Aprendizado sensível ao custo aplicado na detecção de estresse agrícola na cultura do feijão
Resumo
Este trabalho avaliou algoritmos de Aprendizado de Máquina, com abordagens tradicional e sensível ao custo, para detectar estresse em lavouras de feijão a partir de imagens multiespectrais de VANT. Dois ciclos do CRISP-DM foram implementados. Os resultados mostraram a relevância dos índices de vegetação. A abordagem sensível ao custo aumentou a especificidade dos modelos. O melhor modelo foi obtido na fase reprodutiva da lavoura, atingindo 83% de acurácia e 77,63% de média geométrica, indicando potencial para a detecção precoce do estresse.
Referências
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Calou, V. B. C., dos Santos Teixeira, A., Moreira, L. C. J., Lima, C. S., de Oliveira, J. B., and de Oliveira, M. R. R. (2020). The use of uavs in monitoring yellow sigatoka in banana. Biosystems Engineering, 193:115–125.
Gull, A., Lone, A. A., and Wani, N. U. I. (2019). Biotic and abiotic stresses in plants. In de Oliveira, A. B., editor, Abiotic and Biotic Stress in Plants, chapter 1. IntechOpen, Rijeka.
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IBGE (2025). Ibge prevê safra de 322,6 milhões de toneladas para 2025, com crescimento de 10,2% frente a 2024. [link]. Accessed: fev/2025.
Prabhakar, M., Prasad, Y., and Rao, M. N. (2011). Remote sensing of biotic stress in crop plants and its applications for pest management. Crop stress and its management: Perspectives and strategies, pages 517–545.
Reyes-Hung, L. and Soto, I. (2023). Machine learning in spectral imaging for smart farming: A review. In 2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), pages 1–4.
Reyes-Hung, L., Soto, I., Zamorano-Illanes, R., Adasme, P., Ijaz, M., Azurdia, C., and Gutierrez, S. (2023). Crop stress detection with multispectral imaging using ia. In 2023 South American Conference On Visible Light Communications (SACVLC), pages 59–64.
Schröer, C., Kruse, F., and Gómez, J. M. (2021). A systematic literature review on applying crisp-dm process model. Procedia Computer Science, 181:526–534. CENTERIS 2020 - International Conference on ENTERprise Information Systems / ProjMAN 2020 - International Conference on Project MANagement / HCist 2020 - International Conference on Health and Social Care Information Systems and Technologies 2020, CENTERIS/ProjMAN/HCist 2020.
Silva, J., Bacao, F., Dieng, M., Foody, G. M., and Caetano, M. (2017). Improving specific class mapping from remotely sensed data by cost-sensitive learning. International Journal of Remote Sensing, 38(11):3294–3316.
Wirth, R. and Hipp, J. (2000). Crisp-dm: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, pages 29–39. Springer-Verlag London, UK.
Zhang, K., Yan, F., and Liu, P. (2024). The application of hyperspectral imaging for wheat biotic and abiotic stress analysis: A review. Computers and Electronics in Agriculture, 221:109008.
Zhou, J., Zhou, J., Ye, H., Ali, M. L., Nguyen, H. T., and Chen, P. (2020). Classification of soybean leaf wilting due to drought stress using uav-based imagery. Computers and Electronics in Agriculture, 175:105576.
Zhu, H., Lin, C., Liu, G., Wang, D., Qin, S., Li, A., Xu, J.-L., and He, Y. (2024). Intelligent agriculture: deep learning in uav-based remote sensing imagery for crop diseases and pests detection. Frontiers in Plant Science, Volume 15 - 2024.
Calou, V. B. C., dos Santos Teixeira, A., Moreira, L. C. J., Lima, C. S., de Oliveira, J. B., and de Oliveira, M. R. R. (2020). The use of uavs in monitoring yellow sigatoka in banana. Biosystems Engineering, 193:115–125.
Gull, A., Lone, A. A., and Wani, N. U. I. (2019). Biotic and abiotic stresses in plants. In de Oliveira, A. B., editor, Abiotic and Biotic Stress in Plants, chapter 1. IntechOpen, Rijeka.
Herrera-Poyato, D., Domínguez-Rull, J., Montes, R., Hernánde, I., Barrio, I., Poblete-Echeverria, C., Tardaguila, J., Herrera, F., and Herrera-Poyatos, A. (2024). Small data deep learning methodology for in-field disease detection. arXiv preprint arXiv:2409.17119.
IBGE (2025). Ibge prevê safra de 322,6 milhões de toneladas para 2025, com crescimento de 10,2% frente a 2024. [link]. Accessed: fev/2025.
Prabhakar, M., Prasad, Y., and Rao, M. N. (2011). Remote sensing of biotic stress in crop plants and its applications for pest management. Crop stress and its management: Perspectives and strategies, pages 517–545.
Reyes-Hung, L. and Soto, I. (2023). Machine learning in spectral imaging for smart farming: A review. In 2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), pages 1–4.
Reyes-Hung, L., Soto, I., Zamorano-Illanes, R., Adasme, P., Ijaz, M., Azurdia, C., and Gutierrez, S. (2023). Crop stress detection with multispectral imaging using ia. In 2023 South American Conference On Visible Light Communications (SACVLC), pages 59–64.
Schröer, C., Kruse, F., and Gómez, J. M. (2021). A systematic literature review on applying crisp-dm process model. Procedia Computer Science, 181:526–534. CENTERIS 2020 - International Conference on ENTERprise Information Systems / ProjMAN 2020 - International Conference on Project MANagement / HCist 2020 - International Conference on Health and Social Care Information Systems and Technologies 2020, CENTERIS/ProjMAN/HCist 2020.
Silva, J., Bacao, F., Dieng, M., Foody, G. M., and Caetano, M. (2017). Improving specific class mapping from remotely sensed data by cost-sensitive learning. International Journal of Remote Sensing, 38(11):3294–3316.
Wirth, R. and Hipp, J. (2000). Crisp-dm: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, pages 29–39. Springer-Verlag London, UK.
Zhang, K., Yan, F., and Liu, P. (2024). The application of hyperspectral imaging for wheat biotic and abiotic stress analysis: A review. Computers and Electronics in Agriculture, 221:109008.
Zhou, J., Zhou, J., Ye, H., Ali, M. L., Nguyen, H. T., and Chen, P. (2020). Classification of soybean leaf wilting due to drought stress using uav-based imagery. Computers and Electronics in Agriculture, 175:105576.
Zhu, H., Lin, C., Liu, G., Wang, D., Qin, S., Li, A., Xu, J.-L., and He, Y. (2024). Intelligent agriculture: deep learning in uav-based remote sensing imagery for crop diseases and pests detection. Frontiers in Plant Science, Volume 15 - 2024.
Publicado
20/07/2025
Como Citar
MORENO, Clara L.; SOUZA, Vanessa C. O.; MACHADO, Marley L.; FREITAS, Breno H. G. S..
Aprendizado sensível ao custo aplicado na detecção de estresse agrícola na cultura do feijão. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 16. , 2025, Maceió/AL.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 236-245.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2025.9092.
