Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding


Plant height is an essential trait to evaluate in grain sorghum, being positively associated with potential grain yield. Standard manual measures of plant height for large breeding trials are labour-intensive and time-consuming. Due to potential field access issue and the remote nature of breeding trials, Unmanned Aerial vehicles (UAVs) are well-suited to measure plant height if the ground surface can be referenced. In this study, we compared existing algorithms with a new method for estimating plant height for a sorghum breeding trial. Images were captured by a RGB camera mounted on an UAV before emergence and near maturity to generate digital surface models (DSMs). Two existing methods (‘point cloud’ and ‘reference ground’) and a new method (‘self-calibration’) were used to estimate ground level and plant height at the plot level. The self-calibration method required manual measurements of the actual plant height in a sample of plots (fewer than 30), which could be completed during the 30-min flight time. UAV-derived plant heights from each method were compared to manual measurements. The self-calibration method had the best performance (R2=0.63; RMSE=0.07m; repeatability=0.74), with similar repeatability to manual measurement (0.78). The point cloud and reference ground methods had lower repeatabilities (0.34 and 0.38, respectively). For the self-calibration method, we tested different sampling strategies to balance accuracy and the workload of manual measurements, finding that a sample of 30–40 plots from the1440 total could obtain precision similar to manual measurement of the entire trial. The self-calibration method offers a pragmatic, robust and universal approach to high throughput phenotyping of plot plant height with UAV surveys.

Environmental Modelling & Software
Bangyou Zheng
Bangyou Zheng
Data Scientist / Digital Agronomist

a research scientist of digital agriculture at the CSIRO.

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