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

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.

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

Agriculture ◽

10.3390/agriculture11100998 ◽

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.

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Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery

Remote Sensing ◽

10.3390/rs12223811 ◽

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.

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Improvement of Resistance of Wheat Cultivars to Fusarium Head Blight in the Yellow-Huai Rivers Valley Winter Wheat Zone with Functional Marker Selection of Fhb1 Gene

ACTA AGRONOMICA SINICA ◽

10.3724/sp.j.1006.2018.00505 ◽

2018 ◽

Vol 44 (4) ◽

pp. 505 ◽

Cited By ~ 5

Author(s):

Hong-Jun ZHANG ◽

Zhen-Qi SU ◽

Gui-Hua BAI ◽

Xu ZHANG ◽

Hong-Xiang MA ◽

...

Keyword(s):

Winter Wheat ◽

Fusarium Head Blight ◽

Functional Marker ◽

Wheat Cultivars ◽

Head Blight ◽

Marker Selection ◽

Selection Of

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

Phytopathology ◽

10.1094/phyto-100-2-0160 ◽

2010 ◽

Vol 100 (2) ◽

pp. 160-171 ◽

Cited By ~ 69

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.

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

Canadian Journal of Plant Science ◽

10.4141/cjps08053 ◽

2008 ◽

Vol 88 (6) ◽

pp. 1087-1089 ◽

Cited By ~ 14

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

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

Theoretical and Applied Genetics ◽

10.1007/s00122-002-1160-5 ◽

2002 ◽

Vol 106 (6) ◽

pp. 961-970 ◽

Cited By ~ 176

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

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

European Journal of Plant Pathology ◽

10.1007/s10658-012-9968-6 ◽

2012 ◽

Vol 133 (4) ◽

pp. 975-993 ◽

Cited By ~ 23

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

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

Cereal Research Communications ◽

10.1007/bf03543810 ◽

1997 ◽

Vol 25 (3) ◽

pp. 673-675 ◽

Cited By ~ 6

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

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An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

International Journal of Information Security and Privacy ◽

10.4018/ijisp.2021100102 ◽

2021 ◽

Vol 15 (4) ◽

pp. 18-30

Author(s):

Om Prakash Samantray ◽

Satya Narayan Tripathy

Keyword(s):

Supervised Learning ◽

Learning Algorithms ◽

Malware Detection ◽

Support Vector ◽

Detection Accuracy ◽

Detection Techniques ◽

Detection Model ◽

Proposed Model ◽

Tree Classifier ◽

Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

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