Niu Zhi-Bin, Jia Si-Yuan, Xu Hong-He
College of Intelligence and Computing, Tianjin University, Tianjin 300354, China.
State Key Laboratory of Palaeobiology and Stratigraphy, Nanjing Institute of Geology and Palaeontology and Centre for Excellence in Life and Paleoenvironment, Chinese Academy of Sciences, Nanjing 210008, China.
iScience. 2023 Nov 23;27(1):108549. doi: 10.1016/j.isci.2023.108549. eCollection 2024 Jan 19.
Graptolites, fossils significant for evolutionary studies and shale gas exploration, are traditionally identified visually by taxonomists due to their intricate morphologies and preservation challenges. Artificial intelligence (AI) holds great promise for transforming such meticulous tasks. In this paper, we demonstrate that graptolites can be identified with taxonomist accuracy using a deep learning model. We construct the most sophisticated and largest professional single organisms image dataset to date, which is composed of >34,000 images of 113 graptolite species annotated at pixel-level resolution to train the model, develop, and evaluate deep learning networks to classify graptolites. The model's performance surpassed taxonomists in accuracy, time, and generalization, achieving 86% and 81% accuracy in identifying graptolite genus and species, respectively. This AI-based method, capable of recognizing minute morphological details better than taxonomists, can be integrated into web and mobile apps, extending graptolite identification beyond research institutes and enhancing shale gas exploration efficiency.
笔石是对进化研究和页岩气勘探具有重要意义的化石，由于其形态复杂且保存面临挑战，传统上由分类学家通过肉眼识别。人工智能有望改变此类细致的任务。在本文中，我们证明使用深度学习模型可以以分类学家的准确度识别笔石。我们构建了迄今为止最复杂、最大的专业单一生物体图像数据集，该数据集由113种笔石物种的超过34000张图像组成，这些图像在像素级分辨率上进行了标注，用于训练模型、开发和评估用于笔石分类的深度学习网络。该模型在准确性、时间和泛化能力方面超过了分类学家，在识别笔石属和种时的准确率分别达到了86%和81%。这种基于人工智能的方法比分类学家更能识别微小的形态细节，可以集成到网络和移动应用程序中，将笔石识别扩展到研究机构之外，提高页岩气勘探效率。
