Multispectral imaging and unmanned aerial systems for cotton plant phenotyping.
This paper demonstrates the application of aerial multispectral images in cotton plant phenotyping. Four phenotypic traits (plant height, canopy cover, vegetation index, and flower) were measured from multispectral images captured by a multispectral camera on an unmanned aerial system. Data were col...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0205083 |
| _version_ | 1819260038164774912 |
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| author | Rui Xu Changying Li Andrew H Paterson |
| author_facet | Rui Xu Changying Li Andrew H Paterson |
| author_sort | Rui Xu |
| collection | DOAJ |
| description | This paper demonstrates the application of aerial multispectral images in cotton plant phenotyping. Four phenotypic traits (plant height, canopy cover, vegetation index, and flower) were measured from multispectral images captured by a multispectral camera on an unmanned aerial system. Data were collected on eight different days from two fields. Ortho-mosaic and digital elevation models (DEM) were constructed from the raw images using the structure from motion (SfM) algorithm. A data processing pipeline was developed to calculate plant height using the ortho-mosaic and DEM. Six ground calibration targets (GCTs) were used to correct the error of the calculated plant height caused by the georeferencing error of the DEM. Plant heights were measured manually to validate the heights predicted from the imaging method. The error in estimation of the maximum height of each plot ranged from -40.4 to 13.5 cm among six datasets, all of which showed strong linear relationships with the manual measurement (R2 > 0.89). Plot canopy was separated from the soil based on the DEM and normalized differential vegetation index (NDVI). Canopy cover and mean canopy NDVI were calculated to show canopy growth over time and the correlation between the two indices was investigated. The spectral responses of the ground, leaves, cotton flower, and ground shade were analyzed and detection of cotton flowers was satisfactory using a support vector machine (SVM). This study demonstrated the potential of using aerial multispectral images for high throughput phenotyping of important cotton phenotypic traits in the field. |
| first_indexed | 2024-12-23T19:19:33Z |
| format | Article |
| id | doaj.art-345ec74edf714bc8b9f5a904236c8555 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-23T19:19:33Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-345ec74edf714bc8b9f5a904236c85552022-12-21T17:34:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01142e020508310.1371/journal.pone.0205083Multispectral imaging and unmanned aerial systems for cotton plant phenotyping.Rui XuChangying LiAndrew H PatersonThis paper demonstrates the application of aerial multispectral images in cotton plant phenotyping. Four phenotypic traits (plant height, canopy cover, vegetation index, and flower) were measured from multispectral images captured by a multispectral camera on an unmanned aerial system. Data were collected on eight different days from two fields. Ortho-mosaic and digital elevation models (DEM) were constructed from the raw images using the structure from motion (SfM) algorithm. A data processing pipeline was developed to calculate plant height using the ortho-mosaic and DEM. Six ground calibration targets (GCTs) were used to correct the error of the calculated plant height caused by the georeferencing error of the DEM. Plant heights were measured manually to validate the heights predicted from the imaging method. The error in estimation of the maximum height of each plot ranged from -40.4 to 13.5 cm among six datasets, all of which showed strong linear relationships with the manual measurement (R2 > 0.89). Plot canopy was separated from the soil based on the DEM and normalized differential vegetation index (NDVI). Canopy cover and mean canopy NDVI were calculated to show canopy growth over time and the correlation between the two indices was investigated. The spectral responses of the ground, leaves, cotton flower, and ground shade were analyzed and detection of cotton flowers was satisfactory using a support vector machine (SVM). This study demonstrated the potential of using aerial multispectral images for high throughput phenotyping of important cotton phenotypic traits in the field.https://doi.org/10.1371/journal.pone.0205083 |
| spellingShingle | Rui Xu Changying Li Andrew H Paterson Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. PLoS ONE |
| title | Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. |
| title_full | Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. |
| title_fullStr | Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. |
| title_full_unstemmed | Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. |
| title_short | Multispectral imaging and unmanned aerial systems for cotton plant phenotyping. |
| title_sort | multispectral imaging and unmanned aerial systems for cotton plant phenotyping |
| url | https://doi.org/10.1371/journal.pone.0205083 |
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