用双层强化学习优化棉花灌溉与施肥，提升产量和资源效率
NDRL: Cotton Irrigation and Nitrogen Application with Nested Dual-Agent Reinforcement Learning
- 双层智能体分工：主代理选宏观策略，子代理动态调每日方案
- 两年模拟增产4.7%，节水增效5.6%，氮肥效率提升6.3%
- 适合农业资源管理、智慧种植系统研发人员参考
有效的灌溉与氮肥施用对作物产量有显著影响。然而现有研究存在两大局限：一是作物生长期间水-氮组合优化复杂度高，产量优化效果差；二是轻微胁迫信号难以量化，反馈延迟，导致水氮动态调控不精准，资源利用效率低。为此，我们提出嵌套双智能体强化学习（NDRL）方法。主智能体基于预测的累积产量收益，识别有前景的宏观灌溉与施肥动作，减少无效探索，保持目标与产量的一致性。子智能体的奖励函数融合量化的水分胁迫因子（WSF）与氮素胁迫因子（NSF），采用混合概率分布动态优化每日策略，从而提升产量与资源效率。我们使用2023年与2024年的田间实验数据校准并验证了农业技术推广决策支持系统（DSSAT），以模拟真实场景并与NDRL交互。实验结果表明，相较于最优基线，2023年与2024年模拟产量分别提升4.7%，灌溉水生产力分别提高5.6%和5.1%，氮肥偏生产力分别提升6.3%和1.0%。该方法推动了棉花水氮管理的发展，为解决农业资源配置中的复杂性与精准性问题提供了新思路，助力可持续农业。
原文摘要 · Abstract (English)
Effective irrigation and nitrogen fertilization have a significant impact on crop yield. However, existing research faces two limitations: (1) the high complexity of optimizing water-nitrogen combinations during crop growth and poor yield optimization results; and (2) the difficulty in quantifying mild stress signals and the delayed feedback, which results in less precise dynamic regulation of water and nitrogen and lower resource utilization efficiency. To address these issues, we propose a Nested Dual-Agent Reinforcement Learning (NDRL) method. The parent agent in NDRL identifies promising macroscopic irrigation and fertilization actions based on projected cumulative yield benefits, reducing ineffective explorationwhile maintaining alignment between objectives and yield. The child agent's reward function incorporates quantified Water Stress Factor (WSF) and Nitrogen Stress Factor (NSF), and uses a mixed probability distribution to dynamically optimize daily strategies, thereby enhancing both yield and resource efficiency. We used field experiment data from 2023 and 2024 to calibrate and validate the Decision Support System for Agrotechnology Transfer (DSSAT) to simulate real-world conditions and interact with NDRL. Experimental results demonstrate that, compared to the best baseline, the simulated yield increased by 4.7% in both 2023 and 2024, the irrigation water productivity increased by 5.6% and 5.1% respectively, and the nitrogen partial factor productivity increased by 6.3% and 1.0% respectively. Our method advances the development of cotton irrigation and nitrogen fertilization, providing new ideas for addressing the complexity and precision issues in agricultural resource management and for sustainable agricultural development.
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