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Remote Sensing to Distinguish Soybean from Weeds After Herbicide Application

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:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
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

Two experiments, one focusing on preemergence (PRE) herbicides and the other on postemergence (POST) herbicides, were conducted and repeated in time to examine the utility of hyperspectral remote sensing data for discriminating common cocklebur, hemp sesbania, pitted morningglory, sicklepod, and soybean after PRE and POST herbicide application. Discriminant models were created from combinations of multiple indices. The model created from the second experimental run's data set and validated on the first experimental run's data provided an average of 97% correct classification of soybean and an overall average classification accuracy of 65% for all species. These data suggest that these models are relatively robust and could potentially be used across a wide range of herbicide applications in field scenarios. From the data set pooled across time and experiment types, a single discriminant model was created with multiple indices that discriminated soybean from weeds 88%, on average, regardless of herbicide, rate, or species. Signature amplitudes, an additional classification technique, produced variable results with respect to discriminating soybean from weeds after herbicide application and discriminating between controls and plants to which herbicides were applied; thus, this was not an adequate classification technique.

Keywords

Common cocklebur, Xanthium strumarium L.hemp sesbania, Sesbania exaltata (Raf.) Rydb. ex. A.W. Hillpitted morningglory, Ipomoea lacunosa L.sicklepod, Sennna obtusifolia (L.) Irwin and Barnabysoybean, Glycine max (L.) MerrAcifluorfenchlorimuronhyperspectral imageryimazaquinindicesmetribuzinpendimethalinROC curveCCC, canonical correlation coefficientDAA, days after applicationDINO, differential index of normalized observationsLDA, linear discriminant analysisNDVI, normalized difference vegetation indexNIR, near-infraredPOST, postemergencePRE, preemergenceROC, receiver operator characteristicsSA, signature amplitudesSCA, species classification accuracySDM, single discriminant modelSWIR, short-wave infrared

Information

Type
Research
Information
Weed Technology , Volume 18 , Issue 3 , September 2004, pp. 594 - 604
Copyright
Copyright © Weed Science Society of America 

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Footnotes

1 Publication J-10403 Mississippi Agricultural and Forestry Experiment Station, Mississippi State University Journal Series.
Current address: Central Great Plains Research Station, 40335 County Road GG, Akron, CO 80720

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