Temporal field phenomics of transgenic maize events subjected to drought stress: Cross‐validation scenarios and machine learning models
The Plant Phenome Journal,
Journal Year:
2025,
Volume and Issue:
8(1)
Published: Jan. 5, 2025
Abstract
Global
climate
change
has
driven
breeding
programs
to
develop
abiotic
stress‐resilient
plant
varieties.
Traditionally,
assessing
drought
resilience
involves
labor‐intensive
and
time‐consuming
processes.
This
study
used
an
unmanned
aerial
system
(UAS)
predict
key
phenotyping
traits
in
maize
(
Zea
mays
L.)
monitor
response
during
the
crop
cycle.
We
grew
transgenic
hybrids
two
trials,
one
irrigated
another
subjected
stress,
a
drone
equipped
with
red–green–blue
(RGB)
multispectral
sensors
capture
images
of
plots
over
time.
Machine
learning
models
various
prediction
scenarios
revealed
significant
correlations
between
vegetation
indices
Interestingly,
RGB
sensor
outperformed
trait
prediction.
Prediction
accuracy
across
untested
genotypes
environments
ranged
from
0.40
0.70
for
grain
yield,
0.43
0.69
days
anthesis,
0.51
0.67
silking,
0.35
0.57
height.
Ridge
random
forest
consistently
delivered
most
accurate
predictions
environments.
The
normalized
green–red
difference
index,
VARI,
RCC
also
effectively
predicted
captured
drought.
highlights
value
UAS
as
practical
tool
stress
due
its
straightforward
implementation.
Language: Английский
Drought stress memory in maize: understanding and harnessing the past for future resilience
Plant Cell Reports,
Journal Year:
2025,
Volume and Issue:
44(5)
Published: April 25, 2025
Language: Английский
Improvement of maize drought tolerance by foliar application of zinc selenide quantum dots
Venkatesan Kishanth Kanna,
No information about this author
M. Djanaguiraman,
No information about this author
A. Senthil
No information about this author
et al.
Frontiers in Plant Science,
Journal Year:
2024,
Volume and Issue:
15
Published: Dec. 3, 2024
Maize
(
Language: Английский
Appropriate application of organic fertilizer enhanced yield, microelement content, and quality of maize grain under a rotation system
Tong Lu,
No information about this author
Junmei Shi,
No information about this author
Zonglin Lu
No information about this author
et al.
Annals of Agricultural Sciences,
Journal Year:
2024,
Volume and Issue:
69(1), P. 19 - 32
Published: June 1, 2024
Maize
(Zea
mays
L.),
as
a
cornerstone
crop,
is
integral
to
both
livestock
feed
and
human
nutrition.
However,
the
effects
of
long-term
manure
application
on
maize
yield,
micronutrient
levels,
nutritional
quality
under
maize-soybean
rotation
system
have
not
been
fully
elucidated.
This
study
investigates
impact
micronutrients
content
grains,
grain
in
rotation.
Our
results
indicate
that
consistent
significantly
enhances
yield.
Compared
chemical
fertilizers
only,
addition
increased
Fe,
Mn,
Cu,
Zn
concentrations
grains.
In
addition,
highest
protein
concentration
was
observed
when
treated
with
manure.
Concentrations
fractions
such
globulins,
gliadins,
glutenins
were
found
be
higher
low
(13.5
t
ha−1)
compared
high
(27
ha−1).
The
optimum
increments
essential
amino
acids
(EAA)
ratios
nonessential
(EAA/NAA)
treatment.
Collectively,
incorporating
into
crop
only
escalates
yields
but
also
critically
profile
grains
through
an
increase
by
promoting
balance
proteins
within
grain.
long
run,
more
conducive
improving
systems.
Language: Английский