Utilizing computer vision and the latest technological advancements, in this
study, we developed a honey bee monitoring system that aims to enhance our
understanding of Colony Collapse Disorder, honey bee behavior, population
decline, and overall hive health. The system is positioned at the hive entrance
providing real-time data, enabling beekeepers to closely monitor the hive's
activity and health through an account-based website. Using machine learning,
our monitoring system can accurately track honey bees, monitor pollen-gathering
activity, and detect Varroa mites, all without causing any disruption to the
honey bees. Moreover, we have ensured that the development of this monitoring
system utilizes cost-effective technology, making it accessible to apiaries of
various scales, including hobbyists, commercial beekeeping businesses, and
researchers. The inference models used to detect honey bees, pollen, and mites
are based on the YOLOv7-tiny architecture trained with our own data. The
F1-score for honey bee model recognition is 0.95 and the precision and recall
value is 0.981. For our pollen and mite object detection model F1-score is 0.95
and the precision and recall value is 0.821 for pollen and 0.996 for "mite".
The overall performance of our IntelliBeeHive system demonstrates its
effectiveness in monitoring the honey bee's activity, achieving an accuracy of
96.28 % in tracking and our pollen model achieved a F1-score of 0.831.

利用计算机视觉和最新技术，本研究开发了一种蜜蜂监测系统，旨在增进我们对群体崩溃障碍、蜜蜂行为、种群下降和整体蜂巢健康的理解。通过我们的监测系统，蜂农可以实时监测蜂巢的活动和健康，无需干扰蜜蜂。该系统利用性价比高的技术进行开发，使得不同规模的养蜂场包括业余爱好者、商业养蜂企业和研究人员都能够使用。