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

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

Sensors ◽

10.3390/s20010020 ◽

2019 ◽

Vol 20 (1) ◽

pp. 20 ◽

Cited By ~ 6

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.

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

Remote Sensing ◽

10.3390/rs13153024 ◽

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.

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Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale

Sensors ◽

10.3390/s19102281 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2281 ◽

Cited By ~ 16

Author(s):

Anne-Katrin Mahlein ◽

Elias Alisaac ◽

Ali Al Masri ◽

Jan Behmann ◽

Heinz-Wilhelm Dehne ◽

...

Keyword(s):

Fusarium Head Blight ◽

Hyperspectral Imaging ◽

Optical Sensors ◽

Fusarium Culmorum ◽

Maximum Temperature ◽

Support Vector ◽

Head Blight ◽

Maximum Temperature Difference ◽

Simple Ratio

Optical sensors have shown high capabilities to improve the detection and monitoring of plant disease development. This study was designed to compare the feasibility of different sensors to characterize Fusarium head blight (FHB) caused by Fusarium graminearum and Fusarium culmorum. Under controlled conditions, time-series measurements were performed with infrared thermography (IRT), chlorophyll fluorescence imaging (CFI), and hyperspectral imaging (HSI) starting 3 days after inoculation (dai). IRT allowed the visualization of temperature differences within the infected spikelets beginning 5 dai. At the same time, a disorder of the photosynthetic activity was confirmed by CFI via maximal fluorescence yields of spikelets (Fm) 5 dai. Pigment-specific simple ratio PSSRa and PSSRb derived from HSI allowed discrimination between Fusarium-infected and non-inoculated spikelets 3 dai. This effect on assimilation started earlier and was more pronounced with F. graminearum. Except the maximum temperature difference (MTD), all parameters derived from different sensors were significantly correlated with each other and with disease severity (DS). A support vector machine (SVM) classification of parameters derived from IRT, CFI, or HSI allowed the differentiation between non-inoculated and infected spikelets 3 dai with an accuracy of 78, 56 and 78%, respectively. Combining the IRT-HSI or CFI-HSI parameters improved the accuracy to 89% 30 dai.

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Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion

Sensors ◽

10.3390/s20102887 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2887 ◽

Cited By ~ 2

Author(s):

Linsheng Huang ◽

Taikun Li ◽

Chuanlong Ding ◽

Jinling Zhao ◽

Dongyan Zhang ◽

...

Keyword(s):

Fusarium Head Blight ◽

Disease Severity ◽

Feature Fusion ◽

Texture Features ◽

Effective Control ◽

Spectral Features ◽

Head Blight ◽

Color Feature ◽

Model Based ◽

Fusion Features

Fusarium head blight (FHB), one of the most prevalent and damaging infection diseases of wheat, affects quality and safety of associated food. In this study, to realize the early accurate monitoring of FHB, a diagnostic model of disease severity was proposed based on the fusion features of image and spectral features. First, the hyperspectral image of FHB infected in the range of the 400–1000 nm spectrum was collected, and the color parameters of wheat ear and spot region were segmented based on image features. Twelve sensitive bands were extracted using the successive projection algorithm, gray-scale co-occurrence matrix, and RGB color model. Four texture features were extracted from each feature band image as texture variables, and nine color feature variables were extracted from R, G, and B component images. Texture features with high correlation and color features were selected to participate in the final model building parameters via correlation analysis. Finally, the particle swarm optimization support vector machine (PSO-SVM) algorithm was used to build the model based on the diagnosis model of disease severity of FHB with different combinations of characteristic variables. The experimental results showed that the PSO-SVM model based on spectral and color feature fusion was optimal. Moreover, the accuracy of the training and prediction set was 95% and 92%, respectively. The method based on fusion features of image and spectral features can accurately and effectively diagnose the severity of FHB, thereby providing a technical basis for the timely and effective control of FHB and precise application of a pesticide.

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Research on oil-gas Pipeline Leakage Detection Method Based on Particle Swarm Optimization Algorithm Optimized Support Vector Machine

Journal of Physics Conference Series ◽

10.1088/1742-6596/2076/1/012004 ◽

2021 ◽

Vol 2076 (1) ◽

pp. 012004

Author(s):

Jianfeng Gao ◽

Yu Zheng ◽

Kai Ni ◽

Huaizhi Zhang ◽

Bin Hao ◽

...

