AI-Driven Approach for Digital Agriculture: A Case Study on Coffee Leaf Disease
Abstract
This study presents an AI-driven approach to coffee leaf disease classification by integrating deep learning and computer vision. Using DenseNet, MobileNet, and ResNet with transfer learning and data augmentation, the system achieved high accuracy, with DenseNet consistently performing the best. The experimental results highlight the influence of learning rate and class distribution on model performance. Model conversion ensures efficient deployment and supports real-time diagnosis. The proposed solution bridges AI research and practical agriculture, enhancing disease management while paving the way for scalable adaptive precision farming.References
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Albuquerque, L. D. and Guedes, E. B. (2024). Coffee Plant Leaf Disease Detection for Digital Agriculture. Journal on Interactive Systems, 15(1):220–233.
Aufar, Y., Abdillah, M. H., and Romadoni, J. (2023). Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 7(1):71 – 79.
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CONAB (2024). Acompanhamento Da Safra Brasileira de Café. Accessed: 2024-12-10.
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Ferraz, A. F. e. a. (2021). Aplicação da inteligência artificial no diagnóstico de doenças de plantas. Scientia Agricola, 78(4):547–560.
Fones, H. N., Bebber, D. P., Chaloner, T. M., Kay, W. T., Steinberg, G., and Gurr, S. J. (2020). Threats to global food security from emerging fungal and oomycete crop pathogens. Nature Food, 1(6):332–342.
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Moreira, R., Rodrigues Moreira, L. F., Munhoz, P. L. A., Lopes, E. A., and Ruas, R. A. A. (2022). AgroLens: A low-cost and green-friendly Smart Farm Architecture to support real-time leaf disease diagnostics. Internet of Things, 19:100570.
Nawaz, M., Nazir, T., Javed, A., Tawfik Amin, S., Jeribi, F., and Tahir, A. (2024). CoffeeNet: A deep learning approach for coffee plant leaves diseases recognition. Expert Systems with Applications, 237:121481.
Pacal, I., Kunduracioglu, I., Alma, M. H., Deveci, M., Kadry, S., Nedoma, J., Slany, V., and Martinek, R. (2024). A systematic review of deep learning techniques for plant diseases. Artificial Intelligence Review, 57(11):304.
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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., De F. B. Saar, L. N., Moreira, R., Rodrigues, L. G. F., Travençolo, B. A. N., and Backes, A. R. (2024). Enabling Intelligence on Edge Through an Artificial Intelligence as a Service Architecture. In 2024 IEEE 13th International Conference on Cloud Networking (CloudNet), pages 1–8.
Rodrigues Moreira, L. F., Moreira, R., Travençolo, B. A. N., and Backes, A. R. (2023). An Artificial Intelligence-as-a-Service Architecture for deep learning model embodiment on low-cost devices: A case study of COVID-19 diagnosis. Applied Soft Computing, 134:110014.
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.
Saleki, S. and Tahmoresnezhad, J. (2024). Agry: a comprehensive framework for plant diseases classification via pretrained EfficientNet and convolutional neural networks for precision agriculture. Multimedia Tools and Applications, 83(24):64813–64851.
Sharma, A., Azeem, N. A., and Sharma, S. (2023). Coffee Leaf Disease Detection Using Transfer Learning. In Woungang, I., Dhurandher, S. K., Pattanaik, K. K., Verma, A., and Verma, P., editors, Advanced Network Technologies and Intelligent Computing, pages 227–238, Cham. Springer Nature Switzerland.
Silva, A. C. e. a. (2018). Doenças do cafeeiro: manual de identificação e controle. Embrapa.
Yamakawa, D., Sousa, P. M., and Melo, P. H. (2024). Classification of Coffee Biotic Stresses Using Convolutional Neural Networks and Enhanced Image Preprocessing Techniques. In Anais do XV Workshop de Sistemas de Informação, pages 51–56, Porto Alegre, RS, Brasil. SBC.
Zambolim, L. and Brenas, B. M. (2018). Doenças do Café no Brasil - Enfermedades del café en Central-America. UFV, Viçosa, 1ª edition.
Zambolim, L., Caixeta, E. T., Filho, O. G., Sera, G. H., Sera, T., Pereira, A. A., de Oliveira, A. C. B., Filho, A. C. V., de Carvalho, C. H., and Ramalho, A. R. (2024). Breeding Programs Against Coffee Leaf Rust in Brazil: A Review. Journal of Agricultural Science, 16(5):61–75.
Albuquerque, L. D. and Guedes, E. B. (2024). Coffee Plant Leaf Disease Detection for Digital Agriculture. Journal on Interactive Systems, 15(1):220–233.
