Application of Image Processing for Plant Disease Identification Based on SVM Technique

Application of Image Processing for Plant Disease Identification Based on SVM Technique

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2020 by IJETT Journal
Volume-68 Issue-1
Year of Publication : 2020
Authors : M. H. Nayeem, MehrabIbn Newaz, A.K Chakraborty, Md Asaduz Zaman Mamun
DOI :  10.14445/22315381/IJETT-V68I1P213

Citation 

MLA Style: M. H. Nayeem, MehrabIbn Newaz, A.K Chakraborty, Md Asaduz Zaman Mamun  "Application of Image Processing for Plant Disease Identification Based on SVM Technique" International Journal of Engineering Trends and Technology 68.1 (2020):83-90.

APA Style:M. H. Nayeem, MehrabIbn Newaz, A.K Chakraborty, Md Asaduz Zaman Mamun. Application of Image Processing for Plant Disease Identification Based on SVM Techniquee  International Journal of Engineering Trends and Technology, 68(1),83-90.

Abstract
Generally, plant diseases are detected by plant pathologists with the eye observation of different leaf syndromes. In this paper, we propose an automatic system which will be able to detect and classify the plant diseases automatically based on the preset datasets. This project deals with the image processing techniques and SVM algorithm to identify the disease symptoms of different plant leaves based on color, texture, shape, smoothness, variance, skewness and other image properties from an affected leaf image matrix. The diseases focused in this study include AlternariaAlternata, Cercospora leaf spot, Anthracnose, Bacterial canker, Bacterial leaf streak and Bacterial Blight. The goal of this study is to accurately detect and categorize the main symptoms of plant disease by extracting the features from the disease affected portion of a leaf image. The techniques used in our present study are k-means clustering for detection and SVM algorithm for classification of diseases. The experimental results demonstrate the validity of our proposed method as a robust technique for the detection of plant leaf diseases. In this work, a unique dataset of 175 images each containing a total of eleven features of the affected portion of a leaf image was created and it is a major contribution to the study of automatic detection of plant diseases.

