Advances in finance, accounting, and economics book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 457 - 482
Published: Dec. 27, 2024
This
chapter
pioneers
the
utilization
of
YOLOv8,
an
advanced
object
detection
algorithm,
as
a
transformative
tool
to
address
pressing
issues
faced
by
farmers
when
wild
animals
encroach
upon
their
lands.
The
comprehensive
pipeline,
spanning
from
custom
dataset
processing
YOLOv8
model
deployment,
establishes
robust
framework
for
effective
integration
deep
learning
algorithms,
enabling
real-
time
analysis
imagery
and
sensor
data
in
farmlands.
power
lies
its
streamlined
architecture,
eliminating
traditional
reliance
on
Convolutional
Neural
Networks
(CNNs)
Recurrent
(RNNs),
thereby
enhancing
computational
efficiency.
groundbreaking
technology
offers
scalable
solution,
ushering
new
era
sustainable
coexistence
between
agriculture
wildlife
management.
Drones,
Journal Year:
2023,
Volume and Issue:
7(6), P. 398 - 398
Published: June 15, 2023
In
recent
years,
UAV
remote
sensing
has
gradually
attracted
the
attention
of
scientific
researchers
and
industry,
due
to
its
broad
application
prospects.
It
been
widely
used
in
agriculture,
forestry,
mining,
other
industries.
UAVs
can
be
flexibly
equipped
with
various
sensors,
such
as
optical,
infrared,
LIDAR,
become
an
essential
observation
platform.
Based
on
sensing,
obtain
many
high-resolution
images,
each
pixel
being
a
centimeter
or
millimeter.
The
purpose
this
paper
is
investigate
current
applications
well
aircraft
platforms,
data
types,
elements
category;
processing
methods,
etc.;
study
advantages
technology,
limitations,
promising
directions
that
still
lack
applications.
By
reviewing
papers
published
field
we
found
research
classified
into
four
categories
according
field:
(1)
Precision
including
crop
disease
observation,
yield
estimation,
environmental
observation;
(2)
Forestry
forest
identification,
disaster
(3)
Remote
power
systems;
(4)
Artificial
facilities
natural
environment.
We
image
(RGB,
multi-spectral,
hyper-spectral)
mainly
neural
network
methods;
monitoring,
multi-spectral
are
most
studied
type
data;
for
LIDAR
data,
end-to-end
method;
review
examines
development
process
certain
fields
implementation
some
predictions
made
about
possible
future
directions.
Real-time
crowd
monitoring
plays
a
pivotal
role
in
effectively
managing
public
spaces
and
ensuring
safety.
This
study
investigates
the
fusion
of
IoT
devices
YOLO
object
detection
model
to
accurately
count
crowds.
facilitate
instantaneous
collection
data
from
cameras,
while
adeptly
identifies
individuals
within
recorded
video
frames.
The
rigorously
assesses
performance
three
variants:
V5,
V8
NAS.
Findings
reveal
that
NAS
surpasses
V5
mean
average
precision
(mAP),
achieving
an
exceptional
mAP
95.1%.
heightened
is
attributed
integration
Neural
Architecture
Search
(NAS)
into
model,
fine-tuning
its
architecture
specifically
for
counting
tasks.
It
analyzes
various
networking
models
proposed
earlier
studies
analyzing
crowded
scenes
spaces.
emphasizes
potential
hybrid
involving
IP
camera
module
Deep
Network
effective
sensing.
In
this
setup,
captures
footage,
DNN
detects
density
based
on
people
recognized.
approach
presents
encouraging
solution
real-time
management
environments.
Journal of Imaging,
Journal Year:
2025,
Volume and Issue:
11(4), P. 94 - 94
Published: March 24, 2025
Monitoring
coastal
marine
wildlife
is
crucial
for
biodiversity
conservation,
environmental
management,
and
sustainable
utilization
of
tourism-related
natural
assets.
Conducting
in
situ
censuses
population
studies
extensive
remote
habitats
often
faces
logistical
constraints,
necessitating
the
adoption
advanced
technologies
to
enhance
efficiency
accuracy
monitoring
efforts.
This
study
investigates
aerial
imagery
deep
learning
methodologies
automated
detection,
classification,
enumeration
marine-coastal
species.
A
comprehensive
dataset
high-resolution
images,
captured
by
drones
aircrafts
over
southern
elephant
seal
(Mirounga
leonina)
South
American
sea
lion
(Otaria
flavescens)
colonies
Valdés
Peninsula,
Patagonia,
Argentina,
was
curated
annotated.
Using
this
annotated
dataset,
a
framework
developed
trained
identify
classify
individual
animals.
The
resulting
model
may
help
produce
automated,
accurate
metrics
that
support
analysis
ecological
dynamics.
achieved
F1
scores
between
0.7
0.9,
depending
on
type
individual.
Among
its
contributions,
methodology
provided
essential
insights
into
impacts
emergent
threats,
such
as
outbreak
highly
pathogenic
avian
influenza
virus
H5N1
during
2023
austral
spring
season,
which
caused
significant
mortality
these
SAE technical papers on CD-ROM/SAE technical paper series,
Journal Year:
2025,
Volume and Issue:
1
Published: Feb. 7, 2025
<div
class="section
abstract"><div
class="htmlview
paragraph">Human-wildlife
conflicts
pose
significant
challenges
to
both
conservation
efforts
and
community
well-being.
As
these
escalate
globally,
innovative
technologies
become
imperative
for
effective
humane
management
strategies.
This
paper
presents
an
integrated
autonomous
drone
solution
designed
mitigate
human-wildlife
by
leveraging
in
surveillance
artificial
intelligence.
The
proposed
system
consists
of
stationary
IR
cameras
that
are
setup
within
the
conflict
prone
areas,
which
utilizes
machine
learning
identify
presence
wild
animals
send
corresponding
location
a
docking
station.
An
equipped
with
high-resolution
sensors
is
deployed
from
station
provided
location.
camera
object
detection
technology
scan
specified
zone
detect
animal
emit
repelling
ultrasonic
sound
device
achieve
non-invasive
deterrence
provides
approaches
develop
algorithms,
optimize
strategies,
adapt
evolving
dynamics
wildlife
behavior.
promising
avenue
addressing
conflicts,
promoting
coexistence,
contributing
broader
field
technology-driven
ecological
management.</div></div>
Cybernetics and Information Technologies,
Journal Year:
2025,
Volume and Issue:
25(1), P. 19 - 35
Published: March 1, 2025
Abstract
Effective
wildlife
monitoring
in
hilly
and
rural
areas
can
protect
communities
diminish
human-wildlife
conflicts.
A
collaborative
framework
may
overcome
challenges
like
inadequate
data
integrity
security,
declining
detection
accuracy
over
time,
delays
critical
decision-making.
The
proposed
study
aims
to
develop
a
real-time
using
Federated
Learning
blockchain
improve
conservation
strategies.
Min-max
normalization
enhances
training
Elastic
Weight
Consolidation
(EWC)
for
adaptation.
improvised
YOLOv8+EWC
enables
classification
continual
learning
prevents
catastrophic
forgetting.
It
also
automates
actions
based
on
results
smart
contracts
ensures
secure,
transparent
management
with
blockchain.
Compared
existing
classifiers
such
as
Deep
Neural
Network,
Dense-YOLO4,
WilDect:
YOLO,
performs
exceptionally
well
across
several
metrics,
accomplishing
an
of
98.91%.
Thus,
the
model
reliable
decision-making
by
providing
accurate,
information
about
wildlife.
