AI-enabled food waste differentiation for at-home compost nutrients estimation

The main goal of this project is to digitalize food waste at home for a sustainable future using at-home composters. We will develop dedicated machine learning algorithms to detect, segment, and classify various food waste generated in the kitchen, making it possible for everyone to immediately estimate the quality and nutrients of the generated compost using a simple mobile App. The project will collaborate with VCycene Inc., a cleantech company dedicated to providing sustainable solutions to the food-waste problem. The intern will work with the domain experts at VCycene, especially the team for data collection and the team for model deployment and mobile App development, to conduct experiments and evaluate the performance of the proposed machine learning algorithms.

Faculty Supervisor:

Guanghui Wang

Student:

Partner:

VCycene Inc.

Discipline:

Computer science

Sector:

Manufacturing

University:

Toronto Metropolitan University

Program: