This
paper
presents
an
innovative
AI-enhanced
irrigation
system
designed
to
optimize
water
management
in
agriculture.
The
integrates
advanced
technologies
such
as
IoT,
sensor
networks,
and
artificial
intelligence
algorithms
achieve
precise
efficient
scheduling.
Leveraging
real-time
data
from
sensors
including
soil
moisture,
temperature,
humidity,
combined
with
historical
weather
forecasts,
the
employs
a
dynamic
algorithm
make
informed
decisions.
Experimental
evaluation
conducted
over
week-long
period
using
garden
rose
test
subject
demonstrated
system's
ability
maintain
optimal
moisture
levels
within
range
of
60-75%,
while
significantly
reducing
consumption
compared
conventional
methods.
Simulation
results
further
validated
effectiveness
predicting
optimizing
Key
metrics
enhanced
crop
output,
reduced
usage,
adherence
sustainable
farming
practices
were
used
assess
superiority
proposed
model.
Overall,
promising
solution
for
agriculture,
offering
improved
conservation,
productivity,
resource
utilization.
Artificial Intelligence in Agriculture,
Год журнала:
2024,
Номер
12, С. 72 - 84
Опубликована: Апрель 30, 2024
The
issue
of
food
security
continues
to
be
a
prominent
global
concern,
affecting
significant
number
individuals
who
experience
the
adverse
effects
hunger
and
malnutrition.
finding
solution
this
intricate
necessitates
implementation
novel
paradigm-shifting
methodologies
in
agriculture
sector.
In
recent
times,
domain
artificial
intelligence
(AI)
has
emerged
as
potent
tool
capable
instigating
profound
influence
on
sectors.
AI
technologies
provide
advantages
by
optimizing
crop
cultivation
practices,
enabling
use
predictive
modelling
precision
techniques,
aiding
efficient
monitoring
disease
identification.
Additionally,
potential
optimize
supply
chain
operations,
storage
management,
transportation
systems,
quality
assurance
processes.
It
also
tackles
problem
loss
waste
through
post-harvest
reduction,
analytics,
smart
inventory
management.
This
study
highlights
that
how
utilizing
power
AI,
we
could
transform
way
produce,
distribute,
manage
food,
ultimately
creating
more
secure
sustainable
future
for
all.
Agriculture,
Год журнала:
2024,
Номер
14(8), С. 1256 - 1256
Опубликована: Июль 30, 2024
Today,
crop
suggestions
and
necessary
guidance
have
become
a
regular
need
for
farmer.
Farmers
generally
depend
on
their
local
agriculture
officers
regarding
this,
it
may
be
difficult
to
obtain
the
right
at
time.
Nowadays,
datasets
are
available
different
websites
in
sector,
they
play
crucial
role
suggesting
suitable
crops.
So,
decision
support
system
that
analyzes
dataset
using
machine
learning
techniques
can
assist
farmers
making
better
choices
selections.
The
main
objective
of
this
research
is
provide
quick
with
more
accurate
effective
recommendations
by
utilizing
methods,
global
positioning
coordinates,
cloud
data.
Here,
recommendation
personalized,
which
enables
predict
crops
specific
geographical
context,
taking
into
account
factors
like
climate,
soil
composition,
water
availability,
conditions.
In
regard,
an
existing
historical
contains
state,
district,
year,
area-wise
production
rate,
name,
season
was
collected
246,091
sample
records
from
Dataworld
website,
holds
data
37
areas
India.
Also,
analysis,
offices
Rayagada,
Koraput,
Gajapati
districts
Odisha
Both
these
were
combined
stored
Firebase
service.
Thirteen
algorithms
been
applied
identify
dependencies
within
To
facilitate
process,
Android
application
developed
Studio
(Electric
Eel
|
2023.1.1)
Emulator
(Version
32.1.14),
Software
Development
Kit
(SDK,
SDK
33),
Tools.
A
model
has
proposed
implements
SMOTE
(Synthetic
Minority
Oversampling
Technique)
balance
dataset,
then
allows
implementation
13
classifiers,
such
as
logistic
regression,
tree
(DT),
K-Nearest
Neighbor
(KNN),
SVC
(Support
Vector
Classifier),
random
forest
(RF),
Gradient
Boost
(GB),
Bagged
Tree,
extreme
gradient
boosting
(XGB
classifier),
Ada
Classifier,
Cat
Boost,
HGB
(Histogram-based
Boosting),
SGDC
(Stochastic
Descent),
MNB
(Multinomial
Naive
Bayes)
dataset.
