Sensors,
Год журнала:
2025,
Номер
25(8), С. 2362 - 2362
Опубликована: Апрель 8, 2025
Traditional
farming
has
evolved
from
standalone
computing
systems
to
smart
farming,
driven
by
advancements
in
digitalization.
This
led
the
proliferation
of
diverse
information
(IS),
such
as
IoT
and
sensor
systems,
decision
support
farm
management
(FMISs).
These
often
operate
isolation,
limiting
their
overall
impact.
The
integration
IS
into
connected
is
widely
addressed
a
key
driver
tackle
these
issues.
However,
it
complex,
multi-faceted
issue
that
not
easily
achievable.
Previous
studies
have
offered
valuable
insights,
but
they
focus
on
specific
cases,
individual
certain
aspects,
lacking
comprehensive
overview
various
dimensions.
systematic
review
74
scientific
papers
addresses
this
gap
providing
an
digital
technologies
involved,
levels
types,
barriers
hindering
integration,
available
approaches
overcoming
challenges.
findings
indicate
primarily
relies
point-to-point
approach,
followed
cloud-based
integration.
Enterprise
service
bus,
hub-and-spoke,
semantic
web
are
mentioned
less
frequently
gaining
interest.
study
identifies
discusses
27
challenges
three
main
areas:
organizational,
technological,
data
governance-related
Technologies
blockchain,
spaces,
AI,
edge
microservices,
service-oriented
architecture
methods
solutions
for
governance
interoperability
insights
can
help
enhance
interoperability,
leading
data-driven
increases
food
production,
mitigates
climate
change,
optimizes
resource
usage.
Computer Science & IT Research Journal,
Год журнала:
2024,
Номер
5(2), С. 473 - 497
Опубликована: Фев. 18, 2024
This
study
provides
a
comprehensive
review
of
the
integration
and
impact
Artificial
Intelligence
(AI)
in
agricultural
supply
chains,
focusing
on
its
role
enhancing
demand
forecasting
optimizing
supply.
The
primary
objective
was
to
assess
how
AI-driven
predictive
analytics
transforms
practices,
addressing
challenges,
shaping
future
trends.
A
systematic
literature
content
analysis
methodology
were
employed,
utilizing
academic
databases
digital
libraries
source
peer-reviewed
articles
conference
papers
published
between
2014
2024.
inclusion
criteria
focused
studies
related
AI
applications
while
exclusion
filtered
out
non-peer-reviewed
irrelevant
literature.
Key
findings
reveal
that
significantly
improves
accuracy
efficiency
chain
operations
agriculture.
technologies,
including
machine
learning
big
data
analytics,
have
led
advancements
real-time
analysis,
maintenance,
resource
optimization.
However,
challenges
such
as
quality,
infrastructure
development,
skill
gaps
among
professionals
persist.
landscape
agriculture
is
marked
by
growth
opportunities
need
for
equitable
technology
access
ethical
considerations.
recommends
industry
leaders
policymakers
invest
infrastructure,
promote
research
provide
training
facilitate
adoption.
Future
should
focus
developing
robust
models
tailored
agriculture,
exploring
AI's
with
emerging
assessing
long-term
socio-economic
impacts.
contributes
understanding
current
potential
transforming
offering
valuable
insights
stakeholders
sector.
Keywords:
Intelligence,
Agricultural
Supply
Chains,
Predictive
Analytics,
Demand
Forecasting.
Open Access Research Journal of Engineering and Technology,
Год журнала:
2024,
Номер
6(2), С. 023 - 032
Опубликована: Апрель 7, 2024
This
study
systematically
reviews
the
transformative
role
of
Artificial
Intelligence
(AI)
in
enhancing
agricultural
productivity
and
sustainability
United
States.
With
aim
understanding
how
AI
technologies
can
be
effectively
integrated
into
farming
practices,
this
research
employs
a
systematic
literature
review
methodology,
focusing
on
peer-reviewed
journal
articles,
conference
proceedings,
reputable
reports
from
2010
to
2024.
The
methodology
includes
structured
search
strategy,
defined
inclusion
exclusion
criteria,
thematic
analysis
categorize
findings
relevant
themes.
Key
reveal
that
technologies,
such
as
machine
learning
models,
predictive
analytics,
robotics,
are
revolutionizing
U.S.
agriculture
by
optimizing
resource
use,
improving
crop
health
monitoring,
decision-making
processes.
