Novel Approach for Plant Disease Detection Based on Textural Feature Analysis

  • Varinderjit Kaur
  • Ashish OberoiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1042)


The image processing is the technique which can propose the information stored in the form of pixels. The plant disease detection is the technique which can detect the disease from the leaf. The plant disease detection algorithms have various steps like preprocessing, feature extraction, segmentation, and classification. The KNN classifier technique is applied which can classify input data into certain classes. The performance of KNN classifier is compared with the existing techniques and it is analyzed that KNN classifier has high accuracy, less fault detection as compared to other techniques. This paper presents methods that use digital image processing techniques to detect, quantify, and classify plant diseases from digital images in the visible spectrum. In plant leaf classification leaf is classified based on its different morphological features. Some of the classification techniques used are neural network, genetic algorithm, support vector machine, and principal component analysis. In this paper results are compared between KNN classifier and SVM classifier.


GLCM KNN K-means WDDIP-KNN Plant disease detection 


  1. 1.
    Camargo, A., Smith, J.S.: An image-processing based algorithm to automatically identify plant disease visual symptoms. Bio Syst. Eng. 102, 9–21 (2008)Google Scholar
  2. 2.
    Camargo, A., Smith, J.S.: Image processing for pattern classification for the identification of dis-ease causing agents in plants. Comput. Electron. Agric. 66, 121–125 (2009)CrossRefGoogle Scholar
  3. 3.
    Guru, D.S., Mallikarjuna, P.B., Manjunath, S.: Segmentation and classification of tobacco seedling diseases. In: Proceedings of the Fourth Annual ACM Bangalore Conference (2011)Google Scholar
  4. 4.
    Zhao, Y.X., Wang, K.R., Bai, Z.Y., Li, S.K., Xie, R.Z., Gao, S.J.: Research of maize leaf disease identifying models based image recognition. In: Crop Modeling and Decision Support, pp. 317–324. Tsinghua University Press, Beijing (2009)CrossRefGoogle Scholar
  5. 5.
    Fury, T.S., Cristianini, N., Duffy, N.: Support vector machine (SVM) classification and validation of cancer tissue samples using microarray expression data. Proc. BioInform. 16(10), 906–914 (2000)CrossRefGoogle Scholar
  6. 6.
    Al-Hiaryy, H., Bani Yas Ahmad, S., Reyalat, M., Ahmed Braik, M., AL Rahamnehiahh, Z.: Fast and accurate detection and classification of plant diseases. Int. J. Comput. Appl. 17(1), 31–38 (2011)Google Scholar
  7. 7.
    Mohanaiah, P., Sathyanarayana, P., GuruKumar, L.: Image texture feature extraction using GLCM approach. Int. J. Sci. Res. Publ. 3(5), 1 (2013)Google Scholar
  8. 8.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)CrossRefGoogle Scholar
  9. 9.
    Mattihalli, C., Gedefaye, E., Endalamaw, F., Necho, A.: Real time automation of agriculture land, by automatically detecting plant leaf diseases and auto medicine. In: 32nd International Conference on Advanced Information Networking and Applications Workshops (2018)Google Scholar
  10. 10.
    Tichkule, S.K., Gawali, D.H.: Plant diseases detection using image processing techniques. In: Online International Conference on Green Engineering and Technologies (IC-GET) (2016)Google Scholar
  11. 11.
    Tlhobogang, B., Wannous, M.: Design of plant disease detection system: a transfer learning approach work in progress. IEEE (2018)Google Scholar
  12. 12.
    Gandhi, R., Nimbalkar, S., Yelamanchili, N., Ponkshe, S.: Plant disease detection using CNNs and GANs as an augmentative approach. IEEE (2018)Google Scholar
  13. 13.
    Khan, Z.U., Akra, T., Naqvi, S.R., Haider, S.A., Kamran, M., Muhammad, N.: Automatic detection of plant diseases; utilizing an unsupervised cascaded design. IEEE (2018)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of CSERIMT UniversityMandi GobindgarhIndia

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