**5. Summary and Conclusions**

In this study, the texture measures, spectral features, and CSFs of maize in the study area were extracted from UAV-acquired multispectral images, and the optimal texture window size was determined for lodging identification. Based on the above image features, the performance of the two screening methods was analyzed under the MLC, RFC, and BLRC algorithms. The main outcomes were as follows. (1) The optimal texture window sizes of GLCM and GLDM texture were 17 and 21, respectively, as determined by the Kappa coefficient, OA and DI. The area of the former size (1.59 m2) was close to the measured area (1.55 m2) of single lodging maize in the orthographic multispectral image, while the area of the latter (2.40 m2) was quite different from the measured area (0.17 m2) of single nonlodging maize but quite close to the maize lodging area with a small size. (2) Compared with the index method, the AIC method had a higher Kappa coefficient and OA; thus, this method is more suitable for screening lodging recognition features. In a complex field environment, the AIC method has strong generalizability when using MFS. (3) The accuracy of the detection result based on the Texture + CS + Spectral and AIC methods was the highest compared with the other feature sets, and CSF played an important role in lodging recognition. The Texture + CS + Spectral features, screened by the AIC method and classified by the MLC, achieved the highest lodging recognition accuracy: the Kappa coefficient and OA of this combination were 0.92% and 96.00%, respectively. (4) The new crop lodging identification method proposed in this paper can adapt to complex field environments, especially in crop fields with different growth periods. (5) In practice, researchers can obtain the CSF of maize first and then combine it with the BLRC algorithm to achieve fast extraction of crop lodging areas.

**Author Contributions:** Conceptualization, H.G. and H.L.; methodology, H.G.; software, X.M.; validation, Y.M. and Z.Y.; formal analysis, C.L.; investigation, H.G., Y.B., and Z.Y.; resources, X.Z.; data curation, Y.B.; writing—original draft preparation, H.G.; writing—review and editing, H.G.; visualization, H.G.; supervision, H.L.; project administration, H.L.; funding acquisition, H.L. and X.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by the National Natural Science Foundation of China (41671438) and the "Academic Backbone" Project of Northeast Agricultural University (54935112).

**Acknowledgments:** We thank Baiwen Jiang for helping in selection of survey fields and Qiang Ye for helping collect the field data. We are also grateful to the anonymous reviewers for their valuable comments and recommendations.

**Conflicts of Interest:** The authors declare no conflict of interest.
