Towards Improving Agricultural Productivity and Sustainability through Automatic Classification of Soybean Seed Vigor

• Marcelo de Souza Júnior UTFPR
• Rafael S. Bressan UTFPR
• Douglas F. Pereira UTFPR
• Priscila T. M. Saito UTFPR / UNICAMP
• Pedro H. Bugatti UTFPR

DOI: https://doi.org/10.5753/semish.2018.3428

Abstract

Brazil is one of the largest producers and exporters of soybeans in the world. Its great acceptance is given to its nutritional and industrial peculiarities. In view of higher productivity, the use of high quality seed is an important factor. In this sense, the tetrazolium test has been outstanding, due to its precision and speed in the evaluation of the vigor of soybean seeds. However, the analysis process is entirely related to the knowledge and experience of the seed analyst. Thus, this article aims to develop an automatic methodology for the classification of soybean seeds by means of image analysis, using computational vision techniques added to deep learning.

References

Albawi, S., Mohammed, T. A., and Al-Zawi, S. (2017). Understanding of a Convolutional Neural Network. In Proceedings of the International Conference on Engineering and Technology, pages 1–6.

Bezerra, E. (2016). Introdução à Aprendizagem Profunda. In Ogasawara, V., editor, Tópicos em Gerenciamento de Dados e Informações - Simpósio Brasileiro de Banco de Dados, pages 57–86. Sociedade Brasileira de Computação.

Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.

Caroline, A., Vargas, G., Paes, A., and Vasconcelos, C. N. (2016). Um Estudo sobre Redes Neurais Convolucionais e sua Aplicação em Detecção de Pedestres. In Proceedings of the Conference on Graphics, Patterns and Images, pages 1–4.

Chollet, F. (2016). Xception: Deep learning with depthwise separable convolutions. arXiv preprint.

CONAB (2018). Companhia nacional de abastecimento: Acompanhamento da Safra Brasileira Gãos, V.5 - SAFRA 2016/17 - N.6 - Sexto Levantamento — Março 2018. Monitoramento agrícola-Safra 2017, 5:1–126.

Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1):21–27.

Dao-Duc, C., Xiaohui, H., and Morère, O. (2015). Maritime Vessel Images Classification Using Deep Convolutional Neural Networks. In Proceedings of the Sixth International Symposium on Information and Communication Technolog, pages 1–6. ACM Press.

Deng, J., Dong, W., Socher, R., Li, L.-J., Kai Li, and Li Fei-Fei (2009). ImageNet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255. IEEE.

França-Neto, J. B., Krzyzanowski, F. C., and da Costa, N. P. (1998). O Teste de Tetrazólio em Sementes de Soja. Embrapa - Centro Nacional de Pesquisa de Soja, Londrina.

Guo, Z., Zhang, L., and Zhang, D. (2010). Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognition, 43(3):706–719.

Haralick, R. M., Shanmugam, K., and Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6):610–621.

Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., and Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their Applications, 13(4):18–28.

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. CoRR, abs/1704.04861.

Irons, J. R. and Petersen, G. W. (1981). Texture transforms of remote sensing data. Remote Sensing of Environment, 11:359–370.

Moreano, T. B. (2012). Evolução das Qualidades Física e Fisiológica da Semente de Soja no Beneficiamento. Dissertação, Universidade Estadual de Maringá, Maringá.

Papa, J. P., Falcão, A. X., de Albuquerque, V. H. C., and Tavares, J. M. R. S. (2012). Efficient supervised optimum-path forest classification for large datasets. Pattern Recognition, 45:512–520.

Shijie, J., Ping, W., Peiyi, J., and Siping, H. (2017). Research on data augmentation for image classification based on convolution neural networks. In Proceedings of the Chinese Automation Congress, pages 4165–4170. IEEE.

Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556.

Smith, J. R. and Chang, S.-F. (1996). Local color and texture extraction and spatial query. In Proceedings of the International. Conference on Image Processing, pages 1011–1014.

Stehling, R. O., Nascimento, M. A., and Falcão, A. X. (2002). A compact and efficient image retrieval approach based on border/interior pixel classification. In Proceedings of the International Conference on Information and Knowledge Management, pages 102–109.

Stricker, M. A. and Orengo, M. (1995). Similarity of color images. In Storage and Retrieval for Image and Video Databases III, volume 2420, pages 381–393. International Society for Optics and Photonics.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2015). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2818–2826.

Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks? In Proceedings of the International Conference on Neural Information Processing Systems, pages 3320–3328.