基于深度强化学习的柑橘黄龙病智能动态防控策略

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中图分类号：S436.44；TP18 文献标志码：A 文章编号：1001-411X（2026）01-0074-12

Abstract： 【Objective】 Citrus Huanglongbing （HLB） transmission is influenced by the coupling of multiple dynamic factors.Traditional optimal control methodsface the limitations in practical applications due to their high computational complexity and reliance on precise models. To addressthis problem， this paper proposes an inteligent dynamic prevention and control method for HLB based on the Twin Delayed Deep Deterministic Policy Gradient （TD3） algorithm. 【Method】 Firstly， based on the transmission dynamics of HLB，a HLB propagation dynamics model of the interaction mechanism between host and vector was established. On this basis，the HLB transmisson control dynamic model was discretized toconstruct a Markov Decision Process environment suitable for deep reinforcement learning. Subsequently， the TD3 algorithm was introduced， and a multi-objective reward function compatible with biological constraints was designed. Finally， an HLB prevention and control strategy was proposed. 【Result】 Simulation experimental results demonstrated that the proposed dynamic prevention and control strategy for HLB based on TD3 exhibited the significant advantages over traditional algorithms across multiple key performance indicators. Compared to DDPG and PPD， the speed of system state convergence to the disease-free equilibrium point increased by 26.59% and 20.99% respectively， the cumulative control cost reduced by 23.79% and 19.90% respectively， and the peak pesticide usage decreased by about 35.57% . Numerical analysis further showed that timely spraying insecticide during the early stages of HLB outbreak played a critical role in interupting the transmission chain and preventing large-scale epidemics. Compared with constant control strategies，dynamic control strategies had more advantages in suppressing the spread of diseases and reducing the cost of implementing control measures. 【Conclusion】The HLB prevention and control method based on TD3 proposed in this study provides a new perspective for the eficient control of HLB transmission，and demonstrates the potential of deep reinforcement learning methods in agricultural disease prevention and control.

Key words： Citrus Huanglongbing; Deep reinforcement learning; Twin delayed deep deterministic policy gradient; Optimal control; Prevention and control strategy

柑橘黄龙病（Citrus Huanglongbing，HLB），是全球柑橘种植区中最具破坏力的病害之一。（剩余16887字）