Consumer Usability Test of Mobile Food Safety Inquiry Platform Based on Image Recognition

Jun-Woo Park, Young-Hee Cho, Mi-Kyung Park () and Young-Duk Kim ()
Additional contact information
Jun-Woo Park: Division of Automotive Research, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu 42988, Republic of Korea
Young-Hee Cho: School of Food Science and Biotechnology, Kyungpook National University, Daegu 41566, Republic of Korea
Mi-Kyung Park: School of Food Science and Biotechnology, Kyungpook National University, Daegu 41566, Republic of Korea
Young-Duk Kim: Division of Automotive Research, Daegu Gyeongbuk Institute of Science & Technology (DGIST), Daegu 42988, Republic of Korea

Sustainability, 2024, vol. 16, issue 21, 1-21

Abstract: Recently, as the types of imported food and the design of their packaging become more complex and diverse, digital recognition technologies such as barcodes, QR (quick response) codes, and OCR (optical character recognition) are attracting attention in order to quickly and easily check safety information (e.g., food ingredient information and recalls). However, consumers are still exposed to inaccurate and inconvenient situations because legacy technologies require dedicated terminals or include information other than safety information. In this paper, we propose a deep learning-based packaging recognition system which can easily and accurately determine food safety information with a single image captured through a smartphone camera. The detection algorithm learned a total of 100 kinds of product images and optimized YOLOv7 to secure an accuracy of over 95%. In addition, a new SUS (system usability scale)-based questionnaire was designed and conducted on 71 consumers to evaluate the usability of the system from the individual consumer’s perspective. The questionnaire consisted of three categories, namely convenience, accuracy, and usefulness, and each received a score of at least 77, which confirms that the proposed system has excellent overall usability. Moreover, in terms of task completion rate and task completion time, the proposed system is superior when it compared to existing QR code- or Internet-based recognition systems. These results demonstrate that the proposed system provides consumers with more convenient and accurate information while also confirming the sustainability of smart food consumption.

Keywords: food safety; image recognition; artificial intelligence; usability test (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/21/9538/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/21/9538/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:21:p:9538-:d:1512480

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().