FoodSnap: Mobile Application Instant Pricing with Image Recognition

Muhammad Haziq  Azizan; Romiza Md Nor

doi:10.24191/jcrinn.v10i1.493

FoodSnap: Mobile Application Instant Pricing with Image Recognition

Authors

Muhammad Haziq Azizan College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Perlis Branch, Malaysia
Romiza Md Nor College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Perlis Branch, Malaysia

DOI:

https://doi.org/10.24191/jcrinn.v10i1.493

Keywords:

Image recognition, food recognition, Price estmation, machine learning, computer vision

Abstract

This project enhances traditional food pricing methods by employing image recognition and machine learning. Unlike physical price tags which can lead to inconsistencies, this app enables users, particularly financially constrained students, to capture food images and receive accurate pricing information instantly. The mobile application is built on a robust dataset to ensure the model's accuracy across various cuisines. The development process follows the waterfall model, consisting of five phases: Requirement Analysis, Design, Development, Evaluation, and Documentation. Key technologies used include TensorFlow for machine learning model training, MySQL for secure data storage, and Flutter for cross-platform mobile application development. This combination allows users to seamlessly capture images and receive real-time pricing details. The applications accuracy and usability were tested through user acceptance tests (UAT). During testing, all modules worked flawlessly, and the application was praised for its simplicity and effectiveness. The findings highlight that this mobile application significantly improves food pricing accessibility and accuracy. With ongoing updates, it holds the potential to transform consumer interaction with food pricing, increasing transparency and enhancing overall user satisfaction.

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References

Lee, G. G. C., Huang, C. W., Chen, J. H., Chen, S. Y., & Chen, H. L. (2019). AIFood: A large-scale food images dataset for ingredient recognition. IEEE Region 10 Annual International Conference, Proceedings/TENCON (pp. 802-805). IEEE Xplore. https://doi.org/10.1109/TENCON.2019.8929715

Nordin, M. J., Aziz, N., & Xin, O. W. (2019). Food image recognition for price calculation using Convolutional Neural Network. ACM International Conference Proceeding Series, Part F147955, 80–85.

Patil, A. (2021). Image recognition using machine learning. Social Science Research Network. https://doi.org/10.2139/ssrn.3835625

Quinn, I. (2023). Inconsistent pricing “costing retailers lost sales”. The Grocer. https://www.thegrocer.co.uk/consumer-trends/inconsistent-pricing-costing-retailers-lost-sales/567341.article

Reddy, K. U. K., Swathi, S., & Rao, M. S. (2022). Deep learning technique for automatically classifying food images. Mathematical Statistician and Engineering Applications, 71(4), 2362-2370.

Salim, N. O. M., Zeebaree, S. R. M., Sadeeq, M. a. M., Radie, A. H., Shukur, H. M., & Rashid, Z. N. (2021). Study for food recognition system using Deep learning. Journal of Physics: Conference Series, 1963(1), 012014.

Schmidgall, S. (2023, May 18). Brain-inspired learning in artificial neural networks: A review. arXiv.org. https://arxiv.org/abs/2305.11252v1

Yamashita, R., Nishio, M., G, R. K., DO, & Togashi, K. (2018). Convolutional neural networks: An overview and application in radiology. Insights Into Imaging, 9(4), 611–629.

Zakaria, M. Q. (2022, July 5). Terpaksa “ikat perut” sebab makanan mahal. Sinar Harian. https://www.sinarharian.com.my/article/210154/suara-sinar/analisis-sinar/terpaksa-ikat-perut-sebab-makanan-mahal

Zin, O. (2022). Pelajar terkesan harga makanan universiti naik – PKPIM. Utusan Malaysia. https://www.utusan.com.my/nasional/2022/03/pelajar-terkesan-harga-makanan-universiti-naik/