Pesticide Spraying Strategy Optimization for a Tracked Air-Assisted Spraying Robot Based on a Novel Virginia Creeper Optimization Algorithm

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.

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Publication Details

Journal
Applied Sciences
Published
2026-10-14
DOI
https://doi.org/10.3390/app162010194
Primary Topic
Agricultural Engineering and Mechanization
Type
article
Field-Weighted Citation Impact
0.00

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article

Pesticide Spraying Strategy Optimization for a Tracked Air-Assisted Spraying Robot Based on a Novel Virginia Creeper Optimization Algorithm

Shuai Cao, 李蔚博, Yingting Deng, Junyi Zeng et al.
Applied Sciences
Agricultural Engineering and Mechanization
article

Pesticide Spraying Strategy Optimization for a Tracked Air-Assisted Spraying Robot Based on a Novel Virginia Creeper Optimization Algorithm

Shuai Cao, 李蔚博, Yingting Deng, Junyi Zeng, Qing Li, Xiaoqing Deng
article en

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.

Applied SciencesVol. 16(20)
Northwest Minzu University (CN), Wuhan University of Technology (CN)
Science and Technology Department of Hubei Province
Zero hunger
Openalex Percentile: Top 23%
Agricultural Engineering and Mechanization
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