Randomized trial demonstrates automated dietary tracking in users, suggesting improved nutrition monitoring.
This paper presents an AI-powered mobile web application for automated dietary tracking and nutritional analysis. The proposed system simplifies food logging using food image recognition, barcode scanning, and manual search features integrated with cloud-based synchronization. The application utilizes artificial intelligence models for food classification and retrieves nutritional data such as calories, proteins, carbohydrates, and fats from open food databases. Users can track daily calorie intake, monitor hydration levels, analyze macro nutrients, and view progress through interactive dashboards and analytics. The system is developed using Next.js, React.js, Firebase Firestore, Transformers.js, Open Food Facts API, and Recharts to provide a responsive and scalable dietary tracking solution. The study demonstrates how AI, cloud computing, and modern web technologies can improve user engagement and simplify nutrition monitoring and health management.
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vaidya et al. (2026) studied this question.
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