Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2012 (2012), Article ID 484390, 10 pages
http://dx.doi.org/10.1100/2012/484390
Research Article

Crop Row Detection in Maize Fields Inspired on the Human Visual Perception

1Department of Software Engineering and Artificial Intelligence, Faculty of Informatics, University Complutense, 28040 Madrid, Spain
2Department of Computer Architecture and Automatic, Faculty of Informatics, University Complutense, 28040 Madrid, Spain
3Artificial Perception Group, Center for Automation and Robotics (CAR), CSIC-UPM, 28500, Arganda del Rey, Madrid, Spain

Received 14 October 2011; Accepted 28 November 2011

Academic Editors: C. Dell and A. Garcia y Garcia

Copyright © 2012 J. Romeo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. G. Davies, W. Casady, and R. Massey, “Precision agriculture: an introduction. Water Quality Focus Guide. WQ450,” http://extension.missouri.edu/explorepdf/envqual/wq0450.pdf.
  2. 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, no. 3, pp. 141–155, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Tellaeche, X. BurgosArtizzu, G. Pajares, A. Ribeiro, and C. Fernández-Quintanilla, “A new vision-based approach to differential spraying in precision agriculture,” Computers and Electronics in Agriculture, vol. 60, no. 2, pp. 144–155, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Tellaeche, X. P. Burgos-Artizzu, G. Pajares, and A. Ribeiro, “A vision-based method for weeds identification through the Bayesian decision theory,” Pattern Recognition, vol. 41, no. 2, pp. 521–530, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. X. P. Burgos-Artizzu, A. Ribeiro, A. Tellaeche, G. Pajares, and C. Fernández-Quintanilla, “Improving weed pressure assessment using digital images from an experience-based reasoning approach,” Computers and Electronics in Agriculture, vol. 65, no. 2, pp. 176–185, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. N. Sainz-Costa, A. Ribeiro, X. Burgos-Artizzu, M. Guijarro, and G. Pajares, “Mapping wide row crops with video sequences acquired from a tractor moving at treatment speed,” Sensors, vol. 11, no. 7, pp. 7095–7109, 2011. View at Publisher · View at Google Scholar
  7. V. Fontaine and T. G. Crowe, “Development of line-detection algorithms for local positioning in densely seeded crops,” Canadian Biosystems Engineering, vol. 48, pp. 7.19–7.29, 2006. View at Google Scholar · View at Scopus
  8. H. T. Søgaard and H. J. Olsen, “Determination of crop rows by image analysis without segmentation,” Computers and Electronics in Agriculture, vol. 38, no. 2, pp. 141–158, 2003. View at Publisher · View at Google Scholar · View at Scopus
  9. D. M. Woebbecke, G. E. Meyer, K. von Bargen, and D. A. Mortensen, “Shape features for identifying young weeds using image analysis,” Transactions of the American Society of Agricultural Engineers, vol. 38, no. 1, pp. 271–281, 1995. View at Google Scholar · View at Scopus
  10. T. Hague, N. Tillett, and H. Wheeler, “Automated crop and weed monitoring in widely spaced cereals,” Precision Agriculture, vol. 1, no. 1, pp. 95–113, 2006. View at Google Scholar
  11. D. C. Slaughter, D. K. Giles, and D. Downey, “Autonomous robotic weed control systems: a review,” Computers and Electronics in Agriculture, vol. 61, no. 1, pp. 63–78, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. P. V. C. Hough, “A method and means for recognizing complex patterns,” U.S. Patent Office No. 3069654, 1962.
  13. J. Marchant, “Tracking of row structure in three crops using image analysis,” Computers and Electronics in Agriculture, vol. 15, no. 2, pp. 161–179, 1996. View at Publisher · View at Google Scholar · View at Scopus
  14. T. Hague, J. A. Marchant, and D. Tillett, “A system for plant scale husbandry,” Precision Agriculture, pp. 635–642, 1997. View at Google Scholar
  15. B. Åstrand and A. J. Baerveldt, “A vision based row-following system for agricultural field machinery,” Mechatronics, vol. 15, no. 2, pp. 251–269, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. V. Leemans and M. F. Destain, “Application of the Hough transform for seed row localisation using machine vision,” Biosystems Engineering, vol. 94, no. 3, pp. 325–336, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. A. Tellaeche, G. Pajares, X. P. Burgos-Artizzu, and A. Ribeiro, “A computer vision approach for weeds identification through Support Vector Machines,” Applied Soft Computing Journal, vol. 11, no. 1, pp. 908–915, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. Ch. Gée, J. Bossu, G. Jones, and F. Truchetet, “Crop/weed discrimination