Mapping mechanization suitability using machine learning, presence data and spatial co-variates: Methodological approach
| Autores principales: | , , , |
|---|---|
| Formato: | Informe técnico |
| Lenguaje: | Inglés |
| Publicado: |
International Maize and Wheat Improvement Center
2025
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/169945 |
Ejemplares similares: Mapping mechanization suitability using machine learning, presence data and spatial co-variates: Methodological approach
- Geospatial land suitability mapping to support scale-appropriate mechanization in Nepal
- Status of farm mechanization in mid-hill Region of Surkhet, Nepal
- Assessing spatial suitability for agricultural mechanization in Ethiopia using expert-based and data-driven approaches
- Spatial-temporal coupling of malaria vector habitat suitability and biting probability
- Monitoring spatial-temporal variations of surface areas of small reservoirs in Ghana’s Upper East Region using Sentinel-2 satellite imagery and machine learning
- Integrating APSIM model with machine learning to predict wheat yield spatial distribution