High-throughput estimation of sugarcane phenotypic traits using UAV multispectral data under high-density planting conditions.

High-throughput phenotyping using unmanned aerial vehicle (UAV)-based imagery offers substantial potential for improving sugarcane breeding efficiency. This study utilized UAVs-equipped multispectral sensors to capture high-resolution imagery of 652 sugarcane varieties under high-density planting condition, enabling the development of predictive models for key phenotypic traits including plant height, leaf length, leaf width, and relative chlorophyll content (SPAD value). A comprehensive feature extraction process yielded 100 vegetation indices, 7 texture indices, and canopy height parameters derived from the UAV imagery. To develop robust predictive models, we implemented three feature processing strategies-correlation-based filtering (COR), stepwise regression selection (SWR), and principal component analysis (PCA)-in conjunction with five machine learning algorithms: Lasso Regression (LASSO), Ridge Regression (Ridge), Support Vector Machine Regression (SVM), Random Forest (RF), and Gradient Boosting Regression Trees (GBR). Two ensemble methods, Bayesian Model Averaging (BMA) and Stacked Generalization, were also employed. Results demonstrated that LASSO performed best among traditional machine learning models, whereas the Stacking ensemble method, which integrated predictions from all individual algorithms, achieved the highest prediction accuracy (the coefficient of determination (