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.

Small unmanned aerial vehicles are now commonly equipped with integrated navigation systems to obtain high-precision navigation parameters. However, affected by the dual impacts of multipath effects and dynamic environmental changes, their state estimation process is vulnerable to interference from measurement outliers, which in turn leads to the degradation of navigation accuracy and poses a threat to flight safety. To address this issue, this research presents an adaptive minimum error entropy cubature Kalman filter. Firstly, the cubature Kalman filter is introduced to solve the problem of model nonlinear errors; secondly, the cubature Kalman filter based on minimum error entropy is derived to effectively curb the interference that measurement outliers impose on filtering results; finally, a kernel bandwidth adjustment factor is designed, and the kernel bandwidth is estimated adaptively to further improve navigation accuracy. Through numerical simulation experiments, the robustness of the proposed method with respect to measurement outliers is validated; further flight experiment results show that compared with existing related filters, this proposed filter can achieve more accurate navigation and positioning.