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@ARTICLE{Thomas:852606,
author = {Thomas, Stefan and Behmann, Jan and Steier, Angelina and
Kraska, Thorsten and Muller, Onno and Rascher, Uwe and
Mahlein, Anne-Katrin},
title = {{Q}uantitative assessment of disease severity and rating of
barley cultivars based on hyperspectral imaging in a
non-invasive, automated phenotyping platform},
journal = {Plant methods},
volume = {14},
number = {1},
issn = {1746-4811},
address = {London},
publisher = {BioMed Central},
reportid = {FZJ-2018-05510},
pages = {45},
year = {2018},
abstract = {BackgroundPhenotyping is a bottleneck for the development
of new plant cultivars. This study introduces a new
hyperspectral phenotyping system, which combines the high
throughput of canopy scale measurements with the advantages
of high spatial resolution and a controlled measurement
environment. Furthermore, the measured barley canopies were
grown in large containers (called Mini-Plots), which allow
plants to develop field-like phenotypes in greenhouse
experiments, without being hindered by pot size.ResultsSix
barley cultivars have been investigated via hyperspectral
imaging up to 30 days after inoculation with powdery mildew.
With a high spatial resolution and stable measurement
conditions, it was possible to automatically quantify
powdery mildew symptoms through a combination of Simplex
Volume Maximization and Support Vector Machines. Detection
was feasible as soon as the first symptoms were visible for
the human eye during manual rating. An accurate assessment
of the disease severity for all cultivars at each
measurement day over the course of the experiment was
realized. Furthermore, powdery mildew resistance based
necrosis of one cultivar was detected as well.ConclusionThe
hyperspectral phenotyping system combines the advantages of
field based canopy level measurement systems (high
throughput, automatization, low manual workload) with those
of laboratory based leaf level measurement systems (high
spatial resolution, controlled environment, stable
conditions for time series measurements). This allows an
accurate and objective disease severity assessment without
the need for trained experts, who perform visual rating, as
well as detection of disease symptoms in early stages.
Therefore, it is a promising tool for plant resistance
breeding.},
cin = {IBG-2},
ddc = {580},
cid = {I:(DE-Juel1)IBG-2-20101118},
pnm = {582 - Plant Science (POF3-582)},
pid = {G:(DE-HGF)POF3-582},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:29930695},
UT = {WOS:000434911900001},
doi = {10.1186/s13007-018-0313-8},
url = {https://juser.fz-juelich.de/record/852606},
}