I
A
E
S
I
n
t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
r
t
if
ic
ia
l
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
AI
)
V
ol
.
14
, N
o.
3
,
J
une
20
25
, pp.
1719
~
1726
I
S
S
N
:
2252
-
8938
,
D
O
I
:
10.11591/
ij
a
i.
v
14
.i
3
.pp
1719
-
1726
1719
Jou
r
n
al
h
om
e
page
:
ht
tp
:
//
ij
ai
.
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s
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.c
om
C
om
p
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e
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e
n
si
ve
su
r
ve
y of
au
t
om
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d
p
l
an
t
l
e
af
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ase
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d
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t
i
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c
h
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i
q
u
e
s:
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van
c
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s, c
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s, an
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nga
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, I
ndi
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A
B
S
T
R
A
C
T
A
r
ti
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le
h
is
to
r
y
:
R
e
c
e
iv
e
d
N
ov
21
,
2023
R
e
vi
s
e
d
F
e
b
26
,
2025
A
c
c
e
pt
e
d
M
a
r
15
,
2025
This
survey
paper
extensively
researches
into
the
domain
of
timel
y
plant
disease
detection,
crucial
for
alleviating
agricultural
losses
and
en
suring
food
security.
It
accentuate
s
the
significance
of
early
identificat
ion
in
efficient
disease
management
and
informed
agricult
ural
dec
isions
.
Conventiona
l
manual
methods,
constra
ined
by
labor
intensit
y
and
subjectivity,
pave
the
way
for
investigating
autom
ated
disease
de
tection
avenues,
prominen
tly
leveraging
imag
e
processin
g
and
deep
le
arning
techniques.
In
the
subsequent
exploration
of
related
work,
a
panorami
c
view
encompass
es
an
array
of
methodo
logies,
encompass
ing
neural
networ
ks
and
convolut
ional
neural
netw
orks
(CNNs),
paramount
in
automated
disease
detection.
The
synthesis
of
image
processing
intricacies,
pre
-
proc
essing
strategies,
and
feature
extraction
paradigms
alongside
deep
learning
models
is
meticulously
expounded.
As
the
field
advances,
the
paper
acce
ntuates
lingering
challenge
s
in
early
-
stage
detection,
alongside
insightful
so
lutions
like
data
augmentation
and
sophisticated
deep
learning
models.
This
survey
paper
culminates
by
underlining
the
dynamic
trajectory
of
automate
d
plant
disease
identificati
on,
accentuating
its
paramount
role
in
upholding
global
food security
.
K
e
y
w
o
r
d
s
:
C
onvolut
io
na
l
ne
ur
a
l
ne
twor
k
D
e
e
p l
e
a
r
ni
ng
G
lo
ba
l
f
ood s
e
c
ur
it
y
I
m
a
ge
pr
oc
e
s
s
in
g
P
la
nt
l
e
a
f
di
s
e
a
s
e
d
e
te
c
ti
on
This is an
open
acce
ss artic
le unde
r the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
S
hi
lp
a
P
a
ti
l
D
e
pa
r
tm
e
nt
of
E
le
c
tr
ic
a
l
a
nd E
le
c
tr
oni
c
s
E
ngi
ne
e
r
in
g
B
L
D
E
A
'
s
V
P
D
r
P
G
H
a
la
ka
tt
i
C
ol
le
ge
of
E
ngi
ne
e
r
in
g a
nd T
e
c
hnol
ogy
V
ij
a
ya
pur
a
586105, I
ndi
a
E
m
a
il
:
s
hi
lp
a
pa
ti
l1
22024@
gm
a
il
.c
om
1.
I
N
T
R
O
D
U
C
T
I
O
N
T
he
pr
e
s
e
nc
e
of
pl
a
nt
di
s
e
a
s
e
s
ha
s
a
s
ig
ni
f
ic
a
nt
im
pa
c
t
on
a
gr
ic
ul
tu
r
a
l
pr
oduc
ti
vi
ty
.
F
a
il
ur
e
to
pr
om
pt
ly
de
te
c
t
pl
a
nt
di
s
e
a
s
e
s
m
a
y l
e
a
d t
o a
n i
nc
r
e
a
s
e
i
n f
ood i
ns
e
c
ur
it
y
[
1]
. T
he
i
m
pl
e
m
e
nt
a
ti
on of
e
f
f
e
c
ti
ve
m
e
a
s
ur
e
s
to
pr
e
ve
nt
a
nd
c
ont
r
ol
pl
a
nt
di
s
e
a
s
e
s
is
c
r
uc
ia
l
in
th
e
m
a
na
ge
m
e
nt
of
a
gr
ic
ul
tu
r
a
l
pr
oduc
ti
v
it
y.
T
he
e
f
f
ic
ie
nc
y
of
th
e
pr
oc
e
s
s
is
gr
e
a
tl
y
im
pa
c
te
d
by
th
e
e
a
r
ly
id
e
nt
if
ic
a
ti
on
of
ke
y
f
a
c
to
r
s
.
T
he
id
e
nt
if
ic
a
ti
on
of
pl
a
nt
di
s
e
a
s
e
s
ha
s
e
m
e
r
ge
d
a
s
a
s
ig
ni
f
ic
a
nt
c
onc
e
r
n
in
c
ont
e
m
por
a
r
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ti
m
e
s
.
P
la
nt
s
th
a
t
di
s
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a
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om
s
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il
ln
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m
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ni
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t
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s
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m
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le
a
ve
s
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te
m
s
,
f
lo
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r
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um
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di
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pe
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t
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ti
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xhi
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ni
bl
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s
ua
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r
ns
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e
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d
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c
is
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id
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if
ic
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ti
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of
a
bnor
m
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li
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s
.
T
he
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xa
m
in
a
ti
on
of
di
s
e
a
s
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y
m
pt
om
s
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pl
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s
c
om
m
onl
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r
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th
e
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le
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ve
s
,
w
hi
c
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ve
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a
s
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pr
im
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r
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ppr
oa
c
h
f
or
th
e
id
e
nt
if
i
c
a
ti
on
of
pl
a
nt
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il
m
e
nt
s
[
2]
.
T
h
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pr
a
c
ti
c
e
of
id
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if
yi
n
g
di
s
e
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r
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d
out
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y
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gr
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a
l
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
.
14
, N
o.
3
,
J
une
20
25
:
1719
-
1726
1720
a
nd
f
or
e
s
tr
y
p
r
of
e
s
s
io
n
a
l
s
, a
s
w
e
l
l
a
s
b
y
f
a
r
m
e
r
s
w
ho
d
e
p
e
n
d on the
ir
o
w
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xp
e
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is
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a
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b
il
it
ie
s
. T
h
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a
ppr
o
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c
h
de
s
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a
n
d
ha
s
l
im
it
e
d
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f
f
e
c
ti
ve
ne
s
s
.
T
he
pr
im
a
r
y
f
oc
us
w
it
hi
n
th
e
r
e
a
lm
of
c
r
op
pr
oduc
ti
vi
ty
a
nd
p
la
nt
gr
ow
th
pe
r
ta
in
s
to
th
e
de
t
e
c
ti
on
a
nd
c
la
s
s
if
ic
a
ti
on
of
pl
a
nt
di
s
e
a
s
e
s
. T
he
ti
m
e
ly
id
e
nt
if
ic
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ti
on
o
f
th
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s
e
di
s
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s
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ll
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s
f
or
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im
pl
e
m
e
nt
a
ti
on
of
pr
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ve
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iv
e
m
e
a
s
ur
e
s
,
le
a
di
ng
to
de
c
r
e
a
s
e
d
pr
oduc
ti
vi
ty
a
nd
f
in
a
nc
ia
l
lo
s
s
e
s
[
3]
.
A
c
hi
e
vi
ng
opt
im
a
l
c
r
op
qua
li
ty
ne
c
e
s
s
it
a
te
s
th
e
im
pl
e
m
e
nt
a
ti
on
of
c
om
pr
e
he
n
s
iv
e
di
s
e
a
s
e
m
a
na
ge
m
e
nt
a
nd
c
ont
a
in
m
e
nt
s
tr
a
te
gi
e
s
th
r
oughout
th
e
e
nt
ir
e
f
a
r
m
.
T
he
di
a
gnos
ti
c
pr
oc
e
s
s
f
or
a
di
s
e
a
s
e
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nt
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il
s
th
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x
a
m
in
a
ti
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a
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na
ly
s
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of
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ym
pt
om
s
,
th
e
id
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ic
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ti
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th
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unde
r
ly
in
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c
a
us
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,
a
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e
im
pl
e
m
e
nt
a
ti
on
of
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ppr
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m
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s
ur
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to
e
f
f
e
c
ti
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ly
c
ont
r
ol
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ts
t
r
a
ns
m
is
s
io
n. T
he
f
ol
lo
w
in
g s
te
p
s
a
r
e
e
s
s
e
nt
ia
l
f
or
e
f
f
e
c
ti
ve
ly
r
e
duc
in
g t
he
t
r
a
ns
m
is
s
io
n
of
in
f
e
c
ti
ons
.
P
la
nt
di
s
e
a
s
e
s
ha
v
e
th
e
pot
e
nt
ia
l
to
in
f
li
c
t
da
m
a
g
e
on
bot
h
th
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a
bove
gr
ound
a
nd
b
e
lo
w
gr
ound
pa
r
ts
of
a
pl
a
nt
[
4]
.
L
e
a
f
di
s
e
a
s
e
s
c
a
n
be
di
a
gnos
e
d
th
r
ough
th
e
obs
e
r
va
ti
on
of
di
s
ti
nc
t
s
ig
ns
,
in
c
lu
di
ng
th
e
pr
e
s
e
nc
e
of
y
e
ll
ow
a
nd
br
ow
n
di
s
c
ol
or
a
ti
on.
T
o
e
n
s
ur
e
e
f
f
e
c
ti
ve
di
s
e
a
s
e
m
a
na
ge
m
e
nt
,
it
is
c
r
uc
ia
l
to
im
pl
e
m
e
nt
pr
e
ve
nt
iv
e
m
e
a
s
ur
e
s
th
a
t
s
a
f
e
gua
r
d
f
ut
ur
e
ha
r
ve
s
ts
of
r
ic
e
,
w
he
a
t,
oa
ts
,
pot
a
to
e
s
,
to
m
a
to
e
s
,
a
nd
pom
e
gr
a
na
te
s
f
r
om
e
xhi
bi
ti
ng
th
e
s
e
s
ym
pt
om
s
.
V
i
s
ua
l
obs
e
r
va
ti
on
is
a
f
r
e
que
nt
ly
ut
il
iz
e
d
te
c
hni
qu
e
by
f
a
r
m
e
r
s
f
or
di
a
gnos
in
g
th
e
c
ondi
ti
on.
T
he
pr
e
vi
ous
ly
m
e
nt
i
one
d
a
ppr
oa
c
h
is
r
e
c
ogni
z
e
d
f
or
it
s
s
lu
ggi
s
h
pe
r
f
or
m
a
nc
e
a
nd
r
e
s
tr
ic
te
d
r
e
li
a
bi
li
ty
w
it
hi
n
th
e
a
gr
ic
ul
tu
r
a
l
s
e
c
to
r
.
F
a
r
m
e
r
s
ha
v
e
th
e
opt
io
n
to
s
e
e
k
c
ons
ul
ta
ti
on
f
r
om
s
pe
c
ia
li
s
t
s
in
or
de
r
to
obt
a
in
a
n
a
c
c
ur
a
te
di
a
gnos
is
of
th
e
a
il
m
e
nt
.
T
he
im
pl
e
m
e
nt
a
ti
on
of
a
ut
om
a
te
d
te
c
hni
que
s
e
n
a
bl
e
s
a
c
c
ur
a
te
di
s
e
a
s
e
id
e
nt
if
ic
a
ti
on,
th
e
r
e
by
r
e
duc
in
g
th
e
r
e
li
a
n
c
e
on
s
ki
ll
e
d
la
bor
a
nd l
e
a
di
ng t
o c
os
t
s
a
vi
ngs
f
or
f
a
r
m
e
r
s
[
5]
.
