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

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.

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Published

2023-05-11

How to Cite

Arboleda, E. (2023). Classification of Immature and Mature Coffee Beans Using Texture Features and Medium K Nearest Neighbor. Journal of Artificial Intelligence and Technology, 3(3), 114–118. https://doi.org/10.37965/jait.2023.0203

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Section

Research Articles
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