Deep Learning-Based Transfer Learning for Classification of Cassava Disease
Resumo
Esse artigo apresenta um comparativo de desempenho entre quatro arquiteturas de Redes Neurais Convolucionais (EfficientNet-B3, InceptionV3, ResNet50 e VGG16) na classificação de imagens de patologias da mandioca. As imagens foram obtidas de um conjunto de dados desbalanceado de uma competição. Foram utilizadas métricas adequadas para lidar com o desbalanceamento entre classes do conjunto. Os resultados indicam que a EfficientNet-B3 alcançou nessa tarefa acurácia de 87,7%, precisao de 87,8%, revocação de 87,8% e F1-Score de 87,7%. Isso sugere que o EfficientNet-B3 pode ser uma ferramenta valiosa de apoio para Agricultura Digital.
Palavras-chave:
aprendizado profundo, aprendizagem por transferência, doença da mandioca
Referências
Amaral, F. (2016). Introdução à ciência de dados: mineração de dados e big data. Alta Books Editora.
Calvert, L. A. and Thresh, J. (2001). The viruses and virus diseases of cassava. In Cassava: biology, production and utilization, pages 237–260. CABI Wallingford UK.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357.
Companhia Nacional de Abastecimento (2023). Análise mensal da produção de mandioca para fevereiro 2023. Technical report, Companhia Nacional de Abastecimento.
Embrapa (2022). Ciência e tecnologia tornaram o brasil um dos maiores produtores mundiais de alimentos. [link]. Acesso em: 28/02/2024.
Ernest Mwebaze, Jesse Mostipak, J. J. E. S. D. (2020). Cassava leaf disease classification.
FARIAS, A., SOUZA, L., MATTOS, P., et al. (2006). Aspectos socioeconômicos e agronômicos da mandioca. cruz das almas: Embrapa. Mandioca e Fruticultura Tropical.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.
He, H., Bai, Y., Garcia, E. A., and Li, S. (2008). Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), pages 1322–1328. Ieee.
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.
Howeler, R., Lutaladio, E., and Nachury, Z. (2013). Save and grow: Cassava: A guide to sustainable production intensification. Food and Agriculture Organization of the United Nations (FAO).
Hughes, D., Salathé, M., et al. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060.
Kamilaris, A. and Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147:70–90.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553):436–444.
MAPA (2023). Projeções do agronegócio 2022-2023 a 2032-2033. [link]. Acesso em: 28/02/2024.
Massruhá, S. M. F. S., LEITE, M. d. A., OLIVEIRA, S. d. M., Meira, C. A. A., Luchiari Junior, A., and Bolfe, E. (2020). Agricultura digital: pesquisa, desenvolvimento e inovação nas cadeias produtivas.
Mohanty, S. P., Hughes, D. P., and Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in plant science, 7:215232.
Pan, S. J. and Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10):1345–1359.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc.
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.
Sambasivam, G. and Opiyo, G. D. (2021). A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egyptian informatics journal, 22(1):27–34.
Sangbamrung, I., Praneetpholkrang, P., and Kanjanawattana, S. (2020). A novel automatic method for cassava disease classification using deep learning. Journal of Advances in Information Technology Vol, 11(4):241–248.
Santos, T. T., Barbedo, J. G. A., Ternes, S., Camargo Neto, J., Koenigkan, L. V., and de Souza, K. X. S. (2020). Visão computacional aplicada na agricultura.
Shorten, C. and Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of big data, 6(1):1–48.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826.
Tan, M. and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR.
Tieleman, T. and Hinton, G. (2017). Divide the gradient by a running average of its recent magnitude. coursera: Neural networks for machine learning. Technical report.
Van Rossum, G. and Drake, F. L. (2009). Python 3 Reference Manual. CreateSpace, Scotts Valley, CA.
Calvert, L. A. and Thresh, J. (2001). The viruses and virus diseases of cassava. In Cassava: biology, production and utilization, pages 237–260. CABI Wallingford UK.
Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer, W. P. (2002). Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357.
Companhia Nacional de Abastecimento (2023). Análise mensal da produção de mandioca para fevereiro 2023. Technical report, Companhia Nacional de Abastecimento.
Embrapa (2022). Ciência e tecnologia tornaram o brasil um dos maiores produtores mundiais de alimentos. [link]. Acesso em: 28/02/2024.
Ernest Mwebaze, Jesse Mostipak, J. J. E. S. D. (2020). Cassava leaf disease classification.
FARIAS, A., SOUZA, L., MATTOS, P., et al. (2006). Aspectos socioeconômicos e agronômicos da mandioca. cruz das almas: Embrapa. Mandioca e Fruticultura Tropical.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.
He, H., Bai, Y., Garcia, E. A., and Li, S. (2008). Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence), pages 1322–1328. Ieee.
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.
Howeler, R., Lutaladio, E., and Nachury, Z. (2013). Save and grow: Cassava: A guide to sustainable production intensification. Food and Agriculture Organization of the United Nations (FAO).
Hughes, D., Salathé, M., et al. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060.
Kamilaris, A. and Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147:70–90.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553):436–444.
MAPA (2023). Projeções do agronegócio 2022-2023 a 2032-2033. [link]. Acesso em: 28/02/2024.
Massruhá, S. M. F. S., LEITE, M. d. A., OLIVEIRA, S. d. M., Meira, C. A. A., Luchiari Junior, A., and Bolfe, E. (2020). Agricultura digital: pesquisa, desenvolvimento e inovação nas cadeias produtivas.
Mohanty, S. P., Hughes, D. P., and Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in plant science, 7:215232.
Pan, S. J. and Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10):1345–1359.
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc.
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.
Sambasivam, G. and Opiyo, G. D. (2021). A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using convolutional neural networks. Egyptian informatics journal, 22(1):27–34.
Sangbamrung, I., Praneetpholkrang, P., and Kanjanawattana, S. (2020). A novel automatic method for cassava disease classification using deep learning. Journal of Advances in Information Technology Vol, 11(4):241–248.
Santos, T. T., Barbedo, J. G. A., Ternes, S., Camargo Neto, J., Koenigkan, L. V., and de Souza, K. X. S. (2020). Visão computacional aplicada na agricultura.
Shorten, C. and Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of big data, 6(1):1–48.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826.
Tan, M. and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR.
Tieleman, T. and Hinton, G. (2017). Divide the gradient by a running average of its recent magnitude. coursera: Neural networks for machine learning. Technical report.
Van Rossum, G. and Drake, F. L. (2009). Python 3 Reference Manual. CreateSpace, Scotts Valley, CA.
Publicado
17/11/2024
Como Citar
COSTA JUNIOR, Ademir G.; DA SILVA, Fábio S.; RIOS, Ricardo.
Deep Learning-Based Transfer Learning for Classification of Cassava Disease. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 21. , 2024, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 364-375.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2024.244378.
