*3.4. Maize Lodging Identification Result*

Based on the features after AIC method screening, the verification results under different classification methods are given in Table 9. The Texture + CS + Spectral set showed the best performance compared with the other feature sets in the MLC and RFC algorithms. However, MLC provided higher classification accuracy (Kappa coefficient = 0.92, OA = 96%) than RFC. As shown in Figure 5a, RFC overlooks more maize lodging pixels in the UAV image. A favorable lodging identification result (Kappa coefficient = 0.86, OA = 93.00%), which is shown in Figure 5c, can be obtained by utilizing BLRC combined with the CS feature set. Overall, the optimal lodging detection result (Kappa coefficient = 0.92, OA = 96.00%) was generated when using MLC and Texture + CS + Spectral (Table 9). In comparison to other feature sets, the Texture + CS + Spectral had the highest average Kappa coefficient and average OA; thus, it is more suitable for accurately extracting the lodging area.


**Table 9.** Lodging identification accuracy under different classification methods and various feature sets.

**Figure 5.** The lodging identification results with the highest accuracies under MLC, BLRC, and random forest classification (RFC) (**a**) MLC combined with Texture + CS + Spectral; (**b**) RFC combined with Texture + CS + Spectral; (**c**) BLRC combined with CS.

In terms of the SFS in Table 9, CS had the best verification results (average Kappa coefficient = 0.74, average OA = 87.00%). Moreover, the Kappa coefficient and OA of texture + spectra are clearly the lowest among MFS (Table 9). The results of this study show that the CS still provides powerful support for accurate extraction of maize lodging, whether in SFS or MFS.