D
is
e
a
s
e
oc
c
ur
s
in
th
e
pl
a
nt
w
he
n
th
e
c
a
us
a
ti
ve
a
ge
nt
c
on
s
is
te
nt
ly
di
s
r
upt
s
it
s
nor
m
a
l
phys
io
lo
gi
c
a
l
pr
oc
e
s
s
e
s
. T
he
pl
a
nt
e
xpe
r
ie
nc
e
s
di
s
r
upt
io
ns
i
n i
ts
r
e
gul
a
r
s
tr
uc
tu
r
e
, de
ve
lo
pm
e
nt
, f
unc
ti
on, or
ot
he
r
a
c
ti
vi
ti
e
s
due
to
th
e
pr
e
s
e
nc
e
of
a
n
a
bnor
m
a
l
phys
io
lo
gi
c
a
l
pr
oc
e
s
s
.
C
h
a
r
a
c
te
r
is
ti
c
di
s
e
a
s
e
s
or
s
ym
pt
om
s
a
r
is
e
due
to
di
s
tu
r
ba
nc
e
s
in
one
or
m
or
e
vi
ta
l
phys
io
lo
gi
c
a
l
or
bi
oc
he
m
ic
a
l
s
ys
te
m
s
w
it
hi
n
a
pl
a
nt
.
P
la
nt
di
s
e
a
s
e
s
c
a
n
be
c
a
te
gor
iz
e
d
ba
s
e
d
on
th
e
in
f
e
c
ti
ous
or
non
-
in
f
e
c
ti
ous
a
tt
r
ib
ut
e
s
of
th
e
ir
pr
i
m
a
r
y
c
a
us
a
ti
ve
a
ge
nt
[
6]
.
P
la
n
t
di
s
e
a
s
e
s
c
a
n
r
e
s
ul
t
in
s
ubs
ta
nt
ia
l
d
a
m
a
ge
to
pl
a
nt
s
by
im
pe
di
ng
va
r
io
us
e
s
s
e
nt
ia
l
pr
oc
e
s
s
e
s
.
T
hi
s
c
a
te
gor
y
in
c
lu
de
s
va
r
io
us
a
c
ti
vi
ti
e
s
s
u
c
h
a
s
phot
os
ynt
he
s
is
,
f
lo
w
e
r
a
nd
f
r
ui
t
g
r
ow
th
a
nd
de
ve
lo
pm
e
nt
,
ove
r
a
ll
pl
a
nt
gr
ow
th
,
a
s
w
e
ll
a
s
c
e
ll
di
vi
s
io
n
a
nd
e
nl
a
r
ge
m
e
nt
.
A
ddi
ti
ona
ll
y,
th
e
y
ha
ve
th
e
a
bi
li
ty
to
di
s
r
upt
th
e
f
unc
ti
ona
li
ty
of
th
e
m
e
c
ha
ni
s
m
r
e
s
pons
ib
le
f
or
th
e
a
bs
or
pt
io
n
a
nd
tr
a
ns
por
ta
ti
on
of
nut
r
ie
nt
s
a
nd
w
a
te
r
[
7]
.
P
la
n
t
d
is
e
a
s
e
s
c
a
n o
c
c
ur
a
s
a
r
e
s
ul
t
of
t
h
e
pr
e
s
e
nc
e
of
di
v
e
r
s
e
or
g
a
ni
s
m
s
, i
nc
l
ud
in
g
f
un
gi
,
b
a
c
te
r
ia
, p
hyt
opl
a
s
m
a
,
vi
r
u
s
e
s
,
vi
r
o
id
s
,
n
e
m
a
to
de
s
,
a
nd
s
im
i
la
r
e
nt
i
ti
e
s
.
B
a
c
t
e
r
i
a
l
p
a
t
hog
e
n
s
s
e
r
v
e
a
s
t
he
e
ti
o
lo
g
ic
a
l
f
a
c
t
or
s
b
e
h
in
d
va
r
i
ou
s
di
s
e
a
s
e
s
,
s
u
c
h
a
s
ba
c
t
e
r
i
a
l
s
pot
,
b
a
c
te
r
ia
l
b
li
g
ht
,
b
a
c
te
r
i
a
l
w
il
t,
s
c
a
b,
r
ot
,
a
nd
s
i
m
il
a
r
a
il
m
e
nt
s
.
A
p
hi
ds
,
gr
e
y
m
ol
d
,
d
ow
ny
m
il
de
w
,
po
w
d
e
r
y
m
i
ld
e
w
,
c
yl
in
d
r
o
c
l
a
di
um
,
m
e
a
ly
bug
s
,
m
o
s
a
ic
,
s
p
ot
t
e
d
w
i
lt
,
a
nd
c
ur
ly
to
p
r
e
pr
e
s
e
nt
a
r
a
ng
e
of
f
u
ng
a
l
-
i
nd
uc
e
d
di
s
e
a
s
e
s
. E
a
c
h pl
a
nt
e
xh
ib
i
ts
uni
q
u
e
c
ha
r
a
c
t
e
r
i
s
ti
c
s
t
ha
t
e
na
bl
e
u
s
to
i
de
nt
if
y
th
e
und
e
r
l
yi
ng
c
a
u
s
e
of
a
di
s
e
a
s
e
,
be
i
t
a
ba
c
t
e
r
i
a
l,
f
un
ga
l,
or
vi
r
a
l
in
f
e
c
ti
on.
T
he
s
y
m
pt
om
s
of
a
di
s
e
a
s
e
a
r
e
e
m
p
lo
y
e
d
t
o
f
a
c
il
it
a
te
t
he
i
de
nt
if
ic
a
ti
on
of
t
he
s
p
e
c
if
i
c
a
il
m
e
nt
a
nd
it
s
r
oo
t
c
a
u
s
e
[
2]
.
A
r
a
nge
of
de
e
p
le
a
r
ni
ng
te
c
hni
que
s
c
a
n
b
e
ut
il
iz
e
d
f
or
th
e
pur
pos
e
of
id
e
nt
if
yi
ng
a
nd
c
a
te
gor
iz
in
g
di
f
f
e
r
e
nt
ty
pe
s
of
le
a
f
di
s
e
a
s
e
s
. T
he
a
ppl
ic
a
ti
on
of
th
e
s
e
te
c
hni
que
s
e
na
bl
e
s
th
e
c
a
te
gor
iz
a
ti
on
of
v
a
r
io
us
di
s
e
a
s
e
s
,
s
uc
h
a
s
b
a
c
te
r
ia
l
s
pot
s
,
e
a
r
ly
a
nd l
a
te
bl
ig
ht
s
, m
ol
d, s
e
pt
or
ia
l
e
a
f
s
pot
s
, s
pi
de
r
m
it
e
s
, t
w
o
-
s
pot
te
d s
pi
de
r
m
it
e
s
, t
a
r
ge
t
s
pot
s
, m
os
a
ic
vi
r
us
e
s
,
ye
ll
ow
c
ur
l
vi
r
us
e
s
, a
nd he
a
lt
hy l
e
a
ve
s
[
8]
.
I
n
r
e
c
e
nt
ye
a
r
s
,
e
xt
e
ns
iv
e
r
e
s
e
a
r
c
h
ha
s
be
e
n
unde
r
ta
k
e
n
to
e
xpl
or
e
th
e
a
ut
om
a
te
d
id
e
nt
if
ic
a
ti
on
of
pl
a
nt
di
s
e
a
s
e
s
.
T
he
a
c
qui
s
it
io
n
of
pr
e
c
is
e
a
nd
r
e
li
a
bl
e
qua
n
ti
ta
ti
ve
da
ta
is
of
ut
m
os
t
i
m
por
ta
nc
e
in
th
e
di
a
gnos
is
of
pl
a
nt
di
s
e
a
s
e
s
[
9]
.
T
he
s
tu
dy,
c
onduc
te
d
by
r
e
s
e
a
r
c
he
r
s
in
[
10]
a
im
e
d
to
e
xa
m
in
e
th
e
di
s
e
a
s
e
s
th
a
t
c
om
m
onl
y
a
f
f
li
c
t
to
m
a
to
e
s
a
nd
pot
a
to
e
s
, w
it
h
a
pa
r
ti
c
ul
a
r
e
m
pha
s
is
on
a
s
s
e
s
s
in
g
th
e
in
f
lu
e
nc
e
of
vi
r
us
e
s
on
th
e
s
e
c
r
ops
.
T
he
a
ut
hor
s
c
ondu
c
te
d
a
c
om
pr
e
he
ns
iv
e
a
na
ly
s
is
of
va
r
io
us
a
r
ti
c
le
s
pe
r
ta
in
in
g
to
th
e
c
la
s
s
if
ic
a
ti
on
of
r
ic
e
di
s
e
a
s
e
s
in
th
is
s
tu
dy.
T
he
r
e
vi
e
w
c
ondu
c
te
d
a
n
a
s
s
e
s
s
m
e
nt
on
va
r
io
us
f
a
c
to
r
s
,
s
uc
h
a
s
th
e
da
ta
s
e
t
us
e
d, di
s
e
a
s
e
c
la
s
s
if
ic
a
ti
ons
, pr
e
pr
oc
e
s
s
in
g a
nd s
e
g
m
e
nt
a
ti
on t
e
c
hni
que
s
, a
nd t
he
t
ype
of
c
la
s
s
if
ie
r
e
m
pl
oye
d.
T
he
a
ut
hor
s
c
onduc
te
d
a
n
a
na
ly
s
i
s
of
th
e
f
a
c
to
r
s
to
im
pr
ove
th
e
unde
r
s
ta
ndi
ng
of
th
e
e
xi
s
ti
ng
li
te
r
a
tu
r
e
on
th
e
c
la
s
s
if
ic
a
ti
on
of
r
ic
e
di
s
e
a
s
e
s
.
A
s
tu
dy
w
a
s
c
onduc
te
d
by
[
11]
to
in
ve
s
ti
ga
te
th
e
a
ppl
ic
a
ti
on
of
m
a
c
hi
ne
le
a
r
ni
ng
te
c
hni
que
s
f
or
c
la
s
s
if
yi
ng
di
s
e
a
s
e
s
th
a
t
i
m
pa
c
t
c
ot
to
n
pl
a
nt
s
.
T
he
s
tu
dy
c
onduc
te
d
by
[
12]
e
nt
a
il
e
d
a
n
e
xa
m
in
a
ti
on
of
di
s
e
a
s
e
c
la
s
s
if
ic
a
ti
on
in
c
i
tr
us
pl
a
nt
s
th
r
ough
th
e
ut
il
iz
a
ti
on
of
i
m
a
ge
pr
oc
e
s
s
in
g
te
c
hni
que
s
.
T
he
obj
e
c
ti
ve
of
th
e
s
tu
dy
c
onduc
te
d
t
o
e
xa
m
in
e
th
e
c
la
s
s
if
ic
a
ti
on
a
nd
id
e
nt
if
ic
a
ti
on
of
pl
a
nt
di
s
e
a
s
e
s
th
r
ough
th
e
ut
il
iz
a
ti
on
of
le
a
f
im
a
ge
s
.
T
he
r
e
s
e
a
r
c
h
out
li
ne
d
in
r
e
f
e
r
e
nc
e
s
i
s
ba
s
e
d
on
ta
il
or
e
d f
e
a
tu
r
e
s
. I
n
or
de
r
to
c
la
s
s
if
y i
ll
ne
s
s
e
s
ba
s
e
d on ma
nua
l
f
e
a
tu
r
e
s
, i
t
is
ne
c
e
s
s
a
r
y t
o pe
r
f
or
m
a
s
e
que
nc
e
of
s
te
ps
th
a
t
in
vol
ve
p
r
e
-
pr
oc
e
s
s
in
g,
s
e
gm
e
nt
a
ti
on,
a
nd
f
e
a
tu
r
e
e
xt
r
a
c
ti
on
on
th
e
im
a
ge
s
.
