Real-time monitoring of seedling emergence is vital for vegetable crop management and yield estimation. Traditionally, crop seedling emergence monitoring relies on low-efficient and time-consuming manual counting. To address this issue, this research proposed an efficient, fast, and real-time cabbage seedling counting method (combining the improved YOLOv8n, tracking algorithm, and image processing) to accurately track cabbage seedlings in the field and implement counting with an unmanned aerial vehicle (UAV). The improved YOLOv8n replaced the C2f Block in the YOLO backbone with a Swin-conv block and incorporated ParNet attention modules in both the backbone and neck parts. This enhancement enables the YOLOv8n to surpass the base model's performance, achieving a mAP50–95 of 90.3 %, representing a 14.5 % improvement. The experiments demonstrated the superior capabilities of the counting method in terms of speed and accuracy. In field experiments, the proposed Tracking algorithms-Swin-conv blocks-ParNet attention-YOLOv8n (TSP-yolo) counting method demonstrated consistent and reliable accuracy in counting cabbage seedlings while demanding only one-seventh of the time needed compared to the manual counting method. In summary, based on TSP-yolo and implemented through an UAV, the developed seedling emergence counting method demonstrated an excellent capability of counting cabbage seedlings, resulting in significant savings in human resources for crop management.

中文翻译：

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基于TSP-yolo的深度学习甘蓝出苗监测方法

实时监测出苗情况对于蔬菜作物管理和产量估算至关重要。传统上，农作物出苗监测依靠人工计数，效率低且耗时。针对这一问题，本研究提出了一种高效、快速、实时的甘蓝苗计数方法（结合改进的YOLOv8n、跟踪算法和图像处理），以准确跟踪田间甘蓝苗，并利用无人机实现计数。 （无人机）。改进后的 YOLOv8n 用 Swin-conv 块替换了 YOLO 主干中的 C2f 块，并在主干和颈部部分合并了 ParNet 注意力模块。这一增强使 YOLOv8n 超越了基础模型的性能，实现了 90.3% 的 mAP50–95，提高了 14.5%。实验证明了该计数方法在速度和准确性方面的优越性能。在田间实验中，所提出的跟踪算法-Swin-conv块-ParNet注意力-YOLOv8n（TSP-yolo）计数方法在计算甘蓝幼苗方面表现出一致且可靠的精度，而与手动计数方法相比，所需时间仅为七分之一。综上所述，基于TSP-yolo并通过无人机实施，所开发的出苗计数方法表现出优异的甘蓝出苗计数能力，从而大大节省了作物管理的人力资源。