Keyword(s):

Support Vector Machine ◽

Working Conditions ◽

Detection Method ◽

Leak Detection ◽

Gas Pipeline ◽

Support Vector ◽

Leakage Detection ◽

Swarm Optimization ◽

Svm Algorithm ◽

Oil Gas

Abstract In order to solve the problem of low accuracy in oil-gas pipeline leak detection, a pipeline leak detection method based on Particle Swarm Optimization (PSO) algorithm optimized Support Vector Machine (SVM) is introduced. This method uses PSO to solve the penalty factor ‘c’ and kernel function parameter ‘g’, and constructs the pipeline leakage detection model of SVM. We set up an experimental platform to collect negative pressure wave signals under different working conditions. After wavelet domain denoising and data preprocessing, four eigenvalues of Mean, Standard Deviation, Kurtosis and Skewness are extracted from the signals to form the eigenvector samples, which are taken as input of SVM, and four working conditions of normal, leakage, rise and fall are taken as output. Through experimental verification, the comprehensive performance of PSO-SVM algorithm is better than that of traditional SVM, Genetic Algorithm optimized SVM and grid search algorithm optimized SVM. The POS-SVM algorithm can be applied to the leak detection of oil-gas pipeline.

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Identification of Fusarium Head Blight in Winter Wheat Ears Based on Fisher’s Linear Discriminant Analysis and a Support Vector Machine

Applied Sciences ◽

10.3390/app9183894 ◽

2019 ◽

Vol 9 (18) ◽

pp. 3894 ◽

Cited By ~ 4

Author(s):

Linsheng Huang ◽

Zhaochuan Wu ◽

Wenjiang Huang ◽

Huiqin Ma ◽

Jinling Zhao

Keyword(s):

Support Vector Machine ◽

Winter Wheat ◽

Support Vector ◽

Spectral Features ◽

Front Side ◽

Head Blight ◽

Linear Discriminant ◽

First Order ◽

Fisher’S Linear Discriminant ◽

Better Than

Fusarium head blight (FHB) is one of the diseases caused by fungal infection of winter wheat (Triticum aestivum), which is an important cause of wheat yield loss. It produces the deoxynivalenol (DON) toxin, which is harmful to human and animal health. In this paper, a total of 89 samples were collected from FHB endemic areas. The occurrence of FHB is completely natural in experimental fields. Non-imaging hyperspectral data were first processed by spectral standardization. Spectral features of the first-order derivatives, the spectral absorption features of the continuum removal, and vegetation indices were used to evaluate the ability to identify FHB. Then, the spectral feature sets, which were sensitive to FHB and have significant differences between classes, were extracted from the front, side, and erect angles of winter wheat ear, respectively. Finally, Fisher’s linear discriminant analysis (FLDA) for dimensionality reduction and a support vector machine (SVM) based on radical basis function (RBF) were used to construct an effective identification model for disease severity at front, side, and erect angles. Among selected features, the first-order derivative features of SDg/SDb and (SDg-SDb)/(SDg+SDb) were most dominant in the model produced for the three angles. The results show that: (1) the selected spectral features have great potential in detecting ears infected with FHB; (2) the accuracy of the FLDA model for the side, front, and erect angles was 77.1%, 85.7%, and 62.9%. The overall accuracy of the SVM (80.0%, 82.9%, 65.7%) was slightly better than FLDA, but the effect was not obvious; (3) LDA combined with SVM can effectively improve the overall accuracy, user’s accuracy, and producer’s accuracy of the model for the three angles. The over accuracy of the side (88.6%) was better than the front (85.7%), while the over accuracy of the erect angle was the lowest (68.6%).

