Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine Vision

  • Manohar MadgiEmail author
  • Ajit Danti
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


This article presents a neural network and machine vision-based approach to classify the vegetables as normal or affected. The farmers will have great difficulty if there is a change from one disease control to another. The examination through an open eye to classify the diseases by name is more expensive. The texture and color features are used to identify and classify different vegetables into normal or affected using a neural network and machine vision. The mixture of both the features is proved to be more effective. The results of experiments show that the proposed methodology extensively supports the accuracy in automatic detection of affected and normal vegetables. The applications in packing and grading of vegetables are the outcome of this research article.


Vegetable disease Color features Texture features Classifier 



We wish to express gratitude to our beloved Principal, Dr Basavaraj Anami, K.L.E. Institute of Technology, Hubballi for his advice.


  1. 1.
    Karargyris, A., et al.: Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays. Int. J. Pattern Recognit. Artif. Intell. 11(1), 99–106 (2016)Google Scholar
  2. 2.
    Singh, V., Misra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4(1), 41–49 (2016)Google Scholar
  3. 3.
    Raut, S., Fulsunge, A.: Plant disease detection in image processing using matlab. Int. J. Innovative Res. Sci. Eng. Technol. 6(6), 10373–10381 (2017)Google Scholar
  4. 4.
    Mokhtar, U., et al.: SVM-based detection of tomato leaves diseases. In: Filev, D., Filev, D., et al. (eds.) Intelligent Systems 2014. AISC, vol. 323, pp. 641–652. Springer, Cham (2015). Scholar
  5. 5.
    Ghaiwat, S.N., Arora, P.: Detection and classification of plant leaf diseases using image processing techniques: a review. Int. J. Recent Adv. Eng. Technol. 2(3), 1–7 (2014)Google Scholar
  6. 6.
    Badnakhe, M.R., Deshmukh, P.R.: An application of k-means clustering and artificial intelligence in pattern recognition for crop diseases. In: International Conference on Advancements in Information Technology With workshop of ICBMG 2011. IPCSIT, pp. 134–138. IACSIT Press, Singapore (2011)Google Scholar
  7. 7.
    Arivazhagan, S., Newlin, S.R., Ananthi, S., Varthini, S.V.: Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric. Eng. Int. CIGR 15(1), 211–217 (2013)Google Scholar
  8. 8.
    Kulkarni, A.H., Patil, A.R.K.: Applying image processing technique to detect plant diseases. Int. J. Mod. Eng. Res. 2(5), 3661–3664 (2012)Google Scholar
  9. 9.
    Naikwadi, S., Amoda, N.: Advances in image processing for detection of plant diseases. Int. J. Appl. Innovation Eng. Manage. 2(11), 168–175 (2013)Google Scholar
  10. 10.
    Danti, A., Madgi, M., Anami, B.: Identification of common Indian leafy vegetables based on statistical measures on combined color and texture features. In: Sridhar, V., Sheshadri, H., Padma, M. (eds.) Emerging Research in Electronics, Computer Science and Technology. LNEE, vol. 248, pp. 381–389. Springer, New Delhi (2014). Scholar
  11. 11.
    Chaudhary, P., Chaudhari, A.K., Cheeran, N.A., Godara, S.: Color transform based approach for disease spot detection on plant leaf. Int. J. Comput. Sci. Telecommun. 3(6), 65–70 (2012)Google Scholar
  12. 12.
    Rumpf, T., Mahlein, A.-K., Steiner, U., Oerke, E.-C., Dehne, H.-W., Plumer, L.: Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput. Electron. Agric. 74(1), 91–99 (2010)CrossRefGoogle Scholar
  13. 13.
    Al-Bashish, D., Braik, M., Bani-Ahmad, S.: Detection and classification of leaf diseases using K-meansbased segmentation and neural-networks-based classification. Inf. Technol. J. 10(2), 267–275 (2011)CrossRefGoogle Scholar
  14. 14.
    Danti, A., Madgi, M., Anami, B.: Mean and range color features based identification of common Indian leafy vegetables. Int. J. Sig. Process. Image Process. Pattern Recognit. 5(3), 151–160 (2012)Google Scholar
  15. 15.
    Bernardes, A.A., et al.: Identification of foliar diseases in cotton crop. In: Tavares, J., Natal Jorge, R. (eds.) Topics in Medical Image Processing and Computational Vision. LNCVB, vol. 8, pp. 67–84. Springer, Dordrecht (2013). Scholar
  16. 16.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.K. L. E. Institute of TechnologyHubballiIndia
  2. 2.Christ (Deemed to be University)BengaluruIndia

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