27 April 2009 Hyperspectral imaging for detection of black tip damage in wheat kernels
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Abstract
A feasibility study was conducted on the use of hyperspectral imaging to differentiate sound wheat kernels from those with the fungal condition called black point or black tip. Individual kernels of hard red spring wheat were loaded in indented slots on a blackened machined aluminum plate. Damage conditions, determined by official (USDA) inspection, were either sound (no damage) or damaged by the black tip condition alone. Hyperspectral imaging was separately performed under modes of reflectance from white light illumination and fluorescence from UV light (~380 nm) illumination. By cursory inspection of wavelength images, one fluorescence wavelength (531 nm) was selected for image processing and classification analysis. Results indicated that with this one wavelength alone, classification accuracy can be as high as 95% when kernels are oriented with their dorsal side toward the camera. It is suggested that improvement in classification can be made through the inclusion of multiple wavelength images.
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Stephen R. Delwiche, Stephen R. Delwiche, I-Chang Yang, I-Chang Yang, Moon S. Kim, Moon S. Kim, "Hyperspectral imaging for detection of black tip damage in wheat kernels", Proc. SPIE 7315, Sensing for Agriculture and Food Quality and Safety, 73150K (27 April 2009); doi: 10.1117/12.818791; https://doi.org/10.1117/12.818791
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