NUTRISCAN: An AI-Powered Smart Food Scanner for Real-Time Nutrition Analysis Using Deep Learning

NutriScan is an AI-powered food recognition and nutrition analysis system developed to automate calorie and nutrient estimation from food images. The system leverages a Convolutional Neural Network (CNN) model for accurate food classification and retrieves nutritional values including calories, proteins, fats, and carbohydrates in real time. The platform is built using a React and Tailwind CSS frontend for a responsive user interface, while Firebase cloud services handle authentication, real-time data storage, and cloud hosting. The system eliminates common errors arising from manual dietary entry and provides users with an analytics dashboard that visualizes daily intake and dietary trends to support informed decision-making. NutriScan demonstrates the practical integration of deep learning and cloud infrastructure to promote healthier lifestyle choices. Future enhancements include portion size detection via image segmentation and personalized meal recommendation engines.

Authors

Publication Details

Journal
Iconic Research and Engineering Journals
Published
2026-05-11
DOI
https://doi.org/10.64388/irev9i11-1717331
Primary Topic
Nutritional Studies and Diet
Type
article
Field-Weighted Citation Impact
0.00
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article

NUTRISCAN: An AI-Powered Smart Food Scanner for Real-Time Nutrition Analysis Using Deep Learning

S. Jothirharishh, G. Karthick, M. Harshavardhan, A. Jagadeeswaran
Iconic Research and Engineering Journals
Nutritional Studies and Diet
article

NUTRISCAN: An AI-Powered Smart Food Scanner for Real-Time Nutrition Analysis Using Deep Learning

S. Jothirharishh, G. Karthick, M. Harshavardhan, A. Jagadeeswaran
article en

Abstract

NutriScan is an AI-powered food recognition and nutrition analysis system developed to automate calorie and nutrient estimation from food images. The system leverages a Convolutional Neural Network (CNN) model for accurate food classification and retrieves nutritional values including calories, proteins, fats, and carbohydrates in real time. The platform is built using a React and Tailwind CSS frontend for a responsive user interface, while Firebase cloud services handle authentication, real-time data storage, and cloud hosting. The system eliminates common errors arising from manual dietary entry and provides users with an analytics dashboard that visualizes daily intake and dietary trends to support informed decision-making. NutriScan demonstrates the practical integration of deep learning and cloud infrastructure to promote healthier lifestyle choices. Future enhancements include portion size detection via image segmentation and personalized meal recommendation engines.

Iconic Research and Engineering JournalsVol. 9(11)
Openalex Percentile: Top 5%
Nutritional Studies and Diet
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