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High-resolution, time-lapse imaging for ecosystem-scale phenotyping in the field

Brown, Timothy; Zimmermann, Christopher; Panneton, Whitney; Noah, Nina; Borevitz, Justin

Description

The high spatial and temporal resolution of data required for high-throughput phenotyping has typically been all but impossible to obtain in field populations of plants. When studies of individual and population genetic variation and microclimate sensor data are combined with phenology data, a landscape-level view of how populations respond to changing environments can be obtained. This chapter will discuss the development of a multi-billion pixel ("gigapixel") camera system that enables the...[Show more]

dc.contributor.authorBrown, Timothy
dc.contributor.authorZimmermann, Christopher
dc.contributor.authorPanneton, Whitney
dc.contributor.authorNoah, Nina
dc.contributor.authorBorevitz, Justin
dc.date.accessioned2015-12-13T22:41:09Z
dc.identifier.isbn978-1-61779-994-5
dc.identifier.urihttp://hdl.handle.net/1885/78380
dc.description.abstractThe high spatial and temporal resolution of data required for high-throughput phenotyping has typically been all but impossible to obtain in field populations of plants. When studies of individual and population genetic variation and microclimate sensor data are combined with phenology data, a landscape-level view of how populations respond to changing environments can be obtained. This chapter will discuss the development of a multi-billion pixel ("gigapixel") camera system that enables the collection of phenology data at up to hourly intervals from in situ plant populations. Such gigapixel time-lapse imaging systems represent a key technological advancement for enabling high-throughput phenotyping in field settings. Gigapixel resolution image datasets allow researchers to record life-history (phenology) data across an entire landscape over multiple seasons. Image data can be wirelessly transmitted to a remote server where it can be accessed online within hours of capture. The time-lapse panoramic images are browsable through an interactive web tool that can be used to compare plant phenology with environmental sensor data collected simultaneously from the field. The high spatial and temporal resolution data can be used to identify individual plant phenology, which can in turn be used to generate complete population level phenotype data. The Gigavision platform is especially powerful when coupled with next-generation population genomic analysis. The Gigavision system permits the rapid identification of the phenotypes and genotypes responding to natural selection in wild populations.
dc.publisherHumana Press
dc.relation.ispartofHigh-Throughput Phenotyping in Plants: Methods and Protocols
dc.relation.isversionof1st Edition
dc.subjectKeywords: article; camera; computer program; computer system; cost; ecosystem; genotype; information processing; landscape; lens; natural selection; phenology; phenotype; priority journal; reliability; remote sensing; sensor; time lapse imaging; time series analysi Gigapan; High-throughput phenotyping; Landscape ecology; Near remote sensing; Phenology; Phenomics; Time-lapse
dc.titleHigh-resolution, time-lapse imaging for ecosystem-scale phenotyping in the field
dc.typeBook chapter
local.description.notesImported from ARIES
dc.date.issued2012
local.identifier.absfor060411 - Population, Ecological and Evolutionary Genetics
local.identifier.ariespublicationf5625xPUB7035
local.type.statusPublished Version
local.contributor.affiliationBrown, Timothy, College of Medicine, Biology and Environment, ANU
local.contributor.affiliationZimmermann, Christopher, Salt Lake City UT
local.contributor.affiliationPanneton, Whitney , University of Chicago
local.contributor.affiliationNoah, Nina, University of Chicago
local.contributor.affiliationBorevitz, Justin, College of Medicine, Biology and Environment, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage71
local.bibliographicCitation.lastpage96
local.identifier.doi10.1007/978-1-61779-995-2-7
local.identifier.absseo970106 - Expanding Knowledge in the Biological Sciences
dc.date.updated2016-02-24T09:32:39Z
local.bibliographicCitation.placeofpublicationNew York
local.identifier.scopusID2-s2.0-84871890719
CollectionsANU Research Publications

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