Reference

[1] M. K. Barnwal, A. Kotasthane,N. Magculia, P. K. Mukherjee, S. Savary, A. K. Sharma, H. B. Singh, U. S. SinghA, H. Sparks, M. Variar, N. Zaidi “A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps” European Journal of Plant Pathology, July 2013, Volume 136, Issue 3, pp 443–457
[2] JiaChengguo; Zhang Liping; Liu Lihong; Wang Jiansheng; Li Chuanyou; Wang Qiaomei (2013-01-01). "Multiple phytohormone signalling pathways modulate susceptibility of tomato plants to Alternariaalternata f. sp. lycopersici". Journal of Experimental Botany. 64 (2): 637–650. doi:10.1093/jxb/ers360. ISSN 0022-0957. PMC 3542053
[3] D. (1 April 1999). "DNA relatedness among the pathovars of Pseudomonas syringae and description of Pseudomonas tremae sp. nov.and Pseudomonas cannabina sp. nov. (ex Sutic and Dowson 1959)". International Journal of Systematic Bacteriology. 49 (2): 469–478. doi:10.1099/00207713-49-2- 469. PMID 10319466
[4] Gardan, L.; Shafik, H.; Belouin, S.; Broch, R.; Grimont, F.; Grimont, P. A. D. (1 April 1999). "DNA relatedness among the pathovars of Pseudomonas syringae and description of Pseudomonas tremae sp. nov.and Pseudomonas cannabina sp. nov. (ex Sutic and Dowson 1959)". International Journal of Systematic Bacteriology. 49 (2): 469–478. doi:10.1099/00207713-49-2-469. PMID 10319466
[5] Brooklyn Botanic Garden. (2000): Natural disease control: A common-sense approach to plant first aid. Handbook # 164. Brooklyn Botanic Garden, Inc. 1000 Washington Avenue, Brooklyn, NY
[6] Horst, Kenneth; Rev, By R. Cynthia Westcott (2001), Westcott`s plant disease handbook, Boston, Mass.: Kluwer Academic Publishers, pp. 245, 678, ISBN 0-7923-8663-9
[7] Gardiner, Donald M.; Upadhyaya, Narayana M.; Stiller, Jiri; Ellis, Jeff G.; Dodds, Peter N.; Kazan, Kemal; Manners, John M. (2014). "Genomic Analysis of XanthomonasTranslucens Pathogenic on Wheat and Barley Reveals Cross-Kingdom Gene Transfer Events and Diverse Protein Delivery Systems." Ed. TurgayUnver". PLoS ONE. 9 (1): E84995. doi: 10.1371/journal.pone.0084995. PMC 3887016. PMID 24416331
[8] Brooklyn Botanic Garden. (2000): Natural disease control: A common-sense approach to plant first aid. Handbook # 164. Brooklyn Botanic Garden, Inc. 1000 Washington Avenue, Brooklyn, NY
[9] Mathre, D.E. (1997). Compendium of barley diseases. American Phytopathological Society. pp. 120 pp
[10] Wenjiang Huang, Qingsong Guan, JuhuaLuo, Jingcheng Zhang, Jinling Zhao, Dong Liang, Linsheng Huang, and Dongyan Zhang, “New Optimized Spectral Indices for Identifying and Monitoring Winter Wheat Diseases”, IEEE journal of selected topics in applied earth observation and remote sensing,Vol. 7, No. 6, June 2014
[11] Zulkifli Bin Husin, Abdul Hallis Bin Abdul Aziz, Ali Yeon Bin MdShakaffRohaniBinti S Mohamed Farook, “Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques”, 2012 Third International Conference on Intelligent Systems Modelling and Simulation.
[12] Mrunalini R. Badnakhe, Prashant R. Deshmukh, “Infected Leaf Analysis and Comparison by Otsu Threshold and k- Means Clustering”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 3, March 2012.Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Translated J. Magn. Japan, vol. 2, pp. 740-741, August 1987 [Digest 9th Annual Conf. Magnetics Japan, p. 301, 1982].
[13] H. Muhammad Asraf, M. T. Nooritawati, M.S.B. Shah Rizam,” A Comparative Study in Kernel-Based Support Vector Machine of Oil”, Procedia Engineering 41 (2012) 1353 – 1359
[14] SatishMadhogaria, MarekSchikora, Wolfgang Koch, Daniel Cremers,” Pixel-Based Classification Method for Detecting Unhealthy Regions in Leaf Images”, Informatik 2011 - Informatikschafft Communities 41. Jahrestagung der GesellschaftfürInformatik , 4.-7.10.2011, Berlin
[15] Wang, X., M. Zhang, J. Zhu and S. Geng, Spectral prediction of Phytophthorainfestans infection on tomatoes using artificial neural network (ANN), International Journal of Remote Sensing 29 (6) (2008), pp. 1693–1706.
[16] Camargo, A. and Smith, J. S., (2009). An imageprocessing based algorithm to automatically identify plant disease visual symptoms, Biosystems Engineering, Volume 102, Issue 1, January 2009, Pages 9-21, ISSN 1537-5110, DOI: 10.1016/j.biosystemseng.2008.09.030.
[17] Digital Image Processing Techniques by P. Prabhu, Alagappa University.
[18] Hartigan, J. A.; Wong, M. A. (1979). "Algorithm AS 136: A K-Means Clustering Algorithm". Journal of the Royal Statistical Society, Series C (Applied Statistics) 28 (1): 100– 108
[19] Ali, S. A., Sulaiman, N., Mustapha, A. and Mustapha, N., (2009). K-means clustering to improve the accuracy of decision tree response classification. Inform. Technol. J., 8: 1256-1262. DOI: 10.3923/itj.2009.1256.1262
[20] Otsu, N. (1979). "A threshold selection method from graylevel histograms". IEEE Trans. Sys., Man., Cyber. 9: 62– 66. DOI:10.1109/TSMC.1979.4310076
[21] Otsu, N., "A Threshold Selection Method from Gray- Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66
[22] Meyer, David; Leisch, Friedrich; Hornik, Kurt (2003). "The support vector machine under test". Neurocomputing.55 (1–2): 169–186. doi:10.1016/S09252312(03)00431-4
[23] Hsu, Chih-Wei; Chang, Chih-Chung & Lin, Chih-Jen (2003). A Practical Guide to Support Vector Classification (Technical report). Department of Computer Science and Information Engineering, National Taiwan University.
[24] Platt, John C. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. NIPS.
[25] Duan, Kai-Bo; Keerthi, S. Sathiya (2005). "Which Is the Best Multiclass SVM Method? An Empirical Study". Multiple Classifier Systems. LNCS. 3541. pp. 278– 285. CiteSeerX 10.1.1.110.6789. doi:10.1007/11494683_28. ISBN 978-3- 54026306-7.
[26] Hsu, Chih-Wei & Lin, Chih-Jen (2002). "A Comparison of Methods for Multiclass Support Vector Machines". IEEE Transactions on Neural Networks.
[27] Crammer, Koby& Singer, Yoram (2001). "On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines". Journal of Machine Learning Research. 2: 265–2 Lee, Yoonkyung; Lin, Yi &Wahba, Grace (2001). "Multicategory Support Vector Machines". Computing Science and Statistics. 33.

Keywords
Image Processing, Segmentation, plant leaf disease, K-means Method, SVM classifier