Inspiration
As students, we see it every day: untouched broccoli, half-eaten pasta, and full servings thrown away in dining halls. Multiplied across thousands of students, this adds up to an enormous environmental and operational problem. In fact, on average, universities waste 650,000 pounds of food annually.
The core issue is visibility. Once food enters the trash bin, dining halls lose all insight into what was wasted, how much, and why. Without data, dining halls can’t adjust supply, leading to systemic overproduction and unnecessary waste.
We wanted to make food waste measurable, actionable, and preventable.
What it does
Pare has two core components:
Computer Vision Waste Detection Using computer vision, Pare scans a student's plate before food is discarded. Our model identifies and estimates the quantity of uneaten food, automatically logging waste data to a real-time dashboard. This transforms previously invisible waste into structured, actionable data.
Predictive Waste Analytics Pare uses a gradient boosted decision tree model to analyze historical waste patterns and identify trends. Our system can answer questions such as:
Which foods are most frequently wasted?
How does waste vary by day, weather, or academic schedule?
How much of each food should be prepared to minimize waste?
These insights enable dining hall staff to make data-driven decisions, reducing both food waste and operational costs.
What's next for Pare
Ultimately, we hope to deploy Pare in real dining halls to help universities reduce food waste, lower costs, and operate more sustainably.
Built With
- claude
- python
- sklearn
Log in or sign up for Devpost to join the conversation.