Assessing the Self-Recovery Ability of Maize after Lodging Using UAV-LiDAR Data

Lodging is one of the main problems in maize production. Assessing self-recovery ability plants after lodging at different growth stages great significance for yield loss assessment and agricultural insurance claims. The objective this study was to quantitatively analyse effects severity on using UAV-LiDAR data. multi-temporal point cloud data obtained by RIEGL VUX-1 laser scanner were used construct canopy height model maize. Then estimated heights obtained. measured values verify accuracy estimation invert corresponding angle. After verifying height, parameter tasselling stage R2 = 0.9824, root mean square error (RMSE) 0.0613 m, nRMSE 3.745%. That filling 0.9470, RMSE 0.1294 9.889%, which showed that could accurately estimate canopy. By comparing yield, angle maize, it found stronger than stage, but reduction rate 14.16~26.37% higher stage. more serious damage roots support structure plant, weaker ability. Therefore, stem tilt strongest, while folding weakest. results effectively assess lodging.