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.

The tomato picking lifting platform is the carrier for the installation of the picking manipulator, which directly affects the operating range, speed, and picking effect of the picking manipulator. Based on the six degree of freedom motion platform, this paper designs a tomato lifting platform with automatic lifting and automatic leveling functions, which can accurately dock at the designated height to complete the picking operation, and verifies the validity of its parameters and the rationality of the operation movement. First, we analyzed the tomato planting mode and growth characteristics of greenhouse; determined the predetermined trajectory, mechanism travel, and motion form of the lifting platform to complete the lifting process during tomato picking; and determined the basic design parameters of the platform; Secondly, we used SolidWorks to build the three-dimensional model of the lifting platform, and imported it into ADAMS. We used the driving function to complete the dynamic simulation of the virtual prototype of the lifting platform, and obtained the force curve of the electric cylinder and hinge. We analyzed the force on the electric cylinder and hinge of the lifting platform to determine the rationality of the device design. Finally, according to the design requirements and simulation data, we made the prototype of the lifting platform, constructed the motion control system, and carried out relevant experiments. The experimental results show that the maximum rotation angle around the x, y, and z axis is ±10°, the maximum lifting distance is 15 cm, and the maximum load is 50 kg; meanwhile, the average time for the system to reach steady state is 0.309 s, the in-tilt error increases with the increase in biaxial tilt angle, with the maximum error of 1.09°, and the maximum mean square root error of 0.119°, which can meet the automatic operation requirements of the tomato picking manipulator.

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.

Changes in the flight attitude of plant protection unmanned aerial vehicles (UAVs) can lead to oscillations in the liquid level of their medicine tanks, which may affect operational accuracy and stability, and could even pose a threat to flight safety. To address this issue, this article presents the design of a flight attitude simulator for crop protection UAVs, constructed on a six-degree-of-freedom motion platform. This simulator can replicate the various flight attitudes, such as emergency stops, turns, and point rotations, of plant protection UAVs. This article initially outlines the determination and design process for the structural parameters and 3D model of the flight attitude simulator specific to plant protection UAVs. Subsequently, simulations were performed to analyze liquid sloshing in the pesticide tank under various liquid flushing ratios during flight conditions, including emergency stops, climbs, and circling maneuvers. Finally, the influence of liquid sloshing on the flight stability of the plant protection UAVs in different attitudes and with varying liquid flushing ratios is presented. These results serve as a cornerstone for optimizing the flight parameters of plant protection UAVs, analyzing the characteristics of pesticide application, and designing effective pesticide containers.

Based on the physical meaning of wave equation and Singularity Expansion Method, a numerical solution method is designed to extract the pole distributions of infinite cylinder, sphere and ellipsoid. By analysing the wave equation and boundary conditions of the target scattering characteristics, the extractions of poles of target scattering field are transformed into the solution of Hankel function and its derivative roots. Then, the T-matrix method is improved to make the numerical solution process of roots more accurate and faster. The numerical results are compared with strict analytical solutions and experimental results to fully verify the correctness of the method mentioned in this paper this study provides a basis for understanding the scattering mechanism of surface waves, and also provides a method and idea for solving problems such as acoustic scattering and electromagnetic scattering in engineering practice.