A neuronal classification system for plant leaves using genetic image segmentation

Authors

Oluleye Babatunde, Edith Cowan University
Leisa Armstrong, Edith Cowan University
Dean Diepeveen, Department of Agriculture and Food, WAFollow
Jinsong Leng, Edith Cowan University

Document Type

Article

Publication Date

5-26-2015

Journal Title

Journal of Advances in Mathematics and Computer Science

ISSN

ISSN: 2231-0851

Keywords

Radial basis networks, cellular neural networks, genetic algorithm

Disciplines

Agricultural Science | Agronomy and Crop Sciences | Computer Sciences

Abstract

This paper demonstrates the use of radial basis networks (RBF), cellular neural networks (CNN)and genetic algorithm (GA) for automatic classification of plant leaves. A genetic neuronal system herein attempted to solve some of the inherent challenges facing current software being employed for plant leaf classification. 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 efficient 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 classification accuracy.

Recommended Citation

Oluleye, Babatunde, Armstrong Leisa, Diepeveen Dean, and Leng Jinsong. 2015. “A Neuronal Classification System for Plant Leaves Using Genetic Image Segmentation”. Journal of Advances in Mathematics and Computer Science 9 (3):261-78. https://doi.org/10.9734/BJMCS/2015/14611.