Abstract:
In this research work, a digital image analysis technique was used to classify Hararghe coffee
beans of six Weredas (Kombolcha, Meta, Deder, Tulo, Bultum and Mechara) based on their
color, morphological and textural features. From each wereda, 20 images were captured.Thirty
six images were captured from samples collected from mechara research center. Support vector
machine was used to classify HCBs and to cheek weather the genotype HCBs correctly classifies
to their origin of collection or not. For the analysis, 18 color, 8 morphological and 6 textural
features, totally 32 features were extracted from images of coffee beans. Four experimental
classification setups were applied. Setups 1, 2, 3 and 4 are classification designs by using
morphology, color, texture and combination of morphology and color features, respectively. For
all setups 156 images were used as inputs of the machine. From these datasets 84 images were
used for training the machine while the remaining 36 images were used for testing. The accuracy
of classification using morphological feature was 100%, 100%,color features was 94.4%,
83.3%, texture features was 94.45%, 88.9% and combination of morphological and color
features was 88.9%, 100% for Harar A and Harar B, respectively. The classification
performance of the machine was best for morphological and mixed features. Genotypic HCBs
were not correctly classified to their origin of collection. From experiment 1, 2, and 4, the
percentage of images which were not classified to their origin of collection were 33.3%, 77.8%
and 72.2%, respectively and all the images of Harar B genotype coffee beans were classified to
Harar A genotype coffee beans and 27.78% image of Harar A cofee genotype were classified to
Harar B CBs
