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2334
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Plant disea
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Pre
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NSEM
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Un
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m
a
1.
I
NT
RO
D
UCT
I
O
N
Ag
r
icu
ltu
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e
p
lay
s
a
f
u
n
d
am
e
n
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o
le
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s
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s
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in
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m
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life
b
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,
an
d
f
u
el
ess
en
tial
f
o
r
s
u
r
v
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al
[
1
]
.
Su
s
tain
ab
le
ag
r
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ltu
r
al
p
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ac
tices
ar
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cr
u
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e
ec
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n
o
m
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o
f
f
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s
[
2
]
.
T
h
e
im
p
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r
tan
ce
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f
ag
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ltu
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e
in
i
m
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co
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m
u
n
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ties
h
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b
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wid
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ec
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g
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ized
[
3
]
.
B
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p
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u
s
tain
ab
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p
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ac
tices,
ag
r
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.
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n
ts
[
4
]
–
[
6
]
.
T
r
a
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m
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
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I
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N:
2088
-
8
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P
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(
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2335
[
7
]
.
V
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[
8
]
.
A
d
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ti
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y
,
v
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a
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[
9
]
.
T
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a
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,
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il
l
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n
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e
[
7
]
.
T
h
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l
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m
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ti
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n
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v
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as
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d
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te
c
t
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o
n
[
1
0
]
,
[
1
1
]
.
B
y
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n
t
e
g
r
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ti
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g
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tellig
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No
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3
2
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p
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wh
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-
ter
m
m
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(
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STM
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etwo
r
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ical
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p
r
e
cise
ir
r
ig
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n
r
eq
u
i
r
em
en
ts
[
1
2
]
.
A
n
o
t
h
e
r
s
y
s
te
m
u
s
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s
w
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(
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S
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m
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b
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ti
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n
[
1
3
]
.
A
d
d
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t
i
o
n
a
l
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y
,
a
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a
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2
5
6
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b
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t
a
[
1
4
]
.
T
h
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t
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r
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e
t
w
o
r
k
s
(
C
N
Ns
)
,
a
r
e
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n
c
r
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as
in
g
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s
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f
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s
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f
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c
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ti
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f
p
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t
[
1
5
]
,
[
1
6
]
.
T
h
e
s
e
t
e
c
h
n
o
l
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g
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l
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n
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is
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t
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g
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s
,
p
r
o
v
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d
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g
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m
o
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f
f
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c
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t
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n
d
l
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l
a
b
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at
i
v
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t
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m
a
n
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a
l
m
o
n
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t
o
r
i
n
g
[
1
7
]
.
R
e
c
e
n
t
d
is
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a
s
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s
a
d
v
a
n
c
em
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n
t
s
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c
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m
p
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t
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a
n
d
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ta
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d
t
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t
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o
m
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s
d
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c
t
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n
o
f
p
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a
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d
is
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t
h
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h
t
h
e
a
n
a
l
y
s
i
s
o
f
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m
a
g
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c
a
p
t
u
r
e
d
b
y
o
p
t
i
c
a
l
s
e
n
s
o
r
s
,
a
l
l
o
w
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f
o
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t
i
m
el
y
d
i
a
g
n
o
s
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s
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f
c
r
o
p
d
is
e
as
e
s
[
1
8
]
.
F
u
r
t
h
e
r
m
o
r
e
,
t
h
e
u
s
e
o
f
c
o
m
p
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t
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v
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s
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h
n
i
q
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e
s
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n
c
o
m
b
i
n
a
t
i
o
n
w
it
h
A
I
h
a
s
f
a
c
i
l
it
a
t
e
d
t
h
e
e
a
r
l
y
d
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t
ec
t
i
o
n
o
f
p
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a
n
t
d
i
s
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as
e
s
,
a
ll
o
w
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g
f
o
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t
im
e
l
y
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n
t
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v
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n
t
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s
t
o
m
it
i
g
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t
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t
h
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a
d
v
e
r
s
e
e
f
f
e
c
ts
o
f
d
i
s
e
a
s
e
s
[
1
9
]
.
V
i
s
i
o
n
t
r
a
n
s
f
o
r
m
e
r
s
(
Vi
T
s
)
h
av
e
e
m
e
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g
e
d
a
s
a
s
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g
n
i
f
ic
a
n
t
a
d
v
a
n
c
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m
e
n
t
i
n
t
h
e
f
i
el
d
o
f
c
o
m
p
u
t
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r
v
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s
i
o
n
,
b
u
i
l
d
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n
g
o
n
t
h
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s
u
c
c
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s
o
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f
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m
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f
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o
m
n
a
t
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a
l
l
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n
g
u
a
g
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p
r
o
c
e
s
s
i
n
g
(
N
L
P
)
[
2
0
]
.
T
h
e
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t
r
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s
f
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r
m
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r
s
,
s
u
c
h
V
i
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s
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h
a
v
e
d
e
m
o
n
s
t
r
a
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d
i
m
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s
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f
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m
a
n
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o
u
s
m
a
c
h
i
n
e
v
i
s
i
o
n
t
as
k
s
[
2
1
]
.
V
i
T
s
s
h
o
w
c
as
e
d
t
h
e
i
r
a
b
i
l
it
y
to
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-
of
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t
w
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k
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w
h
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l
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r
e
q
u
i
r
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f
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c
o
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p
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i
o
n
a
l
r
e
s
o
u
r
c
es
f
o
r
t
r
a
i
n
i
n
g
[
2
2
]
.
