This study aims to develop a deep learning model to monitor rice serving amounts in institutional foodservice, enhancing personalized nutrition management. The goal is to identify the best convolutional neural network (CNN) for detecting rice quantities on serving trays, addressing balanced dietary intake challenges. Both a vanilla CNN and 12 pre-trained CNNs were tested, using features extracted from images of varying rice quantities on white trays. Configurations included optimizers, image generation, dropout, feature extraction, and fine-tuning, with top-1 validation accuracy as the evaluation metric. The vanilla CNN achieved 60% top-1 validation accuracy, while pre-trained CNNs significantly improved performance, reaching up to 90% accuracy. MobileNetV2, suitable for mobile devices, achieved a minimum 76% accuracy. These results suggest the model can effectively monitor rice servings, with potential for improvement through ongoing data collection and training. This development represents a significant advancement in personalized nutrition management, with high validation accuracy indicating its potential utility in dietary management. Continuous improvement based on expanding datasets promises enhanced precision and reliability, contributing to better health outcomes.

Introduction
Subjects and Methods
    1. Building image datasets
    2. Image feature extraction backbone
Results
    1. Vanilla CNN
    2. Pre-trained CNNs
Discussion
Conflicts of Interest
Funding Source
References

• Young Hoon Park(Full Professor, Dept. of Civil Engineering, Bucheon University, Bucheon 15073, Korea)
• Eun Young Choi(Assistant Professor, Dept. of Food and Nutrition, Bucheon University, Bucheon 15073, Korea) Corresponding author