H
ow
e
ve
r
,
th
is
pr
oc
e
dur
e
c
a
n be
c
ha
r
a
c
t
e
r
iz
e
d a
s
bot
h l
a
bor
-
in
te
ns
iv
e
a
nd t
im
e
-
c
ons
um
in
g.
T
he
pr
io
r
it
iz
a
ti
on
of
e
s
ta
bl
is
hi
ng
a
hi
ghl
y
e
f
f
e
c
ti
ve
a
ppr
oa
c
h
f
or
de
te
c
ti
ng
s
ig
ns
of
il
ln
e
s
s
is
c
r
uc
ia
l,
a
nd
th
is
obj
e
c
ti
ve
c
a
n
be
a
c
c
om
pl
is
he
d
by
ut
il
iz
in
g
s
c
ie
nt
if
ic
e
xpe
r
ti
s
e
a
nd
da
ta
.
T
he
in
it
ia
l
s
te
p
e
nt
a
il
s
ga
th
e
r
in
g
phot
ogr
a
phs
of
th
e
le
a
ve
s
f
r
om
th
e
to
m
a
to
a
nd
p
ot
a
to
c
r
ops
.
P
hot
ogr
a
phs
c
a
n
be
obt
a
in
e
d
by
ut
il
iz
in
g
e
it
he
r
a
hi
gh
-
r
e
s
ol
ut
io
n
s
m
a
r
tp
hone
c
a
m
e
r
a
or
a
c
onv
e
nt
io
na
l
di
gi
ta
l
c
a
m
e
r
a
.
T
he
to
m
a
to
a
nd
pot
a
to
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C
om
pr
e
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s
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ur
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1721
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in
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ve
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io
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im
a
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e
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g
te
c
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ve
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a
l
te
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hni
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e
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a
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ly
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e
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e
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pr
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e
s
s
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r
e
s
to
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a
ti
on,
s
e
gm
e
nt
a
ti
on,
a
ugm
e
nt
a
ti
on,
f
e
a
tu
r
e
e
xt
r
a
c
ti
on,
a
nd
c
la
s
s
if
ic
a
ti
on
[
13]
.
T
he
c
ol
or
c
onve
r
s
io
n
te
c
hni
que
is
e
m
pl
oye
d
dur
in
g
th
e
pr
e
pr
oc
e
s
s
in
g
pha
s
e
to
c
onve
r
t
R
G
B
phot
og
r
a
phs
in
to
g
r
a
ys
c
a
le
im
a
ge
s
.
A
f
te
r
r
e
m
ovi
ng
di
f
f
e
r
e
nt
ty
pe
s
of
noi
s
e
,
a
s
e
t
o
f
c
ont
r
a
s
t
e
nha
nc
e
m
e
nt
t
e
c
hni
que
s
i
s
e
m
pl
oye
d t
o e
nha
n
c
e
t
he
c
o
nt
r
a
s
t
of
t
he
i
m
a
ge
s
.
T
he
a
ppl
ic
a
ti
on
of
de
e
p
le
a
r
ni
ng
in
th
e
a
gr
ic
ul
tu
r
a
l
in
dus
tr
y
ha
s
be
e
n
li
m
it
e
d
a
nd
r
e
la
ti
ve
ly
ne
w
.
T
he
m
e
th
od
de
s
c
r
ib
e
d
in
r
e
f
e
r
e
nc
e
[
14]
ha
s
pr
ove
n
to
be
e
f
f
e
c
t
iv
e
in
th
e
id
e
nt
if
ic
a
ti
on
o
f
pl
a
nt
d
is
e
a
s
e
s
. T
he
s
tu
dy
c
onduc
te
d
by
th
e
a
ut
hor
s
ha
ve
f
oc
u
s
e
d
on
th
e
ut
il
iz
a
ti
on of
de
e
p
le
a
r
ni
ng
te
c
hni
que
s
f
or
th
e
pur
pos
e
of
pl
a
nt
di
s
e
a
s
e
di
a
gnos
i
s
.
T
he
m
a
in
f
oc
us
of
th
e
ir
s
tu
dy
c
e
nt
e
r
e
d
on
th
e
da
ta
s
our
c
e
s
,
m
ode
ls
,
a
nd
pr
e
-
pr
oc
e
s
s
in
g m
e
th
ods
ut
il
iz
e
d i
n t
he
pr
opo
s
e
d c
onvolut
io
na
l
ne
ur
a
l
ne
twor
k (
C
N
N
)
m
ode
ls
.
T
he
oc
c
ur
r
e
nc
e
of
pl
a
nt
di
s
e
a
s
e
s
pos
e
s
a
s
ig
ni
f
ic
a
nt
th
r
e
a
t
to
a
gr
ic
ul
tu
r
a
l
p
r
oduc
ti
vi
ty
,
pot
e
nt
ia
ll
y
le
a
di
ng
to
f
ood
unc
e
r
ta
in
ty
.
E
a
r
ly
a
nd
a
c
c
ur
a
te
de
t
e
c
ti
on
of
th
e
s
e
di
s
e
a
s
e
s
is
e
s
s
e
nt
ia
l
f
or
e
f
f
e
c
ti
ve
di
s
e
a
s
e
m
a
na
ge
m
e
nt
,
in
f
or
m
e
d
de
c
is
io
n
-
m
a
ki
ng,
a
nd
pr
e
ve
nt
io
n
of
e
c
onomi
c
lo
s
s
e
s
.
H
ow
e
ve
r
,
tr
a
di
ti
ona
l
m
e
th
ods
of
pl
a
nt
di
s
e
a
s
e
id
e
nt
if
ic
a
ti
on,
pr
im
a
r
il
y
r
e
ly
in
g
on
m
a
nu
a
l
obs
e
r
va
ti
on
a
nd
e
xpe
r
t
in
te
r
ve
nt
io
n,
a
r
e
s
ubj
e
c
ti
ve
,
ti
m
e
-
c
ons
um
in
g,
a
nd
la
bor
-
in
te
ns
iv
e
.
T
he
s
e
l
im
it
a
ti
ons
hi
nde
r
th
e
ti
m
e
ly
a
nd
pr
e
c
is
e
id
e
nt
if
ic
a
ti
on
of
pl
a
nt
di
s
e
a
s
e
s
,
m
a
ki
ng
it
c
h
a
ll
e
ngi
ng
to
i
m
pl
e
m
e
nt
pr
e
ve
nt
iv
e
m
e
a
s
ur
e
s
a
nd
c
ont
r
ol
s
tr
a
te
gi
e
s
.
A
ddi
ti
ona
ll
y,
th
e
di
ve
r
s
e
na
tu
r
e
of
pl
a
nt
di
s
e
a
s
e
s
,
c
a
us
e
d
by
v
a
r
io
us
a
g
e
nt
s
s
uc
h a
s
b
a
c
te
r
ia
,
f
ungi
,
vi
r
us
e
s
,
a
nd
f
ur
th
e
r
c
om
pl
ic
a
te
s
th
e
id
e
nt
if
ic
a
ti
on
pr
oc
e
s
s
.
T
he
r
e
f
or
e
,
th
e
r
e
is
a
n
unde
r
ly
in
g
ne
e
d
f
o
r
a
dva
nc
e
d
a
ut
om
a
te
d
te
c
hni
que
s
th
a
t
c
a
n
s
w
if
tl
y
a
nd
a
c
c
ur
a
te
ly
de
te
c
t
pl
a
nt
di
s
e
a
s
e
s
,
le
v
e
r
a
gi
ng
te
c
hnol
ogi
e
s
s
uc
h
a
s
de
e
p
le
a
r
ni
ng
a
nd
im
a
ge
pr
oc
e
s
s
in
g.
T
hi
s
pr
obl
e
m
s
ta
te
m
e
nt
s
e
e
k
s
to
a
ddr
e
s
s
th
e
li
m
it
a
ti
ons
of
e
xi
s
ti
ng
m
e
th
ods
a
nd
e
xpl
or
e
in
nova
ti
ve
s
ol
ut
io
ns
to
e
na
bl
e
e
a
r
ly
,
e
f
f
ic
ie
nt
,
a
nd
r
e
li
a
bl
e
pl
a
nt
di
s
e
a
s
e
de
te
c
ti
on, c
ont
r
ib
ut
in
g t
o s
us
ta
in
a
bl
e
a
gr
ic
ul
tu
r
a
l
pr
a
c
ti
c
e
s
a
nd
f
ood s
e
c
ur
it
y.
2.
R
E
L
A
T
E
D
WORK
B
a
s
e
d
on
th
e
f
in
di
ngs
of
[
15]
,
s
pe
c
if
ic
a
lg
or
it
hm
s
f
or
s
e
gm
e
nt
a
ti
on
a
nd
f
e
a
tu
r
e
e
xt
r
a
c
ti
on
c
a
n
be
e
m
pl
oye
d
to
de
te
c
t
pl
a
nt
di
s
e
a
s
e
s
f
r
om
im
a
ge
s
of
l
e
a
ve
s
.
D
e
te
c
ti
ng
pl
a
nt
di
s
e
a
s
e
s
m
a
nu
a
ll
y
is
a
hi
ghl
y
c
ha
ll
e
ngi
ng
ta
s
k
due
to
th
e
le
ngt
hy
le
a
r
ni
ng
c
ur
ve
,
e
xt
e
n
s
iv
e
e
f
f
or
t,
a
nd
ti
m
e
r
e
qui
r
e
m
e
nt
s
in
vol
ve
d.
T
he
a
ut
hor
ha
s
di
vi
de
d
th
e
pr
oc
e
s
s
of
de
te
c
ti
ng
pl
a
nt
le
a
f
il
ln
e
s
s
e
s
in
to
f
iv
e
di
s
ti
nc
t
s
te
p
s
:
im
a
ge
c
a
pt
ur
e
,
pr
e
pr
oc
e
s
s
in
g,
s
e
gm
e
nt
a
ti
on,
f
e
a
tu
r
e
e
xt
r
a
c
ti
on,
a
nd
di
s
e
a
s
e
c
l
a
s
s
if
ic
a
ti
on.
T
h
e
R
G
B
le
a
f
pi
c
tu
r
e
unde
r
w
e
nt
tr
a
ns
f
or
m
a
ti
on
us
in
g
a
f
r
a
m
e
w
or
k
dur
in
g
th
e
pr
oc
e
s
s
of
im
a
ge
c
a
pt
ur
e
.
F
ol
lo
w
in
g
th
is
s
te
p,
th
e
im
a
ge
unde
r
goe
s
pr
e
-
pr
oc
e
s
s
in
g
in
or
de
r
to
m
in
im
iz
e
noi
s
e
a
nd
e
nh
a
nc
e
c
ont
r
a
s
t.
S
e
gm
e
nt
a
ti
on
is
a
pr
oc
e
s
s
th
a
t
in
vol
ve
s
th
e
di
vi
s
io
n
of
a
n
im
a
ge
in
to
di
s
ti
nc
t
f
e
a
tu
r
e
por
ti
o
ns
us
in
g
v
a
r
io
us
te
c
hni
que
s
s
uc
h
a
s
k
-
m
e
a
n
s
c
lu
s
te
r
in
g,
O
ts
u
f
il
te
r
s
,
a
nd
ot
he
r
s
im
il
a
r
m
e
th
ods
.
F
ol
lo
w
in
g
th
e
e
xt
r
a
c
ti
on
of
f
e
a
tu
r
e
s
f
r
om
th
e
s
e
gm
e
nt
e
d
pi
c
tu
r
e
,
va
r
io
us
c
la
s
s
if
ic
a
ti
on
a
ppr
oa
c
he
s
a
r
e
ut
il
iz
e
d
to
g
e
ne
r
a
te
th
e
ul
ti
m
a
te
c
la
s
s
if
ic
a
ti
on.
