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Title: Hyperspectral spectrometry as a means to differentiate uninfested and infested winter wheat by greenbug (Hemiptera: Aphididae)

Author
item MIRIK, MUSTAFA - TEXAS A&M UNIV, AMARILLO
item MICHELS, JR., GERALD - TEXAS A&M UNIV, AMARILLO
item KASSYMZHANOVA-MIRIK, SABINA - TEXAS A&M UNIV, AMARILLO
item Elliott, Norman - Norm
item BOWLING, ROXANNE - WEST TAXAS A&M UNIV

Submitted to: Journal of Economic Entomology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2006
Publication Date: 11/1/2006
Citation: Mirik, M., Michels, Jr., G.J., Kassymzhanova-Mirik, S., Elliott, N.C., Bowling, R. 2006. Hyperspectral spectrometry as a means to differentiate uninfested and infested winter wheat by greenbug (Hemiptera: Aphididae). Journal of Economic Entomology. 99(5):1682-1690.

Interpretive Summary: Spectral remote sensing is the sensing of reflected electromagnetic radiation by instrumentation. Hyperspectral remote sensing instruments partition the electromagnetic spectrum into many narrow bandwidths and sense the amount of reflected radiation in each band. The greenbug is an aphid that is a serious pest of winter wheat, which in many years causes millions of dollars in losses to the wheat industry. Although spectral remote sensing techniques have been used to study stress to agricultural crops for decades, the potential use of these techniques for detecting plant stress and damage caused by greenbug infestations to wheat under field conditions is unknown. This research was conducted to investigate the applicability and feasibility of using a portable hyperspectral remote sensing instrument to identify and discern differences in spectral reflection patterns (spectral signatures) of winter wheat canopies with and without greenbug damage. Greenbug damaged wheat canopies had higher reflectance in the visible range and less in the near infrared regions of the spectrum when compared with undamaged canopies. In addition to percentage of reflectance comparison, a large number of spectral vegetation indices drawn from the literature were calculated and correlated with greenbug density. There were strong relationships between greenbug density and spectral vegetation indices. The results indicate that hyperspectral remote sensing has the potential to portray greenbug density and discriminate its damage to wheat with repeatable accuracy and precision. The importance of our observations lie in the potential for remote sensing to provide an accurate and inexpensive technology for monitoring wheat fields for greenbug infestations so that control measures for the pest can be undertaken before the crop suffers economic losses.

Technical Abstract: Although spectral remote sensing techniques have been used to study many ecological variables and biotic and abiotic stresses to agricultural crops over decades, the potential use of these techniques for greenbug, Schizaphis graminum (Rondani) (Hemiptera: Aphididae) infestations and damage to wheat, Triticum aestivum L., under field conditions is unknown. Hence, this research was conducted to investigate: 1) the applicability and feasibility of using a portable narrow-banded (hyperspectral) remote sensing instrument to identify and discern differences in spectral reflection patterns (spectral signatures) of winter wheat canopies with and without greenbug damage; and 2) the relationship between miscellaneous spectral vegetation indices and greenbug density in wheat canopies growing in two fields and under greenhouse conditions. Both greenbug and reflectance data were collected from 0.25-, 0.37-, and 1-m2 plots in one of the fields, greenhouse, and the other field, respectively. Regardless of the growth conditions, greenbug-damaged wheat canopies had higher reflectance in the visible range and less in the near infrared regions of the spectrum when compared with undamaged canopies. In addition to percentage of reflectance comparison, a large number of spectral vegetation indices drawn from the literature were calculated and correlated with greenbug density. Linear regression analyses revealed high relationships (R2 ranged from 0.62 to 0.85) between greenbug density and spectral vegetation indices. These results indicate that hyperspectral remotely sensed data with an appropriate pixel size have the potential to portray greenbug density and discriminate its damage to wheat with repeated accuracy and precision.