Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Engineering Applied Science 2025 (ICOSTAS-EAS 2025)

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Applying Bootstrapping Language-Image Pre-training for Nutrition Detection from Food Images

Authors
Ida Bagus Putra Manuaba1, *, I Wayan Suasnawa1, Komang Ayu Triana Indah1
1Information Technology Department, Politeknik Negeri Bali, Bali, Indonesia
*Corresponding author. Email: manuabaputra@pnb.ac.id
Corresponding Author
Ida Bagus Putra Manuaba
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-878-3_35How to use a DOI?
Keywords
Bootstrapping Language-Image Pre-training; Food Calorie Detection; LLaMA; Serverless Architecture
Abstract

Accessing accurate and comprehensive food calorie and nutrition data remains a challenge, as existing references are often incomplete, outdated, or difficult to access. This limitation reduces the effectiveness of daily dietary monitoring for both individuals and healthcare professionals. This study proposes a method that integrates Bootstrapping Language-Image Pre-training (BLIP) and the Large Language Model (LLaMA) to automatically detect food calories, providing broader coverage of nutritional data. Bootstrapping Language-Image Pre-training generates textual descriptions from food images uploaded by users, which the Large Language Model then processes to estimate key nutritional values, including calories, protein, fat, and carbohydrates. The experimental results demonstrate that this multimodal approach improves both accuracy and efficiency compared to conventional methods. The proposed method offers a practical tool for individuals to independently track daily nutrition and provides healthcare professionals with adaptive AI-based nutritional analysis, contributing to the advancement of digital health technologies in nutrition monitoring and dietary management.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Engineering Applied Science 2025 (ICOSTAS-EAS 2025)
Series
Advances in Engineering Research
Publication Date
31 October 2025
ISBN
978-94-6463-878-3
ISSN
2352-5401
DOI
10.2991/978-94-6463-878-3_35How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ida Bagus Putra Manuaba
AU  - I Wayan Suasnawa
AU  - Komang Ayu Triana Indah
PY  - 2025
DA  - 2025/10/31
TI  - Applying Bootstrapping Language-Image Pre-training for Nutrition Detection from Food Images
BT  - Proceedings of the International Conference on Sustainable Green Tourism Applied Science - Engineering Applied Science 2025 (ICOSTAS-EAS 2025)
PB  - Atlantis Press
SP  - 304
EP  - 311
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-878-3_35
DO  - 10.2991/978-94-6463-878-3_35
ID  - Manuaba2025
ER  -