Abstract
Many studies have achieved efficient and accurate methods for identifying crop lodging under homogeneous field surroundings. However, under complex field conditions, such as diverse fertilization methods, different crop growth stages, and various sowing periods, the accuracy of lodging identification must be improved. Therefore, a maize plot featuring different growth stages was selected in this study to explore an applicable and accurate lodging extraction method. Based on the Akaike information criterion (AIC), we propose an effective and rapid feature screening method (AIC method) and compare its performance using indexed methods (i.e., variation coefficient and relative difference). Seven feature sets extracted from unmanned aerial vehicle (UAV) images of lodging and nonlodging maize were established using a canopy height model (CHM) and the multispectral imagery acquired from the UAV. In addition to accuracy parameters (i.e., Kappa coefficient and overall accuracy), the difference index (DI) was applied to search for the optimal window size of texture features. After screening all feature sets by applying the AIC method, binary logistic regression classification (BLRC), maximum likelihood classification (MLC), and random forest classification (RFC) were utilized to discriminate among lodging and nonlodging maize based on the selected features. The results revealed that the optimal window sizes of the gray-level cooccurrence matrix (GLCM) and the gray-level difference histogram statistical (GLDM) texture information were 17 × 17 and 21 × 21, respectively. The AIC method incorporating GLCM texture yielded satisfactory results, obtaining an average accuracy of 82.84% and an average Kappa value of 0.66 and outperforming the index screening method (59.64%, 0.19). Furthermore, the canopy structure feature (CSF) was more beneficial than other features for identifying maize lodging areas at the plot scale. Based on the AIC method, we achieved a positive maize lodging recognition result using the CSFs and BLRC. This study provides a highly robust and novel method for monitoring maize lodging in complicated plot environments.
Highlights
Maize plays an important role among the world’s cereal crops; it is used in food, fodder, and bioenergy production
Based on the identification results obtained by using gray-level cooccurrence matrix (GLCM) and gray-level difference histogram statistical (GLDM) textures with different window scales, the variations in the Kappa coefficient and OA are shown in Figure 3, where the overall accuracy and Kappa coefficient clearly have the same trend using both GLCM and GLDM textures
The texture measures, spectral features, and canopy structure feature (CSF) of maize in the study area were extracted from unmanned aerial vehicle (UAV)-acquired multispectral images, and the optimal texture window size was determined for lodging identification
Summary
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]. Harvest losses could be as high as 50% [2]. 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, inefficient, and unsuitable for large-scale lodging surveys
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