A mobile application for food quality recognition - Tang, Connie Ming Xin (2024)
This project seeks to design and develop a mobile application that has the ability to discern with quality of different foods in response to the emerging issue of food quality and its effects on health. One of them is that people have a common difficulty in evaluating the qualities of foods and thus suffer some adverse health effects and the act of food waste among the various generations especially the young ones. This project uses the concepts of computer vision, machine learning, and artifical intelligence to develop an application that rates the quality of foods; particularly fruits and vegetables. The approach entails collections of large datasets of images in food items and using AutoKeras and EfficientNet B7 towards creating models. After that, these models are incorporated into the mobile application interface to help the users to navigate through the app. The utilization of the application was most importantly in validating the application’s capability to recognize food quality and the stage of ripeness of the fruits and vegetables. Thus, by way of revealing details about food quality, the app contributing to food safety. Another strength is its introduction of sophisticated technologies into a basic tool by also providing knowledge in a utilitarian approach. This mobile application can be considered as an innovative solution that goes beyond providing food safety and improving sustainability to acting as an informant that increases people’s understanding of proper eating patterns and how to achieve better food sustainability. Therefore, this work can be indeed considered as a useful contribution to public health, environmental conservation and food security in general.