Features Extraction and Dataset Preparation for Grading of Ethiopian Coffee Beans Using Image Analysis Techniques

Abstract

Coffee is the natural gift of Ethiopia. Generally the export quality washed coffee beans of Ethiopia are classified into two grades and sundried coffee beans into five different grades based on their number of defects. The objective of the research is to extract the features of green coffee beans from the images which would be helpful on classifying the coffee beans to different grades by an automated system. Different image processing techniques are applied on the images to perform preprocessing, segmentation and feature extraction. The extracted features of the coffee beans from images are broadly classified as morphological, textural and color. The morphological feature includes area, perimeter, major axis length, minor axis length, Eccentricity. Energy, Entropy, contrast and homogeneity are the information relevant to texture. Individual color component values of the three primary colors, along with its hue, intensity and saturation values are extracted for color features of the coffee beans. Automated classification and machine learning algorithms needs a data set for further processing. Data mining applications for discovering different patterns for various grades and knowledge discovery need a highly accurate dataset. Hence, preparing such data set by applying image processing techniques is becoming the objective of this research study. The objective is realized with a dataset of 100 observations for each grade with morphological, textural, and color features.