Characterization of rice yield based on biomass and SPAD-based leaf nitrogen for large genotype plots
The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for sele...
| Autores principales: | , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2023
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/132664 |
| _version_ | 1855538048622657536 |
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| author | Duque, Andres F. Patino, Diego Colorado, Julian D. Petro, Eliel E. Rebolledo, Maria C Mondragon, Iván Fernando Espinosa, Natalia Amézquita, Nelson Puentes, Oscar D Mendez, Diego Jaramillo-Botero, Andres |
| author_browse | Amézquita, Nelson Colorado, Julian D. Duque, Andres F. Espinosa, Natalia Jaramillo-Botero, Andres Mendez, Diego Mondragon, Iván Fernando Patino, Diego Petro, Eliel E. Puentes, Oscar D Rebolledo, Maria C |
| author_facet | Duque, Andres F. Patino, Diego Colorado, Julian D. Petro, Eliel E. Rebolledo, Maria C Mondragon, Iván Fernando Espinosa, Natalia Amézquita, Nelson Puentes, Oscar D Mendez, Diego Jaramillo-Botero, Andres |
| author_sort | Duque, Andres F. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs. |
| format | Journal Article |
| id | CGSpace132664 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1326642025-12-08T10:29:22Z Characterization of rice yield based on biomass and SPAD-based leaf nitrogen for large genotype plots Duque, Andres F. Patino, Diego Colorado, Julian D. Petro, Eliel E. Rebolledo, Maria C Mondragon, Iván Fernando Espinosa, Natalia Amézquita, Nelson Puentes, Oscar D Mendez, Diego Jaramillo-Botero, Andres machine learning yields biomass nitrogen unmanned aerial vehicles The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs. 2023-06-26 2023-11-02T10:14:33Z 2023-11-02T10:14:33Z Journal Article https://hdl.handle.net/10568/132664 en Open Access application/pdf MDPI Duque, A.F.; Patino, D.; Colorado, J.D.; Petro, E.; Rebolledo, M.C.; Mondragon, I.F.; Espinosa, N.; Amezquita, N.; Puentes, O.D.; Mendez, D.; Jaramillo-Botero, A. (2023) Characterization of rice yield based on biomass and SPAD-based leaf nitrogen for large genotype plots. Sensors 23(13): 5917. ISSN: 1424-8220 |
| spellingShingle | machine learning yields biomass nitrogen unmanned aerial vehicles Duque, Andres F. Patino, Diego Colorado, Julian D. Petro, Eliel E. Rebolledo, Maria C Mondragon, Iván Fernando Espinosa, Natalia Amézquita, Nelson Puentes, Oscar D Mendez, Diego Jaramillo-Botero, Andres Characterization of rice yield based on biomass and SPAD-based leaf nitrogen for large genotype plots |
| title | Characterization of rice yield based on biomass and SPAD-based leaf nitrogen for large genotype plots |
| title_full | Characterization of rice yield based on biomass and SPAD-based leaf nitrogen for large genotype plots |
| title_fullStr | Characterization of rice yield based on biomass and SPAD-based leaf nitrogen for large genotype plots |
| title_full_unstemmed | Characterization of rice yield based on biomass and SPAD-based leaf nitrogen for large genotype plots |
| title_short | Characterization of rice yield based on biomass and SPAD-based leaf nitrogen for large genotype plots |
| title_sort | characterization of rice yield based on biomass and spad based leaf nitrogen for large genotype plots |
| topic | machine learning yields biomass nitrogen unmanned aerial vehicles |
| url | https://hdl.handle.net/10568/132664 |
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