Smart Nutrition Assistant Using Food Image Recognition and Calorie Estimation Using Deep Learning

konjeti Vijaya Nirmala Devi; Tadiparthi Manasa; Darapaneni Sirisha; Aramalla Dhanush Reddy; Gunturu Markandeyulu

doi:10.36948/ijfmr.2026.v08i02.72548

Smart Nutrition Assistant Using Food Image Recognition and Calorie Estimation Using Deep Learning

Author(s)	Ms. konjeti Vijaya Nirmala Devi, Tadiparthi Manasa, Darapaneni Sirisha, Aramalla Dhanush Reddy, Gunturu Markandeyulu
Country	India
Abstract	Accurate calorie and nutritional tracking is essential for maintaining healthy lifestyles, yet manual food logging remains time-consuming, error-prone, and abandoned by most users. This paper presents the Smart Nutrition Assistant v2.0, an AI-powered web application that uniquely combines state-of-the-art Computer Vision (CV) with deep learning techniques to automatically identify foods from photographs and estimate absolute portion sizes and nutritional values. The system employs Open-Vocabulary Food Recognition using CLIP (Contrastive Language-Image Pretraining) with the ViT-B-32 architecture, supporting over 100 food categories including Indian, Western, Asian, and Mexican cuisines without rigid categorical constraints. Advanced portion estimation leverages OpenCV-based plate detection through Hough Circle Transform, HSV-LAB color segmentation, and hemispherical volume approximation combined with food density mapping to derive accurate meal-level nutritional values scaled from per-100g standards. A multi-strategy barcode scanning pipeline integrates pyzbar, OpenCV BarcodeDetector, and OpenCV QRCodeDetector, querying the OpenFoodFacts API for packaged product nutrition. A pre-meal and post-meal comparative waste analysis module provides real-time breakdowns of calories consumed versus wasted. Experimental results demonstrate that the system achieves robust food recognition accuracy across diverse meal types with an intelligent fallback mechanism ensuring consistent performance even in resource-constrained environments. The proposed system offers a fast, reliable, and user-friendly solution for health-conscious individuals and patients managing dietary requirements.
Keywords	Food Recognition, Deep Learning, CLIP, Calorie Estimation, Computer Vision, Barcode Scanning, Portion Estimation, Nutritional Analysis, Flask, PyTorch
Field	Computer > Data / Information
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-27
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.72548

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Smart Nutrition Assistant Using Food Image Recognition and Calorie Estimation Using Deep Learning

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