Semi-automatic leaf disease detection and classification system for soybean culture

Semi-automatic leaf disease detection and classification system for soybean culture

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Development of automatic disease detection and classification system is significantly explored in precision agriculture. In the past few decades, researchers have studied several cultures exploiting different parts of a plant. A similar study is performed for Soybean using leaf images. A rule based semi-automatic system using concepts of k-means is designed and implemented to distinguish healthy leaves from diseased leaves. In addition, a diseased leaf is classified into one of the three categories (downy mildew, frog eye, and Septoria leaf blight). Experiments are performed by separately utilising colour features, texture features, and their combinations to train three models based on support vector machine classifier. Results are generated using thousands of images collected from PlantVillage dataset. Acceptable average accuracy values are reported for all the considered combinations which are also found to be better than existing ones. This study also attempts to discover the best performing feature set for leaf disease detection in Soybean. The system is shown to efficiently compute the disease severity as well. Visual examination of leaf samples further proves the suitability of the proposed system for detection, classification, and severity calculation.


    1. 1)
      • 1. ‘Nitrogen fixation’, available at, accessed February 2017.
    2. 2)
      • 2. Savary, S., Ficke, A., Aubertot, J.-N., et al: ‘Crop losses due to diseases and their implications for global food production losses and food security’, Food Secur., 2012, 4, (4), pp. 519537.
    3. 3)
      • 3. ‘Diagnosing plant problems: plant diseases and disorders’, available at, accessed February 2017.
    4. 4)
      • 4. ‘Signs and symptoms of plant disease: Is it fungal, viral or bacterial?’, available at, accessed January 2017.
    5. 5)
      • 5. Li, X., Yang, X.B.: ‘Similarity, pattern, and grouping of soybean fungal diseases in the United States: implications for the risk of soybean rust’, Plant Dis., 2009, 93, (2), pp. 162169.
    6. 6)
      • 6. ‘Soybean growth and development’, available at, accessed February 2017.
    7. 7)
      • 7. Mian, M.A., Missaoui, A.M., Walker, D.R., et al: ‘Frogeye leaf spot of soybean: a review and proposed race designations for isolates of Cercospora sojina Hara’, Crop Sci., 2008, 48, (1), pp. 1424.
    8. 8)
      • 8. ‘Characteristics of soybean foliar diseases from bacterial blight to rust’, available at, accessed January 2017.
    9. 9)
      • 9. ‘Soybean diseases’, available at, accessed January 2017.
    10. 10)
      • 10. Giesler, L.J.: ‘Frog eye leaf spot of soybean’, available at, accessed February 2017.
    11. 11)
      • 11. Hughes, D.P., Salathé, M.: ‘An open access repository of images on plant health to enable the development of mobile disease diagnostics’, 2015 CoRR abs/1511.08060.
    12. 12)
      • 12. Shrivastava, S., Hooda, D.S.: ‘Automatic brown spot and frog eye detection from the image captured in the field’, Am. J. Intell. Syst., 2014, 4, (4), pp. 131134.
    13. 13)
      • 13. Shrivastava, S., Singh, S.K., Hooda, D.S.: ‘Statistical texture and normalized discrete cosine transform-based automatic soya plant foliar infection cataloguing’, Br. J. Math. Comput. Sci., 2014, 4, (20), pp. 29012916.
    14. 14)
      • 14. Gharge, S., Singh, P.: ‘Image processing for soybean disease classification and severity estimation’, in Shetty, N., Prasad, N., Nalini, N. (Eds.): ‘Emerging research in computing, information, communication and applications’ (Springer, New Delhi, India, 2016), pp. 493500.
    15. 15)
      • 15. Dandawate, Y., Kokare, R.: ‘An automated approach for classification of plant diseases towards development of futuristic decision support system in Indian perspective’. Proc. IEEE Int. Conf. Advances in Computing, Communications and Informatics (ICACCI), Kerala, India, August 2015, pp. 794799.
