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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/22/5870 |
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| author | Cesar I. Alvarez-Mendoza Diego Guzman Jorge Casas Mike Bastidas Jan Polanco Milton Valencia-Ortiz Frank Montenegro Jacobo Arango Manabu Ishitani Michael Gomez Selvaraj |
| author_facet | Cesar I. Alvarez-Mendoza Diego Guzman Jorge Casas Mike Bastidas Jan Polanco Milton Valencia-Ortiz Frank Montenegro Jacobo Arango Manabu Ishitani Michael Gomez Selvaraj |
| author_sort | Cesar I. Alvarez-Mendoza |
| collection | DOAJ |
| 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 R<sup>2</sup> = 0.60, Linear with R<sup>2</sup> = 0.54, and Extra Trees with R<sup>2</sup> = 0.45 to large scales using satellite. On the other hand, short-scale best regressions are K Neighbors with an R<sup>2</sup> of 0.76, Extra Trees with an R<sup>2</sup> of 0.75, and Bayesian Ridge with an R<sup>2</sup> 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. |
| first_indexed | 2024-03-09T18:01:54Z |
| format | Article |
| id | doaj.art-485b95e74d8e4b09954609b535b47829 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:01:54Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-485b95e74d8e4b09954609b535b478292023-11-24T09:51:46ZengMDPI AGRemote Sensing2072-42922022-11-011422587010.3390/rs14225870Predictive Modeling of Above-Ground Biomass in Brachiaria Pastures from Satellite and UAV Imagery Using Machine Learning ApproachesCesar I. Alvarez-Mendoza0Diego Guzman1Jorge Casas2Mike Bastidas3Jan Polanco4Milton Valencia-Ortiz5Frank Montenegro6Jacobo Arango7Manabu Ishitani8Michael Gomez Selvaraj9Grupo de Investigación Ambiental en el Desarrollo Sustentable GIADES, Carrera de Ingeniería Ambiental, Universidad Politécnica Salesiana, Quito 170702, EcuadorInternational Center for Tropical Agriculture (CIAT), A.A. 6713, Cali 763537, ColombiaInternational Center for Tropical Agriculture (CIAT), A.A. 6713, Cali 763537, ColombiaInternational Center for Tropical Agriculture (CIAT), A.A. 6713, Cali 763537, ColombiaInternational Center for Tropical Agriculture (CIAT), A.A. 6713, Cali 763537, ColombiaInternational Center for Tropical Agriculture (CIAT), A.A. 6713, Cali 763537, ColombiaInternational Center for Tropical Agriculture (CIAT), A.A. 6713, Cali 763537, ColombiaInternational Center for Tropical Agriculture (CIAT), A.A. 6713, Cali 763537, ColombiaInternational Center for Tropical Agriculture (CIAT), A.A. 6713, Cali 763537, ColombiaInternational Center for Tropical Agriculture (CIAT), A.A. 6713, Cali 763537, ColombiaGrassland 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 R<sup>2</sup> = 0.60, Linear with R<sup>2</sup> = 0.54, and Extra Trees with R<sup>2</sup> = 0.45 to large scales using satellite. On the other hand, short-scale best regressions are K Neighbors with an R<sup>2</sup> of 0.76, Extra Trees with an R<sup>2</sup> of 0.75, and Bayesian Ridge with an R<sup>2</sup> 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.https://www.mdpi.com/2072-4292/14/22/5870above-ground biomassprecision agricultureUAVremote sensingmachine learning prediction |
| spellingShingle | Cesar I. Alvarez-Mendoza Diego Guzman Jorge Casas Mike Bastidas Jan Polanco Milton Valencia-Ortiz Frank Montenegro Jacobo Arango Manabu Ishitani Michael Gomez Selvaraj Predictive Modeling of Above-Ground Biomass in Brachiaria Pastures from Satellite and UAV Imagery Using Machine Learning Approaches Remote Sensing above-ground biomass precision agriculture UAV remote sensing machine learning prediction |
| 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 UAV remote sensing machine learning prediction |
| url | https://www.mdpi.com/2072-4292/14/22/5870 |
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