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
Volume 9, Issue 2, December 2020, Pages: 42-48
Received: Jun. 19, 2020;
Accepted: Jul. 29, 2020;
Published: Aug. 13, 2020
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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
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.
Lamessa Dingeta Olika,
Dejenie Demisse Amberber,
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.
Vol. 9, No. 2,
2020, pp. 42-48.
Andrea P., Svore, J. and Miertus, S. 1999, A biosensing method for detection of caffeine in coffee. Journal of the science and Agriculture, Vol. 7a, No. 12, p. 1136.
Endale Asfaw: Physical Quality and Grading Systems of Ethiopian Coffee in Demand-Supply Chain, Four Decades of Coffee Research and Development in Ethiopia 1967 -2007, 2007.
Ethiopia Coffee Quality Inspection & Auction Center: Training Manual for Trainee Coffee Cuppers, 2007.
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.
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.
Gonzalez, R. C. and Woods, R. E., 2002. Digital image processing second edition. Med Data Interactive. University of Tennesse.
Habtamu Minassie, 2008. Image Analysis for Ethiopia Coffee Classification. A Thesis Submitted to the School of Graduate Studies of Addis Ababa University in Partial Fulfillment for the Degree of Master of Science in Computer Science Addis Ababa University School of Graduate studies, Addis Ababa, Ethiopia.
Hammouda, I. and Rudzki, J. 2004. Pattern classification. Tempere University of Technology, institution software systems, 8109103-ohjelmistotuotannon Teoria.
Huiyu Z., Wu, J. &Zhang, J. 2011. Digital image processing part I. Ventus publishing aps, ISBN 978-87-7681-541-4.
International coffee organization, 2003. Coffee and health, new research finding proceeding of the international seminar on coffee and health 40th Anniversary meeting of the ICO Cartagena, Colombia.
International trade centre, 2011. The coffee exporter’s guide manual, third edition. 54-56 re de Montbrillant 1202 Geneva, Switzerland.
International Trade Center UNCTAD/WTO: Coffee An export Guide, Geneva, 2002.
Maheshwari C. V., Jain, K. R. and Modi, C., 2012. Non-destructive quality analysis of Indian Gujarat-17 Oryza sativa SSP Indian (Rice) using image processing. International Journal of computer engineering science (IJCES), volume 2 Issue 3.
Nicolescu, C. and Jonker, P. 2002. A Data and Task Parallel Image Processing environment. Elsevier Science B. V., Parallel Computing 28945-965.
Pathon M., Jussoff, K. and Barkatullah, Q., 2011. Implication of image processing algorithm in remote sensing and GIS application. Journal of theoretical and applied information technology volume 34 No-1.
Seeman, T., 2002. Digital Image Processing using Local Segmentation. Submission for the degree of Doctor of Philosophy, School of computer science and software Engineering. Monash university, Australia.
Surendra Kotecha and Ann Gray: ICO/CFC Study of Marketing and Trading Polices and Systems in Selected Coffee producing countries: Ethiopia Country Profile, 2000.
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.
Waller, J. M., M. Bigger and R. J. Hillocks: Coffee Pests, Diseases and their Management, Column designs Ltd, UK, 2007.