Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental data Article Swipe
Yu Ren
,
Wenjiang Huang
,
Huichun Ye
,
Xianfeng Zhou
,
Huiqin Ma
,
Yingying Dong
,
Yue Shi
,
Yun Geng
,
Yanru Huang
,
Quanjun Jiao
,
Qiaoyun Xie
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.jag.2021.102384
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.jag.2021.102384
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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jag.2021.102384
- OA Status
- gold
- Cited By
- 31
- References
- 57
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3168496760Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.jag.2021.102384Digital Object Identifier
- Title
-
Quantitative identification of yellow rust in winter wheat with a new spectral index: Development and validation using simulated and experimental dataWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-06-11Full publication date if available
- Authors
-
Yu Ren, Wenjiang Huang, Huichun Ye, Xianfeng Zhou, Huiqin Ma, Yingying Dong, Yue Shi, Yun Geng, Yanru Huang, Quanjun Jiao, Qiaoyun XieList of authors in order
- Landing page
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https://doi.org/10.1016/j.jag.2021.102384Publisher landing page
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.jag.2021.102384Direct OA link when available
- Concepts
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Rust (programming language), Hyperspectral imaging, Canopy, Spectral index, Crop, Mean squared error, Agronomy, Mathematics, Environmental science, Statistics, Remote sensing, Biology, Botany, Geography, Computer science, Physics, Programming language, Spectral line, AstronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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31Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 13, 2023: 10, 2022: 3Per-year citation counts (last 5 years)
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57Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.ASD | 106 |
| abstract_inverted_index.L.) | 17 |
| abstract_inverted_index.The | 21, 115 |
| abstract_inverted_index.and | 32, 54, 125, 128, 132, 143, 157, 178, 222, 228 |
| abstract_inverted_index.can | 26, 216 |
| abstract_inverted_index.for | 168, 206, 225 |
| abstract_inverted_index.gap | 80 |
| abstract_inverted_index.has | 38 |
| abstract_inverted_index.new | 91, 141, 204 |
| abstract_inverted_index.our | 214 |
| abstract_inverted_index.sp. | 7 |
| abstract_inverted_index.the | 19, 46, 67, 70, 82, 85, 94, 101, 120, 135, 140, 163, 170, 193, 207, 226, 236, 240 |
| abstract_inverted_index.RMSE | 175, 183 |
| abstract_inverted_index.YROI | 167 |
| abstract_inverted_index.both | 169 |
| abstract_inverted_index.crop | 211, 233 |
| abstract_inverted_index.data | 103 |
| abstract_inverted_index.fill | 78 |
| abstract_inverted_index.idea | 205 |
| abstract_inverted_index.leaf | 171 |
| abstract_inverted_index.over | 235 |
| abstract_inverted_index.rust | 25, 74, 96, 113, 151 |
| abstract_inverted_index.this | 79, 188 |
| abstract_inverted_index.used | 149 |
| abstract_inverted_index.with | 45, 134, 146 |
| abstract_inverted_index.areas | 238 |
| abstract_inverted_index.based | 118 |
| abstract_inverted_index.basis | 224 |
| abstract_inverted_index.field | 107 |
| abstract_inverted_index.index | 98, 116, 142 |
| abstract_inverted_index.large | 237 |
| abstract_inverted_index.leaf- | 156 |
| abstract_inverted_index.loss. | 34 |
| abstract_inverted_index.rust, | 1, 44, 200 |
| abstract_inverted_index.using | 100, 154 |
| abstract_inverted_index.wheat | 14, 198 |
| abstract_inverted_index.which | 201 |
| abstract_inverted_index.yield | 30 |
| abstract_inverted_index.0.070) | 177 |
| abstract_inverted_index.0.085) | 185 |
| abstract_inverted_index.0.542, | 182 |
| abstract_inverted_index.0.822, | 174 |
| abstract_inverted_index.Yellow | 0 |
| abstract_inverted_index.across | 18 |
| abstract_inverted_index.canopy | 179 |
| abstract_inverted_index.caused | 2 |
| abstract_inverted_index.globe. | 20 |
| abstract_inverted_index.index, | 93 |
| abstract_inverted_index.model. | 137 |
| abstract_inverted_index.remote | 36 |
