I
AE
S In
t
er
na
t
io
na
l J
o
urna
l o
f
Art
if
icia
l In
t
ellig
ence
(
I
J
-
AI
)
Vo
l.
1
4
,
No
.
6
,
Dec
em
b
er
2
0
2
5
,
p
p
.
4
7
2
2
~
4
7
3
0
I
SS
N:
2
2
5
2
-
8
9
3
8
,
DOI
: 1
0
.
1
1
5
9
1
/ijai.v
14
.i
6
.
p
p
4
7
2
2
-
4
7
3
0
4722
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ij
a
i
.
ia
esco
r
e.
co
m
Ea
rly
det
ection
of
t
a
r spo
t
disea
se i
n
Zea
m
a
ys
using
hy
perspectral
refl
ectance and
ma
ch
ine learning
Cla
ud
ia
No
hem
y
M
o
nto
y
a
-
E
s
t
ra
da
1
,
O
s
ca
r
Ca
rdo
na
-
M
o
ra
les
2
,
O
s
ca
r
L
ó
pez
-
Na
ra
njo
3
,
F
re
d
d
y
E
lis
eo
H
er
na
nd
ez
-
J
o
rg
e
4
,
Yeiso
n Alber
t
o
G
a
rc
és
-
G
ó
m
ez
3
1
R
e
s
e
a
r
c
h
I
n
st
i
t
u
t
e
i
n
M
i
c
r
o
b
i
o
l
o
g
y
a
n
d
A
g
r
o
-
I
n
d
u
s
t
r
i
a
l
B
i
o
t
e
c
h
n
o
l
o
g
y
,
U
n
i
v
e
r
si
d
a
d
C
a
t
ó
l
i
c
a
d
e
M
a
n
i
z
a
l
e
s,
M
a
n
i
z
a
l
e
s
,
C
o
l
o
m
b
i
a
2
U
n
i
v
e
r
s
i
d
a
d
A
u
t
ó
n
o
ma
d
e
M
a
n
i
z
a
l
e
s
,
M
a
n
i
z
a
l
e
s,
C
o
l
o
mb
i
a
3
F
a
c
u
l
t
y
o
f
En
g
i
n
e
e
r
i
n
g
a
n
d
A
r
c
h
i
t
e
c
t
u
r
e
,
U
n
i
v
e
r
si
d
a
d
C
a
t
ó
l
i
c
a
d
e
M
a
n
i
z
a
l
e
s,
M
a
n
i
z
a
l
e
s,
C
o
l
o
m
b
i
a
4
D
e
p
a
r
t
me
n
t
o
f
A
g
r
i
c
u
l
t
u
r
a
l
P
r
o
d
u
c
t
i
o
n
,
U
n
i
v
e
r
si
d
a
d
d
e
C
a
l
d
a
s
,
M
a
n
i
z
a
l
e
s
,
C
o
l
o
m
b
i
a
Art
icle
I
nfo
AB
S
T
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
u
n
2
7
,
2
0
2
4
R
ev
is
ed
Au
g
2
7
,
2
0
2
5
Acc
ep
ted
Oct
1
6
,
2
0
2
5
En
su
ri
n
g
f
o
o
d
se
c
u
rit
y
a
n
d
m
e
e
ti
n
g
t
h
e
e
c
o
n
o
m
ic
n
e
e
d
s
o
f
fa
rm
e
rs
a
n
d
n
a
ti
o
n
s
d
e
p
e
n
d
h
e
a
v
i
ly
o
n
d
e
tec
ti
n
g
a
n
d
p
re
v
e
n
ti
n
g
c
ro
p
y
ield
l
o
ss
e
s.
Early
d
e
tec
ti
o
n
o
f
tar
sp
o
t
c
a
u
se
d
b
y
P
h
y
ll
a
c
h
o
ra
ma
y
d
is
is
c
ru
c
ial
to
imp
lem
e
n
ti
n
g
e
fficie
n
t
m
it
i
g
a
ti
o
n
a
c
ti
o
n
s
in
th
e
e
a
rli
e
st
sta
g
e
s
o
f
in
fe
sta
ti
o
n
.
Cu
rre
n
tl
y
,
v
is
u
a
l
m
e
th
o
d
s
a
re
u
se
d
fo
r
d
e
tec
ti
o
n
,
wh
ic
h
re
q
u
ire
e
x
ten
siv
e
train
in
g
a
n
d
e
x
p
e
rien
c
e
fro
m
th
e
o
p
e
ra
to
r
.
