A Meteorological Data Quality Control Framework for Tea Plantations Using Association Rules Mined from ERA5 Reanalysis Data
Meteorological data from automatic weather stations (AWS) in tea plantations is critical for agricultural management, but is often compromised by sensor errors and physical implausibilities that traditional quality control (QC) methods fail to detect. This study proposes a novel, meteorologically in...
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| Principais autores: | , , |
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| Format: | Artigo |
| Sprog: | Inglês |
| Udgivet: |
MDPI AG
2026-01-01
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| Serier: | Agriculture |
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| Online adgang: | https://www.mdpi.com/2077-0472/16/2/226 |
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