Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance.

Plant physiology - 1 Jan 2017

Yendrek Craig R, Tomaz Tiago, Montes Christopher M, Cao Youyuan, Morse Alison M, Brown Patrick J, McIntyre Lauren M, Leakey Andrew D B, Ainsworth Elizabeth A

Abstract excerpt

High-throughput, noninvasive field phenotyping has revealed genetic variation in crop morphological, developmental, and agronomic traits, but rapid measurements of the underlying physiological and biochemical traits are needed to fully understand genetic variation in plant-environment interactions. This study tested the application of leaf hyperspectral reflectance (λ = 500-2,400 nm) as a high-throughput...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Chimera
Share to Chlorophyll

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to High-Throughput Screening Assays

Share to Least-Squares Analysis

Share to Nitrogen

Share to Photosynthesis

Share to Plant Leaves

Share to Zea mays

Source

Open in PubMed Open full text

Identifiers

PMID: 28049858
DOI: 10.1104/pp.16.01447
PMCID: PMC5210743

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

High-Throughput Phenotyping of Maize Leaf Physiological and Biochemical Traits Using Hyperspectral Reflectance.PMID 28049858Read ↗

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges.Semantic proximity Read ↗

Spectral Phenotyping of Physiological and Anatomical Leaf Traits Related with Maize Water Status.Semantic proximity Read ↗

Machine Learning Techniques for Predicting Crop Photosynthetic Capacity from Leaf Reflectance Spectra.Semantic proximity Read ↗

Generalizability and transferability of machine learning models using hyperspectral reflectance data for maize traits.Semantic proximity Read ↗

Near-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments.Semantic proximity Read ↗

Conventional and hyperspectral time-series imaging of maize lines widely used in field trials.Semantic proximity Read ↗

A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression.Semantic proximity Read ↗

Phenotyping for abiotic stress tolerance in maize.Semantic proximity Read ↗

Modern phenomics to empower holistic crop science, agronomy, and breeding research.Semantic proximity Read ↗

High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response.Semantic proximity Read ↗

Applications of hyperspectral imaging in plant phenotyping.Semantic proximity Read ↗

A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.