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Related Experiment Video

Updated: Oct 26, 2025

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges.

Marcin Grzybowski^1,2, Nuwan K Wijewardane^3,4, Abbas Atefi³

¹Center for Plant Science Innovation and Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.

Plant Communications

|July 30, 2021

Summary

Hyperspectral imaging offers a non-destructive method for plant phenotyping, enabling geneticists to study traits previously limited by low throughput. This technology can accelerate the discovery of genes controlling natural variation in plant biochemistry and physiology.

Keywords:

hyperspectral reflectance maize phenotyping quantitative genetics

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Area of Science:

Plant Science
Genetics
Remote Sensing

Background:

Many crucial plant traits for breeders and geneticists are difficult to measure due to low throughput or destructive methods.
This limitation hinders the study of natural variation in traits like nutrient abundance, metabolite levels, and photosynthetic capacity in large populations.

Purpose of the Study:

To review advances in hyperspectral reflectance data for plant phenotyping.
To assess the potential benefits and challenges of applying hyperspectral imaging in plant genetics.
To evaluate the heritability of traits predicted by hyperspectral data in maize.

Main Methods:

Review of recent studies on hyperspectral reflectance for plant phenotyping.
Evaluation of existing models for estimating six traits in maize using hyperspectral data on new datasets.
Estimation of heritability for predicted trait values.

Main Results:

Hyperspectral reflectance data shows potential for estimating various plant traits.
Models for trait estimation from hyperspectral data were evaluated on new maize datasets.
Predicted trait values derived from hyperspectral data were found to be heritable.

Conclusions:

Hyperspectral reflectance-based phenotyping can overcome throughput limitations in plant genetics.
This approach can accelerate research into the genetic control of natural variation in plant biochemical and physiological traits.
Further adoption of hyperspectral phenotyping holds significant promise for plant breeding and genetics.