Articles | Volume 45, issue 2
https://doi.org/10.5194/jm-45-623-2026
https://doi.org/10.5194/jm-45-623-2026
Research article
 | 
18 Aug 2026
Research article |  | 18 Aug 2026

Identification of living (Rose Bengal)-stained benthic foraminifera using automated image recognition

Tobias Walla, Christine Barras, Emmanuelle Geslin, Camille Godbillot, Louis Lanoy, Ross Marchant, Delphine Dissard, and Thibault de Garidel-Thoron

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Cited articles

Adebayo, M. B., Bolton, C. T., Marchant, R., Bassinot, F., Conrod, S., and De Garidel-Thoron, T.: Environmental Controls of Size Distribution of Modern Planktonic Foraminifera in the Tropical Indian Ocean, Geochem. Geophys. Geosy., 24, e2022GC010586, doi.org/10.1029/2022GC010586, 2023. 
Al-Sabouni, N., Fenton, I. S., Telford, R. J., and Kučera, M.: Reproducibility of species recognition in modern planktonic foraminifera and its implications for analyses of community structure, J. Micropalaeontol., 37, 519–534, https://doi.org/10.5194/jm-37-519-2018, 2018. 
Alve, E., Korsun, S., Schönfeld, J., Dijkstra, N., Golikova, E., Hess, S., Husum, K., and Panieri, G.: Foram-AMBI: A sensitivity index based on benthic foraminiferal faunas from North-East Atlantic and Arctic fjords, continental shelves and slopes, Mar. Micropaleontol., 122, 1–12, doi.org/10.1016/j.marmicro.2015.11.001, 2016. 
Austen, G. E., Bindemann, M., Griffiths, R. A., and Roberts, D. L.: Species identification by conservation practitioners using online images: accuracy and agreement between experts, PeerJ, 25, 6:e4157, https://doi.org/10.7717/peerj.4157, 2018. 
Barras, C., Jorissen, F. J., Labrune, C., Andral, B., and Boissery, P.: Live benthic foraminiferal faunas from the French Mediterranean Coast: Towards a new biotic index of environmental quality, Ecol. Indic., 36, 719–743, doi.org/10.1016/j.ecolind.2013.09.028, 2014. 
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Short summary
We developed an automated method that uses artificial intelligence to identify marine microscopic organisms called foraminifera, which serve as bioindicators of ocean health. By combining a low-cost imaging system with computer vision, our approach can quickly and accurately recognize both living and dead specimens. This makes environmental monitoring faster, more consistent, and more accessible for studying how coastal ecosystems respond to pollution and climate change.
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