Reconstructed hyperspectral imaging for in-situ nutrient prediction in pine needles

Reconstructed hyperspectral imaging for in-situ nutrient prediction in pine needles

IntroductionHyperspectral imaging (HSI) is a powerful, non-destructive technology that enables precise analysis of plant nutrient content, which can enhance forestry productivity and quality. However, its high cost and complexity hinder practical field applications.MethodsTo overcome these limitatio...

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Main Authors: Yuanhang Li, Jun Du, Chuangjie Zeng, Yongshan Wu, Junxian Chen, Teng Long, Yongbing Long, Yubin Lan, Xiaoliang Che, Tianyi Liu, Jing Zhao
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Plant Science
Subjects:
hyperspectral image reconstruction
deep learning models
machine learning regression
in situ prediction of pine needle nutrients
precision forestry
Online Access:https://www.frontiersin.org/articles/10.3389/fpls.2025.1630758/full
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https://www.frontiersin.org/articles/10.3389/fpls.2025.1630758/full

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