Enhanced wheat yield estimation with UAV multi-source remote sensing and optimized ensemble learning

Accurate prediction of crop yield is a critical issuefor smart field management. To address the limitations of single machine learning models (poor generalization) and single data sources (insufficient capacity to comprehensively characterize crop growth), this study acquired RGB and multispectral (MS) data of winter wheat across three key growth stages via a low-altitude UAV. After extracting vegetation indices (VIs) and crop height (CH) features, and combining them with measured yield data from 123 samples, it systematically evaluated the performance of six machine learning models and explored different combinations of ensemble learning strategies. The results showed that the ​filling stage​ was the optimal period for yield prediction, with ​Support Vector Regression (SVR)​​ achieving the highest accuracy (R² = 0.673), outperforming the ​heading stage​ (R² = 0.618) and ​milking stage​ (R² = 0.534). ​Multi-source data fusion (MS+RGB+CH)​​ significantly improved prediction accuracy (p < 0.05), with SVR performing best (R² = 0.673, RMSE = 1106 kg/ha, RRMSE = 11.1%). Ensemble learning​ further enhanced performance when using a ​Backpropagation Neural Network (BPNN)​​ as the secondary model, increasing accuracy to ​R² = 0.711, RMSE = 1041 kg/ha, RRMSE = 10.4%​. ​Selective removal of underperforming base models​ improved ensemble effectiveness. While BPNN performed best as a secondary model, Random Forest Regression (RFR) exhibited higher stability in this role. This study demonstrates that ​high-precision wheat yield prediction​ can be achieved through ​multi-sensor data fusion and selective ensemble learning, providing essential data support for smart agricultural management