Identification of High Nitrogen Use Efficiency Phenotype in Rice (Oryza sativa L.) Through Entire Growth Duration by Unmanned Aerial Vehicle Multispectral Imagery

Abstract

Identification of high Nitrogen Use Efficiency (NUE) phenotypes has been a long-standing challenge in breeding rice and sustainable agriculture to reduce the costs of nitrogen (N) fertilizers. There are two main challenges: (1) high NUE genetic sources are biologically scarce and (2) on the technical side, few easy, non-destructive, and reliable methodologies are available to evaluate plant N variations through the entire growth duration (GD). To overcome the challenges, we captured a unique higher NUE phenotype in rice as a dynamic time-series N variation curve through the entire GD analysis by canopy reflectance data collected by Unmanned Aerial Vehicle Remote Sensing Platform (UAV-RSP) for the first time. LY9348 was a high NUE rice variety with high Nitrogen Uptake Efficiency (NUpE) and high Nitrogen Utilization Efficiency (NUtE) shown in nitrogen dosage field analysis. Its canopy nitrogen content (CNC) was analyzed by the high-throughput UAV-RSP to screen two mixed categories (51 versus 42 varieties) selected from representative higher NUE indica rice collections. Five Vegetation Indices (VIs) were compared, and the Normalized Difference Red Edge Index (NDRE) showed the highest correlation with CNC (r = 0.80). Six key developmental stages of rice varieties were compared from transplantation to maturation, and the high NUE phenotype of LY9348 was shown as a dynamic N accumulation curve, where it was moderately high during the vegetative developmental stages but considerably higher in the reproductive developmental stages with a slower reduction rate. CNC curves of different rice varieties were analyzed to construct two non-linear regression models between N% or N% × leaf area index (LAI) with NDRE separately. Both models could determine the specific phenotype with the coefficient of determination (R2) above 0.61 (Model I) and 0.86 (Model II). Parameters influencing the correlation accuracy between NDRE and N% were found to be better by removing the tillering stage data, separating the short and long GD varieties for the analysis and adding canopy structures, such as LAI, into consideration. The high NUE phenotype of LY9348 could be traced and reidentified across different years, locations, and genetic germplasm groups. Therefore, an effective and reliable high-throughput method was proposed for assisting the selection of the high NUE breeding phenotype.

Advances in Barley Breeding for Improving Nitrogen Use Efficiency

“…In addition, high-throughput phenomics is being developed in the estimation of responses to nitrogen treatment in crops by using non-invasive imaging systems, spectroscopy, image analysis, robotics, enzyme-based sensors, etc. [75][76][77][78]. These methods can provide a large amount of real-time data, which will have a revolutionary impact on the estimation of phenotypes in the study of NUE in barley and will also be an important research direction.…”

Characterization of N distribution in different organs of winter wheat using UAV-based remote sensing

“…There were only limited studies reported the attempts on the UAV-based estimation of N use efficiency, which for instance is reflected by the correlation between the UAV-based multispectral traits with NUE (Yang et al, 2020). (Liang et al, 2021) has revealed the capability of using UAV multispectral imagery for the identification of high N use efficiency phenotype in rice. Our results demonstrated that, by only using the latent variables extracted from UAV images, we could predict the NAE (Figure 12), highlighting the prospect of using of UAV-based images to estimate the indicators of NUE.…”

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

“…The results revealed that the NDRE had a high consistency and accuracy for NUE, particularly in the mid to late-grain filling stage. In addition, the nitrogen dynamics time-series curves of the two rice populations were captured by UAVs’ multispectral imagery, which were used to identify an available spectral index (NDRE) and a high NUE variety ( Liang et al., 2021 ). Therefore, more studies are needed to explore the potential of UAVs imagery in NUE prediction.…”