A Neuronal Classification System for Plant Leaves Using Genetic Image Segmentation
Babatunde Oluleye *
School of Computer and Security Science, Edith Cowan University, Perth, Western Australia and Department of Information and Communication Technology, Osun State University, Osogbo, Osun State, Nigeria.
Armstrong Leisa
School of Computer and Security Science, Edith Cowan University, Perth, Western Australia.
Diepeveen Dean
Department of Agriculture and Food, Perth, Government of Western Australia.
Leng Jinsong
Security Research Institute, Edith Cowan University, Perth, WA, Australia.
*Author to whom correspondence should be addressed.
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.
Keywords: Radial Basis Networks, Cellular Neural Networks, Genetic Algorithm.