In an era of rapid climate change and its adverse effects on food production,
technological intervention to monitor pollinator conservation is of paramount
importance for environmental monitoring and conservation for global food
security. The survival of the human species depends on the conservation of
pollinators. This article explores the use of Computer Vision and Object
Recognition to autonomously track and report bee behaviour from images. A novel
dataset of 9664 images containing bees is extracted from video streams and
annotated with bounding boxes. With training, validation and testing sets
(6722, 1915, and 997 images, respectively), the results of the COCO-based YOLO
model fine-tuning approaches show that YOLOv5m is the most effective approach
in terms of recognition accuracy. However, YOLOv5s was shown to be the most
optimal for real-time bee detection with an average processing and inference
time of 5.1ms per video frame at the cost of slightly lower ability. The
trained model is then packaged within an explainable AI interface, which
converts detection events into timestamped reports and charts, with the aim of
facilitating use by non-technical users such as expert stakeholders from the
apiculture industry towards informing responsible consumption and production.

使用计算机视觉和目标识别技术自动追踪和报告蜜蜂行为，以实现保护传粉动物，推动环境监测和全球粮食安全，进而提供一个可解释的人工智能接口，方便非技术专家利用蜜蜂检测事件生成时间戳报告和图表，以促进环保产业的负责任消费和生产。