Embedded neural networks for identifying Spodoptera frugiperda in corn plantations
Resumo
A Spodoptera frugiperda é uma das principais pragas da agricultura mundial. Seu monitoramento comumente requer inspeção visual e contagem manual de indivíduos. Avanços em visão computacional, aprendizado de máquina e internet das coisas oferecem meios para alcançar um monitoramento rápido e preciso. Neste cenário, nós investigamos o uso de redes neurais convolucionais leves e densas para extrair características relevantes de imagens desses insetos capturadas em armadilha. Os melhores modelos de extração, MobileNet e DenseNet201, foram aliados à MLP e alcançaram acurácia de classificação de 0,89 e 0,94, respectivamente, embarcados em Raspberry Pi. Os resultados mostram que, embora a DenseNet201 ofereça maior precisão, a MobileNet é mais eficiente em processamento e possui menor tempo de execução. Portanto, a MobileNet desponta como alternativa viável para identificação da Spodoptera frugiperda em campo com dispositivos com restrição computacional. Por fim, este trabalho contribui diretamente para o monitoramento automático e preciso de pragas em plantações.
Palavras-chave:
Monitoramento de pragas, Visão computacional, IA embarcada, Aprendizado profundo
Referências
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Guo, Q., Wang, C., Xiao, D., and Huang, Q. (2024). A lightweight open-world pest image classifier using resnet8-based matching network and nt-xent loss function. Expert Systems with Applications, 237:121395.
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Junior, T. D. C. and Rieder, R. (2020). Uma implementação baseada em mask r-cnn para detecção de insetos em imagens digitais. Rev. Bras. de Entomologia, 64(2):149–157.
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Muhammed, D., Ahvar, E., Ahvar, S., Trocan, M., Montpetit, M.-J., and Ehsani, R. (2024). Artificial Intelligence of Things (AIoT) for smart agriculture: A review of architectures, technologies and solutions. Journal of Network and Computer Applications, 228:103905.
Ngugi, H. N., Ezugwu, A. E., Akinyelu, A. A., and Abualigah, L. (2024). Revolutionizing crop disease detection with computational deep learning: a comprehensive review. Environmental Monitoring and Assessment, 196(3):302.
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Obasekore, H., Fanni, M., Ahmed, S. M., Parque, V., and Kang, B.-Y. (2023). Agricultural robot-centered recognition of early-developmental pest stage based on deep learning: A case study on fall armyworm (spodoptera frugiperda). Sensors, 23(6):3147.
Palei, S., Lenka, R. K., Nayak, S. S., Mohanty, R., Jena, B., and Saxena, S. (2023). Precision Agriculture: ML and DL-Based Detection and Classification of Agricultural Pests. In 2023 2nd International Conference on Ambient Intelligence in Health Care (ICAIHC), pages 1–6, Bhubaneswar, India. IEEE.
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.
Silva, W. d. S., Soares, B., Almeida, V. d. L., Viana, L., Pastori, P. L., Magalhaes, D. M., and da Rocha, A. R. (2024). Detecçao da praga spodoptera frugiperda no cultivo de milho usando armadilhas inteligentes e visao computacional. In Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA), pages 61–70. SBC.
Yodrot, T., Sutacha, C., Orachon, T., Jangjongdee, N., and Boonyasuwanno, S. (2024). A Study on the Potency of Hybrid Models: Detecting Diseases in Cucumber Leaves with Pre-trained CNNs and SVM. In 2024 12th International Electrical Engineering Congress (iEECON), pages 1–4, Pattaya, Thailand. IEEE.
Yu, D., Xu, Q., Guo, H., Zhao, C., Lin, Y., and Li, D. (2020). An efficient and lightweight convolutional neural network for remote sensing image scene classification. Sensors, 20(7):1999.
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.
FAO (2022). Faostat: Crops and livestock products. Accessed: 2024-07-30.
Gallo, D., Nakano, O., Silveira Neto, S., Carvalho, R., Baptista, C., Berti Filho, E., Parra, J., Zucchi, R., Alves, S., Vendramim, J., et al. (2002a). Agricultural entomology= entomologia agrícola. fealq, piracicaba, sp, brazil.
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. (2002b). Entomologia agrícola. FEALQ, Piracicaba.
Ghazal, S., Munir, A., and Qureshi, W. S. (2024). Computer vision in smart agriculture and precision farming: Techniques and applications. Artificial Int. in Agriculture.
