IJBTT

Detection and Classification of Plant Diseases Using Image Processing and Multiclass Support Vector Machine
	© 2020 by IJCTT Journal
	Volume-68 Issue-4
	Year of Publication : 2020
	Authors : Murtaza Ali Khan
	DOI :  10.14445/22312803/IJCTT-V68I4P102

How to Cite?

Murtaza Ali Khan, "Detection and Classification of Plant Diseases Using Image Processing and Multiclass Support Vector Machine," International Journal of Computer Trends and Technology, vol. 68, no. 4, pp. 5-11, 2020. Crossref, https://doi.org/10.14445/22312803/IJCTT-V68I4P102

Abstract
Identification of plant disease is very important to prevent the loss and keep the harvest healthy. Determination of plant disease via visual monitoring is difficult and time consuming. In this paper, we described a method of detection and classification of plant disease using image processing and machine learning techniques. We used standard images of leaves of several types of plants to test our method. Initially, our method segments the input image to isolate disease parts of the leaf. Then we obtain various features from the diseased affected segmented image. Finally, we classify leaves into healthy and disease types based on its features using Multiclass Support Vector Machine (SVM) classifier. Experimental results indicate that our method yields very high accuracy rate for detection and classification of plant diseases.

Reference
[1] Arthur, D., Vassilvitskii, S. K-means++: “The advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms”, SODA `07, Society for Industrial and Applied Mathematics, Philadelphia, PA, USA 2007; 1027-1035. URL http://dl.acm.org/citation.cfm?id=1283383.1283494.
[2] Cigaroudy, L.S., Aghazadeh, N. “A multiphase segmentation method based on binary segmentation method for gaussian noisy image”. Signal, Image and Video Processing 2017; 11(5): 825-831. URL https://doi.org/10.1007/s11760-016-1028-9.
[3] Cortes, C., Vapnik, V. “Support-vector networks”. Machine Learning 1995; 20(3): 273-297. URL https://doi.org/10.1007/BF00994018.
[4] Cristianini, N., Shawe-Taylor, J. “An Introduction to Support Vector Machines and Other Kernel-based Learning Methods”. Cambridge University Press, 2000. URL https://doi.org/10.1017/CBO9780511801389.
[5] Dhingra, G., Kumar, V., Joshi, H.D. “Study of digital image processing techniques for leaf disease detection and classification”. Multimedia Tools and Applications, 2017. URL https://doi.org/10.1007/s11042-017-5445-8.
[6] Gao, F., Lv, W., Zhang, Y., Sun, J., Wang, J., Yang, E. “A novel semi-supervised support vector machine classifier based on active learning and context information”. Multidimensional Systems and Signal Processing 2016; 27(4): 969-988. URL https://doi.org/10.1007/s11045-016-0396-1
[7] Gupta, V., Sengar, N., Dutta, M.K., Travieso, C.M., Alonso, J.B. “Automated segmentation of powdery mildew disease from cherry leaves using image processing”. In: 2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI) 2017; 1-4. URL https://doi.org/10.1109/IWOBI.2017.8006454
[8] Lloyd, S. “Least squares quantization in PCM”. IEEE Transactions on Information Theory 1982; 28(2): 129-137 URL https://doi.org/10.1109/TIT.1982.1056489.
[9] Mignotte, M. “Segmentation by fusion of histogram-based k-means clusters in different color spaces”. IEEE Transactions on Image Processing 2008; 17(5): 780-787 URL https://doi.org/10.1109/TIP.2008.920761.
[10] Nabeel, F., Asghar, S.N., Bashir, S. “Optimizing image segmentation by selective fusion of histogram based k-means clustering”. In: 2015 12th International Bhurban Conference on Applied Sciences and Technology (IBCAST) 2015; 181-185. URL https://doi.org/10.1109/IBCAST.2015.7058501.
[11] Prasad, S., Peddoju, S.K., Ghosh, D. “Multi-resolution mobile vision system for plant leaf disease diagnosis”. Signal, Image and Video Processing 2016; 10(2): 379-388. URL https://doi.org/10.1007/s11760-015-0751-y
[12] Salgadoe, A.S.A., Robson, A.J., Lamb, D.W., Dann, E.K., Searle, C. “Quantifying the severity of phytophthora root rot disease in avocado trees using image analysis”. Remote Sensing 2018; 10(2). URL http://www.mdpi.com/2072-4292/10/2/226.
[13] Shrivastava, S., Singh, S.K., Hooda, D.S. “Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation”. Multimedia Tools and Applications 2015; 74(24), 11467-11484. URL https://doi.org/10.1007/s11042-014-2239-0.
[14] Stoean, C., Stoean, R. “Support Vector Machines and Evolutionary Algorithms for Classification”. Springer International Publishing 2014. URL https://doi.org/10.1007/978-3-319-06941-8

Keywords
Detection, Classification, Plant Diseases, Image Processing, and Multiclass Support Vector Machine