Hyperspectral imaging to classify and monitor quality of agricultural materials.

Mahesh, S., Jayas, D.S., Paliwal, J., and White, N.D.G. (2015). "Hyperspectral imaging to classify and monitor quality of agricultural materials.", Journal of Stored Products Research, 61, pp. 17-26. doi : 10.1016/j.jspr.2015.01.006  Access to full text

Abstract

Hyperspectral imaging has been acknowledged as an emerging technology for monitoring quality parameters and improving grading of agricultural materials, such as field crops (e.g., cereals, pulses, oil seeds) and horticultural crops (e.g., apples, strawberries). It has become a popular research tool that facilitates thorough non-destructive analyses by simultaneous acquisition of both spectral and spatial information of agricultural samples. The technique is an extension of multispectral imaging, which provides a large data set by applying conventional imaging, radiometry, and spectroscopic principles when acquiring images. Hyperspectral imaging was initially used for remote sensing applications, but now has been developed to facilitate complete and reliable analyses of intrinsic properties and external characteristics of samples. This paper reviews applications of using hyperspectral imaging for routine grain industry operations such as grading, classification, and chemometric analyses of major constituents of agricultural materials.

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