Toward Bee Motion Pattern Identification on Hive Landing Board

Abstract

The ability to predict bee behavior through visual analysis of their activity at the hive entrance provides valuable insight into the hive's condition. In this study, we present an algorithm for visual monitoring of bee behavior implemented based on open source tools. The convolutional neural network (CNN) is used to detect bees on the landing board of the hive. The YOLOv8m CNN detects bees with 0.97% mAP@0.5 and 0.65% mAP@0.5:0.95 mean average precision, respectively. Bee behavior types such as foraging, fanning, and wash boarding are presented in heat and path maps. The speed patterns are used to identify the type of motion of the bees.