Enhancing Winter Wheat Soil–Plant Analysis Development Value Prediction through Evaluating Unmanned Aerial Vehicle Flight Altitudes, Predictor Variable Combinations, and Machine Learning Algorithms
Plants,
Год журнала:
2024,
Номер
13(14), С. 1926 - 1926
Опубликована: Июль 12, 2024
Monitoring
winter
wheat
Soil-Plant
Analysis
Development
(SPAD)
values
using
Unmanned
Aerial
Vehicles
(UAVs)
is
an
effective
and
non-destructive
method.
However,
predicting
SPAD
during
the
booting
stage
less
accurate
than
other
growth
stages.
Existing
research
on
UAV-based
value
prediction
has
mainly
focused
low-altitude
flights
of
10-30
m,
neglecting
potential
benefits
higher-altitude
flights.
The
study
evaluates
predictions
Vegetation
Indices
(VIs)
from
UAV
images
at
five
different
altitudes
(i.e.,
20,
40,
60,
80,
100,
120
respectively,
a
DJI
P4-Multispectral
as
example,
with
resolution
1.06
to
6.35
cm/pixel).
Additionally,
we
compare
predictive
performance
various
predictor
variables
(VIs,
Texture
(TIs),
Discrete
Wavelet
Transform
(DWT))
individually
in
combination.
Four
machine
learning
algorithms
(Ridge,
Random
Forest,
Support
Vector
Regression,
Back
Propagation
Neural
Network)
are
employed.
results
demonstrate
comparable
between
m
(with
cm/pixel)
20
This
finding
significantly
improves
efficiency
monitoring
since
flying
UAVs
higher
greater
coverage,
thus
reducing
time
needed
for
scouting
when
same
heading
overlap
side
rates.
overall
trend
accuracy
follows:
VIs
+
TIs
DWT
>
DWT.
set
obtains
frequency
information
(DWT),
compensating
limitations
set.
enhances
effectiveness
agricultural
practices.
Язык: Английский
Coupling Image-Fusion Techniques with Machine Learning to Enhance Dynamic Monitoring of Nitrogen Content in Winter Wheat from UAV Multi-Source
Agriculture,
Год журнала:
2024,
Номер
14(10), С. 1797 - 1797
Опубликована: Окт. 12, 2024
Plant
nitrogen
concentration
(PNC)
is
a
key
indicator
reflecting
the
growth
and
development
status
of
plants.
The
timely
accurate
monitoring
plant
PNC
great
significance
for
refined
management
crop
nutrition
in
field.
rapidly
developing
sensor
technology
provides
powerful
means
PNC.
Although
RGB
images
have
rich
spatial
information,
they
lack
spectral
information
red
edge
near
infrared
bands,
which
are
more
sensitive
to
vegetation.
Conversely,
multispectral
offer
superior
resolution
but
typically
lag
detail
compared
images.
Therefore,
purpose
this
study
improve
accuracy
efficiency
by
combining
advantages
through
image-fusion
technology.
This
was
based
on
booting,
heading,
early-filling
stages
winter
wheat,
synchronously
acquiring
UAV
MS
data,
using
Gram–Schmidt
(GS)
principal
component
(PC)
methods
generate
fused
evaluate
them
with
multiple
image-quality
indicators.
Subsequently,
models
predicting
wheat
were
constructed
machine-selection
algorithms
such
as
RF,
GPR,
XGB.
results
show
that
RGB_B1
image
contains
richer
details
other
bands.
GS
method
PC
method,
performance
fusing
high-resolution
band
optimal.
After
fusion,
correlation
between
vegetation
indices
(VIs)
has
been
enhanced
varying
degrees
different
periods,
significantly
enhancing
response
ability
To
comprehensively
assess
potential
estimating
PNC,
fully
before
after
fusion
machine
learning
Random
Forest
(RF),
Gaussian
Process
Regression
(GPR),
eXtreme
Gradient
Boosting
(XGB).
model
established
high
stability
single
period,
varieties,
treatments,
making
it
better
than
image.
most
significant
enhancements
during
booting
stages,
particularly
RF
algorithm,
achieved
an
18.8%
increase
R2,
26.5%
RPD,
19.7%
decrease
RMSE.
effective
technical
dynamic
nutritional
strong
support
precise
nutrition.
