Sensors, systems and algorithms of 3D reconstruction for smart agriculture and precision farming: A review
Computers and Electronics in Agriculture,
Год журнала:
2024,
Номер
224, С. 109229 - 109229
Опубликована: Июль 10, 2024
Язык: Английский
A corn canopy organs detection method based on improved DBi-YOLOv8 network
European Journal of Agronomy,
Год журнала:
2024,
Номер
154, С. 127076 - 127076
Опубликована: Янв. 18, 2024
Язык: Английский
Review of deep learning-based methods for non-destructive evaluation of agricultural products
Biosystems Engineering,
Год журнала:
2024,
Номер
245, С. 56 - 83
Опубликована: Июль 13, 2024
Язык: Английский
Image processing and artificial intelligence for apple detection and localization: A comprehensive review
Computer Science Review,
Год журнала:
2024,
Номер
54, С. 100690 - 100690
Опубликована: Ноя. 1, 2024
Язык: Английский
DHS-YOLO: Enhanced Detection of Slender Wheat Seedlings Under Dynamic Illumination Conditions
Agriculture,
Год журнала:
2025,
Номер
15(5), С. 510 - 510
Опубликована: Фев. 26, 2025
The
precise
identification
of
wheat
seedlings
in
unmanned
aerial
vehicle
(UAV)
imagery
is
fundamental
for
implementing
precision
agricultural
practices
such
as
targeted
pesticide
application
and
irrigation
management.
This
detection
task
presents
significant
technical
challenges
due
to
two
inherent
complexities:
(1)
environmental
interference
from
variable
illumination
conditions
(2)
morphological
characteristics
characterized
by
slender
leaf
structures
flexible
posture
variations.
To
address
these
challenges,
we
propose
DHS-YOLO,
a
novel
deep
learning
framework
optimized
robust
seedling
under
diverse
intensities.
Our
methodology
builds
upon
the
YOLOv11
architecture
with
three
principal
enhancements:
First,
Dynamic
Slender
Convolution
(DSC)
module
employs
deformable
convolutions
adaptively
capture
elongated
features
leaves.
Second,
Histogram
Transformer
(HT)
integrates
dynamic-range
spatial
attention
mechanism
mitigate
illumination-induced
image
degradation.
Third,
implement
ShapeIoU
loss
function
that
prioritizes
geometric
consistency
between
predicted
ground
truth
bounding
boxes,
particularly
optimizing
plant
structures.
experimental
validation
was
conducted
using
custom
UAV-captured
dataset
containing
images
varying
conditions.
Compared
existing
models,
proposed
model
achieved
best
performance
precision,
recall,
mAP50,
mAP50-95
values
94.1%,
91.0%,
95.2%,
81.9%,
respectively.
These
results
demonstrate
our
model’s
effectiveness
overcoming
variations
while
maintaining
high
sensitivity
fine
research
contributes
an
computer
vision
solution
agriculture
applications,
enabling
automated
field
management
systems
through
reliable
crop
challenging
Язык: Английский
Image Analysis Artificial Intelligence Technologies for Plant Phenotyping: Current State of the Art
AgriEngineering,
Год журнала:
2024,
Номер
6(3), С. 3375 - 3407
Опубликована: Сен. 17, 2024
Modern
agriculture
is
characterized
by
the
use
of
smart
technology
and
precision
to
monitor
crops
in
real
time.
The
technologies
enhance
total
yields
identifying
requirements
based
on
environmental
conditions.
Plant
phenotyping
used
solving
problems
basic
science
allows
scientists
characterize
select
best
genotypes
for
breeding,
hence
eliminating
manual
laborious
methods.
Additionally,
plant
useful
such
as
subtle
differences
or
complex
quantitative
trait
locus
(QTL)
mapping
which
are
impossible
solve
using
conventional
This
review
article
examines
latest
developments
image
analysis
AI,
2D,
3D
reconstruction
techniques
limiting
literature
from
2020.
collects
data
84
current
studies
showcases
novel
applications
various
technologies.
