Autonomous sprayer UAVs are one of the most used aerial machines in modern agriculture. During flight missions, some common narrow obstacles appear in the flying zone. These are non-detectable from satellite images and one of the biggest challenges for autonomous sprayer UAVs in farmland. This work introduces an obstacle avoidance architecture specifically for sprayer UAVs. This architecture has generality in the spraying UAV problem, and it reduces the reliance on the global mapping of farmland. This approach computes the avoiding path based on the onboard sensor fusion system in real-time. Moreover, it autonomously determines the transition of several maneuver states using the current spraying liquid data and the UAV dynamics data obtained by offline system identification. This approach accurately tracks the avoidance path for the nonlinear time-variant spraying UAV systems. To verify the performance of the approach, we performed multiple simulations with different spraying missions, and the method demonstrated a high spraying coverage of more than 98% while successfully avoiding all vertical obstacles. We also demonstrated the adaptability of our control architecture; the safe distance between the UAV and obstacles can be changed by specifying the value of a high-level parameter on the controller. The proposed method adds value to precision agriculture, reduces mission time, and maximizes the spraying area coverage.

Research shows that the accurate acquisition of flight parameters of the plant protection UAV and accurate evaluation of flight parameter quality have great significance for improving the effect and precision of spraying. In order to further improve the accuracy of the flight parameter quality evaluation of the plant protection UAV, this study conducted an evaluation and experiment of the flight parameter quality of the plant protection UAV using a laser tracker. The experimental results showed that the current plant protection UAV used the average altitude and speed of the onboard sensors to determine whether it reached the preset flight operation parameters, but this interpretation method could not accurately reflect the actual flight situation. Laser trackers could obtain more accurate flight parameters, especially instantaneous flight parameters. Compared with the laser tracker, the flight trajectory, altitude, and speed of the UAV reflected by onboard sensors were erroneous and tended to be smooth and stable. This method can obtain more accurate flight parameters, improve the accuracy of the flight parameter quality evaluation of the plant protection UAV, and provide data support and a reference for the precision spraying and performance improvement of the plant protection UAV.

To enhance obstacle avoidance abilities of the plant protection UAV in unstructured farmland, this article improved the traditional A* algorithms through dynamic heuristic functions, search point optimization, and inflection point optimization based on millimeter wave radar and monocular camera data fusion. Obstacle information extraction experiments were carried out. The performance between the improved algorithm and traditional algorithm was compared. Additionally, obstacle avoidance experiments were also carried out. The results show that the maximum error in distance measurement of data fusion method was 8.2%. Additionally, the maximum error in obstacle width and height measurement were 27.3% and 18.5%, respectively. The improved algorithm is more useful in path planning, significantly reduces data processing time, search grid, and turning points. The algorithm at most increases path length by 2.0%, at least reduces data processing time by 68.4%, search grid by 74.9%, and turning points by 20.7%. The maximum trajectory offset error was proportional to the flight speed, with a maximum trajectory offset of 1.4 m. The distance between the UAV and obstacle was inversely proportional to flight speed, with a minimum distance of 1.6 m. This method can provide a new idea for obstacle avoidance of the plant protection UAV.

Electrostatic spraying is considered an effective means to improve the efficacy of pesticide application and reduce pesticide consumption. However, the effectiveness of electrostatic spraying needs further validation in greenhouse environments, especially in long-range air-assisted spraying scenarios. A waist-shaped charging device has been improved to obtain a maximum charge-to-mass ratio of 4.4 mC/kg at an applied voltage of 6 kV in a laboratory setting, representing an increase of approximately 84.9% compared to a commercial circular charging electrode with a fan-shaped nozzle. A comparative air-assisted spray test between electrostatic deactivation (EDAS) and electrostatic activation (EAAS) was conducted on greenhouse tomato crops using a single hanging track autonomous sprayer equipped with a pair of waist-shaped charging devices. The results showed that EAAS yielded an overall average coverage of 28.4%, representing a significant 10.9% improvement over the 25.6% coverage achieved with EDAS. The overall coefficient of variation (CV) for EDAS and EAAS was 62.0% and 48.0%, respectively. Within these, the CV for the average coverage of the sample set reflecting axial distribution uniformity was 33.4% and 31.4%, respectively. Conversely, the CV for the average coverage of the sample group reflecting radial distribution uniformity was 33.7% and 17.9%, respectively. The results indicate that the waist-shaped charging device possesses remarkable charging capabilities, presenting favorable application prospects for long-range air-assisted spraying in greenhouses. The electrostatic application has a positive effect on enhancing the average coverage and improving the overall distribution uniformity. Notably, it significantly improves the radial distribution uniformity of the air-assisted spray at long range, albeit with limited improvement in the axial distribution uniformity.