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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

Information

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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