Enhancing Direct-Seeded Rice Yield Prediction Using UAV-derived Features Acquired During the Reproductive Phase

Pre-harvest yield prediction of direct-seeded rice is critical for guiding crop interventions and food security assessment in precision agriculture. Technology advances in unmanned aerial vehicle (UAV)-based remote sensing has provided an unprecedented opportunity to efficiently retrieve crop growth parameters instead of labor-intensive ground measurements. This study is aiming to evaluate the feasibility of fusing multi-temporal UAV-derived features collected at critical phenological stages in forecasting direct-seeded rice yield across different cultivars and nitrogen (N) management. The results showed that RGB sensor-derived canopy volume, canopy coverage, and spectral features including RBRI, WI etc., were identified to be most sensitive to the differences in aboveground biomass and grain yield. Heading stage was the suitable time for estimating yield performance (R 2 = 0.75) for mono-temporal UAV observation. By contrast, multi-temporal features fusion could remarkably enhance the yield prediction accuracy. Moreover, the yield prediction accuracy can be further improved by integrating UAV features collected at panicle initiation and heading stages (i.e., rice reproductive phase) compared to multi-temporal features fusion (R 2 increased from 0.82 to 0.85 and RMSE decreased from 35.1 to 31.5 g m −2 ). This can be attributed to the fact that the biomass accumulation during the reproductive phase was closely associated to the total spikelets and final yield. By using this proposed approach, the predicted yield showed a good spatial consistency with the measured yield across different cultivars and N management, and yield prediction error in the most of the plots (114 of 128 plots) was less than 45 g m −2 . In summary, this study highlights that the reproductive phase is the optimal time window for UAV observing, which provides an effective method for accurate pre-harvest yield prediction of direct-seeded rice in precision agriculture.