Volume 9, Issue 1, February 2020, Page: 1-5
Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques
Kibru Abera Ganore, Department of Computer Science, Wachemo University, Addis Ababa, Ethiopia
Getahun Tigistu, Faculty of Computing and Software Engineering, Arba Minch University, Arba Minch, Ethiopia
Received: May 14, 2020;       Accepted: May 29, 2020;       Published: Jun. 17, 2020
DOI: 10.11648/j.ijiis.20200901.11      View  186      Downloads  124
Abstract
The diagnosis of diseases on the plant is a very important to provide large quantity and good qualitative agricultural products. Enset is an important food crops produced in Southern parts of the Ethiopia with great role in food security. There are several issues and diseases which try to decline the yield with quality. Particularly, diagnosis of potential diseases on Enset is based on traditional ways. The aim of this study is to design a model for Enset diseases diagnosis using Image processing and Multiclass SVM techniques. This study presented a general process model to classify a given Enset leaf image as normal or infected. The strategy of K-fold stratified cross validation was used to enhance generalization of the model. This diagnosis apply K-means clustering, color distribution, shape measurements, Gabor texture extraction and wavelet transform as key approaches for image processing techniques. The researcher selected two Enset leaf diseases viz. Bacterial Wilt and Fusarium Wilt disease and collected 430 Enset leaf images from Areka agricultural research center and some selected areas in SNNPR. For this research work MATLAB version R2017a tool was used as a platform to simulate the real world data. The proposed model demonstrated with four different kernels, and the overall result indicates that the RBF Kernel achieves the highest accuracy as 94.04% and 92.44% for bacterial wilt and fusarium wilt respectively. Therefore, an efficient practice of IT based solution in this domain will increases productivity and quality of Enset products.
Keywords
Multiclass SVM, Kernels, Enset Disease, K-means Clustering, Image Processing
To cite this article
Kibru Abera Ganore, Getahun Tigistu, Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques, International Journal of Intelligent Information Systems. Vol. 9, No. 1, 2020, pp. 1-5. doi: 10.11648/j.ijiis.20200901.11
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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