NutriVision: YOLO-SAM-Driven Dietary App for Personalized Food Recommendation and Meal Tracking

India's diverse cuisine and lack of nutritional awareness contribute significantly to lifestyle diseases. The rising prevalence of such diseases has increased the demand for personalized dietary advice, highlighting the need for effective nutrition monitoring. Current solutions often rely on time-consuming manual food data entry or costly consultations with dietitians, making them inaccessible to many. The proposed Android application personalizes nutrition management by first gathering user data (age, weight, height, physical activity level, health conditions) to calculate TDEE and individual macronutrient requirements. Users then capture meal images, which are analyzed using YOLOv11 for food detection and SAM for precise segmentation. Based on nutritional analysis and the user's health condition, the app offers safe, healthier food recommendations and updates a dashboard that tracks daily nutrient intake and meal history. The research focuses on integrating YOLO, SAM, and recommendation-based meal tracking to enhance dietary management. In NutriVision, the object detection model reliably identifies and classifies food items, while SAM ensures precise segmentation with clearly defined object masks. The outputs from these models are used for nutritional analysis, which evaluates the macronutrient content of each item. This analysis then informs tailored food recommendations, delivering personalized dietary suggestions and enhancing the overall user experience.