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@ARTICLE{Minervini:276443,
author = {Minervini, Massimo and Scharr, Hanno and Tsaftaris,
Sotirios A.},
title = {{I}mage {A}nalysis: {T}he {N}ew {B}ottleneck in {P}lant
{P}henotyping},
journal = {IEEE signal processing magazine},
volume = {32},
number = {4},
issn = {0740-7467},
address = {New York, NY},
publisher = {IEEE},
reportid = {FZJ-2015-06882},
pages = {126 - 131},
year = {2015},
abstract = {Plant phenotyping is the identification of effects on the
phenotype (i.e., the plant appearance and performance) as a
result of genotype differences (i.e., differences in the
genetic code) and the environmental conditions to which a
plant has been exposed [1]?[3]. According to the Food and
Agriculture Organization of the United Nations, large-scale
experiments in plant phenotyping are a key factor in meeting
the agricultural needs of the future to feed the world and
provide biomass for energy, while using less water, land,
and fertilizer under a constantly evolving environment due
to climate change. Working on model plants (such as
Arabidopsis), combined with remarkable advances in
genotyping, has revolutionized our understanding of biology
but has accelerated the need for precision and automation in
phenotyping, favoring approaches that provide quantifiable
phenotypic information that could be better used to link and
find associations in the genotype [4]. While early on, the
collection of phenotypes was manual, currently noninvasive,
imaging-based methods are increasingly being utilized [5],
[6]. However, the rate at which phenotypes are extracted in
the field or in the lab is not matching the speed of
genotyping and is creating a bottleneck [1].},
cin = {IBG-2},
ddc = {620},
cid = {I:(DE-Juel1)IBG-2-20101118},
pnm = {582 - Plant Science (POF3-582)},
pid = {G:(DE-HGF)POF3-582},
typ = {PUB:(DE-HGF)16},
UT = {WOS:000356539400017},
doi = {10.1109/MSP.2015.2405111},
url = {https://juser.fz-juelich.de/record/276443},
}