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Towards Computer Vision Powered Color-Nutrient Assessment of Puréed Food

With one in four individuals afflicted with malnutrition, computer vision may provide a way of introducing a new level of automation in the nutrition field to reliably monitor food and nutrient intake. We present results on the link between color and vitamin A content using transmittance imaging of a pureed foods dilution series in a computer-vision powered intelligent nutrient sensing system prototype and use a fine-tuned deep autoencoder network to predict the relative concentration of sweet potato purees. Results indicate an network accuracy of 80% across beginner (6 month) and intermediate (8 month) commercially prepared pureed sweet potato samples. Network errors may be explained by fundamental differences in optical properties which are further discussed.