Ho
we
v
e
r,
re
m
o
te
se
n
sin
g
tec
h
n
iq
u
e
s
c
a
n
b
e
u
se
d
to
d
e
tec
t
tar
sp
o
t
in
fe
sta
ti
o
n
th
r
o
u
g
h
th
e
se
lec
ti
o
n
o
f
wa
v
e
len
g
th
s
p
re
se
n
t
i
n
t
h
e
m
a
i
z
e
p
lan
t
sp
e
c
tral
si
g
n
a
t
u
re
.
T
h
i
s
re
se
a
rc
h
p
ro
p
o
se
s
u
si
n
g
m
a
c
h
in
e
lea
rn
i
n
g
tec
h
n
i
q
u
e
s
a
n
d
l
o
g
isti
c
re
g
re
ss
io
n
t
o
d
e
term
in
e
th
e
first
sta
g
e
o
f
tar
sp
o
t
i
n
fe
sta
ti
o
n
.
Th
e
re
su
lt
s
sh
o
w
th
a
t
th
e
lo
g
isti
c
re
g
re
ss
io
n
m
o
d
e
l
is
t
h
e
m
o
st
su
it
a
b
le
fo
r
d
e
tec
ti
n
g
t
h
is
f
irst
sta
g
e
,
a
n
d
t
h
e
k
-
n
e
a
re
st
n
e
i
g
h
b
o
rs
c
l
a
ss
ifi
c
a
ti
o
n
(KN
NC)
a
n
d
ra
n
d
o
m
fo
re
st
c
las
sifica
ti
o
n
(RF
C)
a
lg
o
rit
h
m
s
g
e
n
e
ra
te
th
e
b
e
st
c
las
sifica
ti
o
n
re
s
u
lt
s.
T
h
is
a
p
p
ro
a
c
h
c
a
n
sig
n
ifi
c
a
n
tl
y
re
d
u
c
e
c
o
sts
in
term
s
o
f
ti
m
e
,
l
a
b
o
r,
a
n
d
su
b
jec
ti
v
e
a
n
a
l
y
sis.
K
ey
w
o
r
d
s
:
Ma
ch
in
e
lear
n
in
g
class
if
icatio
n
P
h
ylla
ch
o
r
a
ma
y
d
is
Sp
ec
tr
al
s
ig
n
atu
r
e
T
ar
s
p
o
t
Zea
ma
ys
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Yeiso
n
Alb
er
to
Gar
cé
s
-
Gó
m
e
z
Facu
lty
o
f
E
n
g
in
ee
r
in
g
an
d
Ar
ch
itectu
r
e,
Un
iv
er
s
id
ad
C
ató
lica
d
e
Ma
n
izale
s
C
r
a
2
3
No
6
0
-
6
3
,
Ma
n
izale
s
,
C
o
lo
m
b
ia
E
m
ail: y
g
ar
ce
s
@
u
cm
.
ed
u
.
c
o
1.
I
NT
RO
D
UCT
I
O
N
T
a
r
s
p
o
t
,
n
a
m
e
d
a
f
t
e
r
t
h
e
d
a
r
k
-
b
r
o
w
n
l
e
s
i
o
n
s
[
1
]
,
is
c
u
r
r
e
n
t
l
y
o
n
e
o
f
t
h
e
m
a
i
n
f
o
l
i
a
r
d
i
s
e
as
es
o
f
m
a
i
z
e
i
n
s
e
v
e
r
a
l
L
a
t
i
n
A
m
e
r
i
c
a
n
co
u
n
tr
ies
[
2
]
,
[
3
]
.
A
s
o
f
2
0
1
5
i
n
t
h
e
U
n
it
e
d
St
a
t
es
,
t
h
e
f
i
r
s
t
r
e
p
o
r
t
o
f
d
i
s
e
as
e
o
c
c
u
r
r
e
n
c
e
w
a
s
g
i
v
e
n
[
4
]
,
[
5
]
.
