Machine learning for the study of plankton and marine snow from images
Irisson, J.-O.; Ayata, S.-D.; Lindsay, D.J.; Karp-Boss, L.; Stemmann, L. (2022). Machine learning for the study of plankton and marine snow from images, in: Carlson, C.A. et al. Ann. Rev. Mar. Sci. 14. Annual Review of Marine Science, 14: pp. 277-301. https://dx.doi.org/10.1146/annurev-marine-041921-013023
In: Carlson, C.A.; Giovannoni, S.J. (Ed.) (2022). Ann. Rev. Mar. Sci. 14. Annual Review of Marine Science, 14. Annual Reviews: Palo Alto. i, 492 pp., more
In: Annual Review of Marine Science. Annual Reviews: Palo Alto, Calif. ISSN 1941-1405; e-ISSN 1941-0611, more
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| Keyword |
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| Author keywords |
plankton, marine snow, ecology, quantitative imaging, machine learning, classifier |
| Authors | | Top |
- Irisson, J.-O.
- Ayata, S.-D.
- Lindsay, D.J.
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- Karp-Boss, L.
- Stemmann, L.
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| Abstract |
Quantitative imaging instruments produce a large number of images of plankton and marine snow, acquired in a controlled manner, from which the visual characteristics of individual objects and their in situ concentrations can be computed. To exploit this wealth of information, machine learning is necessary to automate tasks such as taxonomic classification. Through a review of the literature, we highlight the progress of those machine classifiers and what they can and still cannot be trusted for. Several examples showcase how the combination of quantitative imaging with machine learning has brought insights on pelagic ecology. They also highlight what is still missing and how images could be exploited further through trait-based approaches. In the future, we suggest deeper interactions with the computer sciences community, the adoption of data standards, and the more systematic sharing of databases to build a global community of pelagic image providers and users. |
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