Data collection and managing of remote sensing data for Urochloa spp. and Megathyrsus maximus breeding pipelines
Remote sensing has expanded opportunities to enhance genetic gain in breeding programs by increasing selection accuracy, reducing evaluation time, and enabling the incorporation of additional traits through association with multiple color indices (Araus et al., 2018). One of the main advances in rec...
| Main Authors: | , , , |
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| Format: | Informe técnico |
| Language: | Inglés |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/169567 |
| _version_ | 1855513155898179584 |
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| author | Camelo, Rodrigo Espitia Buitrago, Paula Arboleda, Ronald Jauregui, Rosa |
| author_browse | Arboleda, Ronald Camelo, Rodrigo Espitia Buitrago, Paula Jauregui, Rosa |
| author_facet | Camelo, Rodrigo Espitia Buitrago, Paula Arboleda, Ronald Jauregui, Rosa |
| author_sort | Camelo, Rodrigo |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Remote sensing has expanded opportunities to enhance genetic gain in breeding programs by increasing selection accuracy, reducing evaluation time, and enabling the incorporation of additional traits through association with multiple color indices (Araus et al., 2018). One of the main advances in recent years has been the integration of drone technologies into the evaluation of breeding trials, with multispectral cameras providing a significant advantage. Measuring near-infrared and red-edge bands allows for the calculation of vegetation indices that detect changes in chloroplast reflectance, offering valuable insights into plant health, photosynthetic efficiency, and stress responses. Chloroplast reflectance peaks in the near-infrared band (around 850 nm) and undergoes a rapid shift at the red-edge band (around 700 nm), making these wavelengths critical for precise vegetation monitoring and analysis (Guo et al., 2021). However, multispectral data acquisition and processing require not only highly skilled labor but also a robust framework for data management and an adequate cyber infrastructure to handle the storage, analysis, and accessibility of large datasets efficiently (Gano et al., 2024). |
| format | Informe técnico |
| id | CGSpace169567 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | CGSpace1695672025-11-05T12:51:28Z Data collection and managing of remote sensing data for Urochloa spp. and Megathyrsus maximus breeding pipelines Camelo, Rodrigo Espitia Buitrago, Paula Arboleda, Ronald Jauregui, Rosa genetic gain remote sensing plant breeding forage unmanned aerial vehicles multispectral imagery chloroplasts Remote sensing has expanded opportunities to enhance genetic gain in breeding programs by increasing selection accuracy, reducing evaluation time, and enabling the incorporation of additional traits through association with multiple color indices (Araus et al., 2018). One of the main advances in recent years has been the integration of drone technologies into the evaluation of breeding trials, with multispectral cameras providing a significant advantage. Measuring near-infrared and red-edge bands allows for the calculation of vegetation indices that detect changes in chloroplast reflectance, offering valuable insights into plant health, photosynthetic efficiency, and stress responses. Chloroplast reflectance peaks in the near-infrared band (around 850 nm) and undergoes a rapid shift at the red-edge band (around 700 nm), making these wavelengths critical for precise vegetation monitoring and analysis (Guo et al., 2021). However, multispectral data acquisition and processing require not only highly skilled labor but also a robust framework for data management and an adequate cyber infrastructure to handle the storage, analysis, and accessibility of large datasets efficiently (Gano et al., 2024). 2024-01 2025-01-21T16:03:39Z 2025-01-21T16:03:39Z Report https://hdl.handle.net/10568/169567 en Open Access application/pdf Camelo, R.; Espitia Buitrago, P.; Arboleda, R.; Jauregui, R. (2024) Data collection and managing of remote sensing data for Urochloa spp. and Megathyrsus maximus breeding pipelines. 13 p. |
| spellingShingle | genetic gain remote sensing plant breeding forage unmanned aerial vehicles multispectral imagery chloroplasts Camelo, Rodrigo Espitia Buitrago, Paula Arboleda, Ronald Jauregui, Rosa Data collection and managing of remote sensing data for Urochloa spp. and Megathyrsus maximus breeding pipelines |
| title | Data collection and managing of remote sensing data for Urochloa spp. and Megathyrsus maximus breeding pipelines |
| title_full | Data collection and managing of remote sensing data for Urochloa spp. and Megathyrsus maximus breeding pipelines |
| title_fullStr | Data collection and managing of remote sensing data for Urochloa spp. and Megathyrsus maximus breeding pipelines |
| title_full_unstemmed | Data collection and managing of remote sensing data for Urochloa spp. and Megathyrsus maximus breeding pipelines |
| title_short | Data collection and managing of remote sensing data for Urochloa spp. and Megathyrsus maximus breeding pipelines |
| title_sort | data collection and managing of remote sensing data for urochloa spp and megathyrsus maximus breeding pipelines |
| topic | genetic gain remote sensing plant breeding forage unmanned aerial vehicles multispectral imagery chloroplasts |
| url | https://hdl.handle.net/10568/169567 |
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