Title:	AI-assisted plankton monitoring
DNr:	Berzelius-2023-48
Project Type:	LiU Berzelius
Principal Investigator:	Anders Andersson <anders.andersson@scilifelab.se>
Affiliation:	Kungliga Tekniska högskolan
Duration:	2023-03-08 – 2023-10-01
Classification:	10606
Homepage:	http://envgen.github.io/
Keywords:

Abstract

Single-celled eukaryotic plankton (protists) form the productive base of marine ecosystems and are key drivers of global biogeochemical cycles of carbon and nutrients. Monitoring of eukaryotic plankton has traditionally been conducted by manual microscopic detection. Recently, alternative approaches have emerged such as high-throughput imaging and DNA metabarcoding. In this project, we will utilize state-of-the-art image analysis and deep learning approaches to maximise the information gained from these types of data and to translate between them. We will leverage existing imaging data from the new Imaging FlowCytobot (IFC B) instrument mounted on the research vessel Svea as well as generate new parallel IFCB and DNA metabarcoding datasets for 500 water samples spanning the Baltic Sea, Kattegat and Skagerrak. The methodology developed in the project will advance plankton research and ecology in general and plankton monitoring in Sweden in particular. It will bridge the gap between imaging and DNA-based diversity data and increase the information output from both approaches.