Environment Conservation Journal,
Год журнала:
2024,
Номер
25(3), С. 870 - 880
Опубликована: Апрель 22, 2024
The
escalating
global
demand
for
food,
propelled
by
a
burgeoning
population
and
the
unpredictable
shifts
in
climatic
conditions,
presents
challenge
that
traditional
plant
breeding
alone
struggles
to
address.
In
response
this
pressing
need,
infusion
of
intelligent
technologies
emerges
as
pivotal
solution,
poised
not
only
boost
production
but
also
meet
demand.
This
transformative
approach
encompasses
spectrum
cutting-edge
tools,
including
Remote
Sensing
GIS,
Aeroponics,
Drone
Technology,
Biotechnology,
Artificial
Intelligence,
Machine
Learning,
and,
ultimately,
Robotics.
synergistic
integration
these
will
enhance
agricultural
monitoring
facilitating
precise
crop
surveillance,
early
detection
mitigation
diseases
pests,
optimization
water
resources,
accurate
mapping
land
use
types,
comprehensive
environmental
monitoring,
real-time
weather
climate
tracking,
efficient
nutrient
management,
irrigation
spraying
practices,
reliable
yield
prediction,
advanced
forecasting,
genetic
analysis,
informed
decision-making
processes.
amalgamation
with
modern
methodologies
signifies
significant
advancement
towards
achieving
more
sustainable
practices.
convergence
addresses
immediate
need
increased
food
sets
stage
resilient
future-ready
landscape.
era
integration,
we
witness
harmonious
coexistence
tradition
innovation,
paving
way
abundant
secure
future.
The
acceptance
of
genetically
modified
(GM)
crops
is
still
a
controversial
topic
that
presents
major
obstacles
to
their
general
use.
Few
studies,
nevertheless,
have
emphasized
the
use
artificial
intelligence
(AI)
in
forecasting
dangers
GM
crops.
This
review
delves
into
emerging
field
applying
AI
forecast
hazards
linked
and
examines
how
it
could
increase
public
products.
algorithms,
predictive
modeling
approaches
examine
enormous
datasets
include
genetic,
environmental,
agronomic
factors.
Utilizing
AI,
researchers
may
accelerate
risk
assessment
procedures
on
safety
effectiveness
In
addressing
concerns
skepticism,
AI-generated
assessments
foster
transparency
confidence
among
consumers,
regulators,
stakeholders,
thereby
might
fostering
greater
Although
lack
available
data
genetic
modifications
or
developing
crop
varieties,
amount
training
validation
needed
for
algorithms
before
they
can
be
trusted,
complexity
models,
ethical
issues
about
like
privacy
algorithm
bias,
present
difficulties
precise
AI-driven
assessment.
outlines
recent
developments
future
directions
utilizing
as
promising
strategy
enhance
acceptability
Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Май 9, 2024
AbstractCucumis
melo
L.,
commonly
known
as
melon,
is
a
crucial
horticultural
crop.
The
selection
and
breeding
of
superior
melon
germplasm
resources
play
pivotal
role
in
enhancing
its
marketability.
However,
current
methods
for
appearance
phenotypic
analysis
rely
primarily
on
expert
judgment
intricate
manual
measurements,
which
are
not
only
inefficient
but
also
costly.
Therefore,
to
expedite
the
process
we
analyzed
images
117
varieties
from
two
annual
years
utilizing
artificial
intelligence
(AI)
technology.
By
integrating
semantic
segmentation
model
Dual
Attention
Network
(DANet),
object
detection
RTMDet,
keypoint
RTMPose,
Mobile-Friendly
Segment
Anything
Model
(MobileSAM),
deep
learning
algorithm
framework
was
constructed,
capable
efficiently
accurately
segmenting
fruit
pedicel.
On
this
basis,
series
feature
extraction
algorithms
were
designed,
successfully
obtaining
11
traits
melon.
Linear
fitting
verification
results
selected
demonstrated
high
correlation
between
algorithm-predicted
values
manually
measured
true
values,
thereby
validating
feasibility
accuracy
algorithm.
Moreover,
cluster
using
all
revealed
consistency
classification
genotypes.
Finally,
user-friendly
software
developed
achieve
rapid
automatic
acquisition
phenotypes,
providing
an
efficient
robust
tool
breeding,
well
facilitating
in-depth
research
into
genotypes
phenotypes.
Environment Conservation Journal,
Год журнала:
2024,
Номер
25(3), С. 870 - 880
Опубликована: Апрель 22, 2024
The
escalating
global
demand
for
food,
propelled
by
a
burgeoning
population
and
the
unpredictable
shifts
in
climatic
conditions,
presents
challenge
that
traditional
plant
breeding
alone
struggles
to
address.
In
response
this
pressing
need,
infusion
of
intelligent
technologies
emerges
as
pivotal
solution,
poised
not
only
boost
production
but
also
meet
demand.
This
transformative
approach
encompasses
spectrum
cutting-edge
tools,
including
Remote
Sensing
GIS,
Aeroponics,
Drone
Technology,
Biotechnology,
Artificial
Intelligence,
Machine
Learning,
and,
ultimately,
Robotics.
synergistic
integration
these
will
enhance
agricultural
monitoring
facilitating
precise
crop
surveillance,
early
detection
mitigation
diseases
pests,
optimization
water
resources,
accurate
mapping
land
use
types,
comprehensive
environmental
monitoring,
real-time
weather
climate
tracking,
efficient
nutrient
management,
irrigation
spraying
practices,
reliable
yield
prediction,
advanced
forecasting,
genetic
analysis,
informed
decision-making
processes.
amalgamation
with
modern
methodologies
signifies
significant
advancement
towards
achieving
more
sustainable
practices.
convergence
addresses
immediate
need
increased
food
sets
stage
resilient
future-ready
landscape.
era
integration,
we
witness
harmonious
coexistence
tradition
innovation,
paving
way
abundant
secure
future.
