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Using remote sensing to detect weed infestations in Glycine max

Published online by Cambridge University Press:  20 January 2017

Case R. Medlin ,
David R. Shaw ,
Patrick D. Gerard  and
Falba E. LaMastus
Case R. Medlin
Affiliation:
Department of Plant and Soil Sciences, 117 Dorman Hall, Box 9555, Mississippi State University, Mississippi State, MS 39762
Patrick D. Gerard
Affiliation:
Experimental Statistics, 151 Dorman Hall, Box 9653, Mississippi State University, Mississippi State, MS 39762-9653
Falba E. LaMastus
Affiliation:
Department of Plant and Soil Sciences, 117 Dorman Hall, Box 9555, Mississippi State University, Mississippi State, MS 39762
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Abstract

The objective of this research was to evaluate the accuracy of remote sensing for detecting weed infestation levels during early-season Glycine max production. Weed population estimates were collected from two G. max fields approximately 8 wk after planting during summer 1998. Seedling weed populations were sampled using a regular grid coordinate system on a 10- by 10-m grid. Two days later, multispectral digital images of the fields were recorded. Generally, infestations of Senna obtusifolia, Ipomoea lacunosa, and Solanum carolinense could be detected with remote sensing with at least 75% accuracy. Threshold populations of 10 or more S. obtusifolia or I. lacunosa plants m−2 were generally classified with at least 85% accuracy. Discriminant analysis functions formed for detecting weed populations in one field were at least 73% accurate in identifying S. obtusifolia and I. lacunosa infestations in independently collected data from another field. Due to highly variable soil conditions and their effects on the reflectance properties of the surrounding soil and vegetation, accurate classification of weed-free areas was generally much lower. Current remote sensing technology has potential for in-season weed detection; however, further advancements of the technology are needed to insure its use in future prescription weed management systems.

Keywords

Ipomoea lacunosa L. IPOLA, pitted morningglorySenna obtusifolia (L.) Irwin et Barnaby CASOB, sicklepodSolanum carolinense L. SOLCA, horsenettleGlycine max (L.) Merr., soybeanPrecision farmingsite-specific agriculturevariable-rate application
Type
Research Article
Information
Weed Science , Volume 48 , Issue 3 , June 2000 , pp. 393 - 398
Copyright
Copyright © Weed Science Society of America 

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Footnotes

Current address: Department of Botany and Plant Pathology, 1155 Lilly Hall, West Lafayette, IN 47907-1155

References

Literature Cited

Anderson, G. L., Everitt, J. H., Richardson, A. J., and Escobar, D. E. 1993. Using satellite data to map false broomweed (Ericameria austrotexana) infestations on south Texas rangelands. Weed Technol. 7:865–871.CrossRefGoogle Scholar
Cardina, J., Johnson, G. A., and Sparrow, D. H. 1997. The nature and consequence of weed spatial distribution. Weed Sci. 45:364–373.CrossRefGoogle Scholar
Carson, H. W., Lass, L. W., and Callihan, R. H. 1995. Detection of yellow hawkweed (Hieracium pratense) with high resolution multispectral digital imagery. Weed Technol. 9:477–483.Google Scholar
Cousens, R. 1987. Theory and reality of weed control thresholds. Plant Prot. Q. 2:13–20.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 (Euphorbia esula). Weed Technol. 9:599–609.Google Scholar
Everitt, J. H. and Deloach, C. J. 1990. Remote sensing of Chinese tamarisk (Tamarix chinensis) and associated vegetation. Weed Sci. 38:273–278.CrossRefGoogle Scholar
Felton, W. L., Doss, A. F., Nash, P. G., and McCloy, K. R. 1991. To selectively spot spray weeds. Am. Soc. Agric. Eng. Symp. 11- 91:427–432.Google Scholar
Franz, E., Gebhardt, M. R., and Unklesbay, K. B. 1991. The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images. Trans. Am. Soc. Agric. Eng. 34:682–687.Google Scholar
Johnson, G. A., Mortensen, D. A., and Gotway, C. A. 1996. Spatial and temporal analysis of weed seedling populations using geostatistics. Weed Sci. 44:704–710.Google Scholar
Johnson, G. A., Mortensen, D. A., and Martin, A. R. 1995. A simulation of herbicide use based on weed spatial distribution. Weed Res. 35:197–205.Google Scholar
Lass, L. W. and Callihan, R. H. 1997. The effect of phenological stage on detectability of yellow hawkweed (Hieracium pratense) and oxeye daisy (Chrysanthemum leucanthemum) with remote multispectral digital imagery. Weed Technol. 11:248–256.CrossRefGoogle Scholar
Lass, L. W., Carson, H. W., and Callihan, R. H. 1996. Detection of yellow starthistle (Centaurea solstitialis) and common St. Johnswort (Hypericum perforatum) with multispectral digital imagery. Weed Technol. 10:466–474.CrossRefGoogle Scholar
Marshall, E.J.P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191–198.CrossRefGoogle 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. 33:569–581.CrossRefGoogle Scholar
Rankins, A. Jr., Shaw, D. R., and Byrd, J. D. Jr. 1998. HERB and MSU-HERB field validation for soybean (Glycine max) weed control in Mississippi. Weed Technol. 12:88–96.CrossRefGoogle Scholar
Richardson, A. J., Menges, R. M., and Nixon, P. R. 1985. Distinguishing weed from crop plants using video remote sensing. Photogramm. Eng. Remote Sens. 51:1785–1790.Google Scholar
Thompson, J. F., Stafford, J. V., and Miller, P.C.H. 1991. Potential for automatic weed detection and selective herbicide application. Crop Prot. 10:254–259.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 Prot. 9:337–342.CrossRefGoogle Scholar