The Future of Recycling Is Intelligent: AI-Powered Project to Improve Sustainability

A new research initiative funded by Stony Brook University’s AI Innovation Seed Grant is reimagining how to tackle one of the most persistent problems in recycling: contaminated waste streams.

By combining video footage and cutting-edge artificial intelligence, researchers aim to automate the analysis of recycling materials, reduce contamination, and lay the groundwork for smarter, cost-effective, and sustainable waste management.

According to Environmental Protection Agency estimates, approximately 75 percent of the waste produced in the United States is recyclable; however, the actual recycling rate is only 35 percent, resulting in an estimated 68 million tons of recyclables being sent to landfills or incinerators. Meanwhile, the rate of recycling contamination (when non-recyclable items are mistakenly mixed in) is 25 percent, resulting in millions of tons of recyclables being rejected annually, and sent to landfills.

To address this challenge, a team of researchers at Stony Brook is building an AI-assisted design to support sustainable recycling. The project aims to use sensors and develop machine learning algorithms capable of identifying, tracking and counting waste materials as they move through real recycling facilities.

“We’re not just building tools in isolation, we’re collecting data at multiple stages of the sorting process, engaging with recycling workers to understand the pain points, and using those insights to help them work faster, safer, and with greater insight,” said Ruwen Qin, associate professor, Department of Civil Engineering.

Collaborating with municipalities and the Waste Data and Analysis Center housed within Stony Brook’s Department of Technology, AI, and Society and funded by the New York State Department of Environmental Conservation (NYSDEC), the team is collecting high-resolution video data from multiple stages of the sorting process at local material recovery facilities on Long Island.

“We’re not just studying the problem. We’re building tools that can make a measurable difference,” said Qin.

Read the full story by Ankita Nagpal on the AI Innovation Institute website.