We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
Reseach Article

Plant Leaf Recognition using Gabor Filter

by Jyotismita Chaki, Ranjan Parekh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 10
Year of Publication: 2012
Authors: Jyotismita Chaki, Ranjan Parekh
10.5120/8927-3000

Jyotismita Chaki, Ranjan Parekh . Plant Leaf Recognition using Gabor Filter. International Journal of Computer Applications. 56, 10 ( October 2012), 26-29. DOI=10.5120/8927-3000

@article{ 10.5120/8927-3000,
author = { Jyotismita Chaki, Ranjan Parekh },
title = { Plant Leaf Recognition using Gabor Filter },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 10 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 26-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number10/8927-3000/ },
doi = { 10.5120/8927-3000 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:58:28.970595+05:30
%A Jyotismita Chaki
%A Ranjan Parekh
%T Plant Leaf Recognition using Gabor Filter
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 10
%P 26-29
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes an automated system for recognizing plant species based on leaf images. Plant leaf images of three plant types are analyzed using Gabor Filter by varying the filter parameters. Leaf images are convolved with Gabor filters followed by a separation of the real and imaginary portions of the signal. Absolute difference between the real and imaginary signals form the scalar feature value used for discrimination. Associated parameters like filter size, standard deviation, phase shift and orientation are varied to investigate which combination provides the best recognition accuracies. Classification is done by subtracting the test samples from the mean of the training set. The data set consists of 120 images divided into 3 classes. Accuracy obtained is comparable to the best results reported in literature.

References
  1. Sakai. N. , Yonekawa. S. and Matsuzaki. A. 1996. Two-dimensional image analysis of the shape of rice and its applications to separating varieties. Journal of Food Engineering. 397-407.
  2. Timmermans. A. J. M and Hulzebosch. A. A. 1996. Computer vison system for on-line sorting of pot plants using an artificial neural network classifier. Computers and Electronics in Agriculture. 41-55.
  3. Abbasi. S, Mokhtarian. F and Kittler. J. 1997. Reliable classification of chrysanthemum leaves through curvature scale space. Lecture Notes in Computer Science. 284-295.
  4. Perez. A. J, Lopez. F Benlloch. J. V and Christensen. S. 2000. Color and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture. 197-212.
  5. Im. C, Nishida. H. , and Kunii. T. L. 1998. A hierarchical method of recognizing plant species by leaf shapes. IAPR Workshop on Machine Vision Applications. 158-161.
  6. Yang. C-C, Prasher. S. O, Landry. J-A, Perret. J and Ramaswamy. H. S. 2000. Recognition of weeds with image processing and their use with fuzzy logic for precision farming. Canadian Agricultural Engineering. 195-200.
  7. Wang. Z, Chi. Z, Feng. D and Wang. Q. 2000. Leaf image retrieval with shape feature. International Conference on Advances in Visual Information Systems (ACVIS). 477-487.
  8. Wang. Z, Chi. Z and Feng. D. 2003. Shape based leaf image retrieval. IEEE Proceedings on Vision, Image and Signal Processing (VISP). 34-43.
  9. Camarero. J. J. , Siso. S. , and Pelegrin. E. G. 2003. Fractal dimension does not adequately describe the complexity of leaf margin in seedlings of Quercus species. Anales del Jardín Botánico de Madrid. 63-71.
  10. Lee. C-L and Chen. S-Y. 2003. Classification of leaf images. 16th IPPR Conference on Computer Vision, Graphics and Image Processing (CVGIP). 355-362.
  11. Neto. J. C. , Meyer. G. E. , Jones. D. D. and Samal. A. K. 2006. Plant species identification using elliptic Fourier leaf shape analysis. Computers and Electronics in Agriculture. 121-134.
  12. Park. J. K, Hwang. E. J, and Nam. Y. 2006. A vention – based leaf image classification scheme. Alliance of Information and Referral Systems. 416-428.
  13. Pan. J and He Y. 2008. Recognition of plants by leaves digital image and neural network. International Conference on Computer Science and Software Engineering. 906 – 910.
  14. Wang. Q. P, Du J. X, and Zhai. C-M. 2010. Recognition of leaf image based on ring projection wavelet fractal feature. International Journal of Innovative Computing, Information and Control. 240-246.
  15. Beghin. T, Cope. J. S. , Remagnino. P. , and Barman. S. 2010. Shape and texture based plant leaf classification. International Conference on Advanced Concepts for Intelligent Vision Systems (ACVIS). 345-353
  16. Kadir. A, Nugroho. L. E, Susanto. A, and Santosa. P. I. 2011. A comparative experiment of several shape methods in recognizing plants. International Journal of Computer Science & Information Technology (IJCSIT). 256-263.
  17. Cope. James S, Remagnino. Paolo, Barman. Sarah, and Wilkin. Paul. 2010. Plant Texture Classification Using Gabor Co-occurrences. Springer. 669-677.
  18. Cerutti. G, Tougne. L, Mille. J, Vacavant. A. , and Coquin. D. 2011. Guiding active contours for tree leaf segmentation and identification. Cross-Language Evaluation Forum (CLEF).
  19. Plantscan database (URL: http://imedia-ftp. inria. fr:50012/Pl@ntNet/plantscan_v2/).
  20. Chaki. J. and Parekh. R. 2011. Plant Leaf Recognition using Shape based Features and Neural Network classifiers. Int. Journal of Advanced Computer Science and Applications (IJCSA). 41-47.
  21. Chaki . J. and Parekh. R. 2012. Designing an Automated System for Plant Leaf Recognition. Int. Journal of Advances in Engineering & Technology (IJAET). 149-158.
Index Terms

Computer Science
Information Sciences

Keywords

Gabor Filter Leaf Classification