AI-Driven Food Tracking and Diet Recommendation with Calorie Estimation System

Abstract: In today's fast-paced world, maintaining a balanced diet is increasingly challenging, especially with the limitations of manual food tracking systems. This paper presents an AI-driven food tracking and diet recommendation system that utilizes YOLOv8 for real-time food recognition, combined with a Decision Tree algorithm for personalized meal suggestions. Users upload an image of their meal, and the system detects food items, estimates portion sizes, and calculates nutritional values using a pixel-to-gram ratio. Personalized recommendations are generated based on dietary preferences, user goals, and daily intake, while a tracking module monitors consumption patterns over time. The proposed system enhances user convenience, improves accuracy in calorie estimation, and promotes healthier eating habits through data-driven insights. Experimental results demonstrate the system's ability to accurately identify diverse food items, provide meaningful dietary suggestions, and enable continuous health monitoring.

Keywords: Food recognition, calorie estimation, YOLOv8, decision tree, diet recommendation, Indian cuisine, food tracking, nutrition analysis.