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A New Combined Strategy for Discrimination between Types of Weed

  • P. Javier Herrera
  • José Dorado
  • Ángela Ribeiro
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 252)

Abstract

Specific weed management consists on adjusting herbicide treatments depending on the zone infested and the type of weed. In this context, the discrimination between grasses (monocots) and broad-leaved weeds (dicots) is an important objective mainly because the two weed groups can be appropriately controlled by different specific herbicides. This work proposes a method of discrimination between these types of weeds based on a combined strategy, the Sugeno Fuzzy Integral, where the final decision is taken by combining seven attributes, the Hu moments. The main challenge in terms of image analysis is to achieve an appropriate discrimination between both groups in outdoor field images under varying conditions of lighting as well as of soil background texture.

Keywords

Precision Agriculture weed discrimination monocots/dicots discrimination Sugeno Fuzzy Integral colour segmentation Hu moments 

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References

  1. 1.
    Thompson, J.F., Stafford, J.V., Miller, P.C.H.: Potential for automatic weed detection and selective herbicide application. Crop Production 10(4), 254–259 (1991)CrossRefGoogle Scholar
  2. 2.
    Marshall, E.J.P.: Field-scale estimates of grass weed populations in arable land. Weed Research 28(3), 191–198 (1988)CrossRefGoogle Scholar
  3. 3.
    Johnson, G.A., Mortensen, D.A., Martin, A.R.: A simulation of herbicide use based on weed spatial distribution. Weed Research 35(3), 197–205 (1995)CrossRefGoogle Scholar
  4. 4.
    Tian, L., Reid, J.F., Hummel, J.W.: Development of a precision sprayer for site-specific weed management. Transactions of the American Society of Agricultural Engineers 42, 893–900 (1999)CrossRefGoogle Scholar
  5. 5.
    Medlin, C.R., Shaw, D.R.: Economic comparison of broadcast and site-specific herbicide applications in nontransgenic and glyphosate-tolerant Glycine max. Weed Science 48(5), 653–661 (2000)CrossRefGoogle Scholar
  6. 6.
    Timmermann, C., Gerhards, R., Kühbauch, W.: The economic impact of site-specific weed control. Precision Agriculture 4(3), 249–260 (2003)CrossRefGoogle Scholar
  7. 7.
    Tang, L., Tian, L., Steward, B.L.: Classification of broadleaf and grass weeds using Gabor wavelets and an Artificial Neural Network. Transactions of the ASABE 46(4), 1247–1254 (2003)Google Scholar
  8. 8.
    López Granados, F., Jurado-Expósito, M., Atenciano, S., García-Ferrer, A., Sánchez de la Orden, M., García-Torres, L.: Spatial variability of agricultural soils in southern Spain. Plant and Soil 246, 97–105 (2002)CrossRefGoogle Scholar
  9. 9.
    Onyango, C.M., Marchant, J.A.: Segmentation of row crop plants from weeds using colour and morphology. Computers and Electronics in Agriculture 39, 141–155 (2003)CrossRefGoogle Scholar
  10. 10.
    Ribeiro, A., Fernández-Quintanilla, C., Barroso, J., García-Alegre, M.C.: Development of an image analysis system for estimation of weed. In: Stafford, J.V. (ed.) Proceedings 5th European Conf. On Precision Agriculture (5ECPA), pp. 169–174 (2005)Google Scholar
  11. 11.
    Tellaeche, A., Burgos-Artizzu, X., Pajares, G., Ribeiro, A., Fernández-Quintanilla, C.: A new vision-based approach to differential spraying in precision agriculture. Computers and Electronics in Agriculture 60(2), 144–155 (2008)CrossRefGoogle Scholar
  12. 12.
    Tellaeche, A., Burgos-Artizzu, X.P., Pajares, G., Ribeiro, A.: A vision-based method for weeds identification through the Bayesian decision theory. Pattern Recognition 41, 521–530 (2008)CrossRefMATHGoogle Scholar
  13. 13.
    Burgos-Artizzu, X.P., Ribeiro, A., Tellaeche, A., Pajares, G., Fernández-Quintanilla, C.: Improving weed pressure assessment using digital images from an experience-based reasoning approach. Computers and Electronics in Agriculture 65, 176–185 (2009)CrossRefGoogle Scholar
  14. 14.
    Tian, L.F., Slaughter, C.S.: Environmentally adaptive segmentation algorithm for outdoor image segmentation. Computers and Electronics in Agriculture 21, 153–168 (1998)CrossRefGoogle Scholar
  15. 15.
    Brown, R.B., Noble, S.D.: Site-specific weed management: sensing requirements - what do we need to see? Weed Science 53, 252–258 (2005)CrossRefGoogle Scholar
  16. 16.
