과제정보
This work was supported by the New Faculty Startup Fund from Seoul National University. The authors sincerely give thanks to Woo-il Lee, the Extension Specialist of the Rural Development Administration of Korea, for generously providing the historical data of rice blast occurrence from the National Crop Pest Management System.
참고문헌
- Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y. and Zheng, X. 2016. TensorFlow: large-scale machine learning on heterogeneous distributed systems. Preprint at https://arxiv.org/abs/1603.04467.
- Batista, G. E. A. P. A., Prati, R. C. and Monard, M. C. 2004. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor. Newsl. 6:20-29. https://doi.org/10.1145/1007730.1007735
- Bhatia, A., Chug, A. and Prakash Singh, A. 2020. Application of extreme learning machine in plant disease prediction for highly imbalanced dataset. J. Stat. Manage. Syst. 23:1059-1068.
- Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R. and Emadi, A. 2017. Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries. IEEE Trans. Ind. Electron. 65:6730-6739. https://doi.org/10.1109/tie.2017.2787586
- Chung, H., Kang, S., Lee, Y.-H. and Park, S.-Y. 2020. Expression patterns of transposable elements in Magnaporthe oryzae under diverse developmental and environmental conditions. Res. Plant Dis. 26:38-43. https://doi.org/10.5423/RPD.2020.26.1.38
- Fenu, G. and Malloci, F. M. 2019. An application of machine learning technique in forecasting crop disease. In: Proceedings of the 2019 3rd International Conference on Big Data Research, pp. 76-82. Association for Computing Machinery, New York, NY, USA.
- Fenu, G. and Malloci, F. M. 2021. Forecasting plant and crop disease: an explorative study on current algorithms. Big Data Cogn. Comput. 5:2. https://doi.org/10.3390/bdcc5010002
- Hochreiter, S. and Schmidhuber, J. 1996. LSTM can solve hard long time lag problems. In: Proceedings of the 9th International Conference on Neural Information Processing Systems, pp. 473-479. MIT Press, Cambridge, MA, USA.
- Jeon, J. 2019. Phytobiome as a potential factor in nitrogen-induced susceptibility to the rice blast disease. Res. Plant Dis. 25:103-107. https://doi.org/10.5423/RPD.2019.25.3.103
- Juroszek, P. and von Tiedemann, A. 2011. Potential strategies and future requirements for plant disease management under a changing climate. Plant Pathol. 60:100-112. https://doi.org/10.1111/j.1365-3059.2010.02410.x
- Katsantonis, D., Kadoglidou, K., Dramalis, C. and Puigdollers, P. 2017. Rice blast forecasting models and their practical value: a review. Phytopathol. Mediterr. 56:187-216.
- Kaundal, R., Kapoor, A. S. and Raghava, G. P. S. 2006. Machine learning techniques in disease forecasting: a case study on rice blast prediction. BMC Bioinformatics 7:485. https://doi.org/10.1186/1471-2105-7-485
- Kim, K.-H., Cho, J., Lee, Y. H. and Lee, W.-S. 2015. Predicting potential epidemics of rice leaf blast and sheath blight in South Korea under the RCP 4.5 and RCP 8.5 climate change scenarios using a rice disease epidemiology model, EPIRICE. Agric. For. Meteorol. 203:191-207. https://doi.org/10.1016/j.agrformet.2015.01.011
- Kim, K.-H. and Jung, I. 2020. Development of a daily epidemiological model of rice blast tailored for seasonal disease early warning in South Korea. Plant Pathol. J. 36:406-417. https://doi.org/10.5423/PPJ.OA.07.2020.0135
- Kim, K.-H. and Lee, J. 2020. Smart plant disease management using agrometeorological big data. Res. Plant Dis. 26:121-133 (in Korean). https://doi.org/10.5423/RPD.2020.26.3.121
- Kim, Y., Roh, J.-H. and Kim, H. Y. 2018. Early forecasting of rice blast disease using long short-term memory recurrent neural networks. Sustainability 10:34. https://doi.org/10.3390/su10010034
- Kingma, D. P. and Ba, L. J. 2014. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980.
- Lin, T.-Y., Goyal, P., Girshick, R., He, K. and Dollar, P. 2017. Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988. Institute of Electrical and Electronics Engineers, Cambridge, MA, USA.
- Liu, A., Ghosh, J. and Martin, C. 2007. Generative oversampling for mining imbalanced datasets. In: Proceedings of the 2007 International Conference on Data Mining, eds. by R. Stahlbock, S. F. Crone and S. Lessmann, pp. 66-72. CSREA Press, Las Vegas, NV, USA.
- Malicdem, A. R. and Fernandez, P. L. 2015. Rice blast disease forecasting for northern Philippines. WSEAS Trans. Inf. Sci. Appl. 12:120-129.
- Nettleton, D. F., Katsantonis, D., Kalaitzidis, A., Sarafijanovic-Djukic, N., Puigdollers, P. and Confalonieri, R. 2019. Predicting rice blast disease: machine learning versus process-based models. BMC Bioinformatics 20:514. https://doi.org/10.1186/s12859-019-3065-1
- Probst, P., Boulesteix, A.-L. and Bischl, B. 2019. Tunability: importance of hyperparameters of machine learning algorithms. J. Mach. Learn. Res. 20:1-32.
- Sola, J. and Sevilla, J. 1997. Importance of input data normalization for the application of neural networks to complex industrial problems. IEEE Trans. Nucl. Sci. 44:1464-1468. https://doi.org/10.1109/23.589532
- Wang, J. C., Correll, J. C. and Jia, Y. 2015. Characterization of rice blast resistance genes in rice germplasm with monogenic lines and pathogenicity assays. Crop Prot. 72:132-138. https://doi.org/10.1016/j.cropro.2015.03.014
- Yang, I., Jeon, W. H. and Moon, J. 2019. A study on a distance based coordinate calculation method using Inverse Haversine Method. J. Dig. Contents Soc. 20:2097-2102. https://doi.org/10.9728/dcs.2019.20.10.2097