Fruit Disease Detection Using GLCM And SVM Classifier

Authors(4) :-Anu S, Nisha T, Ramya R, Rizuvana Farvin M

Analytics plays a critical role in detecting and analyzing the diseases. The proposed system identifies the fruits that are affected with diseases. It is done by collecting the raw data which is subjected to pre-processing. It results in a HSV (hue, saturation, value) converted image. After pre-processing, the resized format of the data is used to extract the information. In feature extraction the image is segmented and converted into matrix using Gray level co-occurrence matrix algorithm. The further classification is done and result is represented in the form of a decision tree using Support Vector Machine (SVM). The disease that affected the fruit is displayed along with the right fertilizer to be used for the plant.

Authors and Affiliations

Anu S
Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Technology1, Coimbatore, Tamil Nadu, India
Nisha T
Department of Computer Science and Engineering, Sri Krishna College of Technology1, Coimbatore, Tamil Nadu, India
Ramya R
Department of Computer Science and Engineering, Sri Krishna College of Technology1, Coimbatore, Tamil Nadu, India
Rizuvana Farvin M
Department of Computer Science and Engineering, Sri Krishna College of Technology1, Coimbatore, Tamil Nadu, India

Analytics, HSV, Gray level co-occurrence matrix, SVM.

  1. Babu, M.S.P., Srinivasa, B.R., 2010. Leaves recognition using back propagation neural network - advice for pests and disease control on crops. Technical report, Department of Computer Science & Systems Engineering, Andhra University, India.
  2. Roman, T., 2004. Classification and Regression Trees (CART) Theory and Applications, A Master Thesis, Center of Applied Statistics and Economics Humboldt University, Berlin, pp. 1–40.
  3. Kim, K.S., Wang, T.C., Yang, X.B., 2005. Simulation of apparent infection rate to predict severity of soybean rust using a fuzzy logic system. Phytopathology 95 (10), 1122–1131.
  4. Georgiana, E., 2009. A decision tree for weather prediction. Buletinul. LXI 1, 77–82.
  5. Hahn, F., 2009. Actual pathogen detection: sensors and algorithms-a review. Algorithms 2(1), 301–338.
  6. Liu, G., Shen, H., Yang, X., Ge, Y., 2005. Research on prediction about fruit tree diseases and insect pests based on neural network. In: IFIP International Conference onArtificial Intelligence Applications and Innovations. Springer, US, pp. 731–740.
  7. Chakraborty, S., Ghosh, R., Ghosh, M., Fernandes, C.D., Charchar, M.J., Kelemu, S., 2004. Weather-based prediction of anthracnose severity using artificial neural networkmodels. Plant. Pathol. 53, 375–386.
  8. Arsevska, E., Roche, M., Hendrikx, P., Chavernac, D., Falala, S., Lancelot, R., Dufour, B.,2016. Identification of terms for detecting early signals of emerging infectious disease outbreaks on the web. Comput. Electron. Agric. 123, 104–115.
  9. Chaurasia, Vikas, Pal, Saurabh, 2013. Early prediction of heart diseases using data mining techniques. Caribbean J. Sci. Technol. 1, 208–217 ISSN: 0799-3757.
  10. Lucky, M., Christina, C., Kevin, G., Peter, O.S., 2016. Predicting pre-planting risk of Stagonospora nodorum blotch in winter wheat using machine learning models.
  11. Mohammadhassani, M., Nezamabadi-Pour, H., Suhatril, M., Shariati, M., 2014. An evolutionary fuzzy modelling approach and comparison of different methods for shear strength prediction of high-strength concrete beams without stirrups. Smart Struct.Syst. 14 (5), 785–809.
  12. Mirko, I. (Ed.), 1992. Mikoze biljaka (Mycosis of plants). Nauka, Beograd.
  13. Pan, G., Li, K., Ouyang, A., Li, K., 2016. Hybrid immune algorithm based on greedy algorithmand delete-cross operator for solving TSP. Soft. Comput. 20 (2), 555–566.
  14. Rakesh, K., Amar, K.S., Gajendra, R., 2006. Machine learning techniques in disease forecasting: a case study on rice blast prediction. BMC Bioinformatics 7 (1), 485.
  15. Weizheng, S., Yachun, W., Zhanliang, C., Hongda, W., 2008. Grading method of leaf spot disease based on image processing. In: Proceedings of the international Conference on Computer Science and Software Engineering. 6, CSSE. IEEE Computer Society,
  16. Washington, DC, pp. 491–494. http://dx.doi.org/10.1109/CSSE. 2008.1649.
  17. M. Ilic et al. Computers and Electronics in Agriculture 150 (2018) 418–425

Publication Details

Published in : Volume 5 | Issue 2 | March-April 2019
Date of Publication : 2019-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 365-371
Manuscript Number : CSEIT195221
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Anu S, Nisha T, Ramya R, Rizuvana Farvin M, "Fruit Disease Detection Using GLCM And SVM Classifier", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 5, Issue 2, pp.365-371, March-April-2019. Available at doi : https://doi.org/10.32628/CSEIT195221
Journal URL : http://ijsrcseit.com/CSEIT195221

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