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

Phenomic data-driven biological prediction of maize through field-based high-throughput phenotyping integration with genomic data.

Journal of experimental botany - 13 Sept 2023

Adak Alper, Kang Myeongjong, Anderson Steven L, Murray Seth C, Jarquin Diego, Wong Raymond K W, Katzfuß Matthias

Abstract excerpt

High-throughput phenotyping (HTP) has expanded the dimensionality of data in plant research; however, HTP has resulted in few novel biological discoveries to date. Field-based HTP (FHTP), using small unoccupied aerial vehicles (UAVs) equipped with imaging sensors, can be deployed routinely to monitor segregating plant population interactions with the environment under biologically meaningful conditions. Here,...

Read the complete abstract on PubMed

Topics

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

Share to Genome-Wide Association Study
Share to Genomics

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Phenomics

Share to Phenotype

Share to Zea mays

Source

Identifiers

PMID: 37279568
DOI: 10.1093/jxb/erad216

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Phenomic data-driven biological prediction of maize through field-based high-throughput phenotyping integration with genomic data.PMID 37279568Read ↗

Phenomics-derived temporal maize health and environmental index enhance physiology-informed genomic prediction of yield across environments.Semantic proximity Read ↗

Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions.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 ↗

Unoccupied aerial systems discovered overlooked loci capturing the variation of entire growing period in maize.Semantic proximity Read ↗

Using high-throughput multiple optical phenotyping to decipher the genetic architecture of maize drought tolerance.Semantic proximity Read ↗

Deciphering temporal growth patterns in maize: integrative modeling of phenotype dynamics and underlying genomic variations.Semantic proximity Read ↗

Integration of high-throughput phenotyping, GWAS, and predictive models reveals the genetic architecture of plant height in maize.Semantic proximity Read ↗

Validation of functional polymorphisms affecting maize plant height by unoccupied aerial systems discovers novel temporal phenotypes.Semantic proximity Read ↗

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

Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America.Semantic proximity Read ↗

Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.Semantic proximity Read ↗

High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth.Semantic proximity Read ↗

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