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The Effect of Postemergence Herbicides on the Spectral Reflectance of Corn

Published online by Cambridge University Press:  20 January 2017

Wesley J. Everman
Affiliation:
P.O. Box 7620, Crop Science Department, North Carolina State University, Raleigh, NC 27695-7620
Case R. Medlin*
Affiliation:
Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK
Richard D. Dirks Jr.
Affiliation:
Department of Botany and Plant Pathology, and Research Technician, Agronomy Department, Purdue University, West Lafayette, IN 47906
Thomas T. Bauman
Affiliation:
Department of Botany and Plant Pathology, and Research Technician, Agronomy Department, Purdue University, West Lafayette, IN 47906
Larry Biehl
Affiliation:
P.O. Box 7620, Crop Science Department, North Carolina State University, Raleigh, NC 27695-7620
*
Corresponding author's E-mail address: E-mail: [email protected]

Abstract

Studies were conducted in 2001 and 2002 to determine the effect of POST herbicides on the spectral reflectance of corn. POST corn herbicides evaluated included 2,4-D, atrazine, bromoxynil, dicamba + diflufenzopyr, nicosulfuron, and primisulfuron. Multispectral and hyperspectral data were collected and spectral properties were analyzed using SAS procedures and MultiSpec image analysis. Corn treated with POST applications of atrazine and primisulfuron could not be distinguished from nontreated corn regardless of data type or analysis method used. 2,4-D and dicamba + diflufenzopyr were the most readily distinguished from nontreated corn plots using both hyperspectral and multispectral data.

Type
Weed Management — Techniques
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Browner, C. M., Rominger, R., and Kessler, D. A. 1993. Testimony before the Committee on Labor and Human Resources and Subcommittee on Health and the Environment, Committee on Energy Commerce. U.S. House of Representatives. September. 22:1993.Google Scholar
Carter, G. 1993. Responses of leaf spectral reflectance to plant stress. Am. J. Bot. 80:239243.Google Scholar
Carter, G. and Knapp, A. 2001. Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am. J. Bot. 88:677684.Google Scholar
Chang, J., Clay, S. A., Clay, D. E., and Dalsted, K. 2004. Detecting weed-free and weed-infested areas of a soybean field using near-infrared spectral data. Weed Sci. 52:642648.Google Scholar
Daughtry, C. S. T., Vanderbilt, V. C., and Pollara, V. J. 1982. Variability of reflectance measurements with sensor altitude and canopy type. Agron. J. 74:744751.Google Scholar
Everitt, J. H., Anderson, G. L., Escobar, D. E., Davis, M. R., Spencer, N. R., and Andrascik, R. J. 1995. Use of remote sensing for detecting and mapping leafy spurge. Weed Technol. 9:599609.Google Scholar
Fernandez-Cornejo, J. and Jans, S. 1999. Pest management in U.S. agriculture. Washington, DC: Resource Economics Division, Economic Research Service, U.S. Department of Agriculture. Agricultural Handbook No. 717.Google Scholar
Gibson, K. D., Dirks, R., Medlin, C. R., and Johnston, L. 2004. Detection of weed species in soybean using multispectral digital images. Weed Technol. 18:742749.Google Scholar
Henry, W. B., Shaw, D. R., Reddy, K. R., Bruce, L. M., and Tamhankar, H. D. 2004a. Remote sensing to detect herbicide drift on crops. Weed Technol. 18:358368.Google Scholar
Henry, W. B., Shaw, D. R., Reddy, K. R., Bruce, L. M., and Tamhankar, H. D. 2004b. Remote sensing to distinguish soybean from weeds after herbicide application. Weed Technol. 18:594604.Google Scholar
Hughes, G. 1989. Spatial heterogeneity in yield-loss relationships for crop loss assessment. Crop Resources. 29:8794.Google Scholar
Kuo, B. C. and Landgrebe, D. A. 2001. Improved statistics estimation and feature extraction for hyperspectral data classification. West Lafayette, IN Purdue University Department of Electrical and Computer Engineering. TR-ECE 01-6.Google Scholar
Landgrebe, D. A. 1999. Information extraction principles for multispectral and hyperspectral image data. Pages 130. in Chen, C. H., editor. Information Processing for Remote Sensing. River Edge, NJ World Scientific Publishing Co., Inc.Google Scholar
Landgrebe, D. A. and Biehl, L. L. 2001. An Introduction to MultiSpec, Version 5.2001. West Lafayette, IN Purdue Research Foundation. 174.Google Scholar
Landis, J. R. and Kock, G. G. 1977. The measurement of observer agreement for categorical data. Biometrics. 33:159174.Google Scholar
Lass, L. W. and Callihan, R. H. 1997. The effect of phonological stage on detectability of yellow hawkweed and oxeye daisy with remote multispectral digital imagery. Weed Technol. 11:248256.Google Scholar
Lass, L. W., Carson, H. W., and Callihan, R. H. 1996. Detection of yellow starthistle and common St. Johnswort with multispectral digital imagery. Weed Technol. 10:466474.Google Scholar
Leon, C. T., Shaw, D. R., Bruce, L. M., and Watson, C. 2003. Effect of purple (Cyperus rotundus) and yellow nutsedge (C. esculentus) on growth and reflectance characteristics of cotton and soybean. Weed Sci. 51:557564.Google Scholar
Lillesand, T. M. and Kiefer, R. W. 2000. Remote Sensing and Image Interpretation. New York: J Wiley. 724.Google Scholar
Medlin, C. R. and Shaw, D. R. 2000. Economic comparison of broadcast and site-specific herbicide applications in nontrangenic and glyphosate-tolerant Glycine max . Weed Sci. 48:653661.Google Scholar
Medlin, C. R., Shaw, D. R., Gerard, P. D., and LaMastus, F. E. 2000. Using remote sensing to detect weed infestations in Glycine max . Weed Sci. 48:393398.Google Scholar
Menges, R. M., Nixon, P. R., and Richardson, A. J. 1985. Light reflectance and remote sensing of weeds in agronomic and horticultural crops. Weed Sci. 33:569581.CrossRefGoogle Scholar
Robinson, B. F. and Biehl, L. L. 1979. Calibration procedures for measurement of reflectance factor in remote sensing field research. Society of Photo-optical Instrumentation Engineers Vol. 196. Measurements of Optical Radiation. Purdue/LARS Technical Report 082679. Bellingham, WA. 1626.Google Scholar
SAS 1992. SAS/STAT User's Guide, Release 6.03. Cary, NC SAS Institute.Google Scholar
Smith, A. M. and Blackshaw, R. E. 2003. Weed–crop discrimination using remote sensing: a detached leaf experiment. Weed Technol. 17:811820.Google Scholar
Thelen, K. D., Kravchenko, A. N., and Lee, C. D. 2004. Use of optical remote sensing for detecting herbicide injury in soybean. Weed Technol. 18:292297.Google Scholar
Thornton, P. K., Fawcett, R. H., Dent, J. B., and Perkins, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Protection. 9:337342.CrossRefGoogle Scholar
Vrindts, E., De Baerdemaeker, J., and Ramon, H. 2002. Weed detection using canopy reflectance. Precision Agric. 3:6380.CrossRefGoogle Scholar
Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992. Modeling weed distribution for improved post-emergence control decisions. Weed Sci. 40:546553.Google Scholar
Williams, A. P. and Hunt, E. R. Jr. 2002. Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering. Remote Sens. Environ. 82:446456.Google Scholar