Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion.
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TLDR
The method based on fusion features of image and spectral features can accurately and effectively diagnose the severity of F HB, thereby providing a technical basis for the timely and effective control of FHB and precise application of a pesticide.Abstract:
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.read more
Citations
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Automatic evaluation of wheat resistance to fusarium head blight using dual mask-rcnn deep learning frameworks in computer vision
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Monitoring Wheat Fusarium Head Blight Using Unmanned Aerial Vehicle Hyperspectral Imagery
TL;DR: The results demonstrate that hyperspectral images of UAVs can be used to monitor Fusarium head blight in winter wheat and show that bands in the red region provide important information for discriminating between wheat canopies that are either slightly or severely FUSarium-head-blight-infected.
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Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM
TL;DR: In this paper, a method of detecting the severity of fusarium head blight using continuous wavelet analysis and particle swarm optimization support vector machines (PSO-SVM) is proposed.
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UAV-Based Thermal, RGB Imaging and Gene Expression Analysis Allowed Detection of Fusarium Head Blight and Gave New Insights Into the Physiological Responses to the Disease in Durum Wheat
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