Pertanika journal of science & technology,
Год журнала:
2024,
Номер
32(3), С. 1219 - 1241
Опубликована: Апрель 3, 2024
Controlling
weed
infestation
is
pivotal
to
achieving
the
maximum
yield
in
paddy
fields.
At
a
time
of
exponential
human
population
growth
and
depleting
arable
land
mass,
finding
solution
this
problem
crucial.
For
long
time,
herbicides
have
been
most
favoured
approach
for
control
due
their
efficacy
ease
application.
However,
adverse
effects
on
environment
excessive
use
prompted
more
cautious
effective
herbicide
usage.
Many
species
tend
dominate
field,
thrived
patches,
rendering
conventional
broad
spraying
futile.
Site-specific
management
(SSWM)
consists
two
strategies:
mapping
selective
Since
its
introduction
into
agriculture
sector,
unmanned
aerial
vehicles
(UAV)
become
platform
choice
carrying
both
remote
sensing
system
application
herbicide.
Red-Green-Blue
(RGB),
multispectral
hyperspectral
sensors
UAVs
enable
highly
accurate
mapping.
In
Malaysia,
adopting
technology
possible,
given
nature
government-administrated
rice
cultivation.
This
review
provides
insight
practice
using
techniques
UAV
platforms
with
potential
applications
Malaysia's
field.
It
also
discusses
recent
works
imaging
platform.
Agronomy,
Год журнала:
2023,
Номер
13(6), С. 1595 - 1595
Опубликована: Июнь 13, 2023
Over
the
years,
several
agricultural
interventions
and
technologies
have
contributed
immensely
towards
intensifying
food
production
globally.
The
introduction
of
herbicides
provided
a
revolutionary
tool
for
managing
difficult
task
weed
control
contributing
significantly
global
security
human
survival.
However,
in
recent
times,
successes
achieved
with
chemical
taken
turn,
threatening
very
existence
we
tried
to
protect.
side
effects
conventional
farming,
particularly
increasing
cases
herbicide
resistance
weeds,
is
quite
alarming.
Global
calls
sustainable
management
approaches
be
used
mounting.
This
paper
provides
detailed
information
on
molecular
biological
background
resistant
biotypes
highlights
alternative,
non-chemical
methods
which
can
prevent
development
spreading
herbicide-resistant
weeds.
Frontiers in Plant Science,
Год журнала:
2023,
Номер
14
Опубликована: Март 22, 2023
Crop
protection
is
a
key
activity
for
the
sustainability
and
feasibility
of
agriculture
in
current
context
climate
change,
which
causing
destabilization
agricultural
practices
an
increase
incidence
or
invasive
pests,
growing
world
population
that
requires
guaranteeing
food
supply
chain
ensuring
security.
In
view
these
events,
this
article
provides
contextual
review
six
sections
on
role
artificial
intelligence
(AI),
machine
learning
(ML)
other
emerging
technologies
to
solve
future
challenges
crop
protection.
Over
time,
has
progressed
from
primitive
1.0
(Ag1.0)
through
various
technological
developments
reach
level
maturity
closelyin
line
with
Ag5.0
(section
1),
characterized
by
successfully
leveraging
ML
capacity
modern
devices
machines
perceive,
analyze
actuate
following
main
stages
precision
2).
Section
3
presents
taxonomy
algorithms
support
development
implementation
protection,
while
section
4
analyses
scientific
impact
basis
extensive
bibliometric
study
>120
algorithms,
outlining
most
widely
used
deep
(DL)
techniques
currently
applied
relevant
case
studies
detection
control
diseases,
weeds
plagues.
5
describes
39
fields
smart
sensors
advanced
hardware
devices,
telecommunications,
proximal
remote
sensing,
AI-based
robotics
will
foreseeably
lead
next
generation
perception-based,
decision-making
actuation
systems
digitized,
real-time
realistic
Ag5.0.
Finally,
6
highlights
conclusions
final
remarks.
Crop Protection,
Год журнала:
2023,
Номер
176, С. 106522 - 106522
Опубликована: Ноя. 14, 2023
In
the
face
of
increasing
agricultural
demands
and
environmental
concerns,
effective
management
weeds
presents
a
pressing
challenge
in
modern
agriculture.
