UAV Remote Sensing for High-Throughput Phenotyping and for Yield Prediction of Miscanthus by Machine Learning Techniques

Miscanthus holds a great potential in the frame of bioeconomy, and yield prediction can help improve Miscanthus’ logistic supply chain. Breeding programs several countries are attempting to produce high-yielding hybrids better adapted different climates end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) Italy UK 2021 2022 were used investigate feasibility high-throughput phenotyping (HTP) novel for crop traits estimation. An intercalibration procedure was performed using simulated data PROSAIL model link vegetation indices (VIs) derived two multispectral sensors. The random forest algorithm estimated with good accuracy (light interception, plant height, green leaf biomass, standing biomass) 15 VIs time series, predicted peak descriptors these series root mean square error 2.3 Mg DM ha?1. study demonstrates UAVs’ HTP applications prediction, providing important information needed increase sustainable biomass production.