Classification of Different Plant Species Using Deep Learning and Machine Learning Algorithms
Wireless Personal Communications,
Год журнала:
2024,
Номер
136(4), С. 2275 - 2298
Опубликована: Июнь 1, 2024
Язык: Английский
Innovations in seafood freshness quality: Non-destructive detection of freshness in Litopenaeus vannamei using the YOLO-shrimp model
Food Chemistry,
Год журнала:
2024,
Номер
463, С. 141192 - 141192
Опубликована: Сен. 7, 2024
Язык: Английский
BiFPN-enhanced SwinDAT-based cherry variety classification with YOLOv8
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Фев. 13, 2025
Accurate
classification
of
cherry
varieties
is
crucial
for
their
economic
value
and
market
differentiation,
yet
genetic
diversity
visual
similarity
make
manual
identification
challenging,
hindering
efficient
agricultural
trade
practices.
This
study
addresses
this
issue
by
proposing
a
novel
deep
learning-based
hybrid
model
that
integrates
BiFPN
with
the
YOLOv8n-cls
framework,
enhanced
Swin
Transformer
Deformable
Attention
(DAT)
techniques.
The
was
trained
evaluated
on
newly
constructed
dataset
comprising
from
Turkey's
Western
Mediterranean
region.
Experimental
results
demonstrated
effectiveness
proposed
approach,
achieving
precision
91.91%,
recall
92.0%,
F1-score
91.93%,
an
overall
accuracy
91.714%.
findings
highlight
model's
potential
to
optimize
harvest
timing,
ensure
quality
control,
support
export
classification,
thereby
contributing
improved
practices
outcomes.
Язык: Английский
Innovations in Seafood Freshness Quality: Non-Destructive Detection of Freshness in Litopenaeus Vannamei Using the Yolo-Shrimp Model
Опубликована: Янв. 1, 2024
In
this
work,
changes
in
the
quality
of
Litopenaeus
vannamei
stored
at
4
°C
for
one
week
are
investigated,
focusing
on
biochemical
markers
such
as
total
volatile
basic
nitrogen
(TVB-N),
viable
count
(TVC),
and
degree
melanosis,
along
with
their
correlations.
Additionally,
YOLO-Shrimp
model,
an
advanced
version
YOLOv8
architecture
incorporating
focal
EIOU
loss
function
C3X
computation
module,
is
introduced.
These
enhancements
significantly
improve
precision
adaptability
model
assessing
shrimp
freshness.
The
employs
a
non-destructive,
rapid
assessment
method
by
analyzing
texture,
color
morphological
attributes.
Compared
to
YOLOv8,
performance
improvements
were
observed
(5.07%),
recall
(1.58%),
F1
score
(3.25%),
mAP50
(2.84%).
Empirical
validations
confirmed
that
model's
assessments
align
biochemical,
microbiological,
physical
indicators,
highlighting
its
effectiveness
detecting
freshness
potential
enhance
food
safety
control
standards.
Язык: Английский