P
la
nt
di
s
e
a
s
e
s
c
a
n be
a
c
c
ur
a
t
e
ly
i
de
nt
if
ie
d us
in
g t
hi
s
m
e
th
od.
T
he
ut
il
iz
a
ti
on
of
f
e
e
d
f
or
w
a
r
d
b
a
c
k
pr
opa
ga
ti
on
ha
s
be
e
n
im
p
le
m
e
nt
e
d
in
[
16]
.
A
ne
ur
a
l
ne
twor
k
-
ba
s
e
d
a
ppr
oa
c
h
is
pr
opos
e
d
f
or
th
e
id
e
nt
if
ic
a
ti
on
a
nd
c
a
te
gor
iz
a
ti
on
of
il
ln
e
s
s
e
s
in
gr
a
pe
le
a
ve
s
.
T
he
di
a
gnos
is
in
vol
ve
d
th
e
ut
il
iz
a
ti
on
of
gr
a
pe
le
a
f
im
a
ge
s
f
e
a
tu
r
in
g
in
tr
ic
a
te
ba
c
kgr
ounds
.
A
ni
s
ot
r
opi
c
di
f
f
us
io
n
is
a
ppl
ie
d
to
th
e
im
a
ge
in
or
de
r
to
f
ur
th
e
r
e
li
m
in
a
te
noi
s
e
.
S
ubs
e
que
nt
ly
,
th
e
im
a
ge
i
s
s
ubj
e
c
t
e
d
to
k
-
m
e
a
ns
c
lu
s
te
r
in
g
f
or
f
ur
th
e
r
s
e
gm
e
nt
a
ti
on.
T
he
c
la
s
s
if
ic
a
ti
on
of
D
ow
ne
y
M
il
de
w
a
nd
A
nt
hr
a
c
nos
e
w
a
te
r
m
e
lo
n
le
a
f
di
s
e
a
s
e
s
ha
s
be
e
n
a
c
c
om
pl
is
he
d
in
[
17]
th
r
ough
th
e
ut
il
iz
a
ti
on
of
a
ne
ur
a
l
ne
twor
k
-
ba
s
e
d
a
ppr
oa
c
h.
T
he
a
ut
hor
ha
s
e
va
lu
a
te
d t
he
e
f
f
ic
a
c
y of
t
he
pr
opos
e
d c
onc
e
pt
by a
s
s
e
s
s
in
g i
ts
r
a
te
s
of
t
r
ue
pos
it
iv
e
, t
r
ue
ne
ga
ti
ve
,
a
nd
ove
r
a
ll
a
c
c
ur
a
c
y.
T
h
e
c
a
te
gor
iz
a
ti
on
is
de
te
r
m
in
e
d
by
e
xt
r
a
c
ti
ng
th
e
c
ol
or
f
e
a
tu
r
e
f
r
om
th
e
de
te
c
te
d
pi
xe
ls
in
th
e
r
e
gi
on
of
in
te
r
e
s
t,
us
in
g
th
e
R
G
B
c
ol
or
m
ode
l.
T
he
ut
il
iz
a
ti
on
of
pa
tt
e
r
n
r
e
c
ogni
ti
on
m
e
th
ods
ha
s
be
e
n
pr
opos
e
d
in
[
18]
a
s
a
m
e
a
n
s
to
id
e
nt
if
y
a
nd
c
a
te
gor
iz
e
in
f
e
c
ti
ons
c
a
u
s
e
d
by
A
lt
e
r
na
r
ia
,
M
yr
ot
he
c
iu
m
,
a
nd
ba
c
t
e
r
ia
l
bl
ig
ht
on
c
ot
to
n
le
a
ve
s
.
T
he
da
t
a
s
e
t
c
om
pr
is
e
s
im
a
ge
s
th
a
t
w
e
r
e
ga
th
e
r
e
d
w
it
hi
n
th
e
pr
e
m
is
e
s
of
th
e
C
e
nt
r
a
l
I
n
s
ti
tu
te
of
C
ot
to
n
R
e
s
e
a
r
c
h
lo
c
a
te
d
in
N
a
gpur
.
T
he
is
ol
a
ti
on
of
s
ic
k
a
r
e
a
s
is
a
c
hi
e
ve
d
th
r
ough
th
e
ut
il
iz
a
ti
on
o
f
a
n
a
c
ti
ve
c
ont
our
-
ba
s
e
d
s
e
gm
e
nt
a
ti
on
te
c
hni
que
.
T
he
a
ut
hor
ha
s
pr
ovi
de
d
a
num
be
r
of
f
e
a
tu
r
e
r
e
c
om
m
e
nda
ti
ons
f
or
va
r
io
us
c
r
ops
,
s
uc
h
a
s
w
he
a
t,
or
a
nge
,
c
it
r
us
,
a
nd
m
a
iz
e
,
w
hi
c
h
a
r
e
ba
s
e
d on a
c
om
m
on pr
in
c
ip
le
.
A
de
e
p
le
a
r
ni
ng
m
e
th
od
w
a
s
ut
il
iz
e
d
to
c
a
te
gor
iz
e
il
ln
e
s
s
e
s
i
n
be
a
n
le
a
v
e
s
.
T
o
opt
im
iz
e
tr
a
in
in
g
dur
a
ti
ons
,
e
nha
nc
e
a
c
c
ur
a
c
y,
a
nd
s
im
pl
if
y
r
e
tr
a
in
in
g,
th
e
m
o
de
l
w
a
s
tr
a
in
e
d
us
in
g
th
e
M
obi
le
N
e
tV2
[
19]
a
r
c
hi
te
c
tu
r
e
in
a
c
ont
r
ol
le
d
e
nvi
r
onm
e
nt
.
O
ne
s
ig
ni
f
ic
a
nt
a
dv
a
nc
e
m
e
nt
in
th
e
f
ie
ld
of
a
r
ti
f
ic
ia
l
in
te
ll
ig
e
nc
e
pe
r
ta
in
in
g
to
obj
e
c
t
de
te
c
ti
on
is
th
e
ut
il
iz
a
ti
on
of
a
ne
ur
a
l
ne
t
w
or
k.
T
he
s
tu
dy
e
m
pl
oye
d
Y
O
L
O
v
3
[
20]
–
[
23]
to
de
te
c
t
a
nd
c
la
s
s
if
y
s
ix
r
ic
e
le
a
f
di
s
e
a
s
e
s
,
na
m
e
ly
bl
a
s
t,
ba
c
te
r
ia
l
le
a
f
bl
ig
ht
,
br
ow
n
s
pot
,
na
r
r
ow
br
ow
n
s
pot
, ba
c
te
r
ia
l
le
a
f
s
tr
e
a
k, a
nd r
ic
e
r
a
gge
d s
tu
nt
vi
r
a
l
di
s
e
a
s
e
. T
he
a
c
c
ur
a
c
y a
c
hi
e
v
e
d i
n t
hi
s
c
la
s
s
if
ic
a
ti
on t
a
s
k
w
a
s
79.19%
.
I
n
th
e
ir
s
tu
dy,
[
6]
,
[
24]
–
[
26]
in
tr
oduc
e
d
a
two
-
la
ye
r
C
N
N
m
ode
l.
T
hi
s
m
ode
l
w
a
s
de
s
ig
ne
d
to
e
f
f
e
c
ti
ve
ly
c
om
bi
ne
m
ul
ti
pl
e
l
a
ye
r
s
i
n
or
de
r
to
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xt
r
a
c
t
va
lu
a
bl
e
a
nd dis
ti
nc
t
in
f
or
m
a
ti
on
f
r
om
c
it
r
us
f
r
ui
ts
a
nd
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
.
14
, N
o.
3
,
J
une
20
25
:
1719
-
1726
1722
le
a
ve
s
.
T
h
e
m
ode
l
e
f
f
e
c
ti
ve
ly
di
f
f
e
r
e
nt
ia
te
d
be
twe
e
n
he
a
lt
hy
f
r
ui
ts
a
nd
le
a
ve
s
,
a
s
w
e
ll
a
s
th
os
e
e
xhi
bi
ti
ng
c
om
m
on c
it
r
us
di
s
e
a
s
e
s
s
u
c
h a
s
m
e
l
a
nos
e
, bl
a
c
k s
pot
s
,
c
a
nke
r
,
s
c
a
b, a
nd gr
e
e
ni
ng.
T
he
pr
opos
e
d
a
ppr
oa
c
h by
[
27]
in
vol
ve
s
th
e
ut
il
iz
a
ti
on
of
a
hyb
r
id
de
e
p
le
a
r
ni
ng
m
ode
l
.
T
hi
s
m
ode
l
a
im
s
to
r
e
duc
e
th
e
qua
nt
it
y
of
tr
a
in
in
g
pr
oc
e
s
s
pa
r
a
m
e
te
r
s
w
hi
le
s
im
ul
ta
ne
ou
s
ly
e
nha
nc
in
g
c
om
put
a
ti
on
a
c
c
ur
a
c
y.
T
hi
s
i
s
a
c
hi
e
ve
d
by
c
om
bi
ni
ng
th
e
a
dva
nt
a
g
e
ous
c
ha
r
a
c
te
r
is
ti
c
s
of
d
e
ns
e
ne
twor
k
s
a
nd
d
e
e
p
r
e
s
id
ua
l
ne
twor
ks
.
I
n
th
is
s
tu
dy,
[
28]
–
[
31]
a
s
ugge
s
te
d
m
ode
l
is
a
de
e
p
c
onvolut
io
na
l
ne
ur
a
l
ne
twor
k
(
D
C
N
N
)
.
B
y
r
e
pl
a
c
in
g
th
e
c
onve
nt
io
na
l
c
onvolut
io
n
la
ye
r
s
w
it
h
de
e
pe
r
s
e
pa
r
a
bl
e
c
onvolut
io
ns
,
it
i
s
pos
s
ib
le
to
r
e
duc
e
th
e
num
be
r
of
m
od
e
l
pa
r
a
m
e
te
r
s
a
nd
de
c
r
e
a
s
e
th
e
it
e
r
a
ti
on
ti
m
e
.
T
he
in
te
gr
a
ti
on
o
f
lo
c
a
l
bi
na
r
y
pa
tt
e
r
n
(
L
B
P
)
f
e
a
tu
r
e
s
,
w
hi
c
h
e
f
f
ic
ie
nt
ly
c
a
pt
ur
e
l
oc
a
l
in
f
or
m
a
ti
on f
or
e
nha
nc
in
g de
e
p f
e
a
tu
r
e
s
, i
s
pe
r
f
or
m
e
d.
I
n
a
s
tu
dy
c
onduc
te
d
by
[
32]
–
[
34]
,
a
te
c
hni
que
w
a
s
pr
opos
e
d
f
or
th
e
de
te
c
ti
on
of
di
s
e
a
s
e
s
in
te
a
le
a
ve
s
.
T
hi
s
a
ppr
oa
c
h
le
ve
r
a
ge
s
a
m
odi
f
ie
d
D
C
N
N
.
T
he
s
tu
dy
e
m
pl
oye
d
va
r
io
us
de
ve
lo
pe
d/
m
odi
f
ie
d
de
e
p
le
a
r
ni
ng
a
r
c
hi
te
c
tu
r
e
s
to
e
f
f
e
c
ti
ve
ly
id
e
nt
if
y
a
nd
c
a
t
e
gor
iz
e
pl
a
nt
di
s
e
a
s
e
s
.