Despite
promising
potential
address
challenges
like
food
security
environmental
sustainability,
adoption
faces
barriers
including
technological
adoption,
data
privacy
concerns,
need
for
significant
investment
digital
infrastructure.
concludes
leveraging
sustainable
requires
collaborative
efforts
among
stakeholders,
literacy,
development
regulatory
frameworks,
fostering
public-private
partnerships.
Future
directions
emphasize
socio-economic
impacts
ethical
considerations,
scalable
solutions.
underscores
AI's
pivotal
ensuring
sustainable,
productive,
resilient
sector.
Agronomy Journal,
Год журнала:
2023,
Номер
116(3), С. 1217 - 1228
Опубликована: Апрель 5, 2023
Abstract
Artificial
intelligence
(AI)
represents
technologies
with
human‐like
cognitive
abilities
to
learn,
perform,
and
make
decisions.
AI
in
precision
agriculture
(PA)
enables
farmers
farm
managers
deploy
highly
targeted
precise
farming
practices
based
on
site‐specific
agroclimatic
field
measurements.
The
foundational
applied
development
of
has
matured
considerably
over
the
last
30
years.
time
is
now
right
engage
seriously
ethics
responsible
practice
for
well‐being
managers.
In
this
paper,
we
identify
discuss
both
challenges
opportunities
improving
farmers’
trust
those
providing
solutions
PA.
We
highlight
that
can
be
moderated
by
how
benefits
risks
are
perceived,
shared,
distributed.
propose
four
recommendations
trust.
First,
developers
should
improve
model
transparency
explainability.
Second,
clear
responsibility
accountability
assigned
Third,
concerns
about
fairness
need
overcome
human‐machine
partnerships
agriculture.
Finally,
regulation
voluntary
compliance
data
ownership,
privacy,
security
needed,
if
systems
become
accepted
used
farmers.
Frontiers in Artificial Intelligence,
Год журнала:
2024,
Номер
7
Опубликована: Апрель 25, 2024
Food
and
nutrition
are
a
steadfast
essential
to
all
living
organisms.
With
specific
reference
humans,
the
sufficient
efficient
supply
of
food
is
challenge
as
world
population
continues
grow.
Artificial
Intelligence
(AI)
could
be
identified
plausible
technology
in
this
5th
industrial
revolution
bringing
us
closer
achieving
zero
hunger
by
2030—Goal
2
United
Nations
Sustainable
Development
Goals
(UNSDG).
This
goal
cannot
achieved
unless
digital
divide
among
developed
underdeveloped
countries
addressed.
Nevertheless,
developing
regions
fall
behind
economic
resources;
however,
they
harbor
untapped
potential
effectively
address
impending
demands
posed
soaring
population.
Therefore,
study
explores
in-depth
AI
agriculture
sector
for
under-developed
countries.
Similarly,
it
aims
emphasize
proven
efficiency
spin-off
applications
advancement
agriculture.
Currently,
being
utilized
various
spheres
agriculture,
including
but
not
limited
crop
surveillance,
irrigation
management,
disease
identification,
fertilization
practices,
task
automation,
image
manipulation,
data
processing,
yield
forecasting,
chain
optimization,
implementation
decision
support
system
(DSS),
weed
control,
enhancement
resource
utilization.
Whereas
supports
safety
security
ensuring
higher
yields
that
acquired
harnessing
multi-temporal
remote
sensing
(RS)
techniques
accurately
discern
diverse
phenotypes,
monitor
land
cover
dynamics,
assess
variations
soil
organic
matter,
predict
moisture
levels,
conduct
plant
biomass
modeling,
enable
comprehensive
monitoring.
The
present
identifies
challenges,
financial,
infrastructure,
experts,
availability,
customization,
regulatory
framework,
cultural
norms
attitudes,
access
market,
interdisciplinary
collaboration,
adoption
nations
with
their
subsequent
remedies.
identification
challenges
opportunities
ignite
further
research
actions
these
regions;
thereby
supporting
sustainable
development.
Robotics and Autonomous Systems,
Год журнала:
2024,
Номер
174, С. 104642 - 104642
Опубликована: Фев. 2, 2024
Use
of
wheeled
mobile
robot
systems
could
be
crucial
in
addressing
some
the
future
issues
facing
agriculture.
However,
on
wheels
are
currently
unstable
and
require
a
control
mechanism
to
increase
stability,
resulting
much
research
requirement
develop
an
appropriate
controller
algorithm
for
systems.
