Hostname: page-component-7479d7b7d-rvbq7 Total loading time: 0 Render date: 2024-07-13T18:30:32.623Z Has data issue: false hasContentIssue false

Could Weed Sensing in Corn Interrows Result in Efficient Weed Control?

Published online by Cambridge University Press:  20 January 2017

Louis Longchamps*
Affiliation:
Department de Phytologie, Université Laval, Québec, Canada
Bernard Panneton
Affiliation:
Agriculture and Agri-Food Canada, Horticulture Research and Development Centre, Saint-Jean-sur-Richelieu, Canada
Marie-Josée Simard
Affiliation:
Agriculture and Agri-Food Canada, Soils and Crops Research and Development Center, Québec, Canada
Gilles D. Leroux
Affiliation:
Department de Phytologie, Université Laval, Québec, Canada
*
Corresponding author's E-mail: louis.longchamps@gmail.com

Abstract

At the field scale, weeds generally appear aggregated rather than randomly distributed, and this aggregation is linked to the spatial heterogeneity of biotic and abiotic factors. Crop management practices shape the spatial pattern of weed infestations by modifying certain factors having an impact on weed emergence and growth. Although crop seeding is often the last in-field disturbance before crop and weed emergence, its effect on the distribution of weeds has received little attention in the literature. The purpose of this study was to assess the influence of the planting operation on weed cover and presence in corn fields using digital images to investigate the possibility of sensing the interrow to infer the presence or absence of weeds on the corn row. A total of 18 site-years under conventional tillage treated with a single POST application of herbicide were selected across seven locations. Image analysis, at the V2 to V4 growth stage of corn, was used to compare the weed cover in three zones: the undisturbed interrows, the corn rows, and the interrows compacted by tractor wheel traffic. For 61% of site-years, there was no significant difference among the zones. When there was a significant difference compared with the other two zones, the undisturbed interrow was usually less infested. Point-to-point comparisons of weed presence or absence (based on a threshold of five pixels) between the interrow and the corn row revealed 70 or 73% correspondence, depending on the type of interrow (undisturbed or tracked). However the error of inference of the corn row weed cover generated by sensing only adjacent interrows may be too high for efficient commercial weed control.

A una escala de campo, las malezas generalmente aparecen distribuidas en forma agregada y no aleatoriamente, y este agregado está relacionado a la heterogeneidad espacial de los factores bióticos y abióticos. Las prácticas de manejo del cultivo dan forma a los patrones espaciales de las infestaciones de malezas, al modificar ciertos factores que impactan la emergencia y crecimiento de malezas. Aunque la siembra del cultivo es a menudo la última perturbación dentro del campo antes de que se de la emergencia del cultivo y de las malezas, su efecto sobre la distribución de las malezas ha recibido poca atención en la literatura. El objetivo de este estudio fue evaluar la influencia de la operación de siembra sobre la presencia y cobertura de malezas dentro de campos de maíz usando imágenes digitales para investigar la posibilidad de inferir la presencia o ausencia de malezas sobre la hilera de siembra, a partir de datos de los espacios entre-hileras del maíz. Un total de 18 sitios-años bajo labranza convencional tratados con una sola aplicación de herbicida fueron seleccionados a lo largo de siete localidades. Se usó análisis de imágenes, en los estados de crecimiento del maíz de V2 a V4, para comparar la cobertura de malezas en tres zonas: entre-hileras sin perturbación, en la hilera del maíz, y entre-hileras compactadas por el tráfico de las llantas del tractor Para el 61% de los sitios-años, no hubo diferencias significativas entre zonas. Cuando hubo una diferencia significativa en comparación con las otras dos zonas, las entre-hileras sin perturbación estuvieron usualmente menos infestadas. Comparaciones de punto-a-punto de la presencia o ausencia de malezas (con base en un umbral de cinco pixeles) entre la hilera del maíz y entre-hileras revelaron 70 ó 73% de correspondencia, dependiendo del tipo de entre-hilera (sin perturbación o con compactación por las llantas). Sin embargo, el error de la inferencia de la cobertura de malezas en la hilera del maíz, generada solamente con los datos de las entre-hileras adyacentes puede ser muy alto para un control de malezas eficiente a nivel comercial.

