AI-based Food Recognition and Nutrient Prediction

Abstract: Accurate food recognition and nutrient estimation are critical for practical dietary assessment, health monitoring, and personalized nutrition management. Traditional calorie tracking methods rely heavily on manual input and self-reporting, which are often inaccurate, time-consuming, and inconsistent. Existing AI-based food recognition systems primarily rely on basic convolutional neural networks and two-dimensional image analysis, limiting their ability to identify complex, mixed, and regional dishes and failing to estimate portion sizes accurately. These limitations significantly reduce their practical applicability, particularly for diverse cuisines such as Indian food. To address these challenges, this project presents Nutri Vision, an AI-driven framework for food recognition, portion size estimation, nutrient prediction, and personalized dietary guidance. The proposed system integrates advanced deep learning models including YOLOv8, Vision Transformers, and Region-Based Convolutional Neural Networks to accurately detect and classify multiple food items from a single image. Portion size estimation is achieved using pixel-to-gram conversion and depth-aware analysis, enabling reliable calorie and nutrient computation through integrated food databases. Furthermore, machine-learning-based decision models are employed to generate personalized diet recommendations and healthier food alternatives based on users’ goals and health conditions. The system delivers real-time, culturally adaptive, and scalable nutrition insights with high accuracy, making it suitable for applications in healthcare, fitness management, and nutrition research.