Varroa destructor detection on honey bees using hyperspectral imagery

Simple item page
dc.contributor.authorDuma, Zina-Sabrinacs
dc.contributor.authorZemčík, Tomášcs
dc.contributor.authorBilík, Šimoncs
dc.contributor.authorSihvonen, Tuomascs
dc.contributor.authorHonec, Petercs
dc.contributor.authorReinikainen, Satu-Piacs
dc.contributor.authorHorák, Karelcs
dc.coverage.issue9cs
dc.coverage.volume224cs
dc.date.accessioned2025-03-28T06:44:13Z
dc.date.available2025-03-28T06:44:13Z
dc.date.issued2024-09-01cs
dc.description.abstractHyperspectral (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.en
dc.formattextcs
dc.format.extent1-11cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationCOMPUTERS AND ELECTRONICS IN AGRICULTURE. 2024, vol. 224, issue 9, p. 1-11.en
dc.identifier.doi10.1016/j.compag.2024.109219cs
dc.identifier.issn1872-7107cs
dc.identifier.orcid0000-0003-4363-4313cs
dc.identifier.orcid0000-0001-8797-7700cs
dc.identifier.orcid0000-0002-5800-6187cs
dc.identifier.orcid0000-0002-2280-3029cs
dc.identifier.other189100cs
dc.identifier.researcheridJEP-7714-2023cs
dc.identifier.scopus57222421244cs
dc.identifier.urihttps://hdl.handle.net/11012/250681
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofCOMPUTERS AND ELECTRONICS IN AGRICULTUREcs
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0168169924006100cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1872-7107/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectBeehive monitoringen
dc.subjectHyperspectral imagery (HSI)en
dc.subjectKernel partial least-squaresen
dc.subjectVarroa destructoren
dc.subjectWavelength selectionen
dc.titleVarroa destructor detection on honey bees using hyperspectral imageryen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-189100en
sync.item.dbtypeVAVen
sync.item.insts2025.03.28 07:44:13en
sync.item.modts2025.03.28 07:32:28en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav automatizace a měřicí technikycs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1s2.0S0168169924006100main.pdf
Size:
4.21 MB
Format:
Adobe Portable Document Format
Description:
file 1s2.0S0168169924006100main.pdf
Download
Collections
Ústav automatizace a měřicí techniky