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.

Droplet retention during pesticide application is a serious problem because run-off droplets flow out of the target area and pose a hazard to human health and the environment. The present study was conducted with the aim to measure the droplet retention of sprayed droplets on crop leaves in vivo using a constructed test system. In the measurement, three crop species with different surface properties (tomato, chili pepper, and winter wheat) were selected for droplet retention determination, and the variations in the time intervals of maximum retention and stable retention were determined. Contact angle and surface roughness ($R_a$), which are the most important surface properties of crop leaves, were used as independent variables. The $R_a$ values of tomato, pepper, and winter wheat were 24.73 $\mathsf{\mu }\mathrm{m}$, 5.28 $\mathsf{\mu }\mathrm{m}$, and 17.59 $\mathsf{\mu }\mathrm{m},$ respectively, while the contact angles of tomato, pepper, and winter wheat were 97.67°, 70.07° and 131.98°, respectively. The results showed that the curves of droplet retention on sprayed tomato and wheat leaves had similar patterns over time and could be divided into four periods (rapidly increasing period, slowly increasing period, collapsing period, and stable period). The maximum droplet retention on tomato leaf surface was $R_{max}$ = 0.169 $\mathrm{g}\cdot {\mathrm{cm}}^{-2}$, and the stable retention was $R_{st}$ = 0.134 $\mathrm{g}\cdot {\mathrm{cm}}^{-2}$. The maximum droplet retention on the surface of winter wheat leaf was $R_{max}$ = 0.244 $\mathrm{g}\cdot {\mathrm{cm}}^{-2}$, and the stable retention was $R_{st}$ = 0.093 $\mathrm{g}\cdot {\mathrm{cm}}^{-2}$. However, droplet retention on pepper leaves was different from that on tomato and wheat leaves. The curve pattern of droplet retention on pepper leaves over time showed two peaks and two valleys. Moreover, the maximum retention, $R_{max}$, was in the range of 0.149~0.151 $\mathrm{g}\cdot {\mathrm{cm}}^{-2}$, and the stable retention was $R_{st}$ = 0.077 $\mathrm{g}\cdot {\mathrm{cm}}^{-2}$. It is expected that the obtained results can be used to characterize the properties of crop leaves and that this study can contribute to the improvement of droplet retention for effective chemical application and the reduction in the environmental pollution caused by agricultural pesticides.

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.

In order to investigate the effects of various factors (charging voltage, spray distance and spray pattern) on the deposition coverage of tomato leaves, the Box–Behnken surface response methodology was used to design an outdoor air-assisted electrostatic spraying experiment with three factors and three levels. The deposition coverage of tomato leaves in the upper, middle and lower layers was collected under different polarity charging voltages (0, +10 kV, −10 kV), spray distances (1, 3, 5 m) and spray patterns (ascending spray, descending spray, fixed height spray). Regression analysis and variance analysis were performed on the experimental data to determine the optimal working parameters. The results showed that (1) spray distance is the most important factor affecting the droplet coverage rate in the process of air-assisted electrostatic spraying; (2) the droplet coverage rate of air-assisted electrostatic spraying is optimal when the charging voltage polarity is negative voltage, the spray distance is 2.75 m, and the spray pattern is descending spray. The following conclusions were obtained. (1) In air-assisted electrostatic spraying, the distribution of air flow had the greatest effect on droplet deposition on tomato leaf surface. (2) Compared with air-assisted non-electrostatic spray, air-assisted electrostatic spray had a better deposition effect.

To address the insufficient accuracy of traditional single-sensor navigation methods in dense planting environments of pomegranate orchards, this paper proposes a vision and LiDAR fusion-based navigation line extraction method for orchard environments. The proposed method integrates a YOLOv8-ResCBAM trunk detection model, a reverse ray projection fusion algorithm, and geometric constraint-based navigation line fitting techniques. The object detection model enables high-precision real-time detection of pomegranate tree trunks. A reverse ray projection algorithm is proposed to convert pixel coordinates from visual detection into three-dimensional rays and compute their intersections with LiDAR scanning planes, achieving effective association between visual and LiDAR data. Finally, geometric constraints are introduced to improve the RANSAC algorithm for navigation line fitting, combined with Kalman filtering techniques to reduce navigation line fluctuations. Field experiments demonstrate that the proposed fusion-based navigation method improves navigation accuracy over single-sensor methods and semantic-segmentation methods, reducing the average lateral error to 5.2 cm, yielding an average lateral error RMS of 6.6 cm, and achieving a navigation success rate of 95.4%. These results validate the effectiveness of the vision and 2D LiDAR fusion-based approach in complex orchard environments and provide a viable route toward autonomous navigation for orchard robots.