Year: 2025
Type: article
Abstract: This paper presents CalorieInsight, an AI-driven system for automated food calorie estimation and personalized dietary management. Leveraging YOLOv8, a real-time object detection algorithm, the system accurately identifies and quantifies multiple food items from single image inputs, achieving a food recognition accuracy of 99.7%. This surpasses the 95.21% accuracy [1] reported in prior work utilizing convolutional neural networks. An adaptive recommendation engine, employing a decision tree algorithm, generates personalized meal plans based on user-specific dietary preferences, health goals, and activity levels. A tracking module facilitates continuous monitoring of nutritional intake, enabling real-time goal adjustments. The system integrates portion estimation, nutritional API data, and user feedback to provide comprehensive dietary insights. Preliminary evaluations demonstrate CalorieInsight 's potential to significantly enhance personalized nutrition through intelligent food analysis and adaptive dietary planning.
Authors: Burre Ramya, Doddapaneni V. Subbarao
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410606185