Discover Community Create You

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

Automated hyperspectral vegetation index derivation using a hyperparameter optimisation framework for high-throughput plant phenotyping.

The New phytologist - 1 Mar 2022

Koh Joshua C O, Banerjee Bikram P, Spangenberg German, Kant Surya

Abstract excerpt

Hyperspectral vegetation indices (VIs) are widely deployed in agriculture remote sensing and plant phenotyping to estimate plant biophysical and biochemical traits. However, existing VIs consist mainly of simple two-band indices that limit the net performance and often do not generalise well for traits other than those for which they were originally designed. We present an automated hyperspectral vegetation index...

Read the complete abstract on PubMed

Topics

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

Share to Chlorophyll
Share to Least-Squares Analysis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Phenotype

Share to Plant Leaves

Share to Triticum

Source

Open in PubMed Open full text

Identifiers

PMID: 34997968
DOI: 10.1111/nph.17947
PMCID: PMC9305872

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Automated hyperspectral vegetation index derivation using a hyperparameter optimisation framework for high-throughput plant phenotyping.PMID 34997968Read ↗

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

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

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

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

Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat.Semantic proximity Read ↗

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

Field-based high-throughput phenotyping enhances phenomic and genomic predictions for grain yield and plant height across years in maize.Semantic proximity Read ↗

Integration of physiological and remote sensing traits for improved genomic prediction of wheat yield.Semantic proximity Read ↗

Machine Learning Approach for Prescriptive Plant Breeding.Semantic proximity Read ↗

Simultaneously prediction of multiple wheat leaf phenotypes using hyperspectral imaging with multi-task machine and deep learning.Semantic proximity Read ↗

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

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

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