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Have you ever stood in front of apples on display at the grocery store trying to pick out the best ones and wondered, "Is there an app for this?"

Current machine-learning based computer models used for predicting food quality are not as consistent as a human's ability to adapt to environmental conditions. Still, information compiled in an Arkansas Agricultural Experiment Station study may be used someday to develop that app, as well as provide grocery stores with insights on presenting foods in a more appealing manner and optimize software designs for machine vision systems used in processing facilities.

The study led by Dongyi Wang, assistant professor of smart agriculture and food manufacturing in the biological and agricultural engineering department and the food science department, was recently published in the Journal of Food Engineering.

Even though human perception of food quality can be manipulated with illumination, the study showed that computers trained with data from human perceptions of food quality made more consistent food quality predictions under different lighting conditions.

"When studying the reliability of machine-learning models, the first thing you need to do is evaluate the human's reliability," Wang said. "But there are differences in human perception. What we are trying to do is train our machine-learning models to be more reliable and consistent."

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