F
u
r
t
h
e
r
m
o
r
e
,
V
i
T
s
h
a
v
e
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n
a
p
p
l
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d
t
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w
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d
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a
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p
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o
n
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p
p
l
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c
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ti
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n
s
,
h
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l
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v
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s
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t
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t
y
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n
d
p
o
t
e
n
ti
a
l
[
2
3
]
.
V
i
s
i
o
n
t
r
a
n
s
f
o
r
m
e
r
s
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it
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v
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t
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o
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a
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tw
o
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k
s
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p
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V
i
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t
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g
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t
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d
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s
f
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c
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b
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s
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g
e
n
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li
z
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ti
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e
f
f
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n
c
y
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n
d
d
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s
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a
p
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s
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a
v
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t
h
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y
f
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r
f
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f
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el
d
o
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c
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m
p
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v
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s
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.
W
ith
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an
ce
m
en
ts
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ee
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lear
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g
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p
ar
ticu
la
r
ly
th
e
em
er
g
en
ce
o
f
ViT
s
,
th
er
e
h
as
b
ee
n
a
s
ig
n
if
ican
t
s
h
if
t
to
war
d
s
au
to
m
atin
g
th
is
p
r
o
ce
s
s
.
W
e
ex
p
lo
r
ed
r
ec
e
n
t
ar
ticles
ap
p
ly
in
g
ViT
s
in
p
lan
t
d
is
ea
s
e
d
etec
tio
n
.
A
s
m
ar
tp
h
o
n
e
-
b
as
ed
s
o
lu
tio
n
em
p
l
o
y
in
g
ViT
m
o
d
els
is
p
r
o
p
o
s
ed
f
o
r
id
e
n
tify
in
g
h
ea
lth
y
an
d
d
is
ea
s
ed
to
m
ato
p
lan
ts
.
T
h
e
ViT
m
o
d
el,
tr
ain
ed
o
n
a
d
ata
s
et
o
f
to
m
ato
leaf
im
ag
es,
o
u
tp
er
f
o
r
m
s
tr
ad
itio
n
al
C
NN
-
b
ased
ap
p
r
o
ac
h
es,
d
em
o
n
s
tr
atin
g
its
p
o
ten
tial
f
o
r
wi
d
esp
r
ea
d
ad
o
p
tio
n
in
s
m
ar
t
a
g
r
icu
ltu
r
e
s
y
s
tem
s
[
2
4
]
.
B
o
r
h
a
n
i
et
a
l.
[
2
5
]
ex
p
lo
r
es
ViT
s
f
o
r
r
ea
l
-
tim
e
au
to
m
ated
p
lan
t
d
is
ea
s
e
class
if
icatio
n
.
T
h
e
s
tu
d
y
co
m
p
ar
es
ViT
with
tr
ad
itio
n
a
l
C
NN
m
eth
o
d
s
,
h
ig
h
lig
h
tin
g
th
e
tr
ad
e
-
o
f
f
s
b
etwe
en
ac
c
u
r
ac
y
an
d
p
r
e
d
ictio
n
s
p
ee
d
.
I
t
s
u
g
g
ests
p
o
ten
tial
en
h
an
ce
m
en
ts
th
r
o
u
g
h
t
h
e
co
m
b
in
atio
n
o
f
atten
tio
n
b
lo
c
k
s
with
C
NN
b
lo
ck
s
.
I
n
a
d
if
f
er
en
t
ap
p
r
o
ac
h
,
au
th
o
r
s
in
tr
o
d
u
ce
a
f
in
e
-
t
u
n
ed
tech
n
i
q
u
e
ca
lled
Gr
ee
n
ViT
f
o
r
d
etec
tin
g
p
lan
t
in
f
ec
tio
n
s
an
d
d
is
ea
s
es.
B
y
lev
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ag
in
g
ViT
s
,
Gr
ee
n
ViT
o
v
er
c
o
m
es
th
e
lim
itatio
n
s
ass
o
ciate
d
with
C
NN
-
b
ased
m
o
d
els,
d
em
o
n
s
tr
atin
g
s
u
p
er
i
o
r
p
er
f
o
r
m
an
ce
in
d
etec
tin
g
p
lan
t
d
i
s
ea
s
es
[
2
6
]
.
Ad
d
r
ess
in
g
th
e
n
ee
d
f
o
r
en
h
an
ce
d
f
ea
tu
r
e
ex
tr
ac
tio
n
,
r
esear
c
h
er
s
p
r
o
p
o
s
es
an
ed
g
e
-
f
ea
tu
r
e
g
u
id
an
ce
m
o
d
u
le
(
E
FG)
to
im
p
r
o
v
e
th
e
f
ea
t
u
r
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ex
tr
ac
tio
n
ca
p
ab
ilit
ies
o
f
ViT
-
b
ased
m
eth
o
d
s
,
lead
in
g
to
im
p
r
o
v
ed
p
er
f
o
r
m
an
ce
ac
r
o
s
s
m
u
ltip
le
d
atasets
[
2
7
]
.
