The downfall of agriculture is highly rampant in many developing countries such as Ethiopia, Pakistan, Bangladesh, Afghanistan, Eritrea, India and others. This is so a great focus in our country’s strategic plan for contributing growth of economy. There are many issues to decline this potential field, viz. weather, shortage of rain, pollution and diseases. However, Enset crops in Ethiopia are attacked by numerous insect pests and diseases which have been one of the difficulties in the development of agricultural sector. To handle these problems, experienced farmers and domain experts should only use visual inspection for the diagnosis of such plant diseases in-place. This has defects due to lowering the accuracy rate when compare to soft computing approaches. This research dealt with an intelligent based image processing techniques for Enset disease diagnosis to examine the various Enset leaf diseases. In order to create the knowledge base system, a total of 570 sample Enset images for the three diseases including Enset bacterial wilt, Enset black sigatok and Enset panama wilt are employed and this real dataset was demonstrated using MatLab R2020b platform. In the first stage, the image of the Enset disease is subjected to image processing techniques. Particularly, the possibility distribution algorithm applied to enhance the contrast of inputted image, followed the Otsu method used to select region of interest and then features such as GLCM, color and shape are extracted. Next a comparative analysis was made using various machine learning algorithms to identify each class labels based on the trained patterns. The developed system can successfully identify the examined Enset diseases using ANN and Kernel RBF with an accuracy of 91.8% and 79.41% respectively.
| Published in | American Journal of Neural Networks and Applications (Volume 8, Issue 1) |
| DOI | 10.11648/j.ajnna.20220801.12 |
| Page(s) | 6-11 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2022. Published by Science Publishing Group |
Enset Diseases, Possibility Distribution Algorithm, Otsu Method, Color, GLCM, Shape, Kernel-RBF, ANN
| [1] | Avneet Pannu, "Survey on Expert System and its Research Areas," International Journal of Engineering and Innovative Technology, vol. 4, no. 10, April 2015. |
| [2] | Abraham, A. "Rule-based Expert Systems. In: Hand book of Measuring System Design (eds: Sydenham, P. H. and Thorn, R.)," 2009. |
| [3] | Shiferaw. et. al, Identification of Crop Production Constraints and Technology Needs in H1 Agro-Ecology of Shishir Pa in South Ari District of South Omo Zone, Ethiopia. International Journal of Research Granthaalayah. A Knowledge Repository, vol. 3, no. 1, January, 2015. |
| [4] | E. Alehegn. ”Maize Leaf Diseases Recognition and Classification Based on Imaging and Machine Learning Techniques.” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 5, 12, Dec 2017. |
| [5] | G. Tigistu.“Automatic Flower Disease Identification Using Image Processing. M.Sc. Thesis, Addis Ababa University, Addis Ababa, Ethiopia, 2015. |
| [6] | A. Debasu et. al. “Ethiopian Coffee Plant Diseases Recognition Based on Imaging and Machine Learning Techniques.” International Journal of Database Theory and Application, Vols. 9 (4), pp. 79-88, 2016. |
| [7] | Kibru Abera Geinore, 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. |
| [8] | Combined Local Color and Texture Analysis of Stained Cells. Harm, H., and Gunzer, U. 1986, computer vision, graphics, and Image Processing, Vol. 33, pp. PP. 364-376. |
| [9] | Quimio, A. and Mesfine T. “Diseases of enset.” Proceedings of the International Work, 1996. |
| [10] | Gizachew. W. Mechael, "Variation in isolates of Enset wilt pathogen and reaction of enset clones to this disease," Addis Ababa, Appril, 2000. |
| [11] | Luis Pérez-Vicente (PhD). et. al "Technical Manual Prevention and diagnostic of Fusarium Wilt of banana caused by Fusarium oxysporum f. sp. cubense Tropical Race 4," Food and Agriculture Organization of the United Nations, May 2014. |
| [12] | M. G. Welde. “Variation in isolates of Enset wilt pathogen and reaction of enset clones to this disease.” Addis Ababa, Ethiopia, App 2000. |
| [13] | Luis Pérez-Vicente PhD, Miguel A. Dita, PhD and Einar Martínez- de la Parte, MSc. “Technical Manual Prevention and diagnostic of Fusarium Wilt of Enset caused by Fusarium oxysporum f. sp. cubense Tropical Race 4.” Food And Agriculture Organization of the United Nations. May 2014. |
| [14] | A Comparative Study on Digital Mamography Enhancement Algorithms Based on Fuzzy Theory. Aboul Ella Hassanien. march 2003, Studies in Informatics and Control,, Vols. 12, No. 1. |
| [15] | Computer vision approaches to medical image analysis. Lecture Notes in Computer Science. Beichel, et al.,. Springer, Berlin, Vol. 4241. |
| [16] | Which is the best multiclass SVM method? An empirical study, Multiple Classifier Systems. Duan et al. 2005, Springer,, pp. pp. 278-285. |
| [17] | A comparison of methods for multiclass support vector machines and Neural Networks,. Hsu, C.-W., Lin, C.-J.,. 2002, IEEE Transactions on 13,, pp. 415-425. |
