Document Type

Journal Article

Publisher

Science Domain International

School

School of Science

RAS ID

20791

Comments

Originally published as: Babatunde,O.H., Armstrong, L., Diepeveen, D., & Leng,J. (2015). A neuronal classification system for plant leaves using genetic image segmentation. British Journal of Mathematics & Computer Science. 9(3) 261-278. Original article available here

Abstract

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.

DOI

10.9734/BJMCS/2015/14611

Access Rights

free_to_read

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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