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Utilizing computer vision and the latest technological advancements, in thisstudy, we developed a honey bee monitoring system that aims to enhance ourunderstanding of Colony Collapse Disorder, honey bee behavior, populationdecline, and overall hive health. The system is positioned at the hive entranceproviding real-time data, enabling beekeepers to closely monitor the hive'sactivity and health through an account-based website. Using machine learning,our monitoring system can accurately track honey bees, monitor pollen-gatheringactivity, and detect Varroa mites, all without causing any disruption to thehoney bees. Moreover, we have ensured that the development of this monitoringsystem utilizes cost-effective technology, making it accessible to apiaries ofvarious scales, including hobbyists, commercial beekeeping businesses, andresearchers. The inference models used to detect honey bees, pollen, and mitesare based on the YOLOv7-tiny architecture trained with our own data. TheF1-score for honey bee model recognition is 0.95 and the precision and recallvalue is 0.981. For our pollen and mite object detection model F1-score is 0.95and the precision and recall value is 0.821 for pollen and 0.996 for "mite".The overall performance of our IntelliBeeHive system demonstrates itseffectiveness in monitoring the honey bee's activity, achieving an accuracy of96.28 % in tracking and our pollen model achieved a F1-score of 0.831.

IntelliBeeHive: An Automated Honey Bee, Pollen, and Varroa Destructor Monitoring System