Type
Weed Management—Major Crops
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

Agresti, A. 2002. Categorical Data Analysis. 2nd ed. Hoboken, NJ : J. Wiley. Pp. 1114.CrossRefGoogle Scholar
Aitkenhead, M. J., Dalgetty, I. A., Mullins, C. E., McDonald, A.J.S., and Strachan, N.J.C. 2003. Weed and crop discrimination using image analysis and artificial intelligence methods. Comput. Electron. Agric. 39 :157171.CrossRefGoogle Scholar
Andújar, D., Ribeiro, Á., Fernández-Quintanilla, C., and Dorado, J. 2011. Accuracy and feasibility of optoelectronic sensors for weed mapping in wide row crops. Sensors 11 :23042318.CrossRefGoogle ScholarPubMed
Benech-Arnold, D.R.L. and Sanchez, R. A., eds. 2004. Handbook of Seed Physiology. Application to Agriculture. Binghamton, NY : The Haworth Press. 480 p.Google Scholar
Benvenuti, S. 2003. Soil texture involvement in germination and emergence of buried weed seeds. Agron. J. 95 :191198.Google Scholar
Berge, T., Aastveit, A., and Fykse, H. 2008. Evaluation of an algorithm for automatic detection of broad-leaved weeds in spring cereals. Precis. Agric. 9 :391405.CrossRefGoogle Scholar
Biller, R. H. 1998. Reduced input of herbicides by use of optoelectronic sensors. J. Agric. Eng. Res. 71 :357362.CrossRefGoogle Scholar
Blackshaw, R. E., Molnarand, L. J., and Janzen, H. H. 2004. Nitrogen fertilizer timing and application method affect weed growth and competition with spring wheat. Weed Sci. 52 :614622.CrossRefGoogle Scholar
Bonett, D. G. and Price, R. M. 2007. Statistical inference for generalized yule coefficients in 2 × 2 contingency tables. Sociol. Method Res. 35 :429446.CrossRefGoogle Scholar
Boyd, N. and Van Acker, R. 2004. Seed and microsite limitations to emergence of four annual weed species. Weed Sci. 52 :571577.CrossRefGoogle Scholar
Brown, A. D., Dexter, A. R., Chamen, W.C.T., and Spoor, G. 1996. Effect of soil macroporosity and aggregate size on seed-soil contact. Soil Till. Res. 38 :203216.Google Scholar
Buhler, D. D. 1997. Effects of tillage and light environment on emergence of 13 annual weeds. Weed Technol. 11 :496501.CrossRefGoogle Scholar
Casal, J. J. and Sánchez, R. A. 1998. Phytochromes and seed germination. Seed Sci. Res. 8 :317329.CrossRefGoogle Scholar
Dent, J. B., Fawcett, R. H., and Thornton, P. K. 1989. Economics of crop protection in Europe with reference to weed control. British Crop Protection Conference—Weeds. 1989 :917926.Google Scholar
Dexter, A. 1988. Advances in characterization of soil structure. Soil Till. Res. 11 :199238.Google Scholar
Dixon, W. J. and Mood, A. M. 1946. The statistical sign test. J. Am. Stat. Assoc. 41 :557566.CrossRefGoogle ScholarPubMed
Egley, G. H. and Duke, S. O. 1985. Physiology of weed seed dormancy and germination. Pages 2764 in; Duke, S. O., ed. Weed Physiology. Volume I. Reproduction and Ecophysiology. Boca raton, fl : CRC.Google Scholar
Hall, M. R., Swanton, C. J., and Anderson, G. W. 1992. The critical period of weed control in grain corn (Zea mays). Weed Sci. 40 :441447.CrossRefGoogle Scholar
Hartmann, K. M., Goetz, S., Market, R., Kaufmann, T., and Schneider, K. 2003. Photocontrol of weed germination: lightless tillage and variable memory of the seedbank. Aspects Appl. Biol. 69 :237246.Google Scholar
Hollander, M. and Wolfe, D. A. 1973. Nonparametric Statistical Methods. New York : John Wiley & Sons. 503 p.Google Scholar
Holm, R. E. 1972. Volatile metabolites controlling germination in buried weed seeds. Plant Physiol. 50 :293297.CrossRefGoogle ScholarPubMed
Hughes, G. 1996. Incorporating spatial pattern of harmful organisms into crop loss models. Crop Prot. 15 :407421.CrossRefGoogle Scholar
Jensen, T. 2007. Starter fertilizer specifics. Proceedings of the 8th Annual Manitoba Agronomists Conference. Winnipeg, MB : University of Manitoba.Google Scholar
Jurado-Expósito, M., López-Granados, F., García-Torres, L., García-Ferrer, A., Sánchez de la Orden, M., and Atenciano, A. 2003. Multi-species weed spatial variability and site-specific management maps in cultivated sunflower. Weed Sci. 51 :319328.CrossRefGoogle Scholar
Jurik, T. W. and Zhang, S. 1999. Tractor wheel traffic effects on weed emergence in central Iowa. Weed Technol. 13 :741746.CrossRefGoogle Scholar
