Call for Paper - November 2022 Edition
IJCA solicits original research papers for the November 2022 Edition. Last date of manuscript submission is October 20, 2022. Read More

Classification and Detection of Citrus Disease using Feature Extraction and Support Vector Machine (SVM)

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Ayobami I. Ojelabi, Oluwabusayo I. Omotosho, Olajide A. Oladejo

Ayobami I Ojelabi, Oluwabusayo I Omotosho and Olajide A Oladejo. Classification and Detection of Citrus Disease using Feature Extraction and Support Vector Machine (SVM). International Journal of Computer Applications 177(17):17-25, November 2019. BibTeX

	author = {Ayobami I. Ojelabi and Oluwabusayo I. Omotosho and Olajide A. Oladejo},
	title = {Classification and Detection of Citrus Disease using Feature Extraction and Support Vector Machine (SVM)},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2019},
	volume = {177},
	number = {17},
	month = {Nov},
	year = {2019},
	issn = {0975-8887},
	pages = {17-25},
	numpages = {9},
	url = {},
	doi = {10.5120/ijca2019919582},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In agriculture, plant infections are responsible for the reduction in the production of citrus fruits which causes a major economic loss. In plants, citrus is used as a major source of nutrients like vitamin C throughout the universe. However, ‘Citrus’ diseases badly affect the production and quality of citrus fruits. Over the years, image processing techniques have been widely used for detection and classification of citrus diseases but a novel model for such challenge has not been properly explored. Hence, the need for a model that will be able to label the diseases accordingly.

A model for detection and classification of citrus diseases using feature selection and support vector machine (SVM) was developed. The method consists of two primary phases; (a) detection of lesion spots on the citrus fruits; (b) classification of citrus diseases. The citrus lesion spots are extracted by an optimized segmentation method using K-means, which was performed on an enhanced citrus image. The selected features are fed into Support Vector Machine (SVM) for the citrus disease classification, and the model tested with various test images that consist of healthy and diseased citrus fruits.

The model shows a better performance than previous models at 95% accuracy.