| abstract_inverted_index.result | 27 |
| abstract_inverted_index.severe | 29 |
| abstract_inverted_index.simple | 59 |
| abstract_inverted_index.study, | 87 |
| abstract_inverted_index.timely | 229 |
| abstract_inverted_index.yellow | 24, 43, 73, 95, 112, 150, 199 |
| abstract_inverted_index.(YROI), | 99 |
| abstract_inverted_index.Results | 161 |
| abstract_inverted_index.between | 52 |
| abstract_inverted_index.current | 86 |
| abstract_inverted_index.disease | 12, 62 |
| abstract_inverted_index.future. | 241 |
| abstract_inverted_index.grading | 60 |
| abstract_inverted_index.healthy | 55 |
| abstract_inverted_index.indices | 153 |
| abstract_inverted_index.optimal | 97 |
| abstract_inverted_index.results | 215 |
| abstract_inverted_index.scales. | 186 |
| abstract_inverted_index.sensing | 37 |
| abstract_inverted_index.serious | 11 |
| abstract_inverted_index.spores, | 124 |
| abstract_inverted_index.studies | 49 |
| abstract_inverted_index.(VPCPs); | 131 |
| abstract_inverted_index.However, | 64 |
| abstract_inverted_index.Puccinia | 4 |
| abstract_inverted_index.Tritici, | 8 |
| abstract_inverted_index.accuracy | 165 |
| abstract_inverted_index.accurate | 227 |
| abstract_inverted_index.aestivum | 16 |
| abstract_inverted_index.analyzed | 192 |
| abstract_inverted_index.commonly | 148 |
| abstract_inverted_index.compared | 144 |
| abstract_inverted_index.diseased | 53 |
| abstract_inverted_index.diseases | 234 |
| abstract_inverted_index.economic | 33 |
| abstract_inverted_index.employed | 218 |
| abstract_inverted_index.estimate | 111 |
| abstract_inverted_index.limited. | 76 |
| abstract_inverted_index.majority | 47 |
| abstract_inverted_index.obtained | 104 |
| abstract_inverted_index.provided | 202 |
| abstract_inverted_index.research | 65 |
| abstract_inverted_index.response | 122, 195 |
| abstract_inverted_index.severity | 71 |
| abstract_inverted_index.spectral | 92, 121, 159, 194 |
| abstract_inverted_index.superior | 164 |
| abstract_inverted_index.(Triticum | 15 |
| abstract_inverted_index.Moreover, | 213 |
| abstract_inverted_index.attacking | 13 |
| abstract_inverted_index.datasets. | 160 |
| abstract_inverted_index.detecting | 42 |
| abstract_inverted_index.detection | 152 |
| abstract_inverted_index.diseases. | 212 |
| abstract_inverted_index.evaluated | 139 |
| abstract_inverted_index.mechanism | 196 |
| abstract_inverted_index.potential | 40 |
| abstract_inverted_index.reduction | 31 |
| abstract_inverted_index.reference | 221 |
| abstract_inverted_index.research, | 189 |
| abstract_inverted_index.severity. | 63, 114 |
| abstract_inverted_index.PROSPECT-D | 136 |
| abstract_inverted_index.integrated | 133 |
| abstract_inverted_index.occurrence | 22 |
| abstract_inverted_index.parameters | 130 |
| abstract_inverted_index.performing | 58 |
| abstract_inverted_index.vegetation | 126 |
| abstract_inverted_index.biochemical | 129 |
| abstract_inverted_index.biophysical | 127 |
| abstract_inverted_index.constructed | 89 |
| abstract_inverted_index.individuals | 56 |
| abstract_inverted_index.literature, | 83 |
| abstract_inverted_index.striiformis | 5 |
| abstract_inverted_index.theoretical | 223 |
| abstract_inverted_index.canopy-scale | 158 |
| abstract_inverted_index.demonstrated | 39, 162 |
| abstract_inverted_index.experimental | 155 |
| abstract_inverted_index.quantitative | 208, 230 |
| abstract_inverted_index.spectrometer | 108 |
| abstract_inverted_index.Hyperspectral | 35 |
| abstract_inverted_index.hyperspectral | 102 |
| abstract_inverted_index.qualitatively | 51 |
| abstract_inverted_index.distinguishing | 50 |
| abstract_inverted_index.identification | 209, 231 |
| abstract_inverted_index.quantification | 68 |
| abstract_inverted_index.quantitatively | 110, 191 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5100780503, https://openalex.org/A5109619264 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 11 |
| corresponding_institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210137199, https://openalex.org/I4210165038 |
| citation_normalized_percentile.value | 0.94075014 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |