Integrating digital technologies in agriculture for climate change adaptation and mitigation: State of the art and future perspectives
Computers and Electronics in Agriculture,
Год журнала:
2024,
Номер
226, С. 109412 - 109412
Опубликована: Сен. 7, 2024
Язык: Английский
Deep learning-driven behavioral analysis reveals adaptive responses in Drosophila offspring after long-term parental microplastic exposure
Journal of Environmental Management,
Год журнала:
2025,
Номер
376, С. 124502 - 124502
Опубликована: Фев. 15, 2025
Язык: Английский
Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species
PLoS ONE,
Год журнала:
2025,
Номер
20(4), С. e0320224 - e0320224
Опубликована: Апрель 3, 2025
Sandflies
are
vectors
for
several
tropical
diseases
such
as
leishmaniasis,
bartonellosis,
and
sandfly
fever.
Moreover,
sandflies
exhibit
species-specificity
in
transmitting
particular
pathogen
species,
with
females
being
responsible
disease
transmission.
Thus,
effective
classification
of
species
the
corresponding
sex
identification
important
surveillance
control,
managing
breeding/populations,
research
development,
conducting
epidemiological
studies.
This
is
typically
performed
manually
by
observing
internal
morphological
features,
which
maybe
an
error-prone
tedious
process.
In
this
work,
we
developed
a
deep
learning
artificial
intelligence
system
to
determine
gender
differentiate
between
three
two
subgenera
(i.e.,
Phlebotomus
alexandri
,
papatasi
sergenti
).
Using
locally
field-caught
prepared
samples
over
period
years,
based
on
convolutional
neural
networks,
transfer
learning,
early
fusion
genital
pharynx
images,
achieved
exceptional
accuracy
(greater
than
95%)
across
multiple
performance
metrics
using
wide
range
pre-trained
network
models.
study
not
only
contributes
field
medical
entomology
providing
automated
accurate
solution
taxonomy,
but
also
establishes
framework
leveraging
techniques
similar
vector-borne
control
efforts.
Язык: Английский
Future of Information Systems for Pest Management: Data Acquisition and Integration to Guiding Management Decisions
CABI eBooks,
Год журнала:
2024,
Номер
unknown, С. 251 - 262
Опубликована: Авг. 22, 2024
Язык: Английский
Future of Information Systems for Pest Management: Data Acquisition and Integration to Guiding Management Decisions
CABI eBooks,
Год журнала:
2024,
Номер
unknown, С. 251 - 262
Опубликована: Авг. 23, 2024
Язык: Английский
From ethology to behavioral biology
Elsevier eBooks,
Год журнала:
2024,
Номер
unknown
Опубликована: Янв. 1, 2024
Язык: Английский
Efficient Convolutional Neural Network Model for the Taxonomy and Sex Identification of Three Phlebotomine Sandfly Species (Diptera, Psychodidae, and Phlebotominae)
Animals,
Год журнала:
2024,
Номер
14(24), С. 3712 - 3712
Опубликована: Дек. 23, 2024
Sandflies,
small
insects
primarily
from
the
Psychodidae
family,
are
commonly
found
in
sandy,
tropical,
and
subtropical
regions.
Most
active
during
dawn
dusk,
female
sandflies
feed
on
blood
to
facilitate
egg
production.
In
doing
so,
they
can
transmit
infectious
diseases
that
may
cause
symptoms
such
as
fever,
headaches,
muscle
pain,
anemia,
skin
rashes,
ulcers.
Importantly,
species-specific
their
disease
transmission.
Determining
gender
species
of
typically
involves
examining
morphology
internal
anatomy
using
established
identification
keys.
However,
this
process
requires
expert
knowledge
is
labor-intensive,
time-consuming,
prone
misidentification.
paper,
we
develop
a
highly
accurate
efficient
convolutional
network
model
utilizes
pharyngeal
genital
images
sandfly
samples
classify
sex
three
(i.e.,
Phlebotomus
sergenti,
Ph.
alexandri,
papatasi).
A
detailed
evaluation
model’s
structure
classification
performance
was
conducted
multiple
metrics.
The
results
demonstrate
an
excellent
sex-species
accuracy
exceeding
95%.
Hence,
it
possible
automated
artificial
intelligence-based
systems
serve
entomology
community
at
large
specialized
professionals.
Язык: Английский