Submitted to: International Society for Optical Engineering
Publication Type: Proceedings
Publication Acceptance Date: June 1, 2009
Publication Date: July 1, 2009
Citation: Delwiche, S.R., Yang, I., Kim, M.S. 2009. Hyperspectral imaging for detection of black tip damage in wheat kernels. International Society for Optical Engineering. 7315(1). Available: http://spiedl.aip.org/dbt/dbt.jsp?KEY=PSISDG&Volume=7315&Issue=1 Interpretive Summary: In the United States, wheat that undergoes official inspection, such as that for export, is assessed for its level of damaged kernels. The greater the damage, the lower will be the grade, quality, and trade value of the lot. Black point or black tip is a fungal condition that contributes to the kernel damage level. As an alternative to human visual inspection (the traditional standard procedure), we explored the possibility of using digital imaging at discrete wavelengths in the visible light region. Using only one image in a wavelength region of green light, we were successful at identifying black tip damaged kernels at better than 90 percent accuracy. These findings, when coupled with similar image analysis procedures for other forms of damage (heat-, frost-, other molds, and insect-), will be of potential benefit to official inspection offices and commercial processors.
Technical 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.