Satellite imagery for high-throughput phenotyping in breeding plots
Advances in breeding efforts to increase the rate of genetic gains and enhance crop resilience to climate change have been limited by the procedure and costs of phenotyping methods. The recent rapid development of sensors, image-processing technology, and data-analysis has provided opportunities for...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/130586 |
| _version_ | 1855539678285922304 |
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| author | Pinto Espinosa, Francisco Zaman-Allah, Mainassara Reynolds, Matthew P. Schulthess, Urs |
| author_browse | Pinto Espinosa, Francisco Reynolds, Matthew P. Schulthess, Urs Zaman-Allah, Mainassara |
| author_facet | Pinto Espinosa, Francisco Zaman-Allah, Mainassara Reynolds, Matthew P. Schulthess, Urs |
| author_sort | Pinto Espinosa, Francisco |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Advances in breeding efforts to increase the rate of genetic gains and enhance crop resilience to climate change have been limited by the procedure and costs of phenotyping methods. The recent rapid development of sensors, image-processing technology, and data-analysis has provided opportunities for multiple scales phenotyping methods and systems, including satellite imagery. Among these platforms, satellite imagery may represent one of the ultimate approaches to remotely monitor trials and nurseries planted in multiple locations while standardizing protocols and reducing costs. However, the deployment of satellite-based phenotyping in breeding trials has largely been limited by low spatial resolution of satellite images. The advent of a new generation of high-resolution satellites may finally overcome these limitations. The SkySat constellation started offering multispectral images at a 0.5 m resolution since 2020. In this communication we present a case study on the use of time series SkySat images to estimate NDVI from wheat and maize breeding plots encompassing different sizes and spacing. We evaluated the reliability of the calculated NDVI and tested its capacity to detect seasonal changes and genotypic differences. We discuss the advantages, limitations, and perspectives of this approach for high-throughput phenotyping in breeding programs. |
| format | Journal Article |
| id | CGSpace130586 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Frontiers Media |
| publisherStr | Frontiers Media |
| record_format | dspace |
| spelling | CGSpace1305862025-12-08T10:29:22Z Satellite imagery for high-throughput phenotyping in breeding plots Pinto Espinosa, Francisco Zaman-Allah, Mainassara Reynolds, Matthew P. Schulthess, Urs high-throughput phenotyping satellites wheat maize breeding normalized difference vegetation index Advances in breeding efforts to increase the rate of genetic gains and enhance crop resilience to climate change have been limited by the procedure and costs of phenotyping methods. The recent rapid development of sensors, image-processing technology, and data-analysis has provided opportunities for multiple scales phenotyping methods and systems, including satellite imagery. Among these platforms, satellite imagery may represent one of the ultimate approaches to remotely monitor trials and nurseries planted in multiple locations while standardizing protocols and reducing costs. However, the deployment of satellite-based phenotyping in breeding trials has largely been limited by low spatial resolution of satellite images. The advent of a new generation of high-resolution satellites may finally overcome these limitations. The SkySat constellation started offering multispectral images at a 0.5 m resolution since 2020. In this communication we present a case study on the use of time series SkySat images to estimate NDVI from wheat and maize breeding plots encompassing different sizes and spacing. We evaluated the reliability of the calculated NDVI and tested its capacity to detect seasonal changes and genotypic differences. We discuss the advantages, limitations, and perspectives of this approach for high-throughput phenotyping in breeding programs. 2023 2023-06-01T22:21:29Z 2023-06-01T22:21:29Z Journal Article https://hdl.handle.net/10568/130586 en Open Access application/pdf Frontiers Media Pinto, F., Zaman-Allah, M., Reynolds, M., & Schulthess, U. (2023). Satellite imagery for high-throughput phenotyping in breeding plots. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1114670 |
| spellingShingle | high-throughput phenotyping satellites wheat maize breeding normalized difference vegetation index Pinto Espinosa, Francisco Zaman-Allah, Mainassara Reynolds, Matthew P. Schulthess, Urs Satellite imagery for high-throughput phenotyping in breeding plots |
| title | Satellite imagery for high-throughput phenotyping in breeding plots |
| title_full | Satellite imagery for high-throughput phenotyping in breeding plots |
| title_fullStr | Satellite imagery for high-throughput phenotyping in breeding plots |
| title_full_unstemmed | Satellite imagery for high-throughput phenotyping in breeding plots |
| title_short | Satellite imagery for high-throughput phenotyping in breeding plots |
| title_sort | satellite imagery for high throughput phenotyping in breeding plots |
| topic | high-throughput phenotyping satellites wheat maize breeding normalized difference vegetation index |
| url | https://hdl.handle.net/10568/130586 |
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