1. School of Agricultural Engineering，Jiangsu University，Zhenjiang 212013，China
2. Key Laboratory of Modern Agricultural Equipment and Technology，Ministry of Education，Jiangsu University，Zhenjiang 212013，China
3. Institute of Food Crops，Jiangsu Academy of Agricultural Sciences，Nanjing 210014，China
4. Institute of Agricultural Science and Technology，Jiangsu Hongzehu Farm Group Co., Ltd.，Suqian 223900，China
Abstract：In order to explore the applicability of the multi-feature fusion method in the fast and accurate identification of crop lodging, this study used UAVs to obtain wheat field multispectral data with different lodging rates at multi field canopy scales. The original lodging image is preprocessed by image mosaic, radiometric correction, geometric correction, etc., and the normalized difference vegetation index and shadow index are used to remove the soil and shadow background respectively. The wheat lodging DSM model and vegetation index were extracted and fused with the multispectral image for principal component transformation of the multi feature image, respectively, to screen the texture features with greater difference. Support Vector Machine (SVM), Artificial Neural Network (ANN) and Maximum Likelihood (MLC) supervised classification models are used to classify multispectral and DSM fusion images, multispectral and normalized vegetation index (NDVI) fusion images, multispectral images and texture feature images. The overall accuracy (OA), Kappa coefficient and extraction error were used to comprehensively evaluate each supervision model's classification performance and lodging extraction accuracy. The classification results show that the modeling effect of each supervised classification method in different lodging areas is consistent, and the overall extraction accuracy of SVM and ANN is higher than that of MLC. In the high lodging areas, the SVM supervised model (OA: 92.63%, Kappa coefficient: 0.85, extraction error: 1.11%) of multispectral and NDVI fusion images has the best extraction effect; in the middle lodging area, the SVM supervision model (OA: 90.35%, Kappa coefficient: 0.79, extraction error: 9.34%) of multispectral and DSM fusion images has the best extraction effect; in the low lodging area, the ANN supervised model (OA: 91.05%, Kappa coefficient: 0.82, extraction error: 8.20%) of the mean texture feature image has a good extraction result. In this study, the DSM model, vegetation index, texture features and multi spectral images are fused and compared, and whether the multi feature fusion method can effectively extract wheat lodging information with high accuracy is explored. The results show that the UAV multi-spectral remote sensing method combined with feature fusion can effectively extract wheat lodging area, and the extraction effect is better than that of wheat lodging image with a single feature. The results of this study can provide a reference for the accurate acquisition method of the wheat lodging disaster survey data.
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