Varroa destructor detection on honey bees using hyperspectral imagery
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Date
2024-09-01
Authors
Duma, Zina-Sabrina
Zemčík, Tomáš
Bilík, Šimon
Sihvonen, Tuomas
Honec, Peter
Reinikainen, Satu-Pia
Horák, Karel
0000-0003-4363-4313Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
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Abstract
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 present paper is the first to utilize hyperspectral imagery for the task, previous studies existing only for multispectral imagery. 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.
Description
Citation
COMPUTERS AND ELECTRONICS IN AGRICULTURE. 2024, vol. 224, issue 9, p. 1-11.
https://www.sciencedirect.com/science/article/pii/S0168169924006100
https://www.sciencedirect.com/science/article/pii/S0168169924006100
Document type
Peer-reviewed
Document version
Published version
Date of access to the full text
Language of document
en