Optimizing precision agriculture: A real-time detection approach for grape vineyard unhealthy leaves using deep learning improved YOLOv7 with feature extraction capabilities
Computers and Electronics in Agriculture,
Journal Year:
2025,
Volume and Issue:
231, P. 109969 - 109969
Published: Jan. 30, 2025
Language: Английский
Wearable Standalone Sensing Systems for Smart Agriculture
Advanced Science,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 24, 2025
Abstract
Monitoring
crops’
biotic
and
abiotic
responses
through
sensors
is
crucial
for
conserving
resources
maintaining
crop
production.
Existing
often
have
technical
limitations,
measuring
only
specific
parameters
with
limited
reliability
spatial
or
temporal
resolution.
Wearable
sensing
systems
are
emerging
as
viable
alternatives
plant
health
monitoring.
These
employ
flexible
materials
attached
to
the
body
detect
nonchemical
(mechanical
optical)
chemical
parameters,
including
transpiration,
growth,
volatile
organic
compounds,
alongside
microclimate
factors
like
surface
temperature
humidity.
In
smart
farming,
data
from
real‐time
monitoring
using
these
sensors,
integrated
Internet
of
Things
technologies,
can
enhance
production
efficiency
by
supporting
growth
environment
optimization
pest
disease
management.
This
study
examines
core
components
wearable
standalone
systems,
such
circuits,
power
sources,
reviews
their
targets
operational
principles.
It
further
discusses
physiology
metabolite
monitoring,
affordability,
machine
learning
techniques
analyzing
multimodal
sensor
data.
By
summarizing
aspects,
this
aims
advance
understanding
development
sustainable
agriculture.
Language: Английский
Monitoring the Progression of Downy Mildew on Vineyards Using Multi-Temporal Unmanned Aerial Vehicle Multispectral Data
Agronomy,
Journal Year:
2025,
Volume and Issue:
15(4), P. 934 - 934
Published: April 11, 2025
Monitoring
vineyard
diseases
such
as
downy
mildew
(Plasmopara
viticola)
is
important
for
viticulture,
enabling
an
early
intervention
and
optimized
disease
management.
This
crucial
monitoring,
the
use
of
high-spatial-resolution
multispectral
data
from
unmanned
aerial
vehicles
(UAVs)
can
allow
to
a
better
understanding
progression.
study
explores
application
UAV-based
monitoring
infection
in
vineyards
through
multi-temporal
analysis.
was
conducted
plot
Vinho
Verde
region
(Portugal),
where
84
grapevines
were
monitored,
half
which
received
phytosanitary
treatments
while
other
left
untreated
this
way
during
growing
season.
Seven
UAV
flights
performed
across
different
phenological
stages
assess
effects
using
spectral
bands,
vegetation
indices,
morphometric
parameters.
The
results
indicate
that
affects
canopy
area,
height,
volume,
restricting
vegetative
growth.
Spectral
analysis
reveals
infected
show
increased
reflectance
visible
red-edge
bands
progressive
decline
near-infrared
(NIR)
reflectance.
Several
indices
demonstrated
suitable
response
infection,
with
some
them
being
capable
detecting
early-stage
symptoms,
red
edge
NIR
allowed
us
track
These
highlight
potential
remote
sensing
tool
supporting
precision
viticulture
assessment
treatment
effectiveness.
Language: Английский