Unmanned aerial vehicle-based field phenotyping of crop biomass using growth traits retrieved from PROSAIL model

Abstract

Unmanned aerial vehicle (UAV) platform has been perceived as a useful tool for high-throughput field phenotyping of crop growth traits. While interpretation of UAV image data and retrieval of reliable and accurate phenotypic information are still challengeable due to the variations in sensors, crops and environment conditions. The aim of this study, therefore, is to explore the potential of UAV-based field phenotyping with the PROSAIL model to estimate biomass of rice and oilseed rape crops. Field experiments were designed for rice and oilseed rape with different nitrogen (N) treatments, and a UAV platform mounted with a multispectral camera was used to collect multi-temporal field images. Simultaneously, field measurements of leaf chlorophyll content (Cab), leaf area index (LAI), canopy chlorophyll content (CCC) and biomass were conducted. The results showed that coupling UAV-based multispectral images at the spectral region of 604–872 nm with the PROSAIL model successfully retrieved Cab, LAI and CCC of rice with the root mean square error (RMSE) of 5.40 μg/cm2, 1.13, and 43.50 μg/cm2, respectively. Further, the Cab, LAI and CCC retrieved from the PROSAIL model achieved the satisfactory biomass estimation in rice with the RMSE of 0.32 kg/m2, 0.23 kg/m2 and 0.22 kg/m2, respectively, which was comparable or superior to those obtained from commonly used empirical models. The proposed method also presented the robust performance for rice biomass estimation at different growth stages. In addition, model validation with the oilseed rape dataset showed an acceptable accuracy of biomass estimation with the determination coefficient (r2), RMSE and relative RMSE of 0.81, 0.03 kg/m2 and 27.82%, respectively, and still outperformed the empirical models with the better estimation performance. These findings demonstrate the potential of the proposed biomass retrieval strategy for UAV-based multispectral images, which also extend the application of PROSAIL model in field phenotyping of crop growth traits.