AUTHOR=Liu Jikai , Zhu Yongji , Tao Xinyu , Chen Xiaofang , Li Xinwei 

TITLE=Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.1032170

DOI=10.3389/fpls.2022.1032170

ISSN=1664-462X

ABSTRACT=Rapid assessment of yield and nitrogen-use efficiency (NUE) based on universal vegetation index independent of growth period is essential for growth monitoring, efficient use of fertilizer, and crop breeding. This study explored the potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment on winter wheat using universal vegetation index . Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson’s correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R2 = 0.85, root mean square error (RMSE) = 793.96 kg/ha, and mean absolute error (MAE) = 656.31 kg/ha. MERIS terrestrial chlorophyll index (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, the modified normalized difference blue index (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. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for accurate monitoring and early selection of high-yield and high-NUE genotypes and can provide a scientific reference for the development of intelligent breeding and precision agricultural system.