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United States Department of Agriculture

Agricultural Research Service

Title: Hyperspectral Field Spectrometry for Estimating Greenbug (Homoptera: Aphididae) Damage in Wheat

Authors
item Mirik, Mustafa - TEXAS AGRIC EXP STATION
item Michels, Jr, Gerald - TEXAS AGRIC EXP STATION
item Kassymzhanova-Mirik, Sabina - TEXAS AGRIC EXP STATION
item Jones, David - TEXAS AGRIC EXP STATION
item Elliott, Norman
item Catanna, Vasile - OKLAHOMA STATE UNIV
item Bowling, Robert - PIONEER SALES & MARKETING

Submitted to: Biennial Workshop on Aerial Photography, Videography, and High Resolution Digital Imagery for Resource Assessment Proceedings
Publication Type: Proceedings
Publication Acceptance Date: September 1, 2005
Publication Date: November 1, 2005
Citation: Mirik, M., Michels, Jr, G.J., Kassymzhanova-Mirik, S., Jones, D., Elliott, N.C., Catanna, V., Bowling, R. 2005. Hyperspectral field spectrometry for estimating greenbug (Homoptera: Aphididae) damage in wheat. In: Proceedings of the 20th Biennial Workshop on Aerial Photography, Videography, and High Resolution Digital Imagery for Resource Assessment, October 4-6, 2005, Weslaco, Texas. 2005 CDROM.

Interpretive Summary: The greenbug is a severe insect pest of wheat in the Southern and Central Plains regions of the United States. This study was designed to determine the ability to use a digital camera to estimate greenbug damage to wheat in commercial wheat fields. Digital data from the camera were mathematically manipulated and entered into a software program to determine the percentage of damaged wheat in each of digital image. The percentage of damaged wheat was also visually assessed on the ground. A simple linear regression procedure was conducted to investigate the relationships between spectral vegetation indices and percentage damage obtained from the ground and digital images. The results show that there were strong relationships between percentage damage and a newly developed Damage Sensitive Spectral Index. The relationships between the percentage damage collected on the ground and from the digital camera imagery were very strong. The results strongly suggest that percentage reflectance from digital imagery taken with an ordinary digital camera data can be used to estimate greenbug damage in wheat under field conditions with a high degree of accuracy. The significance of the research lies in finding new and more rapid ways to estimate crop damage caused by insect pests so that timely operations can be undertaken to control the pest before serious damage to the crop occurs.

Technical Abstract: Remote sensing techniques have the potential to provide information about vegetation characteristics. Therefore, this study was designed to determine the ability of a hyperspectral field spectrometer along with a digital camera to estimate greenbug (Schizaphis graminum Rondani) damage to wheat (Triticum aestivum L.) in field condition. Percentage reflectance data and digital images were collected over 1 m2 wheat plots in four fields in mid-May of 2005. Percentage reflectance data were transformed into spectral vegetation indices and a damage quantification software package was used to determine percentage damage in each of the digital images. The percentage damage was also visually assessed on the ground. A simple linear regression procedure was conducted to investigate the relationships between spectral vegetation indices and percentage damage obtained from the ground and digital images. The results show that there were strong relationships between percentage damage and a newly developed Damage Sensitive Spectral Index (DSSI1-3). The coefficients of determination (R2) were 0.77, 0.86, 0.92, and 0.95 for the percentage damage determined using the digital images and DSSI1-3 for fields 1-4, respectively. The relationships between the percentage damage collected on the ground and DSSI1-3 were 69%, 81%, 89%, and 92% for the fields 1-4, respectively. These results strongly suggest that percentage reflectance and digital image data can be used to estimate greenbug damage in wheat under field conditions with a high degree of accuracy and precision.

Last Modified: 12/19/2014
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