Evaluation of Convolutional Neural Networks for Coffee Leaf Rust Classification
Abstract
Coffee is a beverage present in the lives of many people worldwide and is of great importance to the economies of various countries. Coffee leaf rust is a serious disease that affects crops worldwide, and identifying it quickly and accurately helps in its control. This paper uses a publicly available image dataset to evaluate the performance of Convolutional Neural Networks (CNNs) in the context of the automatic classification of rust on coffee leaves considering binary and multi-class classification. Among the evaluated networks, ResNet achieved the best results, with an accuracy of 95.19% for binary classification and 78.03% for multi-class classification. This study contributes to the application of deep learning as a tool for farmers, enabling the early detection of rust on coffee leaves and aiding in decision-making related to crop management.References
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Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., and Lew, M. S. (2016). Deep learning for visual understanding: A review. Neurocomputing, 187:27–48. Recent Developments on Deep Big Vision.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K. Q. (2016). Densely connected convolutional networks.
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K. (2016). Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size. arXiv preprint arXiv:1602.07360.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
Krohling, R. A. (2019). BRACOL - A Brazilian Arabica Coffee Leaf images dataset to identification and quantification of coffee diseases and pests. Type: dataset.
Marcos, A. P., Silva Rodovalho, N. L., and Backes, A. R. (2019). Coffee Leaf Rust Detection Using Convolutional Neural Network. In 2019 XV Workshop de Visão Computacional (WVC), pages 38–42, São Bernardo do Campo, Brazil. IEEE.
Montalbo, F. J. and Hernandez, A. (2020). Classifying barako coffee leaf diseases using deep convolutional models. International Journal of Advances in Intelligent Informatics, 6(2):197–209.
Montalbo, F. J. P. (2022). Automated diagnosis of diverse coffee leaf images through a stage-wise aggregated triple deep convolutional neural network. Machine Vision and Applications, 33(1):19.
Ongsulee, P. (2017). Artificial intelligence, machine learning and deep learning. In 2017 15th international conference on ICT and knowledge engineering (ICT&KE), pages 1–6, Bangkok, Thailand. IEEE.
Organization, I. C. (2019). Coffee Development Report 2019 - Growing for Prosperity: Economic viability as the catalyst for a sustainable coffee sector. Technical report, International Coffee Organization, London.
Pandian, J. A., Kumar, V. D., Geman, O., Hnatiuc, M., Arif, M., and Kanchanadevi, K. (2022). Plant disease detection using deep convolutional neural network. Applied Sciences, 12(14).
Parraga-Alava, J., Cusme, K., Loor, A., and Santander, E. (2019). RoCoLe: A robusta coffee leaf images dataset for evaluation of machine learning based methods in plant diseases recognition. Data in Brief, 25:104414.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Wallach, H., Larochelle, H., Beygelzimer, A., d'Alché-Buc, F., Fox, E., and Garnett, R., editors, Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc.
Ponti, M. A., Ribeiro, L. S. F., Nazare, T. S., Bui, T., and Collomosse, J. (2017). Everything You Wanted to Know about Deep Learning for Computer Vision but Were Afraid to Ask. In 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), pages 17–41, Niterói, Brazil. IEEE.
Rodrigues Moreira, L. F., Moreira, R., Travençolo, B. A. N., and Backes, A. R. (2025). Deep learning based image classification for embedded devices: A systematic review. Neurocomputing, 623:129402.
Santana, M. F., Zambolim, E. M., Caixeta, E. T., and Zambolim, L. (2018). Population genetic structure of the coffee pathogen hemileia vastatrix in minas gerais, brazil. Tropical Plant Pathology, 43(5):473–476.
Silva, M. d. C., Guerra-Guimarães, L., Diniz, I., Loureiro, A., Azinheira, H., Pereira, A. P., Tavares, S., Batista, D., and Várzea, V. (2022). An overview of the mechanisms involved in coffee-hemileia vastatrix interactions: Plant and pathogen perspectives. Agronomy, 12(2).
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Suparyanto, T., Firmansyah, E., Wawan Cenggoro, T., Sudigyo, D., and Pardamean, B. (2022). Detecting Hemileia vastatrix using Vision AI as Supporting to Food Security for Smallholder Coffee Commodities. IOP Conference Series: Earth and Environmental Science, 998(1):012044.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015). Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1–9, Boston, MA, USA. IEEE.
Yebasse, M., Shimelis, B., Warku, H., Ko, J., and Cheoi, K. J. (2021). Coffee disease visualization and classification. Plants, 10(6):1257.
Published
2025-07-20
How to Cite
MACHADO, Thiago Vieira; RODRIGUES, Leonardo Gabriel Ferreira; TRAVENÇOLO, Bruno Augusto Nassif; COSTA, Cícero Lima; LIMA, Danielli Araújo; MOREIRA, Larissa Ferreira Rodrigues.
Evaluation of Convolutional Neural Networks for Coffee Leaf Rust Classification. In: WORKSHOP ON COMPUTING APPLIED TO THE MANAGEMENT OF THE ENVIRONMENT AND NATURAL RESOURCES (WCAMA), 16. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 11-20.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2025.6650.
