scholarly journals Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 20 ◽  
Author(s):  
Huiqin Ma ◽  
Wenjiang Huang ◽  
Yuanshu Jing ◽  
Stefano Pignatti ◽  
Giovanni Laneve ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Animal Health ◽  
Reflectance Spectra ◽  
Coefficient Of Determination ◽  
Continuous Wavelet ◽  
Head Blight ◽  
Linear Discriminant ◽  
Wavelet Features

Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem affecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R2 between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R2 values and the statistically significant (p-value of t-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R2 values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coefficient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears.

scholarly journals Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 998
Author(s):  
Linsheng Huang ◽  
Kang Wu ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Huiqin Ma ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Fusarium Head Blight ◽  
Detection Method ◽  
Support Vector ◽  
Spectral Features ◽  
Continuous Wavelet ◽  
Head Blight ◽  
Detection Model ◽  
Svm Algorithm ◽  
Wavelet Features

Fusarium head blight, caused by a fungus, can cause quality deterioration and severe yield loss in wheat. It produces highly toxic deoxynivalenol, which is harmful to human and animal health. In order to quickly and accurately detect the severity of fusarium head blight, a method of detecting the disease using continuous wavelet analysis and particle swarm optimization support vector machines (PSO-SVM) is proposed in this paper. First, seven wavelet features for fusarium head blight detection were extracted using continuous wavelet analysis based on the hyperspectral reflectance of wheat ears. In addition, 16 traditional spectral features were selected using correlation analysis, including two continuous removal transformed spectral features, six differential spectral features, and eight vegetation indices. Finally, wavelet features and traditional spectral features were used as input features to construct fusarium head blight detection models in combination with the PSO-SVM algorithm, and the results were compared with those obtained using random forest (RF) and a back propagation neural network (BPNN). The results show that, under the same feature variables, the PSO-SVM detection method gave an overall higher accuracy than the BPNN detection method, while the overall accuracy of the RF detection model was the lowest. The overall accuracy of the RF, BPNN and PSO-SVM detection models with wavelet features was higher by 3.7%, 2.9% and 8.3% compared to the corresponding methodological models with traditional spectral features. The detection model with wavelet features combining the PSO-SVM algorithm gave the highest overall accuracies (93.5%) and kappa coefficients (0.903) in the six monitoring models. These results suggest that the PSO-SVM algorithm combined with continuous wavelet analysis can significantly improve the accuracy of fusarium head blight detection on the wheat ears scale.

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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