Bo Xu

TL;DR: The current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed and can provide theoretical and technical support to promote the applications of Uav-R SPs for crop phenotypesing.

TL;DR: It is concluded that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB and suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.

TL;DR: The results suggest that crop height determined from the new UAV-based snapshot hyperspectral sensor can improve AGB estimation and is advantageous for mapping applications.

TL;DR: The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling, and the RF model is suitable for estimating LAI when sample plots and variation are relatively large and more than one growth period.

TL;DR: This study develops a semi-automated pipeline for extracting, analyzing and evaluating multiple phenotypic traits and introduces a time series data clustering analysis method into breeding programs as a tool to obtain a novel representative trait: typical curve.