Maize Canopy Height Monitoring Using Multi-Temporal Uav-Lidar

Maize canopy height (CH) monitoring directly reflects maize growth status and potential yield, but is cumbersome through manual in-field measurements. Unmanned Aerial Vehicle (UAV) remote sensing has made this task highly effective. However, the optimal variable to represent maize CH varies in different studies. In this study, we confirmed and compared the feasibility of using UAV-borne LiDAR and RGB to estimate maize CH in multiple treatments and growth stages. The optimal variables for estimating CH were determined for RGB and LiDAR, respectively. The optimal combination (LiDAR, 99<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile of the digital surface model, minimum digital terrain model) proved robust when applied to the independent data in 2021 (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>: 0.972 RMSE: 0.233 m, rRMSE: 19. 1 %). The proposed non - parametric and straightforward method for maize CH monitoring can be easily transferred to other crops, and has the potential to be applied as a common phenotyping procedure for precision agriculture and smart breeding.