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Weed–Crop Discrimination Using Remote Sensing: A Detached Leaf Experiment

Published online by Cambridge University Press:  20 January 2017

Anne M. Smith  and
Robert E. Blackshaw
Anne M. Smith*
Affiliation:
Agriculture and Agri-Food Canada, Research Centre, 5403 1st Avenue South, Lethbridge, AB, T1J 4B1 Canada
Robert E. Blackshaw
Affiliation:
Agriculture and Agri-Food Canada, Research Centre, 5403 1st Avenue South, Lethbridge, AB, T1J 4B1 Canada
*
Corresponding author's E-mail: [email protected]
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Abstract

Mapping weed infestations in an annual crop has implications not only for site-specific herbicide applications but also for planning future management strategies and understanding weed ecology. A controlled laboratory experiment, involving detached leaves, was conducted to investigate the potential to discriminate two crop and five weed species using hyperspectral and multispectral remote sensing. Stepwise discriminant function analyses showed that reflectance in the visible and “red-edge” regions of the spectrum were consistently required for species discrimination. The seven species were correctly identified 90 and 89% of the time using the hyperspectral and multispectral data, respectively, and the classification rules derived from discriminant function analyses. Errant species prediction with the hyperspectral data resulted in a grass being predicted as a grass and a broadleaf as a broadleaf. However, for multispectral data, incorrect classifications were more serious because errant predictions sometimes resulted in a grass being classified as a broadleaf and vice-versa. Further studies using plants at a variety of growth stages, from a variety of environments, and at the canopy level are warranted.

