Remote Sensing of Maize Plant Height at Different Growth Stages Using UAV-Based Digital Surface Models (DSM)

Abstract

Plant height of maize is related to lodging resistance and yield and is highly heritable but also polygenic, and thus is an important trait in maize breeding. Various manual methods exist to determine the plant height of maize, yet they are labor-intensive and time consuming. Therefore, we established digital surface models (DSM) based on RGB-images captured by an unmanned aerial vehicle (UAV) at five different dates throughout the growth period to rapidly estimate plant height of 400 maize genotypes. The UAV-based estimation of plant height (PHUAV) was compared to the manual measurement from the ground to the highest leaf (PHL), to the tip of the manually straightened highest leaf (PHS) and, on the final date, to the top of the tassel (PHT). The best results were obtained for estimating both PHL (0.44 ≤ R2 ≤ 0.51) and PHS (0.50 ≤ R2 ≤ 0.61) from 39 to 68 days after sowing (DAS). After calibration the mean absolute percentage error (MAPE) between PHUAV and PHS was in a range from 12.07% to 19.62%. It is recommended to apply UAV-based maize height estimation from 0.2 m average plant height to maturity before the plants start to senesce and change the leaf color.

Keywords

• plant height
• unmanned aerial vehicle (UAV)
• maize
• high-throughput phenotyping
• digital surface model (DSM)
• photogrammetry