Food Classification and Monitoring System in Refrigerators Using YOLO Algorithm
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Monitoring food freshness in refrigerators remains a challenge for many users, often leading to food spoilage and waste due to the absence of an automatic monitoring system. This study proposes a computer vision–based food monitoring system that leverages the YOLOv5 algorithm to automatically detect and categorize food items through camera input and deliver real-time notifications to users via a connected application. Experimental results demonstrate that YOLOv5 achieves an average accuracy of over 90% across various distances and object positions. Despite challenges related to limited datasets and lighting variations inside the refrigerator, the system offers a practical and innovative solution to reduce food spoilage, minimize household food waste, and support more efficient food storage management.
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