Raw Quality Value Classification of Ethiopian Coffee Using Image Processing Techniques: in the Case of Wollega Region (2011)

Development of an automated computer vision system aiming in the establishment of
technological and innovative approaches towards sample coffee bean raw quality value
classification by extracting the relevant coffee bean features is the focal issue of this
exploratory research. Of paramount significance in this regard is addressing the identified
problems of the tedious and inefficient manual grading and sorting mechanisms of one of
the most important agricultural products in Ethiopia, coffee. Prevalent sorting and
classification approaches are characterized by subjective assessments of the features and
nature of this huge economy representing crop, thereby influencing quality control and
productivity aspects of the product. The major objective of the research spans extraction
and selection of the important coffee bean morphological and color features that are
useful for the purpose of classification of the raw quality grade level of sample coffee
beans by designing, analyzing and testing a digital image processing model.
The automated raw quality value classification experimentation comprised the analysis of
images of washed coffee beans of varying grades from Wollega region, using major
attributes of morphological structures (shape and size), and color features. Grades 2 – 9
of the coffee beans were available, providing a total of 27 samples, which yielded 324
sample images after a series of re-sampling measures of same into 12 sub-samples. The
overall image processing work to develop models and depict trends for an efficient raw
quality value classification involved sequential phases of image acquisition, image
enhancement and segmentation, feature extraction, attribute selection, classification and
performance evaluation.
The Naive Bayes, C4.5 and Artificial neural networks (ANN) were implemented for such
classification purposes. A combined morphological and color features aggregate function
dataset was used to develop the base model, though model attempts with separate features
were conducted. Feed-forward multilayer perceptrons with two hidden layer and backpropagation algorithms are used in the ANN classifiers.
Discretization of the raw quality value in to three interval classes was done to improve
the performance of the model. 75% split evaluation technique was implemented for the
Naive Bayes and ANN classifiers as 10-foldcross validation evaluation techniques
implemented in C4.5. Naive Bayes classifier yielded higher model performance (82.72%
correctly classified), followed by C4.5 (82.09%) and the ANN classifier (80.25%). Model
robustness and sensitivity was analyzed by using perturbation analysis involving
manipulations of model evaluation techniques and dataset characters. Alteration of
number of beans in discretization and the use of different number of hidden layers
constitute the trial modeling in this regard. Classification model was also run with various
combinations of features of the coffee beans as listed with the attribute selection feature
of Weka tool, where the final selection of the 21 features was done at a maximal model
performance level for the Naive Bayes and ANN classification approachs. C4.5 selected
10 features as it has its own attribute selection characteristics.
An additional simulation was done with regression analysis for the sake of evaluation and
trends analysis of the model outputs. A higher relation was resulted from this statistical
approach between the raw quality values and the mentioned coffee bean features,
supporting suitability and accuracy of dataset for classification in this research.