Computer vision identifies ripe fruit

Mikko Toivonen and Chang Rajani, doctoral students in computer science, together with Assistant Professor Arto Klami, have designed computer vision algorithms that can convert photos taken with a phone into extremely accurate hyperspectral images.

Hyperspectral images reveal more
Hyperspectral images are different from regular photographs because they reveal things unseen to the naked eye in the object that was photographed. The technique is not based on transillumination; rather, hyperspectral images interpret the wavelengths of light more accurately than regular photos.

“Normal photos use three colour channels, such as red, blue and green. In hyperspectral imaging, the light wavelength resolution is finer, comprising a hundred colour channels,” Klami explains.

“A simple three-colour camera is unable to distinguish the spectrum of, for example, chlorophyll. In a hyperspectral image taken outdoors, it’s easier to identify the bits with chlorophyll, that is, the areas with vegetation,” Toivonen says.

Above is an RGB photo of avocados taken through a peripheral device. Below are the spectra of the avocados’ surfaces. The colour-coded squares indicate the areas where the spectra have been shot.

The avocado on the right is clearly greener than the others, which can be seen as a spike in the blue spectrum curve at 550 nanometres. The spike indicates that the avocado in question is likely to be less ripe than the others.



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