Design and Development of A WeChat Mini Program for assisting Chinese Tourists in identifying Thai Vegetables
Abstract
Background and Aims: This study aims to bridge the gap in knowledge about Thai vegetables among Chinese tourists visiting Thailand. Through a combination of questionnaires and literature reviews, the researcher identified a lack of familiarity with Thai vegetables among Chinese tourists, potentially influenced by regional cultural differences and eating habits. To address this issue, the study proposes the development of a WeChat mini program as an educational tool to assist Chinese tourists in identifying and understanding Thai vegetables.
Materials and Methods: Data was collected through questionnaires administered to Chinese tourists to determine their level of knowledge and familiarity with Thai vegetables. The findings were complemented by a comprehensive literature review on the types, nutritional values, and promotion status of Thai vegetables in the Chinese market.
The questionnaire survey revealed specific Thai vegetables that were unfamiliar to Chinese tourists. This information was then incorporated into a database to develop the "Identifying Thai Vegetables" WeChat mini program.
Results: The "Identifying Thai Vegetables" WeChat mini program is a well-designed and effective tool that facilitates the identification of Thai vegetables and enhances users' understanding of Thai cuisine and culture. By continuously innovating and expanding its features, the mini program has the potential to become an indispensable resource for individuals interested in exploring the rich culinary landscape of Thailand, promoting awareness and increasing the consumption of Thai vegetables among Chinese tourists.
Conclusion: The mini program features a bilingual interface in both Chinese and Thai, facilitating accurate identification and comprehensive information retrieval. By leveraging machine learning and image recognition technologies, the mini program allows users to upload or take photos of vegetables, which are then analyzed to provide names, descriptions, and pronunciation guides in both languages. This tool aims to bridge the language barrier and enhance the culinary experience for Chinese residents and tourists in Thailand, contributing to a deeper appreciation and knowledge of local produce.
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References
Bangkok Post. (2023). Thailand targets 25m tourists this year. Retrieved from https://www.bangkokpost.com/business/general/2603786/thailand-targets-25 m-tourists-this-year
Karanikolas, N.N. (2014).Knowledge and information in nutrition software design. Procedia-Social and Behavioral Sciences, 147, 398-404. https://doi.org/10.1016/j.sbspro.2014.07.118
Li, R., & Huang, W. (2023). Research on the visual design of food applications based on the regional cultural experience. Packaging and Design, 1, 1-10.
Nadeau, J. L., & Nadeau, J. (2017). Thai: An Essential Grammar. Routledge.
Seubsman, S.-A., Suttinan, P., Dixon, J., & Banwell, C. (2013). Thai meals. Sukhothai Thammathirat Open University, Thailand. In book: Meals in Science and Practice (pp.413-451). DOI:10.1533/9781845695712.6.413
Sukalakamala, S., & Brittin, H. C. (2006). Food practices, changes, preferences, and acculturation of Thais in the United States. Journal of the American Dietetic Association, 106(1), 103-108.
Suryawanshi, Y., Patil, K., Chumchu, P. (2022). VegNet: dataset of vegetable quality images for machine learning applications. Data Brief. 45, 108657. doi: 10.1016/j.dib.2022.108657.
Tang, Y. X. (2021). Study on learning mode of Thai WeChat mini program. Neijiang Science and Technology, 42(1), 98-100.
Wang, L., & Li, J. (2019). The impact of color schemes on the usability of vegetable recognition applications. Journal of Agricultural Informatics, 10(2), 45-58.
Wiwatkitbhuwadol, N. (2023). The art of entrepreneurship: Navigating success through design for business. The Journal of Social Communication Innovation, 11(2), 162-170.
Xu, Y., & Bao, Y. (2017). Research on user experience design of food applications based on users' social characteristics. Design, 1, 1-10.