22.2.8.5.1 Wheat Rust, Blight, Disease

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.[Linyi],
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
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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
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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.[Ruicheng], 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

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.[Linyi], 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.[Zhenrong], Lai, J.B.[Jian-Bin], Zhu, W.[Wanxue], 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.[Linyi], 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.[Linyi], 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
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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.[Changwei],
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
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Tang, Z.Q.[Zi-Qian], Sun, Y.[Yaqin], Wan, G.[Guangtong], Zhang, K.[Kefei], Shi, H.T.[Hong-Tao], Zhao, Y.[Yindi], Chen, S.[Shuo], Zhang, X.[Xuewei],
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


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,
ICIAR15(456-464).
Springer DOI 1507
BibRef

Pande, A.[Arun], Jagyasi, B.G.[Bhushan G.], Choudhuri, R.[Ravidutta],
Late Blight Forecast Using Mobile Phone Based Agro Advisory System,
PReMI09(609-614).
Springer DOI 0912
BibRef

Cataltepe, Z., Cetin, E., Pearson, T.,
Identification of insect damaged wheat kernels using transmittance images,
ICIP04(V: 2917-2920).
IEEE DOI 0505
BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Potato Crop Analysis, Production, Detection, Health, Change .


Last update:May 22, 2023 at 22:32:27