Calibrated Plant Height Estimates with Structure from Motion from Fixed-wing UAV Images
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abstract
2018 SPIE. Field-based high-throughput phenotyping is a bottleneck to future breeding advances. The use of remote sensing with unmanned aerial vehicles (UAVs) can change the way agricultural research operates by increasing the spatiotemporal resolution of data collection to monitor status of plant growth. A fixed-wing UAV (Tuffwing) was operated to collect images of a sorghum breeding research field with 70% overlap at an altitude of 120 m. The study site was located at Texas A and M AgriLife Research's Brazos Bottom research farm near College Station, Texas, USA. Relatively high-resolution (>2.7cm/pixel) images were collected from May to July 2017 over 880 sorghum plots (including six treatments with four replications). The collected images were mosaicked and structure from motion (SfM) calculated, which involves construction of a digital surface model (DSM) by interpolation of 3D point clouds. Maximum plant height for each genotype (plot) was estimated from the DSM and height calibration implemented with aerial measured values of groundcontrol points with known height. Correlations and RMSE values between actual height and estimated height were observed over sorghum across all genotypes and flight dates. Results indicate that the proposed height calibration method has a potential for future application to improve accuracy in plant height estimations from UAVs.
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Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping III
