Scientific project


Artificial intelligence application to the identification of functional traits of zooplankton from high-resolution images

Principal Investigator(s) :

Frédéric Maps

Local Coordinator(s) :

Sakina-Dorothée Ayata

Team(s) involved :


Members :

Jean-Olivier Irisson | Lionel Guidi | Laure Vilgrain | Eric Debeuve (Uca) | Denis Laurendeau (Ulaval) | Cédric Dubois (Uca) | | | | | | | | |
Artifactz gathers marine ecologists and computer scientists to better understand Arctic plankton and their response to environmental changes, through the use of machine learning for plankton imaging analyses.
The base of Arctic marine ecosystems is supported by the growth of planktonic organisms that have adapted the extreme environmental conditions. These adaptations, often shared by many species, represent “functional traits” that influence the fitness of individuals and the ecosystem functioning as well. A better understanding of these traits appears crucial to predict the responses of marine ecosystems to the unprecedented changes affecting the Arctic Ocean. Several traits are associated to morphological features (e.g. size, egg sac, lipid stores, etc.), hence allowing to detect and measure them automatically from images. Imaging methods for plankton studies have multiplied and rapidly improved for the past decade. Thus, our main objective is to develop new tools combining imaging methods and machine learning algorithms in order to automatically measure important functional traits of planktonic organisms.