*4.1. Optimal Texture Window Size*

The precision of image classification based on texture measures relies primarily on the size of the texture window and the size of the target object [34]. At present, accuracy evaluation indexes such as the Kappa coefficient and OA are usually used to determine the optimal texture window size for target recognition in images [55,56]. However, in this study, we found several texture window sizes with the highest accuracy when we used these indexes (Figure 3). Generally, higher separability between the image features of two objects indicates a greater likelihood that they will be correctly classified. Consequently, based on the Kappa coefficient and OA, we utilized DI to analyze the optimal window sizes for the GLCM and GLDM textures (which were 21 and 17, respectively) to ensure the suitability of the window size. Because the spatial resolution of the UAV image is 7.5 cm, the actual areas of the texture features at the 17 and 21 window scales are 1.59 m<sup>2</sup> and 2.40 m2, respectively. The former area (1.59 m2) is close to the measured area (1.55 m2) of single lodging maize in the orthographic multispectral image, while the latter (2.40 m2) is quite different from the measured area (0.17 m2) of single nonlodging maize, but it is close to the area of small-scale maize lodging. Therefore, the texture measures of a single lodging plant and a small-scale lodging area are well described under the optimal window size in this study, which avoids missing maize lodging areas.

As shown in Figure 3, the accuracy of the GLDM texture measures increases sharply at window scales from 3 to 5 and decreases slowly at window scales from 5 to 7. This result occurs principally because the actual area of the 5 window size in the UAV image (0.14 m2) is the closest to the measured area of single nonlodging maize compared to the areas at window sizes of 3 and 7. In this way, the GLDM texture with a window size of 5 maintains good separability between lodging and nonlodging maize. In addition, the randomness of sample selection may cause large fluctuations in the accuracy curve.

### *4.2. Generalizability of the AIC Method in Selecting Lodging Features*

As the fundamental method of identifying the feature selection of lodging crops, the index method [7,15,16] is unable to directly determine the optimal number of features. Conversely, the AIC method not only can quickly determine the optimal number of features but also has higher recognition accuracy (Tables 5–7). The AIC method maintains high recognition accuracy (Table 7) even in two maize regions in different growth stages; therefore, it also achieves good generalizability. Due to the complexity of the plot environment in the study area, we used multiple features to identify maize lodging. Nevertheless, the screening criterion used in the index method—which determines whether a single feature is suitable for lodging identification—does not consider the interaction between multiple features. In contrast, the AIC method establishes the relationships between multiple features using a logistic regression model, thereby enhancing the complementarity of selected features. Therefore, the accuracy of the AIC method is generally higher than that of the index method.

At present, scholars use UAV images to identify lodging crops mainly during the vigorous growth period of crops [12,57]; thus, the method for selecting lodging recognition features is greatly affected by other crop growth periods and field environments. In contrast, the study area in this study includes both lodging and nonlodging maize in different growth stages. Therefore, the AIC method can be extensively applied to various situations (different varieties, growth periods, leaf colors, etc.) In summary, the AIC screening method is more effective for identifying lodging maize in complex field environments or in plots with different fertilization rates, varieties, maturity dates, and sowing periods.

### *4.3. Suitability of Canopy Structure Feature for Extraction of Maize Lodging in Complex Environments*

Texture, spectral, and color features extracted from remote sensing images are frequently used as effective factors to estimate crop lodging [3,11,13]. In addition to these characteristics, CSFs were calculated according to CHM. Among the SFS, the CS had the highest accuracy; its average Kappa coefficient and overall accuracy were 0.74 and 87.00%, respectively (Table 9). Lodging maize in the study area is dominated by Lodging R. The differences in CSFs between Lodging R and No-Lodging LR are significant, but their leaf color and spectral reflectance are similar. Therefore, compared with the texture and spectral features, the CSFs make it easier to identify lodging maize. Although both spectral and texture features are effective for assessing crop lodging [1], different crop types and growing periods still affect the recognition accuracy and stability of the features.

The MFS had the largest difference in the average Kappa coefficient and OA between Texture + Spectral and Texture + CS + Spectral: 0.19 and 9.33% (Table 9), respectively. These results further indicated that CSF is the primary factor that can improve the accuracy of maize lodging detection. Although CSF was able to directly reflect the height difference between lodging and nonlodging maize, their heights were sometimes similar due to the spatial heterogeneity of soil and terrain. Consequently, to compensate for this defect of CSF and accurately distinguish lodging and nonlodging areas, CSF must be combined with other image features such as texture and multispectral reflectance.

### *4.4. Comparison of Optimal Classification Results under Di*ff*erent Classification Algorithms*

Among MLC, RFC, and BLRC, the most accurate respective feature sets were Texture + CS + Spectral, Texture + CS + Spectral, and CS (Table 9). Based on the same feature set, the accuracy of MLC was higher than that of RFC because MLC is more suitable for classifying low-dimensional data than RFC. The BLRC algorithm is very suitable for addressing the issue of binary classification in imagery used for object detection. However, both the lodging and nonlodging maize involved in this study can have multiple forms (Figure 2). Therefore, although high-precision lodging recognition results can be obtained by utilizing BLRC combined with CSF, the results were still lower than those of MLC.

### *4.5. Comparison of Traditional and New Methods for Crop Lodging Identification*

Compared with the traditional crop lodging recognition methods, the new method proposed in this study describes the difference between lodging and nonlodging crops from more aspects—because it extracts more image features. In addition, the optimal and unique texture window size that fully expresses the morphological characteristics of crop lodging in the image was determined, thus improving the probability of successful crop lodging identification. However, extracting a large number of image features requires more computation and consumes more time. In this paper, the AIC method, which can obtain the optimal features more quickly and directly, was applied to feature selection while maintaining a strong relationship between features. Therefore, the AIC method is more suitable for identifying crop lodging using multiple image features. In contrast, the traditional crop lodging identification methods are based on homogeneous field environments in which the crops are all in the same growth period, and their growth conditions are similar. Nevertheless, different maize growth periods and growth statuses existed in the study area. Therefore, compared with traditional methods, the new crop lodging identification method is better able to identify crop lodging in a complex field environment, and it has stronger universality.

### *4.6. Analysis of Optimal Feature Set under Di*ff*erent Data Availability*

In practice, the recognition of lodging maize must consider both data availability and the accuracy of the results. Therefore, based on the reference results in Table 9, we analyzed the optimal feature set of maize lodging extraction features using the AIC method under different data conditions. Compared with multispectral images, the CHM acquisition requires more manpower, material resources, and time. Therefore, we were able to employ only the texture and spectral features derived from the multispectral

imagery to save data collection costs. Based on this approach, the optimal feature set and classification algorithm were Texture + Spectral and MLC, respectively. The Texture + Spectral feature set is suitable for situations in which the color discrepancy between leaves and stems is significant. In the absence of multispectral images, the optimal feature set and classification method were CS and BLRC, respectively. The CS feature set is suitable when there are large differences in lodging and nonlodging maize. When both CHM and multispectral imagery can be obtained, the most suitable feature set and classification method were Texture + CS + Spectral and MLC for lodging detection. In a complex field environment, the Texture + CS + Spectral feature set can accurately discriminate between lodging and nonlodging areas, but the time and economic costs of this approach are high; therefore, it is not practical for rapid monitoring of crop lodging.
