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.
RAS ID
20791
Document Type
Journal Article
Date of Publication
2015
School
School of Science
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Publisher
Science Domain International
Recommended Citation
Babatunde, O., Armstrong, L., Diepeveen, D., & Leng, J. (2015). A neuronal classification system for plant leaves using genetic image segmentation. DOI: https://doi.org/10.9734/BJMCS/2015/14611
Comments
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. Available here