Leaf Recognition Using Feature Point Extraction and Artificial Neural Network

The proposed system focuses on Leaf Recognition System using feature point extraction and artificial neural network (ANN). The leaf recognition system is based on feature point extraction. The feature point’s extraction is base on geometric centre; it compares the input leaf image with already trained leaf image. The numbers of feature points of input leaf images are matched with the already trained leaf feature points. If matched, name of plant is display otherwise system shows detection fail. The objective of this project is to identify the accurate input leaf for feature extraction; two schemes are 28 and 60 feature point extraction. As Feature points are increases recognition rate decreases because of complexity and time require for training and testing is more. Comparative analysis has been made with the three schemes. The first scheme comparison with 28 and 60 feature point extraction with respect to recognition rate, the second scheme is comparison of time require for feature extraction and training time and third scheme is comparison with hidden layers. Results obtained by this algorithm are quite impressive. Unknown leaf samples are also eliminated in greater extent.

[1]  D. Warren.  Automated leaf shape description for variety testing in chrysanthemums , 1997 .

[2]  Rahul Sharma,et al.  An Offline Signature Verification System Using Neural Network Based on Angle Feature and Energy Density , 2011 .

[3]  G. Meyer,et al.  Color indices for weed identification under various soil, residue, and lighting conditions , 1994 .

[4]  M. V. KANAWADE,et al.  OFFLINE SIGNATURE VERIFICATION AND RECOGNITION , 2013 .

[5]  Hiroshi Okamoto,et al.  Automatic detecting system of apple harvest season for robotic apple harvesting , 2001 .

[6]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[7]  J. F. Reid,et al.  Texture-Based Weed Classification Using Gabor Wavelets and Neural Network for Real-time Selective Herbicide Applications , 2000 .

[8]  Kshitij Sisodia,et al.  Off-line Handwritten Signature Verification using Artificial Neural Network Classifier , 2009 .

[9]  Miguel Angel Ferrer-Ballester,et al.  Offline geometric parameters for automatic signature verification using fixed-point arithmetic , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Patrick van der Smagt,et al.  Introduction to neural networks , 1995, The Lancet.

[11]  B. S. El-Desouky,et al.  Plants Images Classification Based on Textural Features using Combined Classifier , 2011 .

[12]  Gaines E. Miles,et al.  Application of machine vision to shape analysis in leaf and plant identification , 1993 .

[13]  Kevin N. Gurney,et al.  An introduction to neural networks , 2018 .

[14]  Yan Li,et al.  Leaf Vein Extraction Using Independent Component Analysis , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[15]  Xiaofeng Wang,et al.  Recognition of Leaf Images Based on Shape Features Using a Hypersphere Classifier , 2005, ICIC.

[16]  Ching Y. Suen,et al.  Cursive Script Recognition: A Sentence Level Recognition Scheme , 1994 .

[17]  C. Viroli,et al.  Supervised locally linear embedding for classification : an application to gene expression data analysis Supervised locally linear embedding in problemi di classificazione : un ’ applicazione all ’ analisi di dati di espressione genica , 2005 .

[18]  De-shuang Huang,et al.  Computer-Aided Plant Species Identification (CAPSI) Based on Leaf Shape Matching Technique , 2006 .

[19]  P. Pattanasethanon,et al.  Thai botanical herbs and its characteristics: Using artificial neural network , 2012 .

[20]  Chomtip Pornpanomchai,et al.  Thai Herb Leaf Image Recognition System (THLIRS) , 2011 .

[21]  Yuxuan Wang,et al.  A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network , 2007, 2007 IEEE International Symposium on Signal Processing and Information Technology.

[22]  Paulus Insap Santosa,et al.  Leaf Classification Using Shape, Color, and Texture Features , 2013, ArXiv.

[23]  N. Z. C. Chaisattapagon,et al.  Effective criteria for weed identification in wheat fields using machine vision , 1995 .

[24]  J. V. Stafford,et al.  Potential for automatic weed detection and selective herbicide application , 1991 .

[25]  George E. Meyer,et al.  Shape features for identifying young weeds using image analysis , 1994 .

[26]  Gaines E. Miles,et al.  MACHINE VISION AND IMAGE PROCESSING FOR PLANT IDENTIFICATION. , 1986 .

[27]  James Michael Coggins,et al.  A framework for texture analysis based on spatial filtering , 1983 .

[28]  Suhail M. Odeh,et al.  Off-line signature verification and recognition: Neural network approach , 2011, 2011 International Symposium on Innovations in Intelligent Systems and Applications.

[29]  E. Franz,et al.  THE USE OF LOCAL SPECTRAL PROPERTIES OF LEAVES AS AN AID FOR IDENTIFYING WEED SEEDLINGS IN DIGITAL IMAGES , 1990 .

[30]  Lei Tian,et al.  MACHINE VISION IDENTIFICATION OF TOMATO SEEDLINGS FOR AUTOMATED WEED CONTROL , 1997 .

[31]  A. Kulkarni,et al.  Applying image processing technique to detect plant diseases , 2012 .

[32]  Sudesh Pawar,et al.  Survey on Techniques for Plant Leaf Classification , 2011 .

[33]  Xiaofeng Wang,et al.  Leaf shape based plant species recognition , 2007, Appl. Math. Comput..

[34]  Lin Kunhui,et al.  Feature extraction and automatic recognition of plant leaf using artificial neural network , 2007 .