An intelligent forecasting system in Internet of Agriculture Things sensor network
Ad Hoc Networks,
Год журнала:
2025,
Номер
unknown, С. 103752 - 103752
Опубликована: Янв. 1, 2025
Язык: Английский
Prediction of the bed expansion and pressure drop in microirrigation media filter backwashing using artificial neural networks and comparison with other machine learning techniques
Smart Agricultural Technology,
Год журнала:
2025,
Номер
unknown, С. 100900 - 100900
Опубликована: Март 1, 2025
Язык: Английский
Development of Mathematical and Computational Models for Predicting Agricultural Soil–Water Management Properties (ASWMPs) to Optimize Intelligent Irrigation Systems and Enhance Crop Resilience
Agronomy,
Год журнала:
2025,
Номер
15(4), С. 942 - 942
Опубликована: Апрель 12, 2025
Soil–water
management
is
fundamental
to
plant
ecophysiology,
directly
affecting
resilience
under
both
anthropogenic
and
natural
stresses.
Understanding
Agricultural
Soil–Water
Management
Properties
(ASWMPs)
therefore
essential
for
optimizing
water
availability,
enhancing
harvest
resilience,
enabling
informed
decision-making
in
intelligent
irrigation
systems,
particularly
the
face
of
climate
variability
soil
degradation.
In
this
regard,
present
research
develops
predictive
models
ASWMPs
based
on
grain
size
distribution
dry
bulk
density
soils,
integrating
traditional
mathematical
approaches
advanced
computational
techniques.
By
examining
900
samples
from
NaneSoil
database,
spanning
diverse
crop
species
(Avena
sativa
L.,
Daucus
carota
Hordeum
vulgare
Medicago
Phaseolus
vulgaris
Sorghum
Pers.,
Triticum
aestivum
Zea
mays
L.),
several
are
proposed
three
key
ASWMPs:
soil-saturated
hydraulic
conductivity,
field
capacity,
permanent
wilting
point.
Mathematical
demonstrate
high
accuracy
(71.7–96.4%)
serve
as
practical
agronomic
tools
but
limited
capturing
complex
soil–plant-water
interactions.
Meanwhile,
a
Deep
Neural
Network
(DNN)-based
model
significantly
enhances
performance
(91.4–99.7%
accuracy)
by
uncovering
nonlinear
relationships
that
govern
moisture
retention
availability.
These
findings
contribute
precision
agriculture
providing
robust
soil–water
management,
ultimately
supporting
against
environmental
challenges
such
drought,
salinization,
compaction.
Язык: Английский
An intelligent framework for monitoring and irrigation prediction for precision agriculture
Iran Journal of Computer Science,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 6, 2025
Язык: Английский
A human-in-the-loop ensemble fusion framework for road crash prediction: coping with imbalanced heterogeneous data from the driver-vehicle-environment system
Transportation Letters,
Год журнала:
2024,
Номер
unknown, С. 1 - 17
Опубликована: Сен. 3, 2024
Язык: Английский
Towards a Modelling, Optimization and Predictive Control Framework for Smart Irrigation
Heliyon,
Год журнала:
2024,
Номер
10(18), С. e38095 - e38095
Опубликована: Сен. 1, 2024
Язык: Английский
Deep Learning Prediction of Vehicle Lane Departure During Night-Times: A Synthetic Over-Sampling Framework with Enhanced Dimensionality Reduction
Lecture notes in networks and systems,
Год журнала:
2024,
Номер
unknown, С. 294 - 301
Опубликована: Янв. 1, 2024
Язык: Английский
Perbandingan Material Superkapasitor Berbasis Karbon dan Oksida Logam untuk Optimalisasi Penyimpanan Energi dalam Aplikasi Sistem Energi Terbarukan: Systematic Literature Review
Nurul Aziz Eka Putra,
Syahrul Aldi Ferdiyanto,
Fuat Qunefi
и другие.
Jurnal Energi Baru dan Terbarukan,
Год журнала:
2024,
Номер
5(3), С. 60 - 74
Опубликована: Окт. 29, 2024
Kebutuhan
akan
solusi
penyimpanan
energi
yang
efisien
dan
berkelanjutan
semakin
meningkat
seiring
dengan
perkembangan
sistem
terbarukan.
Superkapasitor,
dikenal
karena
kepadatan
daya
stabilitas
siklusnya,
menjadi
komponen
penting
dalam
teknologi
ini.
Kajian
literatur
ini
bertujuan
membandingkan
material
berbasis
karbon
oksida
logam
pada
superkapasitor,
fokus
optimalisasi
performa
energi.
Melalui
pendekatan
sistematis,
kajian
menelaah
karakteristik
utama
dari
kedua
jenis
material,
termasuk
densitas
energi,
tahan
siklus,
kestabilan
termal,
potensi
biaya.
Hasil
review
menunjukkan
bahwa
cenderung
memiliki
keunggulan
siklus
hidup,
sementara
menawarkan
kapasitas
lebih
tinggi
tetapi
rentan
terhadap
degradasi.
Analisis
memberikan
wawasan
mengenai
kelebihan
batasan
tiap
dapat
panduan
pemilihan
superkapasitor
untuk
aplikasi
diharapkan
mendukung
pengembangan
berkelanjutan.