Browsing by Author "GETABALEW AMTATE"

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    ETHIOPIAN COFFEE BEAN DETECTION AND CLASSIFICATION USING DEEP LEARNING
    (Hawassa University, 2020-06-02) GETABALEW AMTATE
    Ethiopia is the homeland of Coffee Arabica. Coffee is the major commodity export which covers the highest income source of foreign currency. In addition to this, Coffee has a great role in social interaction between peoples and the source of income for the coffee-producing farmers. Ethiopian coffee beans are distinct from each other in terms of quality based on their geographical origins. Classification and grading of those coffee beans are based on growing origin, altitude, bean shape and color, preparation method and others. However, the quality of the coffee beans is determined by visual inspection, which is subjective, laborious, and prone to error and this requires the development of an alternative method which is precise, non destructive and objective. Thus, the objective of this research is to design and develop a model that characterizes and identifies coffee beans of six different origins of Ethiopia (Jimma, Limmu, Nekemte, Yirgacheffe, Bebeka, and Sidama). Coffee beans for this research are collected from the Ethiopian Coffee Quality Inspection and Auction Center (ECQIAC). Image processing and the state-of-the-art deep-learning techniques were employed to automatically classify coffee bean images into nine different class: washed Limmu, unwashed Limmu, washed Sidamo, unwashed Sidamo, washed Yirgacheffe, unwashed Yirgacheffe, unwashed Jimma, unwashed Nekemte, and washed Bebeka. A total of 9836 coffee bean images were used to train, validate and test the CNN model. We have compared the classification result of the model trained on different dataset sizes and hyperparameters. The model was trained on 80% of the dataset, validated on 10%, and tested on 10% of the colorful coffee bean images, with batch normalization has scored 99.89% overall classification accuracy and 0.92% generalization log loss. In conclusion, the result of the study shows that CNN is an effective deep learning technique in the classification of Ethiopian coffee beans