NUTRISCOPE
1 Mr. R. Krishna Nayak
Dept. of Computer Science and Engineering
Vignan’s Institute of Management and Technology for Women, Hyd.
Email: ramavath.krishna12@gmail.com
2 E. Spandana
Dept. of Computer Science and Engineering
Vignan’s Institute of Management and Technology for Women, Hyd.
Email: spandanaettaboina@gmail.com
3 M. Manasa Lalitha
Dept. of Computer Science and Engineering
Vignan’s Institute of Management and Technology for Women, Hyd.
Email: manasalalitha26@gmail.com
4 A. Geethanjali
Dept. of Computer Science and Engineering
Vignan’s Institute of Management and Technology for Women, Hyd.
Email: apurigeethanjali@gmail.com
Abstract—NutriScope is designed for food product analysis using image recognition and data analytics. It is built with a MERN stack. Users upload the images of food items, and the system processes through the Gemini API for obtaining nutritional information, which includes certain nutrients, possible health risks, and if it’s appropriate for a given age and gender. This enables appropriate dietary choices to be made. NutriScope feature allows users to compare several food products at a time. The system first groups products into high-protein, high- carb, and balanced, using k-means clustering and Chart.js. Nutritional data visualization enables users to see variations in nutrients and determine the most nutritious food product. A history sidebar also enables users to keep track of previous analyses and trends. MongoDB ensures security and stores users’ product analysis history. The application also employs AES encryption for user authentications. Real-time updates, API optimization, image validation, error handling, and user interaction enhance performance. The system can be accessed remotely via a responsive interface. NutriScope serve as an advanced food product analysis platform that enables users to make healthy lifestyle choices using data-driven nutritional decisions.
Key words —AI in nutrition, Chart.js, Gemini API, K-Means clustering, MERN stack, MongoDB Atlas
