu, J., Chang, F., Wang, X., Kang, M., Lu, C., Wang, C., Hu, S., Li, Y., Ma, L., & Su, H. (2025). Smart Irrigation Scheduling for Crop Production Using a Crop Model and Improved Deep Reinforcement Learning. Agriculture, 15(24), 2569. https://doi.org/10.3390/agriculture15242569

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