IJSEA Archive (Volume 7, Issue 8)
International Journal of Science and Engineering Applications (IJSEA) (Volume 7, Issue 8 August 2018)
Support Vector Machine Based Classification of Leaf Diseases
Keywords: leaf diseases; median filter; k-means clustering; gray level co-occurrence matrix; support vector machine
Myanmar is well known for agricultural country; wherein about 65% of the labor force depends on agriculture. Since the leaf diseases are microscopic organism, cannot be detected normal human eyes. Leaves are special indicator to distinguish the diseases because the image information of the leaf are changed when the leaf surf the diseases. So, the image processing techniques can be used in agricultural sector. The research work presents a support vector machine classifier algorithm by using MATLAB R2017a for the classification of leaf diseases such as Alternaria Alternata, Cercospora leaf spot, Bacterial Blight and so on. In this research work, RGB color space is converted into HSI (Hue Saturation Intensity) color space. In segmentation step, k-means clustering is used to select the defected area, and it is extracted the features by using GLCM (Gray Level Co-occurrence Matrix). Prior to the features extraction, the median filter is used for getting noise free feature results. Finally, the leaf disease is classified by using support vector machine (SVM) and computes the accuracy. From the obtained results, the maximum accuracy of the system is 83%.
 Ko Ko Zaw, Zin Ma Ma Myo, and Wah Wah Hlaing. 2018. “Multiclass Support Vector Machine Based Detection and Classification of Leaf Diseases”, 11th National Conference on Science and Engineering, YTU, Yangon, Myanmar
 A. Miyatra, S. Solanki. 2014. “Disease and Nutrient Deficiency Detection in Cotton Plant,” International Journal of Engineering Development and Research, vol. 2, no 2, pp. 2801-2804.
 V. A. Gulhane, A. A. Gurjar. 2011. “Detection of disease on cotton Leaves and Its Possible Diagnosis”, International Journal of Image Processing (IJIP), vol. 5 : no 5 : pp. 590-598.
 S. S. Panchal, R. Sonar. 2016. “Pomegranate Leaf Disease Detection Using Support Vector Machine,” International Journal of Engineering and Computer Science, vol. 5, no 6, pp. 16815-16818.
 V. M. Tiwari, T. Gupta. 2017. “Plant Leaf Disease Analysis using Image Processing Technique with Modified SVM-CS Classifier,” International Journal of Engineering and Management Technology, vol. 5, no. 1, pp. 11-17.
 A. N. Rathod , B. A. Tanawala, V. H. Shah. 2014. “Leaf disease detection using image processing and neural network”, IJAERD vol. 1, no. 6, June, pp. 1-10.
 S. Jayaraman, S. Esakkirajan , and T. Veerakumar. 2009. Digital Image Processing. U.S.A.: McGraw Hill Co,.
 Myo Myo Han. 2017. “Analysis on Detection Improvement of Textile Fabric Defects using Gray Level Co-occurrence Matrix for automatic system,” M.E. thesis, Dept. Electronic Eng., Yangon Technological University., Yangon, Myanmar.
 D. Gadkari. 2004. “Image Quality Analysis Using GLCM,” M.E. thesis, University of Central Florida, Orlando, Florida.
 S. S. Patki, G. S. Sable. 2016. “Cotton Leaf Disease Detection & Classification using Multi SVM,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 10, pp. 165-168.
title = "Support Vector Machine Based Classification of Leaf Diseases ",
journal = "International Journal of Science and Engineering Applications (IJSEA)",
volume = "7",
number = "8",
pages = "143 - 147 ",
year = "2018",
author = " Ko Ko Zaw, Dr. Zin Ma Ma Myo , Daw Thae Hsu Thoung ",