International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.9, No.9, Sep. 2017

Variant-Order Statistics based Model for Real-Time Plant Species Recognition

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Heba F. Eid, Ashraf Darwish

Index Terms

Plant Recognition;Leaf Descriptors Extraction;Gray Level Co-occurrence Matrix (GLCM);Gray Level Run Length Matrices (GLRLM);leaf classification


There are an urgent need of categorizing plant by its species, to help botanist setting up a plant species database. However, plant recognition model is still very challenging task in computer vision and can be onerous and time consuming because of inefficient representation approaches. This paper, proposes a recognition model for classifying botanical species from leaf images, using combination of variant-order statistics based measures. Hence, the spatial coordinates values of gray pixels defines the qualities of texture, for the proposed model a gray-scale approach is adopted  for analyzing the local patterns of leaves images using second and higher order statistical measures. While, first order statistical measures are used to extract color descriptors from leaves images. Evaluation of the proposed model shows the importance of combining variant-order statistics measures for enhancing the plant leaf recognition accuracy. Several experiments on Flavia dataset and swedish dataset are conducted. Experimental results indicates that; the proposed model yields to improve the recognition rate up to 97.1% and 94.7% for both Flavia and Swedish dataset respectively; while taking less execution time.

Cite This Paper

Heba F. Eid, Ashraf Darwish, "Variant-Order Statistics based Model for Real-Time Plant Species Recognition", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.9, pp. 77-84, 2017. DOI: 10.5815/ijitcs.2017.09.08


[1]G. Agarwal, P. Belhumeur, and S. Feiner, “First steps toward an electronic field guide for plants.” Taxon, vol. 55, no. 3, pp. 597–610, 2006.

[2]P. N. Belhumeur, D. Chen, and S. Feiner, “Searching the world’s herbaria:a system for visual identification of plant species,” In: Computer Vision, ECCV , Part IV, LNCS 5305, pp. 116–129, 2008.

[3]Z. Wang, H. Li, Y. Zhu, and T. Xu, “Review of plant identification based on image processing,” Archives of Computational Methods in Engineering, pp. 1–18, 2016.

[4]M. E. Nilsback and A. Zisserman, “An automatic visual flora: Segmentation and classification of flower images,” Ph.D. dissertation, Oxford University, 2009.

[5]S. Fiel and R. Sablatnig, “Automated identification of tree species from images of the bark, leaves and needles,” in Proc. In. 16th Computer Vision Winter Workshop, Mitterberg, Austria, 2011, pp. 1–6.

[6]X. F. Wang, D. S. Huang, J. X. Du, H. Xu, and L. Heutte, “Classification of plant leaf images with complicated background.” Applied Mathematics and Computation, vol. 205, no. 2, pp. 916–926, 2008.

[7]N. Valliammal and S. N. Geethalakshmi, “Analysi of the classification techniques for plant identification through leaf recognition,” Int. J. of Data Mining Knowledge Engineering, vol. 1, no. 5, pp. 239–243, 2009.

[8]M. Kumar, M. Kamble, S. Pawar, P. Patil, and N. Bonde, “Survey on techniques for plant leaf classification.” Int J. of Modern Engineering Research, vol. 1, no. 2, pp. 538–544, 2011.

[9]J. S. Cope, D. P. A. Corney, J. Y. Clark, P. Remagnino, and P. Wilkin, “Plant species identification using digital morphometrics: A review.’,” Expert Syst Appl, vol. 39, no. 8, pp. 7562–7573, 2012.

[10]T. Suk, J. Flusser, and P. Novotny, “Comparison of leaf recognition by moments and fourier descriptors,” Computer Analysis of Images and Patterns Lecture Notes in Computer Science, vol. 8047, pp. 221–228, 2013.

[11]H. F. Eid, “Performance improvement of plant identification model based on pso segmentation,” Int. J Intelligent Systems and Applications, vol. 8, pp. 53– 58, 2016.

