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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/1335

Title: IMAGE ANALYSIS FOR ETHIOPIAN COFFEE CLASSIFICATION
Authors: Habtamu, Minassie
Advisors: Sebsibe Hailemariam
Keywords: Ethiopian coffee
Coffee Bean
Image Analysis
Classification
Neural Networks
Copyright: 2008
Date Added: 2-Sep-2008
Publisher: Addis Ababa University
Abstract: Ethiopia is a homeland of coffee. Coffee is a major export commodity of Ethiopia, which has a significant role in earning foreign currency. There are different varieties of coffee in Ethiopia and they are classified based on their growing region. In view of this, a digital image analysis technique based on morphological and color features was developed to classify different varieties of Ethiopian coffee based on their growing region. Sample coffees were taken from six coffee growing regions (Bale, Harar, Jimma, Limu, Sidamo and Welega) which are popular and widely planted in Ethiopia. On the average 56 images were taken from each region. The total number of images taken was 309 which contain 4844 coffee beans. For the classification analysis, ten morphological and six color features were extracted from each coffee bean images. The processing type of coffee (washed or unwashed) has been also predefined during the analysis. We have compared classification approaches of Naïve Bayes and Neural Network classifiers on each classification parameters of morphology, color and the combination of the two. To evaluate the classification accuracy, from the total of 4844 data sets 80% were used for training and the remaining 20% was used for testing. The classification system was supervised corresponding to the predefined classes of the growing regions. It was found that the classification performance of neural networks classifier was better than Naïve Bayes classifier. It was also showed that the discrimination power of morphology features was better than color features but when both morphology and color features were used together the classification accuracy was increased. The best classification accuracies (80.7%, 72.6%, 56.8%, 96.77%, 95.42% and 69.9% for Bale, Harar, Jimma, Limu, Sidamo and Welega respectively) were obtained using neural networks when both morphology and color features were used together. The overall classification accuracy was 77.4%.
Description: 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
URI: http://hdl.handle.net/123456789/1335
Appears in:Thesis - Computer Science

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