Artificial Intelligence in Agricultural Mapping: A Review
Agriculture,
Journal Year:
2024,
Volume and Issue:
14(7), P. 1071 - 1071
Published: July 3, 2024
Artificial
intelligence
(AI)
plays
an
essential
role
in
agricultural
mapping.
It
reduces
costs
and
time
increases
efficiency
management
activities,
which
improves
the
food
industry.
Agricultural
mapping
is
necessary
for
resource
requires
technologies
farming
challenges.
The
AI
applications
gives
its
subsequent
use
decision-making.
This
study
analyses
AI’s
current
state
through
bibliometric
indicators
a
literature
review
to
identify
methods,
resources,
geomatic
tools,
types,
their
management.
methodology
begins
with
bibliographic
search
Scopus
Web
of
Science
(WoS).
Subsequently,
data
analysis
establish
scientific
contribution,
collaboration,
trends.
United
States
(USA),
Spain,
Italy
are
countries
that
produce
collaborate
more
this
area
knowledge.
Of
studies,
76%
machine
learning
(ML)
24%
deep
(DL)
applications.
Prevailing
algorithms
such
as
Random
Forest
(RF),
Neural
Networks
(ANNs),
Support
Vector
Machines
(SVMs)
correlate
activities
In
addition,
contributes
associated
production,
disease
detection,
crop
classification,
rural
planning,
forest
dynamics,
irrigation
system
improvements.
Language: Английский
Soil Loss Estimation by Water Erosion in Agricultural Areas Introducing Artificial Intelligence Geospatial Layers into the RUSLE Model
Land,
Journal Year:
2024,
Volume and Issue:
13(2), P. 174 - 174
Published: Feb. 1, 2024
The
existing
digital
soil
maps
are
mainly
characterized
by
coarse
spatial
resolution
and
not
up
to
date;
thus,
they
unable
support
the
physical
process-based
models
for
improved
predictions.
overarching
objective
of
this
work
is
oriented
toward
a
data-driven
approach
datacube-based
tools
(Soil
Data
Cube),
leveraging
Sentinel-2
imagery
data,
open
access
databases,
ground
truth
data
Artificial
Intelligence
(AI)
architectures
provide
enhanced
geospatial
layers
into
Revised
Universal
Soil
Loss
Equation
(RUSLE)
model,
improving
both
reliability
final
map.
proposed
methodology
was
implemented
in
agricultural
area
Imathia
Regional
Unit
(northern
Greece),
which
consists
mountainous
areas
lowlands.
Enhanced
Organic
Carbon
(SOC)
texture
were
generated
at
10
m
through
time-series
analysis
satellite
an
XGBoost
(eXtrene
Gradinent
Boosting)
model.
model
trained
84
samples
(collected
from
fields)
taking
account
also
additional
environmental
covariates
(including
elevation
climatic
data)
following
Digital
Mapping
(DSM)
approach.
introduced
RUSLE’s
erodibility
factor
(K-factor),
producing
erosion
layer
with
high
resolution.
Notable
prediction
accuracy
achieved
AI
R2
0.61
SOC
0.73,
0.67
0.63
clay,
sand,
silt,
respectively.
average
annual
loss
unit
found
be
1.76
ton/ha/yr
6%
total
suffering
severe
(>11
ton/ha/yr),
border
regions,
showing
strong
influence
mountains
fields.
overall
could
strongly
regional
decision
making
planning
policies
such
as
European
Common
Agricultural
Policy
(CAP)
Sustainable
Development
Goals
(SDGs).
Language: Английский
A critical systematic review on spectral-based soil nutrient prediction using machine learning
Environmental Monitoring and Assessment,
Journal Year:
2024,
Volume and Issue:
196(8)
Published: July 4, 2024
Language: Английский
Soil Organic Carbon Assessment Using Remote-Sensing Data and Machine Learning: A Systematic Literature Review
Remote Sensing,
Journal Year:
2025,
Volume and Issue:
17(5), P. 882 - 882
Published: March 1, 2025
In
the
current
global
change
scenario,
valuable
tools
for
improving
soils
and
increasing
both
agricultural
productivity
food
security,
together
with
effective
actions
to
mitigate
impacts
of
ongoing
climate
trends,
are
priority
issues.
Soil
Organic
Carbon
(SOC)
acts
on
these
two
topics,
as
C
is
a
core
element
soil
organic
matter,
an
essential
driver
fertility,
becomes
problematic
when
disposed
in
atmosphere
its
gaseous
form.
Laboratory
methods
measure
SOC
expensive
time-consuming.
This
Systematic
Literature
Review
(SLR)
aims
identify
techniques
alternative
ways
estimate
using
Remote-Sensing
(RS)
spectral
data
computer
process
this
database.
SLR
was
conducted
Meta-Analysis
(PRISMA)
methodology,
highlighting
use
Deep
Learning
(DL),
traditional
neural
networks,
other
machine-learning
models,
input
were
used
SOC.
The
concludes
that
Sentinel
satellites,
particularly
Sentinel-2,
frequently
used.
Despite
limited
datasets,
DL
models
demonstrated
robust
performance
assessed
by
R2
RMSE.
Key
data,
such
vegetation
indices
(e.g.,
NDVI,
SAVI,
EVI)
digital
elevation
consistently
correlated
predictions.
These
findings
underscore
potential
combining
RS
advanced
artificial-intelligence
efficient
scalable
monitoring.
Language: Английский
A European soil organic carbon monitoring system leveraging Sentinel 2 imagery and the LUCAS soil data base
Geoderma,
Journal Year:
2024,
Volume and Issue:
452, P. 117113 - 117113
Published: Nov. 26, 2024
Language: Английский
GGW-BDF: an online tool for using earth observation and Chinese ecosystem restoration experiences in support of the Great Green Wall initiative
International Journal of Digital Earth,
Journal Year:
2024,
Volume and Issue:
17(1)
Published: July 2, 2024
The
United
Nations
Sustainable
Development
Goals
(SDGs)
establish
a
fresh
global
framework
for
evaluating
the
development
agenda,
emphasizing
prosperity,
growth,
inclusivity,
and
transparency
while
safeguarding
our
planet
Earth.
Land
Degradation
Neutrality
(LDN)
represents
clear
target
assessing
achievement
of
SDG
15.3.
In
response
to
shortcomings
African
Great
Green
Wall
initiative
with
respect
LDN
monitoring,
reporting,
intervention,
we
developed
an
online
tool,
Big
Data
Facilitator
(GGW-BDF),
based
on
earth
observation
data
(e.g.
SDGSAT-1),
cutting-edge
big
analysis
technologies,
Chinese
experience
in
combating
desertification,
support
implementation
initiative.
GGW-BDF
comprises
portal,
desertification
control
knowledge,
e-learning
resources.
This
study
highlights
functionality
this
tool
stakeholders
across
countries
monitoring
providing
successful
technoloiges
restore
degraded
or
barren
land.
contributions
are
very
useful
supporting
Pan-African
Agency
(PAGGW),
will
hopefully
be
continuously
upgraded
being
part
federated
toolbox
Group
Earth
Observation
(GEO)
Flagship.
Language: Английский
Employing the Soil Data Cube and Digital Soil Mapping Techniques for National Topsoil Predictions of Soil Organic Carbon and Clay Content over the Lithuanian Grasslands
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1585 - 1589
Published: July 7, 2024
Language: Английский