Remote sensing using unmanned aerial vehicles (UAV) combined with machine learning (ML) has significantly advanced field-scale prediction of aboveground biomass. Although ensemble learning frameworks (ELFs) typically outperform individual ML algorithms in accuracy, systematic evaluations of meta learner selection and the effects of base learner quantity and diversity remain limited. Leveraging vegetation indices and texture features extracted from multi-temporal UAV imagery, ELFs were constructed incorporating nine ML algorithms with three meta learners, Linear model, Random forest and Bayesian model averaging (BMA), to systematically evaluate how base learner configuration affects prediction accuracy. Using the fused feature set of sensitive vegetation indices and texture features, Gaussian process regression (GPR) achieved the highest accuracy among all base learners, with R2 = 0.769 and RMSE = 1.83 t·ha–1. Also, the three meta learners outperformed the best base learner, with the BMA meta learner yielding the superior accuracy (R2 = 0.795, RMSE = 1.73 t·ha–1). However, meta learner performance depended strongly on the composition of the base learners pool, stability was optimal with five base learners, and maximum accuracy was achieved by hybrid ensembles that combined linear-kernel models with GPR. This study highlights the importance of both meta learner selection and base learner composition in ELFs for aboveground biomass prediction in rice. These findings offer methodological guidance for UAV-based high-precision monitoring of crop AGB, with practical implications for precision agriculture and crop management.
Liu et al. (Thu,) studied this question.
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