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.
Highlights
Switchgrass (Panicum virgatum) is a perennial C4 grass that is widely considered as a leading candidate for bioenergy production
Using a single Unmanned aerial vehicles (UAVs)-based multispectral camera we developed a methodology for modeling rust severity as well as the contents of chlorophyll, nitrogen, and lignin
Using vegetation indices calculated from a UAV-based multispectral camera, our study developed statistical models for estimating leaf chlorophyll, rust disease, nitrogen, and lignin content
Summary
Switchgrass (Panicum virgatum) is a perennial C4 grass that is widely considered as a leading candidate for bioenergy production. Its natural traits, including high biomass production, wide adaptation, and low agronomic input requirements, make it a highly desirable bioenergy feedstock. Increased productivity and sustainability of bioenergy plant feedstocks are key factors for biofuel production. Factors affecting switchgrass sustainability can be broadly attributed to plant genetics and the growing environment, signifying the importance of performing field studies for successful establishment and subsequent sustainability of feedstocks. Various physiological and biochemical traits in switchgrass, such as chlorophyll, disease severity, nitrogen, and lignin affect the development of high-yielding feedstocks for producing cellulosic ethanol as the end goal. Resistance to diseases such as rust is an important sustainability trait of switchgrass.
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