Precision livestock farming: an overview on the application in extensive systems
Italian Journal of Animal Science,
Journal Year:
2025,
Volume and Issue:
24(1), P. 859 - 884
Published: March 24, 2025
Language: Английский
Livestock Management With Unmanned Aerial Vehicles: A Review
IEEE Access,
Journal Year:
2022,
Volume and Issue:
10, P. 45001 - 45028
Published: Jan. 1, 2022
The
ease
of
use
and
advancements
in
drone
technology
is
resulting
the
widespread
application
Unmanned
Aerial
Vehicles
(UAVs)
to
diverse
fields,
making
it
a
booming
technology.
Among
UAVs'
several
applications,
livestock
agriculture
one
most
promising,
where
UAVs
facilitate
various
operations
for
efficient
animal
management.
But
field
characterized
by
multiple
environmental,
technical,
economic,
strategic
challenges.
However,
advanced
technological
techniques
like
Artificial
Intelligence
(AI),
Internet
Things
(IoT),
Machine
Learning
(ML),
Deep
(DL),
sensors,
etc.,
along
with
assurance
welfare
while
operating
UAVs,
can
lead
adoption
amongst
farmers.
This
paper
discusses
management
research
monitor
farm
animals
via
detection,
counting,
tracking
animals,
etc.
In
this
article,
an
attempt
has
been
made
elucidate
different
aspects
broader
issues
around
highlighting
associated
challenges,
opportunities,
prospects.
work
first
review
on
subject
matter
all
necessary
information
analysis,
best
our
knowledge.
Therefore,
article
promises
provide
interested
researchers
detailed
about
field,
guiding
future
research.
Language: Английский
Mask R-CNN and Centroid Tracking Algorithm to Process UAV Based Thermal-RGB Video for Drylot Cattle Heat Stress Monitoring
Keshawa M. Dadallage,
No information about this author
Basavaraj R. Amogi,
No information about this author
Lav R. Khot
No information about this author
et al.
Drones,
Journal Year:
2024,
Volume and Issue:
8(9), P. 491 - 491
Published: Sept. 17, 2024
This
study
developed
and
evaluated
an
algorithm
for
processing
thermal-RGB
video
feeds
captured
by
unmanned
aerial
vehicle
(UAV)
to
automate
heat
stress
monitoring
in
cattle
housed
the
drylots.
The
body
surface
temperature
(BST)
of
individual
cows
was
used
as
indicator
stress.
UAV
data
were
collected
using
RGB
thermal
infrared
imagers,
respectively,
at
2
6.67
cm
per
pixel
spatial
resolution
Spring
2023
(dataset-1)
Summer
2024
(dataset-2).
Study
sites
two
commercial
drylots
Washington
State.
custom
algorithms
to:
(1)
detect
localize
a
Mask
R-CNN-based
instance
segmentation
model
combined
with
centroid
tracking;
(2)
extract
BST
averaging
thermal-imagery
pixels
each
segmented
cows.
showed
higher
detection
accuracy
images
input
(F1
score:
0.89)
compared
0.64).
extraction
imaging
approach
required
corrections
alignment
problems
associated
differences
optics,
field
view,
resolution,
lens
properties.
Consequently,
imaging-only
adopted
assessing
real-time
cow
localization
estimation.
Operating
one
frame
second,
successfully
detected
72.4%
81.65%
total
frames
from
dataset-1
(38
s)
-2
(48
s),
respectively.
mean
absolute
difference
between
output
ground
truth
(BSTGT)
2.1
°C
3.3
(dataset-2),
demonstrating
satisfactory
performance.
With
further
refinements,
this
could
be
viable
tool
large-scale
drylot
production
systems.
Language: Английский
Pasture Research Using Aerial Photography and Photogrammetry
Published: Oct. 28, 2021
This
article
focuses
on
the
topic
of
using
unmanned
aerial
vehicles
and
modern
software
solutions
systems
in
study
pastures,
photography
photogrammetry.
Language: Английский
Auto-Encoder Learning-Based UAV Communications for Livestock Management
Drones,
Journal Year:
2022,
Volume and Issue:
6(10), P. 276 - 276
Published: Sept. 25, 2022
The
advancement
in
computing
and
telecommunication
has
broadened
the
applications
of
drones
beyond
military
surveillance
to
other
fields,
such
as
agriculture.
Livestock
farming
using
unmanned
aerial
vehicle
(UAV)
systems
requires
monitoring
animals
on
relatively
large
farmland.
A
reliable
communication
system
between
UAVs
ground
control
station
(GCS)
is
necessary
achieve
this.
This
paper
describes
learning-based
strategies
techniques
that
enable
interaction
data
exchange
a
GCS.
