Impedance-Driven Decoupling Water–Nitrogen Stress in Wheat: A Parallel Machine Learning Framework Leveraging Leaf Electrophysiology

Impedance-Driven Decoupling Water–Nitrogen Stress in Wheat: A Parallel Machine Learning Framework Leveraging Leaf Electrophysiology

Accurately monitoring coupled water–nitrogen stress is critical for wheat (<i>Triticum aestivum</i> L.) productivity under climate change. This study developed a machine learning framework utilizing multimodal leaf electrophysiological signals––intrinsic resistance, impedance, capacitive...

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Bibliographic Details
Main Authors: Shuang Zhang, Xintong Du, Bo Zhang, Yanyou Wu, Xinyi Yang, Xinkang Hu, Chundu Wu
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Agronomy
Subjects:
wheat abiotic stress
plant electrophysiology
machine learning
precision agriculture
water stress
nitrogen stress
Online Access:https://www.mdpi.com/2073-4395/15/7/1612
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