Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery

Abstract

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

The Genetic Architecture of Nitrogen Use Efficiency in Switchgrass (Panicum virgatum L.)

“…A highly diverse panel of 331 switchgrass accessions ( Lovell et al, 2021 ) was planted in May 2019 in a 75.2 m × 122.5 m dimension at The University of Tennessee Plant Sciences Unit of the East Tennessee Research and Education Center (ETREC) (latitude: 35°54′11.14″N; longitude: 83°57′33.31″W; and elevation: 255.7 m.). Planting details and experimental design can be found in Li et al (2020) and Xu et al (2021) . Briefly, the 331 accessions were planted under two nitrogen (N) fertility treatments: one with moderate nitrogen (135 kg of N ha –1 ), while the other with low (0 kg of N ha –1 ) supplementation in 2019 and 2020.…”

“…The detail of the nitrogen quantification was previously described in Xu et al (2021) . Briefly, nitrogen content of the aboveground biomass was measured via near-infrared spectroscopy (NIRS) using a FOSS 6500 NIR system (Silver Spring, MD, United States).…”

Using Unmanned Aerial Vehicles and Multispectral Sensors to Model Forage Yield for Grasses of Semiarid Landscapes

“…The NDVI and the NDRE are commonly used as proxies for vegetation quantity and quality [50], and the NDRE, in particular, has been used to model biomass in rangelands with significant spatial and temporal variability [51]. Furthermore, scientists [52] have also found that NDRE can outperform the NDVI to model the nitrogen content for switchgrass (Panicum virgatum). And, concordant with our results, this study found that some traits could be modeled using simple linear models, but other traits (N content) required nonlinear approaches.…”