Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data
Yellow rust, caused by Puccinia striiformis f. sp. Tritici, is a serious disease attacking wheat (Triticum aestivum L.) across the globe. The occurrence of yellow rust can result in severe yield reduction and economic loss. Hyperspectral remote sensing has demonstrated potential in detecting yellow...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-10-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S030324342100091X |
| _version_ | 1818859618257862656 |
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| author | Yu Ren Wenjiang Huang Huichun Ye Xianfeng Zhou Huiqin Ma Yingying Dong Yue Shi Yun Geng Yanru Huang Quanjun Jiao Qiaoyun Xie |
| author_facet | Yu Ren Wenjiang Huang Huichun Ye Xianfeng Zhou Huiqin Ma Yingying Dong Yue Shi Yun Geng Yanru Huang Quanjun Jiao Qiaoyun Xie |
| author_sort | Yu Ren |
| collection | DOAJ |
| description | Yellow rust, caused by Puccinia striiformis f. sp. Tritici, is a serious disease attacking wheat (Triticum aestivum L.) across the globe. The occurrence of yellow rust can result in severe yield reduction and economic loss. Hyperspectral remote sensing has demonstrated potential in detecting yellow rust, with the majority of studies distinguishing qualitatively between diseased and healthy individuals or performing simple grading of disease severity. However, research on the quantification of the severity of yellow rust is limited. To fill this gap in the literature, in the current study, we constructed a new spectral index, the yellow rust optimal index (YROI), using the hyperspectral data obtained by ASD field spectrometer to quantitatively estimate yellow rust severity. The index is based on the spectral response of spores, and vegetation biophysical and biochemical parameters (VPCPs); and integrated with the PROSPECT-D model. We evaluated the new index and compared it with 11 commonly used yellow rust detection indices using experimental leaf- and canopy-scale spectral datasets. Results demonstrated the superior accuracy of YROI for both the leaf (R2 = 0.822, RMSE = 0.070) and canopy (R2 = 0.542, RMSE = 0.085) scales. In this research, we quantitatively analyzed the spectral response mechanism of wheat yellow rust, which provided a new idea for the quantitative identification of crop diseases. Moreover, our results can be employed as a reference and theoretical basis for the accurate and timely quantitative identification of crop diseases over the large areas in the future. |
| first_indexed | 2024-12-19T09:15:03Z |
| format | Article |
| id | doaj.art-a6921cad07994cf0b25fa04ecdebbe30 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-12-19T09:15:03Z |
| publishDate | 2021-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-a6921cad07994cf0b25fa04ecdebbe302022-12-21T20:28:07ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322021-10-01102102384Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental dataYu Ren0Wenjiang Huang1Huichun Ye2Xianfeng Zhou3Huiqin Ma4Yingying Dong5Yue Shi6Yun Geng7Yanru Huang8Quanjun Jiao9Qiaoyun Xie10Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China; Hainan Key Laboratory of Earth Observation, Hainan Institute of Aerospace Information, Chinese Academy of Sciences, Sanya 572029, China; Corresponding authors at: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; Hainan Key Laboratory of Earth Observation, Hainan Institute of Aerospace Information, Chinese Academy of Sciences, Sanya 572029, China; Corresponding authors at: Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou 310018, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaDepartment of Computing and Mathematics, Manchester Metropolitan University, Manchester M1 5GD, UKKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; Hainan Key Laboratory of Earth Observation, Hainan Institute of Aerospace Information, Chinese Academy of Sciences, Sanya 572029, ChinaFaculty of Science, University of Technology Sydney, Sydney, NSW 2138, AustraliaYellow rust, caused by Puccinia striiformis f. sp. Tritici, is a serious disease attacking wheat (Triticum aestivum L.) across the globe. The occurrence of yellow rust can result in severe yield reduction and economic loss. Hyperspectral remote sensing has demonstrated potential in detecting yellow rust, with the majority of studies distinguishing qualitatively between diseased and healthy individuals or performing simple grading of disease severity. However, research on the quantification of the severity of yellow rust is limited. To fill this gap in the literature, in the current study, we constructed a new spectral index, the yellow rust optimal index (YROI), using the hyperspectral data obtained by ASD field spectrometer to quantitatively estimate yellow rust severity. The index is based on the spectral response of spores, and vegetation biophysical and biochemical parameters (VPCPs); and integrated with the PROSPECT-D model. We evaluated the new index and compared it with 11 commonly used yellow rust detection indices using experimental leaf- and canopy-scale spectral datasets. Results demonstrated the superior accuracy of YROI for both the leaf (R2 = 0.822, RMSE = 0.070) and canopy (R2 = 0.542, RMSE = 0.085) scales. In this research, we quantitatively analyzed the spectral response mechanism of wheat yellow rust, which provided a new idea for the quantitative identification of crop diseases. Moreover, our results can be employed as a reference and theoretical basis for the accurate and timely quantitative identification of crop diseases over the large areas in the future.http://www.sciencedirect.com/science/article/pii/S030324342100091XYellow rustSpectral indexHyperspectral remote sensingQuantitative identificationPROSPECT-D modelWinter wheat |
| spellingShingle | Yu Ren Wenjiang Huang Huichun Ye Xianfeng Zhou Huiqin Ma Yingying Dong Yue Shi Yun Geng Yanru Huang Quanjun Jiao Qiaoyun Xie Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data International Journal of Applied Earth Observations and Geoinformation Yellow rust Spectral index Hyperspectral remote sensing Quantitative identification PROSPECT-D model Winter wheat |
| title | Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data |
| title_full | Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data |
| title_fullStr | Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data |
| title_full_unstemmed | Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data |
| title_short | Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data |
| title_sort | quantitative identification of yellow rust in winter wheat with a new spectral index development and validation using simulated and experimental data |
| topic | Yellow rust Spectral index Hyperspectral remote sensing Quantitative identification PROSPECT-D model Winter wheat |
| url | http://www.sciencedirect.com/science/article/pii/S030324342100091X |
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