Weed image classification using wavelet transform, stepwise linear discriminant analysis, and support vector machines for an automatic spray control system

Journal of Information Science and Engineering

Volumn 30, Issue 4, 2014, Pages 1227-1244

  Siddiqi, Muhammad Hameed ^a   Lee, Seok Won ^b   Khan, Adil Mehmood ^b  

^a Kyung Hee University   (South Korea)

^b Ajou University   (South Korea)

Author keywords

Global histogram equalization; Machine vision; Swlda; Wavelet transform; Weed classification

Indexed keywords

COMPUTER VISION; FACE RECOGNITION; IMAGE CLASSIFICATION; WAVELET TRANSFORMS;

CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; DECOMPOSITION LEVEL; HISTOGRAM EQUALIZATIONS; STEPWISE LINEAR DISCRIMINANT ANALYSIS; STRUCTURAL APPROACH; SUPPORT VECTOR MACHINE (SVMS); SWLDA;

SUPPORT VECTOR MACHINES;

EID: 84940281091     PISSN: 10162364     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (33)

1. N. P. Daggupati, "Assessment of the varitarget nozzle for variable rate application of liquid crop protection products," M.S. Dissertation, Department of Biological and Agricultural Engineering, College of Engineering, University of Kansas State, Manhattan, Kansas, 2007.
2. J. Bossu, C. 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, 2009, pp. 133-143.
3. Y. Chen, P. Lin, Y. He, and Z. Xu, "Classification of broadleaf weed images using Gabor wavelets and lie group structure of region covariance on Riemannian manifolds," Biosystems Engineering, Vol. 109, 2011, pp. 220-227.
4. K. H. Ghazali, M. M. Mustafa, and A. Hussain, "Color image processing of weed classification: A comparison of two feature extraction techniques," in Proceedings of International Conference on Electrical Engineering and Informatics, 2007, pp. 607-610.
5. K. H. Ghazali, M. M. Mustafa, and A. Hussain, "Implementation of feature extraction technique from grey level co-occurrence matrix to classify narrow and broad weed in oil palm plantation," European Journal of Scientific Research, Vol. 20, 2008, pp. 68-75.
6. K. H. Ghazali, M. M. Mustafa, and A. Hussain, "Weed classification for automatic weeding strategy: A comparison of two image processing techniques," International Journal of Soft Computing Applications, Vol. 2, 2008, pp. 62-68.
7. K. H. Ghazali, M. M. Mustafa, and A. Hussain, "Machine vision system for automatic weeding strategy using image processing technique," American-Eurasian Journal of Agricultural and Environmental Science, Vol. 3, 2008, pp. 451-458.
8. K. H. Ghazali, M. M. Mustafa, and A. Hussain, "Machine vision system for automatic weeding strategy in oil palm plantation using image filtering technique," in Proceedings of the 3rd International Conference on Information and Communication Technology: From Theory to Applications, 2008, pp. 1-5.
9. A. J. Ishak, A. Hussain, and M. M. Mustafa, "Weed image classification using Gabor wavelet and gradient field distribution," Computers and Electronics in Agriculture, Vol. 66, 2009, pp. 53-61.
10. C. Gee, J. Bossu, G. Jones, and F. Truchetet, "Crop/weed discrimination in perspective agronomic images," Computers and Electronics in Agriculture, Vol. 60, 2008, pp. 49-59.
11. R. M. Haralick and L. S. Shapiro, Computer Vision, Vol. 1 Addison Wesley, Boston, MA, 1992.
12. F. Ernawan, N. A. Abu, and N. Suryana, "Spectrum analysis of speech recognition via discrete Tchebichef transform," in Proceedings of International Conference on Graphic and Image Processing, 2011, pp. 82856L-1-82856L-8.
13. C. Ancuti and P. P. Bekaert, "SIFT-CCH: Increasing the SIFT distinctness by color co-occurrence histograms," in Proceedings of the 5th International Symposium on Image and Signal Processing and Analysis, 2007, pp. 130-135.