Weeds
not
only
compete
with
crops
for
resources
but
also
pose
threats
to
food
safety
sustainability
through
indiscriminate
use
herbicides,
which
can
lead
contamination
herbicide-resistant
weed
populations.
Artificial
Intelligence
(AI)
has
ushered
paradigm
shift
agriculture,
particularly
domain
management.
AI's
utilization
this
extends
beyond
mere
innovation,
offering
precise
eco-friendly
solutions
identification
control
weeds,
thereby
addressing
critical
challenges.
This
article
aims
examine
application
AI
context
detection
impact
deep
learning
techniques
sector.
Through
an
assessment
research
articles,
study
identifies
factors
influencing
adoption
implementation
These
criteria
encompass
(food
safety,
increased
effectiveness,
eco-friendliness
herbicides
reduction),
(capture
technology,
training
datasets,
models,
outcomes
accuracy),
ancillary
technologies
(IoT,
UAV,
field
robots,
herbicides),
related
methods
(economic,
social,
technological,
environmental).
Of
5821
documents
found,
99
full-text
articles
were
assessed,
68
included
study.
The
review
highlights
role
enhancing
by
reducing
herbicide
residues,
effectiveness
strategies,
promoting
judicious
use.
It
underscores
importance
capture
accuracy
metrics
implementation,
emphasizing
their
synergy
revolutionizing
practices.
Ancillary
technologies,
such
as
IoT,
UAVs,
AI-enhanced
complement
capabilities,
holistic
data-driven
approaches
control.
Additionally,
influences
economic,
dimensions
Last
least,
digital
literacy
emerges
crucial
enabler,
empowering
stakeholders
navigate
effectively
contribute
sustainable
transformation
practices
Smart Agricultural Technology,
Год журнала:
2024,
Номер
8, С. 100487 - 100487
Опубликована: Июнь 11, 2024
The
article
provides
a
comprehensive
review
of
the
use
Internet
Things
(IoT)
in
agriculture,
along
with
its
advantages
and
disadvantages.
However,
it's
important
to
recognize
that
IoT
holds
immense
potential
for
generating
new
ideas
could
drive
innovations
modern
agriculture
address
several
challenges
faced
by
farmers
today.
Applications
such
as
smart
irrigation,
precision
farming,
crop
soil
tracking,
greenhouses,
supply
chain
management,
livestock
monitoring,
agricultural
drones,
pest
disease
prevention,
farm
machinery
are
among
areas
considered
implementation
this
paper.
These
innovative
solutions
have
revolutionize
farming
practices,
improve
efficiency,
reduce
resource
wastage,
ultimately
enhance
productivity
sustainability.
analysis
examines
each
application
terms
utility
outlines
measures
necessary
effectiveness.
Key
considerations
include
addressing
connectivity
issues,
managing
costs,
ensuring
data
security
privacy,
scaling
appropriately,
effectively
data,
promoting
awareness
adoption
tools.
Despite
these
challenges,
offers
numerous
benefits
sector.
paper
underscores
importance
collaboration
farmers,
technology
companies,
academia,
policymakers
issues
fully
harness
IoT.
To
achieve
goal,
ongoing
research,
development,
acceptance
IoT-driven
essential
sustain
viable
option
amidst
emerging
climate
change
scarcity.
Sustainability,
Год журнала:
2024,
Номер
16(7), С. 2664 - 2664
Опубликована: Март 24, 2024
Agricultural
technology
integration
has
become
a
key
strategy
for
attaining
agricultural
sustainability.
This
study
examined
the
of
in
practices
towards
sustainability,
using
Greece
as
case
study.
Data
were
collected
questionnaire
from
240
farmers
and
agriculturalists
Greece.
The
results
showed
significant
positive
effect
on
with
p-values
indicating
strong
statistical
relevance
(types
used:
p
=
0.003;
factors
influencing
adoption:
0.001;
benefits
integration:
0.021).
