Results of using machine learning and a proximal canopy reflectance sensor to predict biomass and nutritional quality in tropical forages (Urochloa humidicola) in three different locations in Colombia
Attributes such as biomass and nutritional quality are characteristics that allow for the selection of better pastures and, therefore, for obtaining greater quantities of meat and milk from livestock feed. However, the laboratory analytical method used to calculate these attributes is inefficient fo...
| Autores principales: | , , , |
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| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/179954 |
| _version_ | 1855513804225380352 |
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| author | Camelo, Rodrigo Andres Hernandez, Luis Miguel Cardoso, Juan Andres Jauregui, Rosa Noemi |
| author_browse | Camelo, Rodrigo Andres Cardoso, Juan Andres Hernandez, Luis Miguel Jauregui, Rosa Noemi |
| author_facet | Camelo, Rodrigo Andres Hernandez, Luis Miguel Cardoso, Juan Andres Jauregui, Rosa Noemi |
| author_sort | Camelo, Rodrigo Andres |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Attributes such as biomass and nutritional quality are characteristics that allow for the selection of better pastures and, therefore, for obtaining greater quantities of meat and milk from livestock feed. However, the laboratory analytical method used to calculate these attributes is inefficient for perennial species when there are more than hundreds of plants to evaluate. Therefore, it is necessary to develop methods that allow for the characterization and selection of genotypes using non-destructive, high-throughput techniques. For this purpose, active canopy sensors such as the Crop Circle Phenom have been used, capable of measuring reflectance, structural attributes, and climatic variables. The objective of this study was to build machine learning models to predict traits such as fresh biomass (FB), dry biomass (DBM) and nutritional traits such as crude protein (CP), in Urochloa humidicola genotypes in 3 locations (Palmira, Quilichao and Villavicencio) from harvests and analytical data (direct estimation method) and measurements acquired with portable multispectral sensors. |
| format | Informe técnico |
| id | CGSpace179954 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | CGSpace1799542026-01-17T02:00:36Z Results of using machine learning and a proximal canopy reflectance sensor to predict biomass and nutritional quality in tropical forages (Urochloa humidicola) in three different locations in Colombia Camelo, Rodrigo Andres Hernandez, Luis Miguel Cardoso, Juan Andres Jauregui, Rosa Noemi remote sensing teledetección feed crops-forage crops forraje high-throughput phenotyping nutritive value-nutritional value valor nutritivo fenotipado de alto rendimiento spectral analysis análisis espectral Attributes such as biomass and nutritional quality are characteristics that allow for the selection of better pastures and, therefore, for obtaining greater quantities of meat and milk from livestock feed. However, the laboratory analytical method used to calculate these attributes is inefficient for perennial species when there are more than hundreds of plants to evaluate. Therefore, it is necessary to develop methods that allow for the characterization and selection of genotypes using non-destructive, high-throughput techniques. For this purpose, active canopy sensors such as the Crop Circle Phenom have been used, capable of measuring reflectance, structural attributes, and climatic variables. The objective of this study was to build machine learning models to predict traits such as fresh biomass (FB), dry biomass (DBM) and nutritional traits such as crude protein (CP), in Urochloa humidicola genotypes in 3 locations (Palmira, Quilichao and Villavicencio) from harvests and analytical data (direct estimation method) and measurements acquired with portable multispectral sensors. 2025-12 2026-01-16T07:16:49Z 2026-01-16T07:16:49Z Report https://hdl.handle.net/10568/179954 en Open Access application/pdf Camelo, R.A.; Hernandez, L.M.; Cardoso, J.A.; Jauregui, R.N. (2025) Results of using machine learning and a proximal canopy reflectance sensor to predict biomass and nutritional quality in tropical forages (Urochloa humidicola) in three different locations in Colombia. 18 p. |
| spellingShingle | remote sensing teledetección feed crops-forage crops forraje high-throughput phenotyping nutritive value-nutritional value valor nutritivo fenotipado de alto rendimiento spectral analysis análisis espectral Camelo, Rodrigo Andres Hernandez, Luis Miguel Cardoso, Juan Andres Jauregui, Rosa Noemi Results of using machine learning and a proximal canopy reflectance sensor to predict biomass and nutritional quality in tropical forages (Urochloa humidicola) in three different locations in Colombia |
| title | Results of using machine learning and a proximal canopy reflectance sensor to predict biomass and nutritional quality in tropical forages (Urochloa humidicola) in three different locations in Colombia |
| title_full | Results of using machine learning and a proximal canopy reflectance sensor to predict biomass and nutritional quality in tropical forages (Urochloa humidicola) in three different locations in Colombia |
| title_fullStr | Results of using machine learning and a proximal canopy reflectance sensor to predict biomass and nutritional quality in tropical forages (Urochloa humidicola) in three different locations in Colombia |
| title_full_unstemmed | Results of using machine learning and a proximal canopy reflectance sensor to predict biomass and nutritional quality in tropical forages (Urochloa humidicola) in three different locations in Colombia |
| title_short | Results of using machine learning and a proximal canopy reflectance sensor to predict biomass and nutritional quality in tropical forages (Urochloa humidicola) in three different locations in Colombia |
| title_sort | results of using machine learning and a proximal canopy reflectance sensor to predict biomass and nutritional quality in tropical forages urochloa humidicola in three different locations in colombia |
| topic | remote sensing teledetección feed crops-forage crops forraje high-throughput phenotyping nutritive value-nutritional value valor nutritivo fenotipado de alto rendimiento spectral analysis análisis espectral |
| url | https://hdl.handle.net/10568/179954 |
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