23.2.8.5.2 Wheat Rust, Blight, Disease, Damage

Chapter Contents (Back)
Wheat Classification. Wheat Rust. Wheat Blight. Wheat Disease.

Yuan, L.[Lin], Zhang, J.C.[Jing-Cheng], Shi, Y.[Yeyin], Nie, C.W.[Chen-Wei], Wei, L.G.[Li-Guang], Wang, J.[Jihua],
Damage Mapping of Powdery Mildew in Winter Wheat with High-Resolution Satellite Image,
RS(6), No. 5, 2014, pp. 3611-3623.
DOI Link 1407
BibRef

Ashourloo, D.[Davoud], Mobasheri, M.R.[Mohammad Reza], Huete, A.[Alfredo],
Developing Two Spectral Disease Indices for Detection of Wheat Leaf Rust (Pucciniatriticina),
RS(6), No. 6, 2014, pp. 4723-4740.
DOI Link 1407
BibRef

Ashourloo, D.[Davoud], Mobasheri, M.R.[Mohammad Reza], Huete, A.[Alfredo],
Evaluating the Effect of Different Wheat Rust Disease Symptoms on Vegetation Indices Using Hyperspectral Measurements,
RS(6), No. 6, 2014, pp. 5107-5123.
DOI Link 1407
BibRef

Jin, X.[Xiu], Jie, L.[Lu], Wang, S.[Shuai], Qi, H.J.[Hai Jun], Li, S.W.[Shao Wen],
Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Shi, Y.[Yue], Huang, W.J.[Wen-Jiang], González-Moreno, P.[Pablo], Luke, B.[Belinda], Dong, Y.Y.[Ying-Ying], Zheng, Q.[Qiong], Ma, H.Q.[Hui-Qin], Liu, L.Y.[Lin-Yi],
Wavelet-Based Rust Spectral Feature Set (WRSFs): A Novel Spectral Feature Set Based on Continuous Wavelet Transformation for Tracking Progressive Host-Pathogen Interaction of Yellow Rust on Wheat,
RS(10), No. 4, 2018, pp. xx-yy.
DOI Link 1805
BibRef

Liu, W.W.[Wei-Wei], Huang, J.F.[Jing-Feng], Wei, C.W.[Chuan-Wen], Wang, X.Z.[Xiu-Zhen], Mansaray, L.R.[Lamin R.], Han, J.H.[Jia-Hui], Zhang, D.D.[Dong-Dong], Chen, Y.L.[Yao-Liang],
Mapping Water-Logging Damage on Winter Wheat at Parcel Level Using High Spatial Resolution Satellite Data,
PandRS(142), 2018, pp. 243-256.
Elsevier DOI 1807
Winter wheat, Water-logging, Parcel scale, High resolution satellite data, Biophysical parameters
See also Mapping Above-Ground Biomass of Winter Oilseed Rape Using High Spatial Resolution Satellite Data at Parcel Scale under Waterlogging Conditions. BibRef

Fitzgerald, G.J.[Glenn J.], Perry, E.M.[Eileen M.], Flower, K.C.[Ken C.], Callow, J.N.[J. Nikolaus], Boruff, B.[Bryan], Delahunty, A.[Audrey], Wallace, A.[Ashley], Nuttall, J.[James],
Frost Damage Assessment in Wheat Using Spectral Mixture Analysis,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Bohnenkamp, D.[David], Behmann, J.[Jan], Mahlein, A.K.[Anne-Katrin],
In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Zhang, X.[Xin], Han, L.X.[Liang-Xiu], Dong, Y.Y.[Ying-Ying], Shi, Y.[Yue], Huang, W.J.[Wen-Jiang], Han, L.H.[Liang-Hao], González-Moreno, P.[Pablo], Ma, H.Q.[Hui-Qin], Ye, H.C.[Hui-Chun], Sobeih, T.[Tam],
A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Guo, A.T.[An-Ting], Huang, W.J.[Wen-Jiang], Dong, Y.Y.[Ying-Ying], Ye, H.C.[Hui-Chun], Ma, H.Q.[Hui-Qin], Liu, B.[Bo], Wu, W.B.[Wen-Bin], Ren, Y.[Yu], Ruan, C.[Chao], Geng, Y.[Yun],
Wheat Yellow Rust Detection Using UAV-Based Hyperspectral Technology,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Guo, A.T.[An-Ting], Huang, W.J.[Wen-Jiang], Ye, H.[Huichun], Dong, Y.Y.[Ying-Ying], Ma, H.Q.[Hui-Qin], Ren, Y.[Yu], Ruan, C.[Chao],
Identification of Wheat Yellow Rust Using Spectral and Texture Features of Hyperspectral Images,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005
BibRef

