Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis
The rapid development of unmanned aerial vehicles (UAVs) and imaging technologies has opened new research avenues for precision agriculture, particularly in the context of plant phenotyping where their utilization has been intensive over the last decade. This review focuses on the interplay of machi...
| Main Authors: | , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
IOP Publishing
2025
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/175812 |
Similar Items: Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis
- 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
- Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches
- Advancing Soil Moisture Prediction Using Satellite and UAV-based Imagery with Machine Learning Models
- Cropping systems mapping through high resolution imagery
- Dataset: Forage grasses in crop fields from ultra-high spatial resolution UAV-based imagery
- Scaling-up plant chlorophyll retrieval from proximal to UAV-borne hyperspectral data using a Gaussian process hybrid model
