Most grocery store patrons take for granted just what it takes to transport a humble sweet potato out of the ground and into a shopping basket. The slightly-sweet red root vegetable can come in various sizes and flavor profiles but consumers have come to expect a level of consistency. To meet that market demand, sweet potatoes are subjected to rounds of laborious and time-consuming quality assessments to root out undesirable batches that are either too firm, not sweet enough, or otherwise deemed unlikely to sell. This process is currently performed methodically by humans in a lab, but a new study suggests hyperspectral cameras and AI could help speed up that process.
In a study published this week in Computers and Electronics in Agriculture, researchers from the University of Illinois set out to see if data collected by a hyperspectral imaging camera could help narrow down certain potato attributes typically determined by manual inspectors and tests. Hyperspectral cameras collect vast amounts of data across the electromagnetic spectrum and are often used to help determine the chemical makeup of materials. In this case, the researchers wanted to see if they could analyze data from the potato images to accurately determine a spud’s firmness, soluble solid content, and dry matter content—three key attributes that contribute to the vegetable’s overall taste and market appeal. Ordinarily, this process requires tedious, sometimes wasteful testing that can include leaving test potatoes heated in a 103 degrees celsius oven for 24 hours.
“Traditionally, quality assessment is done using laboratory analytical methods,” University of Illinois College of Agricultural, Consumer and Environmental Sciences assistant professor Mohammed Kamruzzaman said in a statement. “You need different instruments to measure different attributes in the lab and you need to wait for the results.”
The researchers gathered 141 defect-free sweet potatoes and…
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