GPU-Accelerated Sift-Aided Source Identification of Stabilized Videos

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 fall-out 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 smartphone. 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.