Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Kultūraugu ražas prognozēšana× | Precīzā lauksaimniecība ar NDVI× | |
|---|---|---|
| Nozare | Agronomija | Agronomija |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2015 | 1973 |
| Autors≠ | Agronomic research institutions (CIMMYT, ICRISAT, IRRI) | John W. Rouse, Richard H. Haas |
| Tips≠ | Analytical and predictive pipeline | Geospatial monitoring pipeline |
| Pirmavots≠ | Lobell, D. B., Thau, D., Seifert, C., Engle, E., & Shadow, B. (2015). A regional crop yield forecasting system for Sub-Saharan Africa. Global Food Security, 5, 6-15. link ↗ | Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In Third Earth Resources Technology Satellite symposium, Washington, DC. link ↗ |
| Citi nosaukumi | Yield forecasting, Harvest prediction, Yield monitoring | NDVI remote sensing, Vegetation index monitoring, Satellite crop monitoring |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Crop Yield Estimation is an analytical and predictive pipeline for forecasting final crop yield before harvest or monitoring yield accumulation during the growing season. Developed by agronomic research centers (CIMMYT, ICRISAT, IRRI), this method combines field observations, environmental data, and statistical models to predict grain or biomass output, informing harvest planning, market decisions, and performance evaluation. | Precision Agriculture with NDVI is a geospatial monitoring pipeline for assessing crop vigor, health, and productivity using the Normalized Difference Vegetation Index (NDVI) derived from satellite or drone imagery. Developed by Rouse and colleagues (1973), this method enables rapid, non-destructive assessment of spatial variation in crop performance and informs variable-rate management decisions. |
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