Análise e Classificação Automática de Nódulos em Raízes de Cultivares de Soja
Resumo
A soja é a oleaginosa mais cultivada no mundo e requer grandes quantidades de nitrogênio para a nutrição da planta. O nutriente pode ser fornecido por fertilizantes químicos, prejudiciais ao meio ambiente. No entanto, o processo de fixação biológica do nitrogênio (FBN) tem sido uma opção eficaz para aumentar a produtividade e minimizar os impactos ambientais. A FBN ocorre por meio da associação de bactérias diazotróficas no sistema radicular de leguminosas e oleaginosas, onde nódulos radiculares são formados. Este trabalho propõe-se uma análise e classificação automática, por meio do processamento de imagens e visão computacional, destes nódulos radiculares para avaliar a eficácia da nodulação em soja, considerando a quantidade de nódulos.
Palavras-chave:
Soja, Nodulação, Fixação Biológica do Nitrogênio, Visão Computacional, Aprendizado de Máquina
Referências
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Sandler, M., Howard, A. G., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pages 4510–4520.
Shorten, C. and Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, pages 2196–1115.
Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556.
Stricker, M. A. and Orengo, M. (1995). Similarity of color images. In Niblack, W. and Jain, R. C., editors, Storage and Retrieval for Image and Video Databases III, volume 2420, pages 381 – 392. Intl Society for Optics and Photonics, SPIE.
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A. (2017). Inception-v4, inceptionresnet and the impact of residual connections on learning. In AAAI.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. 2016 IEEE Conf. on Computer Vision and Pattern Recognition, pages 2818–2826.
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Witten, I. H., Frank, E., and Hall, M. A. (2011). Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Burlington, 3 edition.
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pages 8697–8710.
Bressan, R. S., Alves, D. H. A., Valerio, L. M., Bugatti, P. H., and Saito, P. T. M. (2018).
Doctor: The role of deep features in content-based mammographic image retrieval. In 2018 IEEE 31st Intl Symposium on Computer-Based Medical Systems, pages 158–163.
Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. 2017 IEEE Conf. on Computer Vision and Pattern Recognition, pages 1800–1807.
Chung, Y. S., Lee, U., Heo, S., Silva, R. R., Na, C.-I., and Kim, Y. (2020). Image-based machine learning characterizes root nodule in soybean exposed to silicon. Frontiers in Plant Science, 11.
Gomes, M. S. (2019). Fertilização Nitrogenada: produção, produtividade e trocas gasosas da soja cultivada em latossolo amarelo em casa de vegetação. Mestrado em agronomia, Universidade Federal Rural da Amazônia.
Géron, A. (2019). Mãos à Obra Aprendizado de Máquina com Scikit-Learn TensorFlow: Conceitos, Ferramentas e Técnicas Para a Construção de Sistemas Inteligentes. Alta Books, Rio de Janeiro.
Haralick, R. M., Shanmugam, K., and Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, (6):610–621.
He, K., Zhang, X., Ren, S., and Sun, J. (2016a). Deep residual learning for image recognition. 2016 IEEE Conf. on Computer Vision and Pattern Recognition, pages 770–778.
He, K., Zhang, X., Ren, S., and Sun, J. (2016b). Identity mappings in deep residual networks. ArXiv, abs/1603.05027.
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, abs/1704.04861.
Hu, M.-K. (1962). Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2):179–187.
Huang, G., Liu, Z., and Weinberger, K. Q. (2017). Densely connected convolutional networks. 2017 IEEE Conf. on Computer Vision and Pattern Recognition, pages 2261–2269.
Hungria, M., Campo, R. J., and Mendes, I. C. (2001). Fixação Biológica do Nitrogênio na Cultura da Soja. Technical Report 13, Embrapa Soja, Londrina.
Hungria, M. and Mendes, I. C. (2015). Nitrogen Fixation with Soybean: The Perfect Symbiosis?, chapter 99, pages 1009–1024. John Wiley Sons, Ltd.
