Machine Learning in Sustainable Agriculture: Systematic Review and Research Perspectives
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.
Язык: Английский
Rapid Classification of Sugarcane Nodes and Internodes Using Near-Infrared Spectroscopy and Machine Learning Techniques
Sensors,
Год журнала:
2024,
Номер
24(22), С. 7102 - 7102
Опубликована: Ноя. 5, 2024
Accurate
and
rapid
discrimination
between
nodes
internodes
in
sugarcane
is
vital
for
automating
planting
processes,
particularly
minimizing
bud
damage
optimizing
material
quality.
This
study
investigates
the
potential
of
visible-shortwave
near-infrared
(Vis-SWNIR)
spectroscopy
(400-1000
nm)
combined
with
machine
learning
this
classification
task.
Spectral
data
were
acquired
from
cultivar
Khon
Kaen
3
at
multiple
orientations,
various
preprocessing
techniques
employed
to
enhance
spectral
features.
Three
algorithms,
linear
discriminant
analysis
(LDA),
K-Nearest
Neighbors
(KNNs),
artificial
neural
networks
(ANNs),
evaluated
their
performance.
The
results
demonstrated
high
accuracy
across
all
models,
ANN
coupled
derivative
achieving
an
F1-score
0.93
on
both
calibration
validation
datasets,
0.92
independent
test
set.
underscores
feasibility
Vis-SWNIR
precise
node/internode
classification,
paving
way
automation
billet
preparation
other
precision
agriculture
applications.
Язык: Английский
SmartGrow DataControl: An IoT architecture for the acquisition of environmental physiological parameters in Cannabis sativa cultivations
SoftwareX,
Год журнала:
2024,
Номер
27, С. 101880 - 101880
Опубликована: Сен. 1, 2024
Язык: Английский
A 3D Printed Air-Tight Cell Adaptable for Far-Infrared Reflectance, Optical Photothermal Infrared Spectroscopy, and Raman Spectroscopy Measurements
Instruments,
Год журнала:
2024,
Номер
8(4), С. 54 - 54
Опубликована: Дек. 16, 2024
Material
characterization
and
investigation
are
the
basis
for
improving
performance
of
electrochemical
devices.
However,
many
compounds
with
applications
sensitive
to
atmospheric
gases
moisture;
therefore,
even
their
should
be
performed
in
a
controlled
atmosphere.
In
some
cases,
it
is
impossible
execute
such
investigations
glove
box,
and,
present
work,
an
air-tight
3D
printed
cell
was
developed
that
preserves
samples
atmosphere
while
allowing
spectroscopic
measurements
reflectance
geometry.
Equipped
cheap
1
mm
thick
CaF2
optical
window
or
more
expensive
0.5
ZnS
window,
used
both
photothermal
infrared
Raman
spectroscopy
measures;
imaging
also
possible.
The
far-infrared
range
were
equipped
diamond
window.
Язык: Английский