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.

Target and off-target spray depositions determine the spray’s effectiveness and impact on the environment. A decisive stage in the measurement of spray deposition and drift is selecting an appropriate sampling approach under field conditions. There are various approaches available for sampling spray deposition and drift, during the evaluation of ground sprayers used for the UAV sprayer assessment, under field conditions. In this study, two sampling approaches (water-sensitive paper, and glass strip collectors) were compared to analyze spray deposition in target and off-target zones. The results showed a variation in the estimation of the spray deposits among the two applied sampling methods. The results showed that the water-sensitive paper recorded the droplet deposition in the target zone with a range from 0.049 to 4.866 µLcm⁻², whereas the glass strip recorded from 0.11 to 0.793 µLcm⁻². The results also showed the water sensitive paper recorded an 80.3% higher deposition than that of the glass strip at zero position during the driving flight height 2 m and flight speed 2 ms⁻¹ (T1 treatment). It can be concluded that variation in recorded depositing is due to the sampling material. It is recommended that the confident deposition results, measurement methods and sampling approaches must be standardized for UAV sprayers according to the field conditions and controlled within artificial assessments.

Agricultural electrostatic spraying can help to reduce the threat of pesticides to human health and the environment. However, the influence of the law of leaf water content on electrostatic spraying has not been studied. In this study, we used leaf water content as an evaluation index of electrostatic spraying technology and verified the correlation between leaf water content and leaf capacitance value by statistical methods in order to achieve in vivo measurements of leaf water content in relation to tomato, pepper, and wheat crop leaves. Using these in vivo measurements of leaf water content and retention, we demonstrate that the retention of electrostatic droplets on the leaves of all three crops increases with increasing water content; the retention per unit area of leaves increased by 6.1 $\mathrm{mg}/{\mathrm{cm}}^2$, an increase of 7.29%. Increasing the electrostatic spray voltage (10~30 kV) enhances the retention of droplets on the leaves of the crops, with a maximum increase of 6.1. The retention of non-electrostatic droplets decreases with increasing water content; retention at the lowest water content was 1.103~1.131 times greater than at the highest water content. This study has implications for research related to improving the retention of electrostatic droplets in leaves.

In agricultural plant protection spraying, dynamic occlusion by droplet swarms on leaf surfaces poses a major challenge to accurately acquiring leaf motion parameters, limiting the optimization of precision spraying and pesticide utilization. Traditional contact-based methods interfere with natural leaf dynamics, while non-contact optical approaches suffer from tracking failures under occlusion. This study proposes an improved framework combining YOLOv8 integrated with a Spatial Attention Module (SAM) and optimized DeepSORT for robust non-contact tracking of marked points on pepper leaves. High-speed binocular cameras were used to collect leaf motion data under controlled droplet occlusion conditions. Results demonstrate that, under 5% occlusion, the improved model achieves a 19.6% increase in detection mAP@0.5 and significantly enhances tracking MOTA, with trajectory breakage rate reduced to 3.2% and ID switches decreased by approximately 71.4% in long-sequence tracking. Quantitative analysis of leaf midrib motion reveals a clear spatial gradient: average speed increases from 0.012 m s⁻¹ at the base to 0.153 m s⁻¹ at the tip, with intensified fluctuations toward the tip and a consistent dominant vibration frequency of 0.403 Hz across all points. This method provides an efficient, reliable non-contact solution for measuring leaf motion parameters in complex spraying scenarios, offering valuable data support for targeted spray parameter optimization and improved deposition efficiency in precision agriculture.