The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards the ability and logic of machines. In our study, we investigate both the differences in accuracy and in the identification logic of taxonomic experts and machines. We propose a systematic approach utilizing deep Convolutional Neural Nets and extensively evaluate it over a multi-pose taxonomic dataset with hierarchical labels specifically created for this comparison. We also study the prediction accuracy on different ranks of taxonomic hierarchy in detail. We compare the results of Convolutional Neural Networks to human experts and support vector machines. Our results revealed that human experts using actual specimens yield the lowest classification error ($\overline{CE}=6.1\text{\%}$). However, a much faster, automated approach using deep Convolutional Neural Nets comes close to human accuracy ($\overline{CE}=11.4\text{\%}$) when a typical flat classification approach is used. Contrary to previous findings in the literature, we find that for machines following a typical flat classification approach commonly used in machine learning performs better than forcing machines to adopt a hierarchical, local per parent node approach used by human taxonomic experts ($\overline{CE}=13.8\text{\%}$). Finally, we publicly share our unique dataset to serve as a public benchmark dataset in this field.

目前，专家分类单元的识别步骤减慢了许多生物评估的响应时间。转向更快，更便宜的最新机器学习方法仍然受到专家对机器能力和逻辑的怀疑。在我们的研究中，我们研究了分类专家和机器在准确性和识别逻辑上的差异。我们提出了一种利用深层卷积神经网络的系统方法，并在具有为此标签专门创建的层次结构标签的多位置分类数据集上进行了广泛的评估。我们还详细研究了不同分类等级上的预测准确性。我们将卷积神经网络的结果与人类专家和支持向量机进行了比较。$\overline{CË}=6。\mathrm{1个}\text{％}$）。但是，使用深度卷积神经网络的更快，自动化的方法接近于人类的准确性（$\overline{CË}=11。4\text{％}$），当使用典型的平面分类方法时。与文献中的先前发现相反，我们发现对于遵循机器学习中常用的典型平面分类方法的计算机，其性能要比强制机器采用人类分类专家使用的分层，局部的每个父节点的方法要好（$\overline{CË}=13。8\text{％}$）。最后，我们公开共享我们独特的数据集，以用作该领域的公共基准数据集。