T
h
e
doc
um
e
nt
pr
ovi
de
d
a
c
om
pr
e
he
ns
iv
e
e
xpl
a
na
ti
on
of
th
e
ut
il
iz
a
ti
on
of
de
e
p
le
a
r
ni
ng
m
ode
ls
f
or
th
e
r
e
pr
e
s
e
nt
a
ti
on
of
va
r
io
us
pl
a
nt
di
s
e
a
s
e
s
. T
he
doc
um
e
nt
l
a
c
k
s
a
c
om
pr
e
he
ns
iv
e
a
na
ly
s
is
of
di
s
e
a
s
e
e
a
r
ly
de
te
c
ti
on me
th
ods
a
nd t
he
pr
oc
e
s
s
of
id
e
nt
if
yi
ng
a
nd
c
a
te
gor
iz
in
g
pl
a
nt
il
ln
e
s
s
e
s
th
r
ough
th
e
e
xa
m
i
na
ti
on
of
m
in
u
te
s
a
m
pl
e
s
.
D
e
e
p
le
a
r
ni
ng
ha
s
be
e
n
in
c
or
por
a
te
d
in
to
va
r
io
us
a
ppl
ic
a
ti
ons
,
s
uc
h
a
s
c
r
op
va
r
ie
ty
de
te
c
ti
on
a
nd
c
la
s
s
if
ic
a
ti
on,
pl
a
nt
id
e
nt
if
ic
a
ti
on
a
nd
c
la
s
s
if
ic
a
ti
on,
a
nd
f
r
ui
t
pi
c
tu
r
e
gr
a
di
ng.
T
he
popula
r
it
y
of
im
a
ge
s
c
a
pt
ur
e
d
us
in
g
m
obi
le
c
a
m
e
r
a
s
or
c
a
m
e
r
a
de
vi
c
e
s
i
nt
e
gr
a
te
d i
nt
o r
obot
s
ha
s
be
e
n
s
te
a
d
il
y i
nc
r
e
a
s
in
g
[
35]
, [
36]
.
R
a
s
hi
d
e
t
al
.
[
37]
pr
opos
e
d
a
nove
l
a
lg
or
it
hm
ba
s
e
d
on
C
N
N
a
nd
im
a
ge
pr
e
-
pr
oc
e
s
s
in
g
to
e
f
f
e
c
ti
ve
ly
id
e
nt
if
y
a
nd
di
f
f
e
r
e
nt
ia
te
r
ic
e
di
s
e
a
s
e
s
.
T
he
a
lg
or
it
hm
le
ve
r
a
ge
s
a
da
t
a
s
e
t
of
500
a
ut
he
nt
ic
phot
ogr
a
phs
c
a
pt
ur
e
d
f
r
om
th
e
r
ic
e
e
m
pi
r
ic
a
l
f
ie
ld
.
T
he
s
ugg
e
s
te
d
m
ode
l
de
m
ons
tr
a
te
s
im
pr
ove
d
a
c
c
ur
a
c
y
c
om
pa
r
e
d
to
th
e
ty
pi
c
a
l
m
a
c
hi
ne
le
a
r
ni
ng
m
ode
l
due
to
s
e
ve
r
a
l
f
a
c
to
r
s
.
T
he
s
e
in
c
lu
de
th
e
ut
il
iz
a
ti
on
of
th
e
10
-
f
ol
d
c
r
os
s
-
va
li
da
ti
on
m
e
th
od,
w
hi
c
h
e
nha
nc
e
s
f
e
a
s
ib
il
it
y
a
n
d
e
c
onomy.
A
ddi
ti
ona
ll
y,
th
e
s
ugge
s
te
d
m
od
e
l
e
xhi
bi
ts
a
qui
c
ke
r
c
onve
r
ge
nc
e
r
a
te
a
nd
im
pr
ove
d
id
e
nt
if
ic
a
ti
on
a
bi
li
ty
.
A
nove
l
di
s
e
a
s
e
d
e
te
c
ti
on
m
ode
l
f
or
a
ut
om
a
ti
c
di
s
e
a
s
e
di
a
gnos
is
w
a
s
de
ve
lo
pe
d
us
in
g
th
e
4
-
f
ol
d
c
r
os
s
-
va
li
da
ti
on
m
e
th
od.
T
he
m
ode
l
ut
il
iz
e
d
800
c
uc
um
be
r
le
a
f
phot
os
c
a
pt
ur
e
d
by
di
gi
ta
l
c
a
m
e
r
a
s
.
T
o
im
pr
ove
a
c
c
ur
a
c
y,
th
e
m
ode
l
e
m
pl
oye
d
C
N
N
m
e
th
od
s
w
it
h i
m
a
ge
pr
e
-
pr
oc
e
s
s
in
g t
e
c
hni
que
s
[
38]
.
T
o
e
nha
n
c
e
pr
e
c
is
io
n
a
nd
obt
a
in
a
vi
a
bl
e
s
ol
ut
io
n,
a
no
ve
l
m
ode
l
na
m
e
d
in
te
gr
a
te
d
n
e
ur
a
l
a
r
c
hi
te
c
tu
r
e
f
or
r
e
a
l
-
ti
m
e
s
in
gl
e
s
hot
m
ul
ti
box
de
te
c
to
r
(
I
N
A
R
-
SSD
)
w
a
s
i
nt
r
oduc
e
d
in
[
39]
–
[
41]
. T
hi
s
m
ode
l
w
a
s
im
pl
e
m
e
nt
e
d
w
it
hi
n
th
e
c
a
f
f
e
f
r
a
m
e
w
or
k,
s
pe
c
if
ic
a
ll
y
i
n
G
oogl
e
'
s
I
nc
e
pt
io
n
s
tr
uc
tu
r
e
,
a
nd
ut
il
iz
e
d
r
a
in
bow
c
onc
a
te
na
ti
on on the
gr
a
phi
c
s
pr
oc
e
s
s
in
g unit
(
G
P
U
)
p
la
tf
or
m
. T
he
pr
opos
e
d m
ode
l
e
m
pl
oys
D
C
N
N
to
pe
r
f
or
m
r
e
a
l
-
ti
m
e
d
is
e
a
s
e
de
te
c
ti
on
on
a
ppl
e
le
a
ve
s
.
T
h
e
da
ta
s
e
t
us
e
d
f
or
tr
a
in
in
g
a
nd
e
va
lu
a
ti
on
c
ons
is
ts
of
26,377
im
a
ge
s
of
a
ppl
e
le
a
f
di
s
e
a
s
e
s
,
c
om
pr
is
in
g
a
c
om
bi
na
ti
on
of
la
bor
a
to
r
y
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ge
ne
r
a
te
d
im
a
ge
s
a
nd
c
om
pl
e
x
im
a
ge
s
c
a
pt
ur
e
d
in
r
e
a
l
f
ie
ld
c
ondi
ti
ons
.
T
he
s
m
a
r
tp
hone
a
ppl
ic
a
ti
on
dC
r
op
w
a
s
de
v
e
lo
pe
d
to
f
or
e
c
a
s
t
c
r
op dis
e
a
s
e
s
i
n m
ode
r
n a
gr
ic
ul
tu
r
e
. I
t
ut
il
iz
e
d a
publ
ic
da
ta
s
e
t
c
ons
is
ti
ng of
54,306 plant lea
f
photos
a
nd
e
m
pl
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d
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e
p
le
a
r
ni
ng
-
ba
s
e
d
te
c
hni
que
s
in
c
lu
di
ng
A
le
xN
e
t,
R
e
s
N
e
t
-
50,
a
nd
R
e
s
N
e
t
-
34.
T
he
s
e
te
c
hni
que
s
w
e
r
e
us
e
d
to
a
c
hi
e
ve
a
c
c
ur
a
te
pr
e
di
c
ti
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.
I
n
a
d
di
ti
on,
a
P
yT
or
c
h
m
ode
l
th
a
t
ha
s
unde
r
gone
tr
a
in
in
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w
a
s
c
onve
r
te
d
in
to
a
T
e
ns
or
F
lo
w
pb
f
il
e
.
T
hi
s
f
il
e
w
a
s
th
e
n
in
te
gr
a
te
d
in
to
th
e
d
C
r
op
a
ppl
ic
a
ti
on,
e
na
bl
in
g
r
e
a
l
-
ti
m
e
pr
e
di
c
ti
ons
f
or
c
r
op
di
s
e
a
s
e
s
.
I
n
a
s
tu
dy
c
onduc
te
d
by
[
42]
,
[
43]
,
a
C
N
N
m
ode
l
w
it
h
a
tr
a
ns
f
e
r
le
a
r
ni
ng
a
ppr
oa
c
h
w
a
s
e
m
pl
oye
d
to
de
t
e
c
t
c
r
op
il
ln
e
s
s
e
s
a
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id
e
nt
if
y
pe
s
t
s
us
in
g
le
a
f
phot
os
s
our
c
e
d
f
r
om
th
e
P
la
nt
V
il
la
ge
da
ta
ba
s
e
.
T
he
im
pl
e
m
e
nt
a
ti
on
of
th
is
m
e
th
od
r
e
s
ul
te
d
in
a
de
c
r
e
a
s
e
in
bot
h
ti
m
e
a
nd
hum
a
n l
a
bor
r
e
qui
r
e
m
e
nt
s
, w
hi
le
s
im
ul
ta
ne
ous
ly
i
m
pr
ovi
ng a
c
c
ur
a
c
y.
A
to
ta
l
of
500
to
m
a
to
le
a
f
phot
os
f
r
om
th
e
P
la
nt
V
il
li
a
ge
da
ta
s
e
t
w
e
r
e
ut
il
iz
e
d
in
th
is
s
tu
dy
to
de
te
c
t
a
nd
c
la
s
s
if
y
500
in
s
ta
nc
e
s
of
le
a
f
il
ln
e
s
s
e
s
.
T
hi
s
w
a
s
a
c
hi
e
ve
d
by
e
m
pl
oyi
ng
a
C
N
N
m
ode
l
in
c
onj
unc
ti
on
w
it
h t
he
le
a
r
ni
ng ve
c
to
r
qua
nt
iz
a
ti
on
(
L
V
Q
)
t
e
c
hni
que
. F
ur
th
e
r
m
or
e
, t
he
ut
il
iz
a
ti
on of
d
if
f
e
r
e
nt
c
onvolut
io
na
l
f
il
te
r
s
w
a
s
im
pl
e
m
e
nt
e
d
to
e
nh
a
nc
e
th
e
l
e
ve
l
of
r
e
c
ogni
ti
on
i
n
th
e
c
la
s
s
if
ic
a
ti
on
m
e
th
odol
ogy.
T
he
upd
a
te
w
a
s
c
r
e
a
te
d
by
[
44]
–
[
47]
.
I
n
or
de
r
to
de
te
c
t
le
a
f
s
pot
di
s
e
a
s
e
s
i
n
s
uga
r
b
e
e
t,
a
n
im
a
gi
ng
-
ba
s
e
d
e
xpe
r
t
s
ys
te
m
is
e
m
pl
oye
d.
T
hi
s
s
ys
te
m
ut
il
iz
e
s
a
f
a
s
te
r
R
-
C
N
N
a
r
c
hi
te
c
tu
r
e
w
it
h
a
dj
us
ta
bl
e
p
a
r
a
m
e
te
r
s
of
a
C
N
N
m
od
e
l.
T
he
pr
im
a
r
y
obj
e
c
ti
ve
s
of
th
is
a
ppr
oa
c
h
a
r
e
to
a
c
hi
e
v
e
opt
im
a
l
a
c
c
ur
a
c
y,
r
e
du
c
e
pr
oc
e
s
s
in
g
ti
m
e
,
a
nd
m
in
im
iz
e
hum
a
n
e
r
r
or
.
T
he
s
ys
te
m
a
n
a
ly
z
e
s
a
d
a
ta
s
e
t
c
ons
is
t
in
g
of
155
im
a
ge
s
to
a
c
c
om
pl
is
h
th
e
s
e
go
a
ls
[
48]
.
