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Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches

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Title Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches
 
Creator Alvarez-Mendoza, Cesar I.
Guzman, Diego
Casas, Jorge
Bastidas, Mike
Polanco, Jan
Valencia Ortiz, Milton
Montenegro, Frank
Arango, Jacobo
Ishitani, Manabu
Gómez Selvaraj, Michael
 
Subject above-ground biomass
precision agriculture
remote sensing
unmanned aerial vehicles
machine learning
modelling
forecasting
biomasa sobre el suelo
agricultura de precisión
teledetección
 
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.
 
Date 2022-11-19
2022-11-23T09:36:26Z
2022-11-23T09:36:26Z
 
Type Journal Article
 
Identifier 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
2072-4292
https://hdl.handle.net/10568/125648
https://doi.org/10.3390/rs14225870
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format 5870
application/pdf
 
Publisher MDPI AG
 
Source Remote Sensing