Coffee Plant Leaf Disease Detection for Digital Agriculture




Deep Learning, Digital Agriculture, Computer Vision, Artificial Neural Networks, Coffee


In an effort to advance Digital Agriculture, this paper provides a comparative assessment of Artificial Neural Networks for intelligent detection of a major biotic stress factors in coffee cultivation. Through a multi-class Computer Vision task, the superior performance of Convolutional Neural Networks, notably the ShuffleNet architecture, was discerned, further substantiated by statistical analyses. This model's performance, akin to state-of-the-art solutions, was achieved with reduced training data and parameter requirements. Robustness was affirmed through external validation using alternative datasets. This contribution directly enhances coffee plantations' quality and supports the development of Edge Computing devices for Agricultural IoT.


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Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from

Albuquerque, L. and Guedes, E. B. (2023). Um comparativo de abordagens com redes neurais artificiais para detecção inteligente de patologias na folha do café. In Anais do XIV Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais, pages 131–140, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/wcama.2023.229712.

Aufar, Y., Abdillah, M. H., and Romadoni, J. (2023). Web-based CNN application for Arabica Coffee leaf disease prediction in smart agriculture. J. RESTI (Rekayasa Sist. Dan Teknol. Inf.), 7(1):71–79.

Brazil (2022). Brazilian Ministry of Agriculture, Livestock, and Supply – Brazilian Agribusi-ness Overview – Coffee in Brazil. Available at [link]. Accessed in October, 2023.

Brito Silva, L., Cavalcante Carneiro, A. L., and Silveira Almeida Renaud Faulin, M. (2020). Rust (Hemileia vasta-trix) and leaf miner (Leucoptera coffeella) in coffee crop (Coffea arabica).

Cao, K., Liu, Y., Meng, G., and Sun, Q. (2020). An overview on edge computing research. IEEE Access, 8:85714–85728. DOI: 10.1109/ACCESS.2020.2991734.

Carneiro, A. L. C., Silva, L. B., and Faulin, M. S. A. R. (2021). Artificial intelligence for detection and quantification of rust and leaf miner in coffee crop.

Carvalho, V., Guedes, E., and Salame, M. (2019). Classificação de ervas daninhas em culturas agrícolas com comitês de redes neurais convolucionais. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional, pages 60–71, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/eniac.2019.9272.

Chollet, F. et al. (2015). Keras. [link].

Coelho, L. P. (2013). Mahotas: Open source software for scriptable computer vision. Journal of Open Research Software. DOI: 10.5334/

CONAB (2022). Companhia Nacional de Abastecimento – 4o Levantamento da Safra de Café. ISSN: 2318-7913. Available at [link]. Acessed in October, 2023.

Cover, T. M. and Thomas, J. A. (2006). Elements of Information Theory. John Wiley & Sons, Nashville, TN, 2 edition.

Dias, J. and Saito, J. (2021). Coffee plant image segmentation and disease detection using JSEG algorithm. In Anais do XVII Workshop de Visão Computacional, pages 42–47, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/wvc.2021.18887.

Embrapa (2014). Visão 2014–2034 – O Futuro do Desenvolvimento Tecnológico da Agricultura Brasileira (Síntese). Embrapa, Distrito Federal, Brasil.

Escolano, F., Suau, P., and Bonev, B. (2009). Information Theory in Computer Vision and Pattern Recognition. Springer, London, England, 2009 edition.

Esgario, J. G. M., Krohling, R. A., and Ventura, J. A. (2020a). Deep learning for classification and severity estimation of coffee leaf biotic stress. Computers and Electronics in Agriculture, 169:105162. DOI:

Esgario, J. G. M., Krohling, R. A., and Ventura, J. A. (2020b). Deep learning for classification and severity estimation of coffee leaf biotic stress. Computers and Electronics in Agriculture, 169:105162. DOI: 10.1016/j.compag.2019.105162.

Faceli, K., Lorena, A. C., Gama, J., and Carvalho, A. C. P. L. F. (2021). Inteligência Artificial – Uma abordagem de aprendizado de máquina. Editora LTC, Rio de Janeiro, 2 edition.

FAO (2023). Food and Agriculture Organization of the United Nations – Coffee. Available at [link]. Accessed in October, 2023.

Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. The MIT Press, Cambridge.

Haralick, R. M., Shanmugam, K., and Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6):610–621.

Hassan, N., Gillani, S., Ahmed, E., Yaqoob, I., and Imran, M. (2018). The role of edge computing in internet of things. IEEE Communications Magazine, 56(11):110–115. DOI: 10.1109/mcom.2018.1700906.

Haykin, S. (2008). Neural Networks and Learning Machines. Pearson Prentice-Hall, F, 3 edition.

Ho, S. Y., Phua, K., Wong, L., and Bin Goh, W. W. (2020). Extensions of the external validation for checking learned model interpretability and generalizability. Patterns, 1(8):100129. DOI:

Howard, A., Sandler, M., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted Residuals and Linear Bottlenecks. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4510–4520. Available at: [link].

Hung, C.-C., Song, E., and Lan, Y. (2019). Image Texture Analysis – Foundations, Models and Algoritms. Springer, Switzerland.

Işik, A. H. and Eskicioglu, Ö. C. (2022). Classification of coffee leaf diseases through image processing techniques. In Artificial Intelligence and Smart Agriculture Applications, pages 233–252. Auerbach Publications, USA.

