Frontiers in Plant Science,
Год журнала:
2024,
Номер
15
Опубликована: Ноя. 22, 2024
High-throughput
phenotyping
(HTP)
provides
new
opportunities
for
efficiently
dissecting
the
genetic
basis
of
drought-adaptive
traits,
which
is
essential
in
current
wheat
breeding
programs.
The
combined
use
HTP
and
genome-wide
association
(GWAS)
approaches
has
been
useful
assessment
complex
traits
such
as
yield,
under
field
stress
conditions
including
heat
drought.
aim
this
study
was
to
identify
molecular
markers
associated
with
yield
(YLD)
elite
durum
that
could
be
explained
using
hyperspectral
indices
(HSIs)
drought
Mediterranean
environments
Southern
Spain.
HSIs
were
obtained
from
imagery
collected
during
pre-anthesis
anthesis
crop
stages
an
airborne
platform.
A
panel
536
lines
genotyped
by
sequencing
(GBS,
DArTseq)
determine
population
structure,
revealing
a
lack
structure
germplasm.
material
phenotyped
YLD
19
six
growing
seasons
at
two
locations
Andalusia,
southern
GWAS
analysis
identified
740
significant
marker-trait
associations
(MTAs)
across
all
chromosomes,
several
common
HSIs,
can
potentially
integrated
into
Candidate
gene
(CG)
uncovered
genes
related
important
plant
processes
photosynthesis,
regulatory
biological
processes,
abiotic
tolerance.
These
results
are
novel
they
combine
high-resolution
imaging
scale
wheat.
They
also
support
tools
identifying
chromosomal
regions
response
wheat,
pave
way
integration
ABSTRACT
The
global
population
is
placing
unprecedented
demand
on
food
systems,
which
can
be
met
only
through
a
complex
interplay
of
technology,
sustainable
production
intensification
methods
and
climate
resilience.
To
address
such
compounded
requirements,
developing
high‐yielding
crop
varieties
using
precise
plant
breeding
bolstered
with
efficient
nondestructive
trait
documentation
approaches
vital.
High‐throughput
phenotyping
(HTCP)
platforms
have
prominently
emerged
as
mainstream
approach
for
reducing
the
bottleneck
in
programmes.
HTCP
has
potential
to
provide
detailed
quantitative
information
large
populations
under
different
growth
stages
across
diverse
environmental
regimes,
facilitating
accelerated
strategies.
New
imaging
also
enable
characterization
wide
range
above
below‐ground
parameters.
specificity
use
sensors,
automation
data
collection,
large‐scale
handling
systems
accurate
analytical
tools
substantial
role
dynamic
monitoring
big
interpretation.
are
capable
making
measurements
physiological,
morphological,
biochemical
stress
responses
plants.
Developments
sensors
improved
precision,
intervention
unmanned
aerial
vehicles,
robotics,
computed
tomography
machine
learning
given
dramatic
developmental
leap
phenotyping.
This
review
provides
an
avenue
understanding
various
high‐throughput
platforms,
working
principles,
current
developments
contributions
crops
laboratory
field
conditions.
A
comparative
idea
advantages
pitfalls
these
available
help
researchers
choosing
right
technology
suiting
specific
practical
requirements.
Furthermore,
aims
novel
future
prospects
requirements
that
potentially
widen
application
utilization
technologies
agriculture.
Plants,
Год журнала:
2025,
Номер
14(6), С. 907 - 907
Опубликована: Март 14, 2025
Climate
change
intensifies
biotic
and
abiotic
stresses,
threatening
global
crop
productivity.
High-throughput
phenotyping
(HTP)
technologies
provide
a
non-destructive
approach
to
monitor
plant
responses
environmental
offering
new
opportunities
for
both
stress
resilience
breeding
research.
Innovations,
such
as
hyperspectral
imaging,
unmanned
aerial
vehicles,
machine
learning,
enhance
our
ability
assess
traits
under
various
including
drought,
salinity,
extreme
temperatures,
pest
disease
infestations.
These
tools
facilitate
the
identification
of
stress-tolerant
genotypes
within
large
segregating
populations,
improving
selection
efficiency
programs.
HTP
can
also
play
vital
role
by
accelerating
genetic
gain
through
precise
trait
evaluation
hybridization
enhancement.
However,
challenges
data
standardization,
management,
high
costs
equipment,
complexity
linking
phenotypic
observations
improvements
limit
its
broader
application.
Additionally,
variability
genotype-by-environment
interactions
complicate
reliable
selection.
Despite
these
challenges,
advancements
in
robotics,
artificial
intelligence,
automation
are
precision
scalability
analyses.
This
review
critically
examines
dual
assessment
tolerance
performance,
highlighting
transformative
potential
existing
limitations.
By
addressing
key
leveraging
technological
advancements,
significantly
research,
discovery,
parental
selection,
scheme
optimization.
While
current
methodologies
still
face
constraints
fully
translating
insights
into
practical
applications,
continuous
innovation
high-throughput
holds
promise
revolutionizing
ensuring
sustainable
agricultural
production
changing
climate.
The
root
system
architecture
(RSA)
determines
plant
growth
and
yield.
characterization
of
optimal
RSA
discovery
genetic
loci
or
candidate
genes
that
control
traits
are
therefore
important
research
goals.
However,
the
hidden
nature
makes
it
difficult
to
perform
nondestructive,
rapid
analyses
RSA.