Fo
r
ca
s
s
av
a
leaf
d
is
ea
s
e
d
ete
ctio
n
,
ViT
was
u
s
ed
with
tec
h
n
iq
u
es
s
u
ch
as
least
im
p
o
r
ta
n
t
atten
tio
n
p
r
u
n
in
g
(
L
eI
AP)
an
d
s
p
ar
s
e
m
at
r
ix
-
m
atr
ix
m
u
ltip
licatio
n
(
SP
MM
)
,
r
esu
ltin
g
in
s
ig
n
if
ican
t
im
p
r
o
v
em
en
ts
in
ac
cu
r
ac
y
an
d
ef
f
icien
c
y
[
2
8
]
.
T
h
e
s
tu
d
y
o
n
p
lan
t
d
is
ea
s
e
class
if
ic
atio
n
p
r
esen
ts
a
n
o
v
el
ap
p
r
o
ac
h
th
at
in
teg
r
ates
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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t J E
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&
C
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m
p
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Vo
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15
,
No
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2
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Ap
r
il
20
25
:
2
3
3
4
-
2
3
4
4
2336
tr
an
s
f
er
lear
n
in
g
with
ViT
s
.
T
h
is
h
y
b
r
id
m
o
d
el
ac
h
ie
v
es
im
p
r
ess
iv
e
v
alid
atio
n
ac
cu
r
ac
y
,
s
u
r
p
ass
in
g
tr
ad
itio
n
al
tr
an
s
f
er
lear
n
in
g
-
b
ased
m
o
d
els.
T
h
e
ef
f
icien
cy
o
f
ViT
s
in
ex
tr
ac
tin
g
d
ee
p
f
ea
tu
r
es
f
r
o
m
p
lan
t
leav
es
is
h
ig
h
lig
h
ted
as
a
k
ey
f
ac
to
r
in
th
e
m
o
d
el'
s
s
u
p
er
i
o
r
p
er
f
o
r
m
an
ce
[
2
9
]
.
I
n
s
u
m
m
ar
y
,
th
e
r
e
v
iewe
d
liter
atu
r
e
h
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h
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h
ts
th
e
g
r
o
w
in
g
in
ter
est
in
lev
er
ag
in
g
ViT
s
f
o
r
p
lan
t
d
is
ea
s
e
d
etec
tio
n
an
d
class
if
icatio
n
.
T
h
ese
s
tu
d
ies
co
n
tr
ib
u
te
to
ad
v
an
cin
g
p
r
ec
is
io
n
ag
r
icu
ltu
r
e
b
y
p
r
o
v
id
in
g
ef
f
icien
t
an
d
ac
c
u
r
ate
s
o
lu
tio
n
s
f
o
r
au
to
m
ated
d
is
ea
s
e
id
en
tific
atio
n
.
Fu
r
t
h
er
r
esear
c
h
in
th
is
ar
ea
co
u
ld
e
x
p
lo
r
e
o
p
tim
izatio
n
tech
n
iq
u
es,
m
o
d
el
in
ter
p
r
etab
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y
,
an
d
r
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l
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w
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lo
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n
t
s
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n
ar
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s
to
en
h
an
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th
e
p
r
ac
tical
ap
p
lic
ab
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y
o
f
ViT
s
in
ag
r
icu
ltu
r
al
s
y
s
tem
s
.
T
h
e
a
i
m
o
f
t
h
i
s
p
a
p
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r
i
s
t
o
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V
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p
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ts
a
v
e
n
u
es
f
o
r
f
u
t
u
r
e
r
e
s
e
a
r
c
h
.
T
h
r
o
u
g
h
t
h
i
s
s
t
r
u
c
t
u
r
e
d
a
p
p
r
o
a
c
h
,
t
h
e
p
a
p
e
r
a
i
m
s
t
o
c
o
n
t
r
i
b
u
t
e
t
o
t
h
e
a
d
v
a
n
c
e
m
e
n
t
o
f
p
l
a
n
t
d
i
s
e
as
e
d
e
t
ec
t
i
o
n
m
e
t
h
o
d
s
a
n
d
t
h
e
p
r
o
m
o
t
i
o
n
o
f
s
u
s
ta
i
n
a
b
l
e
a
g
r
ic
u
l
t
u
r
a
l
p
r
a
ct
i
ce
s
.
2.
M
E
T
H
O
D
I
n
th
is
m
eth
o
d
o
lo
g
ical
s
ec
tio
n
,
we
p
r
esen
t
th
e
ap
p
r
o
ac
h
es
an
d
to
o
ls
u
tili
ze
d
to
co
n
d
u
ct
o
u
r
s
tu
d
y
.
W
e
b
eg
in
b
y
in
tr
o
d
u
cin
g
th
e
c
en
tr
al
d
ataset
th
at
f
o
r
m
s
th
e
b
asis
o
f
o
u
r
an
aly
s
es,
d
etailin
g
its
co
m
p
o
s
itio
n
an
d
p
r
ep
r
o
ce
s
s
in
g
m
eth
o
d
s
.
Su
b
s
eq
u
en
tly
,
we
d
elv
e
in
to
a
n
in
-
d
ep
th
e
x
p
lo
r
atio
n
o
f
t
h
e
in
n
o
v
ativ
e
ViT
ar
ch
itectu
r
e,
a
s
ig
n
if
ica
n
t
ad
v
an
ce
m
en
t
in
c
o
m
p
u
ter
v
is
io
n
.
