Phytopathology,
Год журнала:
2024,
Номер
114(8), С. 1733 - 1741
Опубликована: Май 29, 2024
In
the
past
decade,
there
has
been
a
recognized
need
for
innovative
methods
to
monitor
and
manage
plant
diseases,
aiming
meet
precision
demands
of
modern
agriculture.
Over
last
15
years,
significant
advances
in
detection,
monitoring,
management
diseases
have
made,
largely
propelled
by
cutting-edge
technologies.
Recent
agriculture
driven
sophisticated
tools
such
as
optical
sensors,
artificial
intelligence,
microsensor
networks,
autonomous
driving
vehicles.
These
technologies
enabled
development
novel
cropping
systems,
allowing
targeted
crops,
contrasting
with
traditional,
homogeneous
treatment
large
crop
areas.
The
research
this
field
is
usually
highly
collaborative
interdisciplinary
endeavor.
It
brings
together
experts
from
diverse
fields
pathology,
computer
science,
statistics,
engineering,
agronomy
forge
comprehensive
solutions.
Despite
progress,
translating
advancements
decision-making
or
automation
into
agricultural
practice
remains
challenge.
knowledge
transfer
extension
particularly
challenging.
Enhancing
accuracy
timeliness
disease
detection
continues
be
priority,
data-driven
intelligence
systems
poised
play
pivotal
role.
This
perspective
article
addresses
critical
questions
challenges
faced
implementation
digital
management.
underscores
urgency
integrating
technological
traditional
integrated
pest
highlights
unresolved
issues
regarding
establishment
control
thresholds
site-specific
treatments
necessary
alignment
technology
use
regulatory
frameworks.
Importantly,
paper
calls
intensified
efforts,
widespread
dissemination,
education
optimize
application
management,
recognizing
intersection
technology's
potential
its
current
practical
limitations.
Advanced Agrochem,
Год журнала:
2022,
Номер
2(1), С. 15 - 30
Опубликована: Окт. 28, 2022
Artificial
Intelligence
(AI)
has
been
extensively
applied
in
farming
recently.
To
cultivate
healthier
crops,
manage
pests,
monitor
soil
and
growing
conditions,
analyse
data
for
farmers,
enhance
other
management
activities
of
the
food
supply
chain,
agriculture
sector
is
turning
to
AI
technology.
It
makes
it
challenging
farmers
choose
ideal
time
plant
seeds.
helps
optimum
seed
a
particular
weather
scenario.
also
offers
on
forecasts.
AI-powered
solutions
will
help
produce
more
with
fewer
resources,
increase
crop
quality,
hasten
product
reach
market.
aids
understanding
qualities.
by
suggesting
nutrients
they
should
apply
quality
soil.
can
optimal
their
Intelligent
equipment
calculate
spacing
between
seeds
maximum
planting
depth.
An
system
known
as
health
monitoring
provides
information
crops
that
need
be
given
yield
quantity.
This
study
identifies
analyses
relevant
articles
Agriculture.
Using
AI,
now
access
advanced
analytics
tools
foster
better
farming,
improve
efficiencies,
reduce
waste
biofuel
production
while
minimising
negative
environmental
impacts.
Machine
Learning
(ML)
have
transformed
various
industries,
wave
reached
sector.
Companies
are
developing
several
technologies
make
farmers'
easier.
Hyperspectral
imaging
3D
laser
scanning
leading
AI-based
ensure
health.
These
collect
precise
greater
volume
analysis.
paper
studied
its
The
process
Agriculture
some
parameters
monitored
briefed.
Finally,
we
identified
discussed
significant
applications
agriculture.
Agriculture,
Год журнала:
2024,
Номер
14(7), С. 1141 - 1141
Опубликована: Июль 14, 2024
Water
is
considered
one
of
the
vital
natural
resources
and
factors
for
performing
short-
long-term
agricultural
practices
on
Earth.
Meanwhile,
globally,
most
available
freshwater
are
utilized
irrigation
purposes
in
agriculture.
Currently,
many
world
regions
facing
extreme
water
shortage
problems,
which
can
worsen
if
not
managed
properly.
In
literature,
numerous
methods
remedies
used
to
cope
with
increasing
global
crises.
The
use
precision
water-saving
systems
(PISs)
efficient
management
under
climate
change
them
a
highly
recommended
approach
by
researchers.
It
mitigate
adverse
effects
changing
help
enhance
efficiency,
crop
yield,
environmental
footprints.
Thus,
present
study
aimed
comprehensively
examine
review
PISs,
focusing
their
development,
implementation,
positive
impacts
sustainable
management.
addition,
we
searched
literature
using
different
online
search
engines
reviewed
summarized
main
results
previously
published
papers
PISs.
We
discussed
traditional
method
its
modernization
enhancing
PIS
monitoring
controlling,
architecture,
data
sharing
communication
technologies,
role
artificial
intelligence
water-saving,
future
prospects
PIS.
Based
brief
review,
concluded
that
PISs
seems
bright,
driven
need
systems,
technological
advancements,
awareness.
As
scarcity
problem
intensifies
due
population
growth,
poised
play
critical
optimizing
modernizing
usage,
reducing
footprints,
thus
ensuring
agriculture
development.
Information Processing in Agriculture,
Год журнала:
2023,
Номер
11(4), С. 524 - 541
Опубликована: Сен. 6, 2023
IoT
based
agriculture
(Ag-IoT)
is
an
emerging
communication
technology
that
widely
adopted
by
agricultural
entrepreneurs
and
farmers
to
perform
agro-chores
in
the
farm
improve
productivity,
for
better
monitoring,
reduce
labor
costs.
