Automatic estimation of lipid content from <i>in situ</i> images of Arctic copepods using machine learning - Sorbonne Université Access content directly
Journal Articles Journal of Plankton Research Year : 2023

Automatic estimation of lipid content from in situ images of Arctic copepods using machine learning


In Arctic marine ecosystems, large planktonic copepods form a crucial hub of matter and energy. Their energy-rich lipid stores play a central role in marine trophic networks and the biological carbon pump. Since the past ∼15 years, in situ imaging devices provide images whose resolution allows us to estimate an individual copepod's lipid sac volume, and this reveals many ecological information inaccessible otherwise. One such device is the Lightframe On-sight Keyspecies Investigation. However, when done manually, weeks of work are needed by trained personnel to obtain such information for only a handful of sampled images. We removed this hurdle by training a machine learning algorithm (a convolutional neural network) to estimate the lipid content of individual Arctic copepods from the in situ images. This algorithm obtains such information at a speed (a few minutes) and a resolution (individuals, over half a meter on the vertical), allowing us to revisit historical datasets of in situ images to better understand the dynamics of lipid production and distribution and to develop efficient monitoring protocols at a moment when marine ecosystems are facing rapid upheavals and increasing threats.
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Friday, November 29, 2024
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Dates and versions

hal-04385516 , version 1 (10-01-2024)



Frédéric Maps, Piotr Pasza Storożenko, Jędrzej Świeżewski, Sakina-Dorothée Ayata. Automatic estimation of lipid content from in situ images of Arctic copepods using machine learning. Journal of Plankton Research, 2023, ⟨10.1093/plankt/fbad048⟩. ⟨hal-04385516⟩
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