[12]B. L. Chance and A. J. Rossman, Investigating Statistical Concepts, Applications, and Methods. Duxbury Press, 2005.

[13]J. Flusser and T. Suk, “Rotation moment invariants for recognition of symmetric objects,” IEEE Trans Image Proc, vol. 15, no. 12, pp. 3784–3790, 2006.

[14]F. Chaumette, “Image moments: A general and useful set of features for visual servoing,” IEEE Trans on robotic, vol. 20, no. 4, pp. 713–723, 2004.

[15]A. H. Kulkarni, H. M. Rai, K. A. Jahagirdar, and P. S. Upparamani, “Leaf recognition technique for plant classification using rbpnn and zernike moments.’, int,” J. of Advanced Research in Computer and Communication Engineering, vol. 2, no. 1, pp. 984–988, 2013.

[16]R. M. Haralick, K. Shanmugam, and D. I., “Textural features for image classification.” IEEE Trans. Syst.Man Cybern Smc, vol. Smc3, no. 6, pp. 610–621, 1973.

[17]M. M. Galloway, “Texture analysis using gray level run lengths,” Computer Graphics Image Processing, vol. 4, no. 2, pp. 172–179, 1975.

[18]F. Albregsten, Statistical texture measures computed from gray level run-length matrices. Department of Informatics, University of Oslo, Norway: Technical Note, 1995.

[19]M. Tuceryan and A. K. Jian, The Handbook of Pattern Recognition and Computer Vision, 2nd ed. World Scientific Publishing, 1998.

[20]A. Chu, C. M. A. Sehgal, and G. J. F., “Use of gray value distribution of run lengths for texture analysis,” Pattern Recognition Letters, vol. 11, no. 6, pp. 415– 419, 1990.

[21]E. Wu and F. X. Q. L. Wang, X.and Chang, “A leaf recognition algorithm for plant classification using probabilistic neural network,” in The 7th IEEE International Symposium on Signal Processing and Information Technology. Cairo, Egypt: IEEE, July 2007, pp. 1–7.

[22]O. Soderkvist, “Computer vision classification of leaves from swedish trees.” Master’s thesis, Linkping University, 2001.

[23]R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. USA: JohnWiley & Sons, 2001.

[24]Y. kumar, G. Sahoo, “Study of Parametric Performance Evaluation of Machine Learning and Statistical Classifiers”. I.J. Information Technology and Computer Science, vol.5, no. 6 , pp. 57-64, 2013. 

[25]A. Kadir, L. E. Nugroho, and P. Santosa, “Leaf classification using shape, color, and texture.” Int J. of Computer Trends & Technology, vol. 1, pp. 306–311, 2011.

[26]A. Kadir, “Experiments of zernike moments for leaf identification.” J. of Theoretical and Applied Information Technology, vol. 41, no. 1, pp. 82–93, 2012.

[27]A. H. Kulkarni, H. M. Rai, K. A. Jahagirdar, and P. S. Upparamani, “a leaf recognition technique for plant classification using rbpnn and zernike moments.’, int, J. of Advanced Research in Computer and Communication Engineering, vol. 2, no. 1, pp. 984–988, 2013.

[28]A. Aakif and M. F. Khan, “Automatic classification of plants based on their leaves,” Biosystems Engineering, vol. 139, pp. 66–75, 2015.

[29]H. Ling and D. W. Jacobs, “Shape classification using the inner-distance,” IEEE Trans Pattern Anal. Mach. Intell., vol. 9, no. 2, pp. 286–299, 2007.

[30]M. Sainin and R. Alfred, “Nearest neighbour distance matrix classification.” in Pro. In. 6th Conference on Advanced Data Mining and Applications: Part I. China, Springer-Verlag: Chongqing, 2010, pp. 114–124.

[31]Y. Lei, J. Zou, T. Dong, Z. You, Y. Yuan, and Y. Hu, “Orthogonal locally discriminant spline embedding for plant leaf recognition,” Computer Vision and Image Understanding, vol. 119, pp. 116–126, 2014.