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.
Oluleye et al. REF combined a GA-based CNN edge detector and a RBF learning system for automatic classification of plant leaves.
A Neuronal Classification System for Plant Leaves Using Genetic Image Segmentation
British Journal of Mathematics & Computer Science
British Journal of Mathematics & Computer Science
Mathematics