International Research Journal of Multidisciplinary Technovation,
Journal Year:
2024,
Volume and Issue:
unknown, P. 223 - 240
Published: Nov. 30, 2024
Climate
change
is
a
significant
global
challenge
concerning
agriculture
and
food
security.
The
understanding
of
climate
effects
on
crop
production
necessary
for
developing
an
effective
adaptation
strategies
predicting
yield
accurately.
This
paper
suggests
the
combined
Clustering
Long
Short
Term
Memory
Transformer
(CLSTMT)
model
prediction.
CLSTMT
hybrid
that
integrates
clustering,
deep
learning
based
LSTM
techniques.
outliers
from
historical
data
are
removed
using
k-means
clustering.
Followed
by,
predicted
Transformer-based
neural
network
with
layers
feed-forward
(FNN)
components.
design
effectively
captures
climate-influenced
patterns,
enhances
precision
comprehensiveness
experiment
conducted
dataset
yield,
climate,
pesticide
details
over
101
countries
collected
1990
to
2013.
comparative
analysis
reveals
outperforms
other
regression
models
such
as
SGDRegressor
(SGDR),
Lasso
Regression
(LR),
Support
Vector
(SVR),
ElasticNet
(EN)
Ridge
(RR).
proposed
enhancing
predictions.
findings
indicate
provides
accurate
prediction
high
R2
0.951
lesser
Mean
Absolute
Percentage
Error
(MAPE)
0.195.
value
minimal
average
percentage
deviation
between
actual
yields.
more
compared
others.
Engineering Reports,
Journal Year:
2025,
Volume and Issue:
7(1)
Published: Jan. 1, 2025
ABSTRACT
Agriculture
is
a
crucial
sector
in
many
countries,
particularly
India,
where
it
significantly
influences
the
economy,
food
supply,
and
rural
livelihoods.
The
increased
integration
of
Deep
Learning
(DL)
Machine
(ML)
into
agriculture
has
enabled
substantial
advancements
predicting
crop
yields
analyzing
factors
affecting
them.
counterfactual
reasoning
framework
DICE
outperforms
LIME
offering
finer
insights
feature
importance
relative
impact
different
on
yield
prediction.
provided
clearest
causal
insights,
demonstrating
how
adjustments
to
attributes
like
sandy
alfisols
surface
texture
could
lead
significant
changes
by
water
retention
nutrient
availability.
SHAP
ranked
features
phosphate
potash
based
their
average
across
dataset,
global
view
influential
but
lacking
in‐depth
understanding.
localized
immediate
influences,
such
as
rainfall
nitrogen
content,
although
fell
short
revealing
broader
interactions
essential
for
targeted
agricultural
interventions.
findings
highlight
significance
explanations
ML
models,
they
provide
robust
understanding
relationships,
going
beyond
correlation‐based
attributions.
study
provides
understandable
practical
allowing
focused
actions
enhance
productivity
adaptability
agriculture.
By
improving
interpretability
machine
learning
research
ultimately
supports
creation
predictive
systems
that
strengthen
sustainable
practices
economic
development
within
industry.
Journal of Machine and Computing,
Journal Year:
2025,
Volume and Issue:
unknown, P. 154 - 166
Published: Jan. 3, 2025
The
cornerstone
of
human
civilization,
agriculture
is
essential
to
social
advancement,
financial
viability,
and
food
security.
However,
for
efficient
management,
issues
like
soil
health
variability
climate
change
require
sophisticated
instruments.
This
study
integrates
deep
neural
networks
(DNNs)
using
a
fuzzy
layer
improve
agricultural
decision-making
in
novel
way.
imprecision
unpredictability
inherent
data
can
pose
challenge
traditional
DNNs.
In
order
solve
this,
we
include
phase
that
uses
rules
convert
crisp
inputs
into
sets
values.
By
processing
intricate
correlations
between
variables,
this
hybrid
model
enhances
the
network's
capacity
manage
ambiguous
noisy
data.
Despite
accuracy
around
0.95,
DNNs
perform
well,
but
they
frequently
have
trouble
handling
uncertainty
With
an
0.96,
Convolutional
Neural
Networks
(CNNs)
marginally
surpass
DNNs,
especially
when
it
comes
yield
forecasting
pesticide
recommendation.
Nevertheless,
with
0.97,
DNN
performs
best
overall.
Our
exceptionally
well
predicting
crop
categories,
yields,
suggesting
fertilizers
pesticides
type
crop,
rainfall,
area
are
used.
fuzzy-integrated
noticeably
better
than
conventional
along
different
machine
learning
models,
0.97.
Fuzzy
also
interpretability,
making
easier
farmers
specialists
comprehend
reasoning
behind
suggestions.
approach
useful
tool
improving
cultivation
input
use
since
offers
higher
prediction
accuracy,
resilience,
transparency.
Plants,
Journal Year:
2025,
Volume and Issue:
14(5), P. 671 - 671
Published: Feb. 21, 2025
Soybean
is
a
vital
crop
globally
and
key
source
of
food,
feed,
biofuel.
With
advancements
in
high-throughput
technologies,
soybeans
have
become
target
for
genetic
improvement.
This
comprehensive
review
explores
advances
multi-omics,
artificial
intelligence,
economic
sustainability
to
enhance
soybean
resilience
productivity.
Genomics
revolution,
including
marker-assisted
selection
(MAS),
genomic
(GS),
genome-wide
association
studies
(GWAS),
QTL
mapping,
GBS,
CRISPR-Cas9,
metagenomics,
metabolomics
boosted
the
growth
development
by
creating
stress-resilient
varieties.
The
intelligence
(AI)
machine
learning
approaches
are
improving
trait
discovery
associated
with
nutritional
quality,
stresses,
adaptation
soybeans.
Additionally,
AI-driven
technologies
like
IoT-based
disease
detection
deep
revolutionizing
monitoring,
early
identification,
yield
prediction,
prevention,
precision
farming.
viability
environmental
soybean-derived
biofuels
critically
evaluated,
focusing
on
trade-offs
policy
implications.
Finally,
potential
impact
climate
change
productivity
explored
through
predictive
modeling
adaptive
strategies.
Thus,
this
study
highlights
transformative
multidisciplinary
advancing
global
utility.
