I-Corps: Software application for predicting consumer food acceptability based on appearances under different illumination conditions

Award ID: 2300281

The broader impact/commercial potential of this I-Corps project is the development of a software application to predict consumer acceptability under different illumination conditions. The proposed technology is focused on consumer acceptability prediction related to food appearance, evaluating how external illumination may affect a deep learning-based image understanding model. Consumers may benefit from the proposed technology by having a more accurate understanding of the products they purchase and a reduced risk. In addition, the proposed technology may be used to recommend the correct illumination levels, which may help reduce food waste. With proper illumination recommendations, retailers may find an increase in purchases and a significant cost-savings as there may be a reduction in product returns. This I-Corps project is based on the development of an illumination estimation deep learning model that may be used to predict food acceptability. Illumination estimation is a fundamental prerequisite for many computer vision applications. Unnatural illumination may influence human perceptions of essential characteristics of goods, e.g., food products in retail stores under different lighting conditions. When food products are placed under different lighting conditions, consumers feel differently in response to the products, which may further affect purchase decisions. The goal is to develop an illumination human acceptability prediction model, which may be transferred to general industrial manufacturing and inspection applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Jaime A. Camelio