An Efficient Deep Learning Approach for Food Quality Detection

This research aims to develop a deep learning system for assessing food edibility based on user-uploaded photos, addressing the global challenge of food waste. The system leverages the YOLOv8 model, renowned for its efficiency in object detection and classification, to evaluate visual attributes such as color changes, mold, and texture abnormalities, determining whether food is fresh or spoiled. With its advanced detection capabilities, YOLOv8 is well-suited for real-time classification, ensuring quick and accurate identification of spoilage across diverse food types and varying lighting conditions. Unlike traditional systems that often struggle with accuracy, especially when dealing with a wide variety of foods, this approach significantly enhances reliability. By enabling individuals to identify unsafe or spoiled food before consumption, this project contributes to reducing waste and promoting sustainable food practices while improving food safety.