Classification of Immature  and  Mature Coffee Beans Using Texture  Features and  Medium  K  Nearest Neighbor

Edwin Arboleda

doi:10.37965/jait.2023.0203

Classification of Immature and Mature Coffee Beans Using Texture Features and Medium K Nearest Neighbor

Authors

Edwin Arboleda Department of Computer and Electronics Engineering, College of Engineering and Information Technology, Cavite State University, Indang, Cavite, Philippines https://orcid.org/0000-0001-9371-8895

DOI:

https://doi.org/10.37965/jait.2023.0203

Keywords:

coffee, energy, entropy, homogeneity, machine learning, texture

Abstract

In this study , texture features namely entropy, contrast, energy and homogeneity were extracted from mature and immature coffee beans using image processing and the values were inputted to MATLAB’s Classification Learner App for discrimination. Among the 23 machine learning algorithms the best performance was achieved by medium K nearest neighbor which has 97 % accuracy and 0.14574 seconds in speed. When compared to previous studies that used RGB and HSV color features to differentiate mature and immature coffee beans, it can be concluded that texture features are far superior in distinguishing the two coffee bean groups.