Abstract
A lack of understanding about one's dietary intake can lead to various health difficulties and lifestyle challenges. When it comes to dietary choices, the amount of nutrition intake plays a vital role for the human body. Nutrition provides energy to the human body, takes care of immune system and mental health. If humans do not consume proper nutrition in their daily food intake, they could lead to health concerns like malnutrition, poor cognitive development, and muscle weakness. For humans to be aware of the nutrition intake in their food, they do not have adequate nutrition information handy, or they cannot memorize all the food components nutritional value. Also, they cannot be aware of all the allergens present in each food component.
This project contributes to the advancement of automated multiple food component detection and nutritional analysis. This project aims to provide people with an easy-to-use tool to assess and evaluate the nutritional value of the food they eat that can allow humans to make informed dietary decisions and provide allergen information present in each food component if any. The tool allows users to upload food images of meals, detects the multiple food components present in their meal, evaluates the nutritional value, and provides allergen information if any of each food component is detected. The datasets used are food images, USDA nutrition dataset and FDA allergen data. In this project, Mask R-CNN deep learning model is used to detect multiple food components from the food images. The main goal of this project is to identify food components from the food images and provide nutrition data, achieving good accuracy results such that it can be helpful in the field of health and care for human health and wellness.