Members of Québec-Océan recently published a paper based on a submersible camera system called LOKI. Deployed in the high Arctic, LOKI took images of zooplankton.
Using these high definition images, an automatic identification model based on machine learning was developed that can reliably turn these images into taxonomic information. The camera system’s high vertical resolution (<1m) in combination with a very high resolution taxonomic model, identifying developmental stages of copepod species, enables scientists to study the link between zooplankton species and their abiotic as well as biotic environment with much detail. Especially the coupling between zooplankton and their phytoplankton prey is of high relevance in the face of climate change in the Arctic.
The model was recently used together with in-situ lipid analysis from images, yielding some very interesting results, so stay tuned!
To read the paper :
Schmid, MS, C Aubry, J Grigor, L Fortier (2016). The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean. Methods in Oceanography 15–16: 129–160.
Follow the project at schmidscience.com.