Identification and Classification of Coix seed Storage Years Based on Hyperspectral Imaging Technology Combined with Deep Learning
Foods,
Год журнала:
2024,
Номер
13(3), С. 498 - 498
Опубликована: Фев. 4, 2024
Developing
a
fast
and
non-destructive
methodology
to
identify
the
storage
years
of
Язык: Английский
Detection of drug residues in bean sprouts by hyperspectral imaging combined with 1DCNN with channel attention mechanism
Qinchen Yang,
Lu Yin,
Xidun Hu
и другие.
Microchemical Journal,
Год журнала:
2024,
Номер
206, С. 111497 - 111497
Опубликована: Авг. 23, 2024
Язык: Английский
Recent advance in nondestructive imaging technology for detecting quality of fruits and vegetables: a review
Critical Reviews in Food Science and Nutrition,
Год журнала:
2024,
Номер
unknown, С. 1 - 19
Опубликована: Сен. 18, 2024
As
an
integral
part
of
daily
dietary
intake,
the
market
demand
for
fruits
and
vegetables
is
continuously
growing.
However,
traditional
methods
assessing
quality
are
prone
to
subjective
influences,
destructive
samples,
fail
comprehensively
reflect
internal
quality,
thereby
resulting
in
various
shortcomings
ensuring
food
safety
control.
Over
past
few
decades,
imaging
technologies
have
rapidly
evolved
been
widely
employed
nondestructive
detection
fruit
vegetable
quality.
This
paper
offers
a
thorough
overview
recent
advancements
vegetables,
including
hyperspectral
(HSI),
fluorescence
(FI),
magnetic
resonance
(MRI),
thermal
(TI),
terahertz
imaging,
X-ray
(XRI),
ultrasonic
microwave
(MWI).
The
principles
applications
these
techniques
testing
summarized.
challenges
future
trends
discussed.
Язык: Английский
From farm to market: research progress and application prospects of artificial intelligence in the frozen fruits and vegetables supply chain
Trends in Food Science & Technology,
Год журнала:
2024,
Номер
unknown, С. 104730 - 104730
Опубликована: Сен. 1, 2024
Язык: Английский
Fusion features of microfluorescence hyperspectral imaging for qualitative detection of pesticide residues in Hami melon
Food Research International,
Год журнала:
2024,
Номер
196, С. 115010 - 115010
Опубликована: Сен. 4, 2024
Язык: Английский
Optimized Extreme Learning Machine with Bacterial Colony Optimization Algorithm for Disease Diagnosis in Clinical Datasets
P. Vigneshvaran,
A. Vijaya Kathiravan
SN Computer Science,
Год журнала:
2024,
Номер
5(5)
Опубликована: Май 26, 2024
Язык: Английский
Study on detection of pesticide residues in tobacco based on hyperspectral imaging technology
Frontiers in Plant Science,
Год журнала:
2024,
Номер
15
Опубликована: Сен. 30, 2024
Introduction
Tobacco
is
a
critical
economic
crop,
yet
its
cultivation
heavily
relies
on
chemical
pesticides,
posing
health
risks
to
consumers,
therefore,
monitoring
pesticide
residues
in
tobacco
conducive
ensuring
food
safety.
However,
most
current
research
residue
detection
traditional
methods,
which
cannot
meet
the
requirements
for
real-time
and
rapid
detection.
Methods
This
study
introduces
an
advanced
method
that
combines
hyperspectral
imaging
(HSI)
technology
with
machine
learning
algorithms.
Firstly,
imager
was
used
obtain
spectral
data
of
samples,
variety
pre-processing
technologies
such
as
mean
centralization
(MC),
trend
correction
(TC),
wavelet
transform
(WT),
well
feature
extraction
methods
competitive
adaptive
reweighted
sampling
(CARS)
least
angle
regression
(LAR)
were
process
data,
then,
grid
search
algorithm
(GSA)
optimize
support
sector
(SVM).
Results
The
optimized
MC-LAR-SVM
model
achieved
classification
accuracy
84.1%,
9.5%
higher
than
original
model.
WT-TC-CARS-GSA-SVM
fenvalerate
concentration
experiment
high
91.8
%,
it
also
had
excellent
performance
other
metrics.
Compared
based
accuracy,
precision,
recall,
F1-score
are
improved
by
8.3
8.2
7.5
0.08,
respectively.
Discussion
results
show
combining
preprocessing
algorithms
models
can
significantly
enhance
provide
robust,
efficient,
accurate
solutions
safety
monitoring.
provides
new
technical
means
tobacco,
great
significance
improving
efficiency
Язык: Английский