Agricultural producers and manufacturers often need information about crop attributes, from nutrient content to chemical composition, to make management decisions. In recent years, multispectral imaging has emerged as a useful tool for product analysis, but the required equipment is expensive. Standard RGB cameras are much more affordable, but their images show only visible attributes.
However, if RGB images can be “translated” to multispectral images, pictures taken with a smartphone or any regular camera can yield sophisticated information. This process requires complex computer modeling and machine learning, but once the techniques are developed, they can be applied to simple devices anyone can use.
In two new papers published in Computers and Electronics in Agriculture, researchers at the University of Illinois Urbana-Champaign explore the reconstruction of multispectral and hyperspectral images from RGB for chemical analysis of sweet potatoes and maize.
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