Advances in environmental engineering and green technologies book series,
Год журнала:
2025,
Номер
unknown, С. 431 - 468
Опубликована: Янв. 3, 2025
This
chapter
emphasizes
the
integration
of
IoT
and
computer
vision
technology
improving
precision
farming
also
highlights
crucial
role
that
real-time
data
processing
plays
in
farm
robots.
According
to
research
studies,
enhances
efficiency
operations.
The
spraying
can
be
even
more
accurate
by
up
20%
operating
costs
reduced
12%.
In
addition
discussing
topics
like
accuracy
cybersecurity,
this
still
addressed
benefits
for
crop
monitoring
autonomous
form
instantaneous
feedback.
further
explains
some
future
areas
under
AI,
climate-smart
behaviors,
emergent
technology.
Some
takeaway
points
are
there
is
so
much
potential
greatly
increase
agricultural
output
sustainability
through
these
advancements.
Apart
from
that,
it
includes
requirements
continuous
innovation
adaptations
technologies
ensure
they
meet
today's
agriculture
needs.
IGI Global eBooks,
Год журнала:
2025,
Номер
unknown, С. 197 - 232
Опубликована: Фев. 18, 2025
Amidst
the
escalating
global
challenges
of
climate
change,
limited
resources,
and
population
growth,
adoption
sustainable
land
resource
management
has
become
imperative
to
ensure
food
security
environmental
conservation.
Precision
agriculture
enhances
process
efficiency,
reduces
impact,
improves
agricultural
productivity
through
integration
artificial
intelligence
technologies,
including
machine
learning,
deep
computer
vision.
Key
findings
indicate
a
reduction
10–20%
in
input
costs
an
increase
15–25%
crop
yields
efficient
utilisation.
Furthermore,
precision
irrigation
systems
can
achieve
water
savings
up
50%,
while
targeted
pesticide
treatments
reduce
chemical
usage
by
30–40%.
This
chapter
examines
economic
benefits,
highlighting
20%
CO2
emissions.
Recent
advancements
underscore
potential
AI
foster
agriculture,
promoting
conservation
viability.
International Journal of Science and Research (IJSR),
Год журнала:
2024,
Номер
13(7), С. 737 - 745
Опубликована: Июль 5, 2024
Farm
automation,
in
particular,
has
brought
about
tremendous
changes
agriculture
as
a
result
of
the
rapid
growth
technology.This
study
investigates
farm
automation's
current
state,
recent
developments,
new
trends,
and
potential
applications
India.
The
illustrates
effects
automation
on
agricultural
productivity
sustainability
by
looking
at
integration
modern
technologies
like
robotics,
precision
farming,
Internet
Things.
A
consideration
governmental
regulations,
technical
advancements,
contribution
academic
institutions
to
movement
are
all
included
study.
findings
indicate
that
India
increased
crop
yields
25-40%
levels
lower
northeastern
areas
greater
northwestern
regions.
also
identifies
important
shift
toward
environmentally
friendly
solutions
expanding
significance
digital
technology
agriculture.
Although
great
potential,
discusses
technological,
societal,
economic
obstacles
prevent
it
from
being
widely
used.
Lastly,
offers
strategic
insights
policy
recommendations
accelerate
adoption,
with
ultimate
goal
supporting
India's
industry.
Advances in marketing, customer relationship management, and e-services book series,
Год журнала:
2024,
Номер
unknown, С. 219 - 252
Опубликована: Окт. 25, 2024
This
chapter
discusses
the
importance
of
cooperative
marketing
strategies
in
agriculture,
focusing
particularly
on
value
embracing
diverse
viewpoints
and
harnessing
global
opportunities
for
local
farms.
study
addresses
relationships
between
collaboration,
diversity,
globalization
agricultural
marketing,
with
a
focus
how
these
might
enhance
market
entrance,
encourage
inclusivity,
promote
sustainable
development.
Various
case
studies
current
practice
collaborative
agriculture
industry
are
discussed
greater
depth.
The
also
gives
much
to
teamwork
advantages,
opportunity
identification,
possible
obstacles,
valuable
advisory
improve
understanding
potential
effects
farming
communities,
economic
advancement,
food
systems.
findings
this
may
better
engagement
among
stakeholders
inclusive
Remote Sensing,
Год журнала:
2024,
Номер
16(24), С. 4623 - 4623
Опубликована: Дек. 10, 2024
LiDAR
sensors
have
great
potential
for
enabling
crop
recognition
(e.g.,
plant
height,
canopy
area,
spacing,
and
intra-row
spacing
measurements)
the
of
agricultural
working
environments
field
boundaries,
ridges,
obstacles)
using
machinery.
The
objective
this
study
was
to
review
use
in
crops
environments.
