Visual recognition of honeybee behavior patterns at the hive entrance.

This study presents a novel method for automatically recognizing honeybee behavior patterns at the hive entrance, significantly contributing to beekeeping and hive management. Utilizing advanced YOLOv8 models for detection and segmentation, our approach analyzes various aspects of bee behavior, including location, direction, path trajectory, and movement speed within a designated area on the hive's landing board. The system effectively detects multiple bee activities such as foraging, fanning, washboarding, and defense, achieving a mean detection accuracy of 98% and operating at speeds of up to 36 fps, surpassing state-of-the-art methods in both speed and accuracy. Key contributions include the development of a comprehensive dataset with 7200 frames from eight beehives, the introduction of the first known research focused on recognizing bee behavior patterns through visual analysis at the hive entrance, and a comparative evaluation of various object detection and tracking algorithms tailored for bee detection and behavior recognition. Our findings indicate that this method enhances monitoring capabilities for beekeepers while reducing the need for manual inspections, thereby minimizing disturbances to the bees. By analyzing spatial trajectories and occurrence density maps, the proposed framework provides robust identification of overlapping behaviors, facilitating timely interventions when necessary. This work lays the groundwork for future automated monitoring systems aimed at improving hive health and productivity.