Abstract
Several studies have demonstrated a good predictive performance of ear emergence in rice crops. However, significant regional variations in performance have been discovered and they remain unsolved. In this study, we aim to realize a stable predictive performance for ear emergence in rice crops regardless of its regional variations. Although a variety of data that represents regional characteristics have been adopted as the variables for prediction in related work, stability of the predictive performance has not been attained. These results imply that explicit regional data is insufficient for stabilizing the regional variances of the prediction. This study proposes to use engineered variables that uncover hidden regional characteristics behind the explicit regional data. Pre-examinations of the regional data indicate distinctive patterns of time dependency according to each region. Based on the findings, hidden Markov models are applied to the micro climate data to create engineered variables that represent the implicit time dependent regional characteristics. The efficiency of these variables is empirically studied, and the results show a significant improvement in the regional predictive variance.
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Uehara, H., Shinjo, A.: WAGRI - the agricultural big data platform. In: ProceedingE-AGRICULTURE IN ACTION:BIG DATA FOR AGRICULTURE Food and Agriculture Organization of the United Nations and the International Telecommunication Union, pp. 73–83 (2019)
Streck, N.A., Albert, W., Xue, Q., Stephen, P.B.: Improving predictions of developmental stages in winter wheat: a modified Wang and Engel model. Agric. Forest Meteorol. 115(3–4), 139–150 (2003)
Bogard, M., Ravel, C., Paux, E., Bordes, J.: Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model. J. Exp. Bot. 65(20), 5849–5865 (2014)
Jeong, J.H., Resop, J.P., Mueller, N.D., Fleisher, D.H.: Random forests for global and regional crop yield predictions. PLoS ONE 11(6), e0156571 (2016)
Ying-xue, S., Huan, X., Li-jiao, Y.: Support vector machine-based open crop model (SBOCM): case of rice production in China. Saudi J. Biol. Sci. 24(3), 537–547 (2017)
Chauhan, Y.S., Ryan, M., Chandra, S., Sadras, V.O.: Accounting for soil moisture improves prediction of flowering time in chickpea and wheat. Nature.com, vol. 7510, no.Scientific Reports, pp. 1–11 (2019)
Bogard, M., et al.: Predictions of heading date in bread wheat (Triticum aestivum L.) using QTL-based parameters of an ecophysiological model. J. Exp. Bot. 65(20), 5849–5865 (2014)
Maeda, Y., Goyodani, T., Nishiuchi, S., Kita, E.: Yield prediction of paddy rice with machine learning. In: Proceeding The 24th Int’l Conf on Parallel and Distributed Processing Techniques and Applications, pp. 361–365 (2018)
Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R.L., Mouazen, A.M.: Wheat yield prediction using machine learning and advanced sensing techniques. Comput. Electron. Agric. 121, 57–65 (2016)
Horie, T., Nakagawa, H.: Modelling and prediction of developmental process in rice: I. structure and method of parameter estimation of a model for simulating developmental process toward heading. Crop Sci. Soc. Japan 59(4), 687–695 (1990)
Maeda, Y., Goyodani, T., Nishiuchi, S., Kita, E.: Yield prediction of paddy rice with machine learning. In: Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), pp. 361–365 (2018)
Wang, Q., Shwartz, L., Grabarnik, G.Y., Nidd, M., Hwang, J.: Leveraging AI in service automation modeling: from classical AI through deep learning to combination models. In: Proceeding of Int’l Conf on Service-Oriented Computing, vol. 2019, pp. 186–201 (2019)
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Iuchi, Y., Uehara, H., Fukazawa, Y., Kaneta, Y. (2021). Stabilizing the Predictive Performance for Ear Emergence in Rice Crops Across Cropping Regions. In: Uehara, H., Yamaguchi, T., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2021. Lecture Notes in Computer Science(), vol 12280. Springer, Cham. https://doi.org/10.1007/978-3-030-69886-7_7
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