EIRP model driven by machine learning for predicting the occurrence risk of southern corn rust (Puccinia polysora Underw.) in northern China

文献类型： 外文期刊

第一作者： Yang, Lujia

作者： Yang, Lujia;Li, Lili;Guo, Wenxiu;Song, Yingying;Cui, Hongying;Lv, Suhong;Sindhu, Lara;Men, Xingyuan;Dong, Zhaoke;Zhu, Junsheng

作者机构：

关键词： Southern corn rust; Machine learning; Growth index; Ecoclimatic index; Disease index

期刊名称：AGRICULTURAL AND FOREST METEOROLOGY （ 影响因子：5.6； 五年影响因子：6.3 ）

ISSN： 0168-1923

年卷期： 2024 年 356 卷

页码：

收录情况： SCI

摘要： Global maize production is persistently confronted with the threat of pests and diseases that are migrating to novel regions. Southern corn rust (SCR) is becoming one of the most serious maize diseases within China, with its pathogens overwintering in southern regions and then spreading northward in response to warm and moist airflow. To fill the gap in SCR prediction frameworks in northern China, the ecoclimatic index risk prediction (EIRP) model was developed by integrating the infection risk of SCR spores migrating from southern regions and meteorological parameters. This model is trained on a comprehensive ground meteorological dataset spanning 15 years (2007-2022) from Shandong Province within a high-performance computing environment. Its temperature and leaf moisture index and leaf area index effectively simulate pathogen spore dynamics, identifying the primary infection of maize by the pathogen in the 28th week of the year. A threshold of 29.5 for the SCR ecoclimatic index (SCREI), established by the model, signifies a high risk of SCR infection. Furthermore, by incorporating a spore dispersal index negatively determined by the wind speed at 10 m, the accuracy of the model-predicted SCR disease index (SCRDI) is markedly enhanced. The validation conducted using observational data from 66 maize-growing regions in 2023 demonstrated that the classification algorithm for SCREI achieved a precision rate of 93.51 %. Furthermore, the linear regression model comparing SCRDI to DI yielded a training R2 of 0.93 and a testing R2 of 0.84, confirming the robustness and reliability of the model. The model undoubtedly offers a valuable approach for enhancing the accuracy of predicting serious airborne diseases such as SCR.

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