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S. Schurischuster, M. Kampel:
"Image-based Classification of Honeybees";
in: "2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)", 1; K. Djemal et al. (ed.); issued by: IEEE Computer Society Press; IEEE Computer Society, U.S., 2020, ISBN: 978-1-7281-8750-1, 1 - 6.

Behavioral analysis of honeybees is a key factor for
keeping a healthy bee colony and therefore impacts our lives
indirectly or even directly, due to their pollination of many plant
species. Rapid growth of parasites like Varroa destructor is one
of the main reasons for the elevated mortality of bee colonies. In
this paper we are classifying bees into ´healthy´ and ´infected´
based on the presence of this parasitic mite. A camera facing the
entrance of a beehive is acquiring the images used for a novel
dataset, which is used to segment and detect Varroa destructor.
We are comparing two classification methods based on AlexNet
and ResNet as well as a semantic segmentation approach using
DeepLabV3, with the latter achieving a per-class accuracy of
minimum 90.8% with an overall f-score of 0.95. The evaluations
are performed on a ground truth dataset with more than 13,000
manually labeled images of infected and healthy bees, which is
made publicly available.

http://dx.doi.org/10.1109/IPTA50016.2020.9286673