CLASSIFICATION OF COFFEE LEAF SPOT DISEASES USING THE RESIDUAL NEURAL NETWORKS

Stanislaus Jiwandana Pinasthika; Fadhel Akhmad Hizham; Annisa Fitri Maghfiroh Harvyanti

doi:10.34288/jri.v8i1.425

Authors

Stanislaus Jiwandana Pinasthika Universitas Jember https://orcid.org/0000-0002-3099-8153
Fadhel Akhmad Hizham Universitas Jember
Annisa Fitri Maghfiroh Harvyanti Universitas Jember

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v8i1.425

Keywords:

CNN, ResNet, Coffee Leaf Diseases, Transfer Learning, Image Processing

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

Downloads

Download data is not yet available.

References

Al-Shamasneh, A. R. (2025). Potato leaves disease classification based on generalized Jones polynomials image features. MethodsX, 14, 103421. https://doi.org/10.1016/j.mex.2025.103421

Atul, S. H. M., Sakib, M., Hasan, Md. I., Islam, Md. S., Khan, O. F., & Rahim, A. (2024). Potato Leaf Disease Detection System Using the Convolutional Neural Network (ResNet). 2024 IEEE Conference on Engineering Informatics (ICEI), 1–5. https://doi.org/10.1109/ICEI64305.2024.10912342

Balavani, K., Sriram, D., Shankar, M. B., & Charan, D. S. (2023). An Optimized Plant Disease Classification System Based on Resnet-50 Architecture and Transfer Learning. 2023 4th International Conference for Emerging Technology (INCET), 1–5. https://doi.org/10.1109/INCET57972.2023.10170368

Bharti, R., Srivastava, V., Bajpai, A., & Sahu, S. (2024). Comparative Analysis of Potato Leaf Disease Classification Using CNN and ResNet50. 2024 International Conference on Data Science and Its Applications (ICoDSA), 87–91. https://doi.org/10.1109/ICoDSA62899.2024.10651649

Firdaus, Q., Sigit, R., Harsono, T., & Anwar, A. (2020). Lung Cancer Detection Based On CT-Scan Images With Detection Features Using Gray Level Co-Occurrence Matrix (GLCM) and Support Vector Machine (SVM) Methods. 2020 International Electronics Symposium (IES), 643–648. https://doi.org/10.1109/IES50839.2020.9231663

Guojian, C., & Peisong, L. (2021). Rock Thin-Section Image Classification based on Residual Neural Network. 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), 521–524. https://doi.org/10.1109/ICSP51882.2021.9408983

Harmiansyah, Diptaningsari, D., Wardani, N., Meidaliyantisyah, Mawardi, R., & Hendra, J. (2023). Intensity of Leaf Rust Disease on Four Robusta Coffee Clones in Natar, South Lampung. IOP Conference Series: Earth and Environmental Science, 1230(1), 012097. https://doi.org/10.1088/1755-1315/1230/1/012097

Harvyanti, A. F. M., Baihaki, R. I., Dafik, Ridlo, Z. R., & Agustin, I. H. (2023). Application of Convolutional Neural Network for Identifying Cocoa Leaf Disease. In I. H. Agustin (Ed.), Proceedings of the 1st International Conference on Neural Networks and Machine Learning 2022 (ICONNSMAL 2022) (Vol. 177, pp. 283–304). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-174-6_21

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition. https://arxiv.org/abs/1512.03385

Kant, V. (2024). Automated Corn Leaf Disease Diagnosis using ResNet-based Convolutional Neural Network. 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), 892–897. https://doi.org/10.1109/ICoICI62503.2024.10696710

Kumari, M., Kukreja, V., Raj, A., & Chaudhary, S. K. (2024). Coffee Leaf Diseases Classification: A CNN and Random Forest Approach for Precision Diagnosis. 2024 International Conference on Automation and Computation (AUTOCOM), 210–214. https://doi.org/10.1109/AUTOCOM60220.2024.10486169

Li, X., & Rai, L. (2020). Apple Leaf Disease Identification and Classification using ResNet Models. 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT), 738–742. https://doi.org/10.1109/ICEICT51264.2020.9334214

Malayil, A., & Mosleh, M. A. S. (2024). Detection of Diseases in Cardamom Leaves by Using Digital Image Feature Selection Techniques. 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–7. https://doi.org/10.1109/ICCCNT61001.2024.10724853

Meghana, B., J, J., & Lalitha, S. (2025). Automated Pomegranate Leaf Disease Identification: An Image Processing Perspectives. 2025 IEEE 14th International Conference on Communication Systems and Network Technologies (CSNT), 522–528. https://doi.org/10.1109/CSNT64827.2025.10968085

Mridha Atul, S. H., Isthiakul Hasan, Md., Rahman, Md. M., Sultana, F., Khan, O. F., & Saiful Islam, Md. (2025). Rice Leaf Disease Detection System Using the Convolutional Neural Network (ResNet). 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), 1–6. https://doi.org/10.1109/ECCE64574.2025.11013852

Naga Saranya, N., Vivekanandhan, V., Al-Farouni, M., Shreyas, A. V., & Ranjith Kumar, G. (2024). Crop Classification on Hyperspectral Images Using Residual Network and Multi-Head Self-Attention Feature Extraction. 2024 International Conference on Data Science and Network Security (ICDSNS), 01–04. https://doi.org/10.1109/ICDSNS62112.2024.10691054

Nasra, P., Gupta, S., Jagadish, S., Singh, A. R., & RiadHwsein, R. (2025). ResNet-50 for Apple Leaf Disease Classification: Scalable and Accurate Automated Diagnostics. 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0, 1–5. https://doi.org/10.1109/OTCON65728.2025.11070672

Nhut, T. H., Hoang Khang, N. P., & Nhan, N. C. (2023). An Automatic Diagnosis System of Rice Leaf Diseases Based on ResNet-50 Architecture. 2023 International Conference on Advanced Computing and Analytics (ACOMPA), 54–58. https://doi.org/10.1109/ACOMPA61072.2023.00018

Pakiding, D., & Selao, A. (2025). Implementation of Computer Vision for Detecting Diseases in Chili and Tomato Plants Using the Convolutional Neural Networks Method. 5.

Patil, P., Pawar, Y., Kuldipkar, S., & Deshpande, A. (2024). Banana Plant Disease Detection using Image Processing. 2024 IEEE 9th International Conference for Convergence in Technology (I2CT), 1–5. https://doi.org/10.1109/I2CT61223.2024.10543338

Purnomo, H., Muhlison, W., Sucipto, I., Shaleh, A., Zahro’in, E., & Rahmawati, I. (2025). Survey on Coffee Leaf Miner Incidences and Potential Parasitic Wasps on Arabica Coffee Crops (Coffea arabica L.) at Probolinggo highland East Java Indonesia. IOP Conference Series: Earth and Environmental Science, 1497(1), 012008. https://doi.org/10.1088/1755-1315/1497/1/012008

Sirenjeevi, P., Karthick, J. M., Agalya, K., Srikanth, R., Elangovan, T., & Nareshkumar, R. (2023). Leaf Disease Identification using ResNet. 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), 1–5. https://doi.org/10.1109/ICECONF57129.2023.10083963

Sunarharum, W. B., Ali, D. Y., Mahatmanto, T., Nugroho, P. I., Asih, N. E., Mahardika, A. P., & Geofani, I. (2021). The Indonesian coffee consumers perception on coffee quality and the effect on consumption behavior. IOP Conference Series: Earth and Environmental Science, 733(1), 012093. https://doi.org/10.1088/1755-1315/733/1/012093

Szeliski, R. (2021). Computer Vision: Algorithms and Applications 2nd Edition (2nd ed.). Springer.

Wiyono, S., Suryaningsih, A. S., Wafa, A., Tondok, E. T., Istiaji, B., Triwidodo, H., & Widodo, W. (2019). Kanker Batang: Penyakit Baru pada Kopi di Lampung. Jurnal Fitopatologi Indonesia, 15(1), 9. https://doi.org/10.14692/jfi.15.1.9