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Innovations in Hybrid Intelligent Systems pp 72–79Cite as

A Vision-Based Hybrid Classifier for Weeds Detection in Precision Agriculture Through the Bayesian and Fuzzy k-Means Paradigms

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Part of the book series: Advances in Soft Computing ((AINSC,volume 44))

Abstract

One objective in Precision Agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. This paper outlines an automatic computer vision system for the detection and differential spraying of Avena sterilis, a noxious weed growing in cereal crops. With such purpose we have designed a hybrid decision making system based on the Bayesian and Fuzzy k-Means (FkM) classifiers, where the a priori probability required by the Bayes framework is supplied by the FkM. This makes the main finding of this paper. The method performance is compared against other available strategies.

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References

  1. Stafford, J.V.: The role of Technology in the Emergence and Current Status of Precision Agriculture. In Handbook of precision agriculture (Srinivasan, A. ed.). Food Products Press. New York. (2006) 19–56

    Google Scholar 

  2. Zhang, A., Wang, M. and Wang, N.: Precision Agricuture-a Worldwide Overview. Computers and Electronics in Agriculture, 36 (2002) 113–132

    Article  Google Scholar 

  3. Gerhards, R. and Oebel, H.: Practical Experiences with a System for Site-specific Weed Control in Arable Crops Using Real-time Image Analysis and GPS-controlled Patch Spraying. Weed Research, 46 (2006) 185–193

    Article  Google Scholar 

  4. Pérez, A.J., López, F., Benlloch, J.V. and Christensen, S.: Colour and Shape Analysis Techniques for Weed Detection in Cereal Fields. Computers and Electroncis in Agriculture, 25 (2000) 197–212

    Article  Google Scholar 

  5. Søgaard, H.T. and Olsen, H.J.: Determination of Crop Rows by Image Analysis without Segmentation. Computers and Electronics in Agriculture, 38 (2003) 141–158

    Article  Google Scholar 

  6. Yang, C.C., Prasher, S.O. Landry, J.A. and Ramaswamy, H.S.: Development of an Image Processing System and a Fuzzy Algorithm for Site-specific Herbicide Applications. Precision Agriculture, 4 (2003) 5–18

    Article  Google Scholar 

  7. Thorp, K.R. and Tian, L.F.: A Review on Remote Sensing of Weeds in Agriculture. Precision Agriculture, 5, (2004) 477–508

    Article  Google Scholar 

  8. Ribeiro A., Fernández-Quintanilla, C., Barroso, J., García-Alegre. M.C.: Development of an Image Analysis System for Estimation of Weed. In Proc. 5th European Conf. On. Precision Agriculture (5ECPA), 2005 pp. 169–174

    Google Scholar 

  9. Barroso, J., Fernández-Quintanilla, C., Ruiz, C., Hernaiz, P. and Rew, L.J.: Spatial Stability of Avena Sterilis ssp. Ludoviciana Populations under Annual Applications of Low Rates of Imazamethbenz. Weed Research, 44 (2004) 178–186

    Article  Google Scholar 

  10. Radics, L., Glemnitz, M., Hoffmann, J. and Czimber, G.: Composition of Weed Floras in Different Agricultural Management Systems within the European Climatic Gradient. In Proc. 6th European Weed Research Society (EWRS). Workshop on Physical and Cultural Weed Control, Lillehammer, Norway, (2004) 58–64

    Google Scholar 

  11. Aitkenhead, M.J., Dalgetty, I.A., Mullins, C.E., McDonald, A.J.S. and Strachan, N.J.C.: Weed and Crop Discrimination Using Image Analysis and Artificial Intelligence Methods. Computers and Electronics in Agriculture, 39 (2003) 157–171

    Article  Google Scholar 

  12. Granitto, P.M., Verdes, P.F. and Ceccatto, H.A.: Large-scale Investigation of Weed Seed Identification by Machine Vision. Computers and Electronics in Agriculture, 47 (2005) 15–24

    Article  Google Scholar 

  13. Onyango, C.M. and Marchant, J.A.: Segmentation of Row Crop Plants from Weeds Using Colour and Morphology. Computers and Electronics in Agriculture, 39, (2003) 141–155

    Article  Google Scholar 

  14. Tian, L.F. and Slaughter, D.C.: Environmentally Adaptive Segmentation Algorithm for Outdoor Image Segmentation. Computers and Electronics in Agriculture, 21 (1998) 153–168

    Article  Google Scholar 

  15. Gonzalez, R.C., Woods, R.E. and Eddins, S.L.: Digital Image Processing using Matlab. Prentice Hall, New York (2004)

    Google Scholar 

  16. Astrand, B. and Baerveldt, A.J.: An Agricultural Mobile Robot with Vision-Based Perception for Mechanical Weed Control. Autonomous Robots, 13, (2002) 21–35

    Article  MATH  Google Scholar 

  17. Duda, R.O., Hart, P.E. and Stork, D.G.: Pattern Classification, Jhon Willey and Sons, New York (2001)

    MATH  Google Scholar 

  18. Zimmermann, H.J.: Fuzzy Set Theory and its Applications, Kluwer Academic Publishers, Norwell (1991)

    MATH  Google Scholar 

  19. Sneath, P., Sokal, R.: Numerical Taxonomy: the principle and practice of numerical classification, W.H. Freeman, San Francisco (1973).

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. Informática y Automática, E.T.S. Informática- UNED, 28040, Madrid, Spain

    Alberto Tellaeche

  2. Instituto de Automática Industrial, CSIC. Arganda del Rey, Madrid, Spain

    Xavier-P. BurgosArtizzu & Angela Ribeiro

  3. Dept. Ingeniería del Software e Inteligencia Artificial, Facultad Informática, Universidad Complutense, 28040, Madrid, Spain

    Gonzalo Pajares

Authors
  1. Alberto Tellaeche
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  2. Xavier-P. BurgosArtizzu
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  3. Gonzalo Pajares
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  4. Angela Ribeiro
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Editor information

Editors and Affiliations

  1. Escuela Politécnica Superior Campus Vena, Edifico C, Universidad de Burgos, C/Francisco de Vitoria s/n, 09006, Burgos, Spain

    Emilio Corchado

  2. Departamento de Informática y Automática Facultad de Ciencias, Universidad de Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Juan M. Corchado

  3. Centre for Quantifiable Quality of Service in Communication Systems (Q2S) Centre of Excellence, Norwegian University of Science and Technology, O.S. Bragstads plass 2E, 7491, Trondheim, Norway

    Ajith Abraham

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© 2007 Springer-Verlag Berlin Heidelberg

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Tellaeche, A., BurgosArtizzu, XP., Pajares, G., Ribeiro, A. (2007). A Vision-Based Hybrid Classifier for Weeds Detection in Precision Agriculture Through the Bayesian and Fuzzy k-Means Paradigms. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_11

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  • DOI: https://doi.org/10.1007/978-3-540-74972-1_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74971-4

  • Online ISBN: 978-3-540-74972-1

  • eBook Packages: EngineeringEngineering (R0)

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