PLANT SPECIE CLASSIFICATION USING SINUOSITY COEFFICIENTS OF LEAVES

Authors

  • Jules R Kala University of KwaZulu-Natal School of Mathematics, Statistics and Computer Science
  • Serestina Viriri University of KwaZulu-Natal, School of Maths, Statistics & Computer Science

DOI:

https://doi.org/10.5566/ias.1821

Keywords:

leaf recognition, plant classification, sinuosity coefficients, sinuosity measure

Abstract

Forests are the lungs of our planet. Conserving the plants may require the development of an automated system that will identify plants using leaf features such as shape, color, and texture. In this paper, a leaf shape descriptor based on sinuosity coefficients is proposed. The sinuosity coefficients are defined using the sinuosity measure, which is a measure expressing the degree of meandering of a curve. The initial empirical experiments performed on the LeafSnap dataset on the usage of four sinuosity coefficients to characterize the leaf images using the Radial Basis Function Neural Network (RBF) and Multilayer Perceptron (MLP) classifiers achieved accurate classification rates of 88% and 65%, respectively. The proposed feature extraction technique is further enhanced through the addition of leaf geometrical features, and the accurate classification rates of 93% and 82% were achieved using RBF and MLP, respectively. The overall results achieved showed that the proposed feature extraction technique based on the sinuosity coefficients of leaves, complemented with geometrical features improve the accuracy rate of plant classification using leaf recognition.

References

von Linne C. Systema naturae; sive, Regna tria naturae: systematice proposita per classes, ordines, genera & species. Haak; 1735.

Partington CF. The British Cyclopdia of Natural History: Combining a Scientific Classification of Animals, Plants, and Minerals. Orr & Smith; 1837.

Cope JS, Corney D, Clark JY, Remagnino P, Wilkin P. Plant species identification using digital morphometrics: A review. Expert Systems with Applications. 2012 Jun 15;39(8):7562-73.

Tanguy A, Peuchot B. Research Into Spinal Deformities 3. IOS Press; 2002.

Langbein WB, Leopold LB. River Meanders-Theory of Minimum Variance , USGS Prof. Paper 422-H. 1966;15:1966.

Kala JR, Viriri S, Moodley D. Sinuosity Coefficients for Leaf Shape Characterisation. In Advances in Nature and Biologically Inspired Computing 2016 (pp. 141-150). Springer International Publishing.

Naing L, Winn T, Rusli BN. Practical issues in calculating the sample size for prevalence studies. Archives of orofacial Sciences. 2006;1(1):9-14.

Wu SG, Bao FS, Xu EY, Wang YX, Chang YF, Xiang QL. A leaf recognition algorithm for plant classification using probabilistic neural network. In Signal Processing and Information Technology, 2007 IEEE International Symposium on 2007 Dec 15 (pp. 11-16). IEEE.

Kuhl FP, Giardina CR. Elliptic Fourier features of a closed contour. Computer graphics and image processing. 1982 Mar 1;18(3):236-58.

Mallah C, Cope J, Orwell J. Plant leaf classification using probabilistic integration of shape, texture and margin features. Signal Processing, Pattern Recognition and Applications. 2013 Feb;5:1.

Kalyoncu C, Toygar . Geometric leaf classification. Computer Vision and Image Understanding. 2015 Apr 30;133:102-9.

Lei YK, Zou JW, Dong T, You ZH, Yuan Y, Hu Y. Orthogonal locally discriminant spline embedding for plant leaf recognition. Computer Vision and Image Understanding. 2014 Feb 28;119:116-26.

Cerutti G, Tougne L, Coquin D, Vacavant A. Leaf margins as sequences: A structural approach to leaf identification. Pattern Recognition Letters. 2014

Nov 1;49:177-84.

Yanikoglu B, Aptoula E, Tirkaz C. Automatic plant identification from photographs. Machine vision and applications. 2014 Aug 1;25(6):1369-83.

Wilf P. Computer Vision Crack the Leaf Code. In 2012 GSA Annual Meeting in Charlotte 2012 Nov 6.

Cerutti G, Tougne L, Coquin D, Vacavant A. Curvature-scale-based contour understanding for leaf margin shape recognition and species identification. InInternational Conference on Computer Vision Theory and Applications

(VISAPP) 2013 Feb 22 (Vol. 1, pp. 277-284).

Bendiab E, Kholladi MK. Recognition of plant leaves using the dendritic cell algorithm. International Journal of Digital Information and Wireless

Communications (IJDIWC). 2011;1(1):284-92.

Aickelin U, Bentley P, Cayzer S, Kim J, McLeod J. Danger theory: The link between AIS and IDS?. In Artificial Immune Systems 2003 Sep 1 (pp. 147-

. Springer Berlin Heidelberg.

Fu H, Chi Z, Feng D, Song J. Machine learning techniques for ontology-based leaf classification. In Control, Automation, Robotics and Vision Conference, 2004. ICARCV 2004 8th 2004 Dec 6 (Vol. 1, pp. 681-686). IEEE.

Jaekel M, Wake DB. Developmental processes underlying the evolution of a derived foot morphology in salamanders. Proceedings of the National Academy of Sciences. 2007 Dec 18;104(51):20437-42.

Lazarus ED, Constantine JA. Generic theory for channel sinuosity. Proceedings of the National Academy of Sciences. 2013 May 21;110(21):8447-52.

Kala JR, Viriri S, Tapamo JR. An approximation based algorithm for minimum bounding rectangle computation. InAdaptive Science & Technology (ICAST), 2014 IEEE 6th International Conference on 2014 Oct 29 (pp. 1-6). IEEE.

Chaudhuri D, Samal A. A simple method for fitting of bounding rectangle to closed regions. Pattern recognition. 2007 Jul 31;40(7):1981-9.

Adetiba E, Olugbara OO. Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation. PloS one. 2015 Dec 1;10(12):e0143542.

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Published

2018-07-09

How to Cite

Kala, J. R., & Viriri, S. (2018). PLANT SPECIE CLASSIFICATION USING SINUOSITY COEFFICIENTS OF LEAVES. Image Analysis and Stereology, 37(2), 119–126. https://doi.org/10.5566/ias.1821

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Section

Original Research Paper