Document of bibliographic reference 347529

BibliographicReference record

Type

Bibliographic resource

Type of document

Book chapters

BibLvlCode

AMS

Title

Machine learning for the study of plankton and marine snow from images

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.

WebOfScience code

https://www.webofscience.com/wos/woscc/full-record/WOS:000788648800013

Bibliographic citation

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

Topic

Marine

Is peer reviewed

true