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 unprecedentedl...

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Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9543351/
https://www.ncbi.nlm.nih.gov/pubmed/36247386
http://dx.doi.org/10.1002/lno.12101

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9543351/
https://www.ncbi.nlm.nih.gov/pubmed/36247386
http://dx.doi.org/10.1002/lno.12101

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