AI
algorithms
showcased
predicting
issues
expected
during
growth
cycles
lettuce
plants,
soybeans
different
climates
conditions,
high-yielding
improve
yields.
high
throughput
also
facilitates
monitoring
crop
canopies
genotypes,
root
phenotyping,
late-time
harvesting
weeds.
methods
combined
with
guide
applications,
leading
higher
accuracy
than
cases
that
consider
either
method.
Finally,
a
combination
undertake
operations
involving
automated
robotic
harvesting.
Future
research
directions
where
uptake
smartphone-based
time
series
ML
recommended.
Язык: Английский
PanicleNeRF: low-cost, high-precision in-field phenotyping of rice panicles with smartphone
Plant Phenomics,
Год журнала:
2024,
Номер
6
Опубликована: Янв. 1, 2024
The
rice
panicle
traits
substantially
influence
grain
yield,
making
them
a
primary
target
for
phenotyping
studies.
However,
most
existing
techniques
are
limited
to
controlled
indoor
environments
and
have
difficulty
in
capturing
the
under
natural
growth
conditions.
Here,
we
developed
PanicleNeRF,
novel
method
that
enables
high-precision
low-cost
reconstruction
of
three-dimensional
(3D)
models
field
based
on
video
acquired
by
smartphone.
proposed
combined
large
model
Segment
Anything
Model
(SAM)
small
You
Only
Look
Once
version
8
(YOLOv8)
achieve
segmentation
images.
neural
radiance
fields
(NeRF)
technique
was
then
employed
3D
using
images
with
2D
segmentation.
Finally,
resulting
point
clouds
processed
successfully
extract
traits.
results
show
PanicleNeRF
effectively
addressed
image
task,
achieving
mean
F1
score
86.9%
Intersection
over
Union
(IoU)
79.8%,
nearly
double
boundary
overlap
(BO)
performance
compared
YOLOv8.
As
cloud
quality,
significantly
outperformed
traditional
SfM-MVS
(structure-from-motion
multi-view
stereo)
methods,
such
as
COLMAP
Metashape.
length
accurately
extracted
rRMSE
2.94%
indica
1.75%
japonica
rice.
volume
estimated
from
strongly
correlated
number
(
R
2
=
0.85
0.82
)
mass
(0.80
0.76
).
This
provides
solution
high-throughput
in-field
panicles,
accelerating
efficiency
breeding.
Язык: Английский
Three-dimensional Reconstruction of Tomato Fruit based on Multi-view Images
Опубликована: Май 30, 2024
Язык: Английский
AppleQSM: Geometry-Based 3D Characterization of Apple Tree Architecture in Orchards
Plant Phenomics,
Год журнала:
2024,
Номер
6
Опубликована: Янв. 1, 2024
The
architecture
of
apple
trees
plays
a
pivotal
role
in
shaping
their
growth
and
fruit-bearing
potential,
forming
the
foundation
for
precision
management.
Traditionally,
2D
imaging
technologies
were
employed
to
delineate
architectural
traits
trees,
but
accuracy
was
hampered
by
occlusion
perspective
ambiguities.
This
study
aimed
surmount
these
constraints
devising
3D
geometry-based
processing
pipeline
tree
structure
segmentation
trait
characterization,
utilizing
point
clouds
collected
terrestrial
laser
scanner
(TLS).
consisted
four
modules:
(a)
data
preprocessing
module,
(b)
instance
(c)
(d)
extraction
module.
developed
used
analyze
84
two
representative
cultivars,
characterizing
such
as
height,
trunk
diameter,
branch
count,
angle.
Experimental
results
indicated
that
established
attained
an
R
2
0.92
0.83,
mean
absolute
error
(MAE)
6.1
cm
4.71
mm
height
diameter
at
level,
respectively.
Additionally,
it
achieved
0.77
0.69,
MAE
6.86
7.48°
angle,
accurate
measurement
can
enable
management
high-density
orchards
bolster
phenotyping
endeavors
breeding
programs.
Moreover,
bottlenecks
characterization
general
comprehensively
analyzed
reveal
future
development.
Язык: Английский