In
this
study,
we
developed
an
automated,
high-throughput
phenotyping
platform
(Root-HTP)
a
corresponding
data
processing
pipeline
for
efficient,
large-scale
wheat
(Triticum
aestivum
L.)
This
is
capable
tracking
dynamics
variation
across
all
developmental
stages.
situ
using
Root-HTP
extracted
47
traits,
including
33
novel
in
23
other
crops.
We
used
trait
from
yield
conduct
genome-wide
association
study
(GWAS)
155
accessions,
which
identified
2,650
SNPs
233
quantitative
(QTLs)
associated
with
aspects
architecture.
gene
TaMYB93
was
detected
QTL
tortuosity,
EMS
mutants
confirmed
its
effect
on
wheat.
explored
relationship
between
root-
yield-related
20
root-related
QTLs
were
also
traits.
Furthermore,
have
built
predictive
model
based
18
propose
parsimonious
ideotype
high
yields.
generated
provide
insight
into
support
ideotype-based
breeding
prediction.
Agronomy,
Год журнала:
2025,
Номер
15(5), С. 1157 - 1157
Опубликована: Май 9, 2025
Artificial
intelligence
(AI)
techniques,
particularly
machine
learning
and
deep
learning,
have
shown
great
promise
in
advancing
wheat
crop
monitoring
management.
However,
the
application
of
AI
this
domain
faces
persistent
challenges
that
hinder
its
full
potential.
Key
limitations
include
high
variability
agricultural
environments,
which
complicates
data
acquisition
model
generalization;
scarcity
limited
diversity
labeled
datasets;
substantial
computational
demands
associated
with
training
deploying
models.
Additionally,
difficulties
ground-truth
generation,
cloud
contamination
remote
sensing
imagery,
coarse
spatial
resolution,
“black-box”
nature
models
pose
significant
barriers.
Although
strategies
such
as
augmentation,
semi-supervised
crowdsourcing
been
explored,
they
are
often
insufficient
to
fully
overcome
these
obstacles.
This
review
provides
a
comprehensive
synthesis
recent
advancements
for
applications,
critically
examines
major
unresolved
challenges,
highlights
promising
directions
future
research
aimed
at
bridging
gap
between
academic
development
real-world
practices.
Remote Sensing,
Год журнала:
2025,
Номер
17(10), С. 1735 - 1735
Опубликована: Май 15, 2025
The
potato
is
the
third
most
important
crop
in
world,
and
more
than
375
million
metric
tonnes
of
potatoes
are
produced
globally
on
an
annual
basis.
Potato
Virus
Y
(PVY)
poses
a
significant
threat
to
production
seed
potatoes,
resulting
economic
losses
risks
food
security.
Current
detection
methods
for
PVY
typically
rely
serological
assays
leaves
PCR
tubers;
however,
these
processes
labor-intensive,
time-consuming,
not
scalable.
In
this
proof-of-concept
study,
we
propose
use
unmanned
aerial
vehicles
(UAVs)
integrated
with
hyperspectral
cameras,
including
downwelling
irradiance
sensor,
detect
commercial
growers’
fields.
We
used
400–1000
nm
visible
near-infrared
(Vis-NIR)
camera
trained
several
standard
machine
learning
deep
models
optimized
hyperparameters
curated
dataset.
performance
promising,
convolutional
neural
network
(CNN)
achieving
recall
0.831,
reliably
identifying
PVY-infected
plants.
Notably,
UAV-based
imaging
maintained
levels
comparable
ground-based
methods,
supporting
its
practical
viability.
captures
wide
range
spectral
bands,
many
which
redundant
PVY.
Our
analysis
identified
five
key
regions
that
informative
Two
them
spectrum,
two
one
red-edge
spectrum.
This
research
shows
early-season
feasible
using
UAV
imaging,
offering
potential
minimize
yield
losses.
It
also
highlights
relevant
carry
distinctive
signatures
demonstrates
feasibility
provides
guidance
developing
cost-effective
multispectral
sensors
tailored
task.
Agronomy,
Год журнала:
2025,
Номер
15(6), С. 1315 - 1315
Опубликована: Май 28, 2025
Accurate
grain
yield
(GY)
prediction
is
essential
in
wheat
breeding
to
enhance
selection
and
accelerate
cycles.
This
study
explored
whether
high-throughput
phenotyping
(HTP)
data
collected
from
small
plot
(SP)
trials
can
effectively
predict
GY
outcomes
later-stage
big
(BP)
trials.
Genomic
(G)
were
combined
with
hyperspectral
(H)
multispectral
+
thermal
(M)
imaging
across
the
2022
2023
growing
seasons
at
Plant
Science
Research
Education
Unit,
Citra,
Florida.
A
panel
of
312
genotypes
was
analyzed
using
GBLUP-based
models,
integrating
G
H
M
SP
BP
yield.
models
demonstrated
promising
predictive
ability,
achieving
moderate
within-year
(0.43
0.51)
across-year
(0.43)
accuracies,
while
reached
0.53
0.58
0.45,
respectively.
The
Random
Forest
Regression
(RFR)
model
produced
an
accuracy
0.47
when
SP,
G,
used
2023.
Additionally,
top
25%
specificity
(coincide
index)
evaluated,
showing
up
47–51%
within
a
year
43–45%
between
years
overlap
highest
predicted-yielding
lines
trials,
further
emphasizing
potential
for
early
selection.
These
findings
suggest
that
provide
meaningful
predictions
yields,
enabling
earlier
faster