Aufar, Y., Abdillah, M. H., and Romadoni, J. (2023). Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 7(1):71 – 79.
Avelino, J., Cristancho, M., Georgiou, S., Imbach, P., Aguilar, L., Bornemann, G., Läderach, P., Anzueto, F., Hruska, A. J., and Morales, C. (2015). The coffee rust crises in Colombia and Central America (2008–2013): impacts, plausible causes and proposed solutions. Food Security, 7(2):303–321.
CONAB (2024). Acompanhamento Da Safra Brasileira de Café. Accessed: 2024-12-10.
de Carvalho, V. L. (2013). Boletim Técnico – Doenças do Cafeeiro: diagnose e controle. Epamig, Brasil.
Ferraz, A. F. e. a. (2021). Aplicação da inteligência artificial no diagnóstico de doenças de plantas. Scientia Agricola, 78(4):547–560.
Fones, H. N., Bebber, D. P., Chaloner, T. M., Kay, W. T., Steinberg, G., and Gurr, S. J. (2020). Threats to global food security from emerging fungal and oomycete crop pathogens. Nature Food, 1(6):332–342.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
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, 36:107142.
Moreira, R., Rodrigues Moreira, L. F., Munhoz, P. L. A., Lopes, E. A., and Ruas, R. A. A. (2022). AgroLens: A low-cost and green-friendly Smart Farm Architecture to support real-time leaf disease diagnostics. Internet of Things, 19:100570.
Nawaz, M., Nazir, T., Javed, A., Tawfik Amin, S., Jeribi, F., and Tahir, A. (2024). CoffeeNet: A deep learning approach for coffee plant leaves diseases recognition. Expert Systems with Applications, 237:121481.
Pacal, I., Kunduracioglu, I., Alma, M. H., Deveci, M., Kadry, S., Nedoma, J., Slany, V., and Martinek, R. (2024). A systematic review of deep learning techniques for plant diseases. Artificial Intelligence Review, 57(11):304.
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017). Automatic differentiation in PyTorch. In NIPS-W.
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., De F. B. Saar, L. N., Moreira, R., Rodrigues, L. G. F., Travençolo, B. A. N., and Backes, A. R. (2024). Enabling Intelligence on Edge Through an Artificial Intelligence as a Service Architecture. In 2024 IEEE 13th International Conference on Cloud Networking (CloudNet), pages 1–8.
Rodrigues Moreira, L. F., Moreira, R., Travençolo, B. A. N., and Backes, A. R. (2023). An Artificial Intelligence-as-a-Service Architecture for deep learning model embodiment on low-cost devices: A case study of COVID-19 diagnosis. Applied Soft Computing, 134:110014.
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.
Saleki, S. and Tahmoresnezhad, J. (2024). Agry: a comprehensive framework for plant diseases classification via pretrained EfficientNet and convolutional neural networks for precision agriculture. Multimedia Tools and Applications, 83(24):64813–64851.
Sharma, A., Azeem, N. A., and Sharma, S. (2023). Coffee Leaf Disease Detection Using Transfer Learning. In Woungang, I., Dhurandher, S. K., Pattanaik, K. K., Verma, A., and Verma, P., editors, Advanced Network Technologies and Intelligent Computing, pages 227–238, Cham. Springer Nature Switzerland.
Silva, A. C. e. a. (2018). Doenças do cafeeiro: manual de identificação e controle. Embrapa.
Yamakawa, D., Sousa, P. M., and Melo, P. H. (2024). Classification of Coffee Biotic Stresses Using Convolutional Neural Networks and Enhanced Image Preprocessing Techniques. In Anais do XV Workshop de Sistemas de Informação, pages 51–56, Porto Alegre, RS, Brasil. SBC.
Zambolim, L. and Brenas, B. M. (2018). Doenças do Café no Brasil - Enfermedades del café en Central-America. UFV, Viçosa, 1ª edition.
Zambolim, L., Caixeta, E. T., Filho, O. G., Sera, G. H., Sera, T., Pereira, A. A., de Oliveira, A. C. B., Filho, A. C. V., de Carvalho, C. H., and Ramalho, A. R. (2024). Breeding Programs Against Coffee Leaf Rust in Brazil: A Review. Journal of Agricultural Science, 16(5):61–75.
Published
2025-07-20
How to Cite
VILAS BOAS, Luiz Felipe de Castro; RODRIGUES, Leonardo Gabriel Ferreira; MARINHO E SILVA, Rafael; LIMA, Danielli Araújo; MOREIRA, Larissa Ferreira Rodrigues.
AI-Driven Approach for Digital Agriculture: A Case Study on Coffee Leaf Disease. 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. 1-10.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2025.6606.
