Visual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniques

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dc.contributor.authorBilík, Šimoncs
dc.contributor.authorKratochvíla, Lukášcs
dc.contributor.authorLigocki, Adamcs
dc.contributor.authorBoštík, Ondřejcs
dc.contributor.authorZemčík, Tomášcs
dc.contributor.authorHýbl, Matoušcs
dc.contributor.authorHorák, Karelcs
dc.contributor.authorŽalud, Luděkcs
dc.coverage.issue8cs
dc.coverage.volume21cs
dc.date.issued2021-04-14cs
dc.description.abstractThe 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. In this paper 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 the highest F1 score up to 0.874 in the infected bee detection and up to 0.714 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 the Varroa destructor mite detection on a honey bee. We expect that performance of those object detectors will enable us to inspect the health status of the honey bee colonies in real time.en
dc.formattextcs
dc.format.extent2764-2780cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationSENSORS. 2021, vol. 21, issue 8, p. 2764-2780.en
dc.identifier.doi10.3390/s21082764cs
dc.identifier.issn1424-8220cs
dc.identifier.orcid0000-0001-8797-7700cs
dc.identifier.orcid0000-0001-8425-323Xcs
dc.identifier.orcid0000-0002-6813-4318cs
dc.identifier.orcid0000-0002-7856-2084cs
dc.identifier.orcid0000-0003-4363-4313cs
dc.identifier.orcid0000-0003-1436-000Xcs
dc.identifier.orcid0000-0002-2280-3029cs
dc.identifier.orcid0000-0003-2993-7772cs
dc.identifier.other171160cs
dc.identifier.researcheridJEP-7714-2023cs
dc.identifier.researcheridA-7047-2012cs
dc.identifier.scopus57222421244cs
dc.identifier.scopus8439091900cs
dc.identifier.urihttp://hdl.handle.net/11012/200873
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofSENSORScs
dc.relation.urihttps://www.mdpi.com/1424-8220/21/8/2764cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1424-8220/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectVarroa destructoren
dc.subjectApis melliferaen
dc.subjectwestern honey beeen
dc.subjectbee health monitoringen
dc.subjectobject detectionen
dc.subjectYOLOen
dc.subjectSSDen
dc.subjectdeep learningen
dc.titleVisual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniquesen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-171160en
sync.item.dbtypeVAVen
sync.item.insts2025.02.03 15:39:28en
sync.item.modts2025.01.17 15:30:17en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav automatizace a měřicí technikycs
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