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The Varroa destructor mite is one of the most dangerous Honey Bee (Apismellifera) parasites worldwide and the bee colonies have to be regularlymonitored in order to control its spread. Here we present an object detectorbased method for health state monitoring of bee colonies. This method has thepotential for online measurement and processing. In our experiment, we comparethe YOLO and SSD object detectors along with the Deep SVDD anomaly detector.Based on the custom dataset with 600 ground-truth images of healthy andinfected bees in various scenes, the detectors reached a high F1 score up to0.874 in the infected bee detection and up to 0.727 in the detection of theVarroa Destructor mite itself. The results demonstrate the potential of thisapproach, which will be later used in the real-time computer vision based honeybee inspection system. To the best of our knowledge, this study is the firstone using object detectors for this purpose. We expect that performance ofthose object detectors will enable us to inspect the health status of the honeybee colonies.

Visual diagnosis of the Varroa destructor parasitic mite in honeybees using object detector techniques