Reliable identification of species is a key step to assess biodiversity. In fossil and archaeological contexts, genetic identifications remain often difficult or even impossible and morphological criteria are the only window on past biodiversity. Methods of numerical taxonomy based on geometric morphometric provide reliable identifications at the specific and even intraspecific levels, but they remain relatively time consuming and require expertise on the group under study. Here, we explore an alternative based on computer vision and machine learning. The identification of three rodent species based on pictures of their molar tooth row constituted the case study. We focused on the first upper molar in order to transfer the model elaborated on modern, genetically identified specimens to isolated fossil teeth. A pipeline based on deep neural network automatically cropped the first molar from the pictures, and returned a prediction regarding species identification. The deep-learning approach performed equally good as geometric morphometrics and, provided an extensive reference dataset including fossil teeth, it was able to successfully identify teeth from an archaeological deposit that was not included in the training dataset. This is a proof-of-concept that such methods could allow fast and reliable identification of extensive amounts of fossil remains, often left unstudied in archaeological deposits for lack of time and expertise. Deep-learning methods may thus allow new insights on the biodiversity dynamics across the last 10.000 years, including the role of humans in extinction or recent evolution.

中文翻译：

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深度学习识别现代和化石啮齿动物的臼齿

可靠地识别物种是评估生物多样性的关键步骤。在化石和考古学背景下，遗传识别常常仍然困难甚至不可能，形态学标准是过去生物多样性的唯一窗口。基于几何形态计量学的数字分类法可以在特定甚至特定种内水平提供可靠的识别，但是它们仍然比较耗时，并且需要研究对象的专业知识。在这里，我们探索基于计算机视觉和机器学习的替代方法。根据它们的臼齿排图片确定三种啮齿动物的种类构成了案例研究。我们将重点放在第一个上磨牙上，以便将在现代，经过基因鉴定的标本上阐述的模型转移到孤立的化石牙齿上。基于深度神经网络的管道会自动从图片中裁剪出第一颗臼齿，并返回有关物种识别的预测。深度学习方法的性能与几何形态计量学一样好，并且提供了包括化石牙齿在内的广泛参考数据集，它能够从未包含在训练数据集中的考古矿床中成功识别牙齿。这是一种概念证明，这种方法可以快速，可靠地识别大量化石遗骸，而这些化石遗骸通常由于缺乏时间和专业知识而没有在考古储藏中研究。因此，深度学习方法可能使人们对过去10.000年的生物多样性动态有了新的见解，包括人类在灭绝或近期进化中的作用。