MOBILE APPLICATION TO CALCULATE FOOD CALORIES

Abstract

The research addressed a pressing issue: the absence of a practical, accurate real-time solution for food and calorie detection, impacting individuals' ability to manage their dietary intake effectively amidst a fast-paced lifestyle. To address this, our project employs Java, computer vision, and machine learning, specifically YOLOv8, to create a real-time food and calorie detection application, where YOLOv8 serves as the primary model. Utilizing image recognition, the app analyzes food items captured via the device's camera, providing instant, detailed nutritional information, including calorie content. Our approach prioritizes user-friendly design and real-time processing, ensuring an optimal user experience. The study showcases the successful implementation of the mobile app, achieving high accuracy in food recognition and calorie estimation for various Vietnamese foods. In conclusion, this real-time application offers a promising solution for dietary monitoring, empowering users with an accessible, accurate, and user-friendly tool for proactive steps toward healthier eating habits. This study opens avenues for further AI-driven research in nutrition and health management, contributing to innovations in healthcare and the food industry