Detecção da Praga Spodoptera frugiperda no Cultivo de Milho usando Armadilhas Inteligentes e Visão Computacional
Resumo
A preocupação com a sanidade das plantações tem sido uma prioridade constante na agricultura, impulsionando o desenvolvimento de diversas abordagens tecnológicas para garantir a vitalidade das culturas. Um dos desafios que os agricultores enfrentam é a necessidade de combater pragas, como a Spodoptera frugiperda, que afeta significativamente diversos tipos de plantações, como milho e algodão, em escala global. O monitoramento preciso da densidade populacional de insetos por área é crucial para o Manejo Integrado de Pragas (MIP) e fornece aos agricultores informações essenciais sobre a saúde de suas culturas. No entanto, esse processo de monitoramento é predominantemente manual envolvendo os produtores. Neste artigo é apresentado o desenvolvimento de uma armadilha e de um modelo de aprendizado de máquina para detecção automática desta praga no campo, visando o suporte à tomada de decisão para implementação de programas MIP.
Referências
Alves, A. N., Souza, W. S., and Borges, D. L. (2020). Cotton pests classification in field-based images using deep residual networks. Computers and Electronics in Agriculture, 174:105488.
Bakry, M. M. S. and Abdel-Baky, N. F. (2024). Impact of the fall armyworm, spodoptera frugiperda (lepidoptera: Noctuidae) infestation on maize growth characteristics and yield loss. Brazilian Journal of Biology, 84:e274602.
Bechar, M. E. A., Settouti, N., Daho, M. E. H., Adel, M., and Chikh, M. A. (2019). Influence of normalization and color features on super-pixel classification: application to cytological image segmentation. Australasian physical & engineering sciences in medicine, 42:427–441.
Bergstra, J. and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of machine learning research, 13(2).
Chen, M., Chen, L., Yi, T., Zhang, R., Xia, L., Qu, C., Xu, G., Wang, W., Ding, C., Tang, Q., and Wu, M. (2023). Development of a Low-Power Automatic Monitoring System for Spodoptera frugiperda (J. E. Smith). Agriculture, 13(4).
Du, L., Sun, Y., Chen, S., Feng, J., Zhao, Y., Yan, Z., Zhang, X., and Bian, Y. (2022). A novel object detection model based on faster r-cnn for spodoptera frugiperda according to feeding trace of corn leaves. Agriculture, 12(2):248.
Gallo, D., Nakano, O., Silveira Neto, S. S., Carvalho, R. P. L., Batista, G. C., Filho, E. B., P., P. J. R., Zucchi, R. A., Alves, S. B., Vendramim, J. D., Marchini, L. C., Lopes, J. R. S., and Omoto, C. (2002). Entomologia agrícola. FEALQ, Piracicaba.
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.
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.
Karunathilake, E., Le, A. T., Heo, S., Chung, Y. S., and Mansoor, S. (2023). The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture, 13(8):1593.
Kenis, M., Benelli, G., Biondi, A., Calatayud, P.-A., Day, R., Desneux, N., Harrison, R. D., Kriticos, D., Rwomushana, I., van den Berg, J., et al. (2022). Invasiveness, biology, ecology, and management of the fall armyworm, spodoptera frugiperda. Entomologia Generalis.
López, A.-F. J., Jiménez-López, F.-R., and Alvarez, C. S. L. (2023). Electronic trap for field detection of fall armyworm (spodoptera frugiperda) in corn. In 2023 IEEE 6th Colombian Conference on Automatic Control (CCAC), pages 1–5.
Maino, J. L., Schouten, R., Overton, K., Day, R., Ekesi, S., Bett, B., Barton, M., Gregg, P. C., Umina, P. A., and Reynolds, O. L. (2021). Regional and seasonal activity predictions for fall armyworm in australia. Current Research in Insect Science, 1:100010.
Montezano, D. G., Sosa-Gómez, D., Specht, A., Roque-Specht, V. F., Sousa-Silva, J. C., Paula-Moraes, S. d., Peterson, J. A., and Hunt, T. (2018). Host plants of s. frugiperda (lepidoptera: Noctuidae) in the americas. African entomology, 26(2):286–300.
Rawat, W. and Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural computation, 29(9):2352–2449.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211–252.
Souza, W. S., Alves, A. N., and Borges, D. L. (2019). A deep learning model for recognition of pest insects in maize plantations. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pages 2285–2290. IEEE.
Sparks, A. N. (1979). A review of the biology of the fall armyworm. Florida entomologist, pages 82–87.
Torres, J. B. and Bueno, A. d. F. (2018). Conservation biological control using selective insecticides–a valuable tool for ipm. Biological Control, 126:53–64.