T
h
e
l
o
s
s
o
f
g
r
a
i
n
y
i
e
l
d
c
a
u
s
e
d
b
y
t
h
e
t
a
r
s
p
o
t
c
o
m
p
l
e
x
(
T
S
C
)
c
a
n
b
e
a
s
h
i
g
h
a
s
5
1
%
,
c
o
n
t
i
n
g
e
n
t
u
p
o
n
t
h
e
l
e
v
el
o
f
v
u
l
n
e
r
a
b
i
l
i
t
y
o
f
c
o
r
n
g
e
n
o
t
y
p
e
s
a
n
d
t
h
e
e
x
i
s
te
n
c
e
o
f
s
p
ec
i
f
i
c
e
n
v
i
r
o
n
m
e
n
t
al
f
a
c
t
o
r
s
t
h
a
t
p
r
o
m
o
t
e
t
h
e
g
r
o
w
t
h
o
f
t
h
i
s
d
i
s
e
a
s
e
[
6
]
.
I
n
t
h
e
y
e
a
r
2
0
2
1
,
t
h
e
o
c
c
u
r
r
e
n
c
e
o
f
t
a
r
s
p
o
t
l
e
d
t
o
a
s
u
b
s
t
a
n
t
i
a
l
r
e
d
u
c
t
i
o
n
i
n
g
r
a
i
n
p
r
o
d
u
c
t
i
o
n
,
a
m
o
u
n
t
i
n
g
t
o
a
l
o
s
s
o
f
a
p
p
r
o
x
i
m
a
t
e
l
y
5
.
8
8
m
i
l
l
i
o
n
m
et
r
i
c
t
o
n
s
.
T
h
i
s
d
e
t
r
i
m
e
n
ta
l
e
f
f
e
c
t
o
n
c
r
o
p
y
i
e
l
d
h
a
d
s
i
g
n
i
f
i
c
a
n
t
e
c
o
n
o
m
ic
i
m
p
l
i
c
a
ti
o
n
s
f
o
r
t
h
e
U
n
i
t
e
d
St
a
t
es
,
r
e
s
u
l
ti
n
g
i
n
a
f
i
n
a
n
c
i
a
l
i
m
p
a
ct
o
f
a
p
p
r
o
x
i
m
a
t
e
l
y
U
S
$
1
.
2
5
b
i
l
li
o
n
.
C
o
n
s
eq
u
e
n
t
l
y
,
t
h
e
r
e
w
a
s
a
n
o
t
ic
e
a
b
l
e
d
e
c
l
i
n
e
i
n
g
r
a
i
n
o
u
t
p
u
t
,
a
m
o
u
n
t
i
n
g
t
o
a
d
e
c
r
e
ase
o
f
a
p
p
r
o
x
i
m
a
t
e
l
y
1
.
4
4
%
.
T
h
is
d
is
e
a
s
e
is
c
a
u
s
e
d
b
y
a
n
a
s
s
o
ci
a
t
i
o
n
o
f
tw
o
f
u
n
g
i
:
P
h
y
l
l
a
c
h
o
r
a
m
a
y
d
is
a
n
d
Mo
n
o
g
r
a
p
h
e
l
l
a
m
a
y
d
i
s
,
a
l
o
n
g
w
i
t
h
t
h
e
h
y
p
e
r
p
a
r
as
i
ti
c
f
u
n
g
u
s
C
o
n
i
o
t
h
yr
i
u
m
p
h
y
l
l
a
c
h
o
r
a
e
[
7
]
.
A
l
t
h
o
u
g
h
th
e
r
e
i
s
c
u
r
r
e
n
t
l
y
li
m
i
te
d
c
o
m
p
r
e
h
e
n
s
i
o
n
r
e
g
a
r
d
i
n
g
t
h
e
m
o
d
e
o
f
i
n
t
e
r
a
c
t
i
o
n
o
c
c
u
r
r
i
n
g
b
e
t
w
e
e
n
v
u
l
n
e
r
a
b
l
e
m
a
i
z
e
g
e
n
o
t
y
p
es
a
n
d
t
h
e
p
a
t
h
o
g
e
n
s
,
i
t
is
e
v
i
d
e
n
t
t
h
a
t
th
e
c
o
m
b
i
n
e
d
e
f
f
e
ct
r
e
s
u
l
t
i
n
g
f
r
o
m
t
h
e
i
r
i
n
t
e
r
a
c
ti
o
n
h
o
l
d
s
s
i
g
n
i
f
i
ca
n
t
r
e
le
v
a
n
c
e
.
