Artificial Intelligence Review,
Journal Year:
2025,
Volume and Issue:
58(3)
Published: Jan. 6, 2025
Abstract
Seed
quality
is
of
great
importance
for
agricultural
cultivation.
High-throughput
phenotyping
techniques
can
collect
magnificent
seed
information
in
a
rapid
and
non-destructive
manner.
Emerging
deep
learning
technology
brings
new
opportunities
effectively
processing
massive
diverse
data
from
seeds
evaluating
their
quality.
This
article
comprehensively
reviews
the
principle
several
high-throughput
non-destructively
collection
information.
In
addition,
recent
research
studies
on
application
learning-based
approaches
inspection
are
reviewed
summarized,
including
variety
classification
grading,
damage
detection,
components
prediction,
cleanliness,
vitality
assessment,
etc.
review
illustrates
that
combination
be
promising
tool
various
phenotype
seeds,
which
used
effective
evaluation
industrial
practical
applications,
such
as
breeding,
management,
selection
food
source.
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(4), P. 1727 - 1727
Published: Feb. 8, 2025
Additive
manufacturing
is
gaining
importance
in
a
number
of
application
areas,
and
there
an
increased
demand
for
mechanically
resilient
components.
A
way
to
improve
the
mechanical
properties
parts
made
thermoplastics
by
using
reinforcing
material.
The
study
demonstrates
development
monitoring
procedure
fused
filament
fabrication-based
co-extrusion
process
wire-reinforced
thermoplastic
Test
components
two
variants
are
produced,
data
acquisition
carried
out
with
laser
line
scanner.
collected
employed
train
deep
neural
networks
classify
printed
layers,
aiming
be
able
four
different
classes
identify
layers
insufficient
quality.
dedicated
convolutional
network
designed
taking
into
account
various
factors
such
as
layer
architecture,
pre-processing
optimization
methods.
Several
architectures,
including
transfer
learning
(based
on
VGG16
ResNet50),
without
fine-tuning,
compared
terms
their
performance
based
F1
score.
Both
model
ResNet50
fine-tuning
achieve
score
84%
83%,
respectively,
decisive
class
‘wire
bad’
classifying
inadequate
reinforcement.