Язык: Английский
UAV Remote Sensing Technology for Wheat Growth Monitoring in Precision Agriculture: Comparison of Data Quality and Growth Parameter Inversion
Agronomy,
Год журнала:
2025,
Номер
15(1), С. 159 - 159
Опубликована: Янв. 10, 2025
The
quality
of
the
image
data
and
potential
to
invert
crop
growth
parameters
are
essential
for
effectively
using
unmanned
aerial
vehicle
(UAV)-based
sensor
systems
in
precision
agriculture
(PA).
However,
existing
research
falls
short
providing
a
comprehensive
examination
inversion
parameters,
there
is
still
ambiguity
regarding
how
affects
potential.
Therefore,
this
study
explored
application
RGB
multispectral
(MS)
images
acquired
from
three
lightweight
UAV
platforms
realm
PA:
DJI
Mavic
2
Pro
(M2P),
Phantom
4
Multispectral
(P4M),
3
(M3M).
reliability
pixel-scale
was
evaluated
based
on
assessment
metrics,
winter
wheat
above-ground
biomass
(AGB),
plant
nitrogen
content
(PNC)
soil
analysis
development
(SPAD),
were
inverted
machine
learning
models
multi-source
features
at
plot
scale.
results
indicated
that
M3M
outperformed
M2P,
while
MS
marginally
superior
P4M.
Nevertheless,
these
advantages
did
not
improve
accuracy
Spectral
(SFs)
derived
P4M-based
demonstrated
significant
AGB
(R2
=
0.86,
rRMSE
27.47%),
SFs
M2P-based
camera
exhibited
best
performance
SPAD
0.60,
7.67%).
Additionally,
combining
spectral
textural
yielded
highest
PNC
0.82,
14.62%).
This
clarified
prevalent
mounted
PA
their
influence
parameter
potential,
offering
guidance
selecting
appropriate
sensors
monitoring
key
parameters.
Язык: Английский
Precision Agriculture: Temporal and Spatial Modeling of Wheat Canopy Spectral Characteristics
Agriculture,
Год журнала:
2025,
Номер
15(3), С. 326 - 326
Опубликована: Фев. 1, 2025
This
study
investigates
the
dynamic
changes
in
wheat
canopy
spectral
characteristics
across
seven
critical
growth
stages
(Tillering,
Pre-Jointing,
Jointing,
Post-Jointing,
Booting,
Flowering,
and
Ripening)
using
UAV-based
multispectral
remote
sensing.
By
analyzing
four
key
bands—green
(G),
red
(R),
red-edge
(RE),
near-infrared
(NIR)—and
their
combinations,
we
identify
features
that
reflect
activity,
health,
structure.
Results
show
green
band
is
highly
sensitive
to
chlorophyll
activity
low
coverage
during
Tillering
stage,
while
NIR
captures
structural
complexity
density
Jointing
Booting
stages.
The
combination
of
G
bands
reveals
increased
concentration
RE
effectively
detects
plant
senescence
reduced
uniformity
ripening
stage.
Time-series
analysis
data
improves
accuracy
stage
identification,
with
offering
insights
into
inflection
points.
Spatially,
demonstrates
potential
for
identifying
field-level
anomalies,
such
as
water
stress
or
disease,
providing
actionable
targeted
interventions.
comprehensive
spatio-temporal
monitoring
framework
crop
management
offers
a
cost-effective,
precise
solution
disease
prediction,
yield
forecasting,
resource
optimization.
paves
way
integrating
UAV
sensing
precision
agriculture
practices,
future
research
focusing
on
hyperspectral
integration
enhance
models.
Язык: Английский
Data Integration Based on UAV Multispectra and Proximal Hyperspectra Sensing for Maize Canopy Nitrogen Estimation
Fuhao Lu,
Sun Hai-ming,
Tao Leí
и другие.
Remote Sensing,
Год журнала:
2025,
Номер
17(8), С. 1411 - 1411
Опубликована: Апрель 16, 2025
Nitrogen
(N)
is
critical
for
maize
(Zea
mays
L.)
growth
and
yield,
necessitating
precise
estimation
of
canopy
nitrogen
concentration
(CNC)
to
optimize
fertilization
strategies.
