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

arXiv: https://arxiv.org/abs/2103.03133 · HTML: https://mlsanity.com/paper/2103.03133/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Learning Data Augmentation Strategies for Object Detection](https://mlsanity.com/paper/1906.11172/) (2019)

## Built on by (most cited first, 4 of 4)

- [Machine Learning and Computer Vision Techniques in Continuous Beehive Monitoring Applications: A survey](https://mlsanity.com/paper/2208.00085/) (2022)
- [Varroa destructor detection on honey bees using hyperspectral imagery](https://mlsanity.com/paper/2403.14359/) (2024)
- [Towards Phytoplankton Parasite Detection Using Autoencoders](https://mlsanity.com/paper/2303.08744/) (2023)
- [Structure Analysis of the FRP Rebar Using Computer Vision Techniques](https://mlsanity.com/paper/2304.14358/) (2023)

## Abstract (arXiv)

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.