T
h
i
s
is
p
r
i
m
a
r
i
l
y
d
u
e
t
o
t
h
e
c
o
n
s
e
q
u
e
n
t
i
a
l
ec
o
n
o
m
i
c
i
m
p
a
c
t
i
t
h
as
o
n
g
r
a
i
n
y
i
e
l
d
[
8
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
E
a
r
ly
d
etec
tio
n
o
f ta
r
s
p
o
t
d
is
ea
s
e
in
Zea
ma
ys u
s
in
g
… (
C
l
a
u
d
ia
N
o
h
emy
Mo
n
to
ya
-
E
s
tr
a
d
a
)
4723
T
h
e
p
r
im
ar
y
in
d
icatio
n
s
o
f
t
h
e
ailm
en
t
ar
e
ap
p
ar
en
t
o
n
th
e
p
lan
t'
s
leav
es,
wh
ich
ca
n
b
ec
o
m
e
with
er
ed
in
a
s
p
an
o
f
f
ewe
r
t
h
an
eig
h
t
d
a
y
s
,
b
ec
au
s
e
o
f
th
e
m
er
g
in
g
o
f
wo
u
n
d
s
ca
u
s
ed
b
y
v
ar
io
u
s
ty
p
es
o
f
f
u
n
g
i
an
d
attr
ib
u
te
d
to
th
e
cr
ea
tio
n
o
f
a
h
a
r
m
f
u
l
s
u
b
s
tan
ce
[
6
]
.
E
ac
h
o
f
th
e
f
u
n
g
i
ca
u
s
es
a
s
p
ec
if
ic
s
y
m
p
to
m
ato
lo
g
y
:
P
h
ylla
ch
o
r
a
ma
yd
is
in
itiate
s
with
s
m
all,
s
h
in
y
,
b
u
l
g
in
g
,
o
v
al
o
r
cir
cu
lar
d
ar
k
s
p
o
ts
with
a
d
iam
eter
o
f
0
.
5
to
2
m
m
,
M
o
n
o
g
r
a
p
h
ella
ma
y
d
is
p
r
o
d
u
ce
s
d
ar
k
s
p
o
ts
wh
ich
a
r
e
s
u
r
r
o
u
n
d
ed
b
y
a
s
tr
aw
-
co
lo
r
ed
h
alo
th
at
is
alr
ea
d
y
n
ec
r
o
tic
tis
s
u
e;
th
is
f
u
n
g
u
s
is
t
h
e
o
n
e
th
at
ca
u
s
es
th
e
m
o
s
t
d
am
ag
e,
ca
u
s
in
g
th
e
b
u
r
n
e
d
ap
p
ea
r
an
ce
o
f
th
e
f
o
liag
e,
it
ca
n
ap
p
ea
r
two
to
th
r
ee
d
a
y
s
af
ter
P
.
ma
yd
is
an
d
C
o
n
io
tr
h
yriu
m
p
h
ylla
ch
o
r
a
e
:
I
t
is
a
h
y
p
e
r
p
a
r
asit
ic
f
u
n
g
u
s
th
at
its
m
ec
h
an
is
m
is
s
till
u
n
k
n
o
wn
[
8
]
.
I
n
iti
al
s
y
m
p
to
m
s
h
av
e
ad
d
itio
n
ally
b
e
en
d
o
cu
m
e
n
ted
to
m
an
if
est
as
d
im
in
u
tiv
e
ch
l
o
r
o
tic
lesi
o
n
s
o
n
e
-
wee
k
p
o
s
t
-
in
f
ec
tio
n
,
s
u
cc
ee
d
ed
b
y
cir
cu
lar
b
r
o
wn
to
b
lack
s
tr
o
m
ata
(
in
d
icatio
n
s
)
d
is
p
er
s
e
d
th
r
o
u
g
h
o
u
t
th
e
u
p
p
er
an
d
l
o
wer
leaf
ex
ter
io
r
s
,
o
cc
asio
n
ally
m
er
g
in
g
in
to
b
an
d
s
.
On
o
cc
asio
n
,
s
tr
o
m
as
m
ay
b
e
en
cir
cled
b
y
a
n
ec
r
o
tic
h
al
o
,
b
esto
win
g
u
p
o
n
th
e
lesi
o
n
a
"f
is
h
ey
e"
s
em
b
lan
ce
[
5
]
,
[
9
]
,
[
1
0
]
.