T
h
e
ViT
d
is
tin
g
u
is
h
es
its
elf
th
r
o
u
g
h
its
ab
ilit
y
to
ef
f
ec
tiv
ely
ca
p
t
u
r
e
lo
n
g
-
r
a
n
g
e
d
ep
en
d
en
cies
in
im
ag
e
d
ata
u
s
in
g
s
elf
-
atten
tio
n
m
ec
h
a
n
is
m
s
,
th
er
eb
y
o
f
f
er
i
n
g
p
r
o
m
is
in
g
av
en
u
es
f
o
r
f
ea
t
u
r
e
ex
tr
ac
tio
n
an
d
p
atter
n
r
ec
o
g
n
itio
n
.
T
h
is
m
eth
o
d
o
lo
g
ical
i
n
tr
o
d
u
ctio
n
s
ets
th
e
s
tag
e
f
o
r
u
n
d
er
s
tan
d
in
g
th
e
a
n
aly
s
es a
n
d
f
in
d
in
g
s
p
r
esen
ted
i
n
th
is
p
ap
er
.
2
.
1
.
P
r
o
po
s
ed
s
o
lutio
n
T
o
o
v
er
co
m
e
th
e
lim
itatio
n
s
o
f
tr
a
d
itio
n
al
p
lan
t
d
is
ea
s
e
d
e
tectio
n
m
eth
o
d
s
,
th
is
s
tu
d
y
p
r
o
p
o
s
es
th
e
u
s
e
o
f
ViT
s
.
ViT
s
lev
er
ag
e
s
elf
-
atten
tio
n
m
ec
h
an
is
m
s
to
ca
p
tu
r
e
in
tr
icate
p
atter
n
s
an
d
lo
n
g
-
r
an
g
e
d
ep
en
d
e
n
cies
in
p
lan
t
im
a
g
es,
o
f
f
er
in
g
a
r
o
b
u
s
t
alter
n
ativ
e
to
c
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etw
o
r
k
s
(
C
NNs).
T
h
e
m
eth
o
d
o
l
o
g
y
i
n
v
o
lv
es
tr
ai
n
in
g
a
ViT
m
o
d
el
o
n
a
d
ataset
o
f
d
iv
er
s
e
p
lan
t
im
a
g
es
ca
teg
o
r
i
ze
d
b
y
d
is
ea
s
e
ty
p
e.
B
y
p
ar
titi
o
n
in
g
im
ag
es
in
to
p
atch
es
an
d
ap
p
ly
in
g
s
elf
-
atten
tio
n
m
ec
h
an
is
m
s
,
ViT
s
ca
n
ef
f
ec
tiv
ely
lear
n
co
m
p
lex
f
ea
t
u
r
es
an
d
im
p
r
o
v
e
class
if
icatio
n
ac
cu
r
ac
y
.
T
h
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
in
teg
r
ates
ViT
s
with
ad
v
an
ce
d
d
ata
p
r
ep
r
o
ce
s
s
in
g
an
d
a
u
g
m
en
tatio
n
tech
n
iq
u
es
to
en
h
a
n
ce
m
o
d
el
p
er
f
o
r
m
a
n
ce
an
d
g
en
er
aliza
tio
n
ac
r
o
s
s
d
if
f
er
en
t
p
lan
t sp
ec
ies an
d
d
is
ea
s
e
co
n
d
itio
n
s
.
2
.
2
.
Da
t
a
s
et
T
h
e
d
ataset
f
r
o
m
Kag
g
le
co
n
s
is
ts
o
f
im
ag
es
o
f
p
lan
t
le
av
es
ca
teg
o
r
ized
in
to
8
8
cla
s
s
es
[
3
0
]
,
r
ep
r
esen
tin
g
v
ar
io
u
s
p
lan
t
s
p
ec
ies
an
d
th
eir
h
ea
lth
co
n
d
it
io
n
s
.
T
h
e
d
ataset
u
s
ed
in
t
h
is
s
tu
d
y
co
v
er
s
an
ex
ten
s
iv
e
ar
r
ay
o
f
5
5
class
es
f
r
o
m
th
e
o
r
ig
in
al
d
ataset,
r
ep
r
e
s
en
tin
g
a
s
u
b
s
tan
tial
n
u
m
b
er
o
f
1
4
p
lan
t
s
p
ec
ies
with
8
3
.
6
0
3
im
ag
es.
Fig
u
r
e
1
p
r
esen
ts
a
s
n
ap
s
h
o
t
o
f
r
an
d
o
m
s
am
p
les
f
r
o
m
th
e
d
ataset.
T
h
e
d
ataset
u
tili
ze
d
in
th
is
p
ap
er
was
ex
tr
ac
ted
f
r
o
m
th
e
o
r
ig
in
al
d
atab
ase,
an
d
th
e
im
ag
es
wer
e
au
g
m
en
ted
to
ac
h
iev
e
a
b
alan
ce
d
d
is
tr
ib
u
tio
n
ac
r
o
s
s
all
ca
teg
o
r
ies.
T
h
e
n
ew
d
ataset
en
co
m
p
ass
es
a
wid
e
r
an
g
e
o
f
p
lan
ts
:
ap
p
le,
ca
s
s
av
a,
ch
er
r
y
,
ch
ili,
co
r
n
,
cu
c
u
m
b
e
r
,
g
r
ap
e,
p
o
m
e
g
r
an
ate,
p
o
tato
,
s
o
y
b
ea
n
,
s
tr
awb
er
r
y
,
s
u
g
a
r
ca
n
e
a
n
d
t
o
m
ato
.
