Sensors,
Год журнала:
2024,
Номер
24(20), С. 6608 - 6608
Опубликована: Окт. 14, 2024
An
image
is
a
visual
representation
that
can
be
used
to
obtain
information.
A
camera
on
moving
vector
(e.g.,
rover,
drone,
quad,
etc.)
may
acquire
images
along
controlled
trajectory.
The
maximum
information
captured
during
fixed
acquisition
time
when
consecutive
do
not
overlap
and
have
no
space
(or
gap)
between
them.
said
Cleaner Energy Systems,
Год журнала:
2024,
Номер
9, С. 100132 - 100132
Опубликована: Авг. 11, 2024
Modern
agricultural
practices
encounter
challenges
related
to
operational
efficiency
and
environmental
effects.
This
prompts
a
demand
for
innovative
solutions
foster
sustainability
in
farming
while
emphasizing
the
limitations
of
conventional
methods.
To
address
these
modern
agriculture
systems,
this
research
proposes
comprehensive
framework
smart
farming.
The
proposed
comprises
three
technology
integrations:
1)
an
efficient
integration
renewable
energy
resources
(RERs)
with
solar
panels
battery
storage
systems
(BESS),
2)
IoT-based
monitoring
precision
irrigation,
3)
android
application-controlled
robotic
system
targeted
chemical
application.
investigates
case
study
on
Sharjah,
United
Arab
Emirates
(UAE)
explore
analyze
optimal
scenarios
multiple
resources.
Results
demonstrate
successful
cross-prototype
through
Blynk
IoT
platform
providing
users
unified
interface.
Furthermore,
results
provide
analysis
investigation
into
interactions
between
RERs
grid
across
various
combinations.
findings
indicate
potential
revolutionize
thus
offer
sustainable,
efficient,
technologically
advanced
approach.
It
also
represents
contribution
complete
solution
presenting
tangible
promising
future
sustainable
practices.
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.
Remote Sensing,
Год журнала:
2024,
Номер
16(6), С. 1003 - 1003
Опубликована: Март 12, 2024
Yield
calculation
is
an
important
link
in
modern
precision
agriculture
that
effective
means
to
improve
breeding
efficiency
and
adjust
planting
marketing
plans.
With
the
continuous
progress
of
artificial
intelligence
sensing
technology,
yield-calculation
schemes
based
on
image-processing
technology
have
many
advantages
such
as
high
accuracy,
low
cost,
non-destructive
calculation,
they
been
favored
by
a
large
number
researchers.
This
article
reviews
research
crop-yield
remote
images
visible
light
images,
describes
technical
characteristics
applicable
objects
different
schemes,
focuses
detailed
explanations
data
acquisition,
independent
variable
screening,
algorithm
selection,
optimization.
Common
issues
are
also
discussed
summarized.
Finally,
solutions
proposed
for
main
problems
arisen
so
far,
future
directions
predicted,
with
aim
achieving
more
wider
popularization
image
technology.
Agriculture,
Год журнала:
2024,
Номер
14(9), С. 1596 - 1596
Опубликована: Сен. 13, 2024
Plant
height
is
a
crucial
indicator
of
crop
growth.
Rapid
measurement
facilitates
the
implementation
and
management
planting
strategies,
ensuring
optimal
production
quality
yield.
This
paper
presents
low-cost
method
for
rapid
multiple
lettuce
heights,
developed
using
an
improved
YOLOv8n-seg
model
stacking
characteristics
planes
in
depth
images.
First,
we
designed
lightweight
instance
segmentation
based
on
by
enhancing
architecture
reconstructing
channel
dimension
distribution.
was
trained
small-sample
dataset
augmented
through
random
transformations.
Secondly,
proposed
to
detect
segment
horizontal
plane.
leverages
plane,
as
identified
image
histogram
from
overhead
perspective,
allowing
identification
parallel
camera’s
imaging
Subsequently,
evaluated
distance
between
each
plane
centers
contours
select
cultivation
substrate
reference
bottom
height.
Finally,
plants
determined
calculating
difference
top
plant.
The
experimental
results
demonstrated
that
achieved
25.56%
increase
processing
speed,
along
with
2.4%
enhancement
mean
average
precision
compared
original
model.
accuracy
plant
algorithm
reached
94.339%
hydroponics
91.22%
pot
scenarios,
absolute
errors
7.39
mm
9.23
mm,
similar
sensor’s
direction
error.
With
images
downsampled
factor
1/8,
highest
speed
recorded
6.99
frames
per
second
(fps),
enabling
system
process
174
targets
second.
confirmed
exhibits
promising
accuracy,
efficiency,
robustness.
INMATEH Agricultural Engineering,
Год журнала:
2024,
Номер
unknown, С. 96 - 105
Опубликована: Март 31, 2024
This
research
is
dedicated
to
enhancing
the
accuracy
and
processing
speed
of
grape
disease
recognition.
As
a
result,
real-time
detection
model
named
MSCI-YOLOv8s,
based
on
an
improved
YOLOv8s
framework
proposed.
The
primary
innovation
this
lies
in
replacing
backbone
network
original
with
more
efficient
MobileNetV3.
alteration
not
only
strengthens
ability
capture
features
various
manifestations
leaf
images
but
also
improves
its
generalization
capabilities
stability.
Additionally,
incorporates
SPPFCSPC
pyramid
pooling
structure,
which
maintains
stability
receptive
field
while
significantly
speed.
integration
CBAM
attention
mechanism
further
accentuates
identify
key
features,
substantially
increasing
detection.
Moreover,
employs
Inner-SIoU
as
loss
function,
optimizing
precision
bounding
box
regression
accelerating
convergence,
thereby
efficiency.
Rigorous
testing
has
shown
that
MSCI-YOLOv8s
achieves
impressive
average
(mAP)
97.7%,
inference
time
just
37.2
milliseconds
memory
footprint
39.3
MB.
These
advancements
render
highly
extremely
practical
for
detection,
meeting
actual
demands
orchard
identification
demonstrating
significant
potential
application.
AgriEngineering,
Год журнала:
2024,
Номер
6(3), С. 2494 - 2512
Опубликована: Авг. 1, 2024
In
open-field
agricultural
environments,
the
inherent
unpredictable
situations
pose
significant
challenges
for
effective
human–robot
interaction.
This
study
aims
to
enhance
natural
communication
between
humans
and
robots
in
such
challenging
conditions
by
converting
detection
of
a
range
dynamic
human
movements
into
specific
robot
actions.
Various
machine
learning
models
were
evaluated
classify
these
movements,
with
Long
Short-Term
Memory
(LSTM)
demonstrating
highest
performance.
Furthermore,
Robot
Operating
System
(ROS)
software
(Melodic
Version)
capabilities
employed
interpret
certain
actions
be
performed
unmanned
ground
vehicle
(UGV).
The
novel
interaction
framework
exploiting
vision-based
activity
recognition
was
successfully
tested
through
three
scenarios
taking
place
an
orchard,
including
(a)
UGV
following
authorized
participant;
(b)
GPS-based
navigation
specified
site
orchard;
(c)
combined
harvesting
scenario
participants
aid
transporting
crates
from
harvest
designated
sites.
main
challenge
precise
hand
gesture
“come”
alongside
navigating
intricate
environments
complexities
background
surroundings
obstacle
avoidance.
Overall,
this
lays
foundation
future
advancements
collaboration
agriculture,
offering
insights
how
integrating
can
communication,
trust,
safety.