**4. Discussion**

In this study, nine CSFs, 41 texture features, and eight spectral features of maize in the study area were extracted based on UAV multispectral images and CHM. The different types of image characteristics can not only describe the discrepancies between lodging and nonlodging areas from various aspects but also establish information complementarity during classification. Therefore, we constructed all possible feature sets (SFS and MFS) of the above features as potential factors for the identification of lodging maize. After screening the above feature sets by the AIC method, MLC, BLRC, and RFC were implemented to identify maize lodging. Finally, we obtained the optimal maize lodging recognition result by analyzing the verification results. The present study offers a novel method for monitoring maize lodging. The results revealed that the AIC method is effective and helpful for discriminating among maize lodging and nonlodging areas.