Remote
sensing
technologies,
such
as
proximal
hyperspectral
sensors
unmanned
aerial
vehicle
(UAV)-based
multispectral
imaging,
offer
promising
solutions
non-destructive
CNC
monitoring.
This
study
evaluates
the
effectiveness
sensor
UAV-based
data
integration
in
estimating
spring
during
key
stages
(from
11th
leaf
stage,
V11,
Silking
R1).
Field
experiments
were
conducted
collect
(20
vegetation
indices
[MVI]
24
texture
[MTI]),
(24
[HVI]
20
characteristic
[HCI]),
alongside
laboratory
analysis
120
samples.
The
Boruta
algorithm
identified
important
features
from
integrated
datasets,
followed
by
correlation
between
these
Random
Forest
(RF)-based
modeling,
with
SHAP
(SHapley
Additive
exPlanations)
values
interpreting
feature
contributions.
Results
demonstrated
model
achieved
high
accuracy
Computational
Efficiency
(CE)
(R2
=
0.879,
RMSE
0.212,
CE
2.075),
outperforming
HVI-HCI
0.832,
0.250,
=2.080).
Integrating
yields
a
high-precision
0.903,
0.190),
standalone
models
2.73%
8.53%,
respectively.
However,
decreased
1.93%
1.68%,
Key
included
red-edge
(NREI,
NDRE,
CI)
parameters
(R1m),
(SR,
PRI)
spectral
(SDy,
Rg)
exhibited
varying
directional
impacts
on
using
RF.
Together,
findings
highlight
that
Boruta–RF–SHAP
strategy
demonstrates
synergistic
value
integrating
multi-source
enhancing
management
cultivation.
Язык: Английский
OBM-RFEcv: An adaptive ensemble model for monitoring key growth indicators of Gerbera using multi-spectral image fusion features
PLoS ONE,
Год журнала:
2025,
Номер
20(5), С. e0322851 - e0322851
Опубликована: Май 20, 2025
This
study
aims
to
address
the
challenge
of
monitoring
Plant
Height
(PH),
SPAD,
Leaf
Area
Index
(LAI),
and
Above-Ground
Biomass
(AGB)
in
Gerbera
under
greenhouse
cultivation
conditions.
We
initially
gathered
multi-spectral
images
corresponding
ground
truth
data
these
parameters
at
various
growth
stages
using
a
low-altitude
UAV.
From
collected
images,
we
derived
five
Vegetation
Indices
(VIs):
NDVI,
GNDVI,
LCI,
NDRE,
OSAVI,
extracted
their
textural
features
as
fusion
features.
An
adaptive
ensemble
model,
OBM-RFEcv,
was
then
developed
by
integrating
six
base
models
(Linear
Regression,
Decision
Tree
Regressor,
Random
Forest
XGBoost
Support
Vector
Regressor)
with
Recursive
Feature
Elimination
(RFE)
predict
key
indicators.
The
results
indicate
that
OBM-RFEcv
model
outperforms
other
when
VIs,
particularly
test
dataset,
where
it
achieved
highest
accuracy
for
PH
(NDVI),
SPAD
(GNDVI),
LAI
AGB
(NDRE)
R
2
values
0.92,
0.90,
0.89,
0.93,
respectively.
root
mean
square
error
(RMSE)
were
0.04,
0.07,
0.08,
respectively,
showing
improvements
over
best
individual
0.01,
0.03,
0.09
,
reductions
RMSE
These
findings
confirm
based
on
image
fusion,
effectively
monitors
indicators
Gerbera,
providing
non-invasive
precise
method
crop
monitoring.
Язык: Английский
Precision estimation of winter wheat crop height and above-ground biomass using unmanned aerial vehicle imagery and oblique photoghraphy point cloud data
Frontiers in Plant Science,
Год журнала:
2024,
Номер
15
Опубликована: Сен. 18, 2024
Introduction
Crop
height
and
above-ground
biomass
(AGB)
serve
as
crucial
indicators
for
monitoring
crop
growth
estimating
grain
yield.
Timely
accurate
acquisition
of
wheat
AGB
data
is
paramount
guiding
agricultural
production.
However,
traditional
methods
suffer
from
drawbacks
such
time-consuming,
laborious
destructive
sampling.
