Abstract—For the existence of human living and development, plants play a crucial part. In order to effectively collect and preserve the genetic resources, the intra- and inter-specific variations must be estimated in proper manner. In plant classification, the leaf shape plays a significant role. In machine intelligence, the most significant part essential for both decision-making and data processing is shape recognition. In this paper, a feed forward neural network is used to automate the leaf recognition for plant classification. The classification accuracy of the proposed method Normalized Cubic Spline Feed Forward Neural Network (NCS – FFNN) is compared with RBF, CART and MLP.
Index Terms—CART, correlation based feature selection, normalized cubic spline feed forward neural network, plant leaf classification, RBF.
C. S. Sumathi is an Assistant Professor with O/o Dean (SPGS), Tamil Nadu Agricultural University, Coimbatore- 641 003, Tamil Nadu, India (e-mail: Mail: css1@tnau.ac.in).
A. V. Senthil Kumar, Director with Department of Post Graduate and Research in Computer Applications, Hindusthan College of Arts and Science, Coimbatore- 641 028, Tamil Nadu, India
Cite: C. S. Sumathi and A. V. Senthil Kumar, "Plant Leaf Classification Using Soft Computing Techniques," International Journal of Future Computer and Communication vol. 2, no. 3 pp. 196-199, 2013.
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