Varroa destructor detection on honey bees using hyperspectral imagery
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151729" target="_blank" >RIV/00216305:26220/24:PU151729 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0168169924006100?pes=vor" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0168169924006100?pes=vor</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.compag.2024.109219" target="_blank" >10.1016/j.compag.2024.109219</a>
Alternative languages
Result language
angličtina
Original language name
Varroa destructor detection on honey bees using hyperspectral imagery
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN
0168-1699
e-ISSN
1872-7107
Volume of the periodical
224
Issue of the periodical within the volume
září 2024
Country of publishing house
GB - UNITED KINGDOM
Number of pages
11
Pages from-to
„“-„“
UT code for WoS article
001271974900001
EID of the result in the Scopus database
2-s2.0-85198275920