    Lee, W.S., Slaughter, D.C., Giles, D.K.: Robotic weed control system for tomatoes. Precision Agriculture 1(1), 95–113 (1999)CrossRefGoogle Scholar
  17. 17.
    Meyer, G.E., Mehta, T., Kocher, M.F., Mortensen, D.A., Samal, A.: Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Transactions of the ASABE 41(4), 1189–1197 (1998)CrossRefGoogle Scholar
  18. 18.
    Ishak, A.J., Hussain, A., Mustafa, M.M.: Weed image classification using Gabor wavelet and gradient field distribution. Computers and Electronics in Agriculture 66, 53–61 (2009)CrossRefGoogle Scholar
  19. 19.
    Hemming, J., Rath, T.: Precision agriculture: computer-vision-based weed identification under field conditions using controlled lighting. Journal of Agricultural Engineering Research 78(3), 233–243 (2001)CrossRefGoogle Scholar
  20. 20.
    Burgos-Artizzu, X.P., Ribeiro, A., Guijarro, M., Pajares, G.: Real-time image processing for crop/weed discrimination in maize fields. Comput. Electron. Agr. 75, 337–346 (2011)CrossRefGoogle Scholar
  21. 21.
    Burks, T.F., Shearer, S.A., Heath, J.R., Donohue, K.D.: Evaluation of Neural-network Classifiers for Weed Species Discrimination. Biosystems Engineering 91(3), 293–304 (2005)CrossRefGoogle Scholar
  22. 22.
    Panneton, B., Guillaume, S., Samson, G., Roger, J.: Discrimination of Corn from Monocotyledonous Weeds with Ultraviolet (UV) Induced Fluorescence. Applied Spectroscopy 65(1), 10–19 (2011)CrossRefGoogle Scholar
  23. 23.
    Camargo Neto, J., Meyer, G.E.: Crop species identification using machine vision of computer extracted individual leaves. In: Chen, Y.R., Meyer, G.E., Tu, S. (eds.) Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality, Proc. SPIE, Bellingham WA, vol. 5996, pp. 64–74 (2005)Google Scholar
  24. 24.
    Sainz-Costa, N., Ribeiro, A., Andujar, D., Dorado, J.: Optimización evolutiva para la construcción de un método de estimación de porcentajes de cobertura de gramíneas y dicotiledóneas. In: Lozano, J.A., Gámez, J.A., Moreno Pérez, J.A. (eds.) Proceedings of the Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2011), vol. 1 (2011)Google Scholar
  25. 25.
    Hu, M.K.: Visual pattern recognition by moment invariants. IRE Trans. Information Theory. 8, 179–187 (1962)MATHGoogle Scholar
  26. 26.
    Mercimek, M., Gulez, K., Mumcu, T.K.: Real object recognition using moment in-variants. Sadhana - Springer India 30(6), 765–775 (2005)CrossRefMATHGoogle Scholar
  27. 27.
    Flusser, J., Suk, T., Zitová, B.: Moments and Moment Invariants in Pattern Recognition. John Wiley & Sons, Ltd. (2009)Google Scholar
  28. 28.
    Herrera, P.J., Pajares, G., Guijarro, M., Ruz, J.J., Cruz, J.M., Montes, F.: A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments. Sensors 9(12), 9468–9492 (2009)CrossRefGoogle Scholar
  29. 29.
    Kuncheva, L.: Combining Pattern Classifiers: Methods and Algorithms. Wiley (2004)Google Scholar
  30. 30.
    Klaus, A., Sormann, M., Karner, K.: Segmented-Based Stereo Matching Using Belief Propagation and Self-Adapting Dissimilarity Measure. In: Proc. of 18th Int. Conference on Pattern Recognition, vol. 3, pp. 15–18 (2006)Google Scholar
  31. 31.
    Herrera, P.J., Pajares, G., Guijarro, M., Ruz, J.J., Cruz, J.M.: A Stereovision Matching Strategy for Images Captured with Fish-Eye Lenses in Forest Environments. Sensors 11(2), 1756–1783 (2011)CrossRefGoogle Scholar
  32. 32.
    Herrera, P.J., Pajares, G., Guijarro, M., Ruz, J.J., Cruz, J.M.: Segmentation and stereoscopic correspondence in images obtained with omnidirectional projection for forest environments. In: Torreao, J.R.A. (ed.) Stereo Vision, ch. 3, pp. 41–56. In-Tech (2011)Google Scholar
  33. 33.
    Burgos-Artizzu, X.P., Ribeiro, A., Tellaeche, A., Pajares, G., Fernández-Quintanilla, C.: Analysis of natural images processing for the extraction of agricultural elements. Image Vision Computing 28, 138–149 (2010)CrossRefGoogle Scholar
  34. 34.
    Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, vol. I–II. Addison-Wesley, Reading (1992)Google Scholar
  35. 35.
    Herrera, P.J., Pajares, G., Guijarro, M., Ruz, J.J., De la Cruz, J.M.: Combination of attributes in stereovision matching for fish-eye lenses in forest analysis. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2009. LNCS, vol. 5807, pp. 277–287. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • P. Javier Herrera
    • 1
  • José Dorado
    • 2
  • Ángela Ribeiro
    • 1
  1. 1.Centre for Automation and RoboticsCSIC-UPMMadridSpain
  2. 2.Institute of Agricultural SciencesCSICMadridSpain

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