The use of machine learning tools to identify organisms and contaminants in lake ecosystems

The ultimate objective of this research project is to use a form of artificial intelligence to be able to classify and identify images of microscopic particles. Machine Learning is the term applied to this type of process, in which an algorithm is created by the computer software itself (i.e. mostly hidden from human intervention) to complete the task. The intern will complete a Masters of Science degree at the University of Toronto, and work with EcoVision Consulting Group, to develop a framework for testing machine learning packages and to parameterize some machine learning tools to identify microscopic organisms called zooplankton and classify inorganic contaminants (example, plastic fibres) in lake water samples. This work will benefit academic and government environmental monitors by providing an automated process for identifying microscopic species within lakes, and benefits EcoVision (and private industry in general) in automating contaminant monitoring in environmental effects monitoring projects.

Faculty Supervisor:

Dak de Kerckhove

Student:

Corey Ramkissoon

Partner:

EcoVision Consulting Group

Discipline:

Biology

Sector:

Environmental industry

University:

Program: