Monitoring of Wheat Powdery Mildew under Different Nitrogen Input Levels Using Hyperspectral Remote Sensing

Both wheat powdery mildew severities and nitrogen input levels can lead to changes in spectral reflectance, but they have been rarely studied simultaneously for their effect on spectral reflectance. To determine the effects and influences of different nitrogen input levels on monitoring wheat powdery mildew and estimating yield by near-ground hyperspectral remote sensing, Canopy hyperspectral reflectance data acquired at Feekes growth stage (GS) 10.5.3, 10.5.4, and 11.1 were used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels during the 2016–2017, 2017–2018, 2018–2019 and 2019–2020 seasons. The relationships of powdery mildew and grain yield with vegetation indices (VIs) derived from spectral reflectance data across the visible (VIS) and near-infrared (NIR) regions of the spectrum were studied. The relationships of canopy spectral reflectance or first derivative spectral reflectance with powdery mildew did not differ under different nitrogen input levels. However, the dynamics of VIs differed in their sensitivities to nitrogen input levels, disease severity, grain yield, The area of the red edge peak (Σdr680–760 nm) was a better overall predictor for both disease severity and grain yield through linear regression models. The slope parameter estimates did not differ between the two nitrogen input levels at each GSs. Hyperspectral indices can be used to monitor wheat powdery mildew and estimate grain yield under different nitrogen input levels, but such models are dependent on GS and year, further research is needed to consider how to incorporate the growth stage and year-to-year variation into future applications.

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Monitoring of Wheat Powdery Mildew Disease Severity Using Multiangle Hyperspectral Remote Sensing

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.3000992 ◽

2020 ◽

pp. 1-12

Author(s):

Li He ◽

Shuang-Li Qi ◽

Jian-Zhao Duan ◽

Tian-Cai Guo ◽

Wei Feng ◽

...

Keyword(s):

Remote Sensing ◽

Powdery Mildew ◽

Disease Severity ◽

Hyperspectral Remote Sensing ◽

Wheat Powdery Mildew ◽

Powdery Mildew Disease

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Detecting Wheat Powdery Mildew and Predicting Grain Yield Using Unmanned Aerial Photography

Plant Disease ◽

10.1094/pdis-12-17-1893-re ◽

2018 ◽

Vol 102 (10) ◽

pp. 1981-1988 ◽

Cited By ~ 3

Author(s):

Wei Liu ◽

Xueren Cao ◽

Jieru Fan ◽

Zhenhua Wang ◽

Zhengyuan Yan ◽

...

Keyword(s):

Powdery Mildew ◽

Grain Yield ◽

Disease Severity ◽

Flight Altitude ◽

Imaging Data ◽

Remote Imaging ◽

Wheat Powdery Mildew ◽

Imaging Tool ◽

Random Coefficient Regression ◽

Relationship Of

High-resolution aerial imaging with an unmanned aerial vehicle (UAV) was used to quantify wheat powdery mildew and estimate grain yield. Aerial digital images were acquired at Feekes growth stage (GS) 10.5.4 from flight altitudes of 200, 300, and 400 m during the 2009–10 and 2010–11 seasons; and 50, 100, 200, and 300 m during the 2011–12, 2012–13, and 2013–14 seasons. The image parameter lgR was consistently correlated positively with wheat powdery mildew severity and negatively with wheat grain yield for all combinations of flight altitude and year. Fitting the data with random coefficient regression models showed that the exact relationship of lgR with disease severity and grain yield varied considerably from year to year and to a lesser extent with flight altitude within the same year. The present results raise an important question about the consistency of using remote imaging information to estimate disease severity and grain yield. Further research is needed to understand the nature of interyear variability in the relationship of remote imaging data with disease or grain yield. Only then can we determine how the remote imaging tool can be used in commercial agriculture.

