Welcome to International Network for Natural Sciences | INNSpub

Leaf identification of sesame varieties using artificial neural networks (MLP and Neuro-Fuzzy)

Research Paper | May 1, 2013

| Download 6

Alireza Pazoki, Zohreh Pazoki, Farzad Paknejad

Key Words:

Int. J. Biosci.3( 5), 108-116, May 2013

DOI: http://dx.doi.org/10.12692/ijb/3.5.108-116


IJB 2013 [Generate Certificate]


This study focused on the identification of sesame leaf varieties using two artificial neural networks. Artificial neural network (ANN) is one of the efficient ways for solving complex problems such as identification tasks. This research was done in Islamic Azad University, Shahr-e-Rey Branch, during 2011 on 7 main sesame leaf varieties (Darab14, Dashtestan, Karaj1, Naz, Oltan, Varamin and Yekta) were grown in Varamin region of Iran. Different types of features (morphological, color, shape and chlorophyll) were extracted from color images using various methods. A multi layer perceptron (MLP) and Neuro-Fuzzy neural network were applied to classify leaf varieties. The MLP topological structure consisted of 42 input neurons, 7 output neurons and two hidden layers. The applied Neuro-fuzzy classifier had input and output layers as MLP and 60 rules instead of hidden layers. The identification accuracies computed 88.43% and 87.34% by MLP and Neuro-Fuzzy classifiers consequently, so the MLP classifier had better performance for classifying sesame leaf varieties.


Copyright © 2013
By Authors and International Network for
Natural Sciences (INNSPUB)
This article is published under the terms of the Creative
Commons Attribution Liscense 4.0

Leaf identification of sesame varieties using artificial neural networks (MLP and Neuro-Fuzzy)

Arnon DI. 1949. Copper enzymes in isolated chloroplasts. Polyphenoloxidase in Beta vulgaris. Plant Physiology 24, 1–15. Doi: http://dx.doi.org/ 10.1104/p.24.1.1

Chen X, Xun Y, Li W, Zhang, J. 2010. Combining discriminant analysis and neural networks for corn variety identification. Computers and Electronics in Agriculture 71S, S48-S53.

Du JX, Wang, XF, Zhang, GJ. 2007. Leaf shape based plant species recognition. Applied Mathematics and Computation, vol. 185.

Fu H, Chi Z. 2006. Combined thresholding and neural network approach for vein pattern extraction from leaf images,” IEE Proceedings-Vision, Image and Signal Processing, vol. 153. Doi: http://dx.doi.org/10.1049/ip-vis:20060061

Fu H, Chi Z. 2003. A two-stage approach for leaf vein extraction,” in Proceedings of IEEE International Conference on Neural Networks and Signal Processing, Nanjing, China.

Gu X, Du JX, Wang XF. 2005. Leaf recognition based on the combination of wavelet transform and gaussian interpolation, in Proceedings of International Conference on Intelligent Computing 2005, ser. LNCS 3644, Springer.

Heymans BC, Onema JP, Kuti JO. 1991. A neural network for opuntia leaf-form recognition,” in Proceedings of IEEE International Joint Conference on Neural Networks. Doi: http://dx.doi.org/10.1109/IJCNN.1991.170700.

Hong SM, Simpson, B, Baranoski, GVG. 2005. Interactive venationbased leaf shape modeling, Computer Animation and Virtual Worlds, vol. 16. Doi: http://dx.doi.org/10.1002/cav.88

Image Processing Toolbox for O-Matrix. 2007. Reference manual, Version 1.0, Anona Labs Ltd, www.anonalabs.com , 5 Sep 2010.

Kantardzic  M.  2003.  Data  Mining  Concepts, Models, Methods, and Algorithms. IEEE, Piscataway, NJ, USA.

Li Y, Zhu Q, Cao Y, Wang C. 2005. A leaf vein extraction method based on snakes technique,” in Proceedings of IEEE International Conference on Neural Networks and Brain.

Nam Y, Hwang E, Byeon K. 2005. Elis: An efficient leaf image retrieval system, in Proceedings of International Conference on Advances in Pattern Recognition 2005, ser. LNCS 3687, Springer.

Ohta Y. 1985. Knowledge-Based Interpretation of Outdoor Natural Color Scenes. Pitman Publishing Inc, Marshfield, MA.

Paliwal J, Visen NS, Jayas DS. 2001. Evaluation of Neural Network Architectures for Cereal Grain Classication using Morphological Features. Journal of agricultural engineering research 79(4), 361-370.

Pazoki AR, Pazoki Z. 2011. Classification system for rain fed wheat grain cultivars using artificial neural network. African Journal of Biotechnology, 10(41), 8031-8038. Doi: http://dx.doi.org/10.5897/AJB11.488

Qi H, Yang JG. 2003. Sawtooth feature extraction of leaf edge based on support vector machine, in Proceedings of the Second International Conference on Machine Learning and Cybernetics.

Rumelhart DE, McClell JL, Williams RJ. 1986. Parallel Recognition in Modern Computers, in Processing: Explorations in the Microstructure of Cognition, vol. 1. MIT Press, Foundations, Cambridge, MA. For an edited book.

Rutkowaska D, Starczewski A. 2004. A Multi-NF approach with a hybrid learning algorithm for classification. Machine Intelligence. World Scientific Publishing Co. Pte. Ltd. For an edited book.

Symons SJ, Fulcher RG. 1988a. Determination of wheat kernel morphological variation by digital image  analysis,  I  Variation  in  eastern  Canadian milling quality wheats. Journal of Cereal Science, 8, 211–218. http://dx.doi.org/10.1016/S0733-5210(88)80032-8

Umbaugh SE. 2005. Computer Imaging: Digital Image Analysis and Processing. Taylor & Francis, New York.

Wang LX. 1997. Design of uzzy systems using gradient descent training. A course in fuzzy systems & control. Prentice Hall international.168- 179. Inc Press.

Wu S, Bao F, Xu E, Wang Y, Chang Y, Xiang Q. 2007. A leaf recognition algorithm for plant classification using probabilistic neural network, in Proceedings of 2007 IEEE International Symposium on Signal Processing and Information Technology, Giza.

Zhao-Yan L, Fang C, Yi-bin Y, Xiu-qin R. 2005. Identification of rice seed varieties using neural network. Journal of Zhejiang University Science 6B(11), 1095-1100.