Machine learning techniques to characterize functional traits of plankton from image data
| Type | : | ACL |
|---|---|---|
| Nature | : | Production scientifique |
| Au bénéfice du Laboratoire | : | Oui |
| Statut de publication | : | Publié |
| Année de publication | : | 2022 |
| Auteurs (37) | : | ORENSTEIN Eric 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 T HOEBEKE Laura IVERSEN Morten,h KIØRBOE T LALONDE Jean-françois LANA Arancha LAVIALE Martin LOMBARD Fabien LORIMER Tom MARTINI Séverine MEYER Albin MÖLLER Klas,o NIEHOFF B OHMAN M,d PRADALIER Cedric ROMAGNAN Jean-baptiste SCHRÖDER Simon-martin SONNET Virginie SOSIK Heidi,m STEMMANN Lars STOCK Michiel TERBIYIK-KURT Tuba VALCÁRCEL-PÉREZ Nerea VILGRAIN Laure WACQUET Guillaume WAITE Anya IRISSON Jean-olivier |
| Revue scientifique | : | Limnology And Oceanography |
| Volume | : | 67 |
| Fascicule | : | 8 |
| Pages | : | 1647-1669 |
| DOI | : | 10.1002/lno.12101 |
| URL | : | https://doi.org/10.1002/lno.12101 |
| Abstract | : | 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. |
| Mots-clés | : | - |
| Commentaire | : | https://doi.org/10.1002/lno.12101 |
| Tags | : | - |
| Fichier attaché | : | - |
| Citation | : |
Orenstein E, Ayata S-D, Maps F, Becker ÉC, Benedetti F, Biard T, De Garidel-Thoron T, Ellen JS, Ferrario F, Giering SLC, Guy-Haim T, Hoebeke L, Iversen MH, Kiørboe T, Lalonde J-F, Lana A, Laviale M, Lombard F, Lorimer T, Martini S, Meyer A, Möller KO, Niehoff B, Ohman MD, Pradalier C, Romagnan J-B, Schröder S-M, Sonnet V, Sosik HM, Stemmann L, Stock M, Terbiyik-Kurt T, Valcárcel-Pérez N, Vilgrain L, Wacquet G, Waite A, Irisson J-O (2022) Machine learning techniques to characterize functional traits of plankton from image data. Limnol Oceanogr 67: 1647-1669 | doi: 10.1002/lno.12101
|