Food Quality Detection Using Deep Learning

Problem statement

Food spoilage is a major problem in supermarkets and households. Visual inspection is inconsistent and often inaccurate.

Abstract

This project uses image classification to distinguish between fresh and spoiled produce. A dataset of fruits/vegetables is used to train a CNN model that identifies color changes, mold, and texture differences. The system can run on a Raspberry Pi to build a real-time food scanning station.

Components required

• Camera Module
• TensorFlow/Keras
• Dataset of fresh/spoiled food images
• Raspberry Pi / Laptop
• Web or mobile UI

Block diagram

Working

User places food item in front of the camera. The system preprocesses the image and passes it through the CNN model, which classifies it as fresh or spoiled. The UI displays the result along with confidence score.

Applications

• Supermarkets
• Food quality labs
• Restaurants
• Smart kitchen devices