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Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size

Remote Sensing ◽

10.3390/rs13132437 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2437

Author(s):

Yingxin Xiao ◽

Yingying Dong ◽

Wenjiang Huang ◽

Linyi Liu ◽

Huiqin Ma

Keyword(s):

Fusarium Head Blight ◽

Model Comparison ◽

Characteristic Curve ◽

Texture Feature ◽

Window Size ◽

Texture Features ◽

Spectral Features ◽

Head Blight ◽

Occurrence Matrix ◽

Optimal Window

By combining the spectral and texture features of images captured by unmanned aerial vehicles (UAVs), the accurate and timely detection of wheat Fusarium head blight (FHB) can be realized. This study presents a methodology to select the optimal window size of the gray-level co-occurrence matrix (GLCM) to extract texture features from UAV images for FHB detection. Host conditions and the disease distribution were combined to construct the model, and its overall accuracy, sensitivity, and generalization ability were evaluated. First, the sensitive spectral features and bands of the UAV-derived hyperspectral images were obtained, and then texture features were selected. Subsequently, spectral features and texture features extracted from windows of different sizes were input to classify the area of severe FHB. According to the model comparison, the optimal window size was obtained. With the collinearity between features eliminated, the best performance of the logistic model reached, with an accuracy, F1 score, and area under the receiver operating characteristic curve of 0.90, 0.79, and 0.79, respectively, when the window size of the GLCM was 5×5 pixels on May 3, and of 0.90, 0.83, and 0.82, respectively, when the size was 17×17 pixels on May 8. The results showed that the selection of an appropriate GLCM window size for texture feature extraction enabled more accurate disease detection.

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An Android Malware Detection Model Based on DT-SVM

Security and Communication Networks ◽

10.1155/2020/8841233 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Min Yang ◽

Xingshu Chen ◽

Yonggang Luo ◽

Hang Zhang

Keyword(s):

Decision Tree ◽

Malware Detection ◽

Small Sample ◽

Support Vector ◽

Android Malware ◽

Detection Model ◽

Model Based ◽

Android Malware Detection ◽

Svm Algorithm ◽

N Gram

In order to improve the accuracy and efficiency of Android malware detection, an Android malware detection model based on decision tree (DT) with support vector machine (SVM) algorithm (DT-SVM) is proposed. Firstly, the original opcode, Dalvik opcode, is extracted by reversing Android software, and the eigenvector of the sample is generated by using the n-gram model. Then, a decision tree is generated via training the sample and updating decision nodes as SVM nodes from the bottom up according to the evaluation result of the test set in the decision path. The model effectively combines DT with SVM. Under the premise of maintaining a high-accuracy decision path, SVM is used to effectively reduce the overfitting problem in DT and thus improve the generalization ability, and maintain the superiority of SVM for the small sample training set. Finally, to test our approach, several simulation experiments are carried out, and the results demonstrate that the improved algorithm has better accuracy and higher speed as compared with other malware detection approaches.

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Intrusion Detection Method Based on Sparse Expression for SVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.1606 ◽

2014 ◽

Vol 602-605 ◽

pp. 1606-1609

Author(s):

Xian Ping Yu ◽

Sheng Wu ◽

Hai Ling Xiong

Keyword(s):

Support Vector Machines ◽

Intrusion Detection ◽

Detection Method ◽

Support Vector ◽

Network Data ◽

Network Access ◽

Svm Algorithm ◽

Vector Machines

This paper presents an intrusion detection method based on the sparse SVM (support vector machines). This algorithm is to achieve sparse expression for the network data by constructing a sparse base, which could reduce the number of data to be processed, and then use the SVM algorithm to carry out the classification, thus achieving the efficient intrusion detection for the network access behavior.

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An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1

Advances in Materials Science and Engineering ◽

10.1155/2015/205817 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9

Author(s):

Hai Guo ◽

Jinghua Yin ◽

Jingying Zhao ◽

Yuanyuan Liu ◽

Lei Yao ◽

...

Keyword(s):

Nanocomposite Film ◽

Detection Method ◽

Principal Component ◽

Automatic Detection ◽

Experimental Result ◽

Support Vector ◽

Gray Level ◽

Detection Model ◽

Different Types ◽

Film Element

An automatic detection model adopting pattern recognition technology is proposed in this paper; it can realize the measurement to the element of nanocomposite film. The features of gray level cooccurrence matrix (GLCM) can be extracted from different types of surface morphology images of film; after that, the dimension reduction of film can be handled by principal component analysis (PCA). So it is possible to identify the element of film according to the Adaboost M1 algorithm of a strong classifier with ten decision tree classifiers. The experimental result shows that this model is superior to the ones of SVM (support vector machine), NN and BayesNet. The method proposed can be widely applied to the automatic detection of not only nanocomposite film element but also other nanocomposite material elements.

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