NMR spectroscopy combined with chemometrics for quality assessment of common vegetable oils: A review
Trends in Food Science & Technology,
Год журнала:
2025,
Номер
unknown, С. 104889 - 104889
Опубликована: Янв. 1, 2025
Язык: Английский
A novel lightweight model for tea disease classification based on feature reuse and channel focus attention mechanism
Engineering Science and Technology an International Journal,
Год журнала:
2025,
Номер
61, С. 101940 - 101940
Опубликована: Янв. 1, 2025
Язык: Английский
China’s Rural Revitalization Policy: A PRISMA 2020 Systematic Review of Poverty Alleviation, Food Security, and Sustainable Development Initiatives
Sustainability,
Год журнала:
2025,
Номер
17(2), С. 569 - 569
Опубликована: Янв. 13, 2025
This
systematic
review
evaluates
China’s
Rural
Revitalization
Policy,
focusing
on
sustainable
agriculture,
food
security,
and
poverty
alleviation
initiatives
from
2010
to
2024.
The
study
addresses
critical
gaps
in
understanding
how
these
combined
efforts
impact
long-term
security
ecological
sustainability
impoverished
areas,
moving
beyond
the
short-term
outcomes
often
emphasized
existing
literature.
Following
PRISMA
2020
guidelines,
we
reviewed
33
peer-reviewed
publications
Web
of
Science
Scopus
databases,
employing
bibliometric
analyses
RStudio
assess
citation
patterns,
collaboration
networks,
thematic
evolution.
Our
analysis
reveals
significant
progress
across
three
interconnected
domains.
First,
achieved
a
12.3%
reduction
rural
through
integrated
agricultural
modernization
targeted
support
programs.
Second,
productivity
increased
by
9.8%
technological
integration
farming
practices,
strengthening
outcomes.
Third,
environmental
improved
notably,
with
15.7%
increase
clean
water
access,
demonstrating
successful
balance
between
economic
growth
protection.
China
emerged
as
largest
contributor
(15.2%)
research
this
field,
substantial
international
(42.4%
involving
cross-border
co-authorship).
Despite
achievements,
regional
disparities
persist,
particularly
eastern
western
regions,
where
interventions
are
needed.
findings
highlight
need
for
regionally
tailored
approaches:
regions
require
focus
intensification,
fundamental
infrastructure
development,
central
would
benefit
strengthened
urban–rural
linkages.
provides
valuable
insights
policymakers
researchers
working
development
while
identifying
areas
requiring
further
research,
assessments
climate
resilience
strategies.
Язык: Английский
TBF-YOLOv8n: A Lightweight Tea Bud Detection Model Based on YOLOv8n Improvements
Sensors,
Год журнала:
2025,
Номер
25(2), С. 547 - 547
Опубликована: Янв. 18, 2025
Tea
bud
localization
detection
not
only
ensures
tea
quality,
improves
picking
efficiency,
and
advances
intelligent
harvesting,
but
also
fosters
industry
upgrades
enhances
economic
benefits.
To
solve
the
problem
of
high
computational
complexity
deep
learning
models,
we
developed
Bud
DSCF-YOLOv8n
(TBF-YOLOv8n)lightweight
model.
Improvement
Cross
Stage
Partial
Bottleneck
Module
with
Two
Convolutions(C2f)
module
via
efficient
Distributed
Shift
Convolution
(DSConv)
yields
C2f
DSConv(DSCf)module,
which
reduces
model’s
size.
Additionally,
coordinate
attention
(CA)
mechanism
is
incorporated
to
mitigate
interference
from
irrelevant
factors,
thereby
improving
mean
accuracy.
Furthermore,
SIOU_Loss
(SCYLLA-IOU_Loss)
function
Dynamic
Sample(DySample)up-sampling
operator
are
implemented
accelerate
convergence
enhance
both
average
precision
The
experimental
results
show
that
compared
YOLOv8n
model,
TBF-YOLOv8n
model
has
a
3.7%
increase
in
accuracy,
1.1%
44.4%
reduction
gigabit
floating
point
operations
(GFLOPs),
13.4%
total
number
parameters
included
In
comparison
experiments
variety
lightweight
still
performs
well
terms
accuracy
while
remaining
more
lightweight.
conclusion,
achieves
commendable
balance
between
efficiency
precision,
offering
valuable
insights
for
advancing
harvesting
technologies.
