Know-Your-Plate: An Application for Diet Analysis through Artificial Intelligence

Dr. Shaneth C. Ambat

College of Computer Studies and Multimedia Arts, Program Director, FEU Institute of Technology

Corresponding Author:

Hezekiah John V. Rizan

FEU Institute of Technology, 3780 Blk. 9 Lot 17, Durian St., Centennial II, Pinagbuhatan, Pasig City, 1602, Philippines;

Corresponding Author:

Rom Braveheart P. Leuterio

FEU Institute of Technology, Samson Apartment Unit B, Pagasa Subd., Anabu, 1-B Imus, Cavite, Philippines;

Corresponding Author:

John Patrick G. Chua

FEU Institute of Technology, Blk 21 Lot 7 Pearl Street Citation Homes Barangay Bahay Pare Meycauayan Bulacan, Philippines;


Chrys Uoie A. Salazar5

FEU Institute of Technology, 469 Block 27-A Brgy. Addition Hills, Mandaluyong, Philippines;

Corresponding Author:

Dr. Hadji J. Tejuco

College of Computer Studies and Multimedia Arts, Faculty, FEU Institute of Technology;

Corresponding Author:

Anthony D. Aquino

College of Computer Studies and Multimedia Arts, Faculty, FEU Institute of Technology

Corresponding Author:

Abstract :

With the advent of the current health situation upon the world, the public has promptly heeded health professionals’ advice on strictly keeping a healthy lifestyle. Our diet and nutrition mainly factor our lifestyle. In this study, the researchers aim to develop an application aligned with this objective by providing the Filipino public a means to monitor, assess, and visualize their health through their diet. The development of the application was employed with artificial intelligence through K-means clustering and Image Classification using ResNet architectures for diet and nutrition analysis, Cloud technology for storing diet records, and Augmented Reality for visualization. The development of the application yielded these results; diet analyses were done through the K-means algorithm yielded that diet of individual Filipinos can be divided into three clusters, where each posed health risks and diseases, and a ResNet-18 classifier yielded an 81% accuracy in classifying 15 different Filipino foods.


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