Research library

Honeybee In-Out Monitoring System by Object Recognition and Tracking from Real-Time Webcams

A new honeybee in-out monitoring system is proposed using real-time deep-learning based image recognition and tracking. The specific design of beehive gate is turned out to be an important factor for accurate bee movement monitoring. We check a series of beehive gate designs for the monitoring system. A novel gate design employing heart valve structure is pro­ posed for ensuring one-way traffic for the bees as well as one-at-a-time gate passing, resulting in an improved bee detection accuracy. As for the deep-learning based image recognition framework, YOLOv4 is used in the proposed system for a better honeybee-detection accuracy as well as a faster detection in comparison to YOLOv3 which was employed for our previous.

Download PDF

Publication details

Authors
NuriMedia Contents Team
Organizations
🇰🇷 Incheon National University
Year
2021
Type
Journal

Relevancy to Gratheon

This paper is relevant to Gratheon because it informs entrance and behavior analytics in the Gratheon web app, camera-based hive-scanner and computer-vision models. Its methods and findings can be translated into product requirements for reliable field deployments: what should be sensed, how signals should be interpreted, and which uncertainty or validation limits need to be surfaced to beekeepers. For Gratheon, the work is most useful as an evidence-backed design reference for connecting local hive observations with actionable recommendations in the web app while keeping hardware practical for remote apiaries.