On The Application Of Genetic Probabilistic Neural Networksand Cellular Neural Networks In Precision Agriculture

Authors

Oluleye H. Babatunde, Edith Cowan UniversityFollow
Leisa Armstrong, Edith Cowan UniversityFollow
Jinsong Leng, Edith Cowan UniversityFollow
Dean Diepeveen

Document Type

Journal Article

Publisher

Asian Journal of Computer and Information Systems

Faculty

Faculty of Health, Engineering and Science

School

School of Computer and Security Science / eAgriculture Research Group

RAS ID

18465

Comments

Babatunde, O. H., Armstrong, L. , Leng, J. , & Diepeveen, D. (2014). On the Application of Genetic Probabilistic Neural Networksand Cellular Neural Networks in Precision Agriculture. Asian Journal of Computer and Information Systems, 2(4), 90-101. Available here

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.

Related Publications

Babatunde, O. H. (2015). A neuro-genetic hybrid approach to automatic identification of plant leaves. Retrieved from http://ro.ecu.edu.au/theses/1733

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 License.