T
h
e
d
is
ea
s
e
g
en
er
ally
o
cc
u
r
s
in
tr
o
p
ical
an
d
s
u
b
tr
o
p
ical
ar
ea
s
,
in
m
o
u
n
tain
o
u
s
,
co
o
l
an
d
h
u
m
id
ar
ea
s
,
wh
ich
ar
e
lo
ca
ted
b
etwe
en
1
3
0
0
to
2
3
0
0
m
eter
s
ab
o
v
e
s
ea
lev
el
an
d
h
av
e
f
av
o
r
ab
le
en
v
ir
o
n
m
en
tal
co
n
d
itio
n
s
f
o
r
th
e
d
e
v
elo
p
m
e
n
t
o
f
p
ath
o
g
en
s
s
u
ch
as
m
o
n
th
ly
tem
p
er
atu
r
es
o
f
1
7
to
2
2
°C
,
r
elativ
e
h
u
m
i
d
ity
g
r
ea
ter
th
an
7
5
%,
leaf
s
p
r
ay
in
th
e
m
o
r
n
i
n
g
an
d
e
v
en
in
g
h
o
u
r
s
,
h
ig
h
n
itro
g
e
n
f
er
tili
za
tio
n
an
d
lo
w
lu
m
in
o
s
ity
,
all
th
e
co
n
d
itio
n
s
m
en
tio
n
ed
ab
o
v
e
h
elp
to
f
av
o
r
th
e
p
o
ten
tial
an
d
r
ap
id
d
ev
elo
p
m
e
n
t o
f
t
h
e
p
ath
o
lo
g
y
[
1
1
]
.
Plan
t
d
is
ea
s
e
ev
alu
atio
n
,
in
clu
d
in
g
th
e
p
r
o
ce
s
s
o
f
b
o
th
id
en
tific
atio
n
an
d
q
u
an
tific
atio
n
,
h
as
tr
ad
itio
n
ally
b
ee
n
ca
r
r
ied
o
u
t
b
y
h
ig
h
ly
s
k
illed
in
d
iv
id
u
als,
eith
er
in
th
e
f
ield
o
f
ag
r
icu
ltu
r
al
cu
ltiv
atio
n
o
r
i
n
th
e
d
o
m
ain
o
f
s
cien
tific
in
v
esti
g
atio
n
[
1
2
]
.
On
e
o
f
th
e
p
r
ed
o
m
in
an
t
tech
n
iq
u
es
f
o
r
g
au
g
in
g
th
e
s
ev
er
ity
o
f
a
d
is
ea
s
e
in
v
o
lv
es
th
e
u
tili
za
tio
n
o
f
l
o
g
ar
ith
m
ic
s
ca
les
p
r
esen
ted
in
d
iag
r
am
m
atic
f
o
r
m
.
T
h
ese
s
ca
les
en
tail
d
ep
ictin
g
a
s
eq
u
en
ce
o
f
p
la
n
ts
o
r
p
lan
t
co
m
p
o
n
en
ts
ex
h
ib
itin
g
d
is
ea
s
e
s
y
m
p
to
m
s
ac
r
o
s
s
a
s
p
ec
tr
u
m
o
f
in
ten
s
ities
.
T
h
e
m
eth
o
d
o
lo
g
y
is
r
o
o
te
d
in
th
e
W
eb
er
-
Fec
h
n
er
law,
p
o
s
itin
g
th
at
th
e
p
er
ce
p
tu
al
ac
u
ity
o
f
d
am
ag
e
c
o
r
r
esp
o
n
d
s
p
r
o
p
o
r
tio
n
ally
to
th
e
lo
g
ar
ith
m
o
f
th
e
s
tim
u
lu
s
,
u
p
to
a
5
0
%
s
ev
er
ity
th
r
esh
o
ld
.
B
ey
o
n
d
th
is
th
r
esh
o
ld
,
th
e
c
o
r
r
elatio
n
b
ec
o
m
es
in
v
e
r
s
ely
p
r
o
p
o
r
tio
n
al
to
th
e
lo
g
ar
ith
m
o
f
th
e
s
tim
u
lu
s
,
d
eter
m
i
n
ed
b
y
th
e
q
u
an
tity
o
f
r
em
ain
i
n
g
h
ea
lth
y
tis
s
u
e
[
1
3
]
.
