Effects of mild infrared and convective drying on physicochemical properties, polyphenol compounds, and image features of two date palm cultivars: ‘Mejhoul’ and ‘Boufeggous’
LWT,
Год журнала:
2025,
Номер
unknown, С. 117502 - 117502
Опубликована: Фев. 1, 2025
Язык: Английский
Remote sensing and artificial intelligence: revolutionizing pest management in agriculture
Frontiers in Sustainable Food Systems,
Год журнала:
2025,
Номер
9
Опубликована: Фев. 26, 2025
The
agriculture
sector
is
currently
facing
several
challenges,
including
the
growing
global
human
population,
depletion
of
natural
resources,
reduction
arable
land,
rapidly
changing
climate,
and
frequent
occurrence
diseases
such
as
Ebola,
Lassa,
Zika,
Nipah,
most
recently,
COVID-19
pandemic.
These
challenges
pose
a
threat
to
food
nutritional
security
place
pressure
on
scientific
community
achieve
Sustainable
Development
Goal
2
(SDG2),
which
aims
eradicate
hunger
malnutrition.
Technological
advancement
plays
significant
role
in
enhancing
our
understanding
agricultural
system
its
interactions
from
cellular
level
green
field
for
benefit
humanity.
use
remote
sensing
(RS),
artificial
intelligence
(AI),
machine
learning
(ML)
approaches
highly
advantageous
producing
precise
accurate
datasets
develop
management
tools
models.
technologies
are
beneficial
soil
types,
efficiently
managing
water,
optimizing
nutrient
application,
designing
forecasting
early
warning
models,
protecting
crops
plant
insect
pests,
detecting
threats
locusts.
application
RS,
AI,
ML
algorithms
promising
transformative
approach
improve
resilience
against
biotic
abiotic
stresses
sustainability
meet
needs
ever-growing
population.
In
this
article
covered
leveraging
AI
RS
data,
how
these
enable
real
time
monitoring,
detection,
pest
outbreaks.
Furthermore,
discussed
allows
more
precise,
targeted
control
interventions,
reducing
reliance
broad
spectrum
pesticides
minimizing
environmental
impact.
Despite
data
quality
technology
accessibility,
integration
holds
potential
revolutionizing
management.
Язык: Английский
Artificial intelligence for prediction of shelf-life of various food products: Recent advances and ongoing challenges
Trends in Food Science & Technology,
Год журнала:
2025,
Номер
unknown, С. 104989 - 104989
Опубликована: Март 1, 2025
Язык: Английский
Exploration of Convective and Infrared Drying Effect on Image Texture Parameters of ‘Mejhoul’ and ‘Boufeggous’ Date Palm Fruit Using Machine Learning Models
Foods,
Год журнала:
2024,
Номер
13(11), С. 1602 - 1602
Опубликована: Май 21, 2024
Date
palm
(Phoenix
dactylifera
L.)
fruit
samples
belonging
to
the
‘Mejhoul’
and
‘Boufeggous’
cultivars
were
harvested
at
Tamar
stage
used
in
our
experiments.
Before
scanning,
date
dried
using
convective
drying
60
°C
infrared
with
a
frequency
of
50
Hz,
then
they
scanned.
The
scanning
trials
performed
for
two
hundred
fresh,
convective-dried,
infrared-dried
forms
each
cultivar
flatbed
scanner.
image-texture
parameters
extracted
from
images
converted
individual
color
channels
RGB,
Lab,
XYZ,
UVS
models.
models
classify
fresh
developed
based
on
selected
image
textures
machine
learning
algorithms
groups
Bayes,
Trees,
Lazy,
Functions,
Meta.
For
both
cultivars,
built
Random
Forest
group
Trees
turned
out
be
accurate
successful.
average
classification
accuracy
reached
99.33%,
whereas
distinguished
an
94.33%.
In
case
model,
higher
correctness
discrimination
was
between
samples,
highest
number
misclassified
cases
occurred
convective-dried
fruit.
Thus,
procedure
may
considered
innovative
approach
non-destructive
assessment
impact
external
quality
characteristics
Язык: Английский
Opportunities for Prediction Models to Reduce Food Loss and Waste in the Postharvest Chain of Horticultural Crops
Sustainability,
Год журнала:
2024,
Номер
16(17), С. 7803 - 7803
Опубликована: Сен. 7, 2024
Significant
losses
occur
in
the
fresh
produce
supply
chain,
spanning
from
harvest
to
postharvest
stages,
with
considerable
wastage
during
production
and
consumption.
Developing
predictive
models
for
overall
is
crucial
inform
growers
industry
stakeholders,
facilitating
better
decision-making
resource
management.
These
play
a
pivotal
role
supporting
governments,
as
well
global
food
agricultural
organizations,
their
efforts
alleviate
poverty
ensure
nutrition
security
growing
human
population.
This
review
discusses
opportunity
targets
predicting
total
addresses
strategies
effective
waste
management
aim
of
promoting
sustainable
enhancing
security.
Язык: Английский
Non-Destructive Monitoring of External Quality of Date Palm Fruit (Phoenix dactylifera L.) During Frozen Storage Using Digital Camera and Flatbed Scanner
Sensors,
Год журнала:
2024,
Номер
24(23), С. 7560 - 7560
Опубликована: Ноя. 27, 2024
The
emergence
of
new
technologies
focusing
on
“computer
vision”
has
contributed
significantly
to
the
assessment
fruit
quality.
In
this
study,
an
innovative
approach
based
image
analysis
was
used
assess
external
quality
fresh
and
frozen
‘Mejhoul’
‘Boufeggous’
date
palm
cultivars
stored
for
6
months
at
−10
°C
−18
°C.
Their
evaluated,
in
a
non-destructive
manner,
texture
features
extracted
from
images
acquired
using
digital
camera
flatbed
scanner.
whole
process
processing
carried
out
MATLAB
R2024a
Q-MAZDA
23.10
software.
Then,
were
as
inputs
pre-established
algorithms–groups
within
WEKA
3.9
software
classify
samples
after
0,
2,
4,
storage.
Among
599
features,
only
5
36
attributes
selected
powerful
predictors
build
desired
classification
models
“Functions-Logistic”
classifier.
general
architecture
exhibited
clear
differences
accuracy
depending
mainly
storage
period
imaging
device.
Accordingly,
confusion
matrices
showed
high
(CA),
which
could
reach
0.84
M0
both
two
temperatures.
This
CA
indicated
remarkable
decrease
M2
M4
before
re-increasing
by
M6,
confirming
slight
changes
end
Moreover,
developed
basis
scanner
use
allowed
us
obtain
correctness
rate
that
attain
97.7%
comparison
camera,
did
not
exceed
85.5%.
perspectives,
physicochemical
can
be
added
establish
correlation
with
predict
behavior
under
Язык: Английский