Toward bee behavioral pattern recognition on hive entrance using YOLOv8

Abstract

Prediction of bee behavior by visual analysis of bee activity at the entrance provides valuable information on the condition of the hive. In this work, convolutional neural networks (CNN) are used to detect bees on the landing board of the hive. Types of bee behavior, such as foraging, guarding, and fanning, are presented in heat and path maps. YOLOv8m detects bees with a mean precision of 0.97% mAP@0.5 and 0.65% mAP@0.5:0.95, respectively. The intensive foraging, swarming, or guarding are behaviors to be most confused and therefore require inherent dataset collection and more detailed investigation.