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Differentiation of turfgrass and common weed species using hyperspectral radiometry

Published online by Cambridge University Press:  20 January 2017

David R. Shaw
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
John D. Byrd Jr.
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
Roger L. King
Affiliation:
Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762

Abstract

Hand-held hyperspectral reflectance data were collected in the summers of 2002, 2003, and 2004 to differentiate unique spectral characteristics of common turfgrass and weed species. Turfgrass species evaluated were: bermudagrass, ‘Tifway 419’; zoysiagrass, ‘Meyer’; St. Augustinegrass, ‘Raleigh’; common centipedegrass; and creeping bentgrass, ‘Crenshaw’. Weed species evaluated were: dallisgrass, southern crabgrass, eclipta, and Virginia buttonweed. Reflectance data were collected from greenhouse and field locations. An overall classification accuracy of 85% was achieved for all species in the field. A total of 21 spectral bands between 378 and 1,000 nm that were consistent over the three data collection periods were used for analysis. Only centipedegrass, zoysiagrass, and dallisgrass were correctly classified less than 80% of the time. An overall classification accuracy of 69% was achieved for the greenhouse species. Spectral bands used in this analysis ranged from 353 to 799 nm. Creeping bentgrass and Virginia buttonweed were classified correctly at 96 and 92%, respectively.

Type
Weed Management
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Brown, R. B. and Steckler, J. P. 1993. Weed patch identification in no-till corn using digital imagery. Can. J. Remote Sensing 19:8891.Google 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 9:477483.Google Scholar
Everitt, J. H. and Deloach, C. J. 1990. Remote sensing of Chinese tamarisk (Tamarix chinensis) and associated vegetation. Weed Sci 38:273278.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 escula). Weed Technol 9:599609.Google Scholar
LaMastus, F. E. and Shaw, D. R. 2004. Comparison of different sampling scales to estimate weed populations in three soybean fields. Prec. Agric 6:271280.CrossRefGoogle Scholar
Lamb, D. W., Weedon, M. M., and Rew, L. J. 1999. Evaluating the accuracy of mapping weeds in seedling crops using airborne digital imaging: Avena spp. in seedling triticale. Weed Res 39:481492.Google 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 10:466474.Google Scholar
Medlin, C. R., Shaw, D. R., Gerard, P. D., and LaMastus, F. E. 2000. Using remote sensing to detect weed infestations in Glycine max . Weed Sci 48:393398.Google 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 33:569581.Google Scholar
Peters, A. J., Reed, B. C., Eve, M. D., and McDaniel, K. C. 1992. Remote sensing of broom snakeweed (Gutierrezia sarothrae) with NOAA-10 spectral image processing. Weed Technol 6:10151020.CrossRefGoogle Scholar