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Remote Sensing Technology and Application  2022, Vol. 37 Issue (3): 571-579    DOI: 10.11873/j.issn.1004-0323.2022.3.0571
Research on Prediction of Wheat Stripe Rust with Multi-source Data
Yuru Kong1,2,3(),Lijuan Wang3,Jingcheng Zhang4,Guijun Yang1,Yun Yue5,Xiaodong Yang1()
1.Key Laboratory of Quantitative Remote Sensing in Agriculture of Ministry of Agriculture and Rural Affairs,Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China
2.Jin Cheng He Wei Planning and Design Group,Jincheng 048000,China
3.School of Geography,Geomatics and Planning,Jiangsu Normal University,Xuzhou 221116,China
4.College of Life Information Science and Instrument Engineering,Hangzhou Dianzi University,Hangzhou 310018,China
5.Gansu General Station of Agro-technology Extension,Lanzhou 730020,China
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Wheat stripe rust is an air-borne disease that leads to large reduction in wheat production. The spread process is affected by many factors. Common crop diseases meteorological prediction models are difficult to simulate wheat stripe rust incidence accurately. In order to obtain accurate prediction of wheat stripe rust incidence, a Suscept-Exposed-Infectious-Removed StripeRust (SEIR-StripeRust) dynamic prediction model was constructed based on meteorological and remote sensing data. This paper chose the Longnan area of Gansu Province as a study area. First, meteorological factors and vegetation indexes were constructed based on meteorological data and MODIS data, respectively. Then, the above features were screened by correlation analysis to identify the sensitive factors. A new incidence prediction model named SEIR-StripeRust was constructed, coupled with the sensitive factors. Finally, compared the accuracy of SEIR-StripeRust model with used Back Propagation Neural Network (BPNN), Support Vector Regression (SVR) and Multiple Linear Regression (MLR). The results showed that the average temperature, relative humidity and normalized difference vegetation index were significantly correlated with the incidence of wheat stripe rust. The SEIR-StripeRust model constructed by the above three sensitive factors had the highest prediction accuracy, the coefficient of determination (R2 ) was 0.79, the Root Mean Square Error (RMSE) was 0.10, and the Mean Absolute Error (MAE) was 0.09, which were higher than the BPNN, SVR and MLR models under the same characteristic variables. The results showed that the SEIR-StripeRust model can effectively predict the incidence of wheat stripe rust and provide technical support for wheat stripe rust prediction and accurate prevention at county scale.

Key words:  Wheat stripe rust      Remote sensing      Meteorological data      Incidence      SEIR-Stripe Rust model     
Received:  10 March 2021      Published:  25 August 2022
ZTFLH:  S512.1  
Corresponding Authors:  Xiaodong Yang     E-mail:;
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Yuru Kong
Lijuan Wang
Jingcheng Zhang
Guijun Yang
Yun Yue
Xiaodong Yang

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Yuru Kong,Lijuan Wang,Jingcheng Zhang,Guijun Yang,Yun Yue,Xiaodong Yang. Research on Prediction of Wheat Stripe Rust with Multi-source Data. Remote Sensing Technology and Application, 2022, 37(3): 571-579.

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Fig.1  General situation of study area
Table 1  Calculation formula of vegetation index
Fig.2  The distribution of meteorological stations
Fig.3  SEIR model development process diagram
Table 2  Correlation coefficient matrix of features variables
Table 3  Correlation coefficients r and P values between the preferred variables and the incidence
Table 4  Fit models
Fig.4  Model verification results
Table 5  Model validation results
Fig. 5  Spatial distribution of stripe rust incidence in wheat
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