Agriculture
is
an
essential
occupation
to
the
people
of
India.
It
considered
as
backbone
most
Indian
population.
However,
one
biggest
concerns
agriculture
growth
weeds.
These
weeds
have
be
removed
get
a
fruitful
harvest.
This
process
removing
weeding,
which
must
done
with
utmost
care
without
affecting
valuable
crops.
Using
agricultural
chemicals
popular
ways
manage
weed
identification
challenging
parts
cultivation,
use
throughout
plantation
harmful
environment
and
ecosystem.
In
addition,
manually
possible
but
not
entirely
practical,
considering
human
error
labor
charges
that
paid
them.
leads
demand
for
alternatives
control
techniques.
Therefore,
industries
continue
seek
human-free
automated
mechanisms
are
relatively
inexpensive.
this
regard,
machine
vision
comes
into
action
automation.
Machine
technology
uses
cameras
rather
than
naked
eye
identify.
recent
years,
technologies
rapidly
developed,
progress
achieved
remarkable.
has
been
proven
help
build
automation
in
resulting
cost-effective,
highly
efficient,
high-precision
solutions.
increased
computational
power
hardware,
decreased
costs,
advancements
accuracy
efficiency
algorithms
made
it
construct
feasible
practical
automatic
weeding
strategies.
chapter
focuses
on
exploration
numerous
strategies
involved
their
applications,
cases,
research
challenges.
AgriEngineering,
Journal Year:
2025,
Volume and Issue:
7(4), P. 103 - 103
Published: April 3, 2025
Accurate
weed
segmentation
in
Unmanned
Aerial
Vehicle
(UAV)
imagery
remains
a
significant
challenge
precision
agriculture
due
to
environmental
variability,
weak
contextual
representation,
and
inaccurate
boundary
detection.
To
address
these
limitations,
we
propose
the
Multi-Scale
Edge-Aware
Network
(MSEA-Net),
lightweight
efficient
deep
learning
framework
designed
enhance
accuracy
while
maintaining
computational
efficiency.
Specifically,
introduce
Spatial-Channel
Attention
(MSCA)
module
recalibrate
spatial
channel
dependencies,
improving
local–global
feature
fusion
reducing
redundant
computations.
Additionally,
Edge-Enhanced
Bottleneck
(EEBA)
integrates
Sobel-based
edge
detection
refine
delineation,
ensuring
sharper
object
separation
dense
vegetation
environments.
Extensive
evaluations
on
publicly
available
datasets
demonstrate
effectiveness
of
MSEA-Net,
achieving
mean
Intersection
over
Union
(IoU)
87.42%
Motion-Blurred
UAV
Images
Sorghum
Fields
dataset
71.35%
CoFly-WeedDB
dataset,
outperforming
benchmark
models.
MSEA-Net
also
maintains
compact
architecture
with
only
6.74
M
parameters
model
size
25.74
MB,
making
it
suitable
for
UAV-based
real-time
segmentation.
These
results
highlight
potential
automated
efficiency
deployment.
INMATEH Agricultural Engineering,
Journal Year:
2025,
Volume and Issue:
unknown, P. 706 - 725
Published: April 28, 2025
This
paper
reviews
the
progress
in
innovative
design
and
intelligent
technology
applications
of
threshing
devices
combine
harvesters
for
staple
crops.
To
address
issues
poor
adaptability
low
intelligence
traditional
systems,
researchers
have
significantly
improved
performance
by
optimizing
components
drum
structures.
Meanwhile,
machine
vision
deep
learning
achieved
important
breakthroughs
feed
rate
monitoring,
breakage
impurity
detection,
control.
review
aims
to
provide
a
reference
research
system
structural
optimization
operational
parameter
PeerJ Computer Science,
Journal Year:
2024,
Volume and Issue:
10, P. e2518 - e2518
Published: Nov. 22, 2024
Extracting
the
essential
features
and
learning
appropriate
patterns
are
two
core
character
traits
of
a
convolution
neural
network
(CNN).
Leveraging
traits,
this
research
proposes
novel
feature
extraction
framework
code-named
'HierbaNetV1'
that
retrieves
learns
effective
from
an
input
image.
Originality
is
brought
by
addressing
problem
varying-sized
region
interest
(ROI)
in
image
extracting
using
diversified
filters.
For
every
sample,
3,872
maps
generated
with
multiple
levels
complexity.
The
proposed
method
integrates
low-level
high-level
thus
allowing
model
to
learn
intensive
features.
As
follow-up
research,
crop-weed
dataset
termed
'SorghumWeedDataset_Classification'
acquired
created.
This
tested
on
HierbaNetV1
which
compared
against
pre-trained
models
state-of-the-art
(SOTA)
architectures.
Experimental
results
show
outperforms
other
architectures
accuracy
98.06%.
An
ablation
study
component
analysis
conducted
demonstrate
effectiveness
HierbaNetV1.
Validated
benchmark
weed
datasets,
also
exhibits
our
suggested
approach
performs
well
terms
generalization
across
wide
variety
crops
weeds.
To
facilitate
further
weights
implementation
made
accessible
community
GitHub.
extend
practicality,
incorporated
real-time
application
named
HierbaApp
assists
farmers
differentiating
Future
enhancements
for
outlined
article
currently
underway.
Agriculture
is
an
essential
occupation
to
the
people
of
India.
It
considered
as
backbone
most
Indian
population.
However,
one
biggest
concerns
agriculture
growth
weeds.
These
weeds
have
be
removed
get
a
fruitful
harvest.
This
process
removing
weeding,
which
must
done
with
utmost
care
without
affecting
valuable
crops.
Using
agricultural
chemicals
popular
ways
manage
weed
identification
challenging
parts
cultivation,
use
throughout
plantation
harmful
environment
and
ecosystem.
In
addition,
manually
possible
but
not
entirely
practical,
considering
human
error
labor
charges
that
paid
them.
leads
demand
for
alternatives
control
techniques.
Therefore,
industries
continue
seek
human-free
automated
mechanisms
are
relatively
inexpensive.
this
regard,
machine
vision
comes
into
action
automation.
Machine
technology
uses
cameras
rather
than
naked
eye
identify.
recent
years,
technologies
rapidly
developed,
progress
achieved
remarkable.
has
been
proven
help
build
automation
in
resulting
cost-effective,
highly
efficient,
high-precision
solutions.
increased
computational
power
hardware,
decreased
costs,
advancements
accuracy
efficiency
algorithms
made
it
construct
feasible
practical
automatic
weeding
strategies.
chapter
focuses
on
exploration
numerous
strategies
involved
their
applications,
cases,
research
challenges.
