Abstract:
In this research work, a digital image analysis technique was used to classify Wollega coffee
beans genotype based on their color and morphological features and performance evaluation
(Accuracy, Specificity and Sensitivity) were done. Artificial neural network (ANN) was used to
classify WCBs that are grown in Haru Coffee Research Center (under the same agronomical
management), whether correctly classifies to their origin of collection or not. Three
classification set-ups were used, which are classification based on color feature, morphological
feature, and combination of color and morphological features. For WCBs genotype, all set up
features of 500 images were used as inputs of the ANNs. From those data sets 60% (300 images),
20% (100 images) and 20% (100 images) were used in the network for training, testing and
validating, respectively. The accuracy of classification using color, morphological, and
combination of color and morphological features were respectively, 69%, 71% and 69%. The
evaluation performance of the above samples (Accuracy, Specificity and Sensitivity) was
respectively, 70.2%, 0% and 100%. The best validation was found at epoch 45 and its best
validation performance was 3.0739e-06.
