Agriculture,
Год журнала:
2024,
Номер
15(1), С. 39 - 39
Опубликована: Дек. 27, 2024
Agroforestry
systems
can
influence
the
occurrence
and
abundance
of
pests
diseases
because
integrating
crops
with
trees
or
other
vegetation
create
diverse
microclimates
that
may
either
enhance
inhibit
their
development.
This
study
analyzes
severity
coffee
rust
in
two
agroforestry
provinces
Jaén
San
Ignacio
department
Cajamarca
(Peru).
research
used
a
quantitative
descriptive
approach,
319
photographs
were
collected
professional
camera
during
field
trips.
The
segmented,
classified
analyzed
using
deep
learning
MobileNet
VGG16
transfer
models
methods
for
measuring
from
SENASA
Peru
SENASICA
Mexico.
results
reported
grade
1
is
most
prevalent
according
to
methodology
(1
5%
leaf
affected)
Mexico
(0
2%
affected).
Moreover,
proposed
model
presented
best
classification
accuracy
rate
94%
over
50
epochs.
demonstrates
capacity
machine
algorithms
disease
diagnosis,
which
could
be
an
alternative
help
experts
quantify
broadens
future
low-cost
computational
tools
recognition
Agriculture,
Год журнала:
2025,
Номер
15(4), С. 377 - 377
Опубликована: Фев. 11, 2025
Machine
learning
(ML)
has
revolutionized
resource
management
in
agriculture
by
analyzing
vast
amounts
of
data
and
creating
precise
predictive
models.
Precision
improves
agricultural
productivity
profitability
while
reducing
costs
environmental
impact.
However,
ML
implementation
faces
challenges
such
as
managing
large
volumes
adequate
infrastructure.
Despite
significant
advances
applications
sustainable
agriculture,
there
is
still
a
lack
deep
systematic
understanding
several
areas.
Challenges
include
integrating
sources
adapting
models
to
local
conditions.
This
research
aims
identify
trends
key
players
associated
with
use
agriculture.
A
review
was
conducted
using
the
PRISMA
methodology
bibliometric
analysis
capture
relevant
studies
from
Scopus
Web
Science
databases.
The
study
analyzed
literature
between
2007
2025,
identifying
124
articles
that
meet
criteria
for
certainty
assessment.
findings
show
quadratic
polynomial
growth
publication
on
notable
increase
up
91%
per
year.
most
productive
years
were
2024,
2022,
2023,
demonstrating
growing
interest
field.
highlights
importance
multiple
improved
decision
making,
soil
health
monitoring,
interaction
climate,
topography,
properties
land
crop
patterns.
Furthermore,
evolved
weather
advanced
technologies
like
Internet
Things,
remote
sensing,
smart
farming.
Finally,
agenda
need
deepening
expansion
predominant
concepts,
farming,
develop
more
detailed
specialized
explore
new
maximize
benefits
sustainability.
With
the
increase
in
awareness
regarding
conservation
of
forests,
we
must
be
wary
to
preserve
them
sustainably
from
potential
pathogens.
Statistics
tells
us
that
number
trees
lose
every
year
due
pathogen
attacks
is
huge
and
thus
requires
a
machine
learning
model
identify
presence
pathogens
significantly
reduce
deaths
per
year.
TIn
this
paper
have
done
cumulative
study
about
efficiency
two
different
models
namely
Linear
Regression
CNN(Convolutional
Neural
Networks)
achieved
following
accuracies
with
respect
actual
data.
For
an
accuracy
65.71%
80.85%
for
CNN.
Further
analysis
various
metrics
like
RMS(Root
Mean
Square)
value,
MAE(Mean
Absolute
Error)
MSE(Mean
Squared
Value)
both
models.
