Fast And Accurate System For Leaf Recognition

Authors

  • SI Mostafa Department of Computer Sciences, Higher Institute for Computer Sciences, Cairo, Egypt
  • YM Abd El-Latif Dept. of Computing and Information Technology, Arab Academy for Science, Technology and Maritime Transport, Cairo, Egypt
  • NM Reda Department of Computer Sciences, Faculty of Sciences, Ain Shams University, Cairo, Egypt

DOI:

https://doi.org/10.26438/ijcse/v8i8.7379

Keywords:

Leaf recognition, leaf classification, Morphological features, Online Dictionary Learning, GPU

Abstract

Leaf recognition is used in various applications in domains like agriculture, forest, biodiversity protection. Leaf recognition based on images is a challenging task for computer, due to the appearance and complex structure of leaves, high variability between classes, and small differences between leaves in the same class. This paper reviews a state-of-the- art application for building a fast automatic leaf recognition system. We propose a combination of shape, color, texture features and sparse representation extraction for different leaf recognition tasks. In this paper two features databases have been built using 32 classes with 1980 images for Flavia dataset. In recent trends the Graphics processing units (GPU) emerge with high parallel computing capabilities. In this paper we used the computation ability of modern GPU to execute the proposed leaf classification that achieves classification results of 99% and extreme parallelism recognition.

References

[1] J. Hsiao, L. Kang, C. Chang , C. Lin ," Learning-Based Leaf Image Recognition Frameworks", Springer International Publishing Switzerland , Intelligent Systems in Science and Information 2014, Studies in Computational Intelligence , pp. 77-91,2015.

[2] M. J. Flynn. "Some computer organizations and their effectiveness", IEEE Transactions on Computers, Vol.21, Issue.9 , pp. 948"960, 1972.

[3] A.Kadir, L. E. Nugroho , A. Susanto, P. I. Santosa , " A comparative experiment of several shape methods in recognizing plants", International Journal of Computer Science & Information Technology, vol.3, No 3, pp.256"263 , 2011.

[4] A. Kadir, L. E. Nugroho, "Leaf Classification Using Shape, Color, and Texture Features ", International Journal of Computer Trends and Technology, July to Aug Issue, pp.225-230, 2011.

[5] C.D. Ruberto, L. Putzu, "A fast leaf recognition algorithm based on SVM classifier and high dimensional feature vector", In Proceedings of the 9th International Conference on Computer Vision Theory and Applications, pp. 601-609, 2014.

[6] B.M. Fern, G. bin, "Leaf Recognition Based on Leaf Tip and Leaf Base Using Centroid Contour Gradient", American Scientific Publishers, vol.20, pp.209-212, 2014.

[7] S. Pudaruth, T. Munisami, M. Ramsum, "Plant Leaf recognition using shape features and color histogram with K-nearest neighbour classifier" , Science Direct, Procedia Computer Science , vol. 58 , pp. 740"747, 2015.

[8] S.K. Badugu, R.K. Kontham, V.K. Vakulabharanam, B. Prajna, "Calculation of Texture Features for Polluted Leaves," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.11-21, 2018.

[9] F. Haque ,"Plant recognition system using leaf shape features and minimum Euclidean distance", ICTACT Journal on Image and Video Processing, vol. 9, issue.2, 2018.

[10] R.Hu,"Multiscale Distance Matrix for Fast Plant Leaf Recognition", IEEE Transactions on Image Processing, vol. 21, Issue 11, pp. 4667 - 4672 , 2012

[11] H. Ling , D.W. Jacobs, "Shape Classification Using the Inner-Distance" IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, no. 2, pp. 286-299, 2007

[12] M. Nordin , A. Fakhri , "Image Segmentation Using OpenMP and its application in plant species classification" International Journal of Software Engineering and Its Application, vol.9, no. 5, 2015.

[13] S.M. Walunj, S.V. Gaikar, A.D. Potgantwar, "Accelerate Image Reconstruction Using GPU," International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.68-75, 2017.

[14] V. Satti, A. Satya, S. Sharma, "An Automatic Leaf Recognition System for Plant Identification Using Machine Vision Technology", International Journal of Engineering Science and Technology (IJEST), Vol.5 , No.04 , 2013.

[15] A. N. Cheeran, A. K. Chaudhari, "Fast and Accurate Method for Leaf Area Measurement", International Journal of Computer Applications, Vol.49, No.9, pp.22 " 25, 2012.

[16] A.H. Kulkarni1, H.M.Rai, "A Leaf Recognition Technique for Plant Classification Using RBPNN and Zernike Moments", International Journal of Advanced Research in Computer and Communication Engineering, Vol.2, Issue.1, 2013.

[17] X.Feng, "Recognition of Leaf Images Based on Shape Features Using a Hypersphere Classifier", Springer-Verlag Berlin Heidelberg, vol.3644, pp. 87-96, 2005.

[18] M. Berg, O. Cheong, M. Kreveld , M. Overmars, "Computational Geometry: Algorithms and Applications", Springer-Verlag Press, New York, pp.45-61, 2000.

[19] D. Naglot , P. S. Kasliwal , S. J. Gaikwad , N. D. Agrawal, "Indian Plant Recognition System Using Convolutional Neural Network", International Journal of Computer Sciences and Engineering (JCSE), Vol.7, Issue.6, 2019.

[20] S. Mallat, "A Wavelet Tour of Signal Processing", Third Edition, Elsevier Inc., United States, pp.1-31, 2008.

[21] S. Ibrahem, Y. M. Abd El-Latif, N. M. Reda , "A novel data dictionary learning for leaf recognition", Signal & Image Processing: An International Journal (SIPIJ), Vol.10, No.3, 2019.

Downloads

Published

2020-08-31
CITATION
DOI: 10.26438/ijcse/v8i8.7379
Published: 2020-08-31

How to Cite

[1]
S. Mostafa, Y. A. E. Latif, and N. Reda, “Fast And Accurate System For Leaf Recognition”, Int. J. Comp. Sci. Eng., vol. 8, no. 8, pp. 73–79, Aug. 2020.

Issue

Vol. 8 No. 8 (2020): IJCSE August Edition

Section

Research Article

License

Creative Commons License

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

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.