Visual diagnosis of the Varroa destructor parasitic mite in honeybees using object detector techniques

TL;DR

This paper introduces an object detection approach using YOLO and SSD to identify Varroa destructor mites and infected bees in images, aiming for real-time honey bee colony health monitoring.

Contribution

First application of object detectors for Varroa mite detection in honeybees, demonstrating high accuracy and potential for online colony health assessment.

Findings

F1 score up to 0.874 for infected bee detection

F1 score up to 0.727 for Varroa mite detection

Potential for real-time honey bee health monitoring

Abstract

The Varroa destructor mite is one of the most dangerous Honey Bee (Apis mellifera) parasites worldwide and the bee colonies have to be regularly monitored in order to control its spread. Here we present an object detector based method for health state monitoring of bee colonies. This method has the potential for online measurement and processing. In our experiment, we compare the YOLO and SSD object detectors along with the Deep SVDD anomaly detector. Based on the custom dataset with 600 ground-truth images of healthy and infected bees in various scenes, the detectors reached a high F1 score up to 0.874 in the infected bee detection and up to 0.727 in the detection of the Varroa Destructor mite itself. The results demonstrate the potential of this approach, which will be later used in the real-time computer vision based honey bee inspection system. To the best of our knowledge, this study is the first one using object detectors for this purpose. We expect that performance of those object detectors will enable us to inspect the health status of the honey bee colonies.