Does spatial region of interest (ROI) matter in multispectral and hyperspectral imaging of segmented wheat kernels?

Advances in optics technology and computational processing have brought multispectral and hyperspectral imaging to commercial sorting of fruits and vegetables, yet the application of imaging to single cereal seeds has lagged due to the enormity in numbers of seeds and challenges posed by lighting, shadowing, and seed curvature that are less problematic with larger objects. This study examined the effect of region of interest (ROI) size on the seed surface with respect to the ability to sort seed into accept and reject categories. Regions of interest (ROI) size ranged from 5 centrally located pixels arranged in a cross to all pixels (typically 100) contained in the viewed surface of a kernel. Two modeling structures were used; the first involving all 87 samples, with approximately 220 kernels per sample, in which mixture level of sound and fusarium-damaged kernels is known, but individual kernel class is unknown; and the second involving 5 samples, of which an equal number of 287 known sound and known fusarium-damaged kernels were used. Accordingly, the larger set model characterised the dispersion (by standard deviation) of kernel-to-kernel reflectance at a single representative wavelength, while the smaller set was used to develop linear discriminant analysis classification models using one to three wavelengths. With either case, it was found that the smaller ROIs should be sufficient for a two-class (accepts, rejects) structure.