Refining Content-Based Segmentation for Prediction of Coffee Bean Quality

Abstract

Coffee has substantial economic value and is a key foreign exchange source for numerous nations, including Indonesia. Moreover, it is a primary livelihood for many of the country's farmers. Recently, there have been challenges in accurately predicting the quality of coffee beans, primarily due to time, inconsistency, and imprecision issues. Consequently, this study delves into the application of region-growing segmentation and content-based image retrieval (CBIR) techniques to enhance the prediction of coffee bean quality. The proposed hybrid approach, which combines region growing and CBIR methods, aims to improve the precision for forecasting cacao bean quality. Additionally, the research introduces an automated tool that employs these hybrid techniques for quality prediction. The study conducted experiments using a dataset of 400 premium and 400 low-quality coffee beans sourced from the University of Syiah Kuala in Indonesia. The results of the experiments demonstrate a commendable precision rate of 85.4%, showcasing significant improvement compared to certain previous studies.