Maize Leaf Disease Detection and Classification Using Machine Learning Algorithms

  • Kshyanaprava Panda Panigrahi
  • Himansu Das
  • Abhaya Kumar Sahoo
  • Suresh Chandra Moharana
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1119)


Plant diseases are the major cause of low agricultural productivity. Mostly the farmers encounter difficulties in controlling and detecting the plant diseases. Thus, early detection of these diseases will be beneficial for farmers to avoid further losses. This paper focuses on supervised machine learning techniques such as Naive Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) for maize plant disease detection with the help of the images of the plant. The aforesaid classification techniques are analyzed and compared in order to select the best suitable model with the highest accuracy for plant disease prediction. The RF algorithm results with the highest accuracy of 79.23% as compared to the rest of the classification techniques. All the aforesaid trained models will be used by the farmers for the early detection and classification of the new image diseases as a preventive measure.


Classification Machine learning Maize leaf disease prediction Naive Bayes KNN Decision tree Support vector machine Random forest 


  1. 1.
    Das, H., Naik, B., Behera, H.S.: Classification of diabetes mellitus disease (DMD): a data mining (DM) approach. In: Progress in Computing, Analytics and Networking, pp. 539–549. Springer, Singapore (2018)Google Scholar
  2. 2.
    Sahani, R., Rout, C., Badajena, J.C., Jena, A.K., Das, H.: Classification of intrusion detection using data mining techniques. In: Progress in Computing, Analytics and Networking, pp. 753–764. Springer, Singapore (2018)Google Scholar
  3. 3.
    Das, H., Jena, A. K., Nayak, J., Naik, B., Behera, H.S.: A novel PSO based back propagation learning-MLP (PSO-BP-MLP) for classification. In: Computational Intelligence in Data Mining, vol. 2, pp. 461–471. Springer, New Delhi (2015)Google Scholar
  4. 4.
    Murty, M.N., Devi, V.S.: Pattern Recognition: an Algorithmic Approach. Springer Science & Business Media (2011)Google Scholar
  5. 5.
    Rish, I.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, no. 22, pp. 41–46. IBM, New York (2001)Google Scholar
  6. 6.
    Fix, E., Hodges Jr, J.L.: Discriminatory Analysis-Nonparametric Discrimination: consistency Properties. California Univ Berkeley (1951)Google Scholar
  7. 7.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefGoogle Scholar
  8. 8.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  9. 9.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
  10. 10.
    Ho, T.K.: Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995)Google Scholar
  11. 11.
    Barandiaran, I.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8) (1998)Google Scholar
  12. 12.
    Pattnaik, P.K., Rautaray, S.S., Das, H., Nayak, J.: Progress in computing, analytics and networking. In: Proceedings of ICCAN, p. 710 (2017)Google Scholar
  13. 13.
    Pradhan, C., Das, H., Naik, B., Dey, N.: Handbook of Research on Information Security in Biomedical Signal Processing, pp. 1–414. IGI Global, Hershey, PA (2018)CrossRefGoogle Scholar
  14. 14.
    Sahoo, A.K., Mallik, S., Pradhan, C., Mishra, B.S.P., Barik, R.K., Das, H.: Intelligence-based health recommendation system using big data analytics. In: Big Data Analytics for Intelligent Healthcare Management, pp. 227–246. Academic Press (2019)Google Scholar
  15. 15.
    Dey, N., Das, H., Naik, B., Behera, H.S. (eds.).: Big Data Analytics for Intelligent Healthcare Management. Academic Press (2019)Google Scholar
  16. 16.
    Ishak, S., Rahiman, M.H.F., Kanafiah, S.N.A.M., Saad, H.: Leaf disease classification using artificial neural network. J. Teknologi, 77(17) (2015)Google Scholar
  17. 17.
    Padol, P.B., Yadav, A.A.: SVM classifier based grape leaf disease detection. In: 2016 Conference on Advances in Signal Processing (CASP), pp. 175–179. IEEE (2016)Google Scholar
  18. 18.
    Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)CrossRefGoogle Scholar
  19. 19.
    Dandawate, Y., Kokare, R.: An automated approach for classification of plant diseases towards development of futuristic decision support system in Indian perspective. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 794–799. IEEE (2015)Google Scholar
  20. 20.
    Singh, V., Misra, A.K.: Detection of unhealthy region of plant leaves using image processing and genetic algorithm. In: 2015 International Conference on Advances in Computer Engineering and Applications, pp. 1028–1032. IEEE (2015)Google Scholar
  21. 21.
    Patil, J.K., Kumar, R.: Color feature extraction of tomato leaf diseases. Int. J. Eng. Trends Technol. 2(2), 72–74 (2011)Google Scholar
  22. 22.
    Ghadge, R., Kulkarni, J., More, P., Nene, S., Priya, R.L.: Prediction of crop yield using machine learning. Int. Res. J. Eng. Technol. (IRJET), 5 (2018)Google Scholar
  23. 23.
    Hong, Z., Kalbarczyk, Z., Iyer, R.K.: A data-driven approach to soil moisture collection and prediction. In: 2016 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 1–6. IEEE (2016)Google Scholar
  24. 24.
    Dahikar, S.S., Rode, S.V., Deshmukh, P.: An artificial neural network approach for agricultural crop yield prediction based on various parameters. Int. J. Adv. Res. Electron. Commun. Eng. (IJARECE) 4(1) (2015)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Kshyanaprava Panda Panigrahi
    • 1
  • Himansu Das
    • 1
  • Abhaya Kumar Sahoo
    • 1
  • Suresh Chandra Moharana
    • 1
  1. 1.School of Computer EngineeringKIIT Deemed to be UniversityBhubaneswarIndia

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