Inspiration
Every day, tons of edible food go to waste while millions struggle with hunger. We wanted to build a system that uses technology to bridge this gap efficiently and sustainably.
What it does
FoodRescue AI connects restaurants, events, and stores with nearby NGOs and shelters to redistribute surplus food. It uses AI to predict surplus generation and optimize delivery routes for faster, smarter food recovery.
How we built it
We built the backend using Flask and MongoDB, with a React.js frontend for seamless interaction. The AI model, developed using TensorFlow and Scikit-learn, predicts food surplus trends, while Google Maps API handles location matching and routing.
Challenges we ran into
- Managing real-time data synchronization between donors and NGOs
- Integrating AI predictions with location-based matching
- Handling CORS and API response delays during testing
Accomplishments that we're proud of
- Developed a working prototype that automates food rescue logistics
- Successfully integrated AI with real-time geolocation mapping
- Built a simple, impactful platform addressing a global problem
What we learned
We learned how to integrate AI models with web backends, manage live API data, and design user flows for social impact platforms.
What's next for FoodRescue AI
We plan to enhance AI prediction accuracy, add delivery partner integrations, and launch the platform for pilot testing with local NGOs and restaurants.
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