Piscicultura inteligente: a integração das Tecnologia 4.0 e “Business Intelligence” para gestão ágil na aquicultura
Revista de Gestão e Secretariado (Management and Administrative Professional Review),
Год журнала:
2025,
Номер
16(1), С. e4524 - e4524
Опубликована: Янв. 9, 2025
A
crescente
demanda
por
alimentos
devido
ao
aumento
populacional
pressiona
a
pesca
de
captura
e
esgota
os
estoques
peixes.
Como
alternativa,
aquicultura
avançada
surge,
embora
ainda
não
tenha
alcançado
o
mesmo
nível
tecnológico
outros
setores.
está
em
crescimento,
espera-se
que
até
2030
forneça
maior
parte
do
peixe
consumido
globalmente.
No
entanto,
setor,
muitas
partes
mundo,
enfrenta
desafios.
As
tecnologias
4.0,
podem
proporcionar
ferramentas
para
criação
pisciculturas
inteligentes,
usam
Internet
of
Things,
big
data,
Inteligência
Artificial
blockchain
promover
eficiência
sustentabilidade.
Neste
contexto,
Business
Intelligence
(BI)
aparece
como
uma
alternativa
essencial
auxiliar
transformação
data
conhecimento
gestores
tomadores
decisão
na
aquicultura.
Esta
revisão
tem
objetivo
explorar
conceitos
(BI),
piscicultura
inteligente
digitais
aplicadas
à
aquicultura,
proporcionando
visão
atualizada
dos
avanços
área.
Para
atingir
esse
objetivo,
foram
analisadas
cinco
revisões
recentes
sobre
estado
atual
das
4.0.
Além
disso,
busca
sistemática
resultou
coleta
20
artigos
originais
adicionais.
O
presente
trabalho
oferece
organizada
estudos
abordam
inteligente,
tempo
integra
algumas
pesquisas
focadas
aplicação
conceito
BI.
Os
trabalhos
analisados
destacam
informações
chave
ser
integradas
piscicultura,
no
Brasil
globalmente,
com
facilitar
tomada
decisões
gestão
sustentável
An efficient detection model based on improved YOLOv5s for abnormal surface features of fish
Mathematical Biosciences & Engineering,
Год журнала:
2024,
Номер
21(2), С. 1765 - 1790
Опубликована: Янв. 1, 2024
Detecting
abnormal
surface
features
is
an
important
method
for
identifying
fish.
However,
existing
methods
face
challenges
in
excessive
subjectivity,
limited
accuracy,
and
poor
real-time
performance.
To
solve
these
challenges,
a
accurate
detection
model
of
in-water
fish
proposed,
based
on
improved
YOLOv5s.
The
specific
enhancements
include:
1)
We
optimize
the
complete
intersection
over
union
non-maximum
suppression
through
normalized
Gaussian
Wasserstein
distance
metric
to
improve
model's
ability
detect
tiny
targets.
2)
design
DenseOne
module
enhance
reusability
features,
introduce
MobileViTv2
speed,
which
are
integrated
into
feature
extraction
network.
3)
According
ACmix
principle,
we
fuse
omni-dimensional
dynamic
convolution
convolutional
block
attention
challenge
extracting
deep
within
complex
backgrounds.
carried
out
comparative
experiments
160
validation
sets
fish,
achieving
precision,
recall,
mAP
Язык: Английский
Overview of aquaculture Artificial Intelligence (AAI) applications: enhance sustainability and productivity, reduce labor costs, and increase the quality of aquatic products
Annals of Animal Science,
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 29, 2024
Abstract
The
current
work
investigates
the
prospective
applications
of
Artificial
Intelligence
(AI)
in
aquaculture
industry.
AI
depends
on
collecting,
validating,
and
analyzing
data
from
several
aspects
using
sensor
readings,
feeding
sheets.
is
an
essential
tool
that
can
monitor
fish
behavior
increase
resilience
quality
seafood
products.
