Identification of living (Rose Bengal)-stained benthic foraminifera using automated image recognition
Walla, T.; Barras, C.; Geslin, E.; Godbillot, C.; Lanoy, L.; Marchant, R.; Dissard, D.; de Garidel-Thoron, T. (2026). Identification of living (Rose Bengal)-stained benthic foraminifera using automated image recognition. J. Micropalaeontol. 45(2): 623-652. https://dx.doi.org/10.5194/jm-45-623-2026
In: Journal of Micropalaeontology. Geological Society: London. ISSN 0262-821X; e-ISSN 2041-4978, meer
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| Auteurs | | Top |
- Walla, T.
- Barras, C.
- Geslin, E.
- Godbillot, C.
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- Lanoy, L.
- Marchant, R.
- Dissard, D.
- de Garidel-Thoron, T.
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| Abstract |
Living and fossil benthic foraminifera are widely used as bioindicators of ecosystem quality, but their analysis is time-consuming. With the help of artificial intelligence, models for the automatic identification of fossil planktonic foraminifera and, more recently, benthic foraminifera are emerging. Although large datasets of images can now be acquired automatically, efficient processing pipelines are still needed. Here we use convolutional neural networks (CNNs) to automatically identify living benthic foraminifera at the species level. Rose Bengal staining is widely used in ecological and biomonitoring studies to identify living foraminifera, which reflect present environmental conditions. We investigate both low-diversity assemblages from an intertidal mudflat on the French Atlantic coast (Bourgneuf Bay) and high-diversity assemblages from the French Mediterranean coast. Samples were imaged using a modified 3D printer equipped with a camera. The three trained CNN models are efficient in identifying species of the total community (i.e., living and dead foraminifera together) in both low- and high-diversity settings with an accuracy of 96.0 % and 82.2 %, respectively. The results compare well to published indices for ecosystem quality in high-diversity ecosystems. One CNN model can distinguish both species and vital status (i.e., living and dead specimens) for the low-diversity spot with an accuracy of 94.2 %. The results of this first application of machine learning for identifying living benthic foraminifera are very encouraging for improving the efficiency and applicability of these bio-indicators in biomonitoring studies. This approach highlights the potential of automated image-based identification to accelerate the use of benthic foraminifera as bioindicators in large-scale environmental monitoring programs. |
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