Quantitative estimation of organ-scale phenotypic parameters of field crops through 3D modeling using extremely low altitude UAV images

Plant phenotypic parameters provide key information in modern crop breeding. However, the rapid and accurate estimation of organ-scale phenotypic parameters remains a challenge. In this context, the present study proposed a novel methodology for the automatic quantification of the organ-scale parameters of field crops using the unmanned aerial vehicle (UAV) platform. First, a lightweight UAV was employed to capture the multi-view and high-resolution image sequences of field crops at an extremely-low flight altitude. Subsequently, based on these image sequences, point cloud reconstruction of the canopy was conducted. Next, the geometrical model of individual leaves was reconstructed using the modified Crust algorithm and optimized by abnormal facet elimination and leaf surface repair. Finally, individual leaf phenotypic parameters were calculated based on the reconstructed geometrical models. The method was evaluated by comparing the calculated parameters with actual measurements. The calculated values for leaf length, maximum leaf width, and leaf area were in good agreements with the measured values (maize: R2 > 0.97 for all parameters, RMSE for length, width, and leaf area was 2.6 cm, 0.4 cm, and 33.2 cm2, respectively; soybean: R2 > 0.85 for all parameters, RMSE for the counterparts was 0.3 cm, 0.5 cm, and 2.3 cm2, respectively; tobacco: R2 > 0.89 for all parameters, RMSE for the counterparts was 4.1 cm, 1.4 cm, and 42.6 cm2, respectively). The methodology based on extremely-low altitude UAV images has promising prospects in the crop breeding program for the automatic acquisition of fine organ-scale parameters with high efficiency.