T
o
a
ddr
e
s
s
th
e
c
ha
ll
e
nge
s
of
id
e
nt
if
yi
ng
r
ic
e
il
ln
e
s
s
e
s
a
nd
r
e
s
ol
vi
ng
is
s
ue
s
s
uc
h
a
s
bl
ur
r
in
g
pi
c
tu
r
e
e
dge
s
,
noi
s
e
,
s
ig
ni
f
ic
a
nt
ba
c
kgr
ound
in
te
r
f
e
r
e
nc
e
,
a
nd
lo
w
de
te
c
ti
on
a
c
c
ur
a
c
y,
pr
opos
e
d
th
e
ut
il
iz
a
ti
on
of
th
e
k
-
m
e
a
ns
c
lu
s
te
r
in
g
te
c
hni
que
a
nd
th
e
qui
c
k
e
r
R
-
C
N
N
f
us
i
on
a
lg
or
it
hm
.
T
he
m
or
e
e
f
f
ic
ie
nt
2D
-
O
ts
u
a
lg
or
it
hm
w
a
s
e
m
pl
oye
d
to
c
la
s
s
if
y
th
e
im
a
ge
s
of
r
ic
e
di
s
e
a
s
e
s
f
or
obt
a
in
in
g
r
e
s
ul
ts
.
T
a
bl
e
s
1
a
nd
2
s
how
s
th
e
s
ur
ve
y t
a
bl
e
.
2.1.
R
e
s
e
ar
c
h
gap
S
om
e
of
th
e
r
e
s
e
a
r
c
h
ga
ps
in
th
i
s
s
tu
dy
a
r
e
a
s
f
ol
lo
w
s
:
i)
li
m
it
e
d
f
oc
us
on
e
a
r
ly
de
te
c
ti
on
:
m
a
ny
e
xi
s
ti
ng
s
tu
di
e
s
f
oc
u
s
on
di
s
e
a
s
e
de
te
c
ti
on,
but
th
e
r
e
'
s
a
l
a
c
k
of
e
m
ph
a
s
is
on
e
a
r
ly
de
te
c
ti
on.
E
a
r
ly
Evaluation Warning : The document was created with Spire.PDF for Python.
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J
A
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C
om
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e
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ur
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e
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of
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ant
l
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af
di
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a
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i
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nt
if
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io
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e
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hni
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(
Shi
lp
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1723
id
e
nt
if
ic
a
ti
on i
s
c
r
uc
ia
l
f
o
r
t
im
e
ly
i
nt
e
r
ve
nt
io
n a
nd p
r
e
ve
nt
io
n o
f
e
xt
e
ns
iv
e
c
r
op da
m
a
ge
;
ii
)
ne
e
d
f
or
e
f
f
ic
ie
nt
a
ut
om
a
ti
on
:
w
hi
le
a
ut
om
a
ti
on
is
ga
in
in
g
a
tt
e
nt
io
n,
tr
a
di
ti
on
a
l
m
e
th
ods
of
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s
e
a
s
e
id
e
nt
if
ic
a
ti
on
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e
m
a
in
pr
e
va
le
nt
.
T
he
r
e
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s
a
ga
p
in
de
ve
lo
pi
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m
or
e
e
f
f
ic
ie
nt
a
nd
a
c
c
ur
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te
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ut
om
a
te
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te
c
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to
r
e
duc
e
th
e
r
e
li
a
nc
e
on
m
a
nua
l
a
nd
la
bor
-
in
te
ns
iv
e
pr
oc
e
s
s
e
s
;
ii
i)
di
ve
r
s
e
d
is
e
a
s
e
c
la
s
s
if
ic
a
ti
on:
pl
a
nt
di
s
e
a
s
e
s
a
r
e
c
a
u
s
e
d
by
va
r
io
us
a
ge
nt
s
,
e
a
c
h
e
xhi
bi
ti
ng
uni
que
s
ym
pt
om
s
.
H
ow
e
ve
r
,
e
xi
s
ti
ng
r
e
s
e
a
r
c
h
of
te
n
ove
r
lo
oks
th
e
c
ha
ll
e
nge
s
of
a
c
c
ur
a
te
ly
c
la
s
s
if
yi
ng
di
s
e
a
s
e
s
th
a
t
m
a
y
s
ha
r
e
s
im
il
a
r
s
ym
pt
om
s
;
iv
)
in
te
gr
a
ti
on
of
de
e
p
le
a
r
ni
ng
:
w
hi
le
de
e
p
le
a
r
ni
ng
ha
s
ga
in
e
d
popula
r
it
y,
it
s
a
ppl
ic
a
t
io
n
in
pl
a
nt
di
s
e
a
s
e
de
te
c
ti
on
is
s
ti
ll
r
e
la
ti
ve
ly
ne
w
a
nd
li
m
it
e
d.
T
he
r
e
'
s
pot
e
nt
ia
l
f
or
de
ve
lo
pi
ng
m
or
e
s
o
phi
s
ti
c
a
te
d
de
e
p
le
a
r
ni
ng
m
ode
ls
ta
il
or
e
d
to
s
pe
c
if
ic
c
r
op dis
e
a
s
e
s
;
v)
li
m
it
e
d
s
m
a
ll
s
a
m
pl
e
a
ppr
oa
c
he
s
:
m
a
ny
s
tu
di
e
s
f
oc
us
on l
a
r
ge
da
ta
s
e
ts
, but
t
he
r
e
'
s
a
ga
p
in
de
ve
lo
pi
ng
e
f
f
e
c
ti
ve
de
te
c
ti
on
a
nd
c
la
s
s
if
ic
a
ti
on
te
c
hn
iq
ue
s
f
or
di
s
e
a
s
e
s
ba
s
e
d
on
li
m
it
e
d
or
s
m
a
ll
s
a
m
pl
e
s
iz
e
s
;
a
nd
vi
)
in
te
gr
a
ti
on
of
im
a
ge
pr
e
pr
oc
e
s
s
in
g:
e
x
is
ti
ng
a
ppr
oa
c
he
s
m
a
y
not
f
ul
ly
e
xpl
or
e
th
e
pot
e
nt
ia
l
of
im
a
ge
pr
e
pr
oc
e
s
s
in
g
te
c
hni
que
s
to
e
nha
nc
e
di
s
e
a
s
e
de
te
c
ti
on
a
c
c
ur
a
c
y,
p
a
r
ti
c
ul
a
r
ly
in
th
e
pr
e
s
e
nc
e
of
noi
s
e
a
nd va
r
ia
ti
ons
i
n i
m
a
ge
qua
li
ty
.
T
a
bl
e
1
.
S
ur
ve
y of
c
ha
ll
e
nge
s
a
nd ga
ps
i
n pl
a
nt
di
s
e
a
s
e
de
te
c
ti
o
n a
nd c
la
s
s
if
ic
a
ti
on
A
s
pe
c
t
s
a
ddr
e
s
s
e
d
K
e
y poi
nt
s
A
dva
nt
a
ge
s
D
i
s
a
dva
nt
a
ge
s
I
m
por
t
a
nc
e
of
e
a
r
l
y de
t
e
c
t
i
on
P
l
a
nt
di
s
e
a
s
e
s
n
e
ga
t
i
ve
l
y
i
m
pa
c
t
a
gr
i
c
ul
t
ur
a
l
pr
oduc
t
i
on
a
nd
f
ood
s
e
c
ur
i
t
y.
T
i
m
e
l
y
de
t
e
c
t
i
on
i
s
vi
t
a
l
f
or
e
f
f
e
c
t
i
ve
di
s
e
a
s
e
m
a
na
ge
m
e
nt
a
nd
i
nf
or
m
e
d
de
c
i
s
i
ons
. E
a
r
l
y s
ym
pt
om
s
of
t
e
n m
a
ni
f
e
s
t
on l
e
a
ve
s
.
F
a
c
i
l
i
t
a
t
e
s
t
i
m
e
l
y
i
nt
e
r
ve
nt
i
on
t
o
pr
e
ve
nt
c
r
op
l
os
s
e
s
.
E
na
bl
e
s
i
nf
or
m
e
d
de
c
i
s
i
on
-
m
a
ki
ng
i
n
a
gr
i
c
ul
t
ur
e
.
N
ot
a
l
l
di
s
e
a
s
e
s
e
xhi
bi
t
e
a
r
l
y
de
t
e
c
t
a
bl
e
s
ym
pt
om
s
.
L
i
m
i
t
a
t
i
ons
of
m
a
nua
l
m
e
t
hods
T
r
a
di
t
i
ona
l
m
e
t
hods
i
nvol
ve
s
ubj
e
c
t
i
ve
,
t
i
m
e
-
c
ons
um
i
ng,
a
nd
l
a
bor
-
i
nt
e
ns
i
ve
pr
oc
e
s
s
e
s
.
E
xpe
r
t
i
de
nt
i
f
i
c
a
t
i
on
m
a
y
be
r
e
qui
r
e
d.
N
e
e
d
f
or
e
f
f
i
c
i
e
nt
a
nd a
c
c
ur
a
t
e
a
ut
om
a
t
e
d t
e
c
hni
que
s
.
A
ut
om
a
t
i
on
r
e
duc
e
s
s
ubj
e
c
t
i
vi
t
y
a
nd
hum
a
n
e
r
r
or
.
E
nha
nc
e
s
e
f
f
i
c
i
e
nc
y i
n di
s
e
a
s
e
i
de
nt
i
f
i
c
a
t
i
on.
A
ut
om
a
t
e
d
m
e
t
hods
m
a
y
r
e
qui
r
e
c
om
pl
e
x
a
l
gor
i
t
hm
s
.
C
l
a
s
s
i
f
i
c
a
t
i
on of
pl
a
nt
di
s
e
a
s
e
s
D
i
s
e
a
s
e
s
c
a
us
e
d
by
va
r
i
ous
a
g
e
nt
s
l
i
ke
ba
c
t
e
r
i
a
,
f
ungi
,
vi
r
us
e
s
,
a
nd
m
or
e
.
E
a
c
h
di
s
e
a
s
e
pr
e
s
e
nt
s
uni
que
s
ym
pt
om
s
a
nd
p
a
t
t
e
r
ns
.
S
ym
pt
om
s
u
s
e
d
t
o
i
de
nt
i
f
y s
pe
c
i
f
i
c
di
s
e
a
s
e
s
a
nd t
he
i
r
c
a
u
s
e
s
.
A
l
l
ow
s
t
a
r
ge
t
e
d
t
r
e
a
t
m
e
nt
ba
s
e
d
on
di
s
e
a
s
e
a
ge
nt
.
P
a
t
t
e
r
ns
a
i
d
i
n
a
c
c
ur
a
t
e
di
a
gnos
i
s
.
C
e
r
t
a
i
n
di
s
e
a
s
e
s
m
a
y
s
ha
r
e
s
i
m
i
l
a
r
s
ym
pt
om
s
.
T
a
bl
e
2
.
L
it
e
r
a
tu
r
e
s
ur
ve
y
S
t
udy
A
ppr
oa
c
h a
nd
f
i
ndi
ngs
A
dva
nt
a
ge
s
D
i
s
a
dva
nt
a
ge
s
[
3]
D
i
s
c
us
s
e
s
s
e
gm
e
nt
a
t
i
on
a
nd
f
e
a
t
ur
e
e
xt
r
a
c
t
i
on
a
l
gor
i
t
hm
s
f
or
pl
a
nt
di
s
e
a
s
e
d
e
t
e
c
t
i
on.
P
r
opos
e
s
a
f
i
ve
-
s
t
e
p
pr
oc
e
s
s
f
or
i
de
nt
i
f
i
c
a
t
i
on,
i
nc
l
udi
ng
i
m
a
ge
a
c
qui
s
i
t
i
on,
pr
e
pr
oc
e
s
s
i
ng,
s
e
gm
e
nt
a
t
i
on, f
e
a
t
ur
e
e
xt
r
a
c
t
i
on, a
nd c
l
a
s
s
i
f
i
c
a
t
i
on.
S
ys
t
e
m
a
t
i
c
a
ppr
oa
c
h
f
or
e
f
f
i
c
i
e
nt
di
s
e
a
s
e
i
de
nt
i
f
i
c
a
t
i
on.
U
t
i
l
i
z
e
s
e
s
t
a
bl
i
s
he
d
i
m
a
ge
pr
oc
e
s
s
i
ng t
e
c
hni
que
s
.
