Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-28T12:44:52.107Z Has data issue: false hasContentIssue false
  • English
  • Français

Differentiation of turfgrass and common weed species using hyperspectral radiometry

Published online by Cambridge University Press:  20 January 2017

Kendall C. Hutto ,
David R. Shaw ,
John D. Byrd Jr.  and
Roger L. King
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
Get access Rights & Permissions [Opens in a new window]

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.

Keywords

Eclipta, Eclipta prostrata L. ECLALVirginia buttonweed, Diodia virginiana L. DIQVIdallisgrass, Paspalum dilatatum Poir. PASDIsouthern crabgrass, Digitaria ciliaris (Retz.) Koel. DIGSPbermudagrass, Cynodon dactylon (L.) Pers. × Cynodon transvaalensis Burtt-Davy, ‘Tifway 419’zoysiagrass, Zoysia japonica Steud., ‘Meyer’St. Augustinegrass, Stenotaphrum secundatum (Walt.) Kuntze, ‘Raleigh’common centipedegrass, Eremochloa ophiuroides (Munro) Hackcreeping bentgrass, Agrostis stolonifera L., ‘Crenshaw’Remote sensingturfgrassweed identification
Type
Weed Management
Information
Weed Science , Volume 54 , Issue 2 , April 2006 , pp. 335 - 339
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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 19:88–91.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 9:477–483.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.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 9:599–609.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 6:271–280.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 39:481–492.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 10:466–474.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 48:393–398.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 33:569–581.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 6:1015–1020.CrossRefGoogle Scholar