in perspective agronomic images,” Computers and Electronics in Agriculture, vol. 60, no. 1, pp. 49–59, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. G. Jones, Ch. Gée, and F. Truchetet, “Modelling agronomic images for weed detection and comparison of crop/weed discrimination algorithm performance,” Precision Agriculture, vol. 10, no. 1, pp. 1–15, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. G. Jones, Ch. Gée, and F. Truchetet, “Assessment of an inter-row weed infestation rate on simulated agronomic images,” Computers and Electronics in Agriculture, vol. 67, no. 1-2, pp. 43–50, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. F. Rovira-Más, Q. Zhang, J. F. Reid, and J. D. Will, “Hough-transform-based vision algorithm for crop row detection of an automated agricultural vehicle,” Journal Automobile Engineering, Part D, vol. 219, no. 8, pp. 999–1010, 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Ji and L. Qi, “Crop-row detection algorithm based on Random Hough Transformation,” Mathematical and Computer Modelling, vol. 54, no. 3-4, pp. 1016–1020, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Asif, S. Amir, A. Israr, and M. Faraz, “A vision system for autonomous weed detection robot,” International Journal of Computer and Electrical Engineering, vol. 2, no. 3, pp. 486–491, 2010. View at Google Scholar
  24. F. Pla, J. M. Sanchiz, J. A. Marchant, and R. Brivot, “Building perspective models to guide a row crop navigation vehicle,” Image and Vision Computing, vol. 15, no. 6, pp. 465–473, 1997. View at Google Scholar · View at Scopus
  25. J. Billingsley and M. Schoenfisch, “The successful development of a vision guidance system for agriculture,” Computers and Electronics in Agriculture, vol. 16, no. 2, pp. 147–163, 1997. View at Google Scholar · View at Scopus
  26. M. Kise, Q. Zhang, and F. Rovira Más, “A stereovision-based crop row detection method for tractor-automated guidance,” Biosystems Engineering, vol. 90, no. 4, pp. 357–367, 2005. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Kise and Q. Zhang, “Development of a stereovision sensing system for 3D crop row structure mapping and tractor guidance,” Biosystems Engineering, vol. 101, no. 2, pp. 191–198, 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. F. Rovira-Más, Q. Zhang, and J. F. Reid, “Stereo vision three-dimensional terrain maps for precision agriculture,” Computers and Electronics in Agriculture, vol. 60, no. 2, pp. 133–143, 2008. View at Publisher · View at Google Scholar · View at Scopus
  29. H. J. Olsen, “Determination of row position in small-grain crops by analysis of video images,” Computers and Electronics in Agriculture, vol. 12, no. 2, pp. 147–162, 1995. View at Google Scholar · View at Scopus
  30. J.-B. Vioix, J.-P. Douzals, F. Truchetet, L. Assémat, and J.-P. Guillemin, “Spatial and spectral method for weeds detection and localization,” Eurasip Journal on Advances in Signal Processing, vol. 7, pp. 679–685, 2004. View at Google Scholar
  31. J. Bossu, Ch. Gée, J. P. Guillemin, and F. Truchetet, “Development of methods based on double Hough transform and Gabor filtering to discriminate crop and weeds in agronomic images,” in Proceedings of the SPIE 18th Annual Symposium Electronic Imaging Science and Technology, vol. 6070, no. 23, San Jose, Calif, USA, 2006.
  32. J. Bossu, Ch. Gée, G. Jones, and F. Truchetet, “Wavelet transform to discriminate between crop and weed in perspective agronomic images,” Computers and Electronics in Agriculture, vol. 65, no. 1, pp. 133–143, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. B. Astrand, Vision based perception or mechatronic weed control, Ph.D. thesis, Chalmers and Halmstad Universities, Sweden, Stockholm, 2005.
  34. TheMatworks, http://www.mathworks.com/, 2011.
  35. A. Ribeiro, C. Fernández-Quintanilla, J. Barroso, and M. C. García-Alegre, “Development of an image analysis system for estimation of weed,” in Proceedings of the 5th European Conference on Precision Agriculture (ECPA '05), vol. 1, no. 1, pp. 169–174, 2005.
  36. H. J. Zimmermann, Fuzzy Set Theory and its Applications, Kluwer Academic, Norwell, Mass, USA, 1991.
  37. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, NY, USA, 1981.
  38. R. O. Duda, P. E. Hart, and D. S. Stork, Pattern Classification, John Wiley & Sons, New York, NY, USA, 2000.
  39. B. Balasko, J. Abonyi, and B. Feil, Fuzzy Clustering and Data Analysis Toolbox for Use with Matlab, Veszprem University, Hungary, Budapest, 2008.
  40. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB, Prentice Hall, Upper Saddle River, NJ, USA, 2009.