Image Segmentation Algorithms for Banana Leaf Disease Diagnosis


Identification and classification of leaf diseases in banana crop are an important requirement for farmers to diagnose and to get proper remedies for the pest and disease infection. Development of an automated system using image processing for leaf disease identification reduces time, cost and mainly supports to increase the productivity of banana fruit. In this process of automation, image segmentation is a key component that is required to analyze the image and to extract information from it. Image segmentation is a low-level module of image processing used to segregate the required object from an image for further analysis. The performance accuracy of image segmentation module determines the success of higher-level module of image processing. Therefore, to select an appropriate segmentation method for leaf analysis, different segmentation methods like adaptive thresholding, canny, color segmentation, fuzzy C-means, geodesic, global thresholding, K-means, log, multithresholding, Prewitt, region growing, Robert, Sobel and zero crossing are analyzed and compared in this paper. The quantitative matrices such as mean square error (MSE), peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are considered to measure the performance of different segmentation methods. The results showed that geodesic method had significantly lower MSE value (6610), PSNR value (6608) and higher SSIM value (0.196) than all other methods. It is concluded that geodesic method is better for segmentation of banana leaf disease images.

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  1. 1.

    D. Surya Prabha, J. Satheesh Kumar, Assessment of banana fruit maturity by image processing technique. J. Food Sci. Technol. 52(3), 1316–1327 (2015)

    Article  Google Scholar 

  2. 2.

    G. Dhingra, V. Kumar, H.D. Joshi, Study of digital image processing techniques for leaf disease detection and classification. Multimed. Tools Appl. 77(15), 19951–20000 (2018)

    Article  Google Scholar 

  3. 3.

    D. Surya Prabha, J Satheesh Kumar, Crop disease identification using image processing methods. In: Proceedings of the National Conference on Green Computing Organized by Department of Computer Science & Research Centre, ST Hindu College, Nagercoil, Tamil Nadu, India, 5–6, October 2012 (2012), pp. 174–179

  4. 4.

    V. Singh, A.K. Misra, Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4(1), 41–49 (2017)

    Google Scholar 

  5. 5.

    D. Surya Prabha, J. Satheesh Kumar, Survey on applications of image processing methods in agriculture sector. Proc. Int. Conf. Converg. Technol. 4(1), 997–999 (2014)

    Google Scholar 

  6. 6.

    D. Surya Prabha, J. Satheesh Kumar, Study on banana leaf disease identification using image processing methods. Int. J. Res. Comput. Sci. Inf. Technol. 2(2A), 89–94 (2014)

    Google Scholar 

  7. 7.

    J. Camargoa, S. Smith, An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosyst. Eng. 102, 9–21 (2009)

    Article  Google Scholar 

  8. 8.

    J. Camargoa, S. Smith, Image pattern classification for the identification of disease causing agents. Comput. Electron. Agric 66, 121–125 (2009)

    Article  Google Scholar 

  9. 9.

    J. Canny, A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)

    Article  Google Scholar 

  10. 10.

    L. Cao, Z. Qiu, X. Dai, H. Tan, Y. Lin, S. Zhou, Isolation of endophyticactinomycetes from roots and leaves of banana (Musa Acuminata) plants and their activities against Fusariumoxysporumf. sp. cubense. World J. Microbiol. Biotechnol 20(5), 501–504 (2004)

    Article  Google Scholar 

  11. 11.

    R. Gonzalez, R. Woods, S. Eddins, Digital Image Processing Using MATLAB (Tata McGraw Hill Education Pvt. Ltd, New York, 2010)

    Google Scholar 

  12. 12.

    D. Surya Prabha, J. Satheesh Kumar, Hybrid segmentation of peel abnormalities in banana fruit. IJCA Proc. Int. Conf. Res. Trends Comput. Technol. 3, 38–42 (2013)

    Google Scholar 

  13. 13.

    N. Hashim, R.B. Janius, L. Baranyai, R.A. Rahman, A. Osman, M. Zude, Kinetic model for colour changes in bananas during the appearance of chilling injury symptoms. Food Bioprocess Technol. 5, 2952–2963 (2010)

    Article  Google Scholar 

  14. 14.

    D. Surya Prabha, J. Satheesh Kumar, Improved edge detection method using non-linear constrained optimization, in National Conference of Data Science and Engineering, Elsevier (2014), pp. 195–204

  15. 15.

    D. Surya Prabha, J. Satheesh Kumar, Enhanced edge detection method using unconstrained non-linear optimization technique. Int. J. Appl. Eng. Res. 9(20), 4697–4702 (2015)

    Google Scholar 

  16. 16.

    D. Surya Prabha, J. Satheesh Kumar, Three dimensional object detection and classification methods: a study. Int. J. Eng. Res. Sci. Technol. 2, 33–42 (2013)

    Google Scholar 

  17. 17.

    T.F. Chan, L.A. Vese, Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  Google Scholar 

  18. 18.

    V. Caselles, R. Kimmel, G. Sapiro, Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)

    Article  Google Scholar 

  19. 19.

    V.G. Panse, P.V. Sukhatme, Statistical Methods for Agricultural Workers (ICAR Publications, New Delhi, India, 1989)

    Google Scholar 

  20. 20.

    I. Avcıbas, A. Sankur, K. Sayood, Statistical evaluation of image quality measures. J. Electron. Imaging. 1(2), 206–223 (2002)

    Google Scholar 

  21. 21.

    H. Alain, Z. Djemel, Image quality metrics: PSNR Vs SSIM, in Proceedings of International Conference on Pattern Recognition, Istanbul, Turkey, 23–26, August 2010, IEEE, pp. 2366–2369

  22. 22.

    D. Surya Prabha, J. Satheeshkumar, Performance evaluation of image segmentation using objective methods. Indian J. Sci. Technol. 9(8), 1–8 (2014)

    Google Scholar 

  23. 23.

    L.M. Lorigo, O. Faugeras, W.E.L. Grimson, R. Keriven, R. Kikinis, C.F. Westin, Co-dimension 2 geodesic active contours for MRA segmentation, in Biennial International Conference on Information Processing in Medical Imaging (Springer, Berlin, 1999), pp. 126–139

  24. 24.

    M. Airouche, L. Bentabet, M. Zelmat, Image segmentation using active contour model and level set method applied to detect oil spills, in Proceedings of the World Congress on Engineering. Lecture notes in engineering and computer science, vol 1 (1), (2009). pp. 1–3.

  25. 25.

    Y. Wang, F. Seguro, E. Kao, Y. Zhang, F. Faraji, C. Zhu, J. Liu, Segmentation of lumen and outer wall of abdominal aortic aneurysms from 3D black-blood MRI with a registration based geodesic active contour model. Med. Image Anal. 40, 1–10 (2017)

    Article  Google Scholar 

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Correspondence to Seenivasan Nagachandrabose.

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Deenan, S., Janakiraman, S. & Nagachandrabose, S. Image Segmentation Algorithms for Banana Leaf Disease Diagnosis. J. Inst. Eng. India Ser. C (2020).

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  • Image segmentation
  • Edge detection
  • Region growing
  • Active contour
  • Thresholding