An Interpretable Machine Learning Framework for Next-Day Frost Forecasting in Tea Plantations Using Multi-Source Meteorological Data

Spring frosts pose a major threat to tea production, causing severe damage to tender spring buds and substantial economic losses. To support timely frost protection measures, this study develops an interpretable machine learning framework for next-day frost forecasting in a tea plantation in Danyang...

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Bibliografische gegevens

Hoofdauteurs:	Zhongqiu Zhang, Pingping Li, Jizhang Wang
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	MDPI AG 2026-03-01
Reeks:	Horticulturae
Onderwerpen:	spring frost forecasting tea plantation machine learning deep learning feature selection XGBoost
Online toegang:	https://www.mdpi.com/2311-7524/12/3/392
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