To address challenges in hybrid rice seed production—specifically labor dependence, low uniformity of pollen distribution, and low operational efficiency—which collectively drive up large-scale production costs, technological innovations are critical. However, despite the demonstrated potential of UAV-assisted pollination, the quantitative relationships between its operational parameters (altitude, speed, flight patterns) and pollen dispersal dynamics remain poorly understood, impeding standardization efforts. In this study, guided by agronomic pollination requirements, we developed an integrated analytical framework linking “pollen density-yield” dynamics to elucidate the governing mechanisms of flight parameters on pollination quality. A DJI T50 UAV was used to carry out the assisted pollination test on two varieties of hybrid rice, Changtian You 405 and Wanxiang You 377, to explore the effects of different flight speeds, altitudes, and trajectories of the UAV on pollination quality and to evaluate the cost-effectiveness ratio, taking the yield and its composition as the evaluation indexes. The experimental results showed that the UAV flight operation parameters had a significant effect on the pollination quality, and the best pollination quality was obtained when the flight altitude was 4 m and the speed was 3 m/s, achieving yields of 2.64 and 3.15 t/hm²; the average yields of the UAV-assisted pollination were 2.10 and 2.61 t/hm², and the filled grain percentages were 15.76% and 34.2%, respectively. These increased the yields by 21.4% and 11.06%, respectively, and the filled grain percentages by 8.69% and 3.95%, compared with artificial pollination. The results also showed that the cost-effectiveness ratio of UAV-assisted pollination was 28.11% lower than that of artificial operation. The results indicate that UAVs have great application prospects in hybrid rice pollination.

Water-sensitive paper (WSP) has been widely used to assess the quality of pesticide sprays. However, fog droplets tend to overlap on WSP. In order to accurately measure the droplet size and grasp the droplet distribution pattern, this study proposes a method based on the optimized XGBoost classification model combined with improved concave-point matching to achieve multi-level overlapping-droplet segmentation. For different types of overlapping droplets, the corresponding improved segmentation algorithm is used to improve the segmentation accuracy. For parallel overlapping droplets, the centre-of-mass segmentation method is used; for non-parallel overlapping droplets, the minimum-distance segmentation method is used; and for strong overlapping of a single concave point, the vertical-linkage segmentation method is used. Complex overlapping droplets were gradually segmented by loop iteration until a single droplet was obtained or no further segmentation was possible, and then ellipse fitting was used to obtain the final single-droplet profile. Up to 105 WSPs were obtained in an orchard field through drone spraying experiments, and were used to validate the effectiveness of the method. The experimental results show that the classification model proposed in this paper achieves an average accuracy of 98% in identifying overlapping-droplet types, which effectively meets the needs of subsequent segmentation. The overall segmentation accuracy of the method is 91.35%, which is significantly better than the contour-solidity and watershed-based algorithm (76.19%) and the improved-concave-point-segmentation algorithm (68.82%). In conclusion, the method proposed in this paper provides an efficient and accurate new approach for pesticide spraying quality assessment.