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A Comparison of Visual and Photographic Estimates of Weed Biomass and Weed Control1

Published online by Cambridge University Press:  20 January 2017

Christophe Neeser ,
Alex R. Martin ,
Peter Juroszek  and
David A. Mortensen
Christophe Neeser
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583-0915
Alex R. Martin
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583-0915
Peter Juroszek
Affiliation:
Institut für Pflanzenbau University of Bonn, 53115 Bonn, Germany
David A. Mortensen*
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583-0915
*
Corresponding author's E-mail: dmortensen1@unl.edu.
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Abstract

The objective of this study was to compare the consistency and accuracy of visually estimated weed biomass and weed control data to data obtained through image analysis. Weed biomass and weed control were evaluated in soybean herbicide efficacy trials conducted at the University of Nebraska–Lincoln during 1992 and 1993. Measurements were based on visual estimates and on aerial photographs taken at a height of 3.5 m above the soil surface. Photographs were digitized and classified, producing pixel values for broadleaf weeds, grass weeds, soybean, and soil. Percent weed cover was calculated in relation to the crop canopy, based on the respective number of pixels per image. Visual and photographic ratings of weed biomass and of weed control were not closely correlated. In the first year the visual method discriminated between more treatments than the photographic method, but the opposite occurred in the second year. The photographic method predicted yield more closely than the visual estimates. We concluded that visual estimates were less consistent and more subject to observer bias than measurements obtained with the photographic method.

Keywords

SoybeanGlycine max (L.) MerrImage analysiscanopyPOST, postemergencePPI, preplant incorporatedPRE, preemergence

Information

Type
Research Article
Information
Weed Technology , Volume 14 , Issue 3 , September 2000 , pp. 586 - 590
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Birdsall, J. L., Quimby, P. C. Jr., Rees, N. E., Svejcar, T. J., and Sowell, B. F. 1997. Image analysis of leafy spurge (Euphorbia esula) cover. Weed Technol. 11: 798–803.CrossRefGoogle Scholar
Duncan, D. B. 1975. t-Tests and intevals for comparisons suggested by the data. Biometrics 31: 339–359.CrossRefGoogle Scholar
Felton, W. L., and Nash, P. G. 1998. Role of reflectance techniques in precision weed management. In Medd, R. W. and Pratley, J. E., eds. Precision Weed Management in Crops and PastuRes. Adelaide, Australia: Cooperative Research Center for Weed Management Systems. pp. 62–70.Google Scholar
Frans, R., Talbert, R., Marx, D., and Crowley, H. 1986. Experimental design and techniques for measuring and analyzing plant responses to weed control practices. In Camper, N. D., ed. Research Methods in Weed Science. 3rd ed. Champaign, IL: Southern Weed Science Society. pp. 29–46.Google Scholar
Glasbey, C. A. and Horgan, G. W. 1995. Image Analysis for the Biological Sciences. Chichester, UK: J. Wiley. 218 p.Google Scholar
Gnegy, J. D. 1991. Visual Rating Systems for Target and Crop Species. In Miller, J. H. and Glover, G. R., eds. Standard Methods for Forest Herbicide Research. Southern Weed Science Society. pp. 40–43.Google Scholar
Ngouajio, M., Lemieux, C., Fortier, J. J., Careau, D., and Leroux, G. D. 1998. Validation of an operator-assisted module to measure weed and crop leaf cover by digital image analysis. Weed Technol. 12: 446–453.CrossRefGoogle Scholar
Parker, S. R., Shaw, M. W., and Royle, D. J. 1995a. The reliability of visual estimates of disease severity on cereal leaves. Plant Pathol. 44: 856–864.CrossRefGoogle Scholar
Parker, S. R., Shaw, M. W., and Royle, D. J. 1995b. Reliable measurement of disease severity. Aspects Appl. Biol. 43: 205–214.Google Scholar
Piggot, G. J. and Morgan, H. M. 1985. Visual assessment of dry matter yield of pastures on dairy farms. N.Z. J. Exp. Agric. 13: 219–224.Google Scholar
Robbins, B. 1998. Real-time weed detection and classification via computer vision. In Medd, R. W. and Pratley, J. E., eds. Precision Weed Management in Crops and PastuRes. Adelaide, Australia: Cooperative Research Center for Weed Management Systems. pp. 119–122.Google Scholar
Todd, L. A. and Kommedahl, T. 1994. Image analysis and visual estimates for evaluating disease reactions of corn to Fusarium stalk rot. Plant Dis. 78: 876–878.CrossRefGoogle Scholar
Watkinson, A. R. 1980. Density-dependence in single-species populations of plants. J. Theor. Biol. 83: 345–357.CrossRefGoogle Scholar