Smart waste management app: efficient waste separation from visual footprints – Smart waste classification framework

Plastic has become ubiquitous in our daily lives, finding its way into a wide variety of products and applications. However, the extensive use of plastic, coupled with improper disposal and inadequate recycling practices, has significant economic and environmental consequences, posing a severe threat to our planet.
In Canada alone, a staggering 3 million tons of plastic waste are generated annually, with only 9% of that waste being recycled and a staggering 91% ending up in landfills. This alarming statistic highlights the inadequacy of current recycling policies, underscoring the need for smart, innovative solutions and technologies to address this critical issue.
To tackle this challenge, we propose the development of a smart waste management app that will enhance the efficiency of waste separation at the consumer level. By leveraging the power of technology, this app will empower consumers to make informed decisions about how they dispose of their waste and encourage them to adopt more sustainable habits
Through the use of camera and machine learning algorithms, the app will be able to identify and sort different types of waste, ensuring that materials are appropriately separated for recycling or other forms of disposal. By providing users with real-time feedback on their waste disposal practices and the environmental impact of their actions, the app will promote greater awareness and accountability, ultimately leading to more sustainable outcomes.

Faculty Supervisor:

Nasim Hajari

Student:

Partner:

Roseridge Waste Management Centre

Discipline:

Computer science

Sector:

Administrative and support, waste management and remediation services

University:

Concordia University of Edmonton

Program: