Application of geographically weighted regression to improve grain yield prediction from unmanned aerial system imagery
Phenological data are important ratings of the in‐season growth of crops, though this assessment is generally limited at both spatial and temporal levels during the crop cycle for large breeding nurseries. Unmanned aerial systems (UAS) have the potential to provide high spatial and temporal resoluti...
| Autores principales: | , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2017
|
| Acceso en línea: | https://hdl.handle.net/10568/165026 |
Ejemplares similares: Application of geographically weighted regression to improve grain yield prediction from unmanned aerial system imagery
- Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding
- Calibration of an Unmanned Aerial Vehicle for Prediction of Herbage Mass in Temperate Pasture
- Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure
- An unmanned aerial vehicle (UAV) technology for estimating leaf N content in rice crops, from multispectral imagery
- Assessment of drip irrigation sub-units using airborne thermal imagery acquired with an Unmanned Aerial Vehicle (UAV)
- Evaluating logistic regression and geographically weighted logistic regression models for predicting orange-fleshed sweet potato adoption intention in Benin