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.
Highlights
Wheat is the most important food crop in the world, and it is a staple food for about one-third of the world’s population [1]
random forest (RF), back propagation neural network (BPNN) and particle swarm optimization support vector machines (PSO-support vector machines (SVM)) detection models constructed using wavelet features (WFs) as input features gave overall accuracies of 82.4%, 86.1% and 93.5% with kappa coefficients of 0.736, 0.792 and 0.903, respectively
The WFs were combined with RF, BPNN and Particle swarm optimization (PSO)-SVM algorithms to build detection models and compared with the detection models built using 16 traditional SFs as input features
Summary
Wheat is the most important food crop in the world, and it is a staple food for about one-third of the world’s population [1]. The development of an accurate and fast method of monitoring FHB is important for the control of this disease. Conventional detection methods, such as sampling analysis, are mostly performed by experts and experienced farmers in the field. Hyperspectral remote sensing technology can quickly monitor crop diseases and is suitable for largescale applications [7,8]. Several types of spectral features have been mentioned and applied to the spectral detection of crop diseases, including continuous removal transformed features, vegetation indices and differential spectral features. Huang et al [11] constructed a model for FHB monitoring using firstorder differential spectral features, vegetation indices and continuous removal transformed features combined with a support vector mechanism. Zheng et al [12] precisely identified wheat rust using a three-band photochemical reflectance Index (PRI) and anthocyanin reflectance index (ARI)
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