AI-Powered Smart Food Expiry & Waste Reduction System
DOI:
https://doi.org/10.46610/RTAIA.2025.v04i03.010Keywords:
Barcode scanning, Dietary recommendations, Food inventory tracking, Food waste reduction, Health risk detection, Machine learning, Nutrition analysis, OCR, Personalized health system, Smart food managementAbstract
Household food waste is rising steadily, and this growth is closely linked to several health and safety issues. Most people still manage their food manually, like checking expiry dates on packages or guessing whether an item is still suitable to eat. These methods are often unreliable and take more time than expected. In this study, we propose SmartBite AI, a web-based tool created to make food tracking easier and more accurate. The system uses barcode scanning and optical character recognition (OCR) to automatically record expiry dates and keep the inventory updated. It also allows users to enter their own details to improve tracking results. Beyond storing data, SmartBite AI applies machine learning to understand individual health factors, including allergies, dietary needs, and medical conditions. With this information, it can send alerts, recommend meals, and support better food-related decisions. By reducing unnecessary waste and encouraging more careful food use, the proposed system helps promote sustainable living and supports healthier daily habits.
References
G. Mariammal, S. S, and N. J, “Real-Time Expiry Alert System for Safer Retail using Barcode and QR Code Recognition,” 2025 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pp. 1616–1621, Jun. 2025, doi: https://doi.org/10.1109/icicv64824.2025.11085659
K. Agrawal, P. Goktas, N. Kumar, and M.-F. Leung, “Artificial intelligence in personalized nutrition and food manufacturing: a comprehensive review of methods, applications, and future directions,” Frontiers in Nutrition, vol. 12, Jul. 2025, doi: https://doi.org/10.3389/fnut.2025.1636980
K. A. Kalu, G. Ataguba, O. Onifade, F. Orji, and N. Giweli, “Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review,” Nutrients, vol. 17, no. 20, pp. 3223–3223, Oct. 2025, doi: https://doi.org/10.3390/nu17203223
H. Çetiner and B. Cetişli, “Optic Character Recognition Using Image Processing Techniques,” Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 13, no. 4, pp. 1067–1082, Dec. 2024, doi: https://doi.org/10.17798/bitlisfen.1500558
S. B. Dhal and D. Kar, “Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review,” Deleted Journal, vol. 7, no. 1, Jan. 2025, doi: https://doi.org/10.1007/s42452-025-06472-w
H. K. Gill and Sehgal, “Context Aware Recommender Systems using Deep Neural Network,” Juit.ac.in, 2022, doi: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/6118
L. Gong, M. Yu, W. Duan, X. Ye, K. Gudmundsson, and M. Swainson, “A Novel Camera Based Approach for Automatic Expiry Date Detection and Recognition on Food Packages,” IFIP Advances in Information and Communication Technology, pp. 133–142, 2018, doi: https://doi.org/10.1007/978-3-319-92007-8_12
Praynlin, R. Ganesh, and Santhosh, “Smart Fridge Keeper -An IoT-Driven Food Management System. International Journal of Scientific Research and Engineering Development, vol. 7, no. 6, 2024, Available: https://www.ijsred.com/volume7/issue6/IJSRED-V7I6P139.pdf
L. O. Badru, V. Vasudevan, G. I. Lingam, and M. G. M. Khan, “MERN Stack Web-Based Education Management Information Systems for Pacific Island Countries,” SN Computer Science, vol. 4, no. 1, Nov. 2022, doi: https://doi.org/10.1007/s42979-022-01457-7
G. Farr-Wharton, M. Foth, and J. H.-J. Choi, “Identifying factors that promote consumer behaviours causing expired domestic food waste,” Journal of Consumer Behaviour, vol. 13, no. 6, pp. 393–402, Jul. 2014, doi: https://doi.org/10.1002/cb.1488
