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A YOLOv8-Based Two-Stage Framework for Non-Destructive Detection of Varroa destructor Infestations in Apis mellifera Colonies

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2024, 29(10), pp.137-148
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 27, 2024
  • Accepted : October 18, 2024
  • Published : October 31, 2024

Yongsun Lee 1 Hyunsu Cho 2 Bo-Young Kim 2 Jihoon Moon 1

1순천향대학교
2아산중학교

Accredited

ABSTRACT

The European honeybee (Apis mellifera) is an important pollinator threatened by colony collapse disorder (CCD), primarily due to infestation by the Varroa mite (Varroa destructor). Traditional detection methods are invasive and time-consuming, often causing additional stress to colonies. We propose a two-stage framework using the You Only Look Once version 8 (YOLOv8) model for non-destructive and rapid detection of Varroa mite infestation. The framework uses comb light images from inside the hives. In the first stage, a YOLOv8-n model detects bees and extracts individual bee images. In the second stage, a YOLOv8-cls model classifies the infestation status of each bee. Our object detection model achieved a mAP@0.5 of 0.701, and the classification model achieved an average accuracy of 91%. These results demonstrate the effectiveness of the framework as a non-destructive method for Varroa mite detection. Based on this research, we expect to provide beekeepers with an efficient tool for early detection and management of Varroa mite infestations, potentially reducing the incidence of CCD and supporting the sustainability of apiculture.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.