paper
2023
Applications & impacts
Application study
Classical ML (trees, GP, linear, kernels)Seasonal to annual Journal
2023-09-05
008
Sep 2023
Subin Ha, Yong-Tak Kim, Eun‐Soon Im et al.
International Journal of Biometeorology
Machine learning models link meteorological variables to rice yield in Korea, with the best algorithm identified for crop yield prediction.
Impacts of meteorological variables and machine learning algorithms on rice yield prediction in Korea
In one sentence. Machine learning models link meteorological variables to rice yield in Korea, with the best algorithm identified for crop yield prediction.
Labels
- Topic
- Applications & impacts
- Type
- Application study
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Local / station
- Data
- Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1007/s00484-023-02544-x OpenAlex:W4386413101
- Added
- 2026-10-22
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