Язык: Английский
Machine learning-based classification and prediction of typical Chinese green tea taste profiles
Food Research International,
Год журнала:
2025,
Номер
unknown, С. 115796 - 115796
Опубликована: Янв. 1, 2025
Язык: Английский
Advances of Vis/NIRS and imaging techniques assisted by AI for tea processing
Critical Reviews in Food Science and Nutrition,
Год журнала:
2025,
Номер
unknown, С. 1 - 19
Опубликована: Март 7, 2025
Tea
is
one
of
the
most
popular
drinks
due
to
its
distinct
flavor
and
numerous
health
benefits.
The
quality
tea
closely
related
production
processing.
Human
sensory
evaluation
conventional
method
for
monitoring
in
However,
this
subjective
susceptible
environmental
influences.
Therefore,
visible/near-infrared
spectroscopy
(Vis/NIRS)
hyperspectral
imaging
(HSI)
techniques
offer
great
potential
their
rapid
detection
speed,
nondestructive,
low
cost,
simple
operations.
Artificial
intelligence
(AI)
promising
methodological
approaches
spectral
analysis
decision-making
automated
production.
Vis/NIRS
HSI
assisted
by
AI
further
promote
progress
This
paper
reviewed
updated
applications
processing
from
2019
2025.
In
particular,
process,
theories
techniques,
algorithms
are
briefly
introduced.
Furthermore,
recent
summarized
discussed.
Finally,
challenges
future
trends
associated
with
practical
application
industry
presented.
Язык: Английский
Data integrity of food and machine learning: Strategies, advances and prospective
Food Chemistry,
Год журнала:
2025,
Номер
unknown, С. 143831 - 143831
Опубликована: Март 1, 2025
Язык: Английский
LCLN-CA: A Survival Regression Analysis-Based Prediction Method for Catechin Content in Yunnan Sun-Dried Tea
Horticulturae,
Год журнала:
2024,
Номер
10(12), С. 1321 - 1321
Опубликована: Дек. 11, 2024
Catechins
are
pivotal
determinants
of
tea
quality,
with
soil
environmental
factors
playing
a
crucial
role
in
the
synthesis
and
accumulation
these
compounds.
To
investigate
impact
changes
garden
environments
on
catechin
content
sun-dried
tea,
this
study
measured
samples
corresponding
leaves
from
Nanhua,
Yunnan,
China.
By
integrating
variations
those
17
employing
COX
regression
factor
analysis,
it
was
found
that
pH,
organic
matter
(OM),
fluoride,
arsenic
(As),
chromium
(Cr)
were
significantly
correlated
(p
<
0.05).
Further,
using
LASSO
for
variable
selection,
model
named
LCLN-CA
constructed
four
variables
including
OM,
As.
The
demonstrated
high
fitting
accuracy
AUC
values
0.674,
0.784,
0.749
intervals
CA
≤
10%,
10%
20%,
20%
30%
training
set,
respectively.
validation
set
showed
0.630,
0.756,
0.723,
respectively,
indicating
well-calibrated
curve.
Based
DynNom
framework,
visual
prediction
system
Yunnan
developed.
External
test
dataset
achieved
an
Accuracy
0.870.
This
explored
relationship
between
soil-related
content,
paving
new
way
enhancing
practical
application
value
artificial
intelligence
technology
agricultural
production.
Язык: Английский
CURRENT CHALLENGES, AND FUTURE OPPORTUNITIES FOR FERMENTED TEA LEAF SEGMENTATION, CLASSIFICATION, AND OPTIMIZATION
ShodhKosh Journal of Visual and Performing Arts,
Год журнала:
2024,
Номер
5(1)
Опубликована: Июнь 30, 2024
Fermented
tea
leaves
emerged
as
a
significant
agricultural
commodity
on
the
global
scene.
This
type
of
product
experiences
segmentation,
classification,
and
optimization
due
to
different
textures,
stages
fermentation,
environmental
influences.
The
article
reviews
progresses
limitations
made
by
automatic
systems
in
realm
image-based
analysis
fermented
leaves,
machine
learning
algorithms,
methods.
challenges
high
segmentation
accuracy
heterogeneous
samples,
robust
classification
among
diverse
varieties,
scaling
strategies
for
quality
enhancement
are
some
key
challenges.
Apart
from
hybrid
algorithms
designed
interpret
gap,
future
areas
opportunities
that
utilize
deep
multimodal
fusion.
Highlights
hyperspectral
imaging
approaches
AI-driven
models
providing
quick
solutions
with
cost-effectiveness.
Язык: Английский