scholarly journals Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery

2020 ◽  
Vol 12 (22) ◽  
pp. 3811
Author(s):  
Linyi Liu ◽  
Yingying Dong ◽  
Wenjiang Huang ◽  
Xiaoping Du ◽  
Huiqin Ma
Keyword(s):  
Neural Network ◽  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Bp Neural Network ◽  
Simulated Annealing Algorithm ◽  
Hyperspectral Imagery ◽  
Hyperspectral Images ◽  
Acquisition Time ◽  
Head Blight ◽  
Monitoring Model

The monitoring of winter wheat Fusarium head blight via rapid and non-destructive measures is important for agricultural production and disease control. Images of unmanned aerial vehicles (UAVs) are particularly suitable for the monitoring of wheat diseases because they feature high spatial resolution and flexible acquisition time. This study evaluated the potential to monitor Fusarium head blight via UAV hyperspectral imagery. The field site investigated by this study is located in Lujiang County, Anhui Province, China. The hyperspectral UAV images were acquired on 3 and 8 May 2019, when wheat was at the grain filling stage. Several features, including original spectral bands, vegetation indexes, and texture features, were extracted from these hyperspectral images. Based on these extracted features, univariate Fusarium monitoring models were developed, and backward feature selection was applied to filter these features. The backpropagation (BP) neural network was improved by integrating a simulated annealing algorithm in the experiment. A multivariate Fusarium head blight monitoring model was developed using the improved BP neural network. The results showed that bands in the red region provide important information for discriminating between wheat canopies that are either slightly or severely Fusarium-head-blight-infected. The modified chlorophyll absorption reflectance index performed best among all features, with an area under the curve and standard deviation of 1.0 and 0.0, respectively. Five commonly used methods were compared with this improved BP neural network. The results showed that the developed Fusarium head blight monitoring model achieved the highest overall accuracy of 98%. In addition, the difference between the producer accuracy and user accuracy of the improved BP neural network was smallest among all models, indicating that this model achieved better stability. These results demonstrate that hyperspectral images of UAVs can be used to monitor Fusarium head blight in winter wheat.

scholarly journals Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight

2021 ◽  
Vol 13 (15) ◽  
pp. 3024
Author(s):  
Huiqin Ma ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Linyi Liu ◽  
Anting Guo
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Hyperspectral Imagery ◽  
Spectral Feature ◽  
Support Vector ◽  
Detection Accuracy ◽  
Feature Combination ◽  
Field Scale ◽  
Head Blight ◽  
Detection Model

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively.

scholarly journals Meta-Analysis of the Effects of Triazole-Based Fungicides on Wheat Yield and Test Weight as Influenced by Fusarium Head Blight Intensity

2010 ◽  
Vol 100 (2) ◽  
pp. 160-171 ◽  
Author(s):  
P. A. Paul ◽  
M. P. McMullen ◽  
D. E. Hershman ◽  
L. V. Madden
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Wheat Yield ◽  
Test Weight ◽  
Response To Treatment ◽  
Yield Response ◽  
Head Blight ◽  
Yield Increase ◽  
Soft Red Winter Wheat ◽  
Red Winter Wheat

Multivariate random-effects meta-analyses were conducted on 12 years of data from 14 U.S. states to determine the mean yield and test-weight responses of wheat to treatment with propiconazole, prothioconazole, tebuconazole, metconazole, and prothioconazole+tebuconazole. All fungicides led to a significant increase in mean yield and test weight relative to the check (D; P < 0.001). Metconazole resulted in the highest overall yield increase, with a D of 450 kg/ha, followed by prothioconazole+tebuconazole (444.5 kg/ha), prothioconazole (419.1 kg/ha), tebuconazole (272.6 kg/ha), and propiconazole (199.6 kg/ha). Metconazole, prothioconazole+tebuconazole, and prothioconazole also resulted in the highest increases in test weight, with D values of 17.4 to 19.4 kg/m3, respectively. On a relative scale, the best three fungicides resulted in an overall 13.8 to 15.0% increase in yield but only a 2.5 to 2.8% increase in test weight. Except for prothioconazole+tebuconazole, wheat type significantly affected the yield response to treatment; depending on the fungicide, D was 110.0 to 163.7 kg/ha higher in spring than in soft-red winter wheat. Fusarium head blight (FHB) disease index (field or plot-level severity) in the untreated check plots, a measure of the risk of disease development in a study, had a significant effect on the yield response to treatment, in that D increased with increasing FHB index. The probability was estimated that fungicide treatment in a randomly selected study will result in a positive yield increase (p+) and increases of at least 250 and 500 kg/ha (p250 and p500, respectively). For the three most effective fungicide treatments (metconazole, prothioconazole+tebuconazole, and prothioconazole) at the higher selected FHB index, p+ was very large (e.g., ≥0.99 for both wheat types) but p500 was considerably lower (e.g., 0.78 to 0.92 for spring and 0.54 to 0.68 for soft-red winter wheat); at the lower FHB index, p500 for the same three fungicides was 0.34 to 0.36 for spring and only 0.09 to 0.23 for soft-red winter wheat.

