Sustainability Trait Modeling of Field-Grown Switchgrass (<i>Panicum virgatum</i>) Using UAV-Based Imagery
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 dat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/10/12/2726 |
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| author | Yaping Xu Vivek Shrestha Cristiano Piasecki Benjamin Wolfe Lance Hamilton Reginald J. Millwood Mitra Mazarei Charles Neal Stewart |
| author_facet | Yaping Xu Vivek Shrestha Cristiano Piasecki Benjamin Wolfe Lance Hamilton Reginald J. Millwood Mitra Mazarei Charles Neal Stewart |
| author_sort | Yaping Xu |
| collection | DOAJ |
| description | 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 (<i>Panicum virgatum</i>), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by <i>Puccinia novopanici</i>, 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. |
| first_indexed | 2024-03-10T03:16:40Z |
| format | Article |
| id | doaj.art-93683427b82b4607a149563c640f2e36 |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-03-10T03:16:40Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj.art-93683427b82b4607a149563c640f2e362023-11-23T10:12:19ZengMDPI AGPlants2223-77472021-12-011012272610.3390/plants10122726Sustainability Trait Modeling of Field-Grown Switchgrass (<i>Panicum virgatum</i>) Using UAV-Based ImageryYaping Xu0Vivek Shrestha1Cristiano Piasecki2Benjamin Wolfe3Lance Hamilton4Reginald J. Millwood5Mitra Mazarei6Charles Neal Stewart7Department of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USADepartment of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USADepartment of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USADepartment of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USADepartment of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USADepartment of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USADepartment of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USADepartment of Plant Sciences, University of Tennessee, Knoxville, TN 37996, USAUnmanned 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 (<i>Panicum virgatum</i>), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by <i>Puccinia novopanici</i>, 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.https://www.mdpi.com/2223-7747/10/12/2726sustainabilityswitchgrassrust diseasechlorophyllnitrogenlignin |
| spellingShingle | Yaping Xu Vivek Shrestha Cristiano Piasecki Benjamin Wolfe Lance Hamilton Reginald J. Millwood Mitra Mazarei Charles Neal Stewart Sustainability Trait Modeling of Field-Grown Switchgrass (<i>Panicum virgatum</i>) Using UAV-Based Imagery Plants sustainability switchgrass rust disease chlorophyll nitrogen lignin |
| title | Sustainability Trait Modeling of Field-Grown Switchgrass (<i>Panicum virgatum</i>) Using UAV-Based Imagery |
| title_full | Sustainability Trait Modeling of Field-Grown Switchgrass (<i>Panicum virgatum</i>) Using UAV-Based Imagery |
| title_fullStr | Sustainability Trait Modeling of Field-Grown Switchgrass (<i>Panicum virgatum</i>) Using UAV-Based Imagery |
| title_full_unstemmed | Sustainability Trait Modeling of Field-Grown Switchgrass (<i>Panicum virgatum</i>) Using UAV-Based Imagery |
| title_short | Sustainability Trait Modeling of Field-Grown Switchgrass (<i>Panicum virgatum</i>) Using UAV-Based Imagery |
| title_sort | sustainability trait modeling of field grown switchgrass i panicum virgatum i using uav based imagery |
| topic | sustainability switchgrass rust disease chlorophyll nitrogen lignin |
| url | https://www.mdpi.com/2223-7747/10/12/2726 |
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