The nutritional quality of diets has significantly deteriorated over the past two to three decades, a decline often underestimated by the people. This deterioration, coupled with a hectic lifestyle, has contributed to escalating health concerns. Recognizing this issue, researchers at Harvard have advocated for a balanced nutritional plate model to promote health. Inspired by this research, our paper introduces an innovative Image-Based Dietary Assessment system aimed at evaluating the healthiness of meals through image analysis. Our system employs advanced image segmentation and classification techniques to analyze food items on a plate, assess their proportions, and calculate meal adherence to Harvard's healthy eating recommendations. This approach leverages machine learning and nutritional science to empower individuals with actionable insights for healthier eating choices. Our four-step framework involves segmenting the image, classifying the items, conducting a nutritional assessment based on the Harvard Healthy Eating Plate research, and offering tailored recommendations. The prototype system has shown promising results in promoting healthier eating habits by providing an accessible, evidence-based tool for dietary assessment.

我们的研究介绍了一种创新的基于图像的饮食评估系统，采用先进的图像分割和分类技术来分析盘中食物、评估餐的健康性，并根据哈佛健康饮食盘的研究进行营养评估，从而为个人提供可操作的健康饮食建议，旨在促进更健康的饮食习惯。

基于图像的饮食评估：一个健康饮食评估系统