No
n
eth
eless
,
th
e
v
is
u
al
ev
alu
atio
n
o
f
d
is
ea
s
es
i
s
v
u
ln
er
ab
le
to
s
u
b
jectiv
ity
an
d
p
o
ten
tial
er
r
o
r
s
s
tem
m
in
g
f
r
o
m
h
u
m
an
r
a
ter
s
,
en
co
m
p
ass
in
g
d
is
cr
ep
an
cies
in
s
k
ill
lev
els,
v
alu
e
p
r
e
f
er
en
ce
s
,
th
e
n
u
m
er
i
ca
l
an
d
s
ize
asp
ec
ts
o
f
lesi
o
n
s
in
r
elatio
n
to
th
e
a
f
f
ec
ted
ar
ea
,
th
e
in
tr
icac
y
o
f
s
y
m
p
to
m
s
,
an
d
tim
in
g
co
n
s
id
er
atio
n
s
[
1
4
]
.
Fu
r
th
er
m
o
r
e,
th
e
p
r
o
ce
s
s
is
r
eso
u
r
ce
-
in
ten
s
iv
e,
d
em
a
n
d
in
g
b
o
th
tim
e
an
d
f
in
a
n
cial
in
v
estme
n
t
f
o
r
t
h
e
tr
ain
in
g
o
f
p
er
s
o
n
n
el
an
d
th
e
r
ef
i
n
em
en
t
o
f
ac
cu
r
ac
y
in
v
is
u
al
ass
es
s
m
en
ts
.
Dig
ital
p
h
en
o
ty
p
in
g
tech
n
o
lo
g
ies
p
r
esen
t
a
p
r
o
s
p
ec
t
f
o
r
a
u
g
m
en
tin
g
t
h
e
o
b
jectiv
ity
an
d
ef
f
icien
cy
o
f
d
etec
tin
g
an
d
q
u
an
tify
in
g
p
lan
t d
is
ea
s
es
[
1
2
]
.
R
e
c
e
n
t
i
n
t
e
n
s
i
v
e
r
es
e
a
r
c
h
h
a
s
u
n
v
e
i
l
e
d
n
o
v
e
l
s
e
n
s
o
r
-
b
a
s
e
d
ap
p
r
o
a
c
h
e
s
f
o
r
t
h
e
d
e
t
e
c
t
i
o
n
,
i
d
e
n
t
i
f
i
c
at
i
o
n
,
a
n
d
q
u
a
n
t
i
f
i
c
a
ti
o
n
o
f
p
l
a
n
t
d
is
e
a
s
es
.
T
h
es
e
s
e
n
s
o
r
s
a
n
a
l
y
z
e
th
e
o
p
t
i
c
a
l
c
h
a
r
a
c
t
e
r
is
ti
c
s
o
f
p
la
n
t
s
a
c
r
o
s
s
d
i
v
e
r
s
e
r
e
g
i
o
n
s
o
f
t
h
e
e
l
e
ct
r
o
m
a
g
n
e
t
i
c
s
p
e
c
t
r
u
m
,
l
e
v
e
r
a
g
i
n
g
i
n
f
o
r
m
a
ti
o
n
b
e
y
o
n
d
t
h
e
v
i
s
i
b
l
e
r
a
n
g
e
[
1
5
]
.
T
h
e
s
e
m
e
t
h
o
d
s
f
a
c
i
l
it
a
t
e
t
h
e
ea
r
l
y
d
e
t
e
c
ti
o
n
o
f
a
l
t
e
r
a
ti
o
n
s
i
n
p
l
a
n
t
p
h
y
s
i
o
l
o
g
y
a
t
t
r
i
b
u
t
e
d
t
o
b
i
o
ti
c
s
t
r
e
s
s
es
,
a
s
d
i
s
e
as
e
s
ca
n
i
n
d
u
c
e
c
h
a
n
g
e
s
i
n
t
is
s
u
e
c
o
l
o
r
,
l
e
a
f
m
o
r
p
h
o
l
o
g
y
,
t
r
a
n
s
p
i
r
a
t
i
o
n
r
a
t
e
,
ca
n
o
p
y
s
t
r
u
c
t
u
r
e
,
a
n
d
p
l
a
n
t
d
e
n
s
i
t
y
.