This
also
highlights
sensor
testing
procedures,
focusing
on
critical
parameters,
industry
standards,
accuracy
benchmarks;
it
evaluates
specifications
various
commercially
available
with
applications
feature
characterization
importance
mounting
technology
machinery
effective
Different
studies
shown
promising
results
an
airborne
LiDAR,
such
as
coefficient
determination
(R2)
root-mean-square
error
(RMSE)
values
0.97
0.05
m
wheat,
0.88
5.2
cm
sugar
beet,
0.50
12
potato
height
estimation,
respectively.
A
relative
11.83%
observed
between
manual
measurements,
highest
distribution
correlation
at
0.675
average
5.14%
during
soybean
estimation
LiDAR.
An
object
detection
100%
found
identification
three
scanning
methods:
center
cluster,
lowest
point,
stem–ground
intersection.
effectively
detect
obstacles,
which
is
necessary
precision
agriculture
autonomous
navigation.
Future
directions
emphasize
need
continuous
advancements
technology,
along
integration
complementary
systems
algorithms,
machine
learning,
improve
performance
applications.
strategic
framework
implementing
includes
recommendations
precise
testing,
solutions
current
limitations,
guidance
integrating
other
technologies
enhance
digital
agriculture.
Advances in electronic government, digital divide, and regional development book series,
Год журнала:
2024,
Номер
unknown, С. 33 - 66
Опубликована: Ноя. 15, 2024
The
development
of
artificial
intelligence
(AI)
and
Internet
things
(IoT)
technologies
has
resulted
in
a
groundbreaking
shift
urban
connectivity
that
enabled
the
creation
smart
cities.
This
chapter
looks
at
how
AI-infused
connections
have
greatly
impacted
growth
IoT
communication
networks.
By
utilizing
AI,
cities
can
improve
public
services,
utilization
assets,
increase
living
standards.
Important
subjects
covered
this
include
predictive
maintenance,
real-time
monitoring,
AI-driven
data
analytics,
all
which
efficiency
dependability
infrastructure.
integration
devices
AI
algorithms
settings
is
examined
chapter,
featuring
concerns
including
security,
privacy,
regulatory
frameworks
being
addressed.
Through
case
studies
practical
examples,
it
offers
thorough
explanation
redefining
encouraging
next-generation
International Journal of Scientific Research in Science and Technology,
Год журнала:
2025,
Номер
12(1), С. 183 - 205
Опубликована: Янв. 26, 2025
The
rapid
advancements
in
artificial
intelligence
(AI)
and
automation
are
transforming
post-harvest
technologies,
offering
innovative
solutions
to
improve
food
quality,
safety,
supply
chain
efficiency.
This
paper
reviews
the
role
of
AI-driven
innovations
processing
logistics,
with
a
focus
on
automation,
predictive
analytics,
quality
control.
AI
such
as
machine
learning,
computer
vision,
IoT
integration,
optimizing
processes
like
sorting,
grading,
packaging,
microbial
detection,
reducing
waste
extending
shelf
life.
Moreover,
AI-powered
robotics
smart
warehouses
streamlining
transportation
inventory
management,
enhancing
operational
integration
demand
forecasting
optimization
is
further
improving
traceability,
minimizing
disruptions,
environmental
impact.
Despite
promising
potential,
challenges
data
system
cost
barriers,
regulatory
concerns
remain.
future
technologies
presents
opportunities
for
continued
innovation,
deep
IoT,
global
scalability,
pathways
sustainable
systems.
concludes
by
discussing
impact
sector
its
potential
drive
more
efficient,
resilient,
chains
worldwide.
Precision
agriculture
depends
on
the
automation
and
mechanization
of
agricultural
equipment
vehicles
in
a
variety
terrains,
which
increases
productivity
sustainability.
This
review
presents
comparative
analysis
significant
simulation
software
used
designing
developing
automated
systems,
emphasizing
their
methodologies
significance
advancing
farm
technology.
Artificial
intelligence
(AI)
machine
learning
(ML)
methods
are
modeled,
optimized,
integrated
using
key
technologies
such
as
MATLAB/Simulink,
SolidWorks,
ANSYS,
AirSim,
Gazebo.
The
results
demonstrate
how
these
improve
automation's
real-time
decision-making,
predictive
maintenance,
system
accuracy.
Case
studies
illustrate
practical
application
simulating
all-terrain
specialized
implements.
best
tools
for
autonomous
navigation
AirSim
Gazebo,
although
MATLAB/Simulink
is
particularly
adept
at
system-level
AI
modeling.
study
takes
new
approach
to
improving
design,
control,
environmental
interactions
by
combining
many
modeling
tools.
makes
it
easier
make
systems
that
last
longer
work
better.
It
suggested
future
investigate
relationship
between
automation,
AI,
greater
detail
propel
precision
forward.
