Cross-Year Rapeseed Yield Prediction for Harvesting Management Using UAV-Based Imagery

Accurate estimation of rapeseed yield is crucial for harvesting decisions and improving efficiency and output. Machine learning (ML) models driven by remote sensing data are widely used for yield prediction. This study explores the generality of feature-based rapeseed yield prediction models across different varieties and years. Seven vegetation indices (VIs) and twenty-four texture features (TFs) were calculated from UAV-based imagery. Pearson’s correlation coefficient was used to assess variable sensitivity at different growth stages, and the variable importance score (VIP) from the random forest (RF) model was used for feature selection. Three ML regression methods—RF, support vector regression (SVR), and partial least squares regression (PLSR)—were applied using the single-stage VI, selected multi-stage VI, and multivariate VI-TFs for yield prediction. The best yield model was selected through cross-validation and tested for temporal fit using cross-year data. Results showed that the multi-stage VI and RF model achieved the highest accuracy in the training dataset (R2 = 0.93, rRMSE = 7.36%), while the multi-stage VI and PLSR performed best in the test dataset (R2 = 0.62, rRMSE = 15.20%). However, this study demonstrated that the addition of TFs could not enhance the robustness of rapeseed yield estimation. Additionally, the model updating strategy improved the RF model’s temporal fit, increasing R2 by 25% and reducing the rRMSE to below 10%. This study highlights the potential of the multi-stage VI for rapeseed yield prediction and offers a method to improve the generality of yield prediction models over multiple years, providing a practical approach for meter-scale yield mapping and multi-year prediction.