Estimating maize plant height using a crop surface model constructed from UAV RGB images

Abstract

Plant height (PH) is an essential agronomic trait that can be used to assist in crop breeding pipelines, assess crop productivity and make crop management decisions. Improving the accuracy of the digital terrain model (DTM) and optimising the PH features of the crop surface model obtained from unmanned aerial vehicle (UAV) images contribute to PH estimation. The influence of the fractional vegetation cover (FVC) on DTM reconstruction accuracy was investigated for the first time, and the influence of the view angle (oblique and nadir) and spatial resolution on the accuracy of maize PH estimation was explored. The results show that the accuracy of the DTM constructed using the inverse distance weighted algorithm was significantly influenced by the FVC conditions. Compared with the DTM constructed using UAV images over bare soil, FVC less than 0.4 was necessary for the accurate construction of the DTM, with average estimation errors of 0.15 m in 2018 and 0.09 m in 2019. Compared with the nadir view, the oblique view resulted in a more accurate 3D reconstruction. When the original spatial resolution of 15 mm was upscaled to 20, 30, 60 and 120 mm, a decreasing trend of PH estimation accuracy was observed, with root mean square error increasing from 0.35 to 0.40 m and mean absolute error increasing from 0.30 to 0.36 m. Overall, this study investigated the optimal FVC conditions for accurate DTM construction and the influence of the view angle and spatial resolution on PH estimation based on UAV RGB images.