Stabilizing Predictive Performance for Ear Emergence in Rice Crops across Cropping Regions
Stabilizing Predictive Performance for Ear Emergence in Rice Crops across Cropping Regions
复制标题
稳定各种植区水稻作物出穗的预测性能
DOI：
10.1007/978-3-030-69886-7_7
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Fukazawa Y.,and Kaneta Y.
中科院分区：
文献类型：
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作者：
Iuchi Y.;Uehara H.;Fukazawa Y.,and Kaneta Y.
一些研究已经证明了水稻作物穗出的良好预测性能。然而，已经发现了显著的区域绩效差异，这些差异仍未得到解决。在本研究中，我们的目标是实现水稻作物穗出的稳定预测性能，而不考虑其区域差异。虽然在相关工作中采用了各种代表区域特征的数据作为预测变量，但预测性能的稳定性尚未达到。这些结果表明，明确的区域数据不足以稳定预测的区域方差。本研究建议使用工程变量来揭示显性区域数据背后隐藏的区域特征。对区域数据的预检验表明，每个区域的时间依赖模式各不相同。在此基础上，将隐马尔可夫模型应用于微气候数据，以创建代表隐式随时间变化的区域特征的工程变量。对这些变量的效率进行了实证研究，结果表明区域预测方差显著提高。
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.