High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel

High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel

Abstract Background Hyperspectral reflectance data in the visible, near infrared and shortwave infrared range (VIS–NIR–SWIR, 400–2500 nm) are commonly used to nondestructively measure plant leaf properties. We investigated the usefulness of VIS–NIR–SWIR as a high-throughput tool to measure six leaf...

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Bibliographic Details
Main Authors: Yufeng Ge, Abbas Atefi, Huichun Zhang, Chenyong Miao, Raghuprakash Kastoori Ramamurthy, Brandi Sigmon, Jinliang Yang, James C. Schnable
Format: Article
Language:English
Published: BMC 2019-06-01
Series:Plant Methods
Subjects:
Hyperspectral
Plant phenotyping
Partial least squares regression
Support vector regression
Machine learning
Vegetation indices
Online Access:http://link.springer.com/article/10.1186/s13007-019-0450-8

Internet

http://link.springer.com/article/10.1186/s13007-019-0450-8

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