Automated graptolite identification at high taxonomic resolution using residual networks.

ABSTRACT: 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% a

SUBMITTER: Niu ZB 

PROVIDER: S-EPMC10783601 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature