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Grant: Alliance Grants

Toronto Metropolitan University

$30,000.00

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

EF 2023-24

Résumé
Canada is one of the top food waste generators in the world, with about 396 kilograms of food wasted or lost per capita every year. Turning food waste directly into organic compost at home is an excellent way to solve the food waste crisis. The main goal of this project is to digitalize food waste at home for a sustainable future using at-home composters. By exploring state-of-the-art development of artificial intelligence, the project 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. In addition to the cash contribution, VCycene has committed substantial in-kind contributions to this project, including a team for data collection and annotation and a team for model deployment and mobile App development. They will use their connection and expertise to provide support and guarantee the success of this project.The proposed solution is technically novel and specifically designed to solve the problem of food waste detection, segmentation, and classification. The project will provide a more convenient and economical solution for food waste processing. If successful, it will make substantial societal impacts and change the current practice of food waste management, significantly cutting down our carbon emissions and meeting Canada's goals for the Paris Agreement.
Programme
Alliance Grants
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data

Tel que publié

Application id
576912-2022
Name nom
Wang, Guanghui G
Department département
Computer Science
Organization id
43
Institution établissement
Toronto Metropolitan University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2022
Award amount
30000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
902
Area of application group en
Commercial services
Area of application group fr
Services commerciaux
Area of application en
Waste, waste management and recycling
Area of application fr
Déchets, gestion des déchets et recyclage
Research subject code
9999
Research subject group en
Not available
Research subject group fr
Not available
Research subject en
Non disponible
Research subject fr
Non disponible
Application title
AI-enabled food waste differentiation for at-home compost nutrients estimation
Keyword
Compost; Computer vision; Deep learning; Food waste classification; Image analysis; Machine learning
Application summary
Canada is one of the top food waste generators in the world, with about 396 kilograms of food wasted or lost per capita every year. Turning food waste directly into organic compost at home is an excellent way to solve the food waste crisis. The main goal of this project is to digitalize food waste at home for a sustainable future using at-home composters. By exploring state-of-the-art development of artificial intelligence, the project 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. In addition to the cash contribution, VCycene has committed substantial in-kind contributions to this project, including a team for data collection and annotation and a team for model deployment and mobile App development. They will use their connection and expertise to provide support and guarantee the success of this project.The proposed solution is technically novel and specifically designed to solve the problem of food waste detection, segmentation, and classification. The project will provide a more convenient and economical solution for food waste processing. If successful, it will make substantial societal impacts and change the current practice of food waste management, significantly cutting down our carbon emissions and meeting Canada's goals for the Paris Agreement.