It
observed
performance
method
1.00
accuracy,
precision,
recall,
F1-score,
ROC
AUC
(Receiver
Operating
Characteristics–Area
Under
Curve)
0.91
sensitivity
0.54
specificity
after
applying
SMOTE.
Overall,
compared
all
other
classifiers
implemented
predictions.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 12, 2025
The
agriculture
field
is
the
basis
of
a
country's
change
and
financial
system.
Crops
are
main
source
revenue
for
people.
One
farmer's
most
challenging
problems
choosing
right
crops
their
land.
This
critical
decision
has
direct
impact
on
productivity
profit.
Wrong
crop
selection
not
only
reduces
yields
but
also
causes
food
shortages,
creating
more
farmers.
best
depends
many
parameters
such
as
illustration
humidity,
N,
K,
P,
pH,
rainfall,
temperature
soil.
Getting
advice
from
experts
an
easy
task.
requires
intelligent
models
in
recommendations
that
use
machine-learning
to
suggest
suitable
soil
other
environmental
conditions.
Temperature,
pH
important
data
growing
agriculture.
In
this
study,
we
gather
preprocess
relevant
data.
To
recommend
crop,
propose
novel
ensemble
learning
approach
called
RFXG
based
random
forest
(RF)
extreme
gradient
boosting
(XGB)
out
twenty-two
major
crops.
measure
capability
proposed
approach,
various
machine
utilized
including
extra
tree
classifier,
multilayer
perceptron,
RF,
trees,
logistic
regression,
XGB
classifiers.
get
performance,
optimization
hyperparameter,
K-fold
cross-validation
procedures
performed.
Experimental
outcomes
show
technique
achieves
recommendation
accuracy
98%.
Specifically,
solution
provides
immediate
help
farmers
make
timely
decisions.
Concurrency and Computation Practice and Experience,
Год журнала:
2025,
Номер
37(4-5)
Опубликована: Фев. 19, 2025
ABSTRACT
Decision
Neural
Networks
significantly
improve
the
performance
of
complex
models
and
create
more
transparent
accountable
decision‐making
systems
that
can
be
trusted
in
critical
applications.
However,
their
strongly
depends
on
amount
data
learning
algorithm.
This
article
describes
development
a
simplified
structure
training
algorithm
based
Levenberg–Marquardt
to
enhance
decision
neural
network's
assess
utility
function's
efficacy
multi‐objective
issues.
The
suggested
converges
faster
than
traditional
algorithms.
Also,
designed
scheme
combines
gradient
descent
with
Gauss‐Newton
method,
allowing
it
escape
shallow
local
minima
effectively
other
similar
techniques.
Numerical
examples
demonstrate
how
well
method
estimates
linear
functions,
even
complicated
nonlinear
ones.
Additionally,
findings
applying
enhanced
network
issues
show
this
instructional
technique
produces
responses
higher
quality
convergence.
By
problem
seven
primary
answers,
is
shown
accuracy
improved
by
20%.
Electronics,
Год журнала:
2024,
Номер
13(22), С. 4362 - 4362
Опубликована: Ноя. 7, 2024
This
article
explores
the
transformative
potential
of
artificial
intelligence
(AI)
tools
across
agricultural
value
chain,
highlighting
their
applications,
benefits,
challenges,
and
future
prospects.
With
global
food
demand
projected
to
increase
by
70%
2050,
AI
technologies—including
machine
learning,
big
data
analytics,
Internet
things
(IoT)—offer
critical
solutions
for
enhancing
productivity,
sustainability,
resource
efficiency.
The
study
provides
a
comprehensive
review
applications
at
multiple
stages
including
land
use
planning,
crop
selection,
management,
disease
detection,
yield
prediction,
market
integration.
It
also
discusses
significant
challenges
adoption,
such
as
accessibility,
technological
infrastructure,
need
specialized
skills.
By
examining
case
studies
empirical
evidence,
demonstrates
how
AI-driven
can
optimize
decision-making
operational
efficiency
in
agriculture.
findings
underscore
AI’s
pivotal
role
addressing
with
implications
farmers,
agribusinesses,
policymakers,
researchers.
aims
advance
evolving
research
discussions
on
sustainable
agriculture,
contributing
insights
that
promote
adoption
technologies
influence
farming.