Leaf Classification Techniques for Medicinal Plants : A Detailed Survey

Authors

  • Gayatri Ambulkar  School of Computer Science, MIT World Peace University, Pune, Maharashtra, India
  • Prajakta Soman  School of Computer Science, MIT World Peace University, Pune, Maharashtra, India

Keywords:

Feature extraction, canny edge detector, Gabor wavelet, PCA, PNN, leaf classification

Abstract

Leaf classification is an important use case and has been explored by main researchers. The main features focused for leaf classification in previous work were shape and texture. In this paper we have discussed the comparative study of different technologies used for feature extraction and selection. We observed that less amount of work is done on Medicinal leaves data. As India is a rich country for being the habitat for a variety of medicinal plants it will be helpful if we can detect such leaves for better use which will be our part of future work after comparing and finding best method and model use for leaf classification.

References

  1. Wu, Stephen & Bao, Forrest & Xu, Eric & Wang, Yu-Xuan & Chang, Yi-Fan & Xiang, Qiao-Liang. (2007). A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network. Centre, Kingston University, London, UK {t.beghin,j.cope,p.remagnino,s.barman}@kingston.ac.uk.
  2. Thibaut Beghin, James S Cope, Paolo Remagnino, and Sarah Barman Digital Imaging Research Centre, Kingston University, London, UK {t.beghin,j.cope,p.remagnino,s.barman}@kingston.ac.uk
  3. Casanova, D., de Mesquita Sá Junior, J. J., & Bruno, O. M. (2009). Plant leaf identification using Gabor wavelets. International Journal of Imaging Systems and Technology, 19(3), 236–243. doi:10.1002/ima.20201
  4. Mallah, Charles & Cope, James & Orwell, James. (2013). Plant Leaf Classification using Probabilistic Integration of Shape, Texture and Margin Features. Pattern Recognit. Appl. 3842. 10.2316/P.2013.798-098.
  5. A. Salman, A. Semwal, U. Bhatt and V. M. Thakkar, "Leaf classification and identification using Canny Edge Detector and SVM classifier," 2017 International Conference on Inventive Systems and Control (ICISC), Coimbatore, India, 2017, pp. 1-4, doi: 10.1109/ICISC.2017.8068597.
  6. C. A. Priya, T. Balasaravanan and A. S. Thanamani, "An efficient leaf recognition algorithm for plant classification using support vector machine," International Conference on Pattern Recognition, Informatics and Medical Engineering (PRIME-2012), Salem, India, 2012, pp. 428-432, doi: 10.1109/ICPRIME.2012.6208384.
  7. T. Le, D. Tran and N. Pham, "Kernel descriptor based plant leaf identification," 2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, France, 2014, pp. 1-5, doi: 10.1109/IPTA.2014.7001990.
  8. E. Elhariri, N. El-Bendary and A. E. Hassanien, "Plant classification system based on leaf features," 2014 9th International Conference on Computer Engineering & Systems (ICCES), Cairo, Egypt, 2014, pp. 271-276, doi: 10.1109/ICCES.2014.7030971.
  9. Leafsnap: A Computer Vision System for Automatic Plant Species Identification Neeraj Kumar1, Peter N. Belhumeur2, Arijit Biswas3, David W. Jacobs3, W. John Kress4, Ida Lopez4, and Jo˜ao V. B. Soares3 1 University of Washington, Seattle WA 2 Columbia University, New York NY 3 University of Maryland, College Park MD 4 National Museum of Natural History, Smithsonian Institution, Washington DC
  10. Ayurvedic leaf recognition for Plant Classification Sethulekshmi A V#1, Sreekumar K#2 # Department Of Computer Science, College Of Engineering Poonjar, Cochin University of Science and Technology Poonjar, Kottayam District, Kerala, India.
  11. Z. Zulkifli, P. Saad and I. A. Mohtar, "Plant leaf identification using moment invariants & General Regression Neural Network," 2011 11th International Conference on Hybrid Intelligent Systems (HIS), Melacca, Malaysia, 2011, pp. 430-435, doi: 10.1109/HIS.2011.6122144.
  12. Hall, David, McCool, Christopher, Dayoub, Feras, Suenderhauf, Niko, & Upcroft, Ben (2015) Evaluation of features for leaf classification in challenging conditions. In Das, S, Sarkar, S, Parvin, B, & Porikli, F (Eds.) Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision (WACV 2015). Institute of Electrical and Electronics Engineers (IEEE), United States of America, pp. 797-804.
  13. A. Sabu, K. Sreekumar and R. R. Nair, "Recognition of ayurvedic medicinal plants from leaves: A computer vision approach," 2017 Fourth International Conference on Image Information Processing (ICIIP), Shimla, India, 2017, pp. 1-5, doi: 10.1109/ICIIP.2017.8313782.
  14. Flavia Dataset : http://flavia.sourceforge.net/
  15. ImageCLEF 2013 https://www.imageclef.org/2013
  16. Canny edge detector in python- Computer Vision https://towardsdatascience.com/canny-edge-detection-step-by-step-in-python-computer-vision-b49c3a2d8123
  17. Medicinal Leaf Dataset https://data.mendeley.com/datasets/nnytj2v3n5/1

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-03-13

Issue

Volume 8, Issue 2, March-April-2021

Section

Research Articles

License

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Gayatri Ambulkar, Prajakta Soman, " Leaf Classification Techniques for Medicinal Plants : A Detailed Survey" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 2, pp.27-33, March-April-2021.