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 [email protected] 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, F.; Liu, K.; Ma, J.; Qiu, B. Application of the Improved YOLOv8-DeepSORT Framework in Motion Tracking of Pepper Leaves Under Droplet Occlusion. Agronomy 2026, 16, 384. https://doi.org/10.3390/agronomy16030384
Source: MDPI