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Application of hyperspectral remote sensing reflectance data to photochemical rate calculations in the Duplin River, a tidal river on the coast of Georgia, USA

GIScience & Remote Sensing ◽

10.1080/15481603.2014.895583 ◽

2014 ◽

Vol 51 (2) ◽

pp. 199-211 ◽

Cited By ~ 1

Author(s):

H.E. Reader ◽

W.L. Miller

Keyword(s):

Remote Sensing ◽

Hyperspectral Remote Sensing ◽

Tidal River ◽

Remote Sensing Reflectance ◽

Reflectance Data

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Disease Severity and Grain Yield in Barley Multilines with Resistance to Powdery Mildew

Crop Science ◽

10.2135/cropsci1989.0011183x002900060027x ◽

1989 ◽

Vol 29 (6) ◽

pp. 1459-1463 ◽

Cited By ~ 11

Author(s):

Per Kølster ◽

Lisa Munk ◽

Olav Stølen

Keyword(s):

Powdery Mildew ◽

Grain Yield ◽

Disease Severity

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Hyperspectral Remote Sensing for Determining Water and Nitrogen Stress in Maize during Rabi Season

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2019/v38i630456 ◽

2020 ◽

pp. 1-9

Author(s):

H. R. Naveen ◽

B. Balaji Naik ◽

G. Sreenivas ◽

Ajay Kumar ◽

J. Adinarayana ◽

...

Keyword(s):

Remote Sensing ◽

Spectral Reflectance ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Hyperspectral Remote Sensing ◽

Nitrogen Stress ◽

Fixed Amount ◽

Nitrogen Levels ◽

Stress Environment

Aims/Objectives: Is to examine the use of spectral reflectance characteristics and explore the effectiveness of spectral indices under water and nitrogen stress environment. Study Design: Split-plot. Place and Duration of Study: Agro Climate Research Center, A.R.I., P.J.T.S. Agricultural University, Rajendranagar, Hyderabad, India in 2018-19. Methodology: Fixed amount of 5 cm depth of water was applied to each plot when the ratio of irrigation water and cumulative pan evaporation (IW/CPE) arrives at pre-determined levels of 0.6, 0.8 & 1.2 as main-plot and 3 nitrogen levels viz. 100, 200 & 300 kg N ha-1 as a subplot to create water and nitrogen stress environment. Spectral reflectance from each treatment was measured using Spectroradiometer and analyzed using statistical software package SPSS 17, SAS and trial version of UNSCRABLER. Results: At tasseling and dough stages, the reflectance pattern of maize was found to be higher in visible light spectrum of 400 to700 nm whereas lower in near-infrared region (700 to 900) in both underwater (IW/CPE ratio of 0.6) and nitrogen stress (100 kg N ha-1) environment as compared to moderate and no stress irrigation (IW/CPE ratio of 0.8 & 1.2) and nitrogen (200 and 300 kg N ha-1) treatments. The discriminant analysis of NDVI, GNDVI, WBI and SR indicated that 72.2% and 66.7% of the original grouped cases and 55.6% and 38.9% of the cross-validated grouped cases under irrigation and nitrogen levels, respectively were correctly classified. Conclusion: Hyperspectral remote sensing can be used as a tool to detect and quantify the water and nitrogen stress in maize non-destructively. Spectral vegetation indices viz. Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were found effective to distinguish water and nitrogen stress severity in maize.

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Remote Sensing Based Monitoring of Winter Wheat Powdery Mildew at a Regional Scale Using Random Forest Model

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/234/1/012015 ◽

2019 ◽

Vol 234 ◽

pp. 012015

Author(s):

Linsheng Huang ◽

Wenjuan Ding ◽

Jing Jiang ◽

Zhaochuan Wu ◽

Jinling Zhao

Keyword(s):

Remote Sensing ◽

Random Forest ◽

Powdery Mildew ◽

Winter Wheat ◽

Regional Scale ◽

Random Forest Model ◽

Forest Model ◽

Wheat Powdery Mildew

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Application of Continuum Removal Method for Estimating Disease Severity Level of Wheat Powdery Mildew

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.396-398.2012 ◽

2011 ◽

Vol 396-398 ◽

pp. 2012-2017

Author(s):

Shi Zhou Du ◽

Wen Jiang Huang ◽

Rong Fu Wang ◽

Ju Hua Luo ◽

Jin Ling Zhao ◽

...

