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使用气象数据和机器学习方法,预测中国的小麦粉疫情

Xiao Nie^1,2, Chang Su^1,2, Xue-Hua Wei^1,2

¹College of Agriculture, Yangtze University, Jingzhou, China.

Pest management science

|November 22, 2025

概括

机器学习模型使用气象数据准确预测小麦粉 (WPM) 的严重程度和发生区域. 这些预测为改善中国各地的WPM管理策略提供了宝贵的见解.

科学领域:

农业科学农业科学
植物病理学植物病理学
计算生物学计算生物学

背景情况:

准确预测像小麦粉 (WPM) 这样的植物疾病对于有效的农业管理至关重要.
本研究的重点是使用中国的气象数据开发WPM严重性和发生的预测模型.
以前的方法缺乏用于大规模WPM管理所需的精度.

研究的目的:

开发和验证机器学习模型,用于预测小麦粉的严重程度和发生区域.
确定影响WPM发展的关键气象因素.
为了改善疾病管理,生成全国范围的WPM严重性分布图.

主要方法:

训练和交叉验证了6个机器学习算法,使用来自中国48个县的411个气象变量 (1981-2021).
利用K-最近邻居 (KNN) 进行严重性预测和空间插值模型 (IDW,普通 kriging) 进行事件区域映射.
采用随机森林气候变量重要性排名和基平方/误差参考方法用于模型验证.

主要成果:

支持矢量机和KNN模型在预测WPM严重性方面表现出很高的表现,特别是使用最寒冷月份和小麦联合头部阶段的数据.
确定了八个关键的气象预测因素,提高了预测准确度.
IDW_4.0模型在生成全国范围的WPM严重性分布图 (1990-2019) 中被证明是优越的.

结论:

机器学习模型有效地使用气象数据在全国范围内预测WPM严重程度和发生区域.
可视化WPM严重程度的空间模式有助于制定针对性和改进的中国管理策略.
开发的模型在数据驱动的植物疾病管理方面取得了重大进展.

关键词:

这种植物是Blumeria graminis f. sp. 这里是triticici和tritici.人工智能的人工智能是人工智能.气候变量气候变量植物疾病预测模型的模型空间间对应的空间间对应

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