Development of Mobile Household Dietary Survey Data Collection System with AI-based Food Recognition Tool

The National Nutrition Survey (NNS) is the Philippines' official source of data on nutritional status, diet, and lifestyle-related risk factors for non-communicable diseases. A food consumption survey is one of its major components that collects individual dietary data through 24-h food recall interviews and household weighing of food intake using a dietetic scale. Conventional manual surveys are costly and tedious to conduct, which can also compound the inconsistencies and biases in the data gathered. Technological advances in mobile devices and networking have made it possible to gather data more economically and more efficiently with emerging technologies such as machine learning (ML) and increased use of automation open vast opportunities. In this paper, we explored the digitalization of food component forms of the NNS and developed a fully digital dietary survey collection system from collection to transmission of data. Google AutoML Vision, an AutoML platform based on NAS was used to create a Filipino food image recognition model as an added feature of the developed system. A Filipino food image dataset comprises 32 food classes and 3,295 images were created for this process. The developed food image recognition model scored a modest 76% on the precision metric in correctly identifying foods, which performed at par with other custom food recognition models. Nutrient approximation based on the Philippine Food Composition Table database also proved to be useful in displaying nutrition facts of recognized foods. This study showed the potential of a digital dietary survey system, combined with a unique and specific food image recognition tool, is a very encouraging proposition. Expansion of the food image dataset, exploration of other ML techniques, automated food weighta approximation, and continuous enhancement of the developed system are advocated.