Near-infrared versus visual sorting of Fusarium-damaged kernels in winter wheat

2008 ◽  
Vol 88 (6) ◽  
pp. 1087-1089 ◽  
Author(s):  
Stephen N Wegulo ◽  
Floyd E Dowell
Keyword(s):  
Quality Assurance ◽  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Key Words ◽  
Fusarium Graminearum ◽  
Near Infrared ◽  
Strongly Correlated ◽  
Head Blight ◽  
Rater Reliability ◽  
Key Words Wheat

Fusarium head blight (scab) of wheat, caused by Fusarium graminearum, often results in shriveled and/or discolored kernels, which are referred to as Fusarium-damaged kernels (FDK). FDK is a major grain grading factor and therefore is routinely determined for purposes of quality assurance. Measurement of FDK is usually done visually. Visual sorting can be laborious and is subject to inconsistencies resulting from variability in intra-rater repeatability and/or inter-rater reliability. The ability of a single-kernel near-infrared (SKNIR) system to detect FDK was evaluated by comparing FDK sorted by the system to FDK sorted visually. Visual sorting was strongly correlated with sorting by the SKNIR system (0.89 ≤ r ≤ 0.91); however, the SKNIR system had a wider range of FDK detection and was more consistent. Compared with the SKNIR system, visual raters overestimated FDK in samples with a low percentage of Fusarium-damaged grain and underestimated FDK in samples with a high percentage of Fusarium-damaged grain. Key words: Wheat, Fusarium head blight, Fusarium-damaged kernels, single-kernel near-infrared

Mapping of quantitative trait loci for field resistance to Fusarium head blight in an European winter wheat

2002 ◽  
Vol 106 (6) ◽  
pp. 961-970 ◽  
Author(s):  
L. Gervais ◽  
F. Dedryver ◽  
J.-Y. Morlais ◽  
V. Bodusseau ◽  
S. Negre ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Quantitative Trait ◽  
Field Resistance ◽  
Head Blight ◽  
Trait Loci ◽  
European Winter Wheat

Quantification of the relationship between the environment and Fusarium head blight, Fusarium pathogen density, and mycotoxins in winter wheat in Europe

2012 ◽  
Vol 133 (4) ◽  
pp. 975-993 ◽  
Author(s):  
Alissa B. Kriss ◽  
Pierce A. Paul ◽  
Xiangming Xu ◽  
Paul Nicholson ◽  
Fiona M. Doohan ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Head Blight ◽  
The Relationship

Fusarium Head Blight and Moniliformin Accumulation in Kernels of 18 Winter Wheat Cultivars Inoculated with Fusarium Avenaceum (3 Years Study)

1997 ◽  
Vol 25 (3) ◽  
pp. 673-675 ◽  
Author(s):  
Piotr Goliński ◽  
Marian Kostecki ◽  
Przemysław Kaptur ◽  
Slawomir Wojciechowski ◽  
Zygmunt Kaczmarek ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Fusarium Avenaceum ◽  
Wheat Cultivars ◽  
Head Blight ◽  
Winter Wheat Cultivars

Accuracy of within- and among-family genomic prediction for Fusarium head blight and Septoria tritici blotch in winter wheat

2018 ◽  
Vol 132 (4) ◽  
pp. 1121-1135 ◽  
Author(s):  
Cathérine Pauline Herter ◽  
Erhard Ebmeyer ◽  
Sonja Kollers ◽  
Viktor Korzun ◽  
Tobias Würschum ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Genomic Prediction ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Head Blight

scholarly journals Effects of Integrating Cultivar Resistance and Fungicide Application on Fusarium Head Blight and Deoxynivalenol in Winter Wheat

Plant Disease ◽  
2011 ◽  
Vol 95 (5) ◽  
pp. 554-560 ◽  
Author(s):  
Stephen N. Wegulo ◽  
William W. Bockus ◽  
John Hernandez Nopsa ◽  
Erick D. De Wolf ◽  
Kent M. Eskridge ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Field Experiments ◽  
Economic Losses ◽  
Cultivar Resistance ◽  
Head Blight ◽  
Resistant Cultivars ◽  
Fungicide Application ◽  
Fungicide Efficacy ◽  
Moderately Resistant

Fusarium head blight (FHB) or scab, incited by Fusarium graminearum, can cause significant economic losses in small grain production. Five field experiments were conducted from 2007 to 2009 to determine the effects on FHB and the associated mycotoxin deoxynivalenol (DON) of integrating winter wheat cultivar resistance and fungicide application. Other variables measured were yield and the percentage of Fusarium-damaged kernels (FDK). The fungicides prothioconazole + tebuconazole (formulated as Prosaro 421 SC) were applied at the rate of 0.475 liters/ha, or not applied, to three cultivars (experiments 1 to 3) or six cultivars (experiments 4 and 5) differing in their levels of resistance to FHB and DON accumulation. The effect of cultivar on FHB index was highly significant (P < 0.0001) in all five experiments. Under the highest FHB intensity and no fungicide application, the moderately resistant cultivars Harry, Heyne, Roane, and Truman had less severe FHB than the susceptible cultivars 2137, Jagalene, Overley, and Tomahawk (indices of 30 to 46% and 78 to 99%, respectively). Percent fungicide efficacy in reducing index and DON was greater in moderately resistant than in susceptible cultivars. Yield was negatively correlated with index, with FDK, and with DON, whereas index was positively correlated with FDK and with DON, and FDK and DON were positively correlated. Correlation between index and DON, index and FDK, and FDK and DON was stronger in susceptible than in moderately resistant cultivars, whereas the negative correlation between yield and FDK and yield and DON was stronger in moderately resistant than in susceptible cultivars. Overall, the strongest correlation was between index and DON (0.74 ≤ R ≤ 0.88, P ≤ 0.05). The results from this study indicate that fungicide efficacy in reducing FHB and DON was greater in moderately resistant cultivars than in susceptible ones. This shows that integrating cultivar resistance with fungicide application can be an effective strategy for management of FHB and DON in winter wheat.

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