Automated plant disease analysis (APDA): Performance comparison of machine learning techniques

Proceedings - 11th International Conference on Frontiers of Information Technology, FIT 2013

Volumn , Issue , 2013, Pages 60-65

  Akhtar, Asma ^a   Khanum, Aasia ^a   Khan, Shoab A ^a   Shaukat, Arslan ^a  

^a NATIONAL UNIVERSITY OF SCIENCES AND TECHNOLOGY   (Pakistan)

Author keywords

Artificial Intelligence; Classification; Machine Learning; Plant Disease Analysis

Indexed keywords

EID: 84894154996     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FIT.2013.19     Document Type: Conference Paper

References (18)

1. Camargo, A. and J. S. Smith. 2008. An image-processing based algorithm to automaticallyidentify plant disease visual symptoms. Bio.Sys. Eng., 102: 9-21.
2. Camargo, A. and J. S. Smith. 2009. Image pattern classification for the identification of disease causing agents in plants. Com. Elect. Agr. 66: 121-125.
3. Guru, D. S., P. B. Mallikarjuna and S. Manjunath. 2011. Segmentation and Classification of Tobacco Seedling Diseases. Proceedings of the Fourth Annual ACM Bangalore Conference.
4. Zhao, Y. X., K. R. Wang, Z. Y. Bai, S. K. Li, R. Z. Xie and S. J. Gao. 2009. Research of Maize Leaf Disease Identifying Models Based Image Recognition. Crop Modeling and Decision Support.Tsinghua uni.press. Beiging. pp. 317-324.
5. Al-Hiary, H., S. Bani-Ahmad, M. Reyalat, M. Braik and Z. ALRahamneh. 2011. Fast and Accurate Detection and Classification of Plant Diseases. Int. J. Com. App., 17(1): 31-38.
6. PearlMutter, B. A. 1990. Dynamic Recurrent Neural Network 1 Dr S. M. Mughal, Professor at Plant Pathalogy Department, PMAS Arid Agriculture University Rawalpindi.
7. Aly, M. 2005. Survey on Multiclass Classification Methods
8. Fury, T. S., N. Cristianini and N. Duffy. 2000. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Proc. BioInfo., 16(10): 906-914.
9. Scholkopf, B. and A. J. Smola. 2001. Learning with Kernels Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, Cambridge.
10. Huang, J., V. Blanz and B. Heisele. 2002. Face Recognition Using Component-Based SVM Classification and Morphable Models, pp. 334-341.
11. Mohammed J. Islam., Q. M. Jonathan Wu, MajidAhmadi, A.Maher and Sid-Ahmed.2007. Investigating the Performance of Naive-Bayes Classifiers and K-Nearest Neighbor Classifiers.ICCI Proceedings of International Conference on Convergence Information Technology.IEEE Computer Society
12. Bock, C. H., G. H. Poole, P. E. Parker and T. R. Gottwald. 2010. Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging. Cri. Rev. Pla. Sci., 29: 59-107.
13. http://users.rowan.edu/~polikar/WAVELETS/WTtuto rial.com (Accessed: 25th April 2013)
14. Naveed N., T. S., Choi and A.Jaffa.Malignancy and Abnormality Detection of Mammograms using DWT features and ensembling of classifiers, International Journal of the Physical Sciences,Vol.6(8)
15. Duda, R. O., P. E. Hart and D. G. Stork. 2001. Pattern classification, 2nd edition, John Wiley and Sons, New York.
16. Al Bashish, D., M.Braik and S.Bani-Ahmad.2010. Frame work for detection and classification of plant leaf and stem diseases. Signal and Image Processing(ICSIP) international conference. pp.113-118
17. Hongzhi, W., Ying, D,.2008. An Improved Image Segmentation Algorithm Based on Otsu Method. International Symposium on Photo electronic Detection and Imaging SPIE Vol. 6625
18. Chung, K, L., Liu, Y, W,. and Yan, W, M., 2006. A hybrid grey image representation using spatial-and DCT-based approach with application to moment computation. Journal of Visual Communication and Image Representation Vol. 17, Issue 6.