Influence of images recording height and crop growth stage on leaf cover estimates and their performance in yield prediction models

Crop Protection

Volumn 18, Issue 8, 1999, Pages 501-508

  Ngouajio, M ^a   Leroux, G D ^a   Lemieux, C ^b  

^a UNIVERSITÉ LAVAL   (Canada)

^b AGRICULTURE AND AGRI FOOD CANADA   (Canada)

Author keywords

Integrated weed management; Yield prediction; Zea mays

Indexed keywords

EID: 0032693690     PISSN: 02612194     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0261-2194(99)00052-6     Document Type: Article

References (33)

1. Anonymous, 1995. Mauvaises herbes: répression. Conseil des Productions Végétales du Québec. Agdex 640. Ministère de l'Agriculture, des Pêcheries et de l'Alimentation, Québec, 206 p.
2. Andreasen C., Rudemo M., Sevestre S. Assessment of weed density at an early stage by use of image processing. Weed Res. 37:1997;5-18.
3. Andrieu B., Allirand J.M., Jaggard K. Ground cover and leaf area index of maize and sugar beet crops. Agronomie. 17:1997;315-321.
4. Baziramakenga R., Leroux G.D. Economic and interference threshold densities of quackgrass (Elytrigia repens) in potato (Solanum tuberosum). Weed Sci. 46:1998;176-180.
5. Brain P., Cousens R. The effect of weed distribution on predictions of yield loss. J. Appl. Ecol. 27:1990;735-742.
6. Brivot R., Marchant J.A. Segmentation of plants and weeds for a precision crop protection robot using infrared images. IEE Proc.-Visual Image Process. 143:1996;118-124.
7. Carson H.W., Lass L.W., Callihan R.H. Detection of yellow hawkweed (Hieracium pratense) with high resolution multispectral digital imagery. Weed Technol. 9:1995;477-483.
8. Chism W.J., Birch J.B., Bingham S.W. Nonlinear regressions for analyzing growth stage and quinclorac interactions. Weed Technol. 6:1992;898-903.
9. Duke S.O. Herbicide resistant crops. background and perspectives Duke S.O. Herbicide-Resistant Crops. Agricultural, Environmental, Economic, Regulatory, and Technical Aspects:1996;1-3 CRC Press, Boca Raton, Fl.
10. Gomez, K.A., Gomez, A.A., 1984. Statistical procedures for Agricultural Research, 2nd Edition. Wiley, New York, 680 pp.
11. Hall M.R., Swanton C.J., Anderson G.N. The critical period of weed control in grain corn (Zea mays L.). Weed Sci. 40:1992;441-447.
12. Kershaw, K.A., 1973. Quantitative and Dynamic Plant Ecology, 2nd Edition. Elsevier, New York, 308 p.
13. King R.P., Lybecker D.W., Schweizer E.E., Zimdahl R.L. Bioeconomic modeling to simulate weed control strategies for continuous corn (Zea mays). Weed Sci. 34:1986;972-979.
14. Knezevic S.Z., Horak M.J., Vanderlip R.L. Relative time of redroot pigweed (Amaranthus retroflexus L.) emergence is critical in pigweed-sorghum [Sorghum bicolor (L.) Moench] competition. Weed Sci. 45:1997;502-508.
15. Knezevic S.Z., Weise S.F., Swanton C.J. Comparison of empirical models depicting density of Amaranthus retroflexus L. and relative leaf area as predictors of yield loss in maize (Zea mays L.). Weed Res. 35:1995;207-214.
16. Kropff M.J. Modelling the effects of weeds on crop production. Weed Res. 28:1988;465-471.
17. Kropff M.J., Lotz L.A.P. Optimization of weed management systems. the role of ecological models of interplant competition Weed Technol. 6:1992;462-470.
18. Kropff M.J., Spitters C.J.T. A simple model of crop loss by weed competition from early observations of relative leaf area of the weeds. Weed Res. 31:1991;97-105.
19. Kropff M.J., Weaver S.E., Smits M.A. Use of ecophysiological models for crop-weed interference. relations amongst weed density, relative time of weed emergence relative leaf area, and yield loss Weed Sci. 40:1992;296-301.
20. Lamb D.W., Weedon M. Evaluating the accuracy of mapping weeds in fallow fields using airborne digital imaging. Panicum effusum in oilseed rape stubble Weed Res. 38:1998;443-451.
21. Lass L.W., Callihan R.H. The effect of phenological stage on detectability of yellow hawkweed (Hieracium pratense) and oxeye daisy (Chrysanthemum leucanthemum) with remote multispectral digital imagery. Weed Technol. 11:1997;248-256.
22. Lass L.W., Carson H.W., Callihan R.H. Detection of yellow starthistle (Centaurea solstitialis) and common St. Johnswort (Hypericum perforatum) with multispectral digital imagery. Weed Technol. 10:1997;466-474.
23. Lemieux C., Cloutier D.C., Leroux G.D. Sampling quackgrass (Elytrigia repens) populations. Weed Sci. 40:1992;534-541.
24. Lemieux, C., Panneton, B., Benoit, D., 1995. L'analyse d'image en malherbologie. Actes Colloque international sur la prévision et le dépistage des ennemis des cultures, Québec, Octobre 10-12, pp. 201-208.
25. Leroux, G.D., Laganière, M., Vanasse, A., 1990. Méthodes alternatives de répression des mauvaises herbes dans les cultures fourragères, céréalières, le maïs et le soya. Rapport de recherches, ministère de l'Agriculture des Pêcheries et de l'Alimentation du Québec, Québec, G1R 4X6, 135 p.
26. Lotz L.A.P., Christensen S., Cloutier D., Quintanilla C.F., Légère A., Lemieux C., Lutman P.J.W., Iglesias A.P., Salonen J., Sattin M., Stigliani L., Tei F. Prediction of the competitive effects of weeds on crop yields based on the relative leaf area of weeds. Weed Res. 36:1996;93-101.
27. Lotz L.A.P., Kropff M.J., Wallinga J., Bos H.J., Groeneveld R.M.W. Techniques to estimate relative leaf area and cover of weeds in crops for yield prediction. Weed Res. 34:1994;167-175.
28. Lutman, P.J.W., 1992. Prediction of the competitive ability of weeds on the yield of several spring-sown arable crops. Actes IXème colloque international sur la biologie des mauvaises herbes, Dijon, Paris, France, pp. 337-345.
29. Ngouajio M., Lemieux C., Fortier J.J., Careau D., Leroux G.D. Validation of an operator-assisted module to measure weed and crop leaf cover by digital image analysis. Weed Technol. 12:1998;446-453.
30. Ngouajio, M., Leroux, G.D., Lemieux, C., 1999. A flexible sigmoidal model relating crop yield to weed relative leaf cover and its comparison with other nested models. Weed Res. 39, 000-000 in press.
31. [SAS] Statistical analysis Systems, 1989. SAS/STAT™ User's Guide, Version 6, 4th Edition, Vol. 2. Statistical Analysis Systems Institute, Cary, NC, 846 p.
32. Wiles L.J., Gold H.J., Wilkerson G.G. Modelling the uncertainty of weed density estimates to improve post-emergence herbicide control decisions. Weed Res. 33:1993;241-252.
33. Zwiggelaar R. A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops. Crop Prot. 17:1998;189-206.