Inspiration
Food spoilage in restaurants and warehouses is often detected too late, leading to food waste, financial losses, and health risks. After studying existing methods and speaking with food handlers, we wanted to build a smarter system that predicts spoilage before it happens.
What it does
Smart Food Safety Digital Twin creates a virtual model of food storage using IoT sensors and AI. It monitors temperature, humidity, and gas levels in real time, predicts food spoilage, and sends instant alerts through a dashboard and mobile app.
How we built it
We used an ESP32 microcontroller with DHT11 and MQ135 sensors to collect environmental data. The data is sent to the cloud, where an AI model analyses it and updates a digital twin. A Flutter app displays real-time food conditions and notifications.
Challenges we ran into
The biggest challenges were sensor calibration, ensuring accurate spoilage prediction, integrating hardware with cloud services, and maintaining reliable real-time communication.
Accomplishments that we're proud of
We designed a working concept that combines AI, IoT, and Digital Twin technology into one solution. Our system can monitor food continuously, predict spoilage early, and help reduce food waste while improving food safety.
What we learned
This project helped us learn IoT hardware integration, cloud connectivity, AI-based prediction, mobile app development, teamwork, and solving real-world problems using emerging technologies.
What's next for Smart Food Safety Digital Twin
Our next steps are to improve the AI model with larger datasets, support multiple storage locations, integrate computer vision for food quality analysis, and test the system in restaurants and warehouses for real-world validation.
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