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Remote Sensing to Detect Herbicide Drift on Crops

Published online by Cambridge University Press:  20 January 2017

W. Brien Henry ,
David R. Shaw ,
Kambham R. Reddy ,
Lori M. Bruce  and
Hrishikesh D. Tamhankar
W. Brien Henry*
Affiliation:
Central Great Plains Research Station, 40335 County Road GG, Akron, CO 80720
David R. Shaw
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
Kambham R. Reddy
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
Lori M. Bruce
Affiliation:
Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762
Hrishikesh D. Tamhankar
Affiliation:
Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762
*
Corresponding author's E-mail: brien.henry@ars.usda.gov
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Abstract

Glyphosate and paraquat herbicide drift injury to crops may substantially reduce growth or yield. Determining the type and degree of injury is of importance to a producer. This research was conducted to determine whether remote sensing could be used to identify and quantify herbicide injury to crops. Soybean and corn plants were grown in 3.8-L pots to the five- to seven-leaf stage, at which time, applications of nonselective herbicides were made. Visual injury estimates were made, and hyperspectral reflectance data were recorded 1, 4, and 7 d after application (DAA). Several analysis techniques including multiple indices, signature amplitude (SA) with spectral bands as features, and wavelet analysis were used to distinguish between herbicide-treated and nontreated plants. Classification accuracy using SA analysis of paraquat injury on soybean was better than 75% for both 1/2- and 1/8× rates at 1, 4, and 7 DAA. Classification accuracy of paraquat injury on corn was better than 72% for the 1/2× rate at 1, 4, and 7 DAA. These data suggest that hyperspectral reflectance may be used to distinguish between healthy plants and injured plants to which herbicides have been applied; however, the classification accuracies remained at 75% or higher only when the higher rates of herbicide were applied. Applications of a 1/2× rate of glyphosate produced 55 to 81% soybean injury and 20 to 50% corn injury 4 and 7 DAA, respectively. However, using SA analysis, the moderately injured plants were indistinguishable from the uninjured controls, as represented by the low classification accuracies at the 1/8-, 1/32-, and 1/64× rates. The most promising technique for identifying drift injury was wavelet analysis, which successfully distinguished between corn plants treated with either the 1/8- or the 1/2× rates of paraquat compared with the nontreated corn plants better than 92% 1, 4, and 7 DAA. These analysis techniques, once tested and validated on field scale data, may help determine the extent and the degree of herbicide drift for making appropriate and, more importantly, timely management decisions.

