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All Journal Jurnal Riset Informatika
Harvyanti, Annisa Fitri Maghfiroh
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Computer Science & IT

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CLASSIFICATION OF COFFEE LEAF SPOT DISEASES USING THE RESIDUAL NEURAL NETWORKS Pinasthika, Stanislaus Jiwandana; Hizham, Fadhel Akhmad; Harvyanti, Annisa Fitri Maghfiroh
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1646.353 KB) | DOI: 10.34288/jri.v8i2.425

Abstract

Coffee is one of the competitive commodities that requires detailed quality control. The common diseases that attack coffee plants are miner, rust, and phoma. Despite their visual similarity, the diseases differ in symptoms and treatments, requiring precise identification aided by computer vision. Miner and phoma have similar image features that are challenging in this study. Avoiding treatment error, several deep learning approach is needed to help classify the diseases. One of the robust methods is the Residual Network. Considering the number of datasets and alignment with the state-of-the-art, this study picked ResNet50 and ResNet101 to be observed. This study employed ResNet50 and ResNet101 in two scenarios. The first scenario was training the models on datasets without preprocessing, while the second scenario trained models on processed datasets. The preprocessing involved converting the color model to HSV and taking the range of leaf spot color from light red to dark brown for color segmentation. This study successfully achieved accuracy, precision, and F1-score at 89,16%, 89,42%, and 89,15% respectively, for the ResNet50 model trained on preprocessed data, slightly higher than the metrics of ResNet101. The ResNet101 achieved 87.95% of accuracy, 88.05% of precision, and 87.98% of F1-Score. These results indicate that ResNet50 is more robust for classifying the leaf spot, and the color segmentation helped the model to optimize the performance
Co-Authors Hizham, Fadhel Akhmad Pinasthika, Stanislaus Jiwandana