AI-driven tree monitoring for silvopastoral systems using remote sensing imagery: Progress report
The current consensus within livestock production systems is directed toward more sustainable and environmentally friendly methods and practices. Silvopastoral systems offer a significant opportunity by integrating tree growth with pasture systems to provide shelter, enhance livestock feeding and we...
| Autores principales: | , |
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| Formato: | Internal Document |
| Lenguaje: | Inglés |
| Publicado: |
International Center for Tropical Agriculture
2023
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| Acceso en línea: | https://hdl.handle.net/10568/135077 |
| _version_ | 1855516848713367552 |
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| author | Ruiz-Hurtado, Andres Felipe Cardoso Arango, Juan Andrés |
| author_browse | Cardoso Arango, Juan Andrés Ruiz-Hurtado, Andres Felipe |
| author_facet | Ruiz-Hurtado, Andres Felipe Cardoso Arango, Juan Andrés |
| author_sort | Ruiz-Hurtado, Andres Felipe |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The current consensus within livestock production systems is directed toward more sustainable and environmentally friendly methods and practices. Silvopastoral systems offer a significant opportunity by integrating tree growth with pasture systems to provide shelter, enhance livestock feeding and welfare, improve soil characteristics, and can be profitable for producers. These systems typically exhibit a sparse distribution of trees, and a consistent and reliable monitoring process is essential for their management. Multiple remote sensing services provide images that can be used for this purpose. This project aims to leverage existing AI models to facilitate the monitoring of trees for different types of silvopastoral systems across several regions of Colombia using accessible remote sensing imagery either from popular satellite imagery services or from locally obtained drone photographs. Considering the characteristics and flexibility of AI models, especially for computer vision tasks, there is also a desire to explore the potential of adapting these approaches to other vision-based monitoring tasks in forage systems. |
| format | Internal Document |
| id | CGSpace135077 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | International Center for Tropical Agriculture |
| publisherStr | International Center for Tropical Agriculture |
| record_format | dspace |
| spelling | CGSpace1350772025-11-05T12:57:40Z AI-driven tree monitoring for silvopastoral systems using remote sensing imagery: Progress report Ruiz-Hurtado, Andres Felipe Cardoso Arango, Juan Andrés remote sensing models processing data analysis artificial intelligence monitoring trees silvopastoral systems The current consensus within livestock production systems is directed toward more sustainable and environmentally friendly methods and practices. Silvopastoral systems offer a significant opportunity by integrating tree growth with pasture systems to provide shelter, enhance livestock feeding and welfare, improve soil characteristics, and can be profitable for producers. These systems typically exhibit a sparse distribution of trees, and a consistent and reliable monitoring process is essential for their management. Multiple remote sensing services provide images that can be used for this purpose. This project aims to leverage existing AI models to facilitate the monitoring of trees for different types of silvopastoral systems across several regions of Colombia using accessible remote sensing imagery either from popular satellite imagery services or from locally obtained drone photographs. Considering the characteristics and flexibility of AI models, especially for computer vision tasks, there is also a desire to explore the potential of adapting these approaches to other vision-based monitoring tasks in forage systems. 2023-11 2023-12-06T14:44:24Z 2023-12-06T14:44:24Z Internal Document https://hdl.handle.net/10568/135077 en Open Access application/pdf International Center for Tropical Agriculture Ruiz H., A.F.; Cardoso, J.A. (2023) AI-driven tree monitoring for silvopastoral systems using remote sensing imagery: Progress Report. Cali (Colombia): International Center for Tropical Agriculture. 12 p. |
| spellingShingle | remote sensing models processing data analysis artificial intelligence monitoring trees silvopastoral systems Ruiz-Hurtado, Andres Felipe Cardoso Arango, Juan Andrés AI-driven tree monitoring for silvopastoral systems using remote sensing imagery: Progress report |
| title | AI-driven tree monitoring for silvopastoral systems using remote sensing imagery: Progress report |
| title_full | AI-driven tree monitoring for silvopastoral systems using remote sensing imagery: Progress report |
| title_fullStr | AI-driven tree monitoring for silvopastoral systems using remote sensing imagery: Progress report |
| title_full_unstemmed | AI-driven tree monitoring for silvopastoral systems using remote sensing imagery: Progress report |
| title_short | AI-driven tree monitoring for silvopastoral systems using remote sensing imagery: Progress report |
| title_sort | ai driven tree monitoring for silvopastoral systems using remote sensing imagery progress report |
| topic | remote sensing models processing data analysis artificial intelligence monitoring trees silvopastoral systems |
| url | https://hdl.handle.net/10568/135077 |
| work_keys_str_mv | AT ruizhurtadoandresfelipe aidriventreemonitoringforsilvopastoralsystemsusingremotesensingimageryprogressreport AT cardosoarangojuanandres aidriventreemonitoringforsilvopastoralsystemsusingremotesensingimageryprogressreport |