On the application of genetic probabilistic neural networks and cellular neural networks in precision agriculture
Document Type
Article
Publication Date
8-2014
Journal Title
Asian Journal of Computer and Information Systems
ISSN
ISSN: 2321 – 5658
Keywords
Probabilistic Neural Networks, Cellular Neural Networks, Genetic Algorithm, Feature Extraction
Disciplines
Agricultural Science | Agronomy and Crop Sciences | Applied Mathematics | Mathematics
Abstract
This article details the effect of Gaussian smoothing parameter (spread) on the performance of Probabilistic Neural Networks (PNN). Two (2) different Genetic Algorithms (GAs) were used to optimize the PNN spread in order to avoid under and over fitting. In this work there is a novel combination of Cellular Neural Networks (CNN), Probabilistic Neural Networks (PNN) and GA to address the present challenges on automatic identification of plant species. Such problems include misclassification species of plants that are similar in shapes and image segmentation speed. In this work, GA was used in both feature selection and PNN parameter optimization. The GA developed herein improved the performance of the PNN. This work serves as a framework for building image classification or pattern recognition system.
Recommended Citation
Oluleye, B., Leisa, A., Jinsong, L., & Dean, D. (2014). On the Application of Genetic Probabilistic Neural Networks and Cellular Neural Networks in Precision Agriculture. Asian Journal of Computer and Information Systems, 2(4). Retrieved from https://www.ajouronline.com/index.php/AJCIS/article/view/1578
