FoodBridge AI

Inspiration

Food waste is a major problem, while many people and communities still struggle to access sufficient food. We wanted to build a technology-based solution that could help reduce the gap between surplus food and people who need it. This inspired us to create FoodBridge AI, a platform that uses AI to predict food surplus and improve the redistribution process.

What We Built

FoodBridge AI is an AI-powered food waste prediction and redistribution platform. It allows restaurants, hotels, events, and other food providers to record surplus food. The system analyzes available data to predict potential food waste and helps connect food donors with nearby NGOs or community organizations.

The platform includes a dashboard for monitoring food donations, surplus predictions, redistribution activities, and reports. A matching system helps identify suitable recipients based on factors such as food quantity, location, and availability.

How We Built It

We designed the application using a modern full-stack architecture. The frontend was developed using React, while Spring Boot microservices handle the main backend operations. MySQL is used for storing users, food donations, organizations, and transaction data. A FastAPI-based AI module is used for food surplus prediction and can be integrated with machine learning models.

The system was designed with separate modules for authentication, food management, prediction, donor-NGO matching, and analytics. This modular approach makes the platform easier to maintain and extend.

What We Learned

Through this project, we learned how to combine AI with full-stack application development to solve a real-world problem. We gained practical experience in React, Spring Boot, REST APIs, MySQL, microservices, and Python-based AI services. We also learned how different services communicate with each other and how data can be used to support better decisions.

Challenges We Faced

One of the main challenges was designing a reliable flow between the frontend, backend services, database, and AI module. Another challenge was deciding how to match surplus food with suitable organizations while considering quantity and location. We also had to handle data validation, API communication, and designing the system so that it could be extended in the future.

Future Scope

In the future, the platform can be enhanced with real-time food availability, improved machine learning models, route optimization for food pickup, notifications, and integration with IoT or smart inventory systems. Our goal is to make FoodBridge AI a scalable platform that can help reduce food waste and improve food redistribution.