An Amalgam Approach for Feature Extraction and Classification of Leaves Using Support Vector Machine

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)


This paper describes the need for the development of automatic plant recognition system for classification of plant leaves. In this paper, an automatic Computer Aided Plant Leaf Recognition (CAP-LR) is presented. To implement the above system initially the input image is pre-processed in order to remove the background noise and to enhance the leaf image. As a second stage the system efficiently extracts the different feature vectors of the leaves and gives it as input to the Support Vector Machine (SVM) for classification into plant leaves or tree leaves. Geometric, texture and color features are extracted for classification. The method is validated by K-Map which calculates the accuracy, sensitivity and efficiency. The experimental result shows that the system has faster processing speed and higher recognition rate.


Feature Extraction Classification Plant recognition Geometric Color Texture features SVM 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Singh, K., Gupta, I., Gupta, S.: SVM-BDT PNN and Fourier Moment Technique for Classification of Leaf Shape. International Journal of Signal Processing, Image Processing and Pattern Recognition 3(4), 68–78 (2010)MathSciNetGoogle Scholar
  2. 2.
    Chaki, J., Parekh, R.: Plant Leaf Recognition using Shape based Features and Neural Network classifiers. International Journal of Advanced Computer Science and Applications (IJACSA) 2(10), 41–47 (2011)Google Scholar
  3. 3.
    Kekre, H.B., Thepade, S.D., Sarode, T.K., Suryawanshi, V.: Image Retrieval using Texture Features extracted from GLCM, LBG and KPE. International Journal of Computer Theory and Engineering 2(5), 1793–8201 (2010)Google Scholar
  4. 4.
    Pornpanomchai, C., Supapattranon, P., Siriwisesokul, N.: Leaf and Flower Recognition System (e-Botanist). IACSIT International Journal of Engineering and Technology 3(4), 347–351 (2011)CrossRefGoogle Scholar
  5. 5.
    Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y.-X., Chang, Y.-F., Xiang, Q.-L.: A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network. In: IEEE International Symposium on Signal Processing and Information Technology. IEE explore library, pp. 11–16 (2007)Google Scholar
  6. 6.
    Tang, L., Tian, L., Steward, B.L.: Classification of Broadleaf and Grass Weeds Using Gabor Wavelets and An Artificial Neural Network. Transactions of the ASAE 46(4), 1247–1254 (2003)CrossRefGoogle Scholar
  7. 7.
    Benčo, M., Hudec, R.: Novel Method for Color Textures Features Extraction Based on GLCM. Radio Engineering 16(4), 64–67 (2007)Google Scholar
  8. 8.
    Muralidharan, R., Chandrasekar, C.: Object Recognition using SVM-KNN based on Geometric Moment Invariant. International Journal of Computer Trends and Technology (July- August 2011)Google Scholar
  9. 9.
    Kadir, A., Nugroho, L.E., Susanto, A., Santosa, P.I.: Leaf Classification Using Shape, Color, and Texture Features. International Journal of Computer Trends and Technology, 225–230 (July- August 2011)Google Scholar
  10. 10.
    Patil, J.K., Kumar, R.: Color Feature Extraction of Tomato Leaf Diseases. International Journal of Engineering Trends and Technology 2(2-201), 72–74 (2011)Google Scholar
  11. 11.
    Kadir, A., Nugroho, L.E., Susanto, A., Santosa, P.I.: A Comparative Experiment of Several Shape Methods in Recognizing Plants. International Journal of Computer Science & Information Technology (IJCSIT) 3(3), 256–263 (2011)CrossRefGoogle Scholar
  12. 12.
    Fiel, S., Sablatnig, R.: Automated identification of tree species from images of the bark, leaves and needles. In: 16th Computer Vision Winter Workshop Austria (2011)Google Scholar
  13. 13.
    Porebski, A., Vandenbroucke, N., Macaire, L.: Selection of Color Texture Features from Reduced Size Chromatic Co-occurrence Matrices. In: IEEE International Conference on Signal and Image Processing Applications (2009)Google Scholar
  14. 14.
    Kebapci, H., Yanikoglu, B., Unal, G.: Plant Image Retrieval Using Color, Shape and Texture Features. The Computer Journal Advance Access Published, 1–16 (2010)Google Scholar
  15. 15.
    Yang, M., Kidiyo, K., Joseph, R.: A survey of shape feature extraction techniques. In: Yin, P.-Y. (ed.) Pattern Recognition (2008)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department Computer ScienceAvinashilingam Institute of Home Science and Higher Education for WomenCoimbatoreIndia

Personalised recommendations