Project description
Taxa Identification with Machine Learning Enhanced by DNA Metabarcoding (TIMED) proposes combining DNA metabarcoding and optical machine learning in a novel interleaved way to pave the way toward automated aquatic biomonitoring. We will optimize the overall taxa identification pipeline consisting of imaging, metabarcoding, and optical machine learning blocks and develop novel state-of-the-art machine learning algorithms to three main tasks: 1) identification of taxa in the training set, 2) detection of taxa not in the training set, and 3) biomass estimation. For all of these tasks, we will consider novel ways to exploit the DNA metabarcoding output. During TIMED, we will collect a large-scale dataset suitable for advancing the proposed result.
People in project
PI: Senior Research Scientist Jenni Raitoharju, YTO
Main researcher: Mikko Impiö, YTO
Other support at SYKE:
Director Kristian Meissner, YTO
Senior Research Scientist Eero Siivola, YTO
International collaborators:
Assoc. Prof. Alexandros Iosifidis, Aarhus University, Denmark, expertise in project: machine learning
Senior Researcher Toke T. Høye, Aarhus University, Denmark, expertise in project: ecology
Prof. Florian Leese, University of Duisburg-Essen, Germany, expertise in project: DNA metabarcoding
More information
Senior Research Scientist Jenni Raitoharju