Advancing food security: rice yield estimation framework using time-series satellite data & machine learning
Timely and accurately estimating rice yields is crucial for supporting food security management, agricultural policy development, and climate change adaptation in rice-producing countries such as Bangladesh. To address this need, this study introduced a workflow to enable timely and precise rice yie...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/169956 |
Similar Items: Advancing food security: rice yield estimation framework using time-series satellite data & machine learning
- Advancing Soil Moisture Prediction Using Satellite and UAV-based Imagery with Machine Learning Models
- Predicting aflatoxin risk in maize using machine learning and satellite data in East and Southern Africa
- A machine learning algorithm for mapping small reservoirs using Sentinel-2 satellite imagery in Google Earth Engine
- Insights from above: Satellite and machine learning for agriculture and food security in Myanmar
- Predicting malaria prevalence with machine learning models using satellite-based climate information: technical report
- Monitoring spatial-temporal variations of surface areas of small reservoirs in Ghana’s Upper East Region using Sentinel-2 satellite imagery and machine learning