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Improving UASS pesticide application: optimizing and validating drift and deposition simulations

文献类型： 外文期刊

作者： Tang, Qing 1 ; Zhang, Ruirui 1 ; Chen, Liping 1 ; Zhang, Pan 1 ; Li, Longlong 1 ; Xu, Gang 1 ; Yi, Tongchuan 1 ; Hewitt, Andrew 3 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

2.Natl Ctr Int Res Agr Aerial Applicat Technol, Beijing, Peoples R China

3.Univ Queensland, Ctr Pesticide Applicat & Safety, Brisbane, Australia

关键词： lattice Boltzmann method (LBM); unmanned aerial spraying systems (UASS); Pest management; pesticide drift and deposition; optimization

期刊名称：PEST MANAGEMENT SCIENCE （ 影响因子：3.8； 五年影响因子：4.3 ）

ISSN： 1526-498X

年卷期： 2025 年 81 卷 1 期

页码：

收录情况： SCI

摘要： BACKGROUND: As unmanned aerial spraying systems (UASS) usage grows rapidly worldwide, a critical research study was conducted to optimize the simulation of UASS applications, aiming to enhance pesticide delivery efficiency and reduce environmental impact. The study examined several key aspects for accurate simulation of UASS application with lattice Boltzmann method (LBM). Based on these discussions, the most suitable grid size and simulation parameters were selected to create a robust model for optimizing UASS performance in various pest management scenarios, potentially leading to more targeted and sustainable pest control practices. RESULTS: The effect of stability parameter, grid size around the rotor and near ground, and parameters at wake fl ow were carefully analyzed to improve the precision of pesticide drift predictions and deposition patterns. Optimal grid sizes were identified fi ed as 0.2 m generally, 0.025 m near rotors, and a 0.1 + 0.2 m scheme for ground proximity, with fi ner grids improving accuracy but increasing computation time. Wake resolution and threshold significantly fi cantly influenced fl uenced simulation results, while wake distance had minimal impact beyond a certain point. The LBM's accuracy was validated by comparing simulated downwash fl ow and droplet deposition with fi eld test data. CONCLUSION: This study optimized UASS simulation parameters, balancing computational efficiency fi ciency with accuracy. The validated model enhances our ability to design more effective UASS for pest management, potentially leading to more precise and targeted pesticide applications. These advancements contribute to the development of sustainable pest control strategies, aiming to reduce pesticide usage and environmental impact while maintaining crop protection efficacy. fi cacy. (c) 2024 Society of Chemical Industry.

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