Browse > Article
http://dx.doi.org/10.33851/JMIS.2020.7.1.17

Detection of Rice Disease Using Bayes' Classifier and Minimum Distance Classifier  

Sharma, Vikas (University of Jammu)
Mir, Aftab Ahmad (Shri Venkateshwara University)
Sarwr, Abid (University of Jammu)
Publication Information
Journal of Multimedia Information System / v.7, no.1, 2020 , pp. 17-24 More about this Journal
Abstract
Rice (Oryza Sativa) is an important source of food for the people of our country, even though of world also .It is also considered as the staple food of our country and we know agriculture is the main source country's economy, hence the crop of Rice plays a vital role over it. For increasing the growth and production of rice crop, ground-breaking technique for the detection of any type of disease occurring in rice can be detected and categorization of rice crop diseases has been proposed in this paper. In this research paper, we perform comparison between two classifiers namely MDC and Bayes' classifiers Survey over different digital image processing techniques has been done for the detection of disease in rice crops. The proposed technique involves the samples of 200 digital images of diseased rice leaf images of five different types of rice crop diseases. The overall accuracy that we achieved by using Bayes' Classifiers and MDC are 69.358 percent and 81.06 percent respectively.
Keywords
Bayes' Classifier; Disease spots; Minimum distance classifier;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Islam, M., Dinh, A., Wahid, K., & Bhowmik, P. "Detection of potato diseases using image segmentation and multiclass support vector machine,"in Proceedings of the 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering, pp. 1-4, Apr. 2017.
2 Anthonys G., and N. Wickramarachchi, "An image recognition system for crop disease identification of paddy fields in Sri Lanka," in Proceedings of the International Conference on Industrial and Information Systems, pp. 403-407, 2009.
3 N. N. Kurniawati, S. N. H. S. Abdullah, S. Abdullah and S. Abdullah, "Texture analysis for diagnosing paddy disease," in Proceedings of the International Conference on Electrical Engineering and Informatics, pp. 23-27, Sept., 2009.
4 Byung-Gyu Kim, J. I. Shim, D. J. Park, "Fast Image Segmentation Based on Multi-resoluition Analysis and Wavelets," Pattern Recognition Letters, vol. 24, no. 16, pp. 2995-3006, 2003.   DOI
5 R. Kaur, and M. Kaur. "A Brief Review on Plant Disease Detection using in Image Processing,"International Journal of Computer Science and Mobile Computing, vol. 6, no. 2,pp. 101-106, 2017.
6 S. Phadikar,J. Sil, and A. K. Das. "Classification of Rice Leaf Diseases Based onMorphological Changes," International Journal of Information and Electronics Engineering, vol. 2, no. 3, pp. 460-, 2012.
7 FY. Shih, and S. Cheng,"Automatic seeded region growing for color image segmentation," Image and vision computing, vol. 23, no. 10, pp. 877-886, 2005.   DOI
8 R. Pydipati, T.F. Burks and W.S. Lee, "Identification of citrus disease using color texture features and discriminant analysis," Comput. Electron. Agric., vol.52, no.1, pp.49-59, June 2006   DOI
9 S. Raut, and A. Fulsunge. "Plant Disease Detection in Image Processing Using MATLAB," Int. J. Innov.Res. in Science Engg. and Tech, vol. 6, No. 6, 2017.
10 R.P.Narmadha and G.Arulvadivu, "Detection And Measurement of Paddy Leaf Disease Symptoms using Image Processing,"Appl. Math. J. Chinese Univ. Ser. B, vol.18, no.3, pp. 332-334, Sept. 2003.   DOI
11 Y.X. Zhao, K.R. Wang, Z.Y. Bai, S.K. Li, R.Z. Xie and S.J. Gao, "Bayesian classifier method on maize leaf disease identifying based images," Comput. Engin. Applic., vol.43, no.5, pp.193-195, Feb. 2007.   DOI
12 S. Phadikar, and J. Sil, "Rice disease identification using pattern recognition techniques,"in Proceedings of the 11th International Conference on Computer and Information Technology,pp. 420-423, Dec. 2008.
13 K.Y. Huang, "Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features," Comput. Electron. Agric., vol.57, no.1, pp.3-11, May 2007.   DOI
14 Y.W. Tian, T.L. Li, C.H. Li, Z.L. Piao, G.K. Sun and B. Wang, "Method for recognition of grape disease based on support vector machine," Trans. CSAE, vol.23, no.6, pp.175-180, June 2007
15 A. A. Joshi, &B. D. Jadhav,"Monitoring and controlling rice diseases using Image processing techniques,"in Proceedings of the International Conference on Computing, Analytics and Security Trends,,pp. 471-476, Dec. 2016.
16 Q. Yao, Z. Guan, Y. Zhou, J. Tang, Y. Hu, andB. Yang,"Application of support vector machine for detecting rice diseases using shape and color texture features,"in Proceedings ofthe International Conference on Engineering Computation, pp. 79-83, May 2009.
17 A. Awate, D. Deshmankar, G. Amrutkar, U. Bagul, andS. Sonavane,"Fruit disease detection using color, texture analysis and ANN,"in Proceedings of the 2015 International Conference on Green Computing and Internet of Things, pp. 970-975, Oct. 2015.
18 P. Pawar, V. Turkar, and P. Patil,"Cucumber disease detection using artificial neural network,"in Proceedings of the International Conference on Inventive Computation Technologies, Aug. 2016.