Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches
Grassland pastures are crucial for the global food supply through their milk and meat production; hence, forage species monitoring is essential for cattle feed. Therefore, knowledge of pasture above-ground canopy features help understand the crop status. This paper finds how to construct machine lea...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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MDPI
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/125648 |
| _version_ | 1855528626528714752 |
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| author | Álvarez Mendoza, Cesar I. Guzman, Diego Casas, Jorge Bastidas, Mike Polanco, Jan Valencia Ortiz, Milton Orlando Montenegro, Frank Arango, Jacobo Ishitani, Manabu Gómez Selvaraj, Michael |
| author_browse | Arango, Jacobo Bastidas, Mike Casas, Jorge Guzman, Diego Gómez Selvaraj, Michael Ishitani, Manabu Montenegro, Frank Polanco, Jan Valencia Ortiz, Milton Orlando Álvarez Mendoza, Cesar I. |
| author_facet | Álvarez Mendoza, Cesar I. Guzman, Diego Casas, Jorge Bastidas, Mike Polanco, Jan Valencia Ortiz, Milton Orlando Montenegro, Frank Arango, Jacobo Ishitani, Manabu Gómez Selvaraj, Michael |
| author_sort | Álvarez Mendoza, Cesar I. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Grassland pastures are crucial for the global food supply through their milk and meat production; hence, forage species monitoring is essential for cattle feed. Therefore, knowledge of pasture above-ground canopy features help understand the crop status. This paper finds how to construct machine learning models to predict above-ground canopy features in Brachiaria pasture from ground truth data (GTD) and remote sensing at larger (satellite data on the cloud) and smaller (unmanned aerial vehicles (UAV)) scales. First, we used above-ground biomass (AGB) data obtained from Brachiaria to evaluate the relationship between vegetation indices (VIs) with the dry matter (DM). Next, the performance of machine learning algorithms was used for predicting AGB based on VIs obtained from ground truth and satellite and UAV imagery. When comparing more than twenty-five machine learning models using an Auto Machine Learning Python API, the results show that the best algorithms were the Huber with R² = 0.60, Linear with R² = 0.54, and Extra Trees with R² = 0.45 to large scales using satellite. On the other hand, short-scale best regressions are K Neighbors with an R2 of 0.76, Extra Trees with an R² of 0.75, and Bayesian Ridge with an R² of 0.70, demonstrating a high potential to predict AGB and DM. This study is the first prediction model approach that assesses the rotational grazing system and pasture above-ground canopy features to predict the quality and quantity of cattle feed to support pasture management in Colombia. |
| format | Journal Article |
| id | CGSpace125648 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace1256482025-12-08T10:29:22Z Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches Álvarez Mendoza, Cesar I. Guzman, Diego Casas, Jorge Bastidas, Mike Polanco, Jan Valencia Ortiz, Milton Orlando Montenegro, Frank Arango, Jacobo Ishitani, Manabu Gómez Selvaraj, Michael above-ground biomass precision agriculture remote sensing unmanned aerial vehicles machine learning modelling forecasting biomasa sobre el suelo agricultura de precisión teledetección Grassland pastures are crucial for the global food supply through their milk and meat production; hence, forage species monitoring is essential for cattle feed. Therefore, knowledge of pasture above-ground canopy features help understand the crop status. This paper finds how to construct machine learning models to predict above-ground canopy features in Brachiaria pasture from ground truth data (GTD) and remote sensing at larger (satellite data on the cloud) and smaller (unmanned aerial vehicles (UAV)) scales. First, we used above-ground biomass (AGB) data obtained from Brachiaria to evaluate the relationship between vegetation indices (VIs) with the dry matter (DM). Next, the performance of machine learning algorithms was used for predicting AGB based on VIs obtained from ground truth and satellite and UAV imagery. When comparing more than twenty-five machine learning models using an Auto Machine Learning Python API, the results show that the best algorithms were the Huber with R² = 0.60, Linear with R² = 0.54, and Extra Trees with R² = 0.45 to large scales using satellite. On the other hand, short-scale best regressions are K Neighbors with an R2 of 0.76, Extra Trees with an R² of 0.75, and Bayesian Ridge with an R² of 0.70, demonstrating a high potential to predict AGB and DM. This study is the first prediction model approach that assesses the rotational grazing system and pasture above-ground canopy features to predict the quality and quantity of cattle feed to support pasture management in Colombia. 2022-11-19 2022-11-23T09:36:26Z 2022-11-23T09:36:26Z Journal Article https://hdl.handle.net/10568/125648 en Open Access application/pdf MDPI Alvarez-Mendoza, C.I.; Guzman, D.; Casas, J.; Bastidas, M.; Polanco, J.; Valencia-Ortiz, M.; Montenegro, F.; Arango, J.; Ishitani, M.; Selvaraj, M.G. (2022) Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine Learning approaches. Remote Sensing 14(22):5870. ISSN: 2072-4292 |
| spellingShingle | above-ground biomass precision agriculture remote sensing unmanned aerial vehicles machine learning modelling forecasting biomasa sobre el suelo agricultura de precisión teledetección Álvarez Mendoza, Cesar I. Guzman, Diego Casas, Jorge Bastidas, Mike Polanco, Jan Valencia Ortiz, Milton Orlando Montenegro, Frank Arango, Jacobo Ishitani, Manabu Gómez Selvaraj, Michael Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches |
| title | Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches |
| title_full | Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches |
| title_fullStr | Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches |
| title_full_unstemmed | Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches |
| title_short | Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches |
| title_sort | predictive modeling of above ground biomass in brachiaria pastures from satellite and uav imagery using machine learning approaches |
| topic | above-ground biomass precision agriculture remote sensing unmanned aerial vehicles machine learning modelling forecasting biomasa sobre el suelo agricultura de precisión teledetección |
| url | https://hdl.handle.net/10568/125648 |
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