Karssen, C. M. and Hilhorst, H.W.M. 1992. Effect of chemical environment on seed germination. Pages 327348 in; Fenner, M., ed. Seeds: The Ecology of Regeneration in Plant Communities. Wallingford, UK : CAB International.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42 :568573.CrossRefGoogle Scholar
Leblanc, M. L., Cloutier, D. C., Hamel, C., and Leroux, G. D. 1998. Facteurs impliqués dans la levée des mauvaises herbes au champ. Phytoprotection 79 :111127.CrossRefGoogle Scholar
Lemieux, C., Vallée, L., and Vanasse, A. 2003. Predicting yield loss in maize fields and developing decision support for post-emergence herbicide applications. Weed Res. 43 :323332.CrossRefGoogle Scholar
Longchamps, L., Panneton, B., Samson, G., Leroux, G. D., and Thériault, R. 2010. Discrimination of corn, grasses and dicot weeds by their UV-induced fluorescence spectral signature. Precis. Agric. 11 :181197.CrossRefGoogle Scholar
Mao, W., Hu, X., and Zhang, X. 2008. Weed detection based on the optimized segmentation line of crop and weed. Computer And Computing Technologies In Agriculture. Volume II. First IFIP TC 12 International Conference on Computer and Computing Technologies in Agriculture (CCTA 2007). New-York, NY : Springer.Google Scholar
Meyer, D., Zeileis, A., and Hornik, K. 2002. vcd: Visualizing Categorical Data. R package version 1.2-13.Google Scholar
Morgan, D. C., Child, R., and Smith, H. 1981. Absence of fluence rate dependency of phytochrome modulation of stem extension in light-grown Sinapis alba L. Planta 151 :497498.CrossRefGoogle ScholarPubMed
Onyango, C. M. and Marchant, J. A. 2001. Physics-based color image segmentation for scenes containing vegetation and soil. Image Vision Comp. 19 :523538.CrossRefGoogle Scholar
Paap, A., Askraba, S., Alameh, K., and Rowe, J. 2008. Photonic-based spectral reflectance sensor for ground-based plant detection and weed discrimination. Opt. Express. 16 :10511055.CrossRefGoogle ScholarPubMed
Panneton, B. and Brouillard, M. 2009. Colour representation methods for segmentation of vegetation in photographs. Biosystems Eng. 102 :365378.CrossRefGoogle Scholar
Roberts, E. H. and Smith, R. D. 1977. Dormancy and the pentose phosphate pathway. Pages 385411 in; Khan, A. A., ed. The Physiology and Biochemistry of Seed Dormancy and Germination. Amsterdam, The Netherlands : Elsevier North-Holland Biomedical Press.Google Scholar
Sattin, M., Zuin, M. C., and Sartorato, I. 1994. Light quality beneath field-grown maize, soybean and wheat canopies—red: far red variations. Physiol. Plant 91 :322328.CrossRefGoogle Scholar
Simard, M.-J., Panneton, B., Longchamps, L., Lemieux, C., Légère, A., and Leroux, G. D. 2009. Validation of a management program based on a weed cover threshold model: effects on herbicide use and weed populations. Weed Sci. 57 :187193.CrossRefGoogle Scholar
Sui, R., Thomasson, A., Hanks, J., and Wooten, J. 2008. Ground-based sensing system for weed mapping in cotton. Comput. Electron. Agric. 60 :3138.CrossRefGoogle Scholar
Thompson, D., Thompson, S., and Thompson, R. 1995. Alternatives in Agriculture—Alternative Weed Management System. Boone, IA : Thompson On-Farm Research. 41 p.Google Scholar
Tian, L. 2002. Development of a sensor-based precision herbicide application system. Comput. Electron. Agric. 36 :133149.CrossRefGoogle Scholar
Voorhees, W. B. and Hendrick, J. G. 1977. Compaction: good and bad effects on energy needs. Crops Soils Mag. 229 :1113.Google Scholar
Wang, N., Zhang, N., Wei, J., Stoll, Q., and Peterson, D. E. 2007. A real-time, embedded, weed-detection system for use in wheat fields. Biosystems Eng. 98 :276285.CrossRefGoogle Scholar
Way, T. R. and Kishimoto, T. 2004. Interface pressures of a tractor drive tyre on structured and loose soils. Biosystems Eng. 87 :375386.CrossRefGoogle Scholar
Wiles, L. and Brodahl, M. 2004. Exploratory data analysis to identify factors influencing spatial distributions of weed seed banks. Weed Sci. 52 :936947.CrossRefGoogle Scholar
Woolcock, J. L. and Cousens, R. 2000. A mathematical analysis of factors affecting the rate of spread of patches of annual weeds in an arable field. Weed Sci. 48 :2734.CrossRefGoogle Scholar
Zeileis, A., Meyer, D., and Hornik, K. 2007. Residual-based shadings for visualizing (conditional) independence. J. Comput. Graph. Stat. 16 :507525.Google Scholar