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Hyperspectral imaging is a useful technique for analyzing the chemical composition of food and agricultural products. However, it is a costly and complicated procedure, which limits its practical application.

A team of University of Illinois Urbana-Champaign researchers has developed a method to reconstruct hyperspectral images from standard RGB images using deep machine learning. This technique can greatly simplify the analytical process and potentially revolutionize product assessment in the agricultural industry.

"Hyperspectral imaging uses expensive equipment. If we can use RGB images captured with a regular camera or smartphone, we can use a low-cost, handheld device to predict product quality," said lead author Md Toukir Ahmed.

Ahmed is a doctoral student in the Department of Agricultural and Biological Engineering (ABE), part of the College of Agricultural, Consumer and Environmental Sciences and The Grainger College of Engineering at Illinois.

The researchers tested their method by analyzing the chemical composition of sweet potatoes.

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