Objective  Citrus Huanglongbing (HLB) transmission is influenced by the coupling of multiple dynamic factors. Traditional optimal control methods face the limitations in practical applications due to their high computational complexity and reliance on precise models. To address this problem, this paper proposes an intelligent 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 transmission control dynamic model was discretized to construct 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 interrupting 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 efficient control of HLB transmission, and demonstrates the potential of deep reinforcement learning methods in agricultural disease prevention and control.