Smart shopping assistant using mobile application development
In the contemporary retail landscape, mobile applications have become indispensable tools for grocery shoppers. However, many existing platforms fail to offer a truly integrated and intelligent experience, lacking advanced features for personalized health-conscious decision-making, effortless lis...
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| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
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| Online Access: | http://eprints.utar.edu.my/6962/1/fyp_CN_2025_LWL.pdf http://eprints.utar.edu.my/6962/ |
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| _version_ | 1854094446455422976 |
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| author | Lok, Wai Loon |
| author_facet | Lok, Wai Loon |
| author_sort | Lok, Wai Loon |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | In the contemporary retail landscape, mobile applications have become indispensable
tools for grocery shoppers. However, many existing platforms fail to offer a truly
integrated and intelligent experience, lacking advanced features for personalized
health-conscious decision-making, effortless list management, and practical in-store
support. This project introduces the Smart Shopping Assistant, a cross-platform mobile
application designed to address these limitations by creating a seamless, secure, and
highly personalized shopping journey. The application is developed using the Flutter
framework for broad device compatibility and is powered by Supabase for robust and
secure backend services, including user authentication, data management, and PostGISbased
geospatial queries. At its core, the assistant leverages a sophisticated AI engine,
orchestrated through Dify and utilizing the Gemini API, to provide users with
personalized nutritional analysis based on their specific health profiles. A key
innovation is the on-device integration of Optical Character Recognition (OCR) via
Google's ML Kit, which empowers users to instantly scan and digitize product nutrition
tables for real-time evaluation. Furthermore, the inclusion of the Google Maps Service
provides comprehensive location-based features, including nearby store discovery,
address selection with autocompletion, and route visualization. Developed under an
Agile methodology, the project prioritizes a user-centric design that blends convenience
with valuable insights. By unifying an intelligent AI assistant, real-time nutritional data
analysis via OCR, a full-featured e-commerce system with order management, and
practical location-based services within a single platform, the Smart Shopping Assistant
aims to empower consumers to make more informed, efficient, and health-aware
purchasing decisions. This work contributes to the evolution of mobile commerce by
delivering an integrated solution that enhances user trust, convenience, and dietary
consciousness in everyday shopping.
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| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.6962 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.69622025-12-28T10:45:51Z Smart shopping assistant using mobile application development Lok, Wai Loon T Technology (General) TD Environmental technology. Sanitary engineering In the contemporary retail landscape, mobile applications have become indispensable tools for grocery shoppers. However, many existing platforms fail to offer a truly integrated and intelligent experience, lacking advanced features for personalized health-conscious decision-making, effortless list management, and practical in-store support. This project introduces the Smart Shopping Assistant, a cross-platform mobile application designed to address these limitations by creating a seamless, secure, and highly personalized shopping journey. The application is developed using the Flutter framework for broad device compatibility and is powered by Supabase for robust and secure backend services, including user authentication, data management, and PostGISbased geospatial queries. At its core, the assistant leverages a sophisticated AI engine, orchestrated through Dify and utilizing the Gemini API, to provide users with personalized nutritional analysis based on their specific health profiles. A key innovation is the on-device integration of Optical Character Recognition (OCR) via Google's ML Kit, which empowers users to instantly scan and digitize product nutrition tables for real-time evaluation. Furthermore, the inclusion of the Google Maps Service provides comprehensive location-based features, including nearby store discovery, address selection with autocompletion, and route visualization. Developed under an Agile methodology, the project prioritizes a user-centric design that blends convenience with valuable insights. By unifying an intelligent AI assistant, real-time nutritional data analysis via OCR, a full-featured e-commerce system with order management, and practical location-based services within a single platform, the Smart Shopping Assistant aims to empower consumers to make more informed, efficient, and health-aware purchasing decisions. This work contributes to the evolution of mobile commerce by delivering an integrated solution that enhances user trust, convenience, and dietary consciousness in everyday shopping. 2025-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/6962/1/fyp_CN_2025_LWL.pdf Lok, Wai Loon (2025) Smart shopping assistant using mobile application development. Final Year Project, UTAR. http://eprints.utar.edu.my/6962/ |
| spellingShingle | T Technology (General) TD Environmental technology. Sanitary engineering Lok, Wai Loon Smart shopping assistant using mobile application development |
| title | Smart shopping assistant using mobile application development |
| title_full | Smart shopping assistant using mobile application development |
| title_fullStr | Smart shopping assistant using mobile application development |
| title_full_unstemmed | Smart shopping assistant using mobile application development |
| title_short | Smart shopping assistant using mobile application development |
| title_sort | smart shopping assistant using mobile application development |
| topic | T Technology (General) TD Environmental technology. Sanitary engineering |
| url | http://eprints.utar.edu.my/6962/1/fyp_CN_2025_LWL.pdf http://eprints.utar.edu.my/6962/ |
| url_provider | http://eprints.utar.edu.my |