To improve the prediction accuracy of pest forecasting, we developed a machine-learning-based method of estimating parameters of the effective accumulated temperature (EAT) model on the basis of numerous historical occurrence data on target pests and meteorological data in the field. The parameters were estimated by using past occurrence data of the white-backed planthopper (WBPH), , collected in light traps at 20 sites on Shikoku Island from 1980 to 2000. When the accuracy of the estimated parameters was compared with that of those derived from traditional approaches using field data from 2001 to 2022, the parameters estimated by using the novel method had the best accuracy, with a predictive lower threshold temperature () = 12.29 (°C) and an effective accumulated temperature () = 372.23 (degree-days) for the first generation of WBPH and = –5.00 and = 800.31 for the second generation. Use of these parameters improved the prediction accuracy by approximately 3 days compared with the traditional approach. We also found that prediction of parameters on the basis of the coefficient of variation of the predicted EATs resulted in better forecasting accuracy than prediction based on the standard deviation. Our novel method of estimating parameters for the EAT model will contribute to better forecasting of insect pests.

中文翻译：

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利用季节性害虫发生数据确定有效积温模型参数的新方法

为了提高害虫预报的预测精度，我们根据大量目标害虫历史发生数据和现场气象数据，开发了一种基于机器学习的有效积温（EAT）模型参数估计方法。这些参数是利用 1980 年至 2000 年在四国岛 20 个地点的光阱中收集的白背飞虱 (WBPH) 过去发生的数据进行估计的。与使用2001年至2022年现场数据的传统方法相比，采用新方法估算的参数精度最高，预测下限温度()=12.29(℃)，有效积温()=372.23(度-日) ) 对于第一代 WBPH，对于第二代 = –5.00 和 = 800.31。与传统方法相比，使用这些参数将预测精度提高了大约 3 天。我们还发现，基于预测 EAT 的变异系数进行参数预测比基于标准差的预测具有更好的预测精度。我们估计 EAT 模型参数的新方法将有助于更好地预测害虫。