Early and accurate assessment of crop yield and NUE potential is crucial for rapid screening of excellent germplasm resources. The growth market of consumer-grade unmanned aerial vehicles (UAVs) provides an economical, non-destructive, high-throughput platform to execute this goal. In this study, the potential of DJI Phantom 4 multispectral (P4M) camera was explored to bridge the gap of consumer camera in winter wheat yield and NUE researches. Three vegetation indices that have high correlations with yield and NUE during the entire growth season were determined through Pearson’s correlational analysis, and multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods were adopted for agronomic traits assessment. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, except at the jointing stage, and the late grain-filling stage was deemed as the optimal single stage with R2 = 0.85, RMSE = 793.96 kg/ha, and MAE = 656.31 kg/ha. The MERIS terrestrial chlorophyll index (MTCI) and modified normalized difference blue index (mNDblue) exhibited significant performance for NUE assessment. MTCI performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage with R2 = 0.65, RMSE = 10.53 kg yield/kg N, and MAE = 8.90 kg yield/kg N. At the same time, mNDblue was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage with R2 = 0.61, RMSE = 7.48 kg yield/kg N, and MAE = 6.05 kg yield/kg N. The PLSR model with all vegetation indices serving as the input features was more robust than the other 2 regression models, albeit its accuracy did not exceed that of the selected vegetation indices. Furthermore, increasing the number of input features cannot improve the model accuracy. These results together indicate that the consumer-grade P4M camera is suitable for accurate monitoring and early selection of high yield and NUE genotypes and can provide a scientific reference for the development of intelligent breeding and precision agricultural system.