Batz Philipp, Ruttor Andreas, Thiel Sebastian, Wegener Jakob, Zautke Fred, Schwekendiek Christoph, Bienefeld Kaspar
Adaptiv Lernende Maschinen GmbH, Hauptstraße 25, 56472 Nisterau, Germany.
Artificial Intelligence Group, TU Berlin, Marchstraße 23, 10587 Berlin, Germany.
Biol Methods Protoc. 2022 Feb 16;7(1):bpac005. doi: 10.1093/biomethods/bpac005. eCollection 2022.
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 (). 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 (). 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.
机器学习技术正在改变许多行为学研究领域的可行性边界。在此，我们描述了机器学习在蜜蜂卫生行为检测/测量中的应用，卫生行为是蜜蜂的一项重要繁殖特性。卫生工蜂能够检测并销毁患病幼虫，从而减少诸如瓦螨等具有经济重要性的病原体和寄生虫的繁殖。在受感染的蜂巢脾上对这种行为进行视频观察比其他测量方法具有许多优势，但分析录制的材料极其耗时。我们通过结合视频记录中蜜蜂的自动跟踪、提取相关特征以及在感兴趣行为的正例和负例上训练多层判别器来解决这个问题。将专家知识纳入特征设计中，得到了一个用于识别视频中可安全跳过的无趣部分的高效模型。然后，该算法被用于半自动识别参与该行为的个体工蜂。机器学习方法的应用节省了70%的人工分析所需时间，并大幅增加了正确识别的巢房开口数量。因此，它将对单个巢房开口事件的视频观察转变为一种在经济上具有竞争力的方法，用于选择潜在抗性蜜蜂。该方法展示了机器学习如何用于推动行为学研究，以及如何通过以有意义的参数形式解释所学决策规则来产生新知识。
