Semi-automatic detection of honeybee brood hygiene—an example of artificial learning to facilitate ethological studies on social insects. Issue 1 (16th February 2022)

• Record Type: Journal Article
• Authors: Batz, Philipp; Ruttor, Andreas; Thiel, Sebastian; Wegener, Jakob; Zautke, Fred; Schwekendiek, Christoph; Bienefeld, Kaspar
• Is Part Of: Biology methods & protocols. Volume 7:Issue 1(2022)
• Publication Date: 2022-02-16
• Subjects: video analysis -- behaviour detection -- machine learning; Biology -- Methodology -- Periodicals; 570.1
• DOI: 10.1093/biomethods/bpac005
• Languages: English
• ISSNs: 2396-8923

Abstract

Abstract: Machine-learning techniques are shifting the boundaries of feasibility in many fields of ethological research. Here, we describe an application of machine learning to the detection/measurement of hygienic behaviour, an important breeding trait in the honey bee ( Apis mellifera ). Hygienic worker bees are able to detect and destroy diseased brood, thereby reducing the reproduction of economically important pathogens and parasites such as the Varroa mite ( Varroa destructor ). Video observation of this behaviour on infested combs has many advantages over other methods of measurement, but analysing the recorded material is extremely time-consuming. We approached this problem by combining automatic tracking of bees in the video recordings, extracting relevant features, and training a multi-layer discriminator on positive and negative examples of the behaviour of interest. Including expert knowledge into the design of the features lead to an efficient model for identifying the uninteresting parts of the video which can be safely skipped. This algorithm was then used to semiautomatically identify individual worker bees involved in the behaviour. Application of the machine-learning method allowed to save 70% of the time required for manual analysis, and substantially increased the number of cell openings correctly identified. It thereby turns video-observation of individual cell opening events into an economically competitive method for selecting potentially resistant bees. This method presents an example of how machine learning can be used to boost ethological research, and how it can generate new knowledge by explaining the learned decision rule in form of meaningful parameters.