Optimization of Coffee Bean Maturity Classification by Segmentation on Multispectral Images Using HSV and DBSCAN

Abstract

In the coffee industry, sorting the maturity level of coffee beans is still done conventionally. In an effort to get good quality coffee beans, automatic classification of the maturity level of coffee beans is needed. The data in this research is multispectral image data and still has a background, so the preprocessing process is the main focus in this research to improve the performance of segmentation analysis in identifying objects and background image data. In the image data of 15 types of channels, a combination of 3 channel variations is carried out by applying HSV transformation so that the image data is easily processed by a computer, then the image data will be clustered using DBSCAN to identify coffee bean objects. The results obtained, the best channel combination in segmentation is blue, azure and amber, namely with a final weight value of 611. The segmentation results in the image data preprocessing process resulted in 100% accuracy. Meanwhile, the performance of the model without the segmentation preprocessing stage resulted in an accuracy of 92%. In conclusion, the performance of the model will be more optimal if preprocessing is done, namely segmentation in separating object and background data.