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Paper ID:IJSREM68353

SMART SHELF - LIFE PREDICITION SYSTEM FOR PACKAGED FOODS

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM68353

Volume:

10 |

10 |

Oct 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM68353

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DHARSHAN B V

DHARSHAN B V

Electronics and Communication Engineering

K Ramakrishnan College of Technology

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DINESHKUMAR D

DINESHKUMAR D

Electronics and Communication Engineering

K Ramakrishnan College of Technology

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HARIHARAN S

HARIHARAN S

Electronics and Communication Engineering

K Ramakrishnan College of Technology

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ENIYAVAN M

ENIYAVAN M

Electronics and Communication Engineering

K Ramakrishnan College of Technology

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Mentors

MANI P

MANI P

Electronics and Communication Engineering

K Ramakrishnan College of Technology

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Abstract

The Smart Shelf-Life Prediction System for Packaged Foods is an Internet of Things system that watches storage conditions and calculates how much time a packaged food product still has before it goes bad. The Smart Shelf-Life Prediction System uses an ESP32 microcontroller to gather temperature and humidity readings from a DHT22 sensor in time. Those readings are sent to a database where they are stored and examined. Using the data about how the food's stored the Smart Shelf-Life Prediction System figures out how long the food will last and spots any changes that could lower food quality. Every food package gets its QR code so that a user can scan it with a mobile phone and see the food’s details when it was made, its original expiry date, the current temperature and humidity the storage history and the estimated remaining shelf life. If the storage conditions turn bad or if the Smart Shelf-Life Prediction System thinks the food is, near the end of its life the system can send alerts to the user. The Smart Shelf-Life Prediction System brings together sensor hardware, on‑board processing, wireless links and cloud monitoring to make food storage easier to manage. To confirm that the Smart Shelf-Life Prediction System works well experiments must be done to check how accurate the sensors are, how precise the shelf‑life calculations are and how reliable the alerts are. The Smart Shelf-Life Prediction System wants to cut food waste keep food safe and help people keep track of packaged food products efficiently.

KEYWORDS

Internet of Things (IoT) ESP32 Microcontroller DHT22 Sensor Shelf-Life Prediction Temperature and Humidity Monitoring QR Code Cloud Database Food Safety Food Waste Reduction.

Cite This Article

DHARSHAN B V, DINESHKUMAR D, HARIHARAN S, ENIYAVAN M, MANI P, "SMART SHELF - LIFE PREDICITION SYSTEM FOR PACKAGED FOODS," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-7, Oct 2026. https://doi.org/10.55041/IJSREM68353

Full Article/Conclusion

The smart shelf-life prediction system for packaged foods is designed to make it easier to keep track of how long food stays fresh. It does this by using sensors that measure the temperature and humidity around the food. An ESP32 microcontroller takes the data from these sensors. Sends it over Wi-Fi to a cloud platform where it can be monitored. The system uses the information about the food. How it is stored to figure out how much time is left before the food might go bad. Users can scan a QR code to see details about the product and the estimated shelf life. The system can also send a warning if the shelf life is about to end or if the storage conditions are not good. This way people can manage their food better. Avoid throwing away food that is still safe to eat.. The system needs to be accurate and work well. That depends on how the sensors are set up the details about each type of food and the model used for predictions. More tests with food, in different storage situations are needed to check if the system works as it should.

References

[1] “An RFID-Powered Multisensing Fusion Industrial IoT System for Food Quality Assessment and Sensing,” IEEE Transactions on Industrial Informatics, vol. 20, no. 1, pp. 337–348, 2024. doi: 10.1109/TII.2023.3262197.   [2] “Artificial Intelligence and Digital Twins in Postharvest Food Systems: Transforming Quality Prediction, Shelf-Life Management, and Food Loss Reduction,” Frontiers in Sustainable Food Systems, vol. 10, 2026. doi: 10.3389/fsufs.2026.1932749.   M. G. Corradini, “Shelf Life of Food Products: From Open Labeling to Real-Time Measurements,” Annual Review of Food Science and Technology, vol. 9, pp. 251–269, 2018. doi: 10.1146/annurev-food-030117-012433.   F. Cui, S. Zheng, D. Wang, X. Tan, Q. Li, J. Li, and T. Li, “Recent Advances in Shelf- Life Prediction Models for Monitoring Food Quality,” Comprehensive Reviews in Food Science and Food Safety, vol. 22, no. 2, pp. 1257–1284, 2023. doi: 10.1111/1541-4337.13110.   [5] A. Gupta and V. Jain, “Blockchain-Assisted IoT Framework for Reliable Monitoring and Predictive Analytics in Perishable Food Supply Chains,” Journal of Food Process Engineering, vol. 49, no. 1, Art. no. e70333, 2026. doi: 10.1111/jfpe.70333.   [6] A. Lamberty and J. Kreyenschmidt, “Technical, Process-Related and Sustainability Requirements for IoT-Based Temperature Monitoring in Fruit and Vegetable Supply Chains,” Discover Food, vol. 5, Art. no. 151, 2025. doi: 10.1007/s44187-025-00427-1.   [7] P. Li, J. Yang, A. M. Jiménez-Carvelo, and S. W. Erasmus, “Applications of Food Packaging Quick Response Codes in Information Transmission Toward Food Supply Chain Integrity,” Trends in Food Science & Technology, vol. 146, Art. no. 104384, 2024. doi: 10.1016/j.tifs.2024.104384.   [8] M. Liang et al., “Deep Learning Models for Shelf- Life Prediction and Regulation of Various Foods: A Systematic Review,” Journal of Food Science, vol. 91, no. 3, Art. no. e70945, 2026. doi: 10.1111/1750-3841.70945.   9 M. Masuduzzaman, E. Hassini, M. F. Rahaman, et al., “FreshTrack: An Innovative IoT-Sensor-Driven Food Freshness Estimation Framework Integrating Blockchain,” Scientific Reports, vol. 16, Art. no. 13847, 2026. doi: 10.1038/s41598-026-44579-1.   [10] J. Qiao, M. Zhang, L. Qiu, A. S. Mujumdar, and Y. Ma, “Visual Early Warning and Prediction of Fresh Food Quality Deterioration: Research Progress and Application in Supply Chain,” Food Bioscience, vol. 59, Art. no. 103671, 2024. doi: 10.1016/j.fbio.2024.103671.

Editorial & Review Transparency

Publication Date

10/26/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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