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

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.

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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, 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.,

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