Prediction of Wheat Stripe Rust Occurrence with Time Series Sentinel-2 Images

Chao Ruan, Yingying Dong, Wenjiang Huang, Linsheng Huang, Huichun Ye, Huiqin Ma, Anting Guo and Yu Ren
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Chao Ruan: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yingying Dong: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Wenjiang Huang: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Linsheng Huang: National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
Huichun Ye: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Huiqin Ma: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Anting Guo: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yu Ren: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Agriculture, 2021, vol. 11, issue 11, 1-19

Abstract: Wheat stripe rust has a severe impact on wheat yield and quality. An effective prediction method is necessary for food security. In this study, we extract the optimal vegetation indices (VIs) sensitive to stripe rust at different time-periods, and develop a wheat stripe rust prediction model with satellite images to realize the multi-temporal prediction. First, VIs related to stripe rust stress are extracted as candidate features for disease prediction from time series Sentinel-2 images. Then, the optimal VI combinations are selected using sequential forward selection (SFS). Finally, the occurrence of wheat stripe rust in different time-periods is predicted using the support vector machine (SVM) method. The results of the features selected demonstrate that, before the jointing period, the optimal VIs are related to the biomass, pigment, and moisture of wheat. After the jointing period, the red-edge VIs related to the crop health status play important roles. The overall accuracy and Kappa coefficient of the prediction model, which is based on SVM, is generally higher than those of the k-nearest neighbor (KNN) and back-propagation neural network (BPNN) methods. The SVM method is more suitable for time series predictions of wheat stripe rust. The model obtained accuracy based on the optimal VI combinations and the SVM increased over time; the highest accuracy was 86.2%. These results indicate that the prediction model can provide guidance and suggestions for early disease prevention of the study site, and the method combines time series Sentinel-2 images and the SVM, which can be used to predict wheat stripe rust.

Keywords: wheat; stripe rust; prediction; time series; remote sensing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
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