Real-Time Food Detection and Nutritional Tracking Application for Personalized Health Management Using MobileNetV2

Accurate and timely dietary assessment is essential for health management. This study presents a deep learning-based approach for real-time, personalized food analysis through image analysis. The preparation processaccurately predicts the content of food products based on images captured by a smartphone camera. The model is trained on a comprehensive database of food images along with their corresponding menu items. To enhance user experience and engagement, the system integrates a mobile application that allows users to easily record their meals by taking photos. Test resultsshow the system's effectiveness in accurately estimating macronutrients and calories, providing users with a better understanding of making dietary decisions. This research contributes to the development of smart health devices and has the potential to impact public health through health promotion.