2022 IEEE International Conference on Image Processing (ICIP), p. 3341-3345

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.

L. Picek, A. Novozamsky, R. C. Frydrychova, B. Zitova and P. Mach, "Monitoring of Varroa Infestation Rate in Beehives: A Simple AI Approach," 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 2022, pp. 3341-3345, doi: 10.1109/ICIP46576.2022.9897809.