Commenced in January 2007
Paper Count: 31533
On Combining Support Vector Machines and Fuzzy K-Means in Vision-based Precision Agriculture
Abstract:One important 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. In order to reach this goal, two major factors need to be considered: 1) the similar spectral signature, shape and texture between weeds and crops; 2) the irregular distribution of the weeds within the crop's field. This paper outlines an automatic computer vision system for the detection and differential spraying of Avena sterilis, a noxious weed growing in cereal crops. The proposed system involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and the weeds. From these attributes, a hybrid decision making approach determines if a cell must be or not sprayed. The hybrid approach uses the Support Vector Machines and the Fuzzy k-Means methods, combined through the fuzzy aggregation theory. This makes the main finding of this paper. The method performance is compared against other available strategies.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1332832Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1512
 J.V Stafford, "The role of technology in the emergence and current status of precision agriculture" in Handbook of precision agriculture. (A. Srinivasan Ed.). Food Products Press. New York. pp 19-56, 2006.
 A. Zhang, M. Wang and N. Wang, "Precision Agricuture-a worldwide overview," Computers and Electronics in Agriculture, vol. 36, pp. 113- 132, 2002.
 R. Gerhards and H. Oebel, "Practical experiences with a system for sitespecific weed control in arable crops using real-time image analysis and GPS-controlled patch spraying," Weed Research, vol. 46, pp. 185-193, 2006.
 A.J. Pérez, F. L├│pez, J.V. Benlloch and S. Christensen, "Colour and Shape Analysis techniques for weed detection in cereal fields," Computers and Electroncis in Agriculture, vol. 25, pp. 197-212, 2000.
 H.T. S├©gaard and H.J. Olsen, "Determination of crop rows by image analysis without segmentation," Computers and Electronics in Agriculture, vol. 38, pp. 141-158, 2003.
 C.C. Yang, S.O. Prasher, J.A. Landry and H.S. Ramaswamy, "Development of an image processing system and a fuzzy algorithm for site-specific herbicide applications," Precision Agriculture, vol. 4, pp. 5-18, 2003.
 K.R. Thorp and L.F. Tian, "A Review on Remote Sensing of Weeds in Agriculture," Precision Agriculture vol. 5, pp. 477-508, 2004.
 A. Ribeiro, C. Fern├índez-Quintanilla, J. Barroso and M.C. Garc├¡a- Alegre, "Development of an image analysis system for estimation of weed,". in Proc. 5th European Conf. On Precision Agriculture (5ECPA), 2005 pp. 169-174
 J. Barroso, C. Fern├índez-Quintanilla, C. Ruiz, P. Hernaiz, L.J Rew, "Spatial stability of Avena sterilis ssp. Ludoviciana populations under annual applications of low rates of imazamethbenz," Weed Research, vol. 44, pp. 178-186, 2004.
 L. Radics, M. Glemnitz, J. Hoffmann, G. Czimber, "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, pp. 58-64.
 M.J. Aitkenhead, I.A. Dalgetty, C.E. Mullins, A.J.S. McDonald, N.J.C. Strachan, "Weed and crop discrimination using image analysis and artificial intelligence methods," Computers and Electronics in Agriculture, vol. 39, pp. 157-171, 2003.
 P.M. Granitto, P.F. Verdes, H.A. Ceccatto, "Large-scale investigation of weed seed identification by machine vision," Computers and Electronics in Agriculture vol. 47, pp. 15-24, 2005.
 C.M. Onyango and J.A. Marchant, "Segmentation of row crop plants from weeds using colour and morphology," Computers and Electronics in Agriculture, vol. 39, pp. 141-155, 2003.
 L.F. Tian, D.C. Slaughter, "Environmentally adaptive segmentation algorithm for outdoor image segmentation," Computers and Electronics in Agriculture, vol. 21, pp. 153-168, 1998.
 J. Kapur, P. Sahoo, A. Wong, "A new method for gray-level picture thresholding using the entropy of the histogram," Computer Vision Graphics Image Processing, vol. 29, nº 3, pp. 273-285, 1985.
 P.L. Rosin, E. Ioannidis, "Evaluation of global image thresholding for change detection," Pattern Recognition Letters, vol. 24, pp. 2345-2356, 2003.
 B. Bacher, Weed density estimation from digital images in spring barley, PhD thesis, Dpt. Agricultural Sciences, Section of AgroTechnology, The Royal Veterinary and Agricultural University, Denmark, 2001
 R.C. Gonzalez, R.E. Woods and S.L. Eddins, Digital Image Processing using Matlab. Prentice Hall, New York, 2004.
 B. Astrand, A.J. Baerveldt, "An Agricultural Mobile Robot with Vision- Based Perception for Mechanical Weed Control," Autonomous Robots, vol.13, pp. 21-35, 2002.
 R.P.W Duin, "On the choice of smoothing parameters for Parzen estimators of probability density functions," IEEE Trans. Comput., vol. C-25, pp. 1175-1179, 1976.
 R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, Jhon Willey and Sons, New York ,2001.
 V. Cherkassky, F. Mulier, Learning from data: concepts, theory and methods, Wiley, New York, 1998
 V.N. Vapnik, The nature of statistical learning theory, Springer-Verlag, New York, 2000.
 H.J. Zimmermann, Fuzzy Set Theory and its Applications, Kluwer Academic Publishers, Norwell, 1991.
 P. Sneath, R. Sokal, Numerical Taxonomy: the principle and practice of numerical classification, W.H. Freeman, 1973.