Environment Conservation Journal,
Journal Year:
2024,
Volume and Issue:
25(3), P. 870 - 880
Published: April 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.
Frontiers in Plant Science,
Journal Year:
2024,
Volume and Issue:
14
Published: Jan. 8, 2024
The
sugar
industry,
which
relates
to
people's
livelihood,
is
strategic
and
fundamental
in
the
development
of
agricultural
economy.
In
China,
derived
from
sugarcane
accounts
for
approximately
85%
total
production.
Mechanization
"flower"
industry.
As
saying
goes
"when
there
are
blooming
flowers,
will
be
sweet
honey."
However,
due
limitations
land
resources,
technology,
equipment,
organization,
management,
mechanization
throughout
production
process
has
not
yet
brought
about
economic
benefits
that
a
mechanized
system
should
provide
reached
an
ideal
yield
through
integration
machinery
agronomic
practice.
This
paper
briefly
describes
how
initiate
Chinese
promote
sound,
healthy,
rapid
ultimately
achieve
transformation
breeding
China
modernization
industry
three
perspectives,
namely,
requirements
varieties,
strategies
selecting
new
varieties
suitable
production,
screening
diversification
variety
distribution
or
arrangement
China.
We
also
highlight
current
challenges
surrounding
this
topic
look
forward
its
bright
prospects.
Plants,
Journal Year:
2025,
Volume and Issue:
14(5), P. 671 - 671
Published: Feb. 21, 2025
Soybean
is
a
vital
crop
globally
and
key
source
of
food,
feed,
biofuel.
With
advancements
in
high-throughput
technologies,
soybeans
have
become
target
for
genetic
improvement.
This
comprehensive
review
explores
advances
multi-omics,
artificial
intelligence,
economic
sustainability
to
enhance
soybean
resilience
productivity.
Genomics
revolution,
including
marker-assisted
selection
(MAS),
genomic
(GS),
genome-wide
association
studies
(GWAS),
QTL
mapping,
GBS,
CRISPR-Cas9,
metagenomics,
metabolomics
boosted
the
growth
development
by
creating
stress-resilient
varieties.
The
intelligence
(AI)
machine
learning
approaches
are
improving
trait
discovery
associated
with
nutritional
quality,
stresses,
adaptation
soybeans.
Additionally,
AI-driven
technologies
like
IoT-based
disease
detection
deep
revolutionizing
monitoring,
early
identification,
yield
prediction,
prevention,
precision
farming.
viability
environmental
soybean-derived
biofuels
critically
evaluated,
focusing
on
trade-offs
policy
implications.
Finally,
potential
impact
climate
change
productivity
explored
through
predictive
modeling
adaptive
strategies.
Thus,
this
study
highlights
transformative
multidisciplinary
advancing
global
utility.
Advanced Science,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 25, 2025
Abstract
This
study
integrates
blockchain
technology
into
smart
agriculture
to
enhance
its
productivity
and
sustainability.
By
combining
with
remote
sensing,
artificial
intelligence
(AI),
the
Internet
of
Things
(IoT),
a
Human‐Cyber‐Physical
System
(H‐CPS)
architecture
tailored
for
agricultural
applications
is
proposed.
It
supports
real‐time
crop
management,
data‐driven
decision‐making,
transparent
trading
products.
A
semantic‐based
framework
introduced
address
challenges
in
data
management
AI
model
integration,
optimizing
production,
improving
traceability,
reducing
costs,
enhancing
financial
security.
directly
addresses
real‐world
challenges,
such
as
optimized
irrigation,
improved
breeding
efficiency,
enhanced
supply
chain
transparency.
These
innovations
provide
practical
solutions
modern
agriculture,
contributing
sustainable
development
global
food
Further
research
collaboration
are
encouraged
unlock
full
potential
transforming
practices.
Engineering Research Express,
Journal Year:
2024,
Volume and Issue:
6(2), P. 022202 - 022202
Published: June 1, 2024
Abstract
Deep
learning
has
shown
tremendous
potential
for
transforming
healthcare
by
enabling
more
accurate
diagnoses,
improved
treatment
planning
and
better
patient
outcome
predictions.
In
this
comprehensive
survey,
we
provide
a
detailed
overview
of
the
state-of-the-art
deep
techniques
their
applications
across
ecosystem.
We
first
introduce
fundamentals
discuss
its
key
advantages
compared
to
traditional
machine
approaches.
then
present
an
in-depth
review
major
in
medical
imaging,
electronic
health
record
analysis,
genomics,
robotics
other
domains.
For
each
application,
summarize
advancements,
outline
technical
details
methods,
challenges
limitations
highlight
promising
directions
future
work.
examine
cross-cutting
deploying
clinical
settings,
including
interpretability,
bias
data
scarcity.
conclude
proposing
roadmap
accelerate
translation
adoption
high-impact
learning.
Overall,
survey
provides
reference
researchers
practitioners
working
at
intersection
healthcare.
Agriculture,
Journal Year:
2024,
Volume and Issue:
14(12), P. 2299 - 2299
Published: Dec. 14, 2024
Artificial
intelligence
(AI)
can
revolutionize
agriculture
by
enhancing
genomic
research
and
promoting
sustainable
crop
improvement.
AI
systems
integrate
machine
learning
(ML)
deep
(DL)
with
big
data
to
identify
complex
patterns
relationships
analyzing
vast
genomic,
phenotypic,
environmental
datasets.
This
capability
accelerates
breeding
cycles,
improves
predictive
accuracy,
supports
the
development
of
climate-resilient,
high-yielding
varieties.
Applications
such
as
precision
agriculture,
automated
phenotyping,
analytics,
early
pest
disease
detection
demonstrate
AI’s
ability
optimize
agricultural
practices
while
sustainability.
Despite
these
advancements,
challenges
remain,
including
fragmented
sources,
variability
in
phenotyping
protocols,
ownership
concerns.
Addressing
issues
through
standardized
integration
frameworks,
advanced
analytical
tools,
ethical
will
be
critical
for
realizing
full
potential.
review
provides
a
comprehensive
overview
AI-powered
research,
highlights
role
training
robust
models,
explores
technological
considerations
practices.
AgriEngineering,
Journal Year:
2024,
Volume and Issue:
6(3), P. 3408 - 3426
Published: Sept. 18, 2024
Weeds
reduce
cassava
root
yields
and
infest
furrow
areas
quickly.
The
use
of
mechanical
weeders
has
been
introduced
in
Thailand;
however,
manually
aligning
the
with
each
planting
row
at
headland
turns
is
still
challenging.
It
critical
to
clear
weeds
on
slopes
driveways
via
weeders.
Automation
can
support
this
difficult
work
for
weed
management
driveway
detection.
In
context,
deep
learning
algorithms
have
potential
train
models
detect
through
image
segmentation.
Therefore,
purpose
research
was
develop
an
segmentation
model
automated
control
operations
plantation
fields.
To
achieve
this,
datasets
were
obtained
from
various
fields
aid
detection
management.
Three
models—Mask
R-CNN,
YOLACT,
YOLOv8n-seg—were
used
construct
model,
they
evaluated
according
their
precision,
recall,
FPS.
results
show
that
YOLOv8n-seg
achieved
highest
accuracy
FPS
(114.94
FPS);
it
experienced
issues
frame
during
video
testing.
contrast,
YOLACT
had
no
tests
(23.45
FPS),
indicating
its
plantations.
summary,
detecting
improve
fields,
further
automation
low-cost
tropical
climates
be
performed
based
algorithm.
