Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-16T17:27:44.471Z Has data issue: false hasContentIssue false
  • English
  • Français

Use of Image Analysis to Assess Color Response on Plants Caused by Herbicide Application

Published online by Cambridge University Press:  20 January 2017

Asif Ali ,
Jens C. Streibig ,
Joachim Duus  and
Christian Andreasen
Asif Ali*
Affiliation:
Department of Agriculture and Ecology, Faculty of Science, University of Copenhagen, Hjbakkegaard Allé 13, DK 2630 Taastrup, Denmark
Jens C. Streibig
Affiliation:
Department of Agriculture and Ecology, Faculty of Science, University of Copenhagen, Hjbakkegaard Allé 13, DK 2630 Taastrup, Denmark
Joachim Duus
Affiliation:
Department of Agriculture and Ecology, Faculty of Science, University of Copenhagen, Hjbakkegaard Allé 13, DK 2630 Taastrup, Denmark
Christian Andreasen
Affiliation:
Department of Agriculture and Ecology, Faculty of Science, University of Copenhagen, Hjbakkegaard Allé 13, DK 2630 Taastrup, Denmark
*
Corresponding author's E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

In herbicide-selectivity experiments, response can be measured by visual inspection, stand counts, plant mortality, and biomass. Some response types are relative to nontreated control. We developed a nondestructive method by analyzing digital color images to quantify color changes in leaves caused by herbicides. The range of color components of green and nongreen parts of the plants and soil in Hue, Saturation, and Brightness (HSB) color space were used for segmentation. The canopy color changes of barley, winter wheat, red fescue, and brome fescue caused by doses of a glyphosate and diflufenican mixture, cycloxydim, diquat dibromide, and fluazifop-p-butyl were described with a log-logistic dose–response model, and the relationship between visual inspection and image analysis was calculated at the effective doses that cause 50% and 90% response (ED50 and ED90, respectively). The ranges of HSB components for the green and nongreen parts of the plants and soil were different. The relative potencies were not significantly different from one, indicating that visual and image analysis estimations were about the same. The comparison results suggest that image analysis can be used to assess color changes of plants in response to some herbicides and may have the potential to provide an objective measurement of symptoms.

En experimentos de selectividad de herbicidas, la respuesta puede ser medida mediante inspección visual, conteo de plantas establecidas, mortalidad de plantas y biomasa. Algunos tipos de respuesta son relativos al testigo no-tratado. Nosotros desarrollamos un método no-destructivo que analiza imágenes digitales a color para cuantificar cambios en el color de las hojas causados por herbicidas. El rango de los componentes de color de partes verdes y no-verdes de las plantas y el suelo en el ámbito de tono, saturación y brillo (HSB) de color fue usado para la segmentación. Los cambios en el color del dosel de cebada, trigo de invierno, Festuca rubra y Vulpia bromoides causados por dosis de una mezcla de glyphosate y diflufenican, cycloxydim, diquat dibromide, y fluazifop-p-butyl fueron descritos con un modelo log-logístico de respuesta a dosis, y la relación entre la inspección visual y el análisis de imagen fue calculada a dosis efectivas que causaron una respuesta del 50% y 90% (ED50 y ED90, respectivamente). Los rangos de los componentes de HSB para las partes verdes y no-verdes de las plantas y el suelo fueron diferentes. Las potencias relativas no fueron significativamente diferentes de uno, indicando que las estimaciones del análisis visual y del de imagen fueron casi las mismas. Los resultados de la comparación sugieren que el análisis de imagen puede ser usado para evaluar los cambios de color de las plantas en respuesta a algunos herbicidas y podría tener potencial para brindar una medida objetiva de los síntomas.

Keywords

Cycloxydimdiflufenicandiquat dibromidefluazifop-p-butylglyphosatebrome fescue, Vulpia bromoides (L.) S.F. Graybarley, Hordeum vulgare L.red fescue, Festuca rubra L.wheat, Triticum aestivum L.Herbicide efficacyvisual inspectionlogarithmic sprayerweeds management
Type
Weed Management—Techniques
Information
Weed Technology , Volume 27 , Issue 3 , September 2013 , pp. 604 - 611
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