Zhang, D.Y.[Dong-Yan], Wang, D.Y.[Dao-Yong], Gu, C.Y.[Chun-Yan], Jin, N.[Ning], Zhao, H.T.[Hai-Tao], Chen, G.[Gao], Liang, H.Y.[Hong-Yi], Liang, D.[Dong],
Using Neural Network to Identify the Severity of Wheat Fusarium Head Blight in the Field Environment,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Qiu, R.C.[Rui-Cheng], Yang, C.[Ce], Moghimi, A.[Ali], Zhang, M.[Man], Steffenson, B.J.[Brian J.], Hirsch, C.D.[Cory D.],
Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Wang, S.[Shuai], Chen, J.[Jin], Rao, Y.H.[Yu-Han], Liu, L.C.[Li-Cong], Wang, W.Q.[Wen-Qing], Dong, Q.[Qi],
Response of winter wheat to spring frost from a remote sensing perspective: Damage estimation and influential factors,
PandRS(168), 2020, pp. 221-235.
Elsevier DOI 2009
Spring frost, Winter wheat, Damage assessment, Remote sensing, North China BibRef

Xiao, Y.X.[Ying-Xin], Dong, Y.Y.[Ying-Ying], Huang, W.J.[Wen-Jiang], Liu, L.Y.[Lin-Yi], Ma, H.Q.[Hui-Qin], Ye, H.C.[Hui-Chun], Wang, K.[Kun],
Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Liu, L.Y.[Lin-Yi], Dong, Y.Y.[Ying-Ying], Huang, W.J.[Wen-Jiang], Du, X.P.[Xiao-Ping], Ma, H.Q.[Hui-Qin],
Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Ma, H.Q.[Hui-Qin], Huang, W.J.[Wen-Jiang], Dong, Y.Y.[Ying-Ying], Liu, L.Y.[Lin-Yi], Guo, A.[Anting],
Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Chauhan, S.[Sugandh], Darvishzadeh, R.[Roshanak], Boschetti, M.[Mirco], Nelson, A.[Andrew],
Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data,
PandRS(164), 2020, pp. 138-151.
Elsevier DOI 2005
Lodging score, Lodging severity, Sentinel-1, RADARSAT-2, PLS-DA, Sustainable agriculture BibRef

Zhang, Z.[Zhao], Flores, P.[Paulo], Igathinathane, C., Naik, D.L.[Dayakar L.], Kiran, R.[Ravi], Ransom, J.K.[Joel K.],
Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Dehkordi, R.H.[Ramin Heidarian], El Jarroudi, M.[Moussa], Kouadio, L.[Louis], Meersmans, J.[Jeroen], Beyer, M.[Marco],
Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Zhang, T.X.[Tian-Xiang], Xu, Z.Y.[Zhi-Yong], Su, J.Y.[Jin-Ya], Yang, Z.F.[Zhi-Fang], Liu, C.J.[Cun-Jia], Chen, W.H.[Wen-Hua], Li, J.Y.[Jiang-Yun],
Ir-UNet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

