Visual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniques
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU140620" target="_blank" >RIV/00216305:26220/21:PU140620 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/1424-8220/21/8/2764" target="_blank" >https://www.mdpi.com/1424-8220/21/8/2764</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/s21082764" target="_blank" >10.3390/s21082764</a>
Alternative languages
Result language
angličtina
Original language name
Visual Diagnosis of the Varroa Destructor Parasitic Mite in Honeybees Using Object Detector Techniques
Original language description
The 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.
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
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
SENSORS
ISSN
1424-8220
e-ISSN
1424-3210
Volume of the periodical
21
Issue of the periodical within the volume
8
Country of publishing house
CH - SWITZERLAND
Number of pages
16
Pages from-to
2764-2780
UT code for WoS article
000644789900001
EID of the result in the Scopus database
2-s2.0-85104024610