APA Style
Kibru Abera Geinore. (2022). Intelligent Based Image Processing Model for Ethiopian Enset Diseases Diagnosis. American Journal of Neural Networks and Applications, 8(1), 6-11. https://doi.org/10.11648/j.ajnna.20220801.12
ACS Style
Kibru Abera Geinore. Intelligent Based Image Processing Model for Ethiopian Enset Diseases Diagnosis. Am. J. Neural Netw. Appl. 2022, 8(1), 6-11. doi: 10.11648/j.ajnna.20220801.12
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ajnna.20220801.12,
author = {Kibru Abera Geinore},
title = {Intelligent Based Image Processing Model for Ethiopian Enset Diseases Diagnosis},
journal = {American Journal of Neural Networks and Applications},
volume = {8},
number = {1},
pages = {6-11},
doi = {10.11648/j.ajnna.20220801.12},
url = {https://doi.org/10.11648/j.ajnna.20220801.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20220801.12},
abstract = {The downfall of agriculture is highly rampant in many developing countries such as Ethiopia, Pakistan, Bangladesh, Afghanistan, Eritrea, India and others. This is so a great focus in our country’s strategic plan for contributing growth of economy. There are many issues to decline this potential field, viz. weather, shortage of rain, pollution and diseases. However, Enset crops in Ethiopia are attacked by numerous insect pests and diseases which have been one of the difficulties in the development of agricultural sector. To handle these problems, experienced farmers and domain experts should only use visual inspection for the diagnosis of such plant diseases in-place. This has defects due to lowering the accuracy rate when compare to soft computing approaches. This research dealt with an intelligent based image processing techniques for Enset disease diagnosis to examine the various Enset leaf diseases. In order to create the knowledge base system, a total of 570 sample Enset images for the three diseases including Enset bacterial wilt, Enset black sigatok and Enset panama wilt are employed and this real dataset was demonstrated using MatLab R2020b platform. In the first stage, the image of the Enset disease is subjected to image processing techniques. Particularly, the possibility distribution algorithm applied to enhance the contrast of inputted image, followed the Otsu method used to select region of interest and then features such as GLCM, color and shape are extracted. Next a comparative analysis was made using various machine learning algorithms to identify each class labels based on the trained patterns. The developed system can successfully identify the examined Enset diseases using ANN and Kernel RBF with an accuracy of 91.8% and 79.41% respectively.},
year = {2022}
}
TY - JOUR T1 - Intelligent Based Image Processing Model for Ethiopian Enset Diseases Diagnosis AU - Kibru Abera Geinore Y1 - 2022/06/21 PY - 2022 N1 - https://doi.org/10.11648/j.ajnna.20220801.12 DO - 10.11648/j.ajnna.20220801.12 T2 - American Journal of Neural Networks and Applications JF - American Journal of Neural Networks and Applications JO - American Journal of Neural Networks and Applications SP - 6 EP - 11 PB - Science Publishing Group SN - 2469-7419 UR - https://doi.org/10.11648/j.ajnna.20220801.12 AB - The downfall of agriculture is highly rampant in many developing countries such as Ethiopia, Pakistan, Bangladesh, Afghanistan, Eritrea, India and others. This is so a great focus in our country’s strategic plan for contributing growth of economy. There are many issues to decline this potential field, viz. weather, shortage of rain, pollution and diseases. However, Enset crops in Ethiopia are attacked by numerous insect pests and diseases which have been one of the difficulties in the development of agricultural sector. To handle these problems, experienced farmers and domain experts should only use visual inspection for the diagnosis of such plant diseases in-place. This has defects due to lowering the accuracy rate when compare to soft computing approaches. This research dealt with an intelligent based image processing techniques for Enset disease diagnosis to examine the various Enset leaf diseases. In order to create the knowledge base system, a total of 570 sample Enset images for the three diseases including Enset bacterial wilt, Enset black sigatok and Enset panama wilt are employed and this real dataset was demonstrated using MatLab R2020b platform. In the first stage, the image of the Enset disease is subjected to image processing techniques. Particularly, the possibility distribution algorithm applied to enhance the contrast of inputted image, followed the Otsu method used to select region of interest and then features such as GLCM, color and shape are extracted. Next a comparative analysis was made using various machine learning algorithms to identify each class labels based on the trained patterns. The developed system can successfully identify the examined Enset diseases using ANN and Kernel RBF with an accuracy of 91.8% and 79.41% respectively. VL - 8 IS - 1 ER -
Information
Email: