Classical Image Based Classification of Coffee Beans on Their Botanical Origins in Tongo and Wambara, Benishangul Gumuz, Ethiopia

Lamessa Dingeta Olika; Dejenie Demisse Amberber

doi:doi:10.11648/j.cssp.20200902.12

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Classical Image Based Classification of Coffee Beans on Their Botanical Origins in Tongo and Wambara, Benishangul Gumuz, Ethiopia

Lamessa Dingeta Olika, Dejenie Demisse Amberber

Published in Science Journal of Circuits, Systems and Signal Processing (Volume 9, Issue 2)

Received: 19 June 2020 Accepted: 29 July 2020 Published: 13 August 2020

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Abstract

Ethiopia is a homeland of coffee. Coffee is a major export commodity of Ethiopia, which has a significant role in earning foreign currency. This research was conducted with the objective of developing an appropriate computer routine algorithm that can characterize different varieties of Beneshanguel coffee based on their growing region. Imaging techniques were employed to automatically classify the coffee bean samples according to provenance in Beneshanguel (Tongo and Wombera) which corresponds to their botanical origins. Important coffee bean features, namely, color, shape and size and texture were extracted from 100 images (50 images from each location). For the purpose of classification, altogether 24 features (12 colors, 6 shapes and size and 6 textures) were extracted from images of the coffee samples from the two locations. Artificial neural network (ANN) was employed to automatically categorize the coffee beans according to their provenance. We have compared classification approaches of Neural Network classifiers were employed based on the features used for color, morphology (shapes and size), texture, and the combination of morphology and color respectively. To evaluate the classification accuracy, from the total of 100 sample images of the training 70% (70 images), validation 20% (20 images) and testing 10% (10 images) data. Classification scores of 93%, and 99.3% were achieved for color, morphology, texture and a combination of morphology and color features, respectively. The classification results of the network indicated that morphology and a combination of morphological and color features exhibited the highest accuracy. In conclusion, the results of this study have revealed that imaging technique could be used as the most effective method to determine coffee bean qualities for export. However, it is suggested that the repeatability of this coffee quality testing method be validated using a large data set before employing the algorithm for the purpose of classifying coffee beans as a daily routine.

Published in	Science Journal of Circuits, Systems and Signal Processing (Volume 9, Issue 2)
DOI	10.11648/j.cssp.20200902.12
Page(s)	42-48
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

Beneshangul Coffe, Coffee Beans, Classification, Image Analysis, Neural Networks

References

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[4]	Faridah, G., Parikesit, F. and Ferdiansjah, 2011. Coffee bean grade determination based on image parameter. Department of Physics Engineering, Faculty of Enginering, Univertal Gadjah Mada Jalan Grafika 2 Yogyakarta, Indonesia. TELKOMNOKA, vol. 9, No. 3.
[5]	Furtado, J. J., cai, Z. and Liu, X. 2010. Digital image processing: supervised classification using genetic algorithm in matlab toolbox. China university of geosciences, 388 LuMo road, Wuhan, Hubei, P. R. china Zip code 430074.
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[18]	Unay, D. and Bernard, G., 2005. Artificial Neural Network-baTCTS Labs., Faculty Polytechnique de Mons Multitel Building, Avenue Copernic 1, Parc Initialis, B-7000, Mons, Belgium.
[19]	Waller, J. M., M. Bigger and R. J. Hillocks: Coffee Pests, Diseases and their Management, Column designs Ltd, UK, 2007.

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Lamessa Dingeta Olika, Dejenie Demisse Amberber. (2020). Classical Image Based Classification of Coffee Beans on Their Botanical Origins in Tongo and Wambara, Benishangul Gumuz, Ethiopia. Science Journal of Circuits, Systems and Signal Processing, 9(2), 42-48. https://doi.org/10.11648/j.cssp.20200902.12

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ACS Style

Lamessa Dingeta Olika; Dejenie Demisse Amberber. Classical Image Based Classification of Coffee Beans on Their Botanical Origins in Tongo and Wambara, Benishangul Gumuz, Ethiopia. Sci. J. Circuits Syst. Signal Process. 2020, 9(2), 42-48. doi: 10.11648/j.cssp.20200902.12

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AMA Style

Lamessa Dingeta Olika, Dejenie Demisse Amberber. Classical Image Based Classification of Coffee Beans on Their Botanical Origins in Tongo and Wambara, Benishangul Gumuz, Ethiopia. Sci J Circuits Syst Signal Process. 2020;9(2):42-48. doi: 10.11648/j.cssp.20200902.12

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@article{10.11648/j.cssp.20200902.12,
  author = {Lamessa Dingeta Olika and Dejenie Demisse Amberber},
  title = {Classical Image Based Classification of Coffee Beans on Their Botanical Origins in Tongo and Wambara, Benishangul Gumuz, Ethiopia},
  journal = {Science Journal of Circuits, Systems and Signal Processing},
  volume = {9},
  number = {2},
  pages = {42-48},
  doi = {10.11648/j.cssp.20200902.12},
  url = {https://doi.org/10.11648/j.cssp.20200902.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20200902.12},
  abstract = {Ethiopia is a homeland of coffee. Coffee is a major export commodity of Ethiopia, which has a significant role in earning foreign currency. This research was conducted with the objective of developing an appropriate computer routine algorithm that can characterize different varieties of Beneshanguel coffee based on their growing region. Imaging techniques were employed to automatically classify the coffee bean samples according to provenance in Beneshanguel (Tongo and Wombera) which corresponds to their botanical origins. Important coffee bean features, namely, color, shape and size and texture were extracted from 100 images (50 images from each location). For the purpose of classification, altogether 24 features (12 colors, 6 shapes and size and 6 textures) were extracted from images of the coffee samples from the two locations. Artificial neural network (ANN) was employed to automatically categorize the coffee beans according to their provenance. We have compared classification approaches of Neural Network classifiers were employed based on the features used for color, morphology (shapes and size), texture, and the combination of morphology and color respectively. To evaluate the classification accuracy, from the total of 100 sample images of the training 70% (70 images), validation 20% (20 images) and testing 10% (10 images) data. Classification scores of 93%, and 99.3% were achieved for color, morphology, texture and a combination of morphology and color features, respectively. The classification results of the network indicated that morphology and a combination of morphological and color features exhibited the highest accuracy. In conclusion, the results of this study have revealed that imaging technique could be used as the most effective method to determine coffee bean qualities for export. However, it is suggested that the repeatability of this coffee quality testing method be validated using a large data set before employing the algorithm for the purpose of classifying coffee beans as a daily routine.},
 year = {2020}
}

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TY  - JOUR
T1  - Classical Image Based Classification of Coffee Beans on Their Botanical Origins in Tongo and Wambara, Benishangul Gumuz, Ethiopia
AU  - Lamessa Dingeta Olika
AU  - Dejenie Demisse Amberber
Y1  - 2020/08/13
PY  - 2020
N1  - https://doi.org/10.11648/j.cssp.20200902.12
DO  - 10.11648/j.cssp.20200902.12
T2  - Science Journal of Circuits, Systems and Signal Processing
JF  - Science Journal of Circuits, Systems and Signal Processing
JO  - Science Journal of Circuits, Systems and Signal Processing
SP  - 42
EP  - 48
PB  - Science Publishing Group
SN  - 2326-9073
UR  - https://doi.org/10.11648/j.cssp.20200902.12
AB  - Ethiopia is a homeland of coffee. Coffee is a major export commodity of Ethiopia, which has a significant role in earning foreign currency. This research was conducted with the objective of developing an appropriate computer routine algorithm that can characterize different varieties of Beneshanguel coffee based on their growing region. Imaging techniques were employed to automatically classify the coffee bean samples according to provenance in Beneshanguel (Tongo and Wombera) which corresponds to their botanical origins. Important coffee bean features, namely, color, shape and size and texture were extracted from 100 images (50 images from each location). For the purpose of classification, altogether 24 features (12 colors, 6 shapes and size and 6 textures) were extracted from images of the coffee samples from the two locations. Artificial neural network (ANN) was employed to automatically categorize the coffee beans according to their provenance. We have compared classification approaches of Neural Network classifiers were employed based on the features used for color, morphology (shapes and size), texture, and the combination of morphology and color respectively. To evaluate the classification accuracy, from the total of 100 sample images of the training 70% (70 images), validation 20% (20 images) and testing 10% (10 images) data. Classification scores of 93%, and 99.3% were achieved for color, morphology, texture and a combination of morphology and color features, respectively. The classification results of the network indicated that morphology and a combination of morphological and color features exhibited the highest accuracy. In conclusion, the results of this study have revealed that imaging technique could be used as the most effective method to determine coffee bean qualities for export. However, it is suggested that the repeatability of this coffee quality testing method be validated using a large data set before employing the algorithm for the purpose of classifying coffee beans as a daily routine.
VL  - 9
IS  - 2
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Author Information

Lamessa Dingeta Olika

Department of Electrical and Computer Engineering, Assosa University, Benishangul Gumuz, Ethiopia
Dejenie Demisse Amberber

Department of Physics, Assosa University, Benishangul Gumuz, Ethiopia

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APA Style

Lamessa Dingeta Olika, Dejenie Demisse Amberber. (2020). Classical Image Based Classification of Coffee Beans on Their Botanical Origins in Tongo and Wambara, Benishangul Gumuz, Ethiopia. Science Journal of Circuits, Systems and Signal Processing, 9(2), 42-48. https://doi.org/10.11648/j.cssp.20200902.12

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ACS Style

Lamessa Dingeta Olika; Dejenie Demisse Amberber. Classical Image Based Classification of Coffee Beans on Their Botanical Origins in Tongo and Wambara, Benishangul Gumuz, Ethiopia. Sci. J. Circuits Syst. Signal Process. 2020, 9(2), 42-48. doi: 10.11648/j.cssp.20200902.12

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AMA Style

Lamessa Dingeta Olika, Dejenie Demisse Amberber. Classical Image Based Classification of Coffee Beans on Their Botanical Origins in Tongo and Wambara, Benishangul Gumuz, Ethiopia. Sci J Circuits Syst Signal Process. 2020;9(2):42-48. doi: 10.11648/j.cssp.20200902.12

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@article{10.11648/j.cssp.20200902.12,
  author = {Lamessa Dingeta Olika and Dejenie Demisse Amberber},
  title = {Classical Image Based Classification of Coffee Beans on Their Botanical Origins in Tongo and Wambara, Benishangul Gumuz, Ethiopia},
  journal = {Science Journal of Circuits, Systems and Signal Processing},
  volume = {9},
  number = {2},
  pages = {42-48},
  doi = {10.11648/j.cssp.20200902.12},
  url = {https://doi.org/10.11648/j.cssp.20200902.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20200902.12},
  abstract = {Ethiopia is a homeland of coffee. Coffee is a major export commodity of Ethiopia, which has a significant role in earning foreign currency. This research was conducted with the objective of developing an appropriate computer routine algorithm that can characterize different varieties of Beneshanguel coffee based on their growing region. Imaging techniques were employed to automatically classify the coffee bean samples according to provenance in Beneshanguel (Tongo and Wombera) which corresponds to their botanical origins. Important coffee bean features, namely, color, shape and size and texture were extracted from 100 images (50 images from each location). For the purpose of classification, altogether 24 features (12 colors, 6 shapes and size and 6 textures) were extracted from images of the coffee samples from the two locations. Artificial neural network (ANN) was employed to automatically categorize the coffee beans according to their provenance. We have compared classification approaches of Neural Network classifiers were employed based on the features used for color, morphology (shapes and size), texture, and the combination of morphology and color respectively. To evaluate the classification accuracy, from the total of 100 sample images of the training 70% (70 images), validation 20% (20 images) and testing 10% (10 images) data. Classification scores of 93%, and 99.3% were achieved for color, morphology, texture and a combination of morphology and color features, respectively. The classification results of the network indicated that morphology and a combination of morphological and color features exhibited the highest accuracy. In conclusion, the results of this study have revealed that imaging technique could be used as the most effective method to determine coffee bean qualities for export. However, it is suggested that the repeatability of this coffee quality testing method be validated using a large data set before employing the algorithm for the purpose of classifying coffee beans as a daily routine.},
 year = {2020}
}

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TY  - JOUR
T1  - Classical Image Based Classification of Coffee Beans on Their Botanical Origins in Tongo and Wambara, Benishangul Gumuz, Ethiopia
AU  - Lamessa Dingeta Olika
AU  - Dejenie Demisse Amberber
Y1  - 2020/08/13
PY  - 2020
N1  - https://doi.org/10.11648/j.cssp.20200902.12
DO  - 10.11648/j.cssp.20200902.12
T2  - Science Journal of Circuits, Systems and Signal Processing
JF  - Science Journal of Circuits, Systems and Signal Processing
JO  - Science Journal of Circuits, Systems and Signal Processing
SP  - 42
EP  - 48
PB  - Science Publishing Group
SN  - 2326-9073
UR  - https://doi.org/10.11648/j.cssp.20200902.12
AB  - Ethiopia is a homeland of coffee. Coffee is a major export commodity of Ethiopia, which has a significant role in earning foreign currency. This research was conducted with the objective of developing an appropriate computer routine algorithm that can characterize different varieties of Beneshanguel coffee based on their growing region. Imaging techniques were employed to automatically classify the coffee bean samples according to provenance in Beneshanguel (Tongo and Wombera) which corresponds to their botanical origins. Important coffee bean features, namely, color, shape and size and texture were extracted from 100 images (50 images from each location). For the purpose of classification, altogether 24 features (12 colors, 6 shapes and size and 6 textures) were extracted from images of the coffee samples from the two locations. Artificial neural network (ANN) was employed to automatically categorize the coffee beans according to their provenance. We have compared classification approaches of Neural Network classifiers were employed based on the features used for color, morphology (shapes and size), texture, and the combination of morphology and color respectively. To evaluate the classification accuracy, from the total of 100 sample images of the training 70% (70 images), validation 20% (20 images) and testing 10% (10 images) data. Classification scores of 93%, and 99.3% were achieved for color, morphology, texture and a combination of morphology and color features, respectively. The classification results of the network indicated that morphology and a combination of morphological and color features exhibited the highest accuracy. In conclusion, the results of this study have revealed that imaging technique could be used as the most effective method to determine coffee bean qualities for export. However, it is suggested that the repeatability of this coffee quality testing method be validated using a large data set before employing the algorithm for the purpose of classifying coffee beans as a daily routine.
VL  - 9
IS  - 2
ER  -

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