High-Throughput Switchgrass Phenotyping and Biomass Modeling by UAV
Unmanned aerial vehicle (UAV) technology is an emerging powerful approach for high-throughput plant phenotyping field-grown crops. Switchgrass (Panicum virgatum L.) is a lignocellulosic bioenergy crop for which studies on yield, sustainability, and biofuel traits are performed. In this study, we exp...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-10-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2020.574073/full |
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| author | Fei Li Fei Li Cristiano Piasecki Cristiano Piasecki Reginald J. Millwood Reginald J. Millwood Benjamin Wolfe Benjamin Wolfe Mitra Mazarei Mitra Mazarei C. Neal Stewart C. Neal Stewart |
| author_facet | Fei Li Fei Li Cristiano Piasecki Cristiano Piasecki Reginald J. Millwood Reginald J. Millwood Benjamin Wolfe Benjamin Wolfe Mitra Mazarei Mitra Mazarei C. Neal Stewart C. Neal Stewart |
| author_sort | Fei Li |
| collection | DOAJ |
| description | Unmanned aerial vehicle (UAV) technology is an emerging powerful approach for high-throughput plant phenotyping field-grown crops. Switchgrass (Panicum virgatum L.) is a lignocellulosic bioenergy crop for which studies on yield, sustainability, and biofuel traits are performed. In this study, we exploited UAV-based imagery (LiDAR and multispectral approaches) to measure plant height, perimeter, and biomass yield in field-grown switchgrass in order to make predictions on bioenergy traits. Manual ground truth measurements validated the automated UAV results. We found UAV-based plant height and perimeter measurements were highly correlated and consistent with the manual measurements (r = 0.93, p < 0.001). Furthermore, we found that phenotyping parameters can significantly improve the natural saturation of the spectral index of the optical image for detecting high-density plantings. Combining plant canopy height (CH) and canopy perimeter (CP) parameters with spectral index (SI), we developed a robust and standardized biomass yield model [biomass = (m × SI) × CP × CH] where the m is an SI-sensitive coefficient linearly varying with the plant phenological changing stage. The biomass yield estimates obtained from this model were strongly correlated with manual measurements (r = 0.90, p < 0.001). Taking together, our results provide insights into the capacity of UAV-based remote sensing for switchgrass high-throughput phenotyping in the field, which will be useful for breeding and cultivar development. |
| first_indexed | 2024-12-14T18:45:48Z |
| format | Article |
| id | doaj.art-36645ffc71fc466a939d581aee803363 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-12-14T18:45:48Z |
| publishDate | 2020-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-36645ffc71fc466a939d581aee8033632022-12-21T22:51:24ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2020-10-011110.3389/fpls.2020.574073574073High-Throughput Switchgrass Phenotyping and Biomass Modeling by UAVFei Li0Fei Li1Cristiano Piasecki2Cristiano Piasecki3Reginald J. Millwood4Reginald J. Millwood5Benjamin Wolfe6Benjamin Wolfe7Mitra Mazarei8Mitra Mazarei9C. Neal Stewart10C. Neal Stewart11Department of Plant Sciences, University of Tennessee, Knoxville, Knoxville, TN, United StatesCenter for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesDepartment of Plant Sciences, University of Tennessee, Knoxville, Knoxville, TN, United StatesCenter for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesDepartment of Plant Sciences, University of Tennessee, Knoxville, Knoxville, TN, United StatesCenter for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesDepartment of Plant Sciences, University of Tennessee, Knoxville, Knoxville, TN, United StatesCenter for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesDepartment of Plant Sciences, University of Tennessee, Knoxville, Knoxville, TN, United StatesCenter for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesDepartment of Plant Sciences, University of Tennessee, Knoxville, Knoxville, TN, United StatesCenter for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United StatesUnmanned aerial vehicle (UAV) technology is an emerging powerful approach for high-throughput plant phenotyping field-grown crops. Switchgrass (Panicum virgatum L.) is a lignocellulosic bioenergy crop for which studies on yield, sustainability, and biofuel traits are performed. In this study, we exploited UAV-based imagery (LiDAR and multispectral approaches) to measure plant height, perimeter, and biomass yield in field-grown switchgrass in order to make predictions on bioenergy traits. Manual ground truth measurements validated the automated UAV results. We found UAV-based plant height and perimeter measurements were highly correlated and consistent with the manual measurements (r = 0.93, p < 0.001). Furthermore, we found that phenotyping parameters can significantly improve the natural saturation of the spectral index of the optical image for detecting high-density plantings. Combining plant canopy height (CH) and canopy perimeter (CP) parameters with spectral index (SI), we developed a robust and standardized biomass yield model [biomass = (m × SI) × CP × CH] where the m is an SI-sensitive coefficient linearly varying with the plant phenological changing stage. The biomass yield estimates obtained from this model were strongly correlated with manual measurements (r = 0.90, p < 0.001). Taking together, our results provide insights into the capacity of UAV-based remote sensing for switchgrass high-throughput phenotyping in the field, which will be useful for breeding and cultivar development.https://www.frontiersin.org/articles/10.3389/fpls.2020.574073/fullphenotypeLiDARspectral indexbiomassNitrogen |
| spellingShingle | Fei Li Fei Li Cristiano Piasecki Cristiano Piasecki Reginald J. Millwood Reginald J. Millwood Benjamin Wolfe Benjamin Wolfe Mitra Mazarei Mitra Mazarei C. Neal Stewart C. Neal Stewart High-Throughput Switchgrass Phenotyping and Biomass Modeling by UAV Frontiers in Plant Science phenotype LiDAR spectral index biomass Nitrogen |
| title | High-Throughput Switchgrass Phenotyping and Biomass Modeling by UAV |
| title_full | High-Throughput Switchgrass Phenotyping and Biomass Modeling by UAV |
| title_fullStr | High-Throughput Switchgrass Phenotyping and Biomass Modeling by UAV |
| title_full_unstemmed | High-Throughput Switchgrass Phenotyping and Biomass Modeling by UAV |
| title_short | High-Throughput Switchgrass Phenotyping and Biomass Modeling by UAV |
| title_sort | high throughput switchgrass phenotyping and biomass modeling by uav |
| topic | phenotype LiDAR spectral index biomass Nitrogen |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2020.574073/full |
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