These
highlight
effects
that
cutting-edge
like
artificial
intelligence,
Internet
Things
(IoT),
precision
agriculture
have
improving
resource
efficiency,
lowering
environmental
effects,
raising
yields.
Our
findings
cast
doubt
conventional
dependence
intensive,
resource-depleting
farming
techniques
point
to
move
toward
more
technologically
advanced,
sustainable
approaches.
research
advances
conversation
by
showcasing
how
well
may
improve
sustainability
Greek
agriculture.
emphasizes
significance
infrastructure
investment,
supporting
legislation,
farmer
education
order
facilitate
adoption
technology.
Plants,
Год журнала:
2024,
Номер
13(22), С. 3184 - 3184
Опубликована: Ноя. 13, 2024
The
intensifying
challenges
posed
by
global
climate
change
and
water
scarcity
necessitate
enhancements
in
agricultural
productivity
sustainability
within
arid
regions.
This
review
synthesizes
recent
advancements
genetic
engineering,
molecular
breeding,
precision
agriculture,
innovative
management
techniques
aimed
at
improving
crop
drought
resistance,
soil
health,
overall
efficiency.
By
examining
cutting-edge
methodologies,
such
as
CRISPR/Cas9
gene
editing,
marker-assisted
selection
(MAS),
omics
technologies,
we
highlight
efforts
to
manipulate
drought-responsive
genes
consolidate
favorable
agronomic
traits
through
interdisciplinary
innovations.
Furthermore,
explore
the
potential
of
farming
including
Internet
Things
(IoT),
remote
sensing,
smart
irrigation
systems,
optimize
utilization
facilitate
real-time
environmental
monitoring.
integration
genetic,
biotechnological,
approaches
demonstrates
a
significant
enhance
resilience
against
abiotic
biotic
stressors
while
resource
Additionally,
advanced
along
with
conservation
techniques,
show
promise
for
maximizing
efficiency
sustaining
fertility
under
saline–alkali
conditions.
concludes
recommendations
further
multidisciplinary
exploration
genomics,
sustainable
practices,
agriculture
ensure
long-term
food
security
development
water-limited
environments.
providing
comprehensive
framework
addressing
regions,
emphasize
urgent
need
continued
innovation
response
escalating
pressures.
Remote Sensing,
Год журнала:
2025,
Номер
17(1), С. 120 - 120
Опубликована: Янв. 2, 2025
This
study
explores
the
efficacy
of
drone-acquired
RGB
images
and
YOLO
model
in
detecting
invasive
species
Siam
weed
(Chromolaena
odorata)
natural
environments.
is
a
perennial
scrambling
shrub
from
tropical
sub-tropical
America
that
outside
its
native
range,
causing
substantial
environmental
economic
impacts
across
Asia,
Africa,
Oceania.
First
detected
Australia
northern
Queensland
1994
later
Northern
Territory
2019,
there
an
urgent
need
to
determine
extent
incursion
vast,
rugged
areas
both
jurisdictions
for
distribution
mapping
at
catchment
scale.
tests
drone-based
imaging
train
deep
learning
contributes
goal
surveying
non-native
vegetation
We
specifically
examined
effects
input
training
images,
solar
illumination,
complexity
on
model’s
detection
performance
investigated
sources
false
positives.
Drone-based
were
acquired
four
sites
Townsville
region
test
(YOLOv5).
Validation
was
performed
through
expert
visual
interpretation
results
image
tiles.
The
YOLOv5
demonstrated
over
0.85
F1-Score,
which
improved
0.95
with
exposure
images.
A
reliable
found
be
sufficiently
trained
approximately
1000
tiles,
additional
offering
marginal
improvement.
Increased
did
not
notably
enhance
performance,
indicating
smaller
adequate.
False
positives
often
originated
foliage
bark
under
high
low
reduced
these
errors
considerably.
demonstrates
feasibility
using
models
detect
landscapes,
providing
safe
alternative
current
method
involving
human
spotters
helicopters.
Future
research
will
focus
developing
tools
merge
duplicates,
gather
georeference
data,
report
detections
large
datasets
more
efficiently,
valuable
insights
practical
applications
management
