On the Application of Genetic Probabilistic Neural Networks and Cellular Neural Networks in Precision Agriculture

Authors

  • Babatunde Oluleye eAgriculture Research Group, Edith Cowan University, Perth, WA, Australia
  • Armstrong Leisa
  • Leng Jinsong
  • Diepeveen Dean

Abstract

This article details the effect of Gaussian smoothing parameter (spread) on the performance of Probabilistic Neural Networks (PNN). Two (2) different Genetic Algorithms (GAs) were used to optimize the PNN spread in order to avoid under and over fitting. In this work there is a novel combination of Cellular Neural Networks (CNN), Probabilistic Neural Networks (PNN) and GA to address the present challenges on automatic identification of plant species. Such problems include misclassification species of plants that are similar in shapes and image segmentation speed. In this work, GA was used in both feature selection and PNN parameter optimization.  The GA developed herein improved the performance of the PNN. This work serves as a framework for building image classification or pattern recognition system.

Author Biography

Babatunde Oluleye, eAgriculture Research Group, Edith Cowan University, Perth, WA, Australia

PhD Research student

References

H O Babatunde, C O Akanbi, O G Fadare, A A Eludire, O B Aluko, and O G Egbedokun. On numerical simulation of a boundary-valued neuronal model. World J of Engineering and Pure and Applied Sci, WJEPAS, 20(2):20–25, 2012.

Oluleye Babatunde, L. Armstrong, J Leng, and D Diepeveen. Zernike moments and genetic algorithm: Tutorial and application. British Journal of Mathematics and Computer Science, 4(15):2217–2236, 2014.

Babatunde Oluleye, Armstrong Leisa, Leng Jinsong, and Diepeveen Dean (2014). A Genetic

Algorithm-Based Feature Selection. International Journal of Electronics and Communication and Computer

Engineering, IJECCE 5(4); 889-905

T Z Charles and Z R Ralph. Fourier descriptors for plane closed curves. IEEE Transactions on Computers, C-21(3):269–281, 1972.

Pornpanomchai Chomtip, Kuakiatngam Chawin, Supapathranon Pitchayuk, and Siriwisesokul Nititat. Leaf and flower recognition system (e-botanist). IACSIT International Journal of Engineering and Technology, 3(4):10–15, 2011.

L O Chua and T Roska. Cellular neural networks and vision computing. Cambridge University Press, 2002.

L O Chua and L Yang. Cellular neural networks: theory and applications. IEEE Trans on Circuits and System, 35(10):1257–1272, 1988.

B Clarke, E Fokoue, and H Zhang. Principles and theory for data mining and machine learning. Springer Series in Statistics;

http://www.amazon.com/Principles-Machine-Learning-Springer-Statistics/dp/0387981349:Page 798, 2009.

James S Cope, David Corney, Jonathan Y Clark, and Paul W Remagnino. Plant species identification using digital morphometrics: A review. Digital Imaging Research Centre, Kingston University,London,UK and Department of Computing, University of Surrey, Guildford Surrey, UK, pages 1–21, 2011.

Z Dengsheng and Lu Guojun. A comparative study on shape retrieval using fourier descriptors with different shape signatures. Gippsland School of Computing and Information Technology,Monash University,Australia, 2000.

R Ercsey, M Maria, Z Nda, and T Roska. Statistical physics on cellular neural network computers. Physica D: Nonlinear Phenomena, 237(9):2051–2068, 2008.

J. B. Fourier. The Analytical Theory of Heat. The Universal Press, 1878.

B Hezekiah, A T Akinwale, and O Folorunso. A cellular neural networks- based model for edge detection. Journal of Information and Computing Science, 5(1):003–010, 2010.

MathWorks. Matlab neural network toolbox documentation. MathWorks. Inc. [Online]. Available:, 2007.

Mathworks. Signal processing toobox (discrete fourier transform (dft)). MathWorks. Inc, 2009.

K Meeta, K Mrunali, P Shubhada, P Prajakta, and B Neha. Survey on techniques for plant leaf classification. International Journal of Modern Engineering Research (IJMER), 1(2):538–544, 2012.

K K Pahalawatta. A plant identification system using both global and local features of plant leaves. MSc Thesis at the department of Computer Science and Software Engineering, University of Canterbury, New Zealand, pages 1–127, 2008.

Emanuel Parzen. On estimation of a probability density function and mode. Annals of Mathematical Statistics, Vol 33, Issue 3:1065–1076, 1962.

T Roska, A Zarandy, and C Rekeczky. Cellular neural networks. CRC Press LLC, 2003.

D F Specht. Probabilistic neural networks for classification, mapping, or associative memory. IEEE International Conference on Neural Networks, 1(2):525–532, 1988.

M.R Teague. Image analysis via the general theory of moments. J. Optical Soc. Am. 70, page 920-930, 1980.

Arif Thawar, Krekor Zyad Shaaban, Lala, and Baba Sami. Object classification via geometric, zernike and legendre moments. Journal of Theoretical and Applied Information Technology, Vol 7. No 1:31–37, 2009.

Karrels Tyler. Fourier descriptors: Properties and utility in leaf classification. ECE 533 Fall 2006

B.; Stefan L.; Karsten B. & Andreas Z. Urilch, W.; Peter. Plant species classication using a 3d lidar sensor and machine learning. Ninth International Conference on Machine Learning and Applications, pages 339–345, 2010.

Stephen Gang Wu, Forrest Sheng Bao, Eric You Xu, Yu-Xuan Wang, Yi-Fan Chang, and Qiao-Liang Xiang. A leaf recognition algorithm for plant classification using probabilistic neural network. IEEE 7th International Symposium on Signal Processing and Information Technology, Cario, Egypt; ArXiv 0707.4289 v1 [ CS.AI], 2007.

Yonqing Xin, Pawlak Miroslaw, and X L. Simon. Image reconstruction with polar zernike moments. ICARPR 2005, LNCS 3687, pages 394–403, 2005.

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Published

2014-08-15

How to Cite

Oluleye, B., Leisa, A., Jinsong, L., & Dean, D. (2014). On the Application of Genetic Probabilistic Neural Networks and Cellular Neural Networks in Precision Agriculture. Asian Journal of Computer and Information Systems, 2(4). Retrieved from https://ajouronline.com/index.php/AJCIS/article/view/1578

Issue

Vol. 2 No. 4 (2014): August 2014

Section

Articles

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