Comparative Analysis of Spectroradiometric and Chemical Methods for Nutrient Detection in Black Gram Leaves
M. Balamurugan,
K. Kalaiarasi,
Jayalakshmi Shanmugam
и другие.
Results in Engineering,
Год журнала:
2024,
Номер
24, С. 103065 - 103065
Опубликована: Окт. 9, 2024
Язык: Английский
Tree vitality predicts plant-pathogenic fungal communities in beech forest canopies
Forest Ecology and Management,
Год журнала:
2025,
Номер
585, С. 122588 - 122588
Опубликована: Март 18, 2025
Язык: Английский
Prediction of Vanadium Contamination Distribution Pattern Through Remote Sensing Image Fusion and Machine Learning
Remote Sensing,
Год журнала:
2025,
Номер
17(7), С. 1164 - 1164
Опубликована: Март 25, 2025
Soil
vanadium
contamination
poses
a
significant
threat
to
ecosystems.
Hyperspectral
remote
sensing
plays
critical
role
in
extracting
spectral
features
of
heavy
metal
contamination,
mapping
its
spatial
distribution,
and
monitoring
trends
over
time.
This
study
targets
vanadium-contaminated
area
Panzhihua
City,
Sichuan
Province.
sampling
measurements
occurred
the
laboratory.
(Gaofen-5,
GF-5)
multispectral
(Gaofen-2,
GF-2;
Sentinel-2)
images
were
acquired
preprocessed,
feature
bands
extracted
by
combining
laboratory
data.
A
dual-branch
convolutional
neural
network
(DB-CNN)
fused
hyperspectral
confirmed
fusion’s
effectiveness.
Six
prevalent
machine
learning
models
adopted,
unified
framework
leveraged
Random
Forest
(RF)
as
second-layer
model
enhance
predictive
performance
these
base
models.
Both
ensemble
evaluated
based
on
accuracy.
The
fusion
process
enhanced
models,
improving
R2
values
for
(V)
pentavalent
(V5+)
from
0.54
0.3
0.58
0.39,
respectively,
at
4
m
resolution.
Further
optimization
using
RF
refine
Extreme
Trees
(ETs)
significantly
increased
0.83
0.75
V
V5+,
this
scale.
934
nm
464
wavelengths
identified
most
predicting
soil
contamination.
integrated
approach
robustly
delineates
distribution
characteristics
V5+
soils,
facilitating
precise
ecological
risk
assessments
through
comparative
analysis
accuracy
across
diverse
Язык: Английский
Quantification and mapping of medicinally important Quercitrin compound using hyperspectral imaging and machine learning
International Journal of Applied Earth Observation and Geoinformation,
Год журнала:
2024,
Номер
134, С. 104202 - 104202
Опубликована: Окт. 16, 2024
Язык: Английский