W
ith
in
ea
ch
p
lan
t
ca
teg
o
r
y
,
d
if
f
er
en
t
class
es
d
en
o
te
s
p
ec
if
i
c
d
is
ea
s
es
o
r
h
ea
lth
co
n
d
itio
n
s
T
ab
le
1
,
r
esu
ltin
g
in
a
d
iv
er
s
e
co
llectio
n
o
f
c
o
m
p
r
eh
en
s
iv
e
m
ac
h
in
e
lear
n
i
n
g
m
o
d
el
tr
ain
in
g
.
2
.
3
.
Da
t
a
p
re
pro
ce
s
s
ing
As
im
ag
e
p
r
ep
r
o
ce
s
s
in
g
is
a
cr
u
cial
s
tep
in
p
r
ep
ar
in
g
d
ata
f
o
r
m
ac
h
i
n
e
lear
n
in
g
m
o
d
els,
p
ar
ticu
lar
ly
in
co
m
p
u
ter
v
is
io
n
task
s
.
T
h
e
p
r
o
ce
s
s
o
f
ten
in
v
o
lv
es
au
g
m
en
tin
g
th
e
d
ataset
to
en
h
an
ce
th
e
d
iv
er
s
ity
an
d
q
u
an
tity
o
f
tr
ain
in
g
s
am
p
les,
wh
ich
h
elp
s
im
p
r
o
v
e
th
e
r
o
b
u
s
tn
ess
an
d
p
e
r
f
o
r
m
an
ce
o
f
th
e
m
o
d
els.
T
h
e
au
g
m
en
ter
d
ef
i
n
ed
h
er
e
em
p
l
o
y
s
s
ev
er
al
tec
h
n
iq
u
es
u
s
in
g
th
e
im
ag
e
lib
r
ar
y
.
I
t
in
clu
d
es
h
o
r
iz
o
n
tal
f
li
p
p
in
g
(
.
(
0
.
5
)
)
,
wh
ich
r
ev
e
r
s
es
im
ag
es
h
o
r
izo
n
tally
with
a
p
r
o
b
a
b
ilit
y
o
f
5
0
%,
an
d
cr
o
p
p
in
g
(
.
(
=
(
0
,
0
.
1
)
)
)
,
wh
ich
r
an
d
o
m
l
y
r
em
o
v
es
u
p
to
1
0
%
o
f
th
e
im
ag
e’
s
b
o
r
d
er
s
.
C
o
n
tr
ast
ad
ju
s
tm
en
ts
(
.
(
0
.
75
,
1
.
5
)
)
d
y
n
am
ically
alter
th
e
im
ag
e
co
n
tr
ast,
wh
ile
ad
d
i
tiv
e
Gau
s
s
ian
n
o
is
e
(
.
(
=
(
0
,
0
.
05
∗
255
)
)
)
in
tr
o
d
u
ce
s
s
lig
h
t
r
a
n
d
o
m
n
ess
to
p
ix
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
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2088
-
8
7
0
8
P
la
n
t d
is
ea
s
e
d
etec
tio
n
u
s
in
g
visi
o
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tr
a
n
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fo
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mer
s
(
Mh
a
n
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A
li
)
2337
v
alu
es
to
s
im
u
late
r
ea
l
-
wo
r
ld
v
ar
iatio
n
s
.
B
r
ig
h
tn
ess
ch
an
g
es
(
.
(
0
.
8
,
1
.
2
)
)
ad
ju
s
t
th
e
im
ag
e’
s
b
r
ig
h
tn
ess
,
m
a
k
in
g
th
e
m
o
d
el
r
esil
ien
t
t
o
lig
h
tin
g
co
n
d
itio
n
s
.
Fin
ally
,
af
f
i
n
e
tr
an
s
f
o
r
m
atio
n
s
(
.
(
=
(
−
5
,
5
)
,
ℎ
=
(
−
16
,
16
)
)
)
in
v
o
lv
e
r
o
tatin
g
th
e
im
ag
e
with
in
a
r
an
g
e
o
f
-
5
to
5
d
eg
r
ee
s
an
d
s
h
ea
r
in
g
it
b
et
wee
n
-
1
6
an
d
1
6
d
eg
r
ee
s
,
ef
f
ec
tiv
ely
d
is
to
r
tin
g
th
e
im
ag
e
wh
ile
p
r
eser
v
in
g
its
ess
en
tial
f
ea
tu
r
es.
T
h
ese
au
g
m
en
tatio
n
s
co
llectiv
ely
en
s
u
r
e
th
at
th
e
d
ataset
is
v
ar
ied
an
d
co
m
p
r
eh
en
s
iv
e,
wh
ich
is
v
ital
f
o
r
tr
ain
in
g
ef
f
ec
tiv
e
an
d
g
en
er
alize
d
m
o
d
el
s
.
T
h
e
d
is
tr
ib
u
tio
n
o
f
im
ag
es
in
ea
ch
class
o
f
t
h
e
n
ew
d
ataset
is
s
h
o
wn
in
Fig
u
r
e
2
.
Fig
u
r
e
1
.
Sam
p
le
o
f
th
e
d
ataset
Fig
u
r
e
2
.
Dis
tr
ib
u
tio
n
o
f
im
a
g
es in
ea
ch
class
2
.
4
.