Keywords

Corn, Zea mays L.soybean, Glycine max (L.) MerrGlyphosatehyperspectral imageryindicesparaquatROC curvewavelet analysisDAA, days after applicationDINO, differential index of normalized observationsDWT, discrete wavelet transformEPSP, 5-enolpyruvylshikimate-3-phosphateLDA, linear discriminant analysisNDVI, normalized difference vegetation indexNIR, near-infraredPOST, postemergenceROC, receiver operator characteristicsSA, signature amplitudes
Type
Research
Information
Weed Technology , Volume 18 , Issue 2 , June 2004 , pp. 358 - 368
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Ahrens, W. H. 1994. Herbicide Handbook. 7th ed. Champaign, IL: Weed Science Society of America. Pp. 149–152, 226–228.Google Scholar
Al-Khatib, K. and Peterson, D. 1999. Soybean (Glycine max) response to simulated drift from selected sulfonylurea herbicides, dicamba, glyphosate, and glufosinate. Weed Technol. 13:264–270.CrossRefGoogle Scholar
Amrhein, N., Deus, B., Gehrke, P., and Steinrucken, H. C. 1980. The site of the inhibition of the shikimate pathway by glyphosate. II. Interference of glyphosate with chorismate formation in vivo and in vitro. Plant Physiol. 66:830–834.CrossRefGoogle ScholarPubMed
Anonymous. 1999. Spray drift of pesticides. December. EPA, Office of Pesticide Programs, Pub. 735F99024.Google Scholar
Auch, D. E. and Arnold, W. E. 1978. Dicamba use and injury on soybean (Glycine max) in South Dakota. Weed Sci. 26:471–475.CrossRefGoogle Scholar
Banks, P. A. and Schroeder, J. 2000. Carrier volume affects herbicide activity in simulated spray drift studies. Proc. South. Weed Sci. Soc. 53:173.Google Scholar
Bausch, W. C. and Duke, H. R. 1996. Remote sensing of plant nitrogen status in corn. ASAE. 39:1869–1875.CrossRefGoogle Scholar
Burrus, S., Gopinath, R., and Guo, H. 1998. Introduction to Wavelets and Wavelet Transforms: A Primer. 1st ed. Upper Saddle River, NJ: Prentice-Hall. Pp. 3–7.Google Scholar
Calderbank, A. 1968. The bipyridylium herbicides. Adv. Pest Control Res. 8:127–135.Google ScholarPubMed
Carter, G. A. and Knapp, A. K. 2000. Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am. J. Bot. 88:677–684.CrossRefGoogle Scholar
Cranmer, J. R. and Linscott, D. L. 1990. Droplet makeup and the effect of phytotoxicity of glyphosate in velvetleaf (Abutilon theophrasti). Weed Sci. 38:406–410.CrossRefGoogle Scholar
Crippen, R. E. 1990. Calculating the vegetation index faster. Remote Sens. Environ. 34:71–73.CrossRefGoogle Scholar
Drapala, P. 2001. Bureau of plant industry announces supplemental labeling. Bureau of Plant Industry, Mississippi Department of Agriculture and Commerce.Google Scholar
Duda, R. O., Hart, P. E., and Stark, D. G. 2001. Pattern Classification. 2nd ed. New York: J. Wiley. Pp. 117–124.Google Scholar
Ellis, J. M., Griffin, J. L., Jones, C. A., and Webster, E. P. 2001. Effect of carrier volume on crop response to simulated drift of glyphosate and glufosinate. Proc. South. Weed Sci. Soc. 54:159.Google Scholar
Fuerst, E. P. and Vaughn, K. C. 1990. Mechanisms of paraquat resistance. Weed Technol. 4:150–156.CrossRefGoogle Scholar
Gausman, H. W. 1985. Plant Leaf Optical Properties in Visible and Near-infrared Light. Graduate Studies. No. 29. Lubbock, TX: Texas Technical University, Texas Tech Press. Pp. 1–78.Google Scholar
Gitelson, A., Kaufman, Y., and Merzlyak, M. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens. Environ. 58:289–298.CrossRefGoogle Scholar
Graps, A. 1995. An introduction to wavelets. IEEE Comput. Sci. Eng. 2:1–18.CrossRefGoogle Scholar
Haar, A. 1910. Zur theorie der orthogonalen funktionensysteme. Math. Ann. 69:331–371.CrossRefGoogle Scholar
Hanley, J. and McNeil, B. 1982. The meaning and use of the area under a receiver operating characteristics (ROC) curve. Diagn. Radiol. 143:29–36.Google Scholar
Heller, R. C. 1978. Case applications of remote sensing for vegetation damage assessment. PERS. 44:1159–1166.Google Scholar
Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25:295–309.CrossRefGoogle Scholar
Hunt, E. R. Jr. and Rock, B. N. 1989. Detection of changes in leaf water content using near- and middle-infrared reflectances. Remote Sens. Environ. 30:43–54.Google Scholar
James, C. and Krattiger, A. F. 1996. Global Review of the Field Testing and Commericializaton of Transgenic Plants, 1986 to 1995: The First Decade of Crop Biotechnology. Ithaca, NY: ISAA Briefs No. 1. 31 p.Google Scholar
Jordan, C. F. 1969. Derivation of leaf area index from quality of light on the forest floor. Ecology. 50:663–666.CrossRefGoogle Scholar
Koger, C. H. 2001. Remote Sensing Weeds, Cover Crop Residue, and Tillage Practices in Soybean. Ph.D. dissertation. Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS.Google Scholar
Kokaly, R. F. 2001. Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sens. Environ. 75:153–161.CrossRefGoogle Scholar
Leon, C. T. 2001. Crop Monitoring Utilizing Remote Sensing, Soil Parameters, and GPS Technologies. M.S. Thesis. Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS.Google Scholar
Lillesand, T. M. and Kiefer, R. W. 1987. Remote Sensing and Image Interpretation. 2nd ed. New York: J. Wiley. 721 p.Google Scholar
McKinlay, K. S., Ashford, R., and Ford, R. J. 1974. Effects of drop size, spray volume, and dosage, on paraquat toxicity. Weed Sci. 22:31–34.CrossRefGoogle Scholar
Medlin, C. R., Shaw, D. R., Gerrard, P. D., and Lamastus, F. E. 2000. Using remote sensing to detect weed infestations in Glycine max . Weed Sci. 48:393–398.CrossRefGoogle Scholar
Miller, P. C. H. 1993. Spray drift and its measurement. in Mathews, G. A. and Hislop, E. C., eds. Application Technology for Crop Protection. Wallingford, U.K.: Commonwealth Agriculture Bureaux International. Pp. 101–122.Google Scholar
Nordby, A. and Skuterud, R. 1975. The effects of boom height, working pressure and wind speed on spray drift. Weed Res. 14:385–395.CrossRefGoogle Scholar
Nutter, F. W. Jr. 1989. Detection and measurement of plant disease gradients in peanut with a multispectral radiometer. Phytopathology. 79:958–963.CrossRefGoogle Scholar
Nutter, F. W. Jr. and Guan, J. 2002. Quantifying alfalfa yield losses caused by foliar diseases in Iowa, Ohio, Wisconsin, and Vermont. Plant Dis. 86:269–277.CrossRefGoogle ScholarPubMed
Peters, A. J., Griffin, S. C., Vina, A., and Ji, L. 2000. Use of remotely sensed data for assessing crop hail damage. PERS. 66:1349.Google Scholar
Pringnitz, B. 1999. Pesticide Drift: To Spray or Not to Spray?. Iowa State University, Iowa State University Extension Pesticide Applicator Education Program. PCIC-99d.Google Scholar
Richardson, A. J. and Everitt, J. H. 1992. Using spectra vegetation indices to estimate rangeland productivity. Geocart. Int. 1:63–69.CrossRefGoogle Scholar
Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W. 1973. Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP-351 I:309–317.Google Scholar
Rowland, C. D. 2000. Crop Tolerance to Non-target and Labeled Herbicide Applications. M.S. Thesis. Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS.Google Scholar
Schiller, J. 2001. Interesting Examples of Remote Sensing Applications Emerge as Farmers Sign on to Agrimage. CSIR Satellite Applications Center—Newsletter. March. 21:1–4.Google Scholar
Tucker, C. J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8:127–150.CrossRefGoogle Scholar
Tucker, C. J. 1980. Remote sensing of leaf water content in the near infrared. Remote Sens. Environ. 10:23–32.CrossRefGoogle Scholar