  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.