  1. [Ali, H., Lali, M. I., Nawaz, M. Z., Sharif, M., & Saleem, B. A. (2017). Symptom-based automated detection of citrus diseases using the color histogram and textural descriptors. Computers and Electronics in Agriculture, 138, 92–104.
  2. Hamuda, E., Ginley, B. M., Glavin, M., & Jones, E. (2018). Improved image processing-based crop detection using Kalman filtering and the Hungarian algorithm. Computers and Electronics in Agriculture, 148(February), 37–44.
  3. García-García, I, Taboada-Rodríguez, A, López-Gomez, A and Marín-Iniesta, F. 2013. Active Packaging of Cardboard to Extend the Shelf Life of Tomatoes. Food and Bioprocess Technology 6 (3): 754-761
  4. Sankaran, S., Mishra, A., Ehsani, R. and Davis, C. (2010) A Review of Advanced Techniques for Detecting Plant Diseases. Computers and Electronics in Agriculture, 72, 1-13.
  5. López-García F, Andreu-García G, Blasco J, Aleixos N, Valiente JM (2010) Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Comput Electron Agric 71(2):189–197.
  6. Pydipati R, Burks TF, Lee WS (2005) Statistical and neural network classifiers for citrus disease detection using machine vision. Trans ASAE 48(5):2007–2014
  7. Pydipati, R., Burks, T.F., Lee, W.S., 2006. Identification of citrus disease using color texture features and discriminant analysis. Comput. Electron. Agric. 52, 49–59.
  8. Sanyal P, Patel SC (2008) Pattern recognition method to detect two diseases in rice plants. Imaging Sci J 56(6):7
  9. Sanyal P, Bhattacharya U, Parui SK, Bandyopadhyay SK, Patel S (2007) Color texture analysis of rice leaves diagnosing deficiency in the balance of mineral levels towards improvement of crop productivity. In: 10th International Conference on Information Technology (ICIT 2007). IEEE, Orissa, pp 85–90
  10. Yao Q, Guan Z, Zhou Y, Tang J, Hu Y, Yang B (2009) Application of support vector machine for detecting rice diseases using shape and color texture features. In: 2009 international conference on engineering computation. IEEE, Hong Kong, pp 79–83
  11. Jian Z, Wei Z (2010) Support vector machine for recognition of cucumber leaf diseases. In: 2010 2nd international conference on advanced computer control. IEEE, Shenyang, pp 264–266
  12. Youwen T, Tianlai L, Yan N (2008) The recognition of cucumber disease based on image processing and support vector machine. In: 2008 congress on image and signal processing. IEEE, Sanya, pp 262–267
  13. Huang KY (2007) Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Comput Electron Agric 57:3–11
  14. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3 3:610–62
  15. Hetzroni A, Miles GE, Engel BA, Hammer PA, Latin RX (1994) Machine vision monitoring of plant health. Adv Space Res 14(11):203–212
  16. Jednipat Moonrinta, Supawadee Chaivivatrakul, Matthew N. Dailey, and Mongkol Ekpanyapong (2010) Fruit Detection, Tracking, and 3D Reconstruction for Crop Mapping and Yield Estimation
  17. Mohammed Yesuf (2013) Pseudocercospora leaf and fruit spot disease of citrus: Achievements and challenges in the citrus industry: A review. Vol.4, No.7, 324-328 (2013)
  18. Meunkaewjinda. A, P.Kumsawat, K.Attakitmongcol and A.Sirikaew.2008 Grape leaf diseases from color imaginary using Hybrid intelligent system”, Proceedings of ECTICON.
  19. Nivedit.R. Kakade, Dnyaneswar.D.Ahire (2015) “A Review of Grape Plant Disease Detection” International Research Journal of Engineering and Technology, Volume: 02 Issue: 05 | Aug-2015
  20. Pujari, J.D., Yakkundimath, R., 2013. Grading and classification of anthracnose fungal disease of fruits based on statistical texture features. Int. J. Adv. Sci. Technol. 52, 121–132.
  21. Pujari, J.D., Yakkundimath, R., Byadgi, A.S., 2015. Image processing-based detection of fungal diseases in plants. Procedia Comput. Sci. 46, 1802–1808. http://dx.doi. org/10.1016/j.procs.2015.02.137.
  22. Qin, J., Burks, T.F., Ritenour, M.A., Bonn, W.G., 2009. Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. J. Food Eng.93,183–191.
  23. Hocquellet, A., Toorawa, P., Bové, J.M., Garnier, M., 1999. Detection and identification of the two Candidatus Liberobacter species associated with citrus huanglongbing by PCR amplification of ribosomal protein genes of the beta operon. Mol. Cell. Probes 13, 373–379. mcpr.1999.0263.
  24. Javaid, M.A., Tariq, M.A., Asi, A.A., 2006. Effect of Micronutrients Application on the Yield and Quality of Kinnow Mandarin (Citrus Reticulata Blanco.) 38, 169–172.
  25. Almada-Ruiz, E., Martínez-Téllez, M.Á., Hernández-Álamos, M.M., Vallejo, S., Primo- Yúfera, E., Vargas-Arispuro, I., 2003. Fungicidal potential of methoxylated flavones from citrus for in vitro control of Colletotrichum gloeosporioides, causal agent of anthracnose disease in tropical fruits. Pest Manage. Sci. 59, 1245–1249.
  26. Deng, Z., Huang, S., Xiao, S., Gmitter, F.G., 1997. Development and characterization of SCAR markers linked to the citrus tristeza virus resistance gene from Poncirus trifoliata. Genome 704, 697–704.
  27. Quan-Sen Sun, Sheng-Gen Zeng, Yan Liu, Pheng-Ann Heng, De-Shen Xia; 2005. A new method of feature fusion and its application in image recognition. Volume 38 Issue 12, December, 2005. Pages 2437-2448, doi>10.1016/j.patcog.2004.12.013
  28. Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi; 2016. Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition. IEEE Transactions on Information Forensics and Security ( Volume: 11 , Issue: 9 , Sept. 2016 ). Page(s): 1984 – 1996. DOI: 10.1109/TIFS.2016.2569061
  29. M.M. El-gayar, H. Soliman, N. meky; 2016. A comparative study of image low level feature Extraction algorithms.


Support Vector Machine, Feature Extraction, Feature Selection, Citrus Disease