Recognition on Food Images

Faculty Mentor: Caiwen Ding (UConn)


With nearly a third of the world’s population suffering from food-induced chronic diseases such as obesity, the role of food in community health is required now more than ever. While current research underscores food proximity and density, there is a dearth in regard to its nutrition and quality. However, recent research in geospatial data analysis as well as intelligent deep learning will help us study this further. Employing the efficiency and connectiveness of modern technology, with the help of deep learning in the field of health geography, we aim to utilize image recognition to gather and model the role of the community food environment in shaping obesity and related chronic diseases.


The participants of this project will achieve the following learning and research objectives.

  1. Learn the basic concepts of hands-on machine learning and understand how an end-to-end machine learning project is formulated and completed.
  2. Learn how to utilize Python and its several libraries to retrieve data from various crowdsourced resources through APIs.
  3. Explore deep learning methodologies that most efficiently creates a food image classifier.