Abstract: Abstract: Bending of the lower part of the stalk (lodging) has posed a great threat to the yield, quality, and mechanical harvesting capacity in maize production. It is a high demand to quickly identify the lodging of maize subjected to the large wind load. In this study, an unmanned aerial vehicle (UAV)-based multispectral remote sensing was utilized to extract the maize lodging information after typhoon. A field test was conducted at the ecological unmanned farm of Shandong University of Technology of China. A quadrotor UAV carrying a 6-channel multispectral camera was also used to capture the image of the maize field block. A Pix4Dmapper software was selected to spline the multispectral images, and the band synthesis tool of ENVI software was used to process the six single-band gray images into one image with six bands. Firstly, ten kinds of commonly-used indices of multispectral vegetation were all selected to calculate, where 20 features of near-infrared bands were involved in the classification, due to the sensor included two near-infrared bands (840 and 940 nm). Secondly, a principal component analysis (PCA) was made to transform the original 6-band multispectral image, where the first three principal component bands with the most information were retained to extract texture features. Eight texture features were obtained in each band. The minimum noise fraction rotation (MNF) was applied to reduce the dimensionality of 48 texture features generated by the original 6-band multispectral image, further to screen the first 6 texture features with the most retention information. Finally, a low- and high-pass filtering was used to process the images, where the above 62 features were taken as the full feature set. The numbers of obtained subsets were 10, 13, and 12, respectively, using the support vector machines-recursive feature elimination (SVM-RFE), ReliefF and Least absolute shrinkage and selection operator (Lasso). Five supervised classification models were selected to train the feature subsets of the target region, including SVM, Naive Bayes, K-nearest neighbor (KNN), decision tree, and artificial neural network (ANN). The most suitable classification model for different data sets was selected to classify and evaluate the accuracy of the multi-spectrum of the validation region. The results show that ReliefF, SVM-RFE, and Lasso feature screening algorithms effectively reduce the dimension of the data while maintaining high classification accuracy. The lowest classification accuracy of ReliefF feature screening algorithm was 89.02%. The lowest classification accuracies of SVM-RFE and Lasso feature screening algorithms were both 95.38% that was closer to the lowest classification accuracy of the full-feature data set of 94.80%. There was only a 0.58% difference from the lowest accuracy of the full-feature data set, indicating a higher accuracy while a significant reduction in the number of features involved in classification. A confusion matrix verified that KNN and ANN models could effectively identify soil background, normal maize, and lodging maize, with the highest overall accuracies of 93.49% and 91.77%, respectively, where the Kappa coefficients were 0.90 and 0.88. KNN model combined with SVM-RFE feature screening method had the best classification results. Consequently, the fawer features had participated for the higher classification and recognition accuracy. The finding can provide technical support to the rapid and accurate extraction of maize lodging information after typhoon using the UAV multi-spectral remote sensing.