Machine learning techniques to characterize functional traits of plankton from image data

Orenstein, Eric C.; Ayata, Sakina‐Dorothée; Maps, Frédéric; Becker, Érica C.; Benedetti, Fabio; Biard, Tristan; de Garidel‐Thoron, Thibault; Ellen, Jeffrey S.; Ferrario, Filippo; Giering, Sarah L. C. ORCID: https://orcid.org/0000-0002-3090-1876; Guy‐Haim, Tamar; Hoebeke, Laura; Iversen, Morten Hvitfeldt; Kiørboe, Thomas; Lalonde, Jean‐François; Lana, Arancha; Laviale, Martin; Lombard, Fabien; Lorimer, Tom; Martini, Séverine; Meyer, Albin; Möller, Klas Ove; Niehoff, Barbara; Ohman, Mark D.; Pradalier, Cédric; Romagnan, Jean‐Baptiste; Schröder, Simon‐Martin; Sonnet, Virginie; Sosik, Heidi M.; Stemmann, Lars S.; Stock, Michiel; Terbiyik‐Kurt, Tuba; Valcárcel‐Pérez, Nerea; Vilgrain, Laure; Wacquet, Guillaume; Waite, Anya M.; Irisson, Jean‐Olivier. 2022 Machine learning techniques to characterize functional traits of plankton from image data. Limnology and Oceanography, 67 (8). 1647-1669. https://doi.org/10.1002/lno.12101

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Official URL: http://dx.doi.org/10.1002/lno.12101

Abstract/Summary

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1002/lno.12101
ISSN:	0024-3590
Date made live:	01 Dec 2022 11:55 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/533662

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1002/lno.12101

ISSN:

0024-3590

Date made live:

01 Dec 2022 11:55 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/533662