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British Journal of Mathematics & Computer Science, ISSN: 2231-0851,Vol.: 9, Issue.: 3


A Neuronal Classification System for Plant Leaves Using Genetic Image Segmentation


Babatunde Oluleye1,2*, Armstrong Leisa1, Diepeveen Dean3 and Leng Jinsong4
1School of Computer and Security Science, Edith Cowan University, Perth, Western Australia.
2Department of Information and Communication Technology, Osun State University, Osogbo, Osun State, Nigeria.
3Department of Agriculture and Food, Perth, Government of Western Australia.
4Security Research Institute, Edith Cowan University, Perth, WA, Australia.

Article Information
(1) Victor Carvalho, Polytechnic Institute of Cvado and Ave, Portuguese Catholic University and Lusiada University, Portugal.
(2) Tian-Xiao He, Dept of Mathematics and Computer Science, Illinois Wesleyan University, USA.
(1) Anonymous, Chongqing University of Technology, China.
(2) Gulshan Kumar, Dept of Computer Applications, SBS State Technical Campus, India.
(3) Anonymous, Malaysia.
(4) Anonymous, Lebanon.
(5) Anonymous, Lodz University of Technology, Poland.
Complete Peer review History: http://www.sciencedomain.org/review-history/9437


This paper demonstrates the use of radial basis networks (RBF), cellular neural networks (CNN) and genetic algorithm (GA) for automatic classication of plant leaves. A genetic neuronal system herein attempted to solve some of the inherent challenges facing current software being employed for plant leaf classication. The image segmentation module in this work was genetically optimized to bring salient features in the images of plants leaves used in this work. The combination of GA-based CNN with RBF in this work proved more ecient than the existing systems that use conventional edge operators such as Canny, LoG, Prewitt, and Sobel operators. The results herein showed that GA-based CNN edge detector outperforms other edge detector in terms of speed and classication accuracy.

Keywords :

Radial Basis Networks; Cellular Neural Networks; Genetic Algorithm.

Full Article - PDF    Page 261-278

DOI : 10.9734/BJMCS/2015/14611

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