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.
Highlights
Feature extraction and classification are given followed by feature extraction and Benishangule coffee bean classification are made based on Artificial neural network (ANN) in order to permit comparison of our system with those introduced in the literature
From the collected samples of 100gm, 50gram will be used for image analysis and the rest are used as reference. 10 images will be taken per each Quintal, which means 50 image from each selected coffee growing part of the country and totally 100 images will be captured and out of this 60% to 70% of the image will be used for practice purpose and the rest will be used for thesis work
Classification Model Result Based on All Features In this model, twenty four features corresponding to six shape and size features, twelve color features and six textural features of Benshanguel coffee were used as input to the neural network ; there were twenty four neuron numbers for the input layer
Summary
It is widely used as a beverage but a day’s its use as input in some food processing industries is increasing [3]. There are different types of coffee in the world. Among different types of coffee, the major economic species are coffee Arabica and coffee Robusta. Arabica accounts 80% of the world coffee trade, and Robusta most of the remaining 20%. The origins of the coffee crop can be traced back to the Ethiopian highlands for coffee Arabica and the forest of West and Central Africa for coffee Robusta (Canephora). Ethiopia has a suitable environment to grow all Arabica coffee varieties. In Ethiopia, coffee production is concentrated in the Oromia and Southern regions of the country, though the majority of Ethiopian regions are still suitable for coffee growth [12]. The tradition of serving coffee in Ethiopia is unique
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