Identificação de Doenças de Plantas Baseada em Visão para Sistemas Autônomos de Manejo de Culturas
Resumo
A integração da robótica na agricultura está revolucionando práticas tradicionais para permitir o monitoramento inteligente da saúde das plantas e a detecção de doenças. Este trabalho propõe uma nova abordagem para a identificação de doenças em plantas, utilizando o modelo de aprendizado profundo ConvNeXt para extrair e classificar características visuais de plantas em diferentes espécies e tipos de doenças. Foram utilizados dois conjuntos de dados: Plant Village e Plant Pathology 2020. O método proposto obteve alta acurácia, com 99,47% no Plant Village e 93,83% no Plant Pathology 2020, superando abordagens comparativas. O modelo foi mostrado robusto e escalável para robôs independentes, apoiando uma agricultura mais sustentável.Referências
Ahmed, S. T., Barua, S., Fahim-Ul-Islam, M., and Chakrabarty, A. (2024). Enhancing precision in rice leaf disease detection: A transformer model approach with attention mapping. In 2024 Int. Conf. on Adv. in Comp., Com., Elect., and Smart Sys. (iCAC-CESS), pages 1–6.
Belmir, M., Difallah, W., and Ghazli, A. (2023). Plant leaf disease prediction and classification using deep learning. In 2023 Int. Conference on Decision Aid Sciences and Applications (DASA), pages 536–540. IEEE.
Bhargava, A., Shukla, A., Goswami, O., Alsharif, M. H., Uthansakul, P., and Uthansakul, M. (2024). Plant leaf disease detection, classification and diagnosis using computer vision and artificial intelligence: A review. IEEE Access.
Ermolaeva, A. D. (2024). Plant disease detection using small convolutional neural networks. In 2024 Conference of Young Researchers in Electrical and Electronic Engineering (ElCon), pages 10–12. IEEE.
Fahim-Ul-Islam, M., Chakrabarty, A., Ahmed, S. T., Rahman, R., Kwon, H. H., and Jalil Piran, M. (2024). A comprehensive approach toward wheat leaf disease identification leveraging transformer models and federated learning. IEEE Access, 12:109128–109156.
G., G. and J., A. P. (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers Electrical Engineering, 76:323–338.
Hanif, M. A., Zim, M. K. I., and Kaur, H. (2024). Resnet vs inception-v3 vs svm: A comparative study of deep learning models for image classification of plant disease detection. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), volume 2, pages 1–6. IEEE.
Hashemifar, S. and Zakeri-Nasrabadi, M. (2024). Deep identification of plant diseases. In 2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), pages 1–6. IEEE.
Hendrycks, D. and Gimpel, K. (2016). Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415.
Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K. Q. (2016). Deep networks with stochastic depth. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, pages 646–661. Springer.
Jain, S., Jaidka, P., and Jain, V. (2023). Plant leaf disease classification using deep learning based hybrid approach. In 2023 Int. Conf. on Com., Security and Artificial Intelligence (ICCSAI), pages 383–387. IEEE.
Ji, W., Zhang, T., Xu, B., and He, G. (2024). Apple recognition and picking sequence planning for harvesting robot in a complex environment. Journal of Agricultural Engineering, 55(1).
Kaeser-Chen, C., Pathology, F., Maggie, and Dane, S. (2020). Plant pathology 2020 - fgvc7. [link]. Kaggle.
Kolakaluru, H., Vishal, T., Chandu, M. P., Harshini, M., Vignesh, T., and Padyala, V. V. P. (2023). Crop disease identification using convolutional neural network. In 2023 International Conference on Inventive Computation Technologies (ICICT), pages 366–369. IEEE.
Lakshmanarao, A., Babu, M. R., and Kiran, T. S. R. (2021). Plant disease prediction and classification using deep learning convnets. In 2021 Int. Conf. on Art. Intel. and Machine Vision (AIMV), pages 1–6. IEEE.
Lei Ba, J., Kiros, J. R., and Hinton, G. E. (2016). Layer normalization. ArXiv e-prints, pages arXiv–1607.
Li, L.-H. and Tanone, R. (2022). Mlp-mixer approach for corn leaf diseases classification. In Nguyen, N. T., Tran, T. K., Tukayev, U., Hong, T.-P., Trawiński, B., and Szczerbicki, E., editors, Intelligent Information and Database Systems, pages 204–215, Cham. Springer Nature Switzerland.
Liu, Z., Mao, H., , C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022). A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976–11986.
Ma, C., Hu, Y., Liu, H., Huang, P., Zhu, Y., and Dai, D. (2023). Generating image descriptions of rice diseases and pests based on deit feature encoder. Applied Sciences, 13(18).
Melese, T. and Yayeh, Y. (2023). Hybrid deep-machine learning based performance comparison for soybean plant disease identification. In 2023 International Conference on Information and Communication Technology for Development for Africa (ICT4DA), pages 96–101.
Mu, H., Zeng, X., Liu, W., Sun, J., and Zhang, Y. (2024). Improved mobile-vit model and its application in rolling bearing fault diagnosis. In 2024 6th International Conference on System Reliability and Safety Engineering (SRSE), pages 308–315.
Pavan, C. H. T., Sadha, C. K., Harshini, P., Annepu, V., Bagadi, K., and Chirra, V. R. R. (2023). Plant leaf disease classification using transfer learning using efficientnetb5. In 2023 International Conference on Next Generation Electronics (NEleX), pages 1–6. IEEE.
Pendhari, H., Virkar, R., Edakkalathur, B., and Jadhav, A. (2023). A comparative study on algorithms for plant disease detection using transfer learning. In 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), pages 528–533. IEEE.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE CVPR, pages 4510–4520.
Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., and Jégou, H. (2021). Going deeper with image transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 32–42.
Yu, W., Si, C., Zhou, P., Luo, M., Zhou, Y., Feng, J., Yan, S., and Wang, X. (2024). Metaformer baselines for vision. IEEE Trans. on Pattern Analysis and Machine Intelligence, 46(2):896–912.
Belmir, M., Difallah, W., and Ghazli, A. (2023). Plant leaf disease prediction and classification using deep learning. In 2023 Int. Conference on Decision Aid Sciences and Applications (DASA), pages 536–540. IEEE.
Bhargava, A., Shukla, A., Goswami, O., Alsharif, M. H., Uthansakul, P., and Uthansakul, M. (2024). Plant leaf disease detection, classification and diagnosis using computer vision and artificial intelligence: A review. IEEE Access.
Ermolaeva, A. D. (2024). Plant disease detection using small convolutional neural networks. In 2024 Conference of Young Researchers in Electrical and Electronic Engineering (ElCon), pages 10–12. IEEE.
Fahim-Ul-Islam, M., Chakrabarty, A., Ahmed, S. T., Rahman, R., Kwon, H. H., and Jalil Piran, M. (2024). A comprehensive approach toward wheat leaf disease identification leveraging transformer models and federated learning. IEEE Access, 12:109128–109156.
G., G. and J., A. P. (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers Electrical Engineering, 76:323–338.
Hanif, M. A., Zim, M. K. I., and Kaur, H. (2024). Resnet vs inception-v3 vs svm: A comparative study of deep learning models for image classification of plant disease detection. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), volume 2, pages 1–6. IEEE.
Hashemifar, S. and Zakeri-Nasrabadi, M. (2024). Deep identification of plant diseases. In 2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), pages 1–6. IEEE.
Hendrycks, D. and Gimpel, K. (2016). Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415.
Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K. Q. (2016). Deep networks with stochastic depth. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, pages 646–661. Springer.
Jain, S., Jaidka, P., and Jain, V. (2023). Plant leaf disease classification using deep learning based hybrid approach. In 2023 Int. Conf. on Com., Security and Artificial Intelligence (ICCSAI), pages 383–387. IEEE.
Ji, W., Zhang, T., Xu, B., and He, G. (2024). Apple recognition and picking sequence planning for harvesting robot in a complex environment. Journal of Agricultural Engineering, 55(1).
Kaeser-Chen, C., Pathology, F., Maggie, and Dane, S. (2020). Plant pathology 2020 - fgvc7. [link]. Kaggle.
Kolakaluru, H., Vishal, T., Chandu, M. P., Harshini, M., Vignesh, T., and Padyala, V. V. P. (2023). Crop disease identification using convolutional neural network. In 2023 International Conference on Inventive Computation Technologies (ICICT), pages 366–369. IEEE.
Lakshmanarao, A., Babu, M. R., and Kiran, T. S. R. (2021). Plant disease prediction and classification using deep learning convnets. In 2021 Int. Conf. on Art. Intel. and Machine Vision (AIMV), pages 1–6. IEEE.
Lei Ba, J., Kiros, J. R., and Hinton, G. E. (2016). Layer normalization. ArXiv e-prints, pages arXiv–1607.
Li, L.-H. and Tanone, R. (2022). Mlp-mixer approach for corn leaf diseases classification. In Nguyen, N. T., Tran, T. K., Tukayev, U., Hong, T.-P., Trawiński, B., and Szczerbicki, E., editors, Intelligent Information and Database Systems, pages 204–215, Cham. Springer Nature Switzerland.
Liu, Z., Mao, H., , C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022). A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976–11986.
Ma, C., Hu, Y., Liu, H., Huang, P., Zhu, Y., and Dai, D. (2023). Generating image descriptions of rice diseases and pests based on deit feature encoder. Applied Sciences, 13(18).
Melese, T. and Yayeh, Y. (2023). Hybrid deep-machine learning based performance comparison for soybean plant disease identification. In 2023 International Conference on Information and Communication Technology for Development for Africa (ICT4DA), pages 96–101.
Mu, H., Zeng, X., Liu, W., Sun, J., and Zhang, Y. (2024). Improved mobile-vit model and its application in rolling bearing fault diagnosis. In 2024 6th International Conference on System Reliability and Safety Engineering (SRSE), pages 308–315.
Pavan, C. H. T., Sadha, C. K., Harshini, P., Annepu, V., Bagadi, K., and Chirra, V. R. R. (2023). Plant leaf disease classification using transfer learning using efficientnetb5. In 2023 International Conference on Next Generation Electronics (NEleX), pages 1–6. IEEE.
Pendhari, H., Virkar, R., Edakkalathur, B., and Jadhav, A. (2023). A comparative study on algorithms for plant disease detection using transfer learning. In 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA), pages 528–533. IEEE.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE CVPR, pages 4510–4520.
Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., and Jégou, H. (2021). Going deeper with image transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 32–42.
Yu, W., Si, C., Zhou, P., Luo, M., Zhou, Y., Feng, J., Yan, S., and Wang, X. (2024). Metaformer baselines for vision. IEEE Trans. on Pattern Analysis and Machine Intelligence, 46(2):896–912.
Publicado
01/07/2025
Como Citar
GIL, Emanuelle S.; DIAS, Lucas M. A.; BRITO, Alternei S.; OLIVEIRA, Felipe G..
Identificação de Doenças de Plantas Baseada em Visão para Sistemas Autônomos de Manejo de Culturas. In: CONFERÊNCIA DE TECNOLOGIA DO ICET (CONNECTECH), 2. , 2025, Itacoatiara/AM.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 169-183.
DOI: https://doi.org/10.5753/connect.2025.12342.
