International Journal of Advanced Research in Computer and Communication Engineering

Abstract: In today’s well-being-conscious era, individuals strive to maintain balanced nutrition and monitor their dietary intake, yet manual tracking methods remain cumbersome and prone to inaccuracies. The proposed Artificial Intelligence Driven AI-Powered Nutrition Evaluation System provides a smart and efficient solution to analyze and evaluate the nutritional composition of food items using artificial intelligence and image recognition. By capturing or uploading a meal image, the framework identifies the food components and estimates their nutritional values, including calories, proteins, carbohydrates, fats, and essential micronutrients. The framework employs Convolutional Neural Networks (CNNs) for image classification and integrates a nutritional database for value computation. It further customizes recommendations based on user-specific conditions such as diabetes, obesity, or deficiencies, assisting in well-beingy decision-making. The AI-driven approach significantly minimizes human error, enhances user engagement, and promotes sustainable well-being monitoring through automation and personalization.

Keywords: Artificial Intelligence, Nutrition Tracking, Machine Learning, CNN, Health Monitoring, Image Recognition.

Downloads: | DOI: 10.17148/IJARCCE.2025.14937