P
r
oc
e
s
s
c
om
pl
e
xi
t
y
due
t
o
m
ul
t
i
pl
e
s
t
e
ps
.
M
a
y
r
e
qui
r
e
pa
r
a
m
e
t
e
r
t
uni
ng.
[
4]
U
t
i
l
i
z
e
s
ne
ur
a
l
ne
t
w
or
ks
f
or
gr
a
pe
l
e
a
f
di
s
e
a
s
e
di
a
gnos
i
s
a
nd
c
l
a
s
s
i
f
i
c
a
t
i
on.
E
m
pl
oys
a
ni
s
ot
r
opi
c
di
f
f
us
i
on
a
nd
k
-
m
e
a
ns
c
l
us
t
e
r
i
ng
f
or
i
m
a
ge
pr
e
pr
oc
e
s
s
i
ng
a
nd
s
e
gm
e
nt
a
t
i
on.
N
e
ur
a
l
ne
t
w
or
ks
of
f
e
r
pow
e
r
f
ul
pa
t
t
e
r
n
r
e
c
ogni
t
i
on
c
a
pa
bi
l
i
t
i
e
s
.
A
ut
om
a
t
i
on
m
i
ni
m
i
z
e
s
hum
a
n
i
nt
e
r
ve
nt
i
on.
N
e
ur
a
l
ne
t
w
or
k
t
r
a
i
ni
ng
r
e
qui
r
e
s
s
i
gni
f
i
c
a
nt
c
om
put
a
t
i
ona
l
r
e
s
our
c
e
s
.
[
5]
I
m
pl
e
m
e
nt
s
ne
ur
a
l
ne
t
w
or
k
-
ba
s
e
d
s
ys
t
e
m
f
or
w
a
t
e
r
m
e
l
on
l
e
a
f
di
s
e
a
s
e
c
l
a
s
s
i
f
i
c
a
t
i
on.
F
oc
us
e
s
on
c
ol
or
f
e
a
t
ur
e
e
xt
r
a
c
t
i
on a
nd e
va
l
ua
t
e
s
c
l
a
s
s
i
f
i
c
a
t
i
on a
c
c
ur
a
c
y.
A
ut
om
a
t
i
on
s
pe
e
ds
up
di
s
e
a
s
e
c
l
a
s
s
i
f
i
c
a
t
i
on
pr
oc
e
s
s
.
C
ol
or
f
e
a
t
ur
e
s
a
i
d
i
n
i
de
nt
i
f
yi
ng
s
pe
c
i
f
i
c
di
s
e
a
s
e
s
.
A
c
c
ur
a
c
y c
a
n
be
a
f
f
e
c
t
e
d
by
va
r
yi
ng
l
i
ght
i
ng
c
ondi
t
i
ons
a
nd
i
m
a
ge
qua
l
i
t
y.
[
6]
I
nt
r
oduc
e
s
a
D
C
N
N
m
ode
l
w
i
t
h
de
e
pe
r
s
e
pa
r
a
bl
e
c
onvol
ut
i
ons
f
or
r
e
duc
e
d
pa
r
a
m
e
t
e
r
s
a
nd
i
t
e
r
a
t
i
on
t
i
m
e
.
C
om
bi
ne
s
de
e
p
f
e
a
t
ur
e
s
w
i
t
h
L
B
P
f
e
a
t
ur
e
s
f
or
e
nha
nc
e
d
a
c
c
ur
a
c
y.
D
e
e
pe
r
s
e
pa
r
a
bl
e
c
onvol
ut
i
ons
r
e
duc
e
m
ode
l
c
om
pl
e
xi
t
y.
L
B
P
e
nha
nc
e
s
l
oc
a
l
i
nf
or
m
a
t
i
on
c
a
pt
ur
e
.
M
ode
l
a
r
c
hi
t
e
c
t
ur
e
c
om
pl
e
xi
t
y
m
i
ght
i
m
pa
c
t
t
r
a
i
ni
ng t
i
m
e
.
3.
C
O
N
C
L
U
S
I
O
N
I
n
c
onc
lu
s
io
n, t
hi
s
s
ur
ve
y ha
s
pr
ovi
de
d a
c
om
p
r
e
he
ns
iv
e
ove
r
vi
e
w
of
t
he
c
r
it
ic
a
l
r
ol
e
t
ha
t
a
ut
om
a
te
d
di
s
e
a
s
e
d
e
te
c
ti
on
pl
a
ys
in
th
e
r
e
a
lm
of
a
gr
ic
ul
tu
r
e
.
T
he
de
tr
im
e
nt
a
l
im
pa
c
t
of
unde
te
c
te
d
pl
a
nt
di
s
e
a
s
e
s
on
a
gr
ic
ul
tu
r
a
l
pr
oduc
ti
vi
ty
a
nd
f
ood
s
e
c
ur
it
y
unde
r
s
c
or
e
s
th
e
ur
ge
nc
y
of
a
dopt
in
g
e
f
f
ic
ie
nt
a
nd
a
c
c
ur
a
te
id
e
nt
if
ic
a
ti
on
m
e
th
ods
.
T
he
ju
xt
a
po
s
it
io
n
of
tr
a
di
ti
ona
l
m
a
nua
l
a
ppr
oa
c
he
s
w
it
h
a
dva
nc
e
d
im
a
ge
pr
oc
e
s
s
in
g
a
nd
de
e
p
le
a
r
ni
ng
te
c
hni
que
s
hi
ghl
ig
ht
s
th
e
tr
a
ns
f
or
m
a
ti
ve
p
ot
e
nt
ia
l
of
a
ut
om
a
ti
on.
T
he
s
ur
ve
y
of
r
e
la
te
d
w
or
k
s
how
c
a
s
e
s
th
e
di
ve
r
s
e
m
e
th
odol
ogi
e
s
,
pa
r
ti
c
ul
a
r
ly
ne
ur
a
l
ne
twor
ks
a
nd
C
N
N
s
,
th
a
t
a
r
e
r
e
s
ha
pi
ng
di
s
e
a
s
e
d
e
te
c
ti
on.
T
he
in
te
gr
a
ti
on
of
im
a
ge
pr
oc
e
s
s
in
g
in
tr
ic
a
c
ie
s
,
pr
e
-
pr
oc
e
s
s
in
g
te
c
hni
que
s
,
a
nd
f
e
a
tu
r
e
e
xt
r
a
c
ti
on
m
e
c
ha
ni
s
m
s
w
it
h
d
e
e
p
le
a
r
ni
ng
m
ode
ls
ha
s
e
m
e
r
ge
d
a
s
a
pr
om
is
in
g
a
ve
nue
f
or
a
c
hi
e
vi
ng
a
c
c
ur
a
te
a
nd
ti
m
e
ly
id
e
nt
if
ic
a
ti
on.
D
e
s
pi
te
th
e
s
tr
id
e
s
m
a
d
e
,
th
e
c
h
a
ll
e
nge
of
e
a
r
ly
-
s
ta
ge
di
s
e
a
s
e
de
te
c
ti
on
r
e
m
a
in
s
,
w
a
r
r
a
nt
in
g
f
ur
th
e
r
r
e
s
e
a
r
c
h
a
nd
in
nova
ti
ve
s
ol
ut
io
ns
.
A
s
t
e
c
hnol
ogy
c
ont
in
ue
s
to
e
vol
ve
,
th
e
in
s
ig
ht
s
pr
e
s
e
nt
e
d
in
th
is
s
ur
ve
y
e
m
pha
s
iz
e
th
e
c
ont
in
uous
ne
e
d
to
ha
r
ne
s
s
a
ut
om
a
ti
on
f
or
pl
a
nt
di
s
e
a
s
e
de
te
c
ti
on,
c
ont
r
ib
ut
in
g s
ig
ni
f
ic
a
nt
ly
t
o gl
oba
l
a
gr
ic
ul
tu
r
a
l
s
us
ta
in
a
bi
li
ty
a
nd f
ood s
e
c
ur
it
y.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
.
14
, N
o.
3
,
J
une
20
25
:
1719
-
1726
1724
A
C
K
N
O
WL
E
D
G
E
M
E
N
T
We
w
oul
d
li
ke
to
e
xpr
e
s
s
our
s
in
c
e
r
e
gr
a
ti
tu
de
to
a
ll
th
os
e
w
ho
ha
ve
s
uppor
te
d
a
nd
c
ont
r
ib
ut
e
d
to
th
is
r
e
s
e
a
r
c
h
pr
oj
e
c
t.
P
r
im
a
r
il
y,
we
e
xt
e
nd
our
he
a
r
t
f
e
lt
th
a
n
ks
to
our
gui
de
f
or
hi
s
unw
a
ve
r
in
g
gui
da
nc
e
,
in
va
lu
a
bl
e
i
ns
ig
ht
s
, a
nd e
n
c
our
a
ge
m
e
nt
t
hr
oughout t
he
r
e
s
e
a
r
c
h
pr
oc
e
s
s
.
F
U
N
D
I
N
G
I
N
F
O
R
M
A
T
I
O
N
N
o f
undi
ng i
s
r
a
is
e
d f
or
t
hi
s
r
e
s
e
a
r
c
h.
A
U
T
H
O
R
C
O
N
T
R
I
B
U
T
I
O
N
S
S
T
A
T
E
M
E
N
T
T
hi
s
jo
ur
na
l
us
e
s
th
e
C
ont
r
ib
ut
or
R
ol
e
s
T
a
xonomy
(
C
R
e
d
iT
)
to
r
e
c
ogni
z
e
in
di
vi
dua
l
a
ut
hor
c
ont
r
ib
ut
io
ns
, r
e
duc
e
a
ut
hor
s
hi
p di
s
put
e
s
,
a
nd f
a
c
il
it
a
te
c
ol
la
bo
r
a
ti
on.
N
am
e
o
f
A
u
t
h
or
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
S
hi
lp
a
P
a
ti
l
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
A
s
hokkuma
r
P.
S
undr
a
m
m
a
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
onc
e
pt
ua
l
i
z
a
t
i
on
M
:
M
e
t
hodol
ogy
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
da
t
i
on
Fo
:
Fo
r
m
a
l
a
na
l
ys
i
s
I
:
I
nve
s
t
i
ga
t
i
on
R
:
R
e
s
our
c
e
s
D
:
D
a
t
a
C
ur
a
t
i
on
O
:
W
r
i
t
i
ng
-
O
r
i
gi
na
l
D
r
a
f
t
E
:
W
r
i
t
i
ng
-
R
e
vi
e
w
&
E
di
t
i
ng
Vi
:
Vi
s
ua
l
i
z
a
t
i
on
Su
:
Su
pe
r
vi
s
i
on
P
:
P
r
oj
e
c
t
a
dm
i
ni
s
t
r
a
t
i
on
Fu
:
Fu
ndi
ng a
c
qui
s
i
t
i
on
C
O
N
F
L
I
C
T
O
F
I
N
T
E
R
E
S
T
S
T
A
T
E
M
E
N
T
A
ut
hor
s
s
ta
te
no c
onf
li
c
t
of
in
te
r
e
s
t.
I
N
F
O
R
M
E
D
C
O
N
S
E
N
T
W
e
ha
ve
obt
a
in
e
d i
nf
or
m
e
d c
ons
e
nt
f
r
om
a
ll
i
ndi
vi
dua
ls
i
nc
lu
de
d i
n t
hi
s
s
tu
dy.
D
A
T
A
A
V
A
I
L
A
B
I
L
I
T
Y
D
a
ta
s
e
ts
ut
il
iz
e
d i
n t
hi
s
r
e
s
e
a
r
c
h
a
r
e
c
it
e
d i
n r
e
f
e
r
e
nc
e
[
1]
,
[
36]
,
[
44]
.
R
E
F
E
R
E
N
C
E
S
[
1]
X
.
Z
ha
ng,
Y
.
Q
i
a
o,
F
.
