YOLO-Peach: A High-Performance Lightweight YOLOv8s-Based Model for Accurate Recognition and Enumeration of Peach Seedling Fruits
Agronomy,
Год журнала:
2024,
Номер
14(8), С. 1628 - 1628
Опубликована: Июль 25, 2024
The
identification
and
enumeration
of
peach
seedling
fruits
are
pivotal
in
the
realm
precision
agriculture,
greatly
influencing
both
yield
estimation
agronomic
practices.
This
study
introduces
an
innovative,
lightweight
YOLOv8
model
for
automatic
detection
quantification
fruits,
designated
as
YOLO-Peach,
to
bolster
scientific
rigor
operational
efficiency
orchard
management.
Traditional
methods,
which
labor-intensive
error-prone,
have
been
superseded
by
this
advancement.
A
comprehensive
dataset
was
meticulously
curated,
capturing
rich
characteristics
diversity
through
high-resolution
imagery
at
various
times
locations,
followed
meticulous
preprocessing
ensure
data
quality.
YOLOv8s
underwent
a
series
optimizations,
including
integration
MobileNetV3
its
backbone,
p2BiFPN
architecture,
spatial
channel
reconstruction
convolution,
coordinate
attention
mechanism,
all
significantly
bolstered
model’s
capability
detect
small
targets
with
precision.
YOLO-Peach
excels
accuracy,
evidenced
recall
0.979,
along
mAP50
0.993
mAP50-95
0.867,
indicating
superior
sapling
efficient
computational
performance.
findings
underscore
efficacy
practicality
context
fruit
recognition.
Ablation
studies
shed
light
on
indispensable
role
each
component,
streamlining
complexity
load,
while
ScConv
convolutions,
mechanism
collectively
enhanced
feature
extraction
minute
targets.
implications
research
profound,
offering
novel
approach
recognition
serving
blueprint
young
other
species.
work
holds
significant
theoretical
practical
value,
propelling
forward
broader
field
agricultural
automation.
Язык: Английский
A Novel Multinozzle Targeting Pollination Robot for Clustered Kiwifruit Flowers Based on Air–Liquid Dual‐Flow Spraying
Journal of Field Robotics,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 19, 2025
ABSTRACT
Manual
pollination
of
kiwifruit
flowers
is
a
labor‐intensive
work
that
highly
desired
to
be
replaced
by
robotic
operations.
In
this
research,
robot
was
developed
achieve
precision
clustered
in
the
orchard.
The
consists
five
systems,
including
multinozzle
end‐effector,
mechanical
arm,
vision
system,
crawler‐type
chassis,
and
control
system.
can
select
preferential
then
target
their
pistil
pollination.
First,
statistical
analysis
dimensions
flower
clusters
individual
conducted
fit
normal
distribution
curves,
which
guided
design
spray
coverage
combination
intervals
for
end‐effector.
Second,
optimal
parameters
were
determined
based
on
three‐factor,
five‐level
quadratic
orthogonal
experiment,
is,
air
pressure
70.4
kPa,
rate
flow
86.0
mL/min,
distance
27.8
cm.
A
targeted
strategy
selection
structure
Field
experiments
commercial
orchard
evaluate
its
feasibility
performance,
an
average
success
targeting
93.4%
at
speed
1.0
s
per
achieved.
Furthermore,
compared
with
artificial
assisted
methods,
it
improve
utilization
pollen
consumption
0.20
g
every
60
fruit
set
88.9%.
validations
demonstrated
efficiently
pollinate
save
pollen.
Язык: Английский
A vision-based robotic system for precision pollination of apples
Computers and Electronics in Agriculture,
Год журнала:
2025,
Номер
234, С. 110158 - 110158
Опубликована: Март 9, 2025
Язык: Английский
Robotic precision thinning in apple production – using optimization and Bayesian modeling to assess potentials of automation in horticulture
Acta Horticulturae,
Год журнала:
2025,
Номер
1425, С. 239 - 246
Опубликована: Март 1, 2025
Язык: Английский
Applications of the Internet of Things on Agriculture: Review and Future Apple Fruit Comprehensive Automation System
Smart agriculture,
Год журнала:
2025,
Номер
unknown, С. 45 - 68
Опубликована: Янв. 1, 2025
Язык: Английский
AI Technologies for Apple Leaf Diseases Identification: Scientometric Analysis
Smart agriculture,
Год журнала:
2025,
Номер
unknown, С. 137 - 161
Опубликована: Янв. 1, 2025
Язык: Английский
Research Progress on Thinning Equipment in Orchards: A Review
Smart agriculture,
Год журнала:
2025,
Номер
unknown, С. 23 - 44
Опубликована: Янв. 1, 2025
Язык: Английский
Comparison of robotic precision thinning system and commercial air-blast sprayer for flower thinning on apple trees
C. Andergassen,
Emiliano Bruni,
D. Pichler
и другие.
Acta Horticulturae,
Год журнала:
2024,
Номер
1395, С. 369 - 372
Опубликована: Май 1, 2024
Язык: Английский
Robotics for tree fruit orchards
Acta Horticulturae,
Год журнала:
2024,
Номер
1395, С. 359 - 368
Опубликована: Май 1, 2024
Язык: Английский
Green Fruit‐Stem Pairing and Clustering for Machine Vision System in Robotic Thinning of Apples
Magni Hussain,
Long He,
James R. Schupp
и другие.
Journal of Field Robotics,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 5, 2024
ABSTRACT
Apples
are
one
of
the
most
highly‐valued
specialty
crops
in
United
States.
Recent
labor
shortages
have
made
crop
production
difficult
for
fruit
growers,
including
task
green
thinning.
Current
methods
hand,
chemical,
and
mechanical
thinning
impose
tradeoffs
between
selectivity,
cost,
tree
damage,
speed.
A
robotic
system
could
potentially
selectively
thin
a
quick,
cost‐effective,
non‐damaging
manner.
The
machine
vision
would
be
critical
component
thinning,
not
only
need
to
perform
detection/segmentation,
but
also
fruit‐stem
pairing
clustering
facilitate
proper
decision‐making
neural
network‐based
stem
algorithm
was
devised
evaluated;
an
LSTM‐based
compared
density‐based
algorithm,
OPTICS.
algorithms
were
evaluated
on
image
data
set
consisting
GoldRush,
Fuji,
Golden
Delicious
cultivars
at
stage,
with
evaluations
overall
performance,
cultivar‐wise
cluster
size‐specific
feature
importance.
For
pairing,
achieved
AP
81.4%
all
fruits
stems,
that
reached
90.6%
when
stems
labeled
angles
considered.
clustering,
success
rate
68.4%,
whereas
OPTICS
obtained
50.9%.
will
further
implemented
pipeline
future
system,
as
well
integrate
use
point
clouds
other
3D
orchard
information
improve
performance.
Язык: Английский