Harvester Maintenance Prediction Tool: Machine Learning Model Based on Mechanical Features
AgriEngineering,
Год журнала:
2025,
Номер
7(4), С. 97 - 97
Опубликована: Апрель 1, 2025
One
important
element
influencing
the
efficiency
of
automated
timber
harvesting
is
harvester
maintenance.
However,
understanding
this
effect
limited,
which
can
lead
to
more
frequent
harvest
interruptions
and
consequently
higher
production
costs.
Data
modeling
be
used
evaluate
how
mechanical
aspects
affect
maintenance
in
plantation
forests,
help
with
forest
planning.
This
study
aimed
ascertain
if
characteristics
may
utilized
develop
a
high-performance
model
capable
properly
forecasting
using
machine
learning.
A
free
web
application
managers
implement
approach
was
also
developed
as
part
study.
For
modeling,
we
considered
eight
features
status
target
feature.
In
default
mode,
ran
25
popular
algorithms
through
database
compared
them
based
on
accuracy
error
metrics.
Although
combination
models
performed
well,
Random
Forest
better
mode
an
0.933.
addition,
generated
makes
it
possible
create
prediction
tool
that
provides
quick
visualization
feature
make
informed
decisions.
Along
data
from
experimental
research,
will
available
complete
file
containing
predictive
model,
well
software,
both
Python
language.
Язык: Английский
Forwarder Machine Performance in Eucalyptus Forests in Brazil with Different Productivity Levels: An Analysis of Production Costs
Forests,
Год журнала:
2025,
Номер
16(4), С. 646 - 646
Опубликована: Апрель 8, 2025
The
objective
of
this
study
was
to
evaluate
the
influence
mean
individual
volume
per
tree
(MIV)
on
productivity
forwarder
machines
and
production
cost
in
eucalyptus
plantations
located
southern
Bahia,
Brazil.
MIV
positively
influenced
costs,
promoting
a
more
attractive
latter
when
increased.
machine’s
for
0.13
m3
42.06
cubic
meters
effective
working
hour
(m3Ewh−1),
while
0.58
reached
60.97
m3Ewh−1,
corresponding
an
increase
42.59%
between
minimum
maximum
classes.
extracted
(m3)
decreased
by
30.12%
from
USD
2.49
1.74,
respectively,
comparing
coefficient
determination
obtained
modeling
significant
(R2
=
92%),
indicating
can
be
explained
tree.
highest
yields
average
classes
provided
better
energy
efficiency
indices
machine;
that
is
say,
became
productive,
ratio
fuel
consumption
meter
timber
harvested
decreased,
providing
performance
respective
index.
There
difference
extraction
costs
147.83
hectare
lowest
forests
(MIV
varying
0.15
0.58).
mechanical
availability
operational
all
forwarders
evaluated
were
above
80%,
which
contributed
machine
performance.
Maintenance
repairs
represented
largest
portion
(33.59%),
followed
labor
(22.49%),
depreciation
(14.33%),
(10.11%).
Язык: Английский
Cut-to-Length Harvesting Prediction Tool: Machine Learning Model Based on Harvest and Weather Features
Forests,
Год журнала:
2024,
Номер
15(8), С. 1398 - 1398
Опубликована: Авг. 10, 2024
Weather
is
a
significant
factor
influencing
forest
health,
productivity,
and
the
carbon
cycle.
However,
our
understanding
of
these
effects
limited
for
many
regions
ecosystems.
Assessing
impact
weather
variability
on
harvester
productivity
from
plantation
forests
may
assist
in
planning
through
use
data
modeling.
We
investigated
whether
combined
with
timber
harvesting
attributes
could
be
used
to
create
high-performance
model
that
accurately
predict
Eucalyptus
plantations
using
machine
learning.
Furthermore,
we
aimed
provide
an
online
application
managers
applying
model.
For
modeling,
considered
15
attributes.
as
target
attribute.
subjected
database
24
common
algorithms
default
mode
compared
them
according
error
metrics
accuracy.
From
features
features,
Catboost
can
harvesters
tuned
mode,
coefficient
determination
0.70.
The
accurate
approach
predicting
plantations,
allowing
creation
online,
free
managers.
Язык: Английский
Productivity and Costs of Mechanized Skidding operations at Sao Hill Forest Plantation, Tanzania
Forest Science and Technology,
Год журнала:
2023,
Номер
20(1), С. 91 - 103
Опубликована: Дек. 28, 2023
Due
to
global
advancement
of
technology
in
forest
operations,
utilization
advanced
machineries
such
as
grapple
skidder
(GS)
timber
harvesting
has
been
increasing
the
last
decades.
However,
order
understand
their
contribution
sustainable
it
is
important
performance
under
different
operating
environment.
Therefore,
this
study
aimed
quantify
productivity
and
cost
mechanised
skidding
operations
at
Sao
Hill
Forest
plantation
(SHFP).
Six
variables;
diameter
a
breast
height
(dbh),
tree
height,
distance,
slope,
costs,
cycle
time
(determined
using
detailed
continuous
study)
were
collected
120
GS
observations.GS
costs
estimated
productive
machine
hour
(PMH)
delays
inclusion
approach.
Regression
models
developed
generalized
linear
model
(GLM)
PMH
was
2.6%
higher
than
one
including
delay
time,
while
2.1%
approach
delays.
This
revealed
significant
variations
(p-value
<0.05)
on
various
terrain
classes.
At
0
m
–
50
an
average
free
85.5
m3/h,
with
amounting
1.7
USD/m3.
On
distance
exceeding
150
m,
dropped
20.1
increased
12.7
Likewise,
0.0%
-
10.0%
slope
range,
100
m3/h
1.5
USD/m3
respectively,
20.1%
30.0%
32.6
raised
3.9
Skidding
volume
per
trip
robust
predictors
yielding
pseudo-R2
values
58.1%
64.3%,
respectively.
statistical
useful
for
predicting
however,
applications
are
recommended
be
within
ranges
variables
used
develop
models.
Язык: Английский