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Fruit Disease Recognition and Automatic Classification using MSVM with Multiple Features

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
A. S. M. Shafi, Md. Bayazid Rahman, Mohammad Motiur Rahman

A S M Shafi, Md. Bayazid Rahman and Mohammad Motiur Rahman. Fruit Disease Recognition and Automatic Classification using MSVM with Multiple Features. International Journal of Computer Applications 181(10):12-15, August 2018. BibTeX

	author = {A. S. M. Shafi and Md. Bayazid Rahman and Mohammad Motiur Rahman},
	title = {Fruit Disease Recognition and Automatic Classification using MSVM with Multiple Features},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2018},
	volume = {181},
	number = {10},
	month = {Aug},
	year = {2018},
	issn = {0975-8887},
	pages = {12-15},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2018916773},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Image processing and machine learning play an important role in fruit disease identification and classification by means of image segmentation and pattern recognition. Traditional fault detection in the fruit surface is carried out manually by means of human inspection which is very time consuming and laborious. In this paper we have proposed a method for fruit disease identification using segmentation techniques and use a supervised learning technique for classifying images based on data analyzed from RGB colored images. Three types of common apple diseases are taken into considerations in this paper. The experimental results demonstrate that the proposed approach is promising and effective by showing the classification accuracy which has achieved more than 94% using several features.


  1. Roberts, M. J., Schimmelpfennig, D., Ashley, E., Livingston, M., Ash, M., & Vasavada, U. (2006). The Value of Plant Disease Early- Warning Systems (No. 18). Economic Research Service, United States Department of Agriculture.
  2. Li, Q., Wang, M., & Gu, W. (2002, November). Computer Vision Based System for Apple Surface Defect Detection. Computers and Electronics in Agriculture, 36, 215-223.
  3. Kim, M. S., Lefcourt, A. M., Chen, Y. R., & Tao, Y. (2005). Automated Detection of Fecal Contamination of Apples Based on Multispectral Fluorescence Image Fusion. Journal of food engineering, 71, 85-91.
  4. Kleynen, O., Leemans, V., & Destain, M. F. (2005). Development of a Multi-Spectral Vision System for the Detection of Defects on Apples. Journal of Food Engineering, 69, 41-49.
  5. Leemans, V., Magein, H., & Destain, M. F. (1999). Defect Segmentation on ‘Jonagold‘ Apples using Color Vision and a Bayesian Classification Method. Computers and Electronics in Agriculture, 23, 43-53.
  6. Leemans, V., Magein, H., & Destain, M. F. (1998). Defect Segmentation on ‘Golden Delicious‘ Apples by using Color Machine Vision. Computers and Electronics in Agriculture, 20, 117-130.
  7. N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern Recognition, vol. 26, no. 9, pp. 1227-1294, 1993.
  8. H. Frigui and R. Krishnapuram, “Clustering by competitive agglomeration,” Pattern Recognition, vol. 30, no. 7, pp. 1109-1119, 1997.
  9. Y. Boykov, “Graph cuts and efficient N-D image segmentation,” International Journal of Computer Vision (IJCV), vol. 70, no. 2. pp. 109–131, 2006.
  10. B. Sowmya and B. Sheelarani, “Colour image segmentation using soft computing techniques,” International Journal of Soft Computing Applications, vol. 4, pp. 69-80, 2009.
  11. J. F. David, K. Y. Yau, and A. K. Elmagarmid, “Automatic image segmentation by integrating color-edge extraction and seeded region growing,” IEEE Transactions On Image Processing (TIP), vol. 10, no. 10, pp. 1454-1466, 2001.
  12. R. Adams and L. Bischof, “Seeded region growing,” IEEE Transaction on pattern analysis and machine intelligence (PAMI), vo1. 6, no. 6, pp. 641-647, 1994.
  13. F. Y. Shih and S Cheng, “Automatic seeded region growing for color image segmentation,” Image and Vision Computing (IVC), vol. 23, no. 10, pp. 877-886, 2005.
  14. S. R. Dubey and A. S. Jalal, “Robust Approach for Fruit and Vegetable Classification”, Procedia Engineering, vol. 38, pp. 3449 – 3453, 2012.
  15. S. R. Dubey and A. S. Jalal, “Species and Variety Detection of Fruits and Vegetables from Images”, International Journal of Applied Pattern Recognition (IJAPR), vol. 1, no. 1, pp. 108 – 126, 2013.
  16. Gonzalez, R., Woods, R., 2007. Digital Image Processing, 3rd edition. Prentice-Hall.
  17. T. Ojala, M. Pietik äinen and T. T. Mäenpä ä , Multiresolution gray-scale and rotation invariant texture classifi cation with local binary pattern, IEEE T. Pattern Anal. 24 (2002), 971 – 987.
  18. Dubey, S.R. and Jalal, A.S. (2014) ‘Fruit disease recognition using improved sum and difference histogram from images’, Int. J. Applied Pattern Recognition, Vol. 1, No. 2, pp.199–220.


Image segmentation, filtering, global thresholding, feature extraction, supervised classification