Filipino Meal Recognition Scale with Food Nutrition Calculation and Smart Application

Abstract

Nutritional awareness is considered prevalent in today’s society. In effect, more people have been inclined to perform food calculations in the food they eat to improve their physical fitness and balance the meals they eat. In this study, the Internet of Things was used through a mobile application with Filipino meal recognition that was integrated into a weighing scale to simplify meal recognition and food calculation without individually scaling and measuring the macronutrients of each food. An ESP32 was programmed to determine the weight of the food sample. Moreover, a TensorFlow Lite Model was created using Teachable Machine, whereas the dataset comprised three Filipino meal combinations of rice, pork adobo, and pork giniling; rice, ginataang kalabasa, and pork giniling; and rice, ginataang kalabasa, and pork adobo. The model identified the 15 samples of Filipino meals per combination. The precision was 91.26% for the first meal combination, 82.73% for the second meal combination, and 85.46% for the third meal combination. One-factor ANOVA was conducted to determine the similarities of the actual and predicted macronutrient contents of the food samples, whereas 10 weight values of successfully determined food meals for each combination were used. The model recognized each Filipino food combination with an overall accuracy of 93.33%. The predicted macronutrient contents were similar to the actual macronutrient contents of the meal based on the statistical analysis performed.

Keywords

• food recognition
• TensorFlow
• machine learning