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

ABSTRACT: 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.

SUBMITTER: Orenstein EC 

PROVIDER: S-EPMC9543351 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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

Orenstein Eric C EC   Ayata Sakina-Dorothée SD   Maps Frédéric F   Becker Érica C ÉC   Benedetti Fabio F   Biard Tristan T   de Garidel-Thoron Thibault T   Ellen Jeffrey S JS   Ferrario Filippo F   Giering Sarah L C SLC   Guy-Haim Tamar T   Hoebeke Laura L   Iversen Morten Hvitfeldt MH   Kiørboe Thomas T   Lalonde Jean-François JF   Lana Arancha A   Laviale Martin M   Lombard Fabien F   Lorimer Tom T   Martini Séverine S   Meyer Albin A   Möller Klas Ove KO   Niehoff Barbara B   Ohman Mark D MD   Pradalier Cédric C   Romagnan Jean-Baptiste JB   Schröder Simon-Martin SM   Sonnet Virginie V   Sosik Heidi M HM   Stemmann Lars S LS   Stock Michiel M   Terbiyik-Kurt Tuba T   Valcárcel-Pérez Nerea N   Vilgrain Laure L   Wacquet Guillaume G   Waite Anya M AM   Irisson Jean-Olivier JO  

Limnology and oceanography 20220630 8

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 trai  ...[more]

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