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Título: | Machine learning techniques to characterize functional traits of plankton from image data |
Autor: | 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.; Guy-Haim, Tamar; Hoebeke, Laura; Hvitfeldt, Morten; Kiørboe, Thomas CSIC ORCID; Lalonde, Jean François; Lana, Arancha CSIC ORCID; Laviale, Martin; Lombard, Fabien; Lorimer, Tom; Martini, Séverine; Meyer, Albin; Möller, Klas Ove; Niehoff, Barbara; Ohman, M. D.; Pradalier, Cédric; Romagnan, Jean-Baptiste; Schröder, Simon Martin; Sonnet, Virginie; Sosik, Heidi M.; Stemmann, Lars; Stock, Michiel; Terbiyik Kurt, Tuba; Valcárcel, Nerea; Vilgrain, Laure; Wacquet, Guillaume; Waite, Anya M.; Irisson, Jean-Olivier | Fecha de publicación: | ago-2022 | Editor: | Association for the Sciences of Limnology and Oceanography Wiley-VCH |
Citación: | Limnology and Oceanography 67(8): 1647- 1669 (2022) | Resumen: | 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. | Versión del editor: | http://dx.doi.org/10.1002/lno.12101 | URI: | http://hdl.handle.net/10261/295973 | DOI: | 10.1002/lno.12101 | Identificadores: | doi: 10.1002/lno.12101 e-issn: 1939-5590 issn: 0024-3590 |
Aparece en las colecciones: | (IMEDEA) Artículos (IEO) Artículos |
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