**1. Introduction**

Maize plays an important role among the world's cereal crops; it is used in food, fodder, and bioenergy production. High and stable maize yields are crucial to global food security. Lodging is a natural plant condition that reduces the yield and quality of various crops. In terms of different displacement positions, crop lodging can be divided into stem lodging (Lodging S) and root lodging (Lodging R). Lodging S involves the bending of crop stems from their upright position, while Lodging R refers to damage or failure to the plant's root-soil anchorage system [1]. Due to maize lodging, harvest losses could be as high as 50% [2]. Timely and exact identification of maize lodging is essential for estimating yield loss, making comprehensive production decisions and supporting insurance compensation. The traditional approaches to lodging investigation rely on manual measurements made at plots, which is time consuming, laborious, ine fficient, and unsuitable for large-scale lodging surveys.

Currently, given the rapid evolution of remote sensing techniques, an increasing number of scholars have used remote sensing imagery combined with various feature-extraction tools to obtain crop lodging information. Li et al. [3] investigated the potential for maize lodging extraction and feature screening from satellite remote sensing imagery based on the di fference in texture values between lodging and nonlodging areas. Based on the changes in vegetable index (VI) features under lodging and nonlodging maize, Wang et al. [2] reported that the correlation between the changes and the lodging percentage could be used as a standard to select e ffective features. Crop lodging is frequently accompanied by adverse weather; hence, there is no guarantee that seasonable images can be obtained successfully due to the limited capabilities of optical satellites in poor weather conditions. In contrast to optical sensors, the spaceborne synthetic aperture radar (SAR) technique is not only robust to severe weather adverse e ffects but also provides abundant information regarding the structure of vegetation. SAR technology is currently widely used to discriminate between lodging and nonlodging crops. By utilizing the fully polarimetric C-band radar images, Yang et al. [4] explored the di fference between lodging and nonlodging wheat areas in various growth periods. Chen et al. [5] reported on polarimetric features extracted from consecutive RADARSAT-2 data to identify sugarcane lodging areas. However, due to its low spatial resolution, the SAR technique is more applicable to large and relatively homogeneous crop planting areas than to small and heterogeneous plots [6].

To precisely extract lodging areas within a small patchwork field, unmanned aerial vehicles (UAV) can acquire remote sensing images that o ffer both high temporal and spatial resolution and have strong operability [7]. At present, researchers usually employ UAV images and their features acquired shortly after lodging to identify crop lodging at the field scale. The identified crops are mainly in the vigorous growth period. During this period, lodging crops and nonlodging crops have similar growth conditions, but their leaf color is quite di fferent, which can be observed in UAV images. Therefore, high-accuracy identification results of maize lodging can be achieved by applying only red-green-blue (RGB) or multispectral images.

Using UAV-collected multispectral, RGB, and thermal infrared imagery, image features such as texture features, VI, and canopy structure features can be derived from these UAV images. Based on extracted feature information, single- and multiple-class feature sets (SFS and MFS, respectively) can be constructed and used for lodging identification [8]. SFSs can be used to promptly and efficiently recognize lodging areas due to their low data dimensions and straightforward computation. RGB, multispectral imagery, and VI were introduced to extract crop lodging based on the leaf color discrepancy between lodging and nonlodging areas [9]. However, this discrepancy experiences interference from di fferent crop varieties and fertilization treatments. To address this situation, Liu et al. [10] reported that although thermal infrared images have low spatial resolution, features extracted from them are beneficial for lodging assessment. In addition, the patterns, shapes, and sizes of vegetation in UAV imagery can be quantitatively described by analyzing texture characteristics; thus, they have been widely employed in lodging assessment research [11]. However, these features also have some defects. For instance, it is di fficult to determine the optimal scale and moving direction for the texture window. More recently, the canopy structure feature (CSF) has been broadly applied to determine whether lodging has occurred and, ultimately, to estimate lodging severity, which fosters accurate monitoring of crop lodging [12]. However, these features are likely to be limited by adverse field environments and high experimental consumption; therefore, SFS often cannot fully reflect the crop lodging properties under intricate field environments, and MFSs have been used more frequently in recent years. For example, by combining the digital surface model (DSM) and texture information, crop lodging recognition can be performed with reliable accuracy [13]. Based on a feature set containing color and texture features, adding temperature information also greatly improved the recognition accuracy of indica rice lodging [10]. In addition, the probability that lodging has occurred can be predicted by using a feature set containing the canopy structure and the VI feature [14].

Nevertheless, MFSs have a data redundancy defect that causes greater consumption of computing resources and reduces the identification e fficiency, necessitating optimal feature selection. The number of studies regarding screening methods for lodging identification is limited. The existing methods rely mainly on the di fference evaluation index, which has less computational overhead [1,15,16]. However, these methods do not consider the interactions between features, and there is no clear standard for determining the most appropriate feature dimension. While the feature numbers can be explicitly determined by utilizing single-feature probability values based on a Bayesian network [13], this approach assumes that all features are mutually independent and that no correlation occurs between the selected features. Han et al. [14] reported that univariate and multivariate logistic regression analysis methods can be used to screen lodging features. Although this method explores the underlying relationship between the outcome and the selected factors, it is not suitable for high-dimensional feature sets because of its complex screening process.

Therefore, the main purpose of this study is to construct a simple and e fficient feature screening method for lodging recognition under complicated field environments that also considers the interactions between features. We verify the feasibility and stability of the proposed method under both high- and low-dimensional circumstances. The primary objectives are (1) to analyze the changes in lodging extraction accuracy under di fferent texture window sizes and to determine the optimal window size that results in the maximal accuracy; (2) to determine the optimal screening method by comparing the accuracy of maize lodging results based on di fferent feature screening methods; and (3) to extract maize lodging areas by using the optimal feature screening method and various classifications.
