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Conference Paper (international conference)

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

Picek L., Novozámský Adam, Čapková Frydrychová Radmila, Zitová Barbara, Mach P.

: IEEE International Conference on Image Processing 2022 : Proceedings, p. 3341-3345

: IEEE International Conference on Image Processing 2022 /29./, (Bordeaux, FR, 20221016)

: StrategieAV21/1, AV ČR

: Machine learning algorithms, Costs, Image processing, Machine learning, Frequency measurement, Complexity theory

: 10.1109/ICIP46576.2022.9897809

: http://library.utia.cas.cz/separaty/2022/ZOI/novozamsky-0563135.pdf

(eng): 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.

: JC

: 20206

07.01.2019 - 08:39