Vis
io
n t
ra
ns
f
o
r
m
er
s
T
h
e
ViT
ar
ch
itectu
r
e
r
ep
r
es
en
ts
a
s
ig
n
if
ican
t
ad
v
an
ce
m
en
t
in
th
e
f
ield
o
f
co
m
p
u
t
er
v
is
io
n
,
lev
er
ag
in
g
th
e
s
u
cc
ess
o
f
t
h
e
tr
an
s
f
o
r
m
er
m
o
d
el
in
n
a
tu
r
al
lan
g
u
ag
e
p
r
o
ce
s
s
in
g
task
s
[
2
2
]
.
ViT
h
as
d
em
o
n
s
tr
ated
r
em
ar
k
a
b
le
p
e
r
f
o
r
m
an
ce
in
im
ag
e
class
if
icatio
n
,
e
v
en
s
u
r
p
ass
in
g
tr
ad
itio
n
a
l
ar
ch
itectu
r
es
lik
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
2
,
Ap
r
il
20
25
:
2
3
3
4
-
2
3
4
4
2338
R
esNet
s
[
3
1
]
.
I
n
s
p
ir
ed
b
y
ViT
,
r
esear
ch
er
s
h
av
e
d
ev
elo
p
e
d
v
ar
iatio
n
s
s
u
c
h
as
th
e
s
win
tr
an
s
f
o
r
m
e
r
,
wh
ich
ad
ap
ts
th
e
R
esNet
-
5
0
ar
ch
itectu
r
e
to
cr
ea
te
a
h
ier
ar
ch
ical
ViT
[
3
2
]
.
T
h
ese
ad
ap
tatio
n
s
aim
to
en
h
an
ce
th
e
o
r
ig
in
al
ViT
d
esig
n
b
y
i
n
teg
r
atin
g
m
o
r
e
r
ec
e
n
t
tr
ain
in
g
tec
h
n
iq
u
es
with
o
u
t
in
tr
o
d
u
cin
g
a
d
d
itio
n
al
atten
tio
n
-
b
ased
m
o
d
u
les.
ViT
s
h
av
e
g
ain
ed
p
o
p
u
lar
ity
d
u
e
to
th
eir
s
u
cc
ess
in
v
ar
io
u
s
v
is
io
n
ta
s
k
s
,
lead
in
g
to
th
e
em
er
g
en
ce
o
f
n
o
v
el
ar
ch
itect
u
r
es
lik
e
co
n
v
o
lu
tio
n
al
v
is
io
n
tr
an
s
f
o
r
m
e
r
s
(
C
v
T
)
[
3
3
]
.
C
v
T
co
m
b
in
es
th
e
s
tr
en
g
th
s
o
f
co
n
v
o
lu
tio
n
s
an
d
T
r
an
s
f
o
r
m
e
r
s
to
en
h
an
ce
p
er
f
o
r
m
an
ce
a
n
d
ef
f
icien
cy
.
Ad
d
itio
n
ally
,
ViT
h
as
b
ee
n
ex
p
lo
r
ed
in
d
if
f
er
en
t
d
o
m
ain
s
b
ey
o
n
d
im
ag
e
class
if
icatio
n
,
s
u
ch
as
d
en
s
e
p
r
ed
ictio
n
task
s
[
3
4
]
.
Ov
er
all,
th
e
ViT
ar
ch
itectu
r
e
s
ig
n
if
ies
a
p
iv
o
tal
s
h
if
t
i
n
co
m
p
u
ter
v
i
s
io
n
,
s
h
o
wca
s
in
g
its
v
er
s
atility
an
d
ef
f
ec
tiv
e
n
ess
ac
r
o
s
s
a
wid
e
r
an
g
e
o
f
ap
p
licatio
n
s
.
T
ab
le
1
.
Descr
ip
tiv
e
o
f
th
e
p
lan
t a
n
d
d
is
ea
s
es in
clu
d
ed
in
th
e
d
ataset
P
l
a
n
t
D
i
sea
s
e
s
A
p
p
l
e
B
l
a
c
k
r
o
t
,
r
u
st
,
s
c
a
b
,
h
e
a
l
t
h
y
C
a
ssa
v
a
B
a
c
t
e
r
i
a
l
b
l
i
g
h
t
,
b
r
o
w
n
st
r
e
a
k
d
i
s
e
a
se
,
g
r
e
e
n
mo
t
t
l
e
,
h
e
a
l
t
h
y
,
m
o
s
a
i
c
d
i
se
a
se
C
h
e
r
r
y
H
e
a
l
t
h
y
,
p
o
w
d
e
r
y
mi
l
d
e
w
C
h
i
l
i
H
e
a
l
t
h
y
,
l
e
a
f
c
u
r
l
,
l
e
a
f
sp
o
t
,
w
h
i
t
e
f
l
y
,
y
e
l
l
o
w
i
s
h
C
o
r
n
C
o
mm
o
n
r
u
st
,
g
r
a
y
l
e
a
f
s
p
o
t
,
h
e
a
l
t
h
y
,
n
o
r
t
h
e
r
n
l
e
a
f
b
l
i
g
h
t
C
u
c
u
m
b
e
r
D
i
sea
s
e
d
,
h
e
a
l
t
h
y
G
r
a
p
e
B
l
a
c
k
me
a
sl
e
s,
b
l
a
c
k
r
o
t
,
h
e
a
l
t
h
y
,
l
e
a
