LiDAR-Assisted UAV Variable-Rate Spraying System

Xuhang Liu (), Yicheng Liu, Xinhanyang Chen, Yuhan Wan, Dengxi Gao and Pei Cao
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Xuhang Liu: College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China
Yicheng Liu: College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China
Xinhanyang Chen: College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China
Yuhan Wan: College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China
Dengxi Gao: College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China
Pei Cao: College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang 712100, China

Agriculture, 2025, vol. 15, issue 16, 1-19

Abstract: In wheat pest and disease control methods, pesticide application occupies a dominant position, and the use of UAVs for precise pesticide application is a key technology in precision agriculture. However, it is difficult for existing UAV spraying systems to accurately achieve variable spraying according to crop growth conditions, resulting in pesticide waste and environmental pollution. To address this issue, this paper proposes a LiDAR-assisted UAV variable-speed spraying system. Firstly, a biomass estimation model based on LiDAR data and RGB data is constructed, LiDAR point cloud data and RGB data are extracted from the target farmland, and, after preprocessing, key parameters including LiDAR feature variables, canopy cover, and visible-light vegetation indices are extracted from the two types of data. Using these key parameters as model inputs, multiple machine learning methods are employed to build a wheat biomass estimation model, and a variable spraying prescription map is generated based on the spatial distribution of biomass. Secondly, the variable-speed spraying system is constructed, which integrates a prescription map interpretation module and a PWM control module. Under the guidance of the variable spraying prescription map, the spraying rate is adjusted to achieve real-time variable spraying. Finally, a comparative experiment is designed, and the results show that the LiDAR-assisted UAV variable spraying system designed in this study performs better than the traditional constant-rate spraying system; while maintaining equivalent spraying effects, the usage of chemical agents is significantly reduced by 30.1%, providing a new technical path for reducing pesticide pollution and lowering grain production costs.

Keywords: precision agriculture; wheat pest control; UAV; LiDAR; variable-rate spraying; UAV remote sensing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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