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机器学习方法用于从气候时间序列特征中预测虫头部炎疫情水平

Rui Mao¹, Ying Shi¹, Tao Zhu¹

¹College of Information Engineering, Northwest A&F University, Yangling 712100, China.

|September 10, 2025

概括

在小麦中预测Fusarium head blight (FHB) 对食品安全至关重要. 这项研究发现,使用机器学习模型,天气数据,而不是注射剂水平,有效地预测了FHB严重程度,ANN表现最好.

科学领域:

农业科学农业科学
植物病理学植物病理学
数据科学数据科学数据科学

背景情况:

菌头部病 (Fusarium head blight,FHB) 在全球范围内对小麦生产构成重大威胁.
有效预测FHB对于减轻作物损失和防止食品供应中菌毒素污染至关重要.

研究的目的:

开发和评估机器学习模型,使用历史天气数据和注射剂水平来预测FHB严重程度.
确定影响FHB发生和严重性的关键环境因素.

主要方法:

从55年的每日温度,湿度,降水和阳光数据中提取时间序列特征.
采用随机森林来进行特征选择和六个机器学习模型 (ANNs,逻辑回归,K-NN,SVM,RF,XGBoost) 来进行分类.
在不同地区验证模型性能.

主要成果:

与单独的气候数据相比,Inoculum强度并没有显著提高预测准确性.
来自天气数据的9个复杂时间序列特征有效地分类了FHB流行病水平.
人工神经网络 (ANN) 在分类FHB流行病水平方面表现出卓越的表现.

结论:

气候数据,特别是复杂的时间序列特征,足以预测FHB严重程度.
在实际的FHB管理应用中,ANN显示出前景.
建议对更大的数据集进行进一步研究,以提高模型的实用性.

关键词:

类头部病类头部病气候时间系列的特征是气候.流行病水平的预测和预测.机器学习是机器学习.

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