Keyword(s):

Powdery Mildew ◽

Disease Severity ◽

Hyperspectral Data ◽

Characteristic Parameters ◽

Spectral Reflectivity ◽

Wheat Powdery Mildew ◽

Severity Levels ◽

Wheat Leaves ◽

The Right ◽

Continuum Removal

The hyperspectral bands sensitive to the disease severity levels of wheat powdery mildew was elucidated in this study. The disease severity levels of wheat powdery mildew were also inverted by the extracting characteristic parameters, which provided a basis for detecting the wheat powdery mildew using hyperspectral data. The spectral data of single leaves was obtained at heading stage, the spectral characteristic parameters and sensitivity of wheat leaves were analyzed qualitatively and quantitatively. The result showed that spectral reflectivity within the visible wavebands (400—760 nm) was increased with the aggravating disease severity levels. The spectral sensitivity reached the maximum value within visible wavebands and the optimal sensitive bands for detecting disease severity levels was 630—680nm. After the spectrum was continuum removal-treated, the absorption position moved to longer wavelength with the aggravating disease severity levels and the disease severity levels showed extremely significant negative correlations with the absorption height, absorption width and absorption area. The linear regression equation has high determination coefficient and low root mean square error using the right AAI as independent variable to establish the model. Moreover, the precision verification test also showed that the model performed best in monitoring wheat powdery mildew. In conclusion, disease severity levels of wheat powdery mildew could be inverted effectively by hyperspectral technology, which provides the foundation for detecting wheat powdery mildew.

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The disease severity estimation of bacterial spot disease of processing tomato based on hyperspectral remote sensing

2015 23rd International Conference on Geoinformatics ◽

10.1109/geoinformatics.2015.7378661 ◽

2015 ◽

Author(s):

Yin Xiaojun ◽

Zhu Honghui ◽

Zhang Qing ◽

Liu Jun ◽

Zhao Qingzhan ◽

...

Keyword(s):

Remote Sensing ◽

Disease Severity ◽

Hyperspectral Remote Sensing ◽

Bacterial Spot ◽

Spot Disease ◽

Processing Tomato ◽

Severity Estimation ◽

Bacterial Spot Disease

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Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion

Sensors ◽

10.3390/s22010031 ◽

2021 ◽

Vol 22 (1) ◽

pp. 31

Author(s):

Ziheng Feng ◽

Li Song ◽

Jianzhao Duan ◽

Li He ◽

Yanyan Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Powdery Mildew ◽

Data Fusion ◽

Thermal Infrared ◽

Partial Least Square ◽

Infrared Images ◽

Remote Sensing Monitoring ◽

Wheat Powdery Mildew ◽

Thermal Infrared Images ◽

Source Data

Powdery mildew severely affects wheat growth and yield; therefore, its effective monitoring is essential for the prevention and control of the disease and global food security. In the present study, a spectroradiometer and thermal infrared cameras were used to obtain hyperspectral signature and thermal infrared images data, and thermal infrared temperature parameters (TP) and texture features (TF) were extracted from the thermal infrared images and RGB images of wheat with powdery mildew, during the wheat flowering and filling periods. Based on the ten vegetation indices from the hyperspectral data (VI), TF and TP were integrated, and partial least square regression, random forest regression (RFR), and support vector machine regression (SVR) algorithms were used to construct a prediction model for a wheat powdery mildew disease index. According to the results, the prediction accuracy of RFR was higher than in other models, under both single data source modeling and multi-source data modeling; among the three data sources, VI was the most suitable for powdery mildew monitoring, followed by TP, and finally TF. The RFR model had stable performance in multi-source data fusion modeling (VI&TP&TF), and had the optimal estimation performance with 0.872 and 0.862 of R2 for calibration and validation, respectively. The application of multi-source data collaborative modeling could improve the accuracy of remote sensing monitoring of wheat powdery mildew, and facilitate the achievement of high-precision remote sensing monitoring of crop disease status.

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