Principal Component Analysis (PCA) and feature importance-based dimension reduction for Reference Evapotranspiration (ET0) predictions of Taif, Saudi Arabia
Computers and Electronics in Agriculture,
Journal Year:
2024,
Volume and Issue:
222, P. 109036 - 109036
Published: May 21, 2024
Language: Английский
Experimental Evaluation of Remote Sensing–Based Climate Change Prediction Using Enhanced Deep Learning Strategy
Remote Sensing in Earth Systems Sciences,
Journal Year:
2024,
Volume and Issue:
7(4), P. 642 - 656
Published: Oct. 19, 2024
Language: Английский
Assessing salinity-induced impacts on plant transpiration through machine learning: from model development to deployment
Modeling Earth Systems and Environment,
Journal Year:
2025,
Volume and Issue:
11(3)
Published: March 13, 2025
Language: Английский
Harnessing the power of machine learning for crop improvement and sustainable production
Seyed Mahdi Hosseiniyan Khatibi,
No information about this author
Jauhar Ali
No information about this author
Frontiers in Plant Science,
Journal Year:
2024,
Volume and Issue:
15
Published: Aug. 12, 2024
Crop
improvement
and
production
domains
encounter
large
amounts
of
expanding
data
with
multi-layer
complexity
that
forces
researchers
to
use
machine-learning
approaches
establish
predictive
informative
models
understand
the
sophisticated
mechanisms
underlying
these
processes.
All
aim
fit
target
data;
nevertheless,
it
should
be
noted
a
wide
range
specialized
methods
might
initially
appear
confusing.
The
principal
objective
this
study
is
offer
an
explicit
introduction
some
essential
their
applications,
comprising
most
modern
utilized
have
gained
widespread
adoption
in
crop
or
similar
domains.
This
article
explicitly
explains
how
different
could
applied
for
given
agricultural
data,
highlights
newly
emerging
techniques
users,
lays
out
technical
strategies
agri/crop
research
practitioners
researchers.
Language: Английский
Vegetation and Evapotranspiration Analyses on Climate Maps
Black Sea Journal of Engineering and Science,
Journal Year:
2024,
Volume and Issue:
7(4), P. 616 - 626
Published: May 15, 2024
This
study
focuses
on
the
investigation
of
Evapotranspiration
(ET)
processes
under
climatic
and
geographical
characteristics
Türkiye.
ET
refers
to
process
by
which
plants
transfer
water
vapor
atmosphere
is
an
important
part
cycle.
research
analyzes
in
Türkiye
using
imagery
data
from
NASA
Global
Land
Data
Assimilation
System
Version
2
(GLDAS-2),
MODIS,
TerraClimate,
SMAP
Level-4,
Penman-Monteith-Leuning
V2
(PML_V2).
Surface
Soil
Moisture
(SSM)
for
between
2016
2022
Temperature
(LST)
2000
were
obtained
MODIS
images.
In
study,
regression
analyses
performed
with
values
SSM
LST
data.
The
best
result
was
a
moderate
correlation
(R
0.57)
produced
Level-4
LST.
A
high
0.59)
observed
SSM.
Climate
Hazards
Group
InfraRed
Precipitation
Station
(CHIRPS)
1981
2023
precipitation
Pressure
(PS)
MERRA
image.
Regression
PS
values.
relationship
0.37)
MOD16A2
V105
0.50)
TerraClimate
aims
contribute
development
strategies
effectively
manage
resources
improve
agricultural
sustainability
analyzing
various
regions
Language: Английский