Predictive Modeling of Above-Ground Biomass in Brachiaria Pastures from Satellite and UAV Imagery Using Machine Learning Approaches

Abstract

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.

Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding

“…However, we recognize many other species, from herbaceous annual crops (Grenzdörffer, 2019) all the way up to perennial trees (López‐Granados et al., 2019), are using UAS tools to enhance their small‐plot research and breeding. Forage grasses and silage provide an especially interesting case to look at breeding for the aboveground biomass throughout the season (Alvarez‐Mendoza et al., 2022; de Oliveira et al., 2021; Nakasagga et al., 2022), while cassava ( Manihot esculenta Crantz), peanut ( Arachis hypogaea ), and potato ( Solanum tuberosum L.) UAS research (de Jesus Colwell et al., 2021; Sarkar et al., 2021; Selvaraj et al., 2020) has shown that below ground biomass yield can be estimated from UAS. Each of these species had to develop a unique methodology.…”

Spatio-Temporal Distribution and Trends of Major Agricultural Crops in Romania Using Interactive Geographic Information System Mapping

“…In future research directions, the Landsat Program will be used, which provides researchers with calibrated and at the same time high-resolution spatial data for the land surface of cities and regions worldwide. Landsat-8, which was launched in February 2013, represents one of the most recent terrestrial remote sensing satellites [114][115][116]. The provided data cover a wide spectrum of research areas, for example, land use, agriculture, geology, environmental pollution, etc.…”

Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables

“…Subsequently, the drone images underwent processing with Agisoft Metashape software, culminating in generating spectral ortho mosaics for each band. We then integrated these spectral orthomosaics and harnessed the CIAT Phenoi application to extract vegetation indices (VIs) (Alvarez-Mendoza et al, 2022;Selvaraj et al, 2020). Notably, the most representative index obtained was the normalized vegetation difference index (NDVI), alongside others, as delineated in equations 3 to 10 (Table 1).…”

“…For regional estimates, moderate-resolution imaging spectroradiometer (MODIS) data, along with empirical models extracting wetland area and land surface temperature information, has been employed (Agarwal & Garg, 2007;Collins et al, 2013;Tamiminia et al, 2020). However, the limitations on spatial and temporal resolution in satellite remote sensing for local paddy fields prompt the use of alternative methods like drones to estimate agriculture parameters at local scales (Alvarez-Mendoza et al, 2022). Correlations between CH4 emissions and various multispectral indices, including the widely used normalized difference vegetation index (NDVI), can be established.…”