We
propose
deep
auto-encoder
UAV
design
framework
for
end-to-end
communications.
Simulation
results
show
learns
joint
transmitter
receiver
mapping
functions
various
strategies,
QPSK,
8PSK,
16PSK
16QAM,
without
prior
knowledge.
Language: Английский
Effects of continuous drone herding on behavioral response and spatial distribution of grazing cattle
Hiroki Anzai,
No information about this author
Mahiro Kumaishi
No information about this author
Applied Animal Behaviour Science,
Journal Year:
2023,
Volume and Issue:
268, P. 106089 - 106089
Published: Oct. 21, 2023
Language: Английский
Assessing the Impact of Clearing and Grazing on Fuel Management in a Mediterranean Oak Forest through Unmanned Aerial Vehicle Multispectral Data
Drones,
Journal Year:
2024,
Volume and Issue:
8(8), P. 364 - 364
Published: July 31, 2024
Climate
change
has
intensified
the
need
for
robust
fire
prevention
strategies.
Sustainable
forest
fuel
management
is
crucial
in
mitigating
occurrence
and
rapid
spread
of
fires.
This
study
assessed
impact
vegetation
clearing
and/or
grazing
over
a
three-year
period
herbaceous
shrub
parts
Mediterranean
oak
forest.
Using
high-resolution
multispectral
data
from
an
unmanned
aerial
vehicle
(UAV),
four
flight
surveys
were
conducted
2019
(pre-
post-clearing)
to
2021.
These
used
evaluate
different
scenarios:
combined
grazing,
individual
application
each
method,
control
scenario
that
was
neither
cleared
nor
purposely
grazed.
The
UAV
allowed
detailed
monitoring
dynamics,
enabling
classification
into
arboreal,
shrubs,
herbaceous,
soil
categories.
Grazing
pressure
estimated
through
GPS
collars
on
sheep
flock.
Additionally,
good
correlation
(r
=
0.91)
observed
between
UAV-derived
volume
estimates
field
measurements.
practices
proved
be
efficient
management,
with
grazed
areas
showing
lower
regrowth,
followed
by
only
subjected
clearing.
On
other
hand,
not
any
these
treatments
presented
growth.
Language: Английский
VIPER: Vision-Based System to Detect Potential Predators for Herding with Robots
Xiao Li Yang,
No information about this author
Abel Carnicero,
No information about this author
Lídia Sánchez-González
No information about this author
et al.
Lecture notes in computer science,
Journal Year:
2024,
Volume and Issue:
unknown, P. 214 - 223
Published: Oct. 8, 2024
Language: Английский
Applications of AI in UAVs Using In‐Flight Parameters
Published: Dec. 13, 2024
Recent
developments
in
UAV
(unmanned
aerial
vehicle)
technology
have
led
to
its
use
a
wide
variety
of
applications,
namely,
large-scale
mapping,
autonomous
navigation,
and
package
delivery.
The
present
challenges
that
restrict
further
applications
UAVs
are
their
poor
flight
endurance,
reduced
payload
capacity,
limited
obstacle
avoidance
ability
various
flying
conditions.
To
achieve
optimum
characteristics,
principal
parameters,
which
indispensable
for
UAVs'
operation,
such
as
airspeed,
power
status,
navigation
data,
stabilization,
altitude,
need
be
specifically
controlled.
Besides
the
variations
in-flight
parameters
accordance
with
specific
notable
changes
can
also
occur
result
weather
conditions
wind
gusts,
rain,
clouds,
thermal
formation
environments
topographies.
In
fully
long-range
platforms
susceptible
collision
other
airborne
objects,
living
organisms,
or
man-made
constructions.
exploit
benefits
extend
application
our
daily
lives,
aforementioned
limitations,
optimization
well
improvement
avoidance,
explored.
Artificial
intelligence
(AI)
is
powerful
tool
several
supervised,
semi-supervised,
unsupervised
predictive
models
found
corresponding
cases
realm
well.
Different
come
overlapping
hardware
health
analytics,
path
planning,
avoidance.
order
up
possible
help
solving
these
challenges,
data
collection
pertaining
different
scenarios
required.
types
collected
internal
external
environment
conditions,
image
environment.
this
study,
dataset
positional
energy
has
been
considered.
Various
ML
regression
implemented
MATLAB
using
built-in
functions
two
main
relationships,
consumption
versus
speed
angle.
simulation
results
obtained
when
training
testing
relationships
discussed
presented
chapter.
Results
demonstrated
neural
network
model
performs
best
case
instantaneous
angle
an
RMSE
value
159.58,
while
decision
tree
was
perform
159.80.
Language: Английский