International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Smartbite – Food Image Recognition |
|---|---|
| ABSTRACT | Food image recognition uses computer vision and deep learning techniques to automatically identify and classify food items from images. It supports applications such as dietary monitoring, calorie estimation, and personalized health management. The process generally involves image preprocessing, feature extraction, and classification using convolutional neural networks trained on large food image datasets. However, variations in lighting conditions, occlusions, and diverse food presentations remain significant challenges. Advances in model architectures and data augmentation techniques help improve system robustness. When combined with nutrition databases, these systems can provide real-time dietary feedback and assist users in maintaining healthier eating habits. This paper provides a comprehensive review of food image recognition methodologies focused primarily on Indian Cuisines and suggests improvements for real-world applications. |
| AUTHOR | SHRAVANI BIDVE, VARUN BHANDARKAR, SAKSHI BABAR, JANHAVI BANSODE, PRAJAKTA YADAV, KHUSHBOO SINGH epartment of Computer Science and Design, New Horizons Institute of Technology & Management University of Mumbai, India Professor, Department of Computer Science and Design, New Horizons Institute of Technology & Management University of Mumbai, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403033 |
| pdf/33_Smartbite – Food Image Recognition.pdf | |
| KEYWORDS | |
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