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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM60728

NUTRILENS: AI-Powered Nutrition Tracking and Recommendation System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM60728

Volume:

10 |

4 |

April 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem60728

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MPS

Ms. P. Sasikala

MPDLR

Mrs. P. DeepthiNair Lakshana R

NJ

Nethra J

NR

Netra R

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Abstract

– NutriLens is an AI-powered nutrition tracking and recommendation system designed to improve dietary management through intelligent automation and personalization. The system enables users to log food using multimodal inputs such as image recognition, voice input, barcode scanning, and manual entry. It utilizes Generative AI and machine learning techniques to analyze food data and provide accurate nutritional insights, including calories, macronutrients, and micronutrients. A Retrieval-Augmented Generation (RAG) framework ensures reliable and context-aware responses by integrating verified nutritional knowledge. The platform also provides personalized recommendations and interactive dashboards for monitoring health metrics such as calorie intake, hydration, and weight trends. Developed using Python, Streamlit, and SQLite, NutriLens offers a scalable, user-friendly, and intelligent solution for modern nutrition management.

KEYWORDS

– Artificial Intelligence Machine Learning Nutrition Tracking Food Recognition Retrieval-Augmented Generation (RAG) LSTM Reinforcement Learning Health Analytics Multimodal AI

Cite This Article

Ms. P. Sasikala, Mrs. P. DeepthiNair Lakshana R, Nethra J, Netra R, "NUTRILENS: AI-Powered Nutrition Tracking and Recommendation System," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, April 2026. https://doi.org/10.55041/ijsrem60728

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Publication Date

4/20/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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5

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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