CalorieInsight: A Personalized Dietary Management System Using AI

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.

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Publication Details

Journal
International Research Journal on Advanced Engineering Hub (IRJAEH)
Published
2025-05-20
DOI
https://doi.org/10.47392/irjaeh.2025.0344
Citations
1
Primary Topic
Nutritional Studies and Diet
Type
article
Field-Weighted Citation Impact
1.88
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article

CalorieInsight: A Personalized Dietary Management System Using AI

B. Ramya, D. Subbarao
1 citations
International Research Journal on Advanced Engineering Hub (IRJAEH)
Nutritional Studies and Diet
1.88
article

CalorieInsight: A Personalized Dietary Management System Using AI

B. Ramya, D. Subbarao
article en
1 citations

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.

International Research Journal on Advanced Engineering Hub (IRJAEH)Vol. 3(05)
Dr. NTR University of Health Sciences (IN)
Openalex Percentile: Top 16%
Nutritional Studies and Diet
1.88
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