Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 3
May-June 2026

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Behavioral Refrigerator Shelf Monitoring Using Sequential Image Analysis and Embedded Gas Sensing

Author(s) Mr. Vihaan Madhur
Country United States
Abstract Household food waste remains a significant global challenge, often caused by forgotten, expired, or improperly stored food items inside refrigerators. While industrial food-monitoring systems exist for food production and transportation environments, consumer households still lack practical embedded monitoring solutions for monitoring food freshness and spoilage risk in real-world refrigerator conditions.
This research presents an embedded monitoring system for behavioral refrigerator shelf monitoring using sequential image analysis and embedded gas sensing. The proposed system integrates MQ-series gas sensors, a fisheye camera, an ESP32-S3 microcontroller, and a Python-based backend to continuously monitor refrigerator environments and collect environmental telemetry and shelf images over time.
Initial experiments focused on direct spoilage detection using gas sensor telemetry and computer vision analysis of fresh and decomposing fruits and vegetables in controlled environments. However, real-world refrigerator deployment revealed several practical limitations affecting direct spoilage detection reliability. As a result of these findings, the research evolved beyond direct spoilage classification and led to the development of a behavioral computer vision approach based on sequential refrigerator shelf image analysis. Instead of relying solely on direct food recognition, the system analyzes shelf-state changes and interaction patterns over time to identify low-movement regions associated with potentially forgotten food items.
Experimental observations suggest that combining embedded gas sensing with behavioral shelf interaction analysis may provide a more practical and deployable household food-monitoring approach than gas sensing or image-based spoilage detection alone.
Keywords Embedded AI, Embedded Monitoring System, Machine Learning, Computer Vision, Food Waste Monitoring, IoT
Field Computer > Automation / Robotics
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-06-05
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E-ISSN 3048-7641
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