He, L., Qi, S.L., Duan, J.Z., Guo, T.C., Feng, W., He, D.X.,
Monitoring of Wheat Powdery Mildew Disease Severity Using Multiangle Hyperspectral Remote Sensing,
GeoRS(59), No. 2, February 2021, pp. 979-990.
IEEE DOI 2101
Diseases, Monitoring, Remote sensing, Indexes, Agriculture, Correlation, Vegetation mapping, Band combination, wheat BibRef

Zhu, K.Y.[Kang-Ying], Sun, Z.G.[Zhi-Gang], Zhao, F.[Fenghua], Yang, T.[Ting], Tian, Z.R.[Zhen-Rong], Lai, J.B.[Jian-Bin], Zhu, W.X.[Wan-Xue], Long, B.[Buju],
Relating Hyperspectral Vegetation Indices with Soil Salinity at Different Depths for the Diagnosis of Winter Wheat Salt Stress,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Zheng, Q.[Qiong], Ye, H.C.[Hui-Chun], Huang, W.J.[Wen-Jiang], Dong, Y.Y.[Ying-Ying], Jiang, H.[Hao], Wang, C.Y.[Chong-Yang], Li, D.[Dan], Wang, L.[Li], Chen, S.[Shuisen],
Integrating Spectral Information and Meteorological Data to Monitor Wheat Yellow Rust at a Regional Scale: A Case Study,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Khan, I.H.[Imran Haider], Liu, H.Y.[Hai-Yan], Li, W.[Wei], Cao, A.Z.[Ai-Zhong], Wang, X.[Xue], Liu, H.Y.[Hong-Yan], Cheng, T.[Tao], Tian, Y.C.[Yong-Chao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Yao, X.[Xia],
Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Liu, W.[Wei], Sun, C.F.[Chao-Fei], Zhao, Y.[Yanan], Xu, F.[Fei], Song, Y.[Yuli], Fan, J.[Jieru], Zhou, Y.L.[Yi-Lin], Xu, X.M.[Xiang-Ming],
Monitoring of Wheat Powdery Mildew under Different Nitrogen Input Levels Using Hyperspectral Remote Sensing,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Su, W.H.[Wen-Hao], Zhang, J.J.[Jia-Jing], Yang, C.[Ce], Page, R.[Rae], Szinyei, T.[Tamas], Hirsch, C.D.[Cory D.], Steffenson, B.J.[Brian J.],
Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Xiao, Y.X.[Ying-Xin], Dong, Y.Y.[Ying-Ying], Huang, W.J.[Wen-Jiang], Liu, L.Y.[Lin-Yi], Ma, H.Q.[Hui-Qin],
Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Mustafa, G.[Ghulam], Zheng, H.[Hengbiao], Khan, I.H.[Imran Haider], Tian, L.[Long], Jia, H.Y.[Hai-Yan], Li, G.Q.[Guo-Qiang], Cheng, T.[Tao], Tian, Y.C.[Yong-Chao], Cao, W.X.[Wei-Xing], Zhu, Y.[Yan], Yao, X.[Xia],
Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Li, L.[Lu], Dong, Y.Y.[Ying-Ying], Xiao, Y.X.[Ying-Xin], Liu, L.Y.[Lin-Yi], Zhao, X.[Xing], Huang, W.J.[Wen-Jiang],
Combining Disease Mechanism and Machine Learning to Predict Wheat Fusarium Head Blight,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Jiang, J.[Jiale], Liu, H.Y.[Hai-Yan], Zhao, C.[Chen], He, C.[Can], Ma, J.F.[Ji-Feng], Cheng, T.[Tao], Zhu, Y.[Yan], Cao, W.X.[Wei-Xing], Yao, X.[Xia],
Evaluation of Diverse Convolutional Neural Networks and Training Strategies for Wheat Leaf Disease Identification with Field-Acquired Photographs,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Jing, X.[Xia], Zou, Q.[Qin], Yan, J.[Jumei], Dong, Y.Y.[Ying-Ying], Li, B.[Bingyu],
Remote Sensing Monitoring of Winter Wheat Stripe Rust Based on mRMR-XGBoost Algorithm,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Ruan, C.[Chao], Dong, Y.Y.[Ying-Ying], Huang, W.J.[Wen-Jiang], Huang, L.S.[Lin-Sheng], Ye, H.[Huichun], Ma, H.Q.[Hui-Qin], Guo, A.[Anting], Sun, R.Q.[Rui-Qi],
Integrating Remote Sensing and Meteorological Data to Predict Wheat Stripe Rust,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Jing, X.[Xia], Li, B.[Bingyu], Ye, Q.X.[Qi-Xing], Zou, Q.[Qin], Yan, J.[Jumei], Du, K.Q.[Kai-Qi],
Integrate the Canopy SIF and Its Derived Structural and Physiological Components for Wheat Stripe Rust Stress Monitoring,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Hong, Q.Q.[Qing-Qing], Jiang, L.[Ling], Zhang, Z.H.[Zheng-Hua], Ji, S.[Shu], Gu, C.[Chen], Mao, W.[Wei], Li, W.X.[Wen-Xi], Liu, T.[Tao], Li, B.[Bin], Tan, C.W.[Chang-Wei],
A Lightweight Model for Wheat Ear Fusarium Head Blight Detection Based on RGB Images,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Tang, Z.Q.[Zi-Qian], Sun, Y.Q.[Ya-Qin], Wan, G.T.[Guang-Tong], Zhang, K.[Kefei], Shi, H.T.[Hong-Tao], Zhao, Y.[Yindi], Chen, S.[Shuo], Zhang, X.W.[Xue-Wei],
Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Du, K.Q.[Kai-Qi], Jing, X.[Xia], Zeng, Y.[Yelu], Ye, Q.X.[Qi-Xing], Li, B.[Bingyu], Huang, J.X.[Jian-Xi],
An Improved Approach to Monitoring Wheat Stripe Rust with Sun-Induced Chlorophyll Fluorescence,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Wang, J.[Jian], Shi, L.[Lei], Fu, Y.Y.[Yuan-Yuan], Si, H.P.[Hai-Ping], Liu, Y.[Yi], Qiao, H.B.[Hong-Bo],
Mapping Wheat Take-All Disease Levels from Airborne Hyperspectral Images Using Radiative Transfer Models,
RS(15), No. 8, 2023, pp. 1960.
DOI Link 2305
BibRef