Jepkoech, J., Mugo, D. M., Kenduiywo, B. K., and Too, E. C. (2021). Arabica coffee leaf images dataset for coffee leaf disease detection and classification. Data in Brief, 36:107–142.

Ji, W., Pan, Y., Xu, B., and Wang, J. (2022). A real-time apple targets detection method for picking robot based on ShufflenetV2-YOLOX. Agriculture, 12(6):856. DOI:10.3390/agriculture12060856.

Kamilaris, A. and Prenafeta-Boldú, F. X. (2018). Deep Learning in Agriculture: A Survey. Computers and Electronics in Agriculture, 147:70–90.

Khan, S., Rahmani, H., Shah, S. A. A., and Bennamoun, M. (2018). A Guide to Convolutional Neural Networks for Computer Vision. Synthesis Lectures on Computer Vision. Morgan & Claypool, San Rafael, California, USA, 1a edition.

Kingma, D. P. and Ba, J. L. (2015). Adam: a method for stochastic optimization. In Proceedings of 3rd International Conference on Learning Representations, San Diego, CA. ArXiv. DOI:

Krohling, R. A. (2019). BRACOL - a brazilian arabica coffee leaf images dataset to identification and quantification of coffee diseases and pests.

Mesquita, C. M., Rezende, J. E., Carvalho, J. S., Júnior, M. A. F., Moraes, N. C., Dias, P. T., Carvalho, R. M., and Araújo, W. G. (2016). Manual do Café – Distúrbios Fisiológicos, Pragas e Doenças do Cafeeiro. EMATER, Belo Horizonte, 1 edition.

Mirjalili, F. and Hardeberg, J. Y. (2022). On the quantification of visual texture complexity. Journal of Imaging, 8(9). DOI: 10.3390/jimaging8090248.

Novtahaning, D., Shah, H. A., and Kang, J.-M. (2022). Deep learning ensemble-based automated and high-performing recognition of coffee leaf disease. Agriculture, 12(11). DOI: 10.3390/agriculture12111909.

Palit, A. K. and Popovic, D. (2005). Computational Intelligence in Time Series Forecasting. Springer-Verlag, London.

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).

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.

Prince, S. (2012). Computer Vision: Models, Learning, and Inference. Cambridge University Press, Cambridge.

Quy, V. K., Hau, N. V., Anh, D. V., Quy, N. M., Ban, N. T., Lanza, S., Randazzo, G., and Muzirafuti, A. (2022). IoT-enabled smart agriculture: Architecture, applications, and challenges. Applied Sciences, 12(7):3396. DOI: 10.3390/app12073396.

Rehman, A., Tariq, Z., din Memon, S. u., Zaib, A., Khan, M. U., and Aziz, S. (2021). Cucumber leaf disease classification using local tri-directional patterns and haralick features. In 2021 International Conference on Artificial Intelligence (ICAI), pages 258–263, Islamabad, Pakistan. IEEE. DOI: 10.1109/ICAI52203.2021.9445237.

Santos, F., Canuto, A., Bedregal, B., Palmeira, E., and Silva, I. (2019). Supervised methods applied to the construction of a vision system for the classification of cocoa beans in the cut-test. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional, pages 72–83, Porto Alegre, RS, Brasil. SBC. Available at Accessed at March 18, 2024.

Schwartz, R., Dodge, J., Smith, N. A., and Etzioni, O. (2020). Green ai. Commun. ACM, 63(12):54–63. DOI:10.1145/3381831.

Sharma, A., Jain, A., Gupta, P., and Chowdary, V. (2021). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, 9:4843–4873.

Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. Available at Accessed at March 18, 2024.

Sun, W., Fu, B., and Zhang, Z. (2023). Maize nitrogen grading estimation method based on UAV images and an improved shufflenet network. Agronomy, 13(8):1974. DOI: 10.3390/agronomy13081974.

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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 1–9, Massachusetts, Es-tados Unidos. IEEE.

Tan, M. and Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning, volume 97, pages 6105–6114.

Tang, S., Zhu, Q., Zhou, X., Liu, S., and Wu, M. (2002). A Conception of Digital Agriculture. In IEEE International Geoscience and Remote Sensing Symposium, pages 3026–3028, Canada. IEEE.

Van Rossum, G. and Drake, F. L. (2009). Python 3 Reference Manual. CreateSpace, Scotts Valley, CA.

VijayaLakshmi, B. and Mohan, V. (2016). Kernel-based PSO and FRVM: An automatic plant leaf type detection using texture, shape, and color features. Computers and Electronics in Agriculture, 125:99–112. DOI:

Walpole, R. E., Myers, R. H., Myers, S. L., and Ye, K. (2012). Probability & Statistics for Engineers & Scientists. Prentice Hall, United States, 9 edition.

Xian, T. S. and Ngadiran, R. (2021). Plant diseases classification using machine learning. Journal of Physics: Conference Series, 1962(1):012024. DOI: 10.1088/1742-6596/1962/1/012024.

Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6848–6856, USA. IEEE.




How to Cite

ALBUQUERQUE, L. D.; GUEDES, E. B. Coffee Plant Leaf Disease Detection for Digital Agriculture. Journal on Interactive Systems, Porto Alegre, RS, v. 15, n. 1, p. 220–233, 2024. DOI: 10.5753/jis.2024.3804. Disponível em: Acesso em: 20 may. 2024.



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