Nutrition management method and system based on deep learning food image recognition model

Abstract

The invention belongs to the field of dining food image data processing, and particularly relates to a nutrition management method and system based on a deep learning food image recognition model.The method comprises the steps that a user side obtains an image of food to be taken by a user, and the obtained food image is input into a trained deep learning-based food image recognition model; obtaining different types of food sub-images; calculating the amount of nutrients contained in the sub-images of the different types of food, and accumulating the nutrients in all the food to obtain the total nutrient intake of the user; setting intake thresholds of various nutrients, and comparing the calculated total intake of various nutrients with the corresponding nutrient intake thresholds to obtain a comparison result; according to a comparison result, the type and quantity of ingested food are adjusted, and nutrition management is completed; according to the method, the food intake information uploaded by the user is associated with other data sets through the server, whether the energy and productivity nutrient ratio is in the appropriate recommendation amount or not is obtained, and finally the data obtained through analysis is fed back to the user, so that the user is promoted to improve the diet mode.

Description

technical field [0001] The invention belongs to the field of meal food image data processing, and in particular relates to a nutrition management method and system based on a deep learning food image recognition model. Background technique [0002] With the improvement of living standards, people pay more and more attention to their health, and the health of the body is closely related to the food that the human body consumes every day. Therefore, the rationality of the daily diet plays an important role in the health of the body, and how to judge the reasonableness of the diet. The key to sexuality is accurate estimation of the type and amount of food ingested. Commonly used tools for obtaining dietary intake information include weighing method, dietary review and food frequency questionnaire (FFQ). The weighing method requires weighing each food before and after meals to obtain information on the type and portion of food. Although this method is accurate, it is time-consu...

AI Technical Summary

Problems solved by technology

The weighing method requires weighing each food before and after meals, so as to obtain information on the type and amount of the food. Although this method is accurate, it is time-consuming, laborious, and inoperable, and is only suitable for small-sample research

Meal review relies on the subject to recall all the food names and portions consumed in a short period of time in the past, but the review time of this method should not be too long (usually 24 or 72h), otherwise it is easy to forget, and this method reflects short-term , but cannot reflect dietary intake for a long time; FFQ can be used in large samples, and can reflect the dose-dependent relationship between food types, intake and disease in a long period of time; but the accuracy of FFQ also depends on the test Those with good memory and education, and FFQ evaluation of dietary intake error can be as high as 50%

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