Hyperspectral imagery for characterization of different corn genotypes

Haibo Yao; Zuzana Hruska; Kevin DiCrispino; David Lewis; James Beach; Robert L. Brown; Thomas E. Cleveland

doi:10.1117/12.573907

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19 November 2004 Hyperspectral imagery for characterization of different corn genotypes

Haibo Yao, Zuzana Hruska, Kevin DiCrispino, David Lewis, James Beach, Robert L. Brown, Thomas E. Cleveland

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Proceedings Volume 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources; (2004) https://doi.org/10.1117/12.573907
Event: Optics East, 2004, Philadelphia, Pennsylvania, United States

Abstract

USDA and the Institute for Technology Development are currently collaborating on a project using hyperspectral imagery to detect pathogens such as mycotoxin producing molds in grain products. The initial experiments are being implemented on corn kernels. When molds appear on corn, reflectance spectra from the molds and corn are mixed. Therefore, it is important to characterize the corn reflectance, which is the background reflectance in the image. The objective of this study was to qualitatively identify and quantify kernel signatures of several corn genotypes. Four different corn genotypes (genetically distinct corn lines) and four near isogenic corn lines were prepared at the USDA laboratory. The study used a visible-near-infrared hyperspectral imaging system for data acquisition. The imaging system utilizes focal plane pushbroom scanning for high spatial and high spectral resolution imaging. Procedures were developed for optimum image calibration and image processing. It was expected that the results would be useful for reducing the background influence of corn in mold detection and would also be applicable in corn genotype identification, especially among corn lines with different resistance levels to molds.

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Haibo Yao, Zuzana Hruska, Kevin DiCrispino, David Lewis, James Beach, Robert L. Brown, and Thomas E. Cleveland "Hyperspectral imagery for characterization of different corn genotypes", Proc. SPIE 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources, (19 November 2004); https://doi.org/10.1117/12.573907

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