Biogeosciences
Biogeosciences
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Articles | Volume 23, issue 21
Articles | Volume 23, issue 21
https://doi.org/10.5194/bg-23-8079-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-23-8079-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 23, issue 21
Technical note
 | 
11 Nov 2026
Technical note |  | 11 Nov 2026

Technical note: a Recognition-assisted Camera for Automated Microscopy (RaCAM)

Martin Tetard and Ross Marchant

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Cited articles

Abdelmguid, D., Kloster, M., and Beszteri, B.: Retrieving fine-grained diatom classifications from deep learning models trained on coarse-grained data by over-clustering, Biodivers. Conserv., 35, https://doi.org/10.1007/s10531-025-03218-3, 2026. a
Beaufort, L. and Dollfus, D.: Automatic recognition of coccoliths by dynamical neural networks, Mar. Micropaleontol., 51, 57–73, https://doi.org/10.1016/j.marmicro.2003.09.003, 2004. a
Beaufort, L., Dollfus, D., Gally, Y., Barbarin, N., Tetard, M., Sucheras-Marx, B., Nistor, A., and Biaut, M.: Technical note: From coccolith counts to quantitative carbonate fluxes: three decades of AI-assisted automated coccolith analysis, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-3901, 2026. a, b, c
Bourel, B., Marchant, R., de Garidel-Thoron, T., Tetard, M., Barboni, D., Gally, Y., and Beaufort, L.: Automated recognition by multiple convolutional neural networks of modern, fossil, intact and damaged pollen grains, Comput. Geosci., 140, 104498, https://doi.org/10.1016/j.cageo.2020.104498, 2020. a, b
Carlsson, V., Danelian, T., Boulet, P., Devienne, P., Laforge, A., and Renaudie, J.: Artificial intelligence applied to the classification of eight middle Eocene species of the genus Podocyrtis (polycystine radiolaria), J. Micropalaeontol., 41, 165–182, https://doi.org/10.5194/jm-41-165-2022, 2022. a
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We designed an AI-assisted, and affordable camera, that can be mounted on top of most microscopes (or used as a regular camera) and allows for an automated workflow including image capture, processing and identification of detected objects using artificial neural network to be performed. As this camera runs using a micro-computer and can be powered on a regular power bank, it is ideal to perform microscopy and computer tasks directly on field without the need for an expert to be deployed.
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