M
e
ng,
C
.
F
a
n,
a
nd
M
.
Z
ha
ng,
“
I
de
nt
i
f
i
c
a
t
i
on
of
m
a
i
z
e
l
e
a
f
di
s
e
a
s
e
s
us
i
ng
i
m
pr
ove
d
de
e
p
c
onvol
ut
i
ona
l
ne
ur
a
l
ne
t
w
or
ks
,”
I
E
E
E
A
c
c
e
s
s
, vol
. 6, pp. 30370
–
30377, 2018, doi
:
10.1109/
A
C
C
E
S
S
.2018.2844405.
[
2]
J
.
G
.
A
.
B
a
r
be
do,
“
I
m
pa
c
t
of
da
t
a
s
e
t
s
i
z
e
a
nd
va
r
i
e
t
y
on
t
he
e
f
f
e
c
t
i
ve
ne
s
s
of
d
e
e
p
l
e
a
r
ni
ng
a
nd
t
r
a
ns
f
e
r
l
e
a
r
ni
ng
f
or
pl
a
nt
d
i
s
e
a
s
e
c
l
a
s
s
i
f
i
c
a
t
i
on,”
C
om
put
e
r
s
and E
l
e
c
t
r
oni
c
s
i
n A
gr
i
c
ul
t
ur
e
, vol
. 153, pp. 46
–
53,
O
c
t
. 2018, doi
:
10.1016/
j
.c
om
pa
g.2018.08.013.
[
3]
U
.
P
.
S
i
ngh,
S
.
S
.
C
houha
n,
S
.
J
a
i
n,
a
nd
S
.
J
a
i
n,
“
M
ul
t
i
l
a
ye
r
c
onvol
ut
i
on
ne
ur
a
l
ne
t
w
or
k
f
o
r
t
he
c
l
a
s
s
i
f
i
c
a
t
i
on
of
m
a
ngo
l
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f
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a
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ba
s
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d
pl
a
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di
s
e
a
s
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de
t
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c
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t
a
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e
p
l
e
a
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ba
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d
a
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oa
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f
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a
nt
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s
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i
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t
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a
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ur
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xt
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s
f
or
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nt
l
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s
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a
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m
a
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m
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c
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ba
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d
a
ppr
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c
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f
or
l
e
a
f
di
s
e
a
s
e
of
s
uga
r
c
a
ne
c
l
a
s
s
i
f
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a
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i
on,”
i
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I
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E
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I
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r
nat
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C
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C
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m
uni
c
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Sy
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T
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Evaluation Warning : The document was created with Spire.PDF for Python.
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pr
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ur
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hni
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)
1725
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V
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:
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m
bl
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a
ppr
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c
h
us
i
ng
c
nn
a
nd
s
vm
w
e
i
ght
e
d
a
ve
r
a
g
e
e
n
s
e
m
bl
e
l
e
a
r
ni
ng
f
or
s
uga
r
c
a
ne
l
e
a
f
di
s
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a
s
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de
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e
c
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i
on,”
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I
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r
nat
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C
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e
r
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nc
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Su
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ai
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C
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D
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C
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m
uni
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di
s
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a
s
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r
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l
e
a
f
i
m
a
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s
ba
s
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ut
i
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ne
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a
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E
E
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M
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r
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a
f
di
s
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a
s
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de
t
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c
t
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on
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c
l
a
s
s
i
f
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c
a
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e
p
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ur
a
l
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e
t
w
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m
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hi
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e
l
e
a
r
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m
a
ge
pr
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s
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w
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t
h
pr
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c
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s
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on
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gr
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I
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gr
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ul
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v
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T
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a
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f
i
c
a
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I
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E
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t
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,
E
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c
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O
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T
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c
l
a
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s
i
f
i
c
a
t
i
on
of
c
i
t
r
us
pl
a
nt
di
s
e
a
s
e
s
us
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i
m
a
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pr
oc
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s
s
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t
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c
hni
que
s
:
a
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vi
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w
,”
C
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e
r
s
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E
l
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. K
a
r
a
nt
oum
a
ni
s
,
M
.
L
out
a
, a
nd
N
. P
l
o
s
ka
s
,
“
M
a
c
hi
ne
l
e
a
r
ni
ng a
n
d de
e
p l
e
a
r
ni
ng f
or
pl
a
nt
di
s
e
a
s
e
c
l
a
s
s
i
f
i
c
a
t
i
on
a
n
d
de
t
e
c
t
i
on,”
I
E
E
E
A
c
c
e
s
s
, vol
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:
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A
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gum
a
nd
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.
S
ye
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“
G
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t
t
-
C
M
N
e
t
v3:
pe
ppe
r
l
e
a
f
di
s
e
a
s
e
c
l
a
s
s
i
f
i
c
a
t
i
on
us
i
ng
os
pr
e
y
opt
i
m
i
z
a
t
i
on,”
I
E
E
E
A
c
c
e
s
s
,
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C
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Z
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i
ng,
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“
T
om
a
t
o
l
e
a
f
di
s
e
a
s
e
i
de
nt
i
f
i
c
a
t
i
on
b
y
r
e
s
t
r
uc
t
ur
e
d
de
e
p
r
e
s
i
dua
l
de
n
s
e
ne
t
w
or
k,”
I
E
E
E
A
c
c
e
s
s
, vol
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
,
V
ol
.
14
, N
o.
3
,
J
une
20
25
:
1719
-
1726
1726
[
44]
X
.
L
i
u,
W
.
M
i
n,
S
.
M
e
i
,
L
.
W
a
ng,
a
nd
S
.
J
i
a
ng,
“
P
l
a
nt
di
s
e
a
s
e
r
e
c
ogni
t
i
on:
a
l
a
r
ge
-
s
c
a
l
e
be
nc
hm
a
r
k
da
t
a
s
e
t
a
nd
a
vi
s
ua
l
r
e
gi
on
a
nd
l
os
s
r
e
w
e
i
ght
i
ng
a
ppr
oa
c
h,”
I
E
E
E
T
r
ans
ac
t
i
ons
on
I
m
age
P
r
o
c
e
s
s
i
ng
,
vol
.
30,
pp.
2003
–
2015,
2021,
doi
:
10.1109/
T
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P
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[
45]
P
.
P
uj
a
r
,
A
.
K
um
a
r
,
a
nd
V
.
K
um
a
r
,
“
E
f
f
i
c
i
e
nt
pl
a
nt
l
e
a
f
de
t
e
c
t
i
on
t
hr
ough
m
a
c
hi
ne
l
e
a
r
ni
ng
a
ppr
oa
c
h
b
a
s
e
d
on
c
or
n
l
e
a
f
i
m
a
g
e
c
l
a
s
s
i
f
i
c
a
t
i
on,”
I
A
E
S
I
nt
e
r
nat
i
onal
J
our
nal
of
A
r
t
i
f
i
c
i
al
I
n
t
e
l
l
i
ge
nc
e
(
I
J
-
A
I
)
,
vol
.
13,
no.
1,
p
p.
1139
-
1148
,
M
a
r
.
2024,
doi
:
10.11591/
i
j
a
i
.v13.i
1.pp1139
-
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[
46]
S
.
H
.
S
r
e
e
dha
r
a
,
V
.
K
um
a
r
,
a
nd
S
.
S
a
l
m
a
,
“
E
f
f
i
c
i
e
nt
bi
g
da
t
a
c
l
us
t
e
r
i
ng
us
i
ng
a
dhoc
f
uz
z
y
c
m
e
a
ns
a
nd
a
ut
o
-
e
nc
ode
r
C
N
N
,”
i
n
I
nv
e
nt
i
v
e
C
om
put
at
i
on and I
nf
or
m
at
i
on T
e
c
hnol
ogi
e
s
, 2023, pp. 353
–
368. doi
:
10.1007/
978
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981
-
19
-
7402
-
1_25.
[
47]
A
.
A
.
A
l
A
r
f
a
j
e
t
al
.
,
“
M
ul
t
i
-
s
t
e
p
pr
e
pr
oc
e
s
s
i
ng
w
i
t
h
un
e
t
s
e
gm
e
nt
a
t
i
on
a
nd
t
r
a
ns
f
e
r
l
e
a
r
ni
ng
m
ode
l
f
or
pe
ppe
r
be
l
l
l
e
a
f
di
s
e
a
s
e
de
t
e
c
t
i
on,”
I
E
E
E
A
c
c
e
s
s
, vol
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[
48]
L
.
T
i
a
n
e
t
al
.
,
“
V
M
F
-
s
s
d:
a
nove
l
v
-
s
pa
c
e
ba
s
e
d
m
ul
t
i
-
s
c
a
l
e
f
e
a
t
ur
e
f
us
i
on
s
s
d
f
or
a
ppl
e
l
e
a
f
di
s
e
a
s
e
de
t
e
c
t
i
on,”
I
E
E
E
/
A
C
M
T
r
ans
ac
t
i
ons
on
C
om
put
at
i
onal
B
i
ol
ogy
and
B
i
oi
nf
or
m
at
i
c
s
,
vol
.
20,
no.
3,
pp.
2016
–
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2023,
doi
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T
C
B
B
.2022.3229114.
B
I
O
G
R
A
P
H
I
E
S
O
F
A
U
T
H
O
R
S
Shilpa Pat
il
earned her B
achelor'
s
of Engi
neering
(
B
.
E
.
degree
)
in
electroni
cs and
communi
cation
engineerin
g
from
Visvesvaraya
Technological
Unive
rsity
,
Belagavi
in
2006.
She
has
obtained
her
master'
s
degree
in
M.Tech
.
(
communi
cation
syst
ems
)
from
Visvesvaraya
Technological
University
,
Belagavi
in
2010.
C
urrently
she
is
a
research
s
cholar
at
Visvesvaraya
Technological
University
,
Belagavi
doing
her
Ph
.D
.
in
Department
of
Electronics
and
Communication
Engineering
,
Visvesvaraya
Te
chnological
University
-
Resear
ch Resou
rce Ce
ntre,
Visvesvaraya Technological Un
iversity
, Belagavi and also working
as
assistan
t
professor
in
BLDEA’s
V
P
Dr.
P
G
Halakatti
Colle
ge
of
Engineering
and
Technology.
She
has
attended
many
workshops
and
induction
progra
ms
conducted
by
various
universities.
Her
areas
of
interest
are
communi
cation
systems
,
interne
t
of
things
,
and
machine
learning
. She can be contacted a
t email: shilpapatil122024@
gmail.com.
Dr.
Ashokkumar
P
.
Sundramma
is
a
Professor
in
the
Departmen
t
of
Computer
Scienc
e
and
Engine
ering
at
ACS
College
of
Engine
ering,
Bangal
ore
with
an
exper
ience
of
22
years
in
teaching
.
He
is
qualified
in
Bachelor
of
Engineering
(B.E.
d
egree)
in
electroni
cs
and
communi
cation
engineerin
g
from
Kuvempu
University
and
mas
t
er
deg
rees
in
c
omput
er
applica
tion
to
in
dustr
ial
driv
es
from
Vi
svesv
araya
T
echno
logic
al
U
niversi
ty,
Bela
gavi
,
a
nd
Ph.D
.
i
n
De
partm
ent
of
In
forma
tion
and
Commu
nicat
ion
Engin
eeri
ng
and
spec
ializ
ation
in
compute
r
sci
ence
a
nd
en
ginee
ring
f
rom
AN
NA
Un
ivers
ity,
C
henna
i.
His
are
as
of
i
ntere
st
ar
e
big data an
alyt
ics, ma
chine l
earni
ng, int
erne
t of thin
gs, web s
ervic
es se
curity, i
mage p
roces
sing,
e
-
comme
rce,
a
nd
QoS
at
tribu
tes
and
n
etwor
king.
He
can
b
e
contact
ed
at
em
ail:
ashokdb
it201
7@
gmai
l.com
.
Evaluation Warning : The document was created with Spire.PDF for Python.