Guo, Q., Wang, C., Xiao, D., and Huang, Q. (2024). A lightweight open-world pest image classifier using resnet8-based matching network and nt-xent loss function. Expert Systems with Applications, 237:121395.
Hassan, S. I., Alam, M. M., Illahi, U., and Suud, M. M. (2023). A new deep learning-based technique for rice pest detection using remote sensing. PeerJ Computer Science, 9:e1167.
Hu, T., Zhang, X., Khanal, S., Wilson, R., Leng, G., Toman, E. M., Wang, X., Li, Y., and Zhao, K. (2024). Climate change impacts on crop yields: A review of empirical findings, statistical crop models, and machine learning methods. Environmental Modelling & Software, page 106119.
Jiang, S., Luo, B., Jiang, H., Zhou, Z., and Sun, S. (2024). Research on dense object detection methods in congested environments of urban streets and roads based on dcyolo. Scientific Reports, 14(1):1127.
Junior, T. D. C. and Rieder, R. (2020). Uma implementação baseada em mask r-cnn para detecção de insetos em imagens digitais. Rev. Bras. de Entomologia, 64(2):149–157.
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.
Kathole, A. B., Katti, J., Lonare, S., and Dharmale, G. (2023). Identify and classify pests in the agricultural sector using metaheuristics deep learning approach. Franklin Open, 3(1):100024.
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.
Mira, J. L., Gómez, S., del Río, A., Díaz, F., Lozano, M., and García, L. (2024). Benchmarking of computer vision methods for energy-efficient high-accuracy olive fly detection on edge devices. Multimedia Tools and Applications, 83(1):1203–1220.
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.
Muhammed, D., Ahvar, E., Ahvar, S., Trocan, M., Montpetit, M.-J., and Ehsani, R. (2024). Artificial Intelligence of Things (AIoT) for smart agriculture: A review of architectures, technologies and solutions. Journal of Network and Computer Applications, 228:103905.
Ngugi, H. N., Ezugwu, A. E., Akinyelu, A. A., and Abualigah, L. (2024). Revolutionizing crop disease detection with computational deep learning: a comprehensive review. Environmental Monitoring and Assessment, 196(3):302.
Nixon, M. S. and Aguado, A. S. (2020). Image processing. In Feature Extraction and Image Processing for Computer Vision, pages 83–139. Elsevier.
Obasekore, H., Fanni, M., Ahmed, S. M., Parque, V., and Kang, B.-Y. (2023). Agricultural robot-centered recognition of early-developmental pest stage based on deep learning: A case study on fall armyworm (spodoptera frugiperda). Sensors, 23(6):3147.
Palei, S., Lenka, R. K., Nayak, S. S., Mohanty, R., Jena, B., and Saxena, S. (2023). Precision Agriculture: ML and DL-Based Detection and Classification of Agricultural Pests. In 2023 2nd International Conference on Ambient Intelligence in Health Care (ICAIHC), pages 1–6, Bhubaneswar, India. IEEE.
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.
Silva, W. d. S., Soares, B., Almeida, V. d. L., Viana, L., Pastori, P. L., Magalhaes, D. M., and da Rocha, A. R. (2024). Detecçao da praga spodoptera frugiperda no cultivo de milho usando armadilhas inteligentes e visao computacional. In Workshop de Computação Aplicada à Gestão do Meio Ambiente e Recursos Naturais (WCAMA), pages 61–70. SBC.
Yodrot, T., Sutacha, C., Orachon, T., Jangjongdee, N., and Boonyasuwanno, S. (2024). A Study on the Potency of Hybrid Models: Detecting Diseases in Cucumber Leaves with Pre-trained CNNs and SVM. In 2024 12th International Electrical Engineering Congress (iEECON), pages 1–4, Pattaya, Thailand. IEEE.
Yu, D., Xu, Q., Guo, H., Zhao, C., Lin, Y., and Li, D. (2020). An efficient and lightweight convolutional neural network for remote sensing image scene classification. Sensors, 20(7):1999.
Publicado
17/11/2024
Como Citar
SOARES, Bianca; SILVA, Wendell; PONCIANO, Gabriela; STEFANIE, Bruna; ALMEIDA, Valentine; PASTORI, Patrick; MAGALHÃES, Deborah; ROCHA, Atslands.
Embedded neural networks for identifying Spodoptera frugiperda in corn plantations. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 376-387.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.244552.
