Open AccessIdentifying Arabica Raw Coffee Bean Varieties through Feature Extraction GLCM and Circularity
Author(s) -
Muhammad Agung Nugroho,
Maria Mediatrix Sebatubun,
Sumiyatun
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.44.26964
Subject(s) - coffee bean , mathematics , artificial intelligence , green coffee , raw material , feature (linguistics) , pattern recognition (psychology) , computer science , food science , biology , ecology , linguistics , philosophy
The morphology of raw coffee bean which has colour, texture, size and circularity features are used as standardization to calculate the price and the quality of the raw coffee bean. Meanwhile, coffee farmers have difficulties to distinguish the coffee varieties based on the features of raw coffee bean. Generally, the way of the distinguish the varieties of the coffee is through their own visual perception in form of the tree, leaves, and raw coffee bean. They find it difficult to distinguish the coffee varieties due to the similarity of the varieties forms and colours. This research proposes to solve the problem through the image processing method as the second opinion to help the coffee farmers in identifying the coffee varieties. The research is conducted in three steps processes: The first is pre-processing by cropping the image of raw coffee bean. The second is extracting the image feature of raw coffee bean with Gray Level Co-occurrence Matrices (GLCM) and circularity feature. The last is classifying the feature with multilayer perceptron. The results of the image processing method indicate that the accuracy is 90% with sensitivity is 90%, and 90% specific.