The authors have written an extensive survey of the published literature related to food computing. The survey is about 26 pages long, with an additional ten pages of (about 300) references.

The authors note:

Food computing mainly utilizes the methods from computer science for food-related study. It involves the acquisition and analysis of food data with different modalities (e.g., food images, food logs, recipe, taste, and smell) from different data sources (e.g., the social network, recipe-sharing websites, and cameras). Such analysis resorts to computer vision, machine learning, data mining, and other advanced technologies to connect food and humans. (p. 92:3)

The authors cover databases that include recipes, dish images, cooking videos, food attributes, food logs, restaurant-relevant food information, healthiness, and other miscellaneous food data.

One challenge of this field is that it is rapidly changing. Many of the database references are no longer available. Some of the databases may require a login and password, and some of the databases may require proprietary software.

One problem that has to be solved in this area concerns the detection and analysis of irregularly shaped images. When food is served at a restaurant, its presentation results in an irregularly shaped object. The workaround for this is to have the consumer provide the name of the dish along with its picture.

One of the goals of food computing is to provide consumers with a summary of sources for food information. Suppose a consumer goes to a restaurant and orders a serving of spaghetti and meatballs. In this situation, the consumer could take a picture of the served food and send it off (along with a description) for processing. After processing, the consumer would receive the calories and ingredients associated with a serving of spaghetti and meatballs, which could then be downloaded into a food log. A dietitian could then analyze the food log in order to make diet recommendations.

This article contains references that could be used by food scientists, dietitians, nutritionists, agricultural scientists, and instructors associated with family economics.

Within computer science, this article touches on image databases, data mining, textual databases, image digitization, image capture, and computer vision associated with pattern recognition.