CFP last date
20 May 2024
Reseach Article

A Novel Approach for Plant Leaf Image Segmentation using Fuzzy Clustering

by N.valliammal, S.n.geethalakshmi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 13
Year of Publication: 2012
Authors: N.valliammal, S.n.geethalakshmi
10.5120/6322-8669

N.valliammal, S.n.geethalakshmi . A Novel Approach for Plant Leaf Image Segmentation using Fuzzy Clustering. International Journal of Computer Applications. 44, 13 ( April 2012), 10-20. DOI=10.5120/6322-8669

@article{ 10.5120/6322-8669,
author = { N.valliammal, S.n.geethalakshmi },
title = { A Novel Approach for Plant Leaf Image Segmentation using Fuzzy Clustering },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 13 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 10-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number13/6322-8669/ },
doi = { 10.5120/6322-8669 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:35:27.306489+05:30
%A N.valliammal
%A S.n.geethalakshmi
%T A Novel Approach for Plant Leaf Image Segmentation using Fuzzy Clustering
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 13
%P 10-20
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we propose an algorithm based on fuzzy threshold and clustering segmentation for different plant analysis. Segmentation of the plant from background objects is a challenging task for different plant leaf recognition and classification. Before applying the proposed method pre-processing technique like image conversion, noise reduction by median filter, morphological operation and finally wavelet transformation has to be processed. The proposed method provides good results based on fuzzy threshold and clustering techniques for detection of most homogeneity region in plant leaf images. The relative performance of the conventional and proposed methods is evaluated using Variation of Information, Energy, Entropy and Evaluation Time. It proves that the proposed method gives suitable results for efficient classification and recognition.

References
  1. H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik and Z. ALRahamneh, Fast and Accurate Detection and Classification of Plant Diseases, International Journal of Computer Applications Vol. 17, No. 1, pp. (0975-8887), 2011.
  2. Kebapci, H. ; Yanikoglu, B. ; Unal, G. , Plant image retrieval using color and texture features, Proceeding of International symposium on Computer and Information Sciences, pp. 82-87,2009.
  3. Jyotismita Chaki, Ranjan Parekh, Plant Leaf Recognition using Shape based Features and Neural Network classifiers, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 10, pp. 41-47, 2011.
  4. Cai, J, Golzarian, M & Miklavcic, S, 'Novel Image Segmentation Based on Machine Learning and Its Application to Plant Analysis', IJIEE, vol. 1, no. 1, pp. 79-84, 2011.
  5. Mr. Vinay S. Mandlik, Prof. Sanjay B. Dhaygude, Agricultural Plant Image Retrieval System Using CBIR, International Journal of Emerging Technology and Advanced Enginee, Vol. 1, No. 2, pp. 93-96, 2011.
  6. Huang, R. ; Lum, E. ; Ma, K. -L. ; Multi-scale morphological volume segmentation and visualization, Proceeding of International Asia Pacific Symposium on Visualization, pp. 121-128, 2007.
  7. Ronghua Ji, Zetian Fu & Lijun Qi, Real?time plant image segmentation algorithm under natural outdoor light conditions, New Zealand Journal of Agricultural Research, Vol. 50, No. 5, pp. 847-854,2007.
  8. T. Anken et. al. , Automatic Detection of Broad Leaved Dock in grasslend, In: Conference AgEng 2010, European Society of Agricultural Engineers. Clermont-Ferrand, France. September 6-8, 2010.
  9. H C Sateesh Kumar, K B Raja, Venugopal K R and L M Patnaik, Automatic Image Segmentation using Wavelets, International Journal of Computer Science and Network Security, VOL. 9 No. 2, pp. 305-313, 2009 .
  10. Paul R. Hill, Image Segmentation Using a Tecture Gradient Based Water Transform, IEEE Transction on Image Processing, Vol. 12, No. 12, pp. 1618-1633,2002.
  11. Mahmood R Golzarian and Ross A Frick, Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis,Vol. 7, No. 28, pp. 2-11, 2011.
  12. Lei F. Tian, David C. Slaughter, Environmentally adaptive segmentation algorithm for outdoor image segmentation, Elsiever Journal, Computers and Electronics in Agriculture , Vol. 21, pp. 153–168, 1998.
  13. Abdul Kadir, Lukito Edi Nugroho, Adhi Susanto and Paulus Insap Santosa , Foliage Plant Retrieval using Polar Fourier Transform, Color moments and Vein features, Signal & Image Processing : An International Journal (SIPIJ) Vol. 2, No. 3, pp. 1-13, 2011.
  14. Charles A. Price, Olga Symonova, Yuriy Mileyko, Troy Hilley, and Joshua S. Weitz, eaf Extraction and Analysis Framework Graphical User Interface: Segmenting and Analyzing the Structure of Leaf Veins and Areoles, Plant Physiology, , Vol. 155, pp. 236–245, 2011.
  15. Mahmood R. Golzarian, Jinhai Cai, Ross A. Frick, and Stan J. Miklavcic, Segmentation f Cereal Plant Images Using Level Set Methods – A Comparative Study, International Journal of Information and Electronics Engineering, Vol. 1 , No. 1 , pp. 72-78, 2011.
  16. Ronghua Ji, Real-time plant image segmentation algorithm under natural outdoor light conditions, New Zealand Journal of Agricultural Research, 2007, Vol. 50, pp. 847-854, 2007.
  17. Y. Yong, Z. Chongxun, L. Pan, A Novel Fuzzy C-Means Clustering Algorithm for Image Thresholding Measurement Science Review, Vol. 4, No. 1, 2004
Index Terms

Computer Science
Information Sciences

Keywords

Segmentation Fuzzy Thresholding Morphological Operation Wavelet Transformation Fuzzy Clustering And Hausdorff Distance Method