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

Author

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  • Linsheng Huang

    (National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China)

  • Kang Wu

    (National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China)

  • Wenjiang Huang

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Yingying Dong

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Huiqin Ma

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

  • Yong Liu

    (National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China)

  • Linyi Liu

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China)

Abstract

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.

Suggested Citation

  • Linsheng Huang & Kang Wu & Wenjiang Huang & Yingying Dong & Huiqin Ma & Yong Liu & Linyi Liu, 2021. "Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM," Agriculture, MDPI, vol. 11(10), pages 1-13, October.
  • Handle: RePEc:gam:jagris:v:11:y:2021:i:10:p:998-:d:655121
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    Citations

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    Cited by:

    1. Chunfeng Gao & Xingjie Ji & Qiang He & Zheng Gong & Heguang Sun & Tiantian Wen & Wei Guo, 2023. "Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery," Agriculture, MDPI, vol. 13(2), pages 1-16, January.
    2. Yuan-Kai Tu & Chin-En Kuo & Shih-Lun Fang & Han-Wei Chen & Ming-Kun Chi & Min-Hwi Yao & Bo-Jein Kuo, 2022. "A 1D-SP-Net to Determine Early Drought Stress Status of Tomato ( Solanum lycopersicum ) with Imbalanced Vis/NIR Spectroscopy Data," Agriculture, MDPI, vol. 12(2), pages 1-17, February.
    3. Guillaume Grégoire & Josée Fortin & Isa Ebtehaj & Hossein Bonakdari, 2022. "Novel Hybrid Statistical Learning Framework Coupled with Random Forest and Grasshopper Optimization Algorithm to Forecast Pesticide Use on Golf Courses," Agriculture, MDPI, vol. 12(7), pages 1-19, June.

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