Real-time characterization of transient seed-soil contact dynamics remains a major challenge in high-speed precision seeding. This study proposes a dynamic monitoring and analysis framework that integrates a YOLO-based object detection algorithm with high-speed imaging to quantify seed motion during release and soil penetration. A YOLOv11 model is employed for real time detection and tracking of individual seeds in from high-speed video sequences, with seed velocity derived from inter-frame displacement and time intervals. A three-factor orthogonal experimental design is implemented to investigate the coupled effects of soil moisture content, operating speed of the seed metering device and seed orientation on seed motion characteristics including velocity, kinetic energy, displacement. The results demonstrate that the integrated YOLO and high-speed vision system enables high-precision seed detection and dynamic tracking. Strong three-factor coupling effects are identified, indicating that seed penetration dynamics are governed by non-additive interactions. Representative parameter-dependent patterns were identified: under 15% soil moisture and an operating speed of 1.67 m/s, lateral seed orientation showed higher velocity and kinetic energy, whereas under 25% soil moisture and 3.33 m/s, vertical orientation exhibited better post-impact motion stability. These findings indicate that coordinated adjustment of seed orientation and operating speed under different soil moisture conditions can effectively suppress post-impact rebound and slippage, thereby reducing seeding-point displacement and improving seed spacing uniformity. The proposed framework provides a technical foundation for real-time parameter regulation in intelligent precision seeding systems, enabling higher operational speeds without compromising placement accuracy.
Wang et al. (Wed,) studied this question.