Field-Scale Precision: Predicting Grain Yield of Diverse Wheat Breeding Lines Using High-Throughput UAV Multispectral Imaging

This study explored how to use UAV-based multispectral imaging, a plot detection model, and machine learning (ML) algorithms to predict wheat grain yield at the field scale. Multispectral data were collected over several weeks using the MicaSense RedEdge-P camera. Ground truth data on vegetation indices were collected utilizing portable phenotyping instruments, and agronomic data were collected manually. The YOLOv8 detection model was utilized for field-scale wheat plot detection. Four ML algorithms—decision tree (DT), random forest (RF), gradient boosting (GB), and extreme GB (XGBoost were used to evaluate wheat grain yield prediction using normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green NDVI (G-NDVI) data. The results demonstrated the RF algorithm's predicting ability across all growth stages, with a root-mean-square error (RMSE) of 43 grams per plot (g/p) and a coefficient of determination ($R^{2}$) value of 0.90 for NDVI data. For NDRE data, DT outperformed other models, with an RMSE of 43 g/p and an $R^{2}$ of 0.88. GB exhibited the highest predictive accuracy for G-NDVI data, with an RMSE of 42 g/p and an $R^{2}$ value of 0.89. The study integrated isogenic bread wheat sister lines and checked cultivars differing in grain yield, grain protein, and other agronomic traits to facilitate the identification of high-yield performers. The results show the potential use of UAV-based multispectral imaging combined with a detection model and ML in various precision agriculture applications, including wheat breeding, agronomy research, and broader agricultural practices.