Academy & Industry Research Collaboration Center (AIRCC)

Volume 9, Number 17, December 2019

An Intelligent Mobile Application to Automate Food Health Recommendation Using Deep Learning


Peiqi Gu1, Yu Sun1 and Fangyan Zhang2, 1California State Polytechnic University, USA and 2ASML, USA


As the global health condition declines, people have started to be more conscious about their health. In addition, the development of deep learning, especially in the sector of image recognition, proliferates, provides more convenience for people to monitor their health. Even though some food recognition applications appear on the internet, most of them are inaccurate, and there aren’t any researches that focus on the correlation between the accuracy of the model and attribute of the model. In addition, it is still inconvenient for people to gather information about how the food they eat everyday affects their health. Hence, in this project, the advanced development of deep learning was utilized for making an app which could be used to recognize a picture of the food taken by a phone and to display the food’s effect on a person’s certain health conditions. This project, or the application, has two main components: a model that can recognize the actual food through the camera of the phone and a database that stores the effects of the foods toward different kinds of health problems. After taking the photo, the application will display the effect of the foods to certain health problems that the user wants to see.

The experiment part of this project was inclined more on the optimization of the image recognition model. The result of this experiment indicated that more pictures in one category, less categories in total, and higher image resolution can improve the accuracy of the recognition model. This finding will be used on optimizing both the model and the application.


Deep Learning, Food Health Recommendation