Research on Prediction of Wheat Stripe Rust with Multi-source Data

doi:10.11873/j.issn.1004-0323.2022.3.0571

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 Kong^1,^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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Abstract

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: 15162261380@163.com;yangxd@nercita.org.cn

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	Yuru Kong
	Lijuan Wang
	Jingcheng Zhang
	Guijun Yang
	Yun Yue
	Xiaodong Yang

Cite this article:

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.

URL:

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.3.0571 OR http://www.rsta.ac.cn/EN/Y2022/V37/I3/571

Fig.1 General situation of study area

植被指数	计算公式
归一化植被指数NDVI^[20]	$(R N I R - R R) / (R N I R + R R)$
比值植被指数SR^[21]	$R N I R / R R$
结构加强色素植被指数SIPI^[22]	$(R N I R - R B) / (R N I R + R B)$
优化的土壤调节植被指数OSAVI^[23]	$(R N I R - R R) / (R N I R + R R + 0.16)$
作物氮反应指数NRI^[22]	$(R G - R R) / (R G + R R)$

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

模型名称	方程式	参数
BPNN模型	-	隐藏层节点数为3
SVR模型	-	$c = 0.35; g = 0.5$
MLR模型	$Y = 0.21 × N D V I + 0.01 × T + 0.01 × H - 0.35$	-
SEIR-StripeRust模型	$d H d t = - β H I d L d t = β H I - ω L d I d t = ω L - μ I d R d t = μ I β = k a × T × H + k b + k c × N D V I Y = L + I + R$	$k a = 2; k b = 0.9; k c = 1; 1 / ω = 18; 1 / μ = 30$

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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