Keywords

Hyperspectralleaf reflectancemultispectralAmaranthus retroflexusAvena fatuaBrassica kaberChenopodium albumSetaria viridisBrassica napusTriticum aestivumAMAREAVEFACHEALSETVISINARSMA, spectral mixture analysis
Type
Research
Information
Weed Technology , Volume 17 , Issue 4 , December 2003 , pp. 811 - 820
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Adams, J. B., Smith, M. O., and Gillespie, A. R. 1993. Imaging spectroscopy interpretation based on spectral mixture analysis. in Peters, C. M. and Englert, P., eds. Remote Geochemical Analysis: Elements and Mineralogical Composition. Cambridge, UK: LPI and Cambridge University Press. Pp. 145–166.Google Scholar
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.Google Scholar
Anonymous. 2002. 2000 Sales Survey Pest Control Products. Canada Report and Discussion. Web page: http://www.cropro.org/english/aboutcpi/industrystatistics.html#a. Accessed: September 2002.Google Scholar
Beckie, H. J., Hall, L. M., and Tardif, F. J. 2001. Herbicide resistance in Canada—where are we today?. in Blackshaw, R. E. and Hall, L. M., eds. Integrated Weed Management: Explore the Potential. Sainte-Anne-de-Bellevue, Canada: Expert Committee on Weeds. Pp. 1–36.Google Scholar
Brown, R. B. and Steckler, J-P. G. A. 1993. Weed patch identification in no-till corn using digital imagery. Can. J. Remote Sens 19:88–91.Google Scholar
Brown, R. B. and Steckler, J-P. G. A. 1995. Prescription maps for spatially variable herbicide applications in no-till corn. Trans. ASAE 38:1659–1666.Google Scholar
Brown, R. B., Steckler, J-P. G. A., and Anderson, G. W. 1994. Remote sensing for identification of weeds in no-till corn. Trans. ASAE 37:297–302.Google Scholar
Brown, R. J., Staenz, K., McNairn, H., Hopp, B., and Van Acker, R. 1997. Application of high resolution optical imagery to precision agriculture. in International Symposium, Geomatics in the Era of RADARSAT (GER'97); Ottawa, ON, Canada; May 25–30, 1997. Ottawa, Canada: Natural Resources. P. 9.Google Scholar
Cardina, J., Sparrow, D. H., and McCoy, E. L. 1996. Spatial relationships between seedbank and seedling populations of common lambsquarters (Chenopodium album) and annual grasses. Weed Sci. 44:298–308.CrossRefGoogle Scholar
Carson, H. W., Lass, L. W., and Callihan, R. H. 1995. Detection of yellow hawkweed with high resolution digital images. Weed Technol. 9:477–483.Google Scholar
Cochrane, M. A. 2000. Using vegetation reflectance variability for species level classification of hyperspectral data. Int. J. Remote Sens 21:2075–2087.Google Scholar
Congalton, R. G. 1991. A review of assessing the accuracy of classification of remotely sensed data. Remote Sens. Environ 37:35–46.Google Scholar
Danson, F. M., Steven, M. D., Malthus, T. J., and Clarke, J. A. 1992. High-spectral resolution data for determining leaf water content. Int. J. Remote Sens 13:461–470.CrossRefGoogle Scholar
Daughtry, C. and Biehl, L. 1985. Changes in spectral properties of detached birch leaves. Remote Sens. Environ 17:281–289.Google Scholar
Deguise, J. C., Staenz, K., and Lefebvre, J. 1999. Agricultural applications of airborne hyperspectral data: weed detection. in Fourth International Airborne Remote Sensing conference and Exhibition/21st Canadian Symposium on Remote Sensing; Ottawa, ON, Canada; June 21–24, 1999. Volume 2. Pp. 352–358: Web page: http://www.ccrs.nrcan.gc.ca/ccrs/rd/sci_pub/bibpdf/4635.pdf. Accessed March 2003.Google Scholar
Everitt, J. H., Escobar, D. E., and Davis, M. R. 1995. Using remote sensing for detecting and mapping noxious plants. Weed Abstr 44:639–649.Google Scholar
Everitt, J. H., Pettit, R. D., and Alaniz, M. A. 1987. Remote sensing of broom snakeweed (Gutierrezia sarothrae) and spiny aster (Aster spinosus). Weed Sci. 35:295–302.Google Scholar
Gausman, H. W., Allen, W. A., Wiegand, C. L., Escobar, D. E., Rodriquez, R. R., and Richardson, A. J. 1973. The leaf mesophyll of twenty crops, their light spectra and optical and geometrical parameters. USDA Tech. Bull 1465:59.Google Scholar
Gausman, H. W., Menges, R. M., Richardson, A. J., Rodriquez, R. R., and Tamez, S. 1981. Optical parameters of leaves of seven weed species. Weed Sci. 29:24–26.CrossRefGoogle Scholar
Gerhards, R., Wyse-Pester, D. Y., Mortensen, D., and Johnson, G. A. 1997. Characterizing spatial stability of weed populations using interpolated maps. Weed Sci. 45:108–119.Google Scholar
Gong, P., Pu, R., and Yu, B. 1997. Conifer species recognition: an exploratory analysis of in situ hyperspectral data. Remote Sens. Environ 62:189–200.Google Scholar