Proportional,
integral,
derivative
(PID)
controllers
widely
used
this
purpose,
but
PID
approach
is
frequently
inappropriate
due
disruptions
or
fluctuations
parameters.
Other
approaches,
such
as
linear-quadratic
regulator
(LQR)
control,
can
address
associated
with
controllers.
In
study,
kinematic
model
four-wheel
skid-steering
was
developed
test
functionality
LQR
control.
Three
scenarios
(control
cheap,
non-zero
state
expensive;
expensive,
cheap;
only
expensive)
were
examined
using
characteristics
robot.
Peak
time,
settling
rising
time
cheap
based
these
found
0.1
s,
7.82
4.39
respectively.
Frontiers in Artificial Intelligence,
Год журнала:
2025,
Номер
7
Опубликована: Янв. 23, 2025
The
integration
of
artificial
intelligence
(AI)
technologies
into
agriculture
holds
urgent
and
transformative
potential
for
enhancing
food
security
across
Sub-Saharan
Africa
(SSA),
a
region
acutely
impacted
by
climate
change
resource
constraints.
This
paper
examines
experiences
from
the
Artificial
Intelligence
Agriculture
Food
Systems
(AI4AFS)
Innovation
Research
Network,
which
provided
funding
to
innovative
projects
in
eight
SSA
countries.
Through
set
case
studies,
we
explore
AI-driven
solutions
pest
disease
detection
crops
such
as
cashew,
maize,
tomato,
cassava,
including
real-time
health
monitoring
tool
Nsukka
Yellow
pepper.
Using
participatory
design,
key
informant
interview,
robust
evaluation,
incorporating
ethical
frameworks,
research
prioritizes
gender
equality,
social
inclusion,
environmental
sustainability
AI
development
deployment.
Our
results
demonstrate
that
responsible
practices
can
significantly
enhance
agricultural
productivity
while
maintaining
low
carbon
footprints.
offers
unique,
localized
perspective
on
AI’s
role
addressing
SSA’s
challenges,
with
implications
global
demand
rises
resources
shrink.
Key
recommendations
include
establishing
policy
strengthening
capacity-building
efforts,
securing
sustainable
mechanisms
support
long-term
adoption.
work
provides
community,
policymakers,
stakeholders
critical
insights
ethical,
responsible,
inclusive
be
adapted
similar
contexts
worldwide,
contributing
systems
an
international
scale.
IGI Global eBooks,
Год журнала:
2025,
Номер
unknown, С. 187 - 212
Опубликована: Фев. 7, 2025
In
a
world
where
sustainability
has
been
given
utmost
priority,
agriculture
plays
pivotal
role.
Artificial
Intelligence
in
the
agricultural
sector
changed
landscape
of
across
globe.
‘Agvolution'
(evolution
agriculture)
including
AI
supported
precision
farming
methods,
data
analytics,
and
robotics
is
novel
strategy
which
increases
crop
yields
using
less
fertilizers,
energy.
supports
ethical
farming,
boost
revenue,
lessen
negative
environmental
effects.
systems
aggregate
from
weather
stations,
sensors,
satellites
to
produce
improved
forecasts.
This
mechanism
enhances
sustainability.
Despite
numerous
advantages
with
AI,
community
face
challenges
like
security
privacy,
high
cost
machines
tools.
light
above,
authors
explore
usage
attain
sustainability,
analyze
need
establish
governance
structures
for
increasing
food
overcome
faced
by
farmers.
Heliyon,
Год журнала:
2024,
Номер
10(7), С. e28752 - e28752
Опубликована: Март 25, 2024
Pesticides
play
an
important
role
in
modern
agriculture
by
protecting
crops
from
pests
and
diseases.
However,
the
negative
consequences
of
pesticides,
such
as
environmental
contamination
adverse
effects
on
human
ecological
health,
underscore
importance
accurate
toxicity
predictions.
To
address
this
issue,
artificial
intelligence
models
have
emerged
valuable
methods
for
predicting
organic
compounds.
In
review
article,
we
explore
application
machine
learning
(ML)
pesticide
prediction.
This
provides
a
detailed
summary
recent
developments,
prediction
models,
datasets
used
analysis,
compared
results
several
algorithms
that
predict
harmfulness
various
classes
pesticides.
Furthermore,
article
identified
emerging
trends
areas
future
direction,
showcasing
transformative
potential
promoting
safer
usage
sustainable
agriculture.
