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A Bayesian Network Model for Yellow Rust Forecasting in Winter Wheat

  • Xiaodong YangEmail author
  • Chenwei Nie
  • Jingcheng Zhang
  • Haikuan Feng
  • Guijun Yang
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 545)

Abstract

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.

Keywords

Yellow rust Meteorological factor Bayesian network Forecasting model 

Notes

Acknowledgments

This work was supported by the National Key R&D Program (2016YFD0300602) and the National Natural Science Foundation of China (41101395, 41601346).

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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Xiaodong Yang
    • 1
    • 2
    Email author
  • Chenwei Nie
    • 3
  • Jingcheng Zhang
    • 4
  • Haikuan Feng
    • 1
    • 2
  • Guijun Yang
    • 1
    • 2
  1. 1.Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture P. R. ChinaBeijing Research Center for Information Technology in AgricultureBeijingChina
  2. 2.National Engineering Research Center for Information Technology in AgricultureBeijingChina
  3. 3.Institute of Remote Sensing and Digital Earth of Chinese Academy of SciencesBeijingChina
  4. 4.College of Life Information and Instrument EngineeringHangzhou Dianzi UniversityHangzhouChina

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