Furthermore,
algorithms
early
detect
potential
pathogen
infections
disease
outbreaks,
allowing
stakeholders
to
take
timely
preventive
measures
subsequently
make
proper
decision
appropriate
time.
predict
ecological
conditions
should
help
farmers
adopt
strategies
plans
avoid
negative
impacts
farms
create
easy
safe
environment
for
production.
In
addition,
aids
analyze
collect
regarding
nutritional
requirements,
nutrient
availability,
price
could
adjust
modify
their
diets
optimize
feed
formulations.
Thus,
reduce
labor
costs,
aquatic
animal’s
growth,
health,
formulation
waste
output
detection
outbreaks.
Overall,
this
review
highlights
importance
achieve
sustainability
boost
net
profits
Язык: Английский
State of charge estimation for lithium‐ion battery pack based on real vehicle data and optimized backpropagation method by adaptive cross mutation sparrow search algorithm
Energy Science & Engineering,
Год журнала:
2023,
Номер
12(3), С. 896 - 912
Опубликована: Дек. 25, 2023
Abstract
In
response
to
the
issues
of
traditional
backpropagation
(BP)
neural
networks
in
state
charge
(SOC)
estimation,
including
easy
convergence
local
optima,
slow
speed,
and
low
accuracy,
this
paper
proposes
a
novel
adaptive
crossover
mutation
strategy
dynamic
sparrow
search
algorithm
optimize
BP
networks'
initial
values
thresholds
(ACMSSA‐BP).
The
proposed
method
is
based
on
algorithm,
where
number
producers
scroungers
adjusted
through
an
factor.
This
improvement
effectively
transitions
process
from
extensive
full
exploration
localized
fine‐tuning
search.
position
update
phase
producers,
strategies
are
introduced
increase
diversity
good
populations,
prevent
converging
maintain
its
capability
later
stage.
Using
real
transportation
data
coal
mining
flame‐proof
tracked
vehicles,
we
applied
correlation
theory
extract
model
feature
parameters
constructed
training
set
estimate
SOC.
results
both
static
validation
experiments
have
indicated
that
ACMSSA‐BP
has
delivered
impressive
performance
predicting
SOC,
as
reflected
mean
absolute
error,
root
squared
percentage
error
less
than
1.5%,
1.6%,
respectively.
Compared
with
BP,
SSA‐BP,
CMSSA‐BP,
PSO‐BP,
NARX_NN
methods,
approach
demonstrates
enhanced
accuracy
significant
robustness,
generalization
capabilities.
Язык: Английский
A Survey of Deep Learning for Intelligent Feeding in Smart Fish Farming
Опубликована: Ноя. 24, 2023
The
rapid
development
of
deep
learning
has
been
successfully
applied
in
various
fields,
including
aquaculture,
providing
new
methods
and
ideas
to
realize
unmanned
intelligent
aquaculture.
This
paper
focuses
on
the
technology
research
used
for
feeding
fish
farming
past
decade,
discusses
application
detail,
behavior
analysis,
detection
tracking
live
fish,
growth
state
monitoring,
residual
bait
identification
counting,
water
quality
prediction,
etc.,
summarizes
evaluates
methods,
at
same
time,
analyzes
technical
details
precision
is
analyzed
details,
data,
algorithms,
evaluation
performance
indexes.
summarized
results
show
that
advantage
lies
automatic
extraction
features,
which
also
provides
support
construction
system.
However,
due
large
differences
species,
aquaculture
environments
data
acquisition
less
portable,
it
still
stage
weak
artificial
intelligence,
requires
a
amount
train
model,
cost
high,
become
bottleneck
restricts
further
Nevertheless,
made
breakthroughs
processing
complex
data.
In
summary,
purpose
this
review
provide
researchers
producers
with
better
understanding
current
status
strong
theoretical
production
process.
Язык: Английский