Andreasen, C., Rudemo, M., and Sevestre, S. 1997. Assessment of weed density at an early stage by use of image processing. Weed Res. 37:5–18.Google Scholar
Andujar, D., Ribeiro, A., Carmona, R., Fernandez-Quintanilla, C., and Dorado, J. 2010. An assessment of the accuracy and consistency of human perception of weed cover. Weed Res. 50:638–647.Google Scholar
Bulanona, D. M., Kataokab, T., Otac, Y., and Hiromac, T. 2002. A segmentation algorithm for the automatic recognition of Fuji apples at harvest. Biosyst. Eng. 83:405–412.Google Scholar
Carper, W. J., Lillesand, T. M., and Kiefer, R. W. 1990. The use of intensity–hue–saturation transformations for merging SPOT panchromatic and multispectral image data. Photogramm. Eng. Remote Sens. 56:459–467.Google Scholar
Cobb, A. H. and Reade, J.P.H. 2010. Herbicides and Plant Physiology. 2nd ed. Hoboken, NH J. Wiley.Google Scholar
Ferreira, T. and Rasband, W. 2011. ImageJ User Guide (IJ 1.45m). http://rsbweb.nih.gov/ij/docs/user-guide.pdf. Accessed: August 13, 2012.Google Scholar
Gitelson, A. A., Zur, Y., Chivkunova, O. B., and Merzlyak, M. N. 2002. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem. Photobiol. 75:272–281.Google Scholar
Hack, H., Bleiholder, H., Buhr, L., Meier, U., Schnock-Fricke, U., Weber, E., and Witzenberger, A., 1992. Einheitliche Codierung der phänologischen Entwicklungsstadien mono- und dikotyler Pflanzen–Erweiterte BBCH–Skala, Allgemein. Nachr.bl. Dtsch. Pflanzenschutzd. 44:265–270.Google Scholar
Henry, W. B., Shaw, D. R., Reddy, K. R., Bruce, L. M., and Tamhankar, H. D., 2004. Remote sensing to detect herbicide drift on crops. Weed Technol. 18:358–368.Google Scholar
Huang, Y., Thomson, S. J., Ortiz, B. V., Reddy, K. N., Ding, W., Zablotowicz, R. M., and Brigh, J. R. 2010. Airborne remote sensing assessment of the damage to cotton caused by spray drift from aerially applied glyphosate through spray deposition measurements. Biosyst. Eng. 107:212–220.Google Scholar
Jafari, A. and Bakhshipour, A. 2011. A novel algorithm to recognize and locate pomegranate on the tree for a harvesting robot using a stereo vision system. Precis. Agric. Pages 14133–142.Google Scholar
Koutsias, N., Karteris, M., and Chuvieco, E. 2000. The use of intensity-hue-saturation transformation of landsat-5 thematic mapper data for burned land mapping. Photogramm. Eng. Remote Sens. 66:829–839.Google Scholar
Landini, G. 2009. Auto Threshold: ImageJ. Landini, G. 2009. Threshold_color: ImageJ. http://www.dentistry.bham.ac.uk/landinig/software/software.html. Accessed May 29, 2011. Li, Y., D. Chen, C. N. Walker, and J. F. Angus. 2010. Estimating the nitrogen status of crops using a digital camera. Field Crops Res. 118: 221–227.Google Scholar
Massa, D. and Gerhards, R. 2011. Investigations on herbicide resistance in European silky bent grass (Apera spica-venti) populations. J. Plant Dis. Prot. 118:31–39.Google Scholar
Meier, U. 2001. Weed species. Growth stages of mono and dicotyledonous plants. Berlin, Germany Federal Biological Research Centre for Agriculture and Forestry, Blackwell Wissenschafts-Verlag BBCH Monograph, Pp. 145–152.Google Scholar
Meyer, G., Mehta, T., Kocher, M., Mortensen, D., and Samal, A. 1998. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Trans. ASAE (Am. Soc. Agric. Eng.). 41:1189–1197.Google Scholar
Moghaddam, P. A., Derafshi, M. H., and Shirzad, V. 2011. Estimation of single leaf chlorophyll content in sugar beet using machine vision. Turk. J. Agric. For. 35:563–568.Google Scholar
Pfeiffer, R., Brunskill, R. T., and Hartley, G. S. 1955. Variable dosage sprayer for agricultural experiments. Nature. 176:472–473.Google Scholar
Rasmussen, J., Nørremark, M., and Bibby, B. M. 2007. Assessment of leaf cover and crop soil cover in weed harrowing research using digital images. Weed Res. 47:299–310.Google Scholar
Ritz, C. 2010. Toward a unified approach to dose–response modeling in ecotoxicology. Environ. Toxicol. Chem. 29:220–229.Google Scholar
Ritz, C. and Streibig, J. C. 2005. Bioassay analysis using R. J. Stat. Softw. 12(5):1–22.Google Scholar
Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., and Harlan, J. C. 1974. Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation. Greenbelt, MD National Aeronautics and Space Administration GSFC Type III final report, 371 p.Google Scholar
Rowland, C. D. 2000. Crop Tolerance to Non-Target and Labeled Herbicide Applications. . Mississippi State, MS: Mississippi State University. Pp. 45, 100, 548.Google Scholar
R Development Core Team. 2008. R: A Language and Environment for Statistical Computing. Vienna, Austria R Foundation for Statistical Computing.Google Scholar
Sainis, J. K., Rastogi, R., and Chadda, V. K. 1998. Applications of image processing in biology and agriculture. Nucl. India. 32:12–13.Google Scholar
Streibig, J. C. 1987. Joint action of root-absorbed mixtures of auxin herbicides in Sinapis alba L. and barley (Hordeum vulgare L.). Weed Res. 27:337–347.Google Scholar
Streibig, J. C., Rudemo, M., and Jensen, J. E. 1993. Dose–response curves and statistical models. Pages 29–55 in Streibig, J. C. and Kudsk, P., eds. Herbicide Bioassays. Boca Raton, FL CRC.Google Scholar
Tind, T., Mathiesen, T. J., Jensen, J. E., Ritz, C., and Streibig, J. C. 2009. Using a selectivity index to evaluate logarithmic spraying in grass seed crops. Pest Manag. Sci. 65:1257–1262.Google Scholar
Weis, M. and Sökefeld, M. 2010. Detection and identification of weeds. Pages 119–134 in Precision Crop Protection—The Challenge and Use of Heterogeneity. Houten, Netherlands Springer.Google Scholar