Huang, L.S.[Lin-Sheng], Chen, X.Y.[Xin-Yu], Dong, Y.Y.[Ying-Ying], Huang, W.J.[Wen-Jiang], Ma, H.Q.[Hui-Qin], Zhang, H.[Hansu], Xu, Y.L.[Yun-Lei], Wang, J.[Jing],
Dynamic Analysis of Regional Wheat Stripe Rust Environmental Suitability in China,
RS(15), No. 8, 2023, pp. 2021.
DOI Link 2305
BibRef

Feng, Z.H.[Zi-Heng], Zhang, H.Y.[Hai-Yan], Duan, J.Z.[Jian-Zhao], He, L.[Li], Yuan, X.[Xinru], Gao, Y.Z.[Yue-Zhi], Liu, W.[Wandai], Li, X.[Xiao], Feng, W.[Wei],
Improved Spectral Detection of Nitrogen Deficiency and Yellow Mosaic Disease Stresses in Wheat Using a Soil Effect Removal Algorithm and Machine Learning,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
BibRef

Liu, W.W.[Wei-Wei], Chen, Y.Y.[Yuan-Yuan], Sun, W.W.[Wei-Wei], Huang, R.[Ran], Huang, J.[Jingfeng],
Mapping Waterlogging Damage to Winter Wheat Yield Using Downscaling-Merging Satellite Daily Precipitation in the Middle and Lower Reaches of the Yangtze River,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306
BibRef

