SaveAPlate

Inspiration

At the University of Maryland, we couldn't ignore the staggering amount of food waste in our dining halls. Witnessing trays of perfectly good food being discarded daily, we were inspired to take action. Our journey began with a simple question: How can we reduce this waste and make a meaningful difference?

What it does

SaveAPlate utilizes predictive modeling to analyze past consumption patterns, seasonal trends, and real-time feedback in dining halls. By doing so, it optimizes meal preparation to reduce excess food production, thereby minimizing waste.

How we built it

We built SaveAPlate by leveraging our expertise in data analytics and sustainability. We developed algorithms to predict food consumption patterns and integrated them into a user-friendly platform tailored for dining hall operations.

Challenges we ran into

During the development process, we faced challenges such as fine-tuning algorithms for accuracy and ensuring seamless integration with existing systems in the frontend. Overcoming these obstacles required perseverance and innovative problem-solving but mainly perseverance :)

Accomplishments that we're proud of

We're proud to have created SaveAPlate, a solution that can significantly reduced food waste in UMD dining halls. Our platform has the potential to not only make a tangible impact on sustainability but it can also foster a culture of conscious consumption within the campus community.

What we learned

Through building SaveAPlate, we learned the importance of interdisciplinary collaboration and the power of technology in addressing pressing societal issues. We also gained insights into the complexities of food waste management and the significance of data-driven solutions.

What's next for SaveAPlate

Moving forward, we aim to expand SaveAPlate to other campuses and dining establishments, scaling our impact on a broader scale. Additionally, we're exploring opportunities to enhance our predictive modeling capabilities and integrate additional features to further optimize food waste reduction efforts.