Monitoring of Varroa Infestation rate in Beehives: A Simple AI Approach

0563135 - ÚTIA 2023 RIV US eng C - Conference Paper (international conference)
Picek, L. - Novozámský, Adam - Čapková Frydrychová, Radmila - Zitová, Barbara - Mach, P.
Monitoring of Varroa Infestation rate in Beehives: A Simple AI Approach.
IEEE International Conference on Image Processing 2022 : Proceedings. Piscataway: IEEE, 2022, s. 3341-3345. ISBN 978-1-6654-9620-9. ISSN 2381-8549.
[IEEE International Conference on Image Processing 2022 /29./. Bordeaux (FR), 16.10.2022-19.10.2022]
Grant - others:AV ČR(CZ) StrategieAV21/1
Program: StrategieAV
Institutional support: RVO:67985556 ; RVO:60077344
Keywords : Machine learning algorithms * Costs * Image processing * Machine learning * Frequency measurement * Complexity theory
OECD category: Computer hardware and architecture; Zoology (BC-A)
http://library.utia.cas.cz/separaty/2022/ZOI/novozamsky-0563135.pdf

This paper addresses the monitoring of Varroa destructor infestation in Western honey bee colonies. We propose a simple approach using automatic image-based analysis of the fallout on beehive bottom boards. In contrast to the existing high-tech methods, our solution does not require extensive and expensive hardware components, just a standard smart-phone. The described method has the potential to replace the time-consuming, inaccurate, and most common practice where the infestation level is evaluated manually. The underlining machine learning method combines a thresholding algorithm with a shallow CNN—VarroaNet. It provides a reliable estimate of the infestation level with a mean infestation level accuracy of 96.0% and 93.8% in the autumn and winter, respectively. Furthermore, we introduce the developed end-to-end system and its deployment into the online beekeeper’s diary—ProBee—that allows users to identify and track infestation levels on bee colonies.
Permanent Link: https://hdl.handle.net/11104/0336399