Hungria, M., Moretti, L. G., Crusciol, C. A. C., Kuramae, E. E., Bossolani, J. W., Moreira, A., Costa, N. R., Alves, C. J., Pascoaloto, I. M., and Rondina, A. B. L. (2020). Effects of growth-promoting bacteria on soybean root activity, plant development, and yield. Agronomy Journal, 112(1):418–428.
Irons, J. R. and Petersen, G. W. (1981). Texture transforms of remote sensing data. Remote Sensing of Environment, 11:359–370.
Jubery, T. Z., Carley, C. N., Singh, A., Sarkar, S., Ganapathysubramanian, B., and Singh, A. K. (2021). Using machine learning to develop a fully automated soybean nodule acquisition pipeline (snap). Plant Phenomics, 2021.
Lin, T.-Y., Goyal, P., Girshick, R. B., He, K., and Dollár, P. (2017). Focal loss for dense object detection. 2017 IEEE Intl Conf. on Computer Vision, pages 2999–3007.
Medeiros, A. D. d., Capobiango, N. P., Silva, J. M. d., Silva, L. J. d., Silva, C. B. d., and Santos Dias, D. C. F. d. (2020). Interactive machine learning for soybean seed and seedling quality classification. Scientific Reports, 10.
Meshram, V., Patil, K., Meshram, V., Hanchate, D., and Ramkteke, S. (2021). Machine learning in agriculture domain: A state-of-art survey. AI in the Life Sciences, 1:100010.
Nogueira, M. A., Prando, A. M., de Oliveira, A. B., de Lima, D., Conte, O., Harger, N., de Oliveira, F. T., and Hungria, M. (2018). Ações de transferência de tecnologia em inoculação/coinoculação com bradyrhizobium e azospirillum na cultura da soja na safra 2017/18 no estado do paraná. Technical Report 143, Embrapa Soja, Londrina.
Ojala, T., Pietikäinen, M., and Mäenpää, T. (2000). Gray scale and rotation invariant texture classification with local binary patterns. In Computer Vision, pages 404–420, Berlin, Heidelberg. Springer Berlin Heidelberg.
Refaeilzadeh, P., Tang, L., and Liu, H. (2009). Cross-Validation, pages 532–538. Springer US, Boston, MA.
Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In MICCAI.
Sandler, M., Howard, A. G., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pages 4510–4520.
Shorten, C. and Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, pages 2196–1115.
Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556.
Stricker, M. A. and Orengo, M. (1995). Similarity of color images. In Niblack, W. and Jain, R. C., editors, Storage and Retrieval for Image and Video Databases III, volume 2420, pages 381 – 392. Intl Society for Optics and Photonics, SPIE.
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A. (2017). Inception-v4, inceptionresnet and the impact of residual connections on learning. In AAAI.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. 2016 IEEE Conf. on Computer Vision and Pattern Recognition, pages 2818–2826.
Teague, M. R. (1980). Image analysis via the general theory of moments. J. Opt. Soc. Am., 70(8):920–930.
USDA (2022). United States Departamento of Agriculture: PSD Reports.
Wada, K. (2022). Labelme. disponível em: https://github.com/wkentaro/labelme.
Witten, I. H., Frank, E., and Hall, M. A. (2011). Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Burlington, 3 edition.
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V. (2018). Learning transferable architectures for scalable image recognition. 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pages 8697–8710.
Publicado
31/07/2022
Como Citar
PACANHELA, Eber Fabiano; RONDINA, Artur Berbel Lirio; NOGUEIRA, Marco Antonio; HUNGRIA, Mariangela; SAITO, Priscila Tiemi Maeda; LOPES, Fabrício Martins.
Análise e Classificação Automática de Nódulos em Raízes de Cultivares de Soja. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 16. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 41-48.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2022.222796.
