Field phenotyping of plant height in an upland rice field in Laos using low-cost small unmanned aerial vehicles (UAVs)

Abstract

Plant height (PH) is an important agronomical parameter to assess the growth status in upland rice fields. Recently, field-based phenotyping using unmanned aerial vehicles (UAVs) has received increasing attention as a cost-effective, well-suited sensing technology to measure PH. In this study, we evaluated feasibility of a low-cost small UAV for estimating PH in upland rice fields in Laos with a canopy height model (CHM). Images of the upland field, including 501 plots (= 167 accessions × 3 replicates), were captured by a commercial small UAV (DJI Phantom 4) before emergence and in the near-flowering stage to generate digital surface models (DSMs). The CHM was developed from the difference of the DSMs using UAV images obtained before emergence and before flowering. The CHM metrics of each plot were then calculated using 90–99th percentiles and the top 1–10% largest pixel values of CHM and were compared with the manually measured field PH (78.25–189.75 cm). The predictive accuracy was assessed in the 90–99th percentiles and top 1–10% values of CHM metrics with 5-fold cross-validation procedures. Simple linear regression analyses between the field PH and CHM metrics showed that the top 3% CHM metrics had the best correlation with the field PH (R2 = 0.712, root-mean-square error (RMSE) = 9.142 cm, p < 0.001). Cross-validation procedures also confirmed that the top 3% CHM metrics were the best in terms of accuracy for estimating PH, with an error of 6.963% (8.823 cm) error.

Keywords

• canopy height model
• digital surface model
• phenotyping
• structure from motion
• uav
• upland rice