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Fast and Accurate Detection and Classification of Plant Diseases

International Journal of Computer Applications
© 2011 by IJCA Journal
Number 1 - Article 6
Year of Publication: 2011
H. Al-Hiary
S. Bani-Ahmad
M. Reyalat
M. Braik
Z. ALRahamneh

H Al-Hiary, S Bani-Ahmad, M Reyalat, M Braik and Z ALRahamneh. Article: Fast and Accurate Detection and Classification of Plant Diseases. International Journal of Computer Applications 17(1):31-38, March 2011. Full text available. BibTeX

	author = {H. Al-Hiary and S. Bani-Ahmad and M. Reyalat and M. Braik and Z. ALRahamneh},
	title = {Article: Fast and Accurate Detection and Classification of Plant Diseases},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {17},
	number = {1},
	pages = {31-38},
	month = {March},
	note = {Full text available}


We propose and experimentally evaluate a software solution for automatic detection and classification of plant leaf diseases. The proposed solution is an improvement to the solution proposed in [1] as it provides faster and more accurate solution. The developed processing scheme consists of four main phases as in [1]. The following two steps are added successively after the segmentation phase. In the first step we identify the mostly-green colored pixels. Next, these pixels are masked based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that the proposed technique is a robust technique for the detection of plant leaves diseases. The developed algorithm’s efficiency can successfully detect and classify the examined diseases with a precision between 83% and 94%, and can achieve 20% speedup over the approach proposed in [1].


  • Al-Bashish, D., M. Braik and S. Bani-Ahmad, 2011. Detection and classification of leaf diseases using K-means-based segmentation and neural-networks-based classification. Inform. Technol. J., 10: 267-275. DOI: 10.3923/itj.2011.267.275
  • Aldrich, B.; Desai, M. (1994) "Application of spatial grey level dependence methods to digitized mammograms," Image Analysis and Interpretation, 1994., Proceedings of the IEEE Southwest Symposium on, vol., no., pp.100-105, 21-24 Apr 1994. DOI: 10.1109/IAI.1994.336675
  • Ali, S. A., Sulaiman, N., Mustapha, A. and Mustapha, N., (2009). K-means clustering to improve the accuracy of decision tree response classification. Inform. Technol. J., 8: 1256-1262. DOI: 10.3923/itj.2009.1256.1262
  • Bauer, S. D.; Korc, F., Förstner, W. (2009): Investigation into the classification of diseases of sugar beet leaves using multispectral images. In: E.J. van Henten, D. Goense and C. Lokhorst: Precision agriculture ’09. Wageningen Academic Publishers, p. 229-238. URL:
  • Camargo, A. and Smith, J. S., (2009). An image-processing based algorithm to automatically identify plant disease visual symptoms, Biosystems Engineering, Volume 102, Issue 1, January 2009, Pages 9-21, ISSN 1537-5110, DOI: 10.1016/j.biosystemseng.2008.09.030.
  • Ford, A. and Roberts, A., (2010) Colour Space Conversions, August 11, 1998., viewed on May 2010.
  • Hartigan, J. A.; Wong, M. A. (1979). "Algorithm AS 136: A K-Means Clustering Algorithm". Journal of the Royal Statistical Society, Series C (Applied Statistics) 28 (1): 100–108.
  • Hillnhuetter, C. and A.-K. Mahlein, Early detection and localisation of sugar beet diseases: new approaches, Gesunde Pfianzen 60 (4) (2008), pp. 143–149.
  • Jun, W. and S. Wang, (2008). Image thresholding using weighted parzen-window estimation. J. Applied Sci., 8: 772-779. DOI: 10.3923/jas.2008.772.779. URL:
  • MacQueen, J.. (1967). Some methods for classification and analysis of multivariate observations. In L. M. LeCam and J. Neyman, editors, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, volume 1, pages 281--297, Berkeley, CA, 1967. University of California Press. URL:
  • Martinez, A., (2007). 2007 Georgia Plant Disease Loss Estimates. Viewed on Saturday, 15, January, 2011.
  • Otsu, N. (1979). "A threshold selection method from gray-level histograms". IEEE Trans. Sys., Man., Cyber. 9: 62–66. DOI:10.1109/TSMC.1979.4310076.
  • Otsu, N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.
  • Prasad Babu, M. S. and Srinivasa Rao , B. (2010) Leaves recognition using back-propagation neural network - advice for pest and disease control on crops. Technical report, Department of Computer Science & Systems Engineering, Andhra University, India. Downloaded from on May 2010.
  • Sezgin, M. and Sankur, B. (2003). "Survey over image thresholding techniques and quantitative performance evaluation". Journal of Electronic Imaging 13 (1): 146–165. DOI:10.1117/1.1631315.
  • Soltanizadeh, H. and B.S. Shahriar, 2008. Feature extraction and classification of objects in the rosette pattern using component analysis and neural network. J. Applied Sci., 8: 4088-4096. DOI: 10.3923/jas.2008.4088.4096. URL:
  • Stone, M. C. (August 2001). “A Survey of Color for Computer Graphics”. Course at SIGGRAPH 2001.
  • Rumpf, T., A.-K. Mahlein, U. Steiner, E.-C. Oerke, H.-W. Dehne, L. Plumer, Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance, Computers and Electronics in Agriculture, Volume 74, Issue 1, October 2010, Pages 91-99, ISSN 0168-1699, DOI: 10.1016/j.compag.2010.06.009.
  • Wang, X., M. Zhang, J. Zhu and S. Geng, Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN), International Journal of Remote Sensing 29 (6) (2008), pp. 1693–1706.
  • Weizheng, S., Yachun, W., Zhanliang, C., and Hongda, W. (2008). Grading Method of Leaf Spot Disease Based on Image Processing. In Proceedings of the 2008 international Conference on Computer Science and Software Engineering - Volume 06 (December 12 - 14, 2008). CSSE. IEEE Computer Society, Washington, DC, 491-494. DOI=