Zhang, H., Zhao, S., Song, Y., Ge, S., Liu, D., Yang, X., and Wu, K. (2022). A deep learning and grad-cam-based approach for accurate identification of the fall armyworm (spodoptera frugiperda) in maize fields. Computers and Electronics in Agriculture, 202:107440.
Bakry, M. M. S. and Abdel-Baky, N. F. (2024). Impact of the fall armyworm, spodoptera frugiperda (lepidoptera: Noctuidae) infestation on maize growth characteristics and yield loss. Brazilian Journal of Biology, 84:e274602.
Bechar, M. E. A., Settouti, N., Daho, M. E. H., Adel, M., and Chikh, M. A. (2019). Influence of normalization and color features on super-pixel classification: application to cytological image segmentation. Australasian physical & engineering sciences in medicine, 42:427–441.
Bergstra, J. and Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of machine learning research, 13(2).
Chen, M., Chen, L., Yi, T., Zhang, R., Xia, L., Qu, C., Xu, G., Wang, W., Ding, C., Tang, Q., and Wu, M. (2023). Development of a Low-Power Automatic Monitoring System for Spodoptera frugiperda (J. E. Smith). Agriculture, 13(4).
Du, L., Sun, Y., Chen, S., Feng, J., Zhao, Y., Yan, Z., Zhang, X., and Bian, Y. (2022). A novel object detection model based on faster r-cnn for spodoptera frugiperda according to feeding trace of corn leaves. Agriculture, 12(2):248.
Gallo, D., Nakano, O., Silveira Neto, S. S., Carvalho, R. P. L., Batista, G. C., Filho, E. B., P., P. J. R., Zucchi, R. A., Alves, S. B., Vendramim, J. D., Marchini, L. C., Lopes, J. R. S., and Omoto, C. (2002). Entomologia agrícola. FEALQ, Piracicaba.
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.
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.
Karunathilake, E., Le, A. T., Heo, S., Chung, Y. S., and Mansoor, S. (2023). The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture, 13(8):1593.
Kenis, M., Benelli, G., Biondi, A., Calatayud, P.-A., Day, R., Desneux, N., Harrison, R. D., Kriticos, D., Rwomushana, I., van den Berg, J., et al. (2022). Invasiveness, biology, ecology, and management of the fall armyworm, spodoptera frugiperda. Entomologia Generalis.
López, A.-F. J., Jiménez-López, F.-R., and Alvarez, C. S. L. (2023). Electronic trap for field detection of fall armyworm (spodoptera frugiperda) in corn. In 2023 IEEE 6th Colombian Conference on Automatic Control (CCAC), pages 1–5.
Maino, J. L., Schouten, R., Overton, K., Day, R., Ekesi, S., Bett, B., Barton, M., Gregg, P. C., Umina, P. A., and Reynolds, O. L. (2021). Regional and seasonal activity predictions for fall armyworm in australia. Current Research in Insect Science, 1:100010.
Montezano, D. G., Sosa-Gómez, D., Specht, A., Roque-Specht, V. F., Sousa-Silva, J. C., Paula-Moraes, S. d., Peterson, J. A., and Hunt, T. (2018). Host plants of s. frugiperda (lepidoptera: Noctuidae) in the americas. African entomology, 26(2):286–300.
Rawat, W. and Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural computation, 29(9):2352–2449.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115:211–252.
Souza, W. S., Alves, A. N., and Borges, D. L. (2019). A deep learning model for recognition of pest insects in maize plantations. In 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pages 2285–2290. IEEE.
Sparks, A. N. (1979). A review of the biology of the fall armyworm. Florida entomologist, pages 82–87.
Torres, J. B. and Bueno, A. d. F. (2018). Conservation biological control using selective insecticides–a valuable tool for ipm. Biological Control, 126:53–64.
Zhang, H., Zhao, S., Song, Y., Ge, S., Liu, D., Yang, X., and Wu, K. (2022). A deep learning and grad-cam-based approach for accurate identification of the fall armyworm (spodoptera frugiperda) in maize fields. Computers and Electronics in Agriculture, 202:107440.
Publicado
21/07/2024
Como Citar
SILVA, Wendell dos S.; SOARES, Bianca; ALMEIDA, Valentine de L.; VIANA, Leonardo; PASTORI, Patrik L.; MAGALHÃES, Deborah M. V.; ROCHA, Atslands R. da.
Detecção da Praga Spodoptera frugiperda no Cultivo de Milho usando Armadilhas Inteligentes e Visão Computacional. In: WORKSHOP DE COMPUTAÇÃO APLICADA À GESTÃO DO MEIO AMBIENTE E RECURSOS NATURAIS (WCAMA), 15. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 61-70.
ISSN 2595-6124.
DOI: https://doi.org/10.5753/wcama.2024.2376.
