Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.
Crop yield monitoring demonstrated the potential to improve agricultural productivity through improved crop breeding, farm management and commodity planning. Remote and proximal sensing offer the possibility to cut crop monitoring costs traditionally associated with surveys and censuses. Fraction of...
Guardado en:
Autores principales: | Ephrem Habyarimana, Faheem S Baloch |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/cd3633b59c6e43f18b8c3b1fe1adf9f5 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy
por: Yuki Hamada, et al.
Publicado: (2021) -
Domain-Guided Machine Learning for Remotely Sensed In-Season Crop Growth Estimation
por: George Worrall, et al.
Publicado: (2021) -
Sorghum extract: Phytochemical, proximate, and GC-MS analyses
por: Olayinka O. Ajani, et al.
Publicado: (2021) -
Potential utilization of satellite remote sensing for field-based agricultural studies
por: Keiji Jindo, et al.
Publicado: (2021) -
Remote sensing of seasonal light use efficiency in temperate bog ecosystems
por: R. Tortini, et al.
Publicado: (2017)