# Varroa destructor detection on honey bees using hyperspectral imagery

arXiv: https://arxiv.org/abs/2403.14359 · HTML: https://mlsanity.com/paper/2403.14359/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [Kernel Flows: from learning kernels from data into the abyss](https://mlsanity.com/paper/1808.04475/) (2018)
- [Visual diagnosis of the Varroa destructor parasitic mite in honeybees using object detector techniques](https://mlsanity.com/paper/2103.03133/) (2021)
- [Machine Learning and Computer Vision Techniques in Continuous Beehive Monitoring Applications: A survey](https://mlsanity.com/paper/2208.00085/) (2022)
- [KF-PLS: Optimizing Kernel Partial Least-Squares (K-PLS) with Kernel Flows](https://mlsanity.com/paper/2312.06547/) (2023)
- [Raspberry Pi Bee Health Monitoring Device](https://mlsanity.com/paper/2304.14444/) (2023)

## Built on by (most cited first, 2 of 2)

- [Towards Varroa destructor mite detection using a narrow spectra illumination](https://mlsanity.com/paper/2504.06099/) (2025)
- [A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management](https://mlsanity.com/paper/2509.08822/) (2025)

## Abstract (arXiv)

Hyperspectral (HS) imagery in agriculture is becoming increasingly common. These images have the advantage of higher spectral resolution. Advanced spectral processing techniques are required to unlock the information potential in these HS images. The present paper introduces a method rooted in multivariate statistics designed to detect parasitic Varroa destructor mites on the body of western honey bee Apis mellifera, enabling easier and continuous monitoring of the bee hives. The methodology explores unsupervised (K-means++) and recently developed supervised (Kernel Flows - Partial Least-Squares, KF-PLS) methods for parasitic identification. Additionally, in light of the emergence of custom-band multispectral cameras, the present research outlines a strategy for identifying the specific wavelengths necessary for effective bee-mite separation, suitable for implementation in a custom-band camera. Illustrated with a real-case dataset, our findings demonstrate that as few as four spectral bands are sufficient for accurate parasite identification.