Food Nutrient Extraction Based on Image Recognition and Entity Extraction

Nutrition is an important aspect of public health, and in recent years, there has been increasing interest in the nutritional information of food. However, processing this information can be a challenging task due to the large amounts of data involved. Machine learning (ML) has emerged as a useful tool to address this challenge. In this paper, we present a data resource that uses the FoodData Central (FDC) nutrient database to explore the combination of food images, nutritional information, and text with ML. We begin by providing an overview of machine learning and its applications in nutrition research, including the use of ML algorithms to identify food intake patterns, predict nutrient intakes, and evaluate dietary guidelines. We then describe the features and applications of Inception-v3, Inception-v4, and MobileNetV2 in ML, highlighting how these models can be used to extract nutritional information from food images. To further explore the potential of ML in nutrition research, we developed a quick search app that integrates images, text, and nutritional information. This app uses image recognition algorithms to identify food items in pictures, and text processing techniques to extract food information from text data. Users can simply take a picture of a food item and the app will provide the details of its nutritional content. This app can be used to facilitate the study of food and nutrition information and help promote healthier eating habits. In conclusion, the development of data resources and apps that use ML algorithms can be particularly helpful in processing large amounts of nutrition data and making it more accessible to the public. By harnessing the power of ML, we can advance our understanding of the relationship between diet and health, and ultimately work towards improving public health outcomes.