f
b
l
i
g
h
t
(
i
s
a
r
i
o
p
s
i
s
l
e
a
f
sp
o
t
)
P
o
me
g
r
a
n
a
t
e
D
i
sea
s
e
d
,
h
e
a
l
t
h
y
P
o
t
a
t
o
Ea
r
l
y
b
l
i
g
h
t
,
h
e
a
l
t
h
y
,
l
a
t
e
b
l
i
g
h
t
S
o
y
b
e
a
n
C
a
t
e
r
p
i
l
l
a
r
,
d
i
a
b
r
o
t
i
c
a
s
p
e
c
i
o
sa
,
h
e
a
l
t
h
y
S
t
r
a
w
b
e
r
r
y
H
e
a
l
t
h
y
,
l
e
a
f
sc
o
r
c
h
S
u
g
a
r
c
a
n
e
B
a
c
t
e
r
i
a
l
b
l
i
g
h
t
,
h
e
a
l
t
h
y
,
r
e
d
r
o
t
,
r
e
d
s
t
r
i
p
e
,
r
u
st
To
ma
t
o
B
a
c
t
e
r
i
a
l
sp
o
t
,
e
a
r
l
y
b
l
i
g
h
t
,
h
e
a
l
t
h
y
,
l
a
t
e
b
l
i
g
h
t
,
l
e
a
f
m
o
l
d
,
m
o
s
a
i
c
v
i
r
u
s
,
s
e
p
t
o
r
i
a
l
e
a
f
s
p
o
t
,
sp
i
d
e
r
m
i
t
e
s
(
t
w
o
s
p
o
t
t
e
d
s
p
i
d
e
r
m
i
t
e
)
,
t
a
r
g
e
t
s
p
o
t
,
y
e
l
l
o
w
l
e
a
f
c
u
r
l
v
i
r
u
s
W
h
e
a
t
B
r
o
w
n
r
u
s
t
,
h
e
a
l
t
h
y
,
se
p
t
o
r
i
a
,
y
e
l
l
o
w
r
u
st
T
h
e
ViT
m
o
d
el
is
tailo
r
ed
f
o
r
v
is
u
al
task
s
lik
e
im
ag
e
class
if
icatio
n
,
d
iv
er
g
es
f
r
o
m
tr
ad
itio
n
al
C
NNs
b
y
d
iv
id
in
g
in
p
u
t
im
ag
es
in
to
f
ix
ed
-
s
ize
p
atch
es,
ea
ch
tr
an
s
f
o
r
m
ed
in
to
a
lo
wer
-
d
im
en
s
i
o
n
al
v
ec
to
r
s
p
ac
e.
T
h
ese
p
atch
em
b
ed
d
in
g
s
th
en
f
ee
d
in
to
a
s
tack
o
f
T
r
an
s
f
o
r
m
er
en
co
d
e
r
lay
er
s
.
W
ith
in
ea
ch
en
co
d
e
r
lay
er
,
two
m
ain
s
u
b
-
m
o
d
u
les
o
p
er
at
e:
a
m
u
lti
-
h
ea
d
s
elf
-
atten
tio
n
m
ec
h
an
is
m
to
ca
p
t
u
r
e
lo
n
g
-
r
a
n
g
e
d
e
p
en
d
e
n
cies
an
d
a
p
o
s
itio
n
-
wis
e
f
u
lly
co
n
n
ec
ted
f
ee
d
f
o
r
war
d
n
eu
r
al
n
etwo
r
k
s
f
o
r
co
n
tex
t
-
awa
r
e
r
ep
r
esen
tatio
n
s
.
T
o
ad
d
r
ess
th
e
lack
o
f
in
h
er
e
n
t
s
e
q
u
en
ce
o
r
d
er
t
o
u
n
d
e
r
s
tan
d
in
T
r
an
s
f
o
r
m
er
s
,
p
o
s
itio
n
al
e
n
c
o
d
in
g
s
a
r
e
a
d
d
ed
to
co
n
v
ey
s
p
atial
in
f
o
r
m
atio
n
.
F
in
ally
,
a
class
if
icatio
n
h
ea
d
,
o
f
ten
a
lin
ea
r
lay
er
with
So
f
t
Ma
x
ac
tiv
atio
n
,
is
ap
p
en
d
e
d
to
th
e
o
u
tp
u
t
f
o
r
g
en
er
atin
g
class
p
r
ed
ictio
n
s
.
T
h
is
ar
ch
itectu
r
e'
s
k
ey
h
y
p
er
p
ar
am
eter
is
th
e
d
im
en
s
io
n
ality
o
f
p
atch
em
b
e
d
d
in
g
s
,
cr
u
cial
f
o
r
b
alan
cin
g
m
o
d
el
ca
p
ac
ity
a
n
d
c
o
m
p
u
tati
o
n
al
ef
f
icien
c
y
.
W
e
p
r
esen
t
th
e
p
r
o
p
o
s
ed
s
y
s
tem
in
Fig
u
r
e
3
.
T
h
e
s
y
s
tem
was
d
ev
elo
p
ed
u
s
in
g
th
e
d
atab
a
s
e
o
f
p
lan
t
d
is
ea
s
e
im
ag
es.
T
h
e
d
ataset
was
s
y
s
tem
atica
lly
d
iv
id
e
d
in
to
tr
ain
in
g
,
v
alid
atio
n
,
a
n
d
test
s
u
b
s
ets,
with
p
r
o
p
o
r
tio
n
s
o
f
8
0
%,
1
0
%,
an
d
1
0
%,
r
esp
ec
tiv
ely
.