However,
use
of
Internet
Ag-IoT
facilitates
real-time
functionality
system,
it
can
increase
risk
security
breaches
cyber
attacks
would
cause
system
malfunction
affect
its
productivity.
overlooked
parameters,
which
have
severe
impacts
on
trustworthiness
adoption
communities.
To
address
this
gap,
article
presents
a
systematic
study
literature
published
between
2001
2023
discusses
advances
technology.
The
subjects
included
are
applications,
different
architectures,
suspected
crimes,
challenges
incident
response
digital
forensics.
findings
encourage
reader
explore
future
potential
research
avenues
related
risks
Ag-IoT,
as
well
readiness
forensic
investigation
smart
sector.
main
conclusion
must
be
ensured
environments
offer
uninterrupted
services
also
there
need
effective
event
unanticipated
incidents.
Advances in systems analysis, software engineering, and high performance computing book series,
Год журнала:
2024,
Номер
unknown, С. 1 - 27
Опубликована: Май 15, 2024
The
chapter
examines
how
machine
learning
(ML)
and
artificial
intelligence
(AI)
could
be
used
to
solve
environmental
problems
throughout
the
world.
It
emphasizes
crucial
AI
ML
are
optimizing
energy
distribution,
including
demand
forecasting,
improving
smart
grid
performance,
incorporating
renewable
sources.
also
covers
use
of
methods
sustainable
agriculture,
emphasizing
predictive
analytics
for
pest
management,
soil
health
monitoring,
precision
farming.
highlights
effectiveness
resource
encourages
actions
that
ecologically
friendly.
ethical
issues,
societal
ramifications,
legal
systems,
synergies
between
agricultural
solutions.
imagines
a
day
when
advances
led
by
will
essential
environmentally
balanced
planet.
Open Access Research Journal of Science and Technology,
Год журнала:
2024,
Номер
10(2), С. 060 - 070
Опубликована: Апрель 7, 2024
This
comparative
review
explores
the
advancements
and
applications
of
Artificial
Intelligence
(AI)
in
agriculture,
focusing
on
developments
United
States
(USA)
Africa.
The
integration
AI
technologies
agriculture
has
witnessed
significant
progress
globally,
addressing
challenges
transforming
traditional
farming
practices.
In
USA,
precision
smart
techniques
driven
by
have
become
integral
components
modern
agricultural
systems.
These
innovations
include
autonomous
machinery,
drone
technology
for
crop
monitoring,
predictive
analytics
yield
optimization.
contrast,
application
African
presents
a
distinct
set
opportunities.
delves
into
initiatives
aimed
at
leveraging
to
enhance
productivity,
improve
resource
management,
address
food
security
concerns
various
nations.
efforts
deployment
pest
disease
detection,
monitoring
remote
areas,
implementation
data-driven
decision-making
tools
support
smallholder
farmers.
analysis
sheds
light
disparities
adoption
between
USA
Africa,
emphasizing
factors
such
as
infrastructure,
technological
accessibility,
availability.
Additionally,
it
collaborative
partnerships
that
bridge
gap
contribute
sustainable
development
agriculture.
As
both
regions
navigate
complexities
implementing
this
underscores
potential
play
pivotal
role
global
challenges.
findings
highlight
need
tailored
approaches,
policy
frameworks,
international
collaborations
ensure
inclusive
equitable
access
AI-driven
fostering
shared
commitment
technologically
empowered
Horticulturae,
Год журнала:
2024,
Номер
10(1), С. 49 - 49
Опубликована: Янв. 4, 2024
This
review
article
conducts
an
in-depth
analysis
of
the
role
next-generation
technologies
in
soilless
vegetable
production,
highlighting
their
groundbreaking
potential
to
revolutionize
yield,
efficiency,
and
sustainability.
These
technologies,
such
as
AI-driven
monitoring
systems
precision
farming
methods,
offer
unparalleled
accuracy
critical
variables
nutrient
concentrations
pH
levels.
However,
paper
also
addresses
multifaceted
challenges
that
hinder
widespread
adoption
these
technologies.
The
high
initial
investment
costs
pose
a
significant
barrier,
particularly
for
small-
medium-scale
farmers,
thereby
risking
creation
technological
divide
industry.
Additionally,
technical
complexity
demands
specialized
expertise,
potentially
exacerbating
knowledge
gaps
among
farmers.
Other
considerations
are
scrutinized,
including
data
privacy
concerns
job
displacement
due
automation.
Regulatory
challenges,
international
trade
regulations
policy
frameworks,
discussed,
they
may
need
revision
accommodate
new
concludes
by
emphasizing
while
sustainable
transformative
benefits,
broad
is
constrained
complex
interplay
financial,
technical,
regulatory,
social
factors.
Artificial Intelligence in Agriculture,
Год журнала:
2022,
Номер
6, С. 111 - 128
Опубликована: Янв. 1, 2022
Artificial
intelligence
(AI)
has
advanced
at
an
astounding
rate
and
transformed
numerous
economic
sectors.
Nevertheless,
a
comprehensive
understanding
of
how
AI
can
improve
the
agri-food
industry
is
lacking.
In
addition,
there
notable
dearth
research
on
that
investigates
influence
resources
educates
practitioners
significance
knowledge-based
smart
agriculture.
We
utilized
bibliometric
analysis
to
investigate
present
state
art
emerging
trends
in
relationship
between
industry.
The
identified
three
distinct
growth
phases
most
prevalent
strategies
we
analysed
key
offered
researchers
insightful
recommendations
for
future
research.
Using
resource-based
view
(RBV)
as
theoretical
lens,
this
study
established
framework
emphasising
long-term
effects
various
proposed
several
propositions.
AI-related
obstacles
have
been
categorised
into
four
major
categories.
Lastly,
originality
article
lies
its
suggestions
advancing
field