14. I. Ahmad, A. M. Naeem, and M. Islam, "Real-time specific weed recognition system using histogram analysis," Transactions on on Engineering, Computer and Technology, Vol. 16, 2006 pp. 145-148.
15. I. Ahmad, A. M. Naeem, M. Islam, and S. Nawaz, "Weed classification using variance for real-time selective herbicide applications," in Proceedings of the 3rd International Colloquium on Signal Processing and its Application, 2007, pp. 40-43.
16. A. M. Naeem, I. Ahmad, and M. Islam, "Weed classification using two dimensional weed coverage rate (2D-WCR) for real-time selective herbicide applications," in Proceedings of the 9th International Conference on Computer, Information and Systems Science and Engineering, 2007. pp. 335-339.
17. I. Ahmad, M. Islam, S. I. A. Shah, and A. Adnan, "Real-time specific weed recognition system using statistical methods," International Journal of Computer, Information and System Science, and Engineering, Vol. 1, 2007 pp. 24-248.
18. 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, Vol. 11, 2011, pp. 908-915.
19. H. T. Søgaard, "Weed classification by active shape models," Biosystems Engineering, Vol. 91, 2005, pp. 271-281.
20. J. I. Arribas, V. F. S. Gonzalo, G. Ruiz-Ruiz, and J. Gómez-Gil, "Leaf classification in sunflower crops by computer vision and neural networks," Computers Electronics Agriculture, Vol. 78, 2011, pp. 9-18.
21. M. H. Siddiqi, S. Sulaiman, I. Faye, and I. Ahmad, "A real time specific weed discrimination system using multi-level wavelet decomposition," International Journal of Agriculture and Biology, Vol. 11, 2009, pp. 559-565.
22. R. Sharmila and R. Uma, "A new approach to image contrast enhancement using weighted threshold histogram equalization with improved switching median filter," International Journal of Advanced Engineering Sciences and Technologies, Vol. 7, 2011, pp. 206-211.
23. J. Turunen, "A wavelet-based method for estimating damping in power systems," Ph.D. Thesis, Department of Electrical Engineering Power Transmission Systems, Aalto University, 2011.
24. C. Boutsidis, M. W. Mahoney, and P. Drineas "Unsupervised feature selection for principal components analysis" in Proceedings of the 14th International Conference on Knowledge Discovery and Data Mining, 2008, pp. 61-69.
25. S. Abe, Support Vector Machines for Pattern Classification, Springer, London, 2005.
26. S. Mika, "Kernel fisher discriminants," Ph.D. Thesis, Department of Electrical Engineering, University of Technology, Berlin, 2002.
27. D. J. Krusienski, E. W. Sellers, D. J. McFarland, T. M. Vaughan, and J. R. Wolpaw, "Toward enhanced P300 speller performance," Journal of Neuroscience Methods, Vol. 167, 2008, pp. 15-21.
28. T.-J. Chin and D. Suter, "Incremental kernel principal component analysis," IEEE Transactions on Image Processing, Vol. 16, 2007, pp. 1662-1674.
29. H. Erdogan, "Subspace kernel discriminant analysis for speech recognition," ITRW on Robustness Issues in Conversational Interaction, Norwich, UK, 2004.
30. H. Erdogan, "Subspace kernel discriminant analysis for speech recognition," ITRW on Robustness Issues in Conversational Interaction, Norwich, UK, 2004.
31. S. Milka, G. Ratsch, J. Weston, B. Scholkopf, and K.-R. Muller, "Fisher discriminant analysis with kernels," in Proceedings of Neural Networks for Signal Processing, 1999, pp. 41-48.
32. M. Weis, T. Rumpf, R. Gerhards, and L. Plümer, "Comparison of different classification algorithms for weed detection from images based on shape parameters," Image Analysis for Agricultural Products and Processes, Vol. 69, 2009, pp. 53-64.
33. C. W. Hsu and C. J. Lin, "A comparison on methods for multiclass support vector machines," IEEE Transactions on Neural Networks, Vol. 13, 2002, pp. 415-425.