Artificial Intelligence of Things (AIoT) Advances in Aquaculture: A Review
Processes,
Journal Year:
2025,
Volume and Issue:
13(1), P. 73 - 73
Published: Jan. 1, 2025
The
integration
of
artificial
intelligence
(AI)
and
the
internet
things
(IoT),
known
as
(AIoT),
is
driving
significant
advancements
in
aquaculture
industry,
offering
solutions
to
longstanding
challenges
related
operational
efficiency,
sustainability,
productivity.
This
review
explores
latest
research
studies
AIoT
within
focusing
on
real-time
environmental
monitoring,
data-driven
decision-making,
automation.
IoT
sensors
deployed
across
systems
continuously
track
critical
parameters
such
temperature,
pH,
dissolved
oxygen,
salinity,
fish
behavior.
AI
algorithms
process
these
data
streams
provide
predictive
insights
into
water
quality
management,
disease
detection,
species
identification,
biomass
estimation,
optimized
feeding
strategies,
among
others.
Much
adoption
advantageous
various
fronts,
there
are
still
numerous
challenges,
including
high
implementation
costs,
privacy
concerns,
need
for
scalable
adaptable
models
diverse
environments.
also
highlights
future
directions
aquaculture,
emphasizing
potential
hybrid
models,
improved
scalability
large-scale
operations,
sustainable
resource
management.
Language: Английский
Review of state-of-the-art improvements in recirculating aquaculture systems: Insights into design, operation, and statistical modeling approaches
Aquaculture,
Journal Year:
2025,
Volume and Issue:
unknown, P. 742545 - 742545
Published: April 1, 2025
Language: Английский
Integrating AIoT Technologies in Aquaculture: A Systematic Review
Future Internet,
Journal Year:
2025,
Volume and Issue:
17(5), P. 199 - 199
Published: April 30, 2025
The
increasing
global
demand
for
seafood
underscores
the
necessity
sustainable
aquaculture
practices.
However,
several
challenges,
including
rising
operational
costs,
variable
environmental
conditions,
and
threat
of
disease
outbreaks,
impede
progress
in
this
field.
This
review
explores
transformative
role
Artificial
Intelligence
Things
(AIoT)
mitigating
these
challenges.
We
analyse
current
research
on
AIoT
applications
aquaculture,
with
a
strong
emphasis
use
IoT
sensors
real-time
data
collection
AI
algorithms
effective
analysis.
Our
focus
areas
include
monitoring
water
quality,
implementing
smart
feeding
strategies,
detecting
diseases,
analysing
fish
behaviour,
employing
automated
counting
techniques.
Nevertheless,
gaps
remain,
particularly
regarding
integration
broodstock
management,
development
multimodal
systems,
challenges
model
generalization.
Future
advancements
should
prioritise
adaptability,
cost-effectiveness,
sustainability
while
emphasizing
importance
advanced
biosensing
capabilities,
digital
twin
technologies.
In
conclusion,
presents
substantial
opportunities
enhancing
practices,
successful
implementation
will
depend
overcoming
related
to
scalability,
cost,
technical
expertise,
improving
models’
ensuring
sustainability.
Language: Английский