Abstract
To address the limitations of conventional constant-rate pesticide application in adapting to spatial variations in crop canopy structure, this study proposes a pesticide spraying strategy optimization method for a tracked air-assisted spraying robot based on a novel Virginia Creeper Optimization Algorithm (VCOA). First, a multivariable nonlinear constrained optimization model is established by considering the coupled effects of robot chassis travelling speed, ducted-fan rotational speed, pump pressure, equivalent nozzle atomization diameter parameter and spray-boom pitch angle. A comprehensive spraying effectiveness objective function is constructed and maximized objective function is constructed to coordinate droplet deposition performance, canopy coverage uniformity, pesticide utilization efficiency, off-target loss, and operational energy consumption. Then, inspired by the random spreading, adhesive climbing, and phototropic growth mechanisms of Virginia creeper, VCOA is developed to enhance the balance between global exploration and local exploitation in complex non-convex optimization problems. The convergence behavior and optimization capability of the proposed algorithm are evaluated using benchmark functions and comparative algorithms. Furthermore, the algorithm is introduced into the variable-rate spraying control process of a tracked air-assisted spraying robot, and its practical effectiveness is verified through simulation and field spraying experiments. The results show that VCOA exhibits superior convergence robustness and global search capability compared with the selected benchmark algorithms. In field experiments, the VCOA-based spraying strategy significantly improves droplet deposition coverage, deposition density, and spatial uniformity, demonstrating its effectiveness and application potential for precision pesticide application in intelligent plant protection equipment.