Methods
The
current
approach
to
using
unmanned
aerial
vehicles
(UAVs)
remote
sensing
relies
solely
on
spectral
data,
resulting
in
low
accuracy
estimation.
This
method
fails
address
the
ill-posed
inverse
problem
mapping
two-dimensional
three-dimensional
issues
related
saturation.
To
overcome
these
challenges,
RGB
multispectral
sensors
mounted
UAVs
were
employed
acquire
image
data.
five-directional
oblique
photography
technique
was
utilized
construct
point
cloud
extracting
height.
Results
Discussion
study
comparatively
analyzed
potential
mean
Accumulated
Incremental
Height
(AIH)
extraction.
Utilizing
Vegetation
Indices
(VIs),
AIH
their
feature
combinations,
models
including
Random
Forest
Regression
(RFR),
eXtreme
Gradient
Boosting
(XGBoost),
Trees
(GBRT),
Support
Vector
(SVR)
Ridge
(RR)
constructed
estimate
winter
AGB.
research
results
indicated
that
performed
well
extraction,
with
minimal
differences
between
95%
measured
values
observed
across
various
stages
wheat,
yielding
R
2
ranging
0.768
0.784.
Compared
individual
features,
combination
multiple
features
significantly
improved
model’s
accuracy.
incorporation
helps
alleviate
effects
Coupling
VIs
increases
0.694-0.885
only
0.728-0.925.
In
comparing
performance
five
machine
learning
algorithms,
it
discovered
based
decision
trees
superior
other
algorithms.
Among
them,
RFR
algorithm
optimally,
0.9
0.93.
Conclusion
conclusion,
leveraging
multi-source
algorithms
overcomes
limitations
methods,
offering
a
technological
reference
precision
agriculture
management
decision-making.
Язык: Английский
Impact of remote sensing data fusion on agriculture applications: A review
European Journal of Agronomy,
Год журнала:
2024,
Номер
164, С. 127478 - 127478
Опубликована: Дек. 18, 2024
Язык: Английский
Winter Wheat SPAD Prediction Based on Multiple Preprocessing, Sequential Module Fusion, and Feature Mining Methods
櫻井 克年,
Xiangxiang Su,
Yue Hu
и другие.
Agriculture,
Год журнала:
2024,
Номер
14(12), С. 2258 - 2258
Опубликована: Дек. 10, 2024
Chlorophyll
is
a
crucial
indicator
for
monitoring
crop
growth
and
assessing
nutritional
status.
Hyperspectral
remote
sensing
plays
an
important
role
in
precision
agriculture,
offering
non-destructive
approach
to
predicting
leaf
chlorophyll.
However,
canopy
spectra
often
face
background
noise
data
redundancy
challenges.
To
tackle
these
issues,
this
study
develops
integrated
processing
strategy
incorporating
multiple
preprocessing
techniques,
sequential
module
fusion,
feature
mining
methods.
Initially,
the
original
spectrum
(OS)
from
2021,
2022,
fusion
year
underwent
through
Fast
Fourier
Transform
(FFT)
smoothing,
scattering
correction
(MSC),
first
derivative
(FD),
second
(SD).
Secondly,
was
conducted
using
Competitive
Adaptive
Reweighted
Sampling
(CARS),
Iterative
Retention
of
Information
Variables
(IRIV),
Principal
Component
Analysis
(PCA)
based
on
optimal
order
data.
Finally,
Partial
Least
Squares
Regression
(PLSR)
used
construct
prediction
model
winter
wheat
SPAD
compare
effects
different
years
stages.
The
findings
show
that
FFT-MSC
(firstly
pre-processing
FFT,
secondly
secondary
FFT
spectral
MSC)
effectively
reduced
issues
such
as
noisy
signals
baseline
drift.
FFT-MSC-IRIV-PLSR
(based
combined
preprocessed
data,
screening
IRIV,
then
combining
with
PLSR
model)
predicts
highest
overall
accuracy,
R2
0.79–0.89,
RMSE
4.51–5.61,
MAE
4.01–4.43.
performed
best
0.84–0.89
4.51–6.74.
during
stages
occurred
early
filling
stage,
0.75
0.58.
On
basis
research,
future
work
will
focus
optimizing
process
richer
environmental
so
further
enhance
predictive
capability
applicability
model.
Язык: Английский