CNN Integrated Mobile Application: Food Image Recognition for Recipe Generation
DOI:
https://doi.org/10.24191/jcrinn.v10i2.554Keywords:
CNN, Mobile Application, Food Image, RecognitionAbstract
The rapidly advancing of the digital world has encouraged the use of mobile applications in almost every aspects of our everyday life. This includes transforming the way we obtain our meal, whether to order from food providers or simply cook for ourselves. The CNN Integrated Mobile Application for Food Recipe Generation is intended to improve users' culinary experiences by offering suggestions for recipes and intelligent ingredient recognition. This research explores the Convolutional Neural Networks (CNN) algorithm to tackle the problems of effective ingredient management and minimize food waste through smartphone application. Through the mobile application, the ingredient photos can be scanned by users or uploaded, after which the CNN model processes the images to precisely identify the ingredients. The application provides users with a wide range of meal alternatives that are customized to their available ingredients by retrieving relevant recipes from external databases such as the Spoonacular API based on the recognized ingredients. The research methodology consists of 3 main phases, which are Data Preprocessing, CNN Implementation and Performance Evaluation. In this research, the CNN algorithm has generated a good and acceptable performance with more than 96% accuracy. This research has shown how machine learning, mobile development, and user-centric design can be successfully combined to create a useful tool for contemporary culinary demands. The app encourages a move towards more sustainable and thoughtful eating habits by acting as an incentive for change at the community level. When communities embrace these ideas, the app plays a key role in tackling more significant social issues associated with food waste, supporting international initiatives that are detailed in the UN Sustainable Development Goals (SDGs) for a more sustainable and responsible society.
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Copyright (c) 2025 Muhammad Imran Nor Azlan Shah, Norlina Mohd Sabri, Gloria Jennis Tan, Zhiping Zhang (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.