Detection of the Maturity Level of Lempuyangan Robusta Coffee Fruit Using HSV Segmentation and K-Means Clustering
DOI:
https://doi.org/10.30871/jaic.v10i2.12541Keywords:
Clustering, Coffee maturity detection, HSV segmentation, Image processing, Silhouette scoreAbstract
This study proposes an image based approach to detect the ripeness level of Lempuyangan Robusta coffee using HSV color segmentation and K-Means clustering. Manual sorting conducted by farmers is subjective and inconsistent due to lighting variation and perceptual differences. A total of 901 coffee fruit images were collected and processed through resizing, denoising using Non-Local Means, RGB to HSV conversion, and color based segmentation. Mean HSV features were extracted and clustered into three maturity levels: unripe, semi-ripe, and ripe. Cluster quality was evaluated using the Silhouette Score to measure compactness and separation, and an additional evaluation metric, the Davies Bouldin Index (DBI), to assess the quality of separation between clusters by measuring the ratio between intra cluster dispersion and inter cluster distance. Experimental results show that the proposed method achieves an overall Silhouette Score of 0.6119 and a Davies Bouldin Index of 0.5019, indicating good cluster structure stability with minimal difference between training and testing subsets. The findings demonstrate that HSV segmentation combined with K-Means provides an effective and objective approach to support automated coffee sorting systems under real-field conditions.
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Copyright (c) 2026 Muhammad Ferdy Irmawan, Wenty Dwi Yuniarti, Moh Hadi Subowo
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