Articles | Volume 45, issue 1
https://doi.org/10.5194/jm-45-95-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/jm-45-95-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jan 2026
Research article |
| 26 Jan 2026
| 26 Jan 2026
Automated identification of fossil benthic foraminifera from the Peruvian margin using convolutional neural networks
Sikandar Hayat
CORRESPONDING AUTHOR
Univ Angers, Nantes Université, Le Mans Université, CNRS, Laboratoire de Planétologie et Géosciences, LPG UMR 6112 – Université d'Angers, 49000 Angers, France
Meryem Mojtahid
Univ Angers, Nantes Université, Le Mans Université, CNRS, Laboratoire de Planétologie et Géosciences, LPG UMR 6112 – Université d'Angers, 49000 Angers, France
Mary Elliot
Univ Angers, Nantes Université, Le Mans Université, CNRS, Laboratoire de Planétologie et Géosciences, LPG UMR 6112 – Université d'Angers, 49000 Angers, France
Jorge Cardich
CIDIS-Facultad de Ciencias e Ingenieria, Universidad Peruana Cayetano Heredia, Lima, Peru
Emmanuelle Geslin
Univ Angers, Nantes Université, Le Mans Université, CNRS, Laboratoire de Planétologie et Géosciences, LPG UMR 6112 – Université d'Angers, 49000 Angers, France
Thibault de Garidel-Thoron
Aix-Marseille Université, CNRS, IRD, INRAE, CEREGE, Aix-en-Provence 13545, France
Matthieu Carré
IPSL/Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques – CNRS-IRD-MNHN-Sorbonne Universités, Paris, France
Christine Barras
Univ Angers, Nantes Université, Le Mans Université, CNRS, Laboratoire de Planétologie et Géosciences, LPG UMR 6112 – Université d'Angers, 49000 Angers, France
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Babette A.A. Hoogakker, Catherine Davis, Yi Wang, Stephanie Kusch, Katrina Nilsson-Kerr, Dalton S. Hardisty, Allison Jacobel, Dharma Reyes Macaya, Nicolaas Glock, Sha Ni, Julio Sepúlveda, Abby Ren, Alexandra Auderset, Anya V. Hess, Katrin J. Meissner, Jorge Cardich, Robert Anderson, Christine Barras, Chandranath Basak, Harold J. Bradbury, Inda Brinkmann, Alexis Castillo, Madelyn Cook, Kassandra Costa, Constance Choquel, Paula Diz, Jonas Donnenfield, Felix J. Elling, Zeynep Erdem, Helena L. Filipsson, Sebastián Garrido, Julia Gottschalk, Anjaly Govindankutty Menon, Jeroen Groeneveld, Christian Hallmann, Ingrid Hendy, Rick Hennekam, Wanyi Lu, Jean Lynch-Stieglitz, Lélia Matos, Alfredo Martínez-García, Giulia Molina, Práxedes Muñoz, Simone Moretti, Jennifer Morford, Sophie Nuber, Svetlana Radionovskaya, Morgan Reed Raven, Christopher J. Somes, Anja S. Studer, Kazuyo Tachikawa, Raúl Tapia, Martin Tetard, Tyler Vollmer, Xingchen Wang, Shuzhuang Wu, Yan Zhang, Xin-Yuan Zheng, and Yuxin Zhou
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Short summary
We studied how artificial intelligence can speed up the study of tiny ocean organisms called benthic foraminifera, whose shells help scientists understand past ocean changes. Normally, identifying and counting them under a microscope takes a lot of time and skill. Here, we compared automated image analysis and human counting for 31 samples. The automated method showed promising results for faster and reliable research.
We studied how artificial intelligence can speed up the study of tiny ocean organisms called...
