Agriculture,
Год журнала:
2024,
Номер
14(12), С. 2326 - 2326
Опубликована: Дек. 19, 2024
This
study
accurately
inverts
key
growth
parameters
of
rice,
including
Leaf
Area
Index
(LAI),
chlorophyll
content
(SPAD)
value,
and
height,
by
integrating
multisource
remote
sensing
data
(including
MODIS
ERA5
imagery)
deep
learning
models.
Dehui
City
in
Jilin
Province,
China,
was
selected
as
the
case
area,
where
multidimensional
vegetation
indices,
ecological
function
parameters,
environmental
variables
were
collected,
covering
seven
stages
rice.
Data
analysis
parameter
prediction
conducted
using
a
variety
machine
models
Partial
Least
Squares
(PLSs),
Support
Vector
Machine
(SVM),
Random
Forest
(RF),
Long
Short-Term
Memory
Networks
(LSTM),
among
which
LSTM
model
demonstrated
superior
performance,
particularly
at
multiple
critical
time
points.
The
results
show
that
performed
best
inverting
three
with
LAI
inversion
accuracy
on
21
August
reaching
coefficient
determination
(R2)
0.72,
root
mean
square
error
(RMSE)
0.34,
absolute
(MAE)
0.27.
SPAD
same
date
achieved
an
R2
0.69,
RMSE
1.45,
MAE
1.16.
height
25
July
reached
0.74,
2.30,
2.08.
not
only
verifies
effectiveness
combining
advanced
algorithms
but
also
provides
scientific
basis
for
precision
management
decision-making
rice
cultivation.
Notulae Botanicae Horti Agrobotanici Cluj-Napoca,
Год журнала:
2024,
Номер
52(2), С. 13728 - 13728
Опубликована: Май 21, 2024
Nitrogen
fertilizer
levels
significantly
affect
crop
growth
and
development,
necessitating
precision
management.
Most
studies
focus
on
nitrogen
nutrient
estimation
using
vegetation
indices
textural
features,
overlooking
the
diagnostic
potential
of
color
features.
Hence,
we
investigated
cotton
nutrition
status
unmanned
aerial
vehicle
(UAV)
image
features
index
(NNI).
Random
frog
algorithm
-
random
forest-screened
feature
sets
correlated
with
NNI,
which
were
substituted
into
four
machine
learning
algorithms
for
NNI
modeling.
The
composite
scores
(F)
optimal
calculated
coefficient
variation
method
comprehensive
diagnosis.
Validation
model
determining
critical
concentration
in
yielded
a
determination
R2
=
0.89,
root
mean
square
error
RMSE
0.50
g
(100
g)-1,
absolute
MAE
0.44,
demonstrating
improved
performance.
Additionally,
our
novel
constructed
based
exhibited
R2c
0.97,
RMSEc
0.02,
MAEc
R2v
0.85,
RMSEv
0.05,
MAEv
0.04.
Polynomial
fitting
indicated
that
was
reliable
following
criterion:
0.48
<
F2
0.67
overapplication,
whereas
or
>
deficiency.
This
study
demonstrates
superior
effectiveness
UAV
RGB
quick,
accurate
diagnosis
levels,
will
help
guide
application.
Agronomy,
Год журнала:
2024,
Номер
14(12), С. 2760 - 2760
Опубликована: Ноя. 21, 2024
Leaf
nitrogen
content
(LNC)
is
a
vital
agronomic
parameter
in
rice,
commonly
used
to
evaluate
photosynthetic
capacity
and
diagnose
crop
nutrient
levels.
Nitrogen
deficiency
can
significantly
reduce
yield,
underscoring
the
importance
of
accurate
LNC
estimation
for
practical
applications.
This
study
utilizes
hyperspectral
UAV
imagery
acquire
rice
canopy
data,
applying
various
machine
learning
regression
algorithms
(MLR)
develop
an
model
create
concentration
distribution
map,
offering
valuable
guidance
subsequent
field
management.
The
analysis
incorporates
four
types
spectral
data
extracted
throughout
growth
cycle:
original
reflectance
bands
(OR
bands),
vegetation
indices
(VIs),
first-derivative
(FD
variable
parameters
(HSPs)
as
inputs,
while
measured
serves
output.
Results
demonstrate
that
random
forest
(RFR)
gradient
boosting
decision
tree
(GBDT)
performed
effectively,
with
GBDT
achieving
highest
average
R2
0.76
across
different
treatments.
Among
models
varieties,
RFR
exhibited
superior
accuracy,
0.95
SuXiangJing100
variety,
reached
0.93.
Meanwhile,
support
vector
(SVMR)
showed
slightly
lower
partial
least-squares
(PLSR)
was
least
effective.
developed
method
applicable
whole
stage
common
varieties.
suitable
estimating
stages,
treatments,
it
also
provides
reference
fertilization
planning
at
flight
altitudes
other
than
120
m
this
study.
Agriculture,
Год журнала:
2024,
Номер
14(12), С. 2326 - 2326
Опубликована: Дек. 19, 2024
This
study
accurately
inverts
key
growth
parameters
of
rice,
including
Leaf
Area
Index
(LAI),
chlorophyll
content
(SPAD)
value,
and
height,
by
integrating
multisource
remote
sensing
data
(including
MODIS
ERA5
imagery)
deep
learning
models.
Dehui
City
in
Jilin
Province,
China,
was
selected
as
the
case
area,
where
multidimensional
vegetation
indices,
ecological
function
parameters,
environmental
variables
were
collected,
covering
seven
stages
rice.
Data
analysis
parameter
prediction
conducted
using
a
variety
machine
models
Partial
Least
Squares
(PLSs),
Support
Vector
Machine
(SVM),
Random
Forest
(RF),
Long
Short-Term
Memory
Networks
(LSTM),
among
which
LSTM
model
demonstrated
superior
performance,
particularly
at
multiple
critical
time
points.
The
results
show
that
performed
best
inverting
three
with
LAI
inversion
accuracy
on
21
August
reaching
coefficient
determination
(R2)
0.72,
root
mean
square
error
(RMSE)
0.34,
absolute
(MAE)
0.27.
SPAD
same
date
achieved
an
R2
0.69,
RMSE
1.45,
MAE
1.16.
height
25
July
reached
0.74,
2.30,
2.08.
not
only
verifies
effectiveness
combining
advanced
algorithms
but
also
provides
scientific
basis
for
precision
management
decision-making
rice
cultivation.