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Detecting Late-Season Weed Infestations in Soybean (Glycine max)

Published online by Cambridge University Press:  20 January 2017

Clifford H. Koger ,
David R. Shaw ,
Clarence E. Watson  and
Krishna N. Reddy
Clifford H. Koger*
Affiliation:
USDA-ARS, Southern Weed Science Research Unit, 141 Experiment Station Road, P.O. Box 350, Stoneville, MS 38776
David R. Shaw
Affiliation:
Department of Plant and Soil Sciences, Forestry and Extension Service, Mississippi State University, Mississippi State, MS 39762
Clarence E. Watson
Affiliation:
Mississippi Agriculture, Forestry and Extension Service, Mississippi State University, Mississippi State, MS 39762
Krishna N. Reddy
Affiliation:
USDA-ARS, Southern Weed Science Research Unit, 141 Experiment Station Road, P.O. Box 350, Stoneville, MS 38776
*
Corresponding author's E-mail: ckoger@ars.usda.gov
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Abstract

Field experiments were conducted in 1999 at Stoneville, MS, to determine the potential of multispectral imagery for late-season discrimination of weed-infested and weed-free soybean. Plant canopy composition for soybean and weeds was estimated after soybean or weed canopy closure. Weed canopy estimates ranged from 30 to 36% for all weed-infested soybean plots, and weeds present were browntop millet, barnyardgrass, and large crabgrass. In each experiment, data were collected for the green, red, and near-infrared (NIR) spectrums four times after canopy closure. The red and NIR bands were used to develop a normalized difference vegetation index (NDVI) for each plot, and all spectral bands and NDVI were used as classification features to discriminate between weed-infested and weed-free soybean. Spectral response for all bands and NDVI were often higher in weed-infested soybean than in weed-free soybean. Weed infestations were discriminated from weed-free soybean with at least 90% accuracy. Discriminant analysis models formed from one image were 78 to 90% accurate in discriminating weed infestations for other images obtained from the same and other experiments. Multispectral imagery has the potential for discriminating late-season weed infestations across a range of crop growth stages by using discriminant models developed from other imagery data sets.

Keywords

Barnyardgrass, Echinochloa crus-galli (L.) Beauv. #3 ECHCGbrowntop millet, Brachiaria ramosa (L.) Stapf # PANRAlarge crabgrass, Digitaria sanguinalis (L.) Scop. # DIGSAsoybean, Glycine max (L.) MerrDiscriminant analysismultispectralremote sensingnormalized difference vegetation indexweed detectionDGPS, differential global positioning systemexp., experimentNDVI, normalized difference vegetation indexNIR, near infraredPOST, postemergencePRE, preemergence
Type
Research
Information
Weed Technology , Volume 17 , Issue 4 , December 2003 , pp. 696 - 704
Copyright
Copyright © Weed Science Society of America 

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