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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Bibliografiske detaljer

Principais autores:	Zhongqiu Zhang, Pingping Li, Jizhang Wang
Format:	Artigo
Sprog:	Inglês
Udgivet:	MDPI AG 2026-03-01
Serier:	Horticulturae
Fag:	spring frost forecasting tea plantation machine learning deep learning feature selection XGBoost
Online adgang:	https://www.mdpi.com/2311-7524/12/3/392
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