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

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 sa...

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Autores principales:	Chao Ruan, Yingying Dong, Wenjiang Huang, Linsheng Huang, Huichun Ye, Huiqin Ma, Anting Guo, Yu Ren
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	wheat stripe rust prediction time series remote sensing Agriculture (General) S1-972
Acceso en línea:	https://doaj.org/article/d6974abfa54b41b88117006f5602d1af
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id	oai:doaj.org-article:d6974abfa54b41b88117006f5602d1af
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spelling	oai:doaj.org-article:d6974abfa54b41b88117006f5602d1af2021-11-25T15:58:52ZPrediction of Wheat Stripe Rust Occurrence with Time Series Sentinel-2 Images10.3390/agriculture111110792077-0472https://doaj.org/article/d6974abfa54b41b88117006f5602d1af2021-11-01T00:00:00Zhttps://www.mdpi.com/2077-0472/11/11/1079https://doaj.org/toc/2077-0472Wheat 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.Chao RuanYingying DongWenjiang HuangLinsheng HuangHuichun YeHuiqin MaAnting GuoYu RenMDPI AGarticlewheatstripe rustpredictiontime seriesremote sensingAgriculture (General)S1-972ENAgriculture, Vol 11, Iss 1079, p 1079 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	wheat stripe rust prediction time series remote sensing Agriculture (General) S1-972
spellingShingle	wheat stripe rust prediction time series remote sensing Agriculture (General) S1-972 Chao Ruan Yingying Dong Wenjiang Huang Linsheng Huang Huichun Ye Huiqin Ma Anting Guo Yu Ren Prediction of Wheat Stripe Rust Occurrence with Time Series Sentinel-2 Images
description	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.
format	article
author	Chao Ruan Yingying Dong Wenjiang Huang Linsheng Huang Huichun Ye Huiqin Ma Anting Guo Yu Ren
author_facet	Chao Ruan Yingying Dong Wenjiang Huang Linsheng Huang Huichun Ye Huiqin Ma Anting Guo Yu Ren
author_sort	Chao Ruan
title	Prediction of Wheat Stripe Rust Occurrence with Time Series Sentinel-2 Images
title_short	Prediction of Wheat Stripe Rust Occurrence with Time Series Sentinel-2 Images
title_full	Prediction of Wheat Stripe Rust Occurrence with Time Series Sentinel-2 Images
title_fullStr	Prediction of Wheat Stripe Rust Occurrence with Time Series Sentinel-2 Images
title_full_unstemmed	Prediction of Wheat Stripe Rust Occurrence with Time Series Sentinel-2 Images
title_sort	prediction of wheat stripe rust occurrence with time series sentinel-2 images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d6974abfa54b41b88117006f5602d1af
work_keys_str_mv	AT chaoruan predictionofwheatstriperustoccurrencewithtimeseriessentinel2images AT yingyingdong predictionofwheatstriperustoccurrencewithtimeseriessentinel2images AT wenjianghuang predictionofwheatstriperustoccurrencewithtimeseriessentinel2images AT linshenghuang predictionofwheatstriperustoccurrencewithtimeseriessentinel2images AT huichunye predictionofwheatstriperustoccurrencewithtimeseriessentinel2images AT huiqinma predictionofwheatstriperustoccurrencewithtimeseriessentinel2images AT antingguo predictionofwheatstriperustoccurrencewithtimeseriessentinel2images AT yuren predictionofwheatstriperustoccurrencewithtimeseriessentinel2images
_version_	1718413409190412288

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

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