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Journal of Applied Remote Sensing • Open Access

High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier
Author(s): Mustafa Mirik; R. James Ansley; Karl Steddom; Charles M. Rush; Gerald J. Michels; Fedkede Workneh; Song Cui; Norman C. Elliott

Paper Abstract

The effects of insect infestation in agricultural crops are of major ecological and economic interest because of reduced yield, increased cost of pest control and increased risk of environmental contamination from insecticide application. The Russian wheat aphid (RWA, Diuraphis noxia) is an insect pest that causes damage to wheat (Triticum aestivum L.). We proposed that concentrated RWA feeding areas, referred to as “hot spots,” could be identified and isolated from uninfested areas within a field for site specific aphid management using remotely sensed data. Our objectives were to (1) investigate the reflectance characteristics of infested and uninfested wheat by RWA and (2) evaluate utility of airborne hyperspectral imagery with 1-m spatial resolution for detecting, quantifying, and mapping RWA infested areas in commercial winter wheat fields using the constrained energy minimization classifier. Percent surface reflectance from uninfested wheat was lower in the visible and higher in the near infrared portions of the spectrum when compared with RWA-infested wheat. The overall classification accuracies of <89% for damage detection were achieved. These results indicate that hyperspectral imagery can be effectively used for accurate detection and quantification of RWA infestation in wheat for site-specific aphid management.

Paper Details

Date Published: 21 March 2014
PDF: 15 pages
J. Appl. Rem. Sens. 8(1) 083661 doi: 10.1117/1.JRS.8.083661
Published in: Journal of Applied Remote Sensing Volume 8, Issue 1
Show Author Affiliations
Mustafa Mirik, Texas A&M Univ. (United States)
R. James Ansley, Texas A&M Univ. (United States)
Karl Steddom, Kilgore College (United States)
Charles M. Rush, Texas A&M Univ. (United States)
Gerald J. Michels, Texas A&M Univ. (United States)
Fedkede Workneh, Texas A&M Univ. (United States)
Song Cui, Middle Tennessee State Univ. (United States)
Norman C. Elliott, USDA-ARS Plant Science Research Lab. (United States)

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