    16. 16)
      • 16. Barbedo, J.G.A., Godoy, C.V.: ‘Automatic classification of soybean diseases based on digital images of leaf symptoms’. SBI AGRO, October 2015.
    17. 17)
      • 17. Shrivastava, S., Singh, S.K., Hooda, D.S.: ‘Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation’, Multimedia Tools Appl., 2015, 74, (24), pp. 1146711484.
    18. 18)
      • 18. Jadhav, S.B., Patil, S.B.: ‘Grading of soybean leaf disease based on segmented image using k-means clustering’, Int. J. Adv. Res. Electron. Commun. Eng., 2015, 4, (6), pp. 18161822.
    19. 19)
      • 19. Pujari, J.D., Yakkundimath, R.S., Jahagirdar, S., et al: ‘Quantitative detection of soybean rust using image processing techniques’, J. Crop Prot., 2015, 5, (1), pp. 7587.
    20. 20)
      • 20. Hunter lab color scale’, Insight on Color, Hunter Labs Reston, 1996, 8, (9), pp. 14, available at
    21. 21)
      • 21. Wang, X., Hänsch, R., Ma, L., et al: ‘Comparison of different color spaces for image segmentation using graph-cut’. Proc. IEEE Int. Conf. in Computer Vision Theory and Applications (VISAPP), January 2014, pp. 301308.
    22. 22)
      • 22. Seber, G.A.F.: ‘Multivariate observations’ (Wiley, New York, 1984).
    23. 23)
      • 23. Spath, H.: ‘Cluster dissection and analysis: theory, FORTRAN programs, examples’, translated by J. Goldschmidt (Halsted Press, New York, 1985).
    24. 24)
      • 24. Gavhale, K.R., Gawande, U.: ‘An overview of the research on plant leaves disease detection using image processing techniques’, IOSR J. Comput. Eng., 2014, 16, (1), pp. 1016.
    25. 25)
      • 25. Sachdeva, J., Kumar, V., Gupta, I., et al: ‘A package-SFERCB-segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors’, Appl. Soft Comput., 2016, 47, pp. 151167.
    26. 26)
      • 26. Huang, J.: ‘Color-spatial image indexing and applications’. PhD thesis, Cornell University, 1998.
    27. 27)
      • 27. Mistry, Y., Ingole, D.T., Ingole, M.D.: ‘Content based image retrieval using hybrid features and various distance metric’, J. Electr. Syst. Inf. Technol., 2017, article in press, doi:
    28. 28)
      • 28. Haralick, R.M., Shanmugam, K., Dinstein, I.: ‘Textural features for image classification’, Trans. Syst. Man Cybern., 1973, 3, (6), pp. 610621.
    29. 29)
      • 29. Manjunath, B.S., Ma, W.Y.: ‘Texture features for browsing and retrieval of image data’, IEEE Trans. Pattern Anal. Mach. Intell., 1996, 18, (8), pp. 837842.
    30. 30)
      • 30. Cheng, F.H., Chen, Y.L.: ‘Real time multiple objects tracking and identification based on discrete wavelet transform’, Pattern Recognit., 2006, 39, pp. 11261139.
    31. 31)
      • 31. Yadav, R., Anand, R.S., Dewal, M.L., et al: ‘Performance analysis of discrete wavelet transform based first-order statistical texture features for hardwood species classification’, Procedia Comput. Sci., 2015, 57, pp. 214221.
    32. 32)
      • 32. Amsaveni, V., Singh, N.A., Dheeba, J.: ‘Application of support vector machine classifier for computer aided diagnosis of brain tumor from MRI’. Proc. Int. Conf. on Swarm, Evolutionary, and Memetic Computing, Bhubaneswar, India, December 2014, pp. 514522.
    33. 33)
      • 33. Yu, H., Kim, S.: ‘SVM tutorial-classification, regression and ranking’, in Rozenberg, G., Bäck, T., Kok, J.N. (Eds.): ‘Handbook of natural computing’ (Springer, Berlin Heidelberg, 2012), pp. 479506.
    34. 34)
      • 34. Kaur, S., Pandey, S., Goel, S.: ‘An automatic leaf disease detection system for legume species’, J. Biol. Today's World, 2017, 6, pp. 115122.
    35. 35)
      • 35. ‘IPM Images’, available at, accessed January 2017.
    36. 36)
      • 36. Barbedo, J.G.A.: ‘A review on the main challenges in automatic plant disease identification based on visible range images’, Biosyst. Eng., 2016, 144, pp. 5260.
    37. 37)
      • 37. Cai, J., Miklavcic, S.: ‘Automated extraction of three-dimensional cereal plant structures from two-dimensional orthographic images’, IET Image Process., 2012, 6, (6), pp. 687696.

Related content

This is a required field
Please enter a valid email address