Comparison of the performance of Multi-source Three-dimensional structural data in the application of monitoring maize lodging

Abstract

Lodging is one of the common stresses in maize production, which affects the benefit of farmers and impacts food security. Rapid monitoring of crop lodging can support loss assessment and agricultural insurance claims. Unmanned aerial vehicle (UAV) technology has been widely used in maize lodging monitoring in recent years. Compared with remote sensing monitoring based on spectral and texture features, the method based on crop three-dimensional structure data can accurately and quantitatively monitor the severity of maize lodging. The purpose of this study was to compare the effectiveness of UAV-LiDAR, backpack LiDAR, and 3D model reconstruction of UAV digital image (UAV-DIM) data on monitoring maize lodging. The canopy height model (CHM) was constructed using three kinds of data in the same conditions, and the canopy height of different lodging types of maize was extracted. The angle inversion model was proposed and the lodging angle of maize in the experimental plots was calculated. The results showed that UAV-LiDAR had the best performance in monitoring maize lodging. The determination coefficient (R2) of the canopy height was 0.956, while the RMSE was 0.097 m and NRMSE was 0.067. The correlation coefficient (ρ) of the lodging angle was about 0.9. It is the best method to monitor the lodging of maize based on three-dimensional structural information. The R2, RMSE, and NRMSE of canopy height by the UAV-DIM method were 0.790, 0.482 m, and 0.336, ρ ≥ 0.8, respectively. It shows that the accuracy of maize lodging monitoring by this method is weaker than those of the UAV-LiDAR and backpack LiDAR methods. However, considering the advantages of low cost and high efficiency of data acquisition, the UAV-DIM method still has a wide application prospect in crop lodging monitoring.