Aquaculture,
Journal Year:
2022,
Volume and Issue:
566, P. 739175 - 739175
Published: Dec. 22, 2022
Accurate
measurements
of
breeding
traits
on
individuals
are
critical
in
aquaculture
for
obtaining
values
and
tracking
the
progress
program.
Modern
programs
prioritize
not
only
production
but
also
complex
related
to
production,
product
quality,
body
composition,
disease
resistance,
fish
health,
such
as
slaughter
traits.
Slaughter
can
be
selected
indirectly
incorporated
into
programs.
Indirect
selection
is
cost-effective,
there
often
little
genetic
correlation
between
measured
target
phenotypic
prediction
using
modern
phenotyping
technology
game-changing
indirect
selection.
This
paper
proposes
an
analytical
framework
predicting
images.
The
demonstrated
that
images
addition
weight
improved
fat
percentage
accuracy
from
0.4
0.7
when
compared
a
model
used
its
numerical
derivations.
allowed
interpretation
by
providing
imaginal
features.
In
case
study,
dorsal
side,
upper
edge
pectoral
fin,
operculum
were
discovered
three
regions
seabream
have
properties
negatively
correlated
with
fillet
percentage.
showed
both
visceral
highly
total
area.
revealed
lower
edge,
anal
fin
explain
variation
Future
research
will
required
segment
quantify
each
predictive
feature
calculate
heritability.
potentially
predict
other
harvest,
post-slaughter,
metabolic
aquacultural
study.
Journal of Marine Science and Engineering,
Journal Year:
2024,
Volume and Issue:
12(12), P. 2177 - 2177
Published: Nov. 28, 2024
The
gut
is
the
first
organ
to
contact
food,
and
it
often
target
of
nutrition
studies
performed
on
aquaculture
fish.
Histological
analysis
reveals
morphological
changes
in
fish
intestines
caused
by
ingredients
formulated
feeds.
However,
this
type
mainly
based
a
semi-quantitative
approach,
restricted
specialized
researchers,
may
provide
inconsistent
results
between
studies.
This
study
addresses
these
limitations
combining
quantitative
features
characterize
anterior,
intermediate,
distal
sections
intestine
meagre
(Argyrosomus
regius)
subjected
different
nutritional
status.
Collected
data
were
used
build
machine
learning
models,
select
most
accurate
ones,
identify
key
for
predicting
malnutrition.
Logistic
regression,
support
vector
machines,
ensemble
stacking
best
across
all
intestinal
sections.
Combining
yielded
predictions,
with
villi
number,
density
area,
goblet
cell
count
being
crucial
classification
task.
When
considering
alone,
outperformed
ones.
intermediate
section
showed
model
accuracy,
indicating
higher
sensitivity
changes.
These
demonstrate
potential
models
streamline
histomorphological
analyses
evaluate
status,
making
them
more
accessible
standard
users.
LatIA,
Journal Year:
2024,
Volume and Issue:
2, P. 116 - 116
Published: Dec. 1, 2024
AI
incorporation
in
aquaculture
has
transformed
the
industry
completely,
making
crucial
processes
automated,
maximizing
productivity,
and
promoting
sustainability.
AI,
specifically
machine
learning,
refers
to
application
of
modern
smart
systems
for
tasks
such
as
fish
species
classification,
health
monitoring,
feed
regulation,
management
water
quality.
It
thereby
sets
inefficiency
issues
right
while
reducing
impacts
on
environment
through
real-time
data-driven
decision-making.
This
article
deals
with
very
recent
developments
applications
learning
aquaculture,
pointing
out
their
importance
increasing
production
well
eco-friendly
aquatic
environments
Aquaculture,
Journal Year:
2022,
Volume and Issue:
566, P. 739175 - 739175
Published: Dec. 22, 2022
Accurate
measurements
of
breeding
traits
on
individuals
are
critical
in
aquaculture
for
obtaining
values
and
tracking
the
progress
program.
Modern
programs
prioritize
not
only
production
but
also
complex
related
to
production,
product
quality,
body
composition,
disease
resistance,
fish
health,
such
as
slaughter
traits.
Slaughter
can
be
selected
indirectly
incorporated
into
programs.
Indirect
selection
is
cost-effective,
there
often
little
genetic
correlation
between
measured
target
phenotypic
prediction
using
modern
phenotyping
technology
game-changing
indirect
selection.
This
paper
proposes
an
analytical
framework
predicting
images.
The
demonstrated
that
images
addition
weight
improved
fat
percentage
accuracy
from
0.4
0.7
when
compared
a
model
used
its
numerical
derivations.
allowed
interpretation
by
providing
imaginal
features.
In
case
study,
dorsal
side,
upper
edge
pectoral
fin,
operculum
were
discovered
three
regions
seabream
have
properties
negatively
correlated
with
fillet
percentage.
showed
both
visceral
highly
total
area.
revealed
lower
edge,
anal
fin
explain
variation
Future
research
will
required
segment
quantify
each
predictive
feature
calculate
heritability.
potentially
predict
other
harvest,
post-slaughter,
metabolic
aquacultural
study.
