Hostname: page-component-7dd5485656-bvgqh Total loading time: 0 Render date: 2025-10-31T12:07:34.132Z Has data issue: false hasContentIssue false
  • English
  • Français

Validation of an Operator-Assisted Module to Measure Weed and Crop Leaf Cover by Digital Image Analysis

Published online by Cambridge University Press:  12 June 2017

Mathieu Ngouajio ,
Claudel Lemieux ,
Jean-Jacques Fortier ,
Denis Careau  and
Gilles D. Leroux
Mathieu Ngouajio
Affiliation:
Department of Phytology, Laval University, Québec, QC, Canada G1K 7P4
Claudel Lemieux
Affiliation:
Agriculture and Agri-Food Canada, 2560 Hochelaga Boulevard, Sainte-Foy, QC, Canada G1V 2J3
Jean-Jacques Fortier
Affiliation:
Société de Mathématiques Appliquées Inc., 59 d'Auteuil Street, Quebec, QC, Canada G1R 4C2
Denis Careau
Affiliation:
Department of Phytology, Laval University, Québec, QC, Canada G1K 7P4
Gilles D. Leroux
Affiliation:
Department of Phytology, Laval University, Québec, QC, Canada G1K 7P4
Get access Rights & Permissions [Opens in a new window]

Abstract

The practical application of yield loss prediction models using relative leaf area of weeds is limited due to the lack of a quick and accurate method of leaf area estimation. Leaf cover (the vertical projection of plant canopy on the ground) can be used to approximate leaf area at early stages of plant development. An automated digital image analysis system for measuring leaf cover has been developed. The system has an operator-assisted module aimed at validating the automated functions. The objective of this research was to demonstrate the accuracy of the operator-assisted module under different weed–crop conditions. A laboratory experiment was conducted using simulated weed–crop populations. Two additional field experiments were conducted using corn in competition with: (1) common lambsquarters, barnyardgrass, or a mixture of both species, and (2) a natural weed community. In the laboratory experiment, a narrow linear relation was observed between leaf cover estimated with the operator-assisted module and leaf area measured with an optical area meter (r 2 > 0.98). In field experiments, the regression between corn leaf cover estimated by the operator-assisted module and corn leaf area measured with the optical area meter was not as good (r 2 < 0.55). The poor performance of the module was probably due to the overlapping and the architecture of corn leaves (especially unexpanded leaves). Nevertheless, the system showed high precision in estimating leaf area of both grassy weeds and broadleaf weeds (r 2 > 0.89). Generally, the accuracy of the estimates decreased as the growth stage became more advanced. Apart from its initial purpose as a calibration tool for the automated system, the operator-assisted module can have several potential research applications. It can be used: (1) as an alternative to destructive leaf area measurement at early stages of plant development, (2) as a tool in the study of plant competitive ability, and (3) as an objective and quantitative support to visual observations.

Keywords

Common lambsquarters, Chenopodium album L. # CHEALbarnyardgrass, Echinochloa crus-galli (L.) Beauv. # ECHCGcorn, Zea mays L.Machine visionpixel classificationleaf arealeaf coverCHEALECHCG

Information

Type
Research
Information
Weed Technology , Volume 12 , Issue 3 , September 1998 , pp. 446 - 453
Copyright
Copyright © 1998 by the 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.)

Article purchase

Temporarily unavailable

References

Literature Cited

Anonymous. 1984. Maïs, Culture. Agdex 111/20. Quebec, Canada: Conseil des Productions Végétales du Québec. 21 p.Google Scholar
Brain, P. and Cousens, R. 1990. The effect of weed distribution on predictions of yield loss. J. Appl. Ecol. 27:735–742.Google Scholar
Cousens, R. 1985a. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.Google Scholar
Cousens, R. 1985b. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci. 105:513–521.Google Scholar
Cousens, R., Peters, N.C.B., and Marshall, C. J. 1984. Models of yield loss–weed density relationships. In Proceedings of the 7th International Symposium on Weed Biology, Ecology and Systematics. Paris: Columa-EWRS. pp. 367–374.Google Scholar
Dew, D. A. 1972. An index of competition for estimating crop loss due to weeds. Can. J. Plant Sci. 52:921–927.Google Scholar
Dieleman, A., Hamill, A. S., Weise, S. F., and Swanton, C. J. 1995. Empirical models of pigweed (Amaranthus spp.) interference in soybean (Glycine max). Weed Sci. 43:612–618.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1995. Comparison of empircal models depicting density of Amaranthus retroflexus L. and relative leaf area as predictors of yield loss in maize (Zea mays L.). Weed Res. 35:207–214.Google Scholar
Kropff, M. J. 1988. Modeling the effects of weeds on crop production. Weed Res. 28:465–471.Google Scholar
Kropff, M. J. and Lotz, L.A.P. 1992a. Optimization of weed management systems: the role of ecological models of interplant competition. Weed Technol. 6:462–470.CrossRefGoogle Scholar
Kropff, M. J. and Lotz, L.A.P. 1992b. Systems approach to quantify crop–weed interactions and their application to weed management. Agric. Syst. 40:265–282.Google Scholar
Kropff, M. J., Lotz, L.A.P., Weaver, S. E., Bos, H. J., Willinga, J., and Migo, T. 1995. A two-parameter model for prediction of crop loss by weed competition from early observations of relative leaf area of weeds. Ann. Appl. Biol. 126:329–346.Google Scholar
Kropff, M. J. and Spiders, C.J.T. 1991. A simple model of crop loss by weed competition from early observations of relative leaf area of the weeds. Weed Res. 31:97–105.Google Scholar
Lemieux, C., Panneton, B., and Benoit, D. 1995. L'analyse d'image en malherbologie. In Actes Colloque international sur la prévision et le dépistage des ennemis des cultures, Québec, October 10–12. pp. 201–208.Google Scholar
Lotz, L.A.P., Kropff, M. J., Bos, B., and Wallinga, J. 1992. Prediction of yield loss based on relative leaf cover of weed. Proc. First Int. Weed Control Congr., Melbourne, February 17–22. 2:290–293.Google Scholar
Lotz, L.A.P., Kropff, M. J., Wallinga, J., Bos, H. J., and Groeneveld, R.M.W. 1994. Techniques to estimate relative leaf area and cover of weeds in crops for yield prediction. Weed Res. 34:167–175.Google Scholar
Lotz, L.A.P., Wallinga, J., and Kropff, M. J. 1995. Crop–weed interaction: quantification and prediction. In Glen, D. M., Greaves, M. P., and Anderson, H. M., eds. Ecology and Integrated Farming Systems. London: J. Wiley. pp. 31–47.Google Scholar
Lutman, P.J.W. 1992. Prediction of the competitive ability of weeds on the yield of several spring-sown arable crops. In Actes IXème colloque international sur la biologie des mauvaises herbes, Dijon, Paris, France. pp. 337–345.Google Scholar
[SAS] Statistical Analysis Systems. 1989. SAS/STAT™ User's Guide. Version 6, 4th ed. Volume 2. Cary, NC: Statistical Analysis Systems Institute. 846 p.Google Scholar
Snedecor, G. W. and Cochran, W. G. 1980. Statistical Methods. 7th ed. Ames, IA: Iowa State University Press. 507 p.Google Scholar
Wiles, L. J., Gold, H. J., and Wilkerson, G. G. 1993. Modeling the uncertainty of weed density estimates to improve post-emergence herbicide control decisions. Weed Res. 33:241–252.Google Scholar