Classification of Healthy and Diseased Arecanuts using SVM Classifier
DOI:
https://doi.org/10.26438/ijcse/v7i2.544548Keywords:
Arecanut Images, SMD, SVM ClassifierAbstract
Arecanut is the seed of the areca palm (Areca catechu), Arecanut palm is one of the important commercial crops in India. Majority of arecanut are produced and consumed by Indian populations when compared to other countries. This paper proposes, to Classify Healthy and Diseased Arecanut images. In this paper Healthy and diseased arecanut are have been done. Structured matrix decomposition model (SMD) is used to segment the images and LBP features are extracted using SVM classifier. Experimental results demonstrate proposed method perform well and obtained accuracy of 98%.
References
[1] M. Suresha, A. Danti and S. K. Narasimhamurthy, “Classification of Diseased Arecanut based on TextureFeatures”, 2014.
[2] S. H. Tsai, Y. H. Tseng, “ A novel color detection method based on HSL color space for robotic soccer competition”, Computers & Mathematics with Applications, 64(5), 1291-1300, 2012.
[3] N.V , Ganganagowdar, H. K Siddaramappa “Recognition and ‘classification of White Wholes (WW) grade cashew kernel using artificial neural networks”, ActaScientiarum. Agronomy, 38(2), 145-155., 2012, 2016.
[4] H. Peng, B. Li, H. Ling, W. Hu, W. Xiong, & S.J. Maybank, “Salient object detection via structured matrix decomposition.”, IEEE transactions on pattern analysis and machine intelligence, 39(4), 818-832, 2017.
[5] H. N. Patel, R. K. Jain, & M. V. Joshi, “Fruit detection using improved multiple features based algorithm.”, International journal of computer applications13(2), 1-5, 2011
[6] A. Danti & M. Suresha, “Arecanut grading based on three sigma controls and SVM.”, In Advances in Engineering, Science and Management (ICAESM), International Conference on (pp. 372-376). IEEE. 2012.
[7] T. Ojala, M. Pietikainen and D. Harwood. “A comparative study of texture measures with classification based on feature distributions.”, International Pattern Recognition vol. 29, 1996.
[8] T. Mäenpaa, “The local binary pattern approach to texture analysis—extensions and applications.”, PhD thesis,Acta Universitatis Ouluensis C 187, University of Oulu 2003.
[9] V. N. Vapnik, “Estimation of dependencies based on empirical data ”,Springer. New York, 1982.
[10] M. Weis, T. Rumpf, R. Gerhards, & L. Plümer, “Comparison of different classification algorithms forWeed detection from images based on shape parameters.”, Bornimer Agrartechn. , 69, 53-64. 2009.
[11] B. E. Boser, I. M. Guyon & V. N. Vapnik, “A training algorithm for optimal margin classifiers.”, In Proceedings of the fifth annual workshop on Computational learning theory (pp. 144-152). ACM.1992 July.
[12] C. Cortes & V. Vapnik, “Support-vector networks. Machine learning.”, 20(3), 273-297.1995.
[13] M. Suresha K. N. Shreekanth and B. V. Thirumalesh B V. “Diagnosis and Classification of diseases in Paddy Leaves`.”, Journal of Current Advanced Research , 06(10), pp. 6448-6452.DOI: http://dx.doi.org/10.24327/ijcar.2017.6452.0945 ,2017.
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