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 di...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/11/10/998 |
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| author | Linsheng Huang Kang Wu Wenjiang Huang Yingying Dong Huiqin Ma Yong Liu Linyi Liu |
| author_facet | Linsheng Huang Kang Wu Wenjiang Huang Yingying Dong Huiqin Ma Yong Liu Linyi Liu |
| author_sort | Linsheng Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:40:42Z |
| format | Article |
| id | doaj.art-210f59e6ae3448ee8c3a3cb3752bce44 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T04:40:42Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-210f59e6ae3448ee8c3a3cb3752bce442023-12-03T13:21:03ZengMDPI AGAgriculture2077-04722021-10-01111099810.3390/agriculture11100998Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVMLinsheng Huang0Kang Wu1Wenjiang Huang2Yingying Dong3Huiqin Ma4Yong Liu5Linyi Liu6National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, ChinaNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaNational Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaFusarium 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.https://www.mdpi.com/2077-0472/11/10/998fusarium head blighthyperspectralcontinuous wavelet analysissupport vector machineparticle swarm optimization |
| spellingShingle | Linsheng Huang Kang Wu Wenjiang Huang Yingying Dong Huiqin Ma Yong Liu Linyi Liu Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM Agriculture fusarium head blight hyperspectral continuous wavelet analysis support vector machine particle swarm optimization |
| title | Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM |
| title_full | Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM |
| title_fullStr | Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM |
| title_full_unstemmed | Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM |
| title_short | Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM |
| title_sort | detection of fusarium head blight in wheat ears using continuous wavelet analysis and pso svm |
| topic | fusarium head blight hyperspectral continuous wavelet analysis support vector machine particle swarm optimization |
| url | https://www.mdpi.com/2077-0472/11/10/998 |
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