Ethiopian <i>Enset</i> Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques

Kibru Abera Ganore; Getahun Tigistu

doi:doi:10.11648/j.ijiis.20200901.11

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Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques

Kibru Abera Ganore, Getahun Tigistu

Published in International Journal of Intelligent Information Systems (Volume 9, Issue 1)

Received: 14 May 2020 Accepted: 29 May 2020 Published: 17 June 2020

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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.

Published in	International Journal of Intelligent Information Systems (Volume 9, Issue 1)
DOI	10.11648/j.ijiis.20200901.11
Page(s)	1-5
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), 2020. Published by Science Publishing Group

Keywords

Multiclass SVM, Kernels, Enset Disease, K-means Clustering, Image Processing

References

[1]	MoFED “Macro-economic development in Ethiopia.” Addis Ababa, 2015.
[2]	A. Ayale and M. Yeshitila. “The response of enset (enset ventricosum) production to rate and frequency of N and P N utrients Application at Areka, in southern Ethiopia.” Journal of Economics and Sustainable Development, Vol. 2 (7), pp. 423-431, 2011.
[3]	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.
[4]	G. Tigistu.” Automatic Flower Disease Identification Using Image Processing.” M. Sc. Thesis, Addis Ababa University, Addis Ababa, Ethiopia, 2015.
[5]	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.
[6]	Quimio, A. and Mesfine T. “Diseases of enset.” Proceedings of the International Work, 1996.
[7]	M. G. Welde. “Variation in isolates of Enset wilt pathogen and reaction of enset clones to this disease.” Addis Ababa, Ethiopia, App 2000.
[8]	Luis Pérez-Vicente PhD, Miguel A. Dita, PhD and Einar Martínez- de la Parte, MSc. “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.
[9]	S. Arivazhagan, R. Newlin Shebiah, S. Ananthi, S. Vishnu Varthini. “Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture feature.” CIGR Journal, Vols. 15 (1), Mar 2013.
[10]	Saxena, L., & Armstrong, L. “A survey of image processing techniques for agriculture.” Proceedings of Asian Federation for Information Technology in Agriculture, pp. 401-413, 2014.
[11]	Prof. Patil Ashish, Patil Tanuja. Professor. “Survey on Detection and Classification of Plant Leaf Disease in Agriculture Environment.” International Advanced Research Journal in Science, Engineering and Technology, National Conference on Innovative Applications and Research in Computer Science and Engineering (NCIARCSE-2017), Vol. 4, 4, Jan 2017.
[12]	R. C. Gonzalez, R. E. Woods. Digital Image Processing. Addisen-Wesley, 1992.
[13]	Kragujevac, J. Math. Image Preprocessing Tool, Olga Miljkovic, College of Computer Science, Megatrend University of Belgrade, 11000 Novi Beograd, Serbia. 2009.
[14]	M. Sonka, V. Hlavac and R. Boyle. Image Processing. Analysis And Machine Vision. International Student Edition (3rd).
[15]	P. Sivakumar (Research Scholar), Dr. S. Meenakshi. “A Review On Image Segmentation Techniques.” (IJARCET), Vol. 5, 3, Mar 2016.
[16]	Sh. Medhi, Ch. Ahmed and R. Gayan. “A Study on Feature Extraction Techniques in Image Processing.” International Journal of Computer Sciences and Engineering, Vol. 4, 7, Dec 2016.
[17]	Y. Zhang and L. Wu, School of Information Science and Engineering, Southeast University, Nanjing, China. “An MRI Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine.” Progress in Electromagnetics Research, Vol. 130, pp. 369-388, 2012.

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APA Style

Kibru Abera Ganore, Getahun Tigistu. (2020). Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques. International Journal of Intelligent Information Systems, 9(1), 1-5. https://doi.org/10.11648/j.ijiis.20200901.11

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ACS Style

Kibru Abera Ganore; Getahun Tigistu. Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques. Int. J. Intell. Inf. Syst. 2020, 9(1), 1-5. doi: 10.11648/j.ijiis.20200901.11

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AMA Style

Kibru Abera Ganore, Getahun Tigistu. Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques. Int J Intell Inf Syst. 2020;9(1):1-5. doi: 10.11648/j.ijiis.20200901.11

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@article{10.11648/j.ijiis.20200901.11,
  author = {Kibru Abera Ganore and Getahun Tigistu},
  title = {Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques},
  journal = {International Journal of Intelligent Information Systems},
  volume = {9},
  number = {1},
  pages = {1-5},
  doi = {10.11648/j.ijiis.20200901.11},
  url = {https://doi.org/10.11648/j.ijiis.20200901.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20200901.11},
  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.},
 year = {2020}
}

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TY - JOUR
T1 - Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques
AU - Kibru Abera Ganore
AU - Getahun Tigistu
Y1 - 2020/06/17
PY - 2020
N1 - https://doi.org/10.11648/j.ijiis.20200901.11
DO - 10.11648/j.ijiis.20200901.11
T2 - International Journal of Intelligent Information Systems
JF - International Journal of Intelligent Information Systems
JO - International Journal of Intelligent Information Systems
SP - 1
EP - 5
PB - Science Publishing Group
SN - 2328-7683
UR - https://doi.org/10.11648/j.ijiis.20200901.11
AB - 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.
VL - 9
IS - 1
ER -

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Author Information

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

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APA Style

Kibru Abera Ganore, Getahun Tigistu. (2020). Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques. International Journal of Intelligent Information Systems, 9(1), 1-5. https://doi.org/10.11648/j.ijiis.20200901.11

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ACS Style

Kibru Abera Ganore; Getahun Tigistu. Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques. Int. J. Intell. Inf. Syst. 2020, 9(1), 1-5. doi: 10.11648/j.ijiis.20200901.11

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AMA Style

Kibru Abera Ganore, Getahun Tigistu. Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques. Int J Intell Inf Syst. 2020;9(1):1-5. doi: 10.11648/j.ijiis.20200901.11

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@article{10.11648/j.ijiis.20200901.11,
  author = {Kibru Abera Ganore and Getahun Tigistu},
  title = {Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques},
  journal = {International Journal of Intelligent Information Systems},
  volume = {9},
  number = {1},
  pages = {1-5},
  doi = {10.11648/j.ijiis.20200901.11},
  url = {https://doi.org/10.11648/j.ijiis.20200901.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20200901.11},
  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.},
 year = {2020}
}

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