Ground beetles are a highly sensitive and speciose biological indicator, making them vital for monitoring biodiversity. However, they are currently an underutilized resource due to the manual effort required by taxonomic experts to perform challenging species differentiations based on subtle morphological differences, precluding widespread applications. In this paper, we evaluate 12 vision models on taxonomic classification across four diverse, long-tailed datasets spanning over 230 genera and 1769 species, with images ranging from controlled laboratory settings to challenging field-collected (in-situ) photographs. We further explore taxonomic classification in two important real-world contexts: sample efficiency and domain adaptation. Our results show that the Vision and Language Transformer combined with an MLP head is the best performing model, with 97% accuracy at genus and 94% at species level. Sample efficiency analysis shows that we can reduce train data requirements by up to 50% with minimal compromise in performance. The domain adaptation experiments reveal significant challenges when transferring models from lab to in-situ images, highlighting a critical domain gap. Overall, our study lays a foundation for large-scale automated taxonomic classification of beetles, and beyond that, advances sample-efficient learning and cross-domain adaptation for diverse long-tailed ecological datasets.

翻译：地面甲虫作为高度敏感且物种丰富的生物指示器，对于生物多样性监测至关重要。然而，由于目前仍需分类学专家基于细微形态差异进行极具挑战性的物种鉴定，这一过程依赖大量人工工作，导致该资源未能得到充分利用，阻碍了其广泛应用。本研究在四个多样化的长尾数据集上评估了12种视觉模型在分类学鉴定任务中的表现，这些数据集涵盖超过230个属和1769个物种，图像范围从受控实验室环境到具有挑战性的野外采集（原位）照片。我们进一步探讨了分类学鉴定在两个重要现实场景中的应用：样本效率和领域自适应。实验结果表明，结合MLP头的视觉语言Transformer模型表现最佳，在属级和种级分类准确率分别达到97%和94%。样本效率分析显示，我们可将训练数据需求降低多达50%而性能损失极小。领域自适应实验揭示了模型从实验室图像迁移至原位图像时面临的重大挑战，凸显了关键的领域差异。总体而言，本研究为大规模自动化甲虫分类学鉴定奠定了基础，并进一步推动了针对多样化长尾生态数据集的样本高效学习与跨领域自适应研究。