Yang, H.B.[Hai-Bo], Li, Z.[Zenglan], Du, Q.Y.[Qing-Ying], Duan, Z.[Zheng],
Winter Wheat Drought Risk Assessment by Coupling Improved Moisture-Sensitive Crop Model and Gridded Vulnerability Curve,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
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Zhou, K.[Ke], Zhang, Z.Y.[Zheng-Yan], Liu, L.[Le], Miao, R.[Ru], Yang, Y.[Yang], Ren, T.C.[Tong-Can], Yue, M.[Ming],
Research on SUnet Winter Wheat Identification Method Based on GF-2,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307
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Nguyen, C.[Canh], Sagan, V.[Vasit], Skobalski, J.[Juan], Severo, J.I.[Juan Ignacio],
Early Detection of Wheat Yellow Rust Disease and Its Impact on Terminal Yield with Multi-Spectral UAV-Imagery,
RS(15), No. 13, 2023, pp. 3301.
DOI Link 2307
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Hussain, S.[Sarfraz], Mustafa, G.[Ghulam], Khan, I.H.[Imran Haider], Liu, J.Y.[Jia-Yuan], Chen, C.[Cheng], Hu, B.[Bingtao], Chen, M.[Min], Ali, I.[Iftikhar], Liu, Y.H.[Yu-Hong],
Global Trends and Future Directions in Agricultural Remote Sensing for Wheat Scab Detection: Insights from a Bibliometric Analysis,
RS(15), No. 13, 2023, pp. 3431.
DOI Link 2307
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Zhang, K.[Kai], Zhang, R.D.[Run-Dong], Yang, Z.Q.[Zi-Qian], Deng, J.[Jie], Abdullah, A.[Ahsan], Zhou, C.Y.[Cong-Ying], Lv, X.[Xuan], Wang, R.[Rui], Ma, Z.[Zhanhong],
Efficient Wheat Lodging Detection Using UAV Remote Sensing Images and an Innovative Multi-Branch Classification Framework,
RS(15), No. 18, 2023, pp. 4572.
DOI Link 2310
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Zhao, M.X.[Ming-Xian], Dong, Y.Y.[Ying-Ying], Huang, W.J.[Wen-Jiang], Ruan, C.[Chao], Guo, J.[Jing],
Regional-Scale Monitoring of Wheat Stripe Rust Using Remote Sensing and Geographical Detectors,
RS(15), No. 18, 2023, pp. 4631.
DOI Link 2310
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Najafian, K.[Keyhan], Jin, L.L.[Ling-Ling], Kutcher, H.R.[H. Randy], Hladun, M.[Mackenzie], Horovatin, S.[Samuel], Oviedo-Ludena, M.A.[Maria Alejandra], de Andrade, S.M.P.[Sheila Maria Pereira], Wang, L.[Lipu], Stavness, I.[Ian],
Detection of Fusarium Damaged Kernels in Wheat Using Deep Semi-Supervised Learning on a Novel WheatSeedBelt Dataset,
CVPPA23(660-669)
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Pryzant, R., Ermon, S., Lobell, D.,
Monitoring Ethiopian Wheat Fungus with Satellite Imagery and Deep Feature Learning,
EarthVision17(1524-1532)
IEEE DOI 1709
Agriculture, Diseases, Monitoring, Remote sensing, Satellites, Spatial, resolution BibRef

Siricharoen, P.[Punnarai], Scotney, B.[Bryan], Morrow, P.[Philip], Parr, G.[Gerard],
Automated Wheat Disease Classification Under Controlled and Uncontrolled Image Acquisition,
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Pande, A.[Arun], Jagyasi, B.G.[Bhushan G.], Choudhuri, R.[Ravidutta],
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Cataltepe, Z., Cetin, E., Pearson, T.,
Identification of insect damaged wheat kernels using transmittance images,
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
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