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.
Guo et al. (2026) studied this question.