Heap, I. 2003. The International Survey of Herbicide Resistant Weeds:. Web page: www.weedscience.com. Accessed: March 2003.Google Scholar
Horler, D. N. H., Dockray, M., and Barber, J. 1983. The red edge of plant leaf reflectance. Int. J. Remote Sens 4:272–288.Google Scholar
Jacquemoud, S., Baret, F., Andrieu, B., Danson, F. M., and Jaggard, K. 1995. Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors. Remote Sens. Environ 52:163–172.Google Scholar
Jensen, J. R. 1996. Introductory Digital Image Processing: A Remote Sensing Perspective. 2nd ed. Upper Saddle River, NJ: Prentice-Hall. 40 p.Google Scholar
Johnson, G. A., Mortensen, D. A., Young, L. J., and Martin, A. R. 1995. The stability of weed seedling population models and parameters in eastern Nebraska corn and soybean fields. Weed Sci. 43:604–611.Google Scholar
Lamb, D. W. and Weedon, M. 1998. Evaluating the accuracy of mapping weeds in fallow fields using airborne digital imaging: Panicum effusum in oilseed rape stubble. Weed Res. 38:443–451.Google 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
Landis, J. R. and Koch, G. G. 1977. The measurement of observer agreement for categorical data. Biometrics 33:159–174.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.Google Scholar
Lass, L. W., Carson, H. W., and Callihan, R. H. 1996. Detection of yellow starthistle (Centauria solstitialis) and common St. Johnswort (Hypericum perforatum) with multispectral digital imagery. Weed Technol. 10:466–474.CrossRefGoogle Scholar
Lass, L. W., Shafii, B., Price, W. J., and Thill, D. C. 2000. Assessing agreement in multispectral images of yellow starthistle (Centaurea solstitialis) with ground truth data using a Bayesian methodology. Weed Technol. 14:539–544.Google Scholar
Lewis, M. 2002. Spectral characterization of Australian arid zone plants. Can. J. Remote Sens 28:219–230.CrossRefGoogle Scholar
Mack, A. R., Brach, E. J., and Rao, V. R. 1978. Investigation of crop spectra and selection of optimal spectral channels. Can. J. Spectrosc 23:42–51.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
Mortensen, D. A., Johnson, G. A., Wyse, D. Y., and Martin, A. R. 1995. Managing spatially variable weed populations. in Roberts, P. C., Rust, R. H., and Larson, W. E., eds. Proceedings of Site-Specific Management for Agricultural Systems. 2nd Annual Conference; Minneapolis, MN; March 27–30, 1994. Madison, WI: ASA-CSSA-SSSA. Pp. 398–415.Google Scholar
Munden, R., Curran, P. J., and Catt, J. A. 1994. The relationship between red edge and chlorophyll concentration in the Broadbalk winter wheat experiment at Rothamsted. Int. J. Remote Sens 15:705–709.Google Scholar
Noble, S. D., Brown, R. B., and Crowe, T. G. 2002. The use of spectral properties for weed detection and identification—a review. in Canadian Society of Agricultural Engineers; Saskatoon, SK; July 14–17, 2002. CSAE Paper No. 02-208. Mansonville, QC, Canada: CSAE/SCGR.Google Scholar
Palacios-Orueta, A. and Ustin, S. L. 1996. Multivariate statistical classification of soil spectra. Remote Sens. Environ 57:108–118.Google Scholar
Price, J. C. 1994. How unique are spectral signatures? Remote Sens. Environ 49:181–186.Google Scholar
Radhakrishnan, J., Teasdale, J. R., Liang, S., and Shuey, C. J. 2002. Remote sensing of weed canopies. in Muttiah, R. S., ed. From Laboratory Spectroscopy to Remotely Sensor Spectra of Terrestrial Ecosystems. Netherlands: Kluwer Academic Pp. 175–202.Google Scholar
[SAS] Statistical Analysis Systems. 1999. SAS/STAT User's Guide. Version 8. Cary, NC: Statistical Analysis Systems Institute. 3884 p.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.Google Scholar
Vrindts, E. and De Baerdemaeker, J. 1997. Optical discrimination of crop, weed and soil for on-line weed detection. in Stafford, J., ed. Precision Agriculture '97 Proceedings of the First European Conference on Precision Agriculture. Volume 2: Technology, IT and Management. Oxford, UK: BIOS Scientific. Pp. 537–544.Google Scholar
Vrindts, E. and De Baerdemaeker, J. 2000. Using Spectral Information for Weed Detection in Field Circumstances. AgEng 2000 Warwick, EurAgEng, Paper No. 00-PA-010.Google Scholar
Vrindts, E., De Baerdemaeker, J., and Ramon, H. 2002. Weed detection using canopy reflection. Precis. Agric 3:63–80.Google Scholar
Zwiggelaar, R. 1998. A review of spectral properties of plants and their potential use in crop/weed discrimination in row-crops. Crop Prot 17:189–203.Google Scholar