A
m
o
d
el
was
th
en
cr
ea
ted
,
tr
ai
n
ed
,
a
n
d
v
alid
ated
u
s
in
g
th
e
tr
ain
in
g
an
d
v
alid
atio
n
s
u
b
s
et
s
.
Fo
llo
win
g
th
is
,
th
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
was
r
ig
o
r
o
u
s
ly
test
ed
u
s
in
g
th
e
test
s
u
b
s
et.
T
h
e
u
ltima
te
g
o
al
o
f
th
is
wo
r
k
was
to
d
ev
elo
p
a
m
o
d
el
ca
p
ab
le
o
f
ac
cu
r
atel
y
p
r
ed
ictin
g
th
e
class
o
f
p
lan
t d
is
ea
s
es f
r
o
m
im
ag
es,
th
er
eb
y
p
r
o
v
i
d
in
g
a
v
alu
ab
le
to
o
l f
o
r
ag
r
icu
ltu
r
al
d
ia
g
n
o
s
tics
an
d
m
an
a
g
em
en
t.
Fig
u
r
e
3
.
Pro
p
o
s
ed
ViT
s
y
s
tem
f
o
r
p
lan
t d
is
ea
s
e
d
etec
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
P
la
n
t d
is
ea
s
e
d
etec
tio
n
u
s
in
g
visi
o
n
tr
a
n
s
fo
r
mer
s
(
Mh
a
n
ed
A
li
)
2339
2
.
5
.
E
v
a
lua
t
i
o
n m
et
rics
T
h
e
p
r
im
a
r
y
e
v
alu
atio
n
m
etr
ic
o
f
o
u
r
m
o
d
el
is
th
e
F1
-
s
co
r
e,
wh
ich
is
th
e
h
ar
m
o
n
ic
m
ea
n
o
f
p
r
ec
is
io
n
an
d
r
ec
all.
T
h
e
F1
-
s
co
r
e
is
ca
lcu
lated
as f
o
llo
ws:
F1
s
co
r
e
=
2
∗
(
∗
)
(
+
)
[
3
5
]
Pre
cisi
o
n
=
+
[
3
6
]
R
ec
all
=
+
[
3
7
]
T
h
e
ter
m
s
T
P,
FP
,
a
n
d
FN
s
ta
n
d
f
o
r
:
tr
u
e
p
o
s
itiv
e
(
TP
)
:
T
h
e
n
u
m
b
er
o
f
c
o
r
r
ec
t
p
o
s
itiv
e
p
r
ed
ictio
n
s
.
I
t
r
ef
er
s
to
in
s
tan
ce
s
wh
er
e
th
e
m
o
d
el
co
r
r
ec
tly
p
r
ed
icts
th
e
p
o
s
itiv
e
class
.
Fals
e
p
o
s
itiv
e
(
FP
)
:
T
h
e
n
u
m
b
er
o
f
in
co
r
r
ec
t
p
o
s
itiv
e
p
r
ed
ictio
n
s
.
I
t
r
e
f
er
s
to
in
s
tan
ce
s
wh
er
e
th
e
m
o
d
el
in
c
o
r
r
ec
tly
p
r
e
d
icts
th
e
p
o
s
itiv
e
class
,
wh
en
th
e
ac
tu
al
class
is
n
eg
ati
v
e.
Fals
e
n
eg
ativ
e
(
FN
)
:
T
h
e
n
u
m
b
er
o
f
in
co
r
r
ec
t
n
eg
ativ
e
p
r
ed
ictio
n
s
.
I
t
r
ef
e
r
s
to
in
s
tan
ce
s
wh
er
e
th
e
m
o
d
el
i
n
co
r
r
ec
tly
p
r
ed
icts
th
e
n
e
g
ativ
e
class
,
wh
en
th
e
ac
tu
al
class
is
p
o
s
itiv
e.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
ViT
m
o
d
el
p
r
esen
ted
in
T
ab
le
2
im
p
lem
en
ts
a
p
io
n
ee
r
in
g
ar
ch
itectu
r
e
f
o
r
im
ag
e
-
b
ased
p
lan
t
d
is
ea
s
es
class
if
icatio
n
task
s
,
lev
er
ag
in
g
b
o
th
p
atch
-
b
ased
e
n
co
d
in
g
an
d
t
r
an
s
f
o
r
m
e
r
la
y
e
r
s
.
B
eg
in
n
in
g
with
th
e
Patch
E
n
co
d
er
lay
e
r
,
in
p
u
t
im
ag
es
ar
e
p
ar
titi
o
n
ed
in
to
p
atch
es,
ty
p
ically
in
s
ize
1
6
×
1
6
p
ix
els,
e
x
tr
ac
ted
u
s
in
g
a
s
lid
in
g
win
d
o
w
ap
p
r
o
ac
h
.
E
ac
h
p
atch
u
n
d
er
g
o
es
a
lin
ea
r
p
r
o
jectio
n
f
o
llo
wed
b
y
p
o
s
itio
n
al
em
b
ed
d
in
g
s
,
em
b
e
d
d
in
g
s
p
a
tial
in
f
o
r
m
atio
n
in
to
th
e
d
a
ta.
T
h
is
p
r
o
ce
s
s
cr
ea
tes
a
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Evaluation Warning : The document was created with Spire.PDF for Python.
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tac
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m
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:
s.m
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u
a
tas
sim
@e
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se
m
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a
c
.
m
a
.
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