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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
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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.
Beneshangul Coffe, Coffee Beans, Classification, Image Analysis, Neural Networks
To cite this article
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. doi: 10.11648/j.cssp.20200902.12
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