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: | , , , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/125648 |
Similar Items: Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches
- Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis
- Scaling-up plant chlorophyll retrieval from proximal to UAV-borne hyperspectral data using a Gaussian process hybrid model
- Advancing Soil Moisture Prediction Using Satellite and UAV-based Imagery with Machine Learning Models
- Dataset: Forage grasses in crop fields from ultra-high spatial resolution UAV-based imagery
- Seeing beyond the visible: how drones are revolutionizing rice farming in India and the Philippines
- UAV based survey on portions of the Crocodile, Sabie, and Olifants rivers at Kruger National Park, Limpopo, South Africa to obtain LiDAR, RGB and multispectral imagery
