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

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

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

2021 ◽  
Vol 13 (18) ◽  
pp. 3753
Author(s):  
Wei Liu ◽  
Chaofei Sun ◽  
Yanan Zhao ◽  
Fei Xu ◽  
Yuli Song ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Powdery Mildew ◽  
Grain Yield ◽  
Disease Severity ◽  
Spectral Reflectance ◽  
Growth Stage ◽  
Hyperspectral Remote Sensing ◽  
Nitrogen Input ◽  
Wheat Powdery Mildew ◽  
Reflectance Data

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.

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

Plant Disease ◽  
2018 ◽  
Vol 102 (10) ◽  
pp. 1981-1988 ◽  
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.

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

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.

scholarly journals BIOCONTROL OF WHEAT POWDERY MILDEW DISEASE UNDER FIELD CONDITIONS IN EGYPT

2019 ◽  
Vol 46 (6) ◽  
pp. 2255-2270 ◽  
Author(s):  
Atef Shahin ◽  
M. Ashmawy ◽  
Samar Esmail ◽  
S. El-Moghazy
Keyword(s):  
Powdery Mildew ◽  
Field Conditions ◽  
Wheat Powdery Mildew ◽  
Powdery Mildew Disease

scholarly journals Susceptibility of Eastern Ninebark (Physocarpus opulifolius (L.) Maxim.) Cultivars to Powdery Mildew

2011 ◽  
Vol 29 (3) ◽  
pp. 105-107 ◽  
Author(s):  
J.D. Lubell ◽  
M.H. Brand ◽  
J.M. Lehrer
Keyword(s):  
Powdery Mildew ◽  
Disease Severity ◽  
Powdery Mildew Resistance ◽  
The Other ◽  
Mildew Resistance ◽  
Leaf Drop ◽  
Powdery Mildew Disease ◽  
Physocarpus Opulifolius ◽  
Morning Star

Abstract Powdery mildew disease severity was assessed on ten eastern ninebark (Physocarpus opulifolius (L.) Maxim.) cultivars. The green foliage cultivar ‘Nanus’ was resistant to powdery mildew. Among the deep purple foliage cultivars, ‘ Seward’ Summer Wine® exhibited better resistance than ‘Monlo’ Diablo® and was nearly as resistant as ‘Nanus’. ‘Seward’ Summer Wine® is a hybrid between ‘Nanus’ and ‘Monlo’ Diablo® and probably derives its mildew resistance from ‘Nanus’. ‘Monlo’ Diablo® had reasonably good mildew resistance. Yellow foliage cultivars ‘Dart's Gold’, ‘Morning Star’ and ‘Nugget’, which were highly susceptible to powdery mildew, were unattractive due to substantial leaf drop, leaf disfigurement and shoot brooming. ‘Luteus’ exhibited better powdery mildew resistance than the other yellow foliage cultivars. ‘Mindia’ Coppertina® and ‘Center Glow’, two recent purple foliage introductions with orange-copper new foliage, exhibited levels of mildew intermediate between purple and yellow foliage cultivars. These ‘Monlo’ Diablo® × ‘Dart's Gold’ hybrids probably owe their reduced mildew resistance to their ‘Dart's Gold’ lineage.

scholarly journals Pumpkin powdery mildew disease severity influences the fungal diversity of the phyllosphere

2017 ◽  
Author(s):  
Zhuo Zhang ◽  
Luyun Luo ◽  
Xinqiu Tan ◽  
Xiao Kong ◽  
Jianguo Yang ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Disease Severity ◽  
Fungal Community ◽  
Abiotic Factors ◽  
Its Region ◽  
Illumina Miseq ◽  
Fungal Community Structure ◽  
Plant Environment ◽  
Powdery Mildew Disease ◽  
Rna Genes

Phyllosphere microbiota play a crucial role in plant-environment interactions and are influenced by biotic and abiotic factors. However, there is little research on how pathogen s affect the microbial community. In this study, we collected 16 pumpkin (Cucurbita moschata) leaf samples showing symptoms of powdery mildew disease with different disease severity levels ranging from L1 (least severe) to L4 (most severe). We examined the fungal community structure and diversity by Illumina MiSeq sequencing of the internal transcribed spacer (ITS) region of ribosomal RNA genes. The fungal communities were dominated by members of the Basidiomycota and Ascomycota. The dominant genus was Podosphaera on the diseased leaves, which was the key pathogen responsible for the pumpkin powdery mildew. Ascomycota and Podosphaera increased in abundance as disease severity increased from L1 to L4, and were significantly more abundant than other microorganisms at disease severity L4 (P<0.05). The richness and diversity of the fungal community increased from L1 to L2, and then declined from L2 to L4, likely due to the biotic pressure at disease severity L4. Maintaining species richness in the phyllosphere will be an important part of managing disease control in this agroecological system and an essential step toward predictable biocontrol of powdery mildew in pumpkin.

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