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A Bayesian Network Model for Yellow Rust Forecasting in Winter Wheat

文献类型： 外文期刊

作者： Yang, Xiaodong 1 ; Nie, Chenwei 3 ; Zhang, Jingcheng 4 ; Feng, Haikuan 1 ; Yang, Guijun 1 ;

作者机构： 1.Minist Agr PR China, Key Lab Quantitat Remote Sensing Agr, Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

3.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing, Peoples R China

4.Hangzhou Dianzi Univ, Coll Life Informat & Instrument Engn, Hangzhou, Peoples R China

关键词： Yellow rust; Meteorological factor; Bayesian network; Forecasting model

期刊名称：COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE XI, PT I

ISSN： 1868-4238

年卷期： 2019 年 545 卷

页码：

收录情况： SCI

摘要： Yellow rust (YR) is one of the most destructive diseases of wheat. We introduced the Bayesian network analysis as a core method and develop a large-scale YR forecasting model based on several important meteorological variables that associate with disease occurrence. To guarantee an effective model calibration and validation, we used multiple years (2010-2012) of meteorological data and the ground survey data in Gansu Province where the YR intimidated most severely in China. The validation results showed that the disease forecasting model is able to produce a reasonable risk map to indicate the disease pressure across the region. In addition, the temporal dispersal of YR can also be delineated by the model. Through a comparison with some classic methods, the Bayesian network outperformed BP neutral network and FLDA in accuracy, which thereby suggested a great potential of Bayesian network in disease forecasting at a regional scale.

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