Existing image-based pest counting methods rely on single static images and often produce inaccurate results under occlusion. To address this issue, this paper proposes an automated pest counting method in water traps through active robotic stirring. First, an automated robotic arm-based stirring system is developed to redistribute pests and reveal occluded individuals for counting. Then, the effects of different stirring patterns on pest counting performance are investigated. Six stirring patterns are designed and evaluated across different pest density scenarios to identify the optimal one. Finally, a heuristic counting confidence-driven closed-loop control system is proposed for adaptive-speed robotic stirring, adjusting the stirring speed based on the average change rate of counting confidence between consecutive frames. Experimental results show that the four circles is the optimal stirring pattern, achieving the lowest overall mean absolute counting error of 4.384 and the highest overall mean counting confidence of 0.721. Compared with constant-speed stirring, adaptive-speed stirring reduces task execution time by up to 44.7% and achieves more stable performance across different pest density scenarios. Moreover, the proposed pest counting method reduces the mean absolute counting error by up to 3.428 compared to the single static image counting method under high-density scenarios where occlusion is severe.

翻译：现有的基于图像的害虫计数方法依赖单张静态图像，在遮挡情况下常产生不准确结果。为解决此问题，本文提出一种通过主动机器人搅拌实现水诱捕器害虫自动计数的方法。首先，开发了一套基于机械臂的自动搅拌系统，通过重新分布害虫以显露被遮挡个体进行计数。随后，研究了不同搅拌模式对害虫计数性能的影响。针对不同害虫密度场景，设计并评估了六种搅拌模式以确定最优方案。最后，提出一种启发式计数置信度驱动的闭环控制系统，用于自适应速度的机器人搅拌，该系统根据连续帧间计数置信度的平均变化率调整搅拌速度。实验结果表明，"四圆环"为最优搅拌模式，其整体平均绝对计数误差最低（4.384），整体平均计数置信度最高（0.721）。与恒速搅拌相比，自适应速度搅拌将任务执行时间最多减少44.7%，并在不同害虫密度场景下表现出更稳定的性能。此外，在遮挡严重的高密度场景下，与单张静态图像计数方法相比，所提出的害虫计数方法将平均绝对计数误差最多降低3.428。