<![CDATA[Coffee is one of Indonesia’s key strategic commodities with substantial economic value for farmers and exporters. However, inconsistencies in post-harvest coffee bean quality remain a major challenge due to manual, subjective, and expertise-dependent classification. This study addresses this issue by developing an automated and objective computer vision–based classification system using a hybrid deep learning architecture. The proposed model, named RI-Net, integrates the residual learning capability of ResNet with the multi-scale feature extraction of the Inception module to improve the precision and robustness of coffee bean classification across four roasting levels: Green, Light, Medium, and Dark. The model was trained and evaluated on a locally collected dataset and benchmarked against three standard architectures—ResNet50, InceptionV3, and a Fully Convolutional Neural Network (FCNN). Experimental results show that RI-Net outperforms all baseline models, achieving perfect scores of 100% in accuracy, precision, recall, and F1-score. These findings confirm the effectiveness of combining residual and multi-scale features in capturing subtle visual differences across roasting levels. The study demonstrates the potential of advanced hybrid CNN architectures to enhance post-harvest quality control, supporting faster, more consistent, and standardized classification processes that strengthen the competitiveness of Indonesia’s coffee industry.]]>
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