M
o
r
e
o
v
e
r
,
t
h
e
y
a
l
l
o
w
f
o
r
t
h
e
as
s
ess
m
e
n
t
o
f
v
a
r
i
a
t
i
o
n
s
i
n
t
h
e
i
n
t
e
r
a
c
t
i
o
n
b
e
tw
ee
n
s
o
l
a
r
r
a
d
i
at
i
o
n
a
n
d
p
l
a
n
t
s
[
1
6
]
.
Var
io
u
s
p
l
atf
o
r
m
s
,
i
n
cl
u
d
in
g
s
m
a
r
t
p
h
o
n
es
,
r
o
b
o
ts
,
ai
r
p
l
an
es,
u
n
m
an
n
e
d
ai
r
c
r
a
f
t
s
y
s
t
em
s
(
U
ASs
)
,
a
n
d
s
atel
lit
es,
h
a
v
e
b
e
e
n
u
t
ili
ze
d
f
o
r
d
a
ta
a
c
q
u
is
iti
o
n
i
n
t
h
e
r
ea
l
m
o
f
p
la
n
t
d
is
e
ase
d
e
tec
ti
o
n
[
1
7
]
,
[
1
8
]
.
Am
o
n
g
th
es
e
p
l
at
f
o
r
m
s
,
UASs
g
a
r
n
e
r
n
o
ta
b
le
att
en
ti
o
n
f
r
o
m
r
ese
a
r
c
h
e
r
s
an
d
p
r
o
d
u
ce
r
s
d
u
e
t
o
th
e
ir
e
f
f
ici
e
n
t
d
a
ta
ac
q
u
is
iti
o
n
,
d
e
p
l
o
y
m
en
t
f
le
x
i
b
ilit
y
,
a
n
d
r
el
ati
v
e
ly
l
o
we
r
c
o
s
t
s
c
o
m
p
a
r
e
d
to
i
m
a
g
i
n
g
m
et
h
o
d
s
i
n
v
o
l
v
i
n
g
r
o
b
o
ts
,
air
p
l
a
n
es
,
a
n
d
s
a
tel
lit
es
[
9
]
,
[
1
2
]
,
[
1
6
]
,
[
1
9
]
.
C
o
m
p
l
em
en
ti
n
g
th
ese
p
la
tf
o
r
m
s
,
d
iv
e
r
s
e
s
en
s
o
r
s
a
n
d
i
m
a
g
er
s
,
s
u
c
h
as
r
ed
-
g
r
ee
n
-
b
lu
e
o
r
R
G
B
[
2
0
]
,
m
u
lt
is
p
ec
t
r
al
[
1
2
]
,
h
y
p
er
s
p
e
ct
r
al
,
a
n
d
t
h
e
r
m
al
c
am
e
r
a
s
,
ar
e
em
p
l
o
y
e
d
f
o
r
d
at
a
c
o
l
lec
ti
o
n
.
M
ac
h
i
n
e
le
ar
n
in
g
a
lg
o
r
it
h
m
s
a
r
e
co
m
m
o
n
l
y
em
p
l
o
y
e
d
t
o
a
u
t
o
m
at
ica
ll
y
i
d
e
n
ti
f
y
,
class
if
y
,
a
n
d
q
u
a
n
ti
f
y
p
l
an
t
d
is
ea
s
es
u
s
i
n
g
t
h
e
c
o
ll
ec
te
d
d
a
ta
o
r
e
x
t
r
a
cte
d
f
ea
t
u
r
es
[
1
2
]
,
[
1
6
]
,
[
1
7
]
.
T
h
e
p
u
r
p
o
s
e
o
f
t
h
is
s
t
u
d
y
is
t
o
p
r
o
p
o
s
e
m
et
h
o
d
o
l
o
g
ies
t
h
at
all
o
w
f
o
r
t
h
e
e
ar
ly
d
et
ec
t
io
n
o
f
ta
r
s
p
o
t
d
is
ea
s
e
c
au
s
e
d
b
y
P
h
yll
a
c
h
o
r
a
m
a
y
d
is
in
m
aiz
e,
t
o
g
en
er
at
e
m
it
ig
ati
o
n
ac
t
io
n
s
t
h
at
p
r
e
v
e
n
t
s
i
g
n
if
ica
n
t
l
o
s
s
es
i
n
cr
o
p
y
i
el
d
.
2.
M
AT
E
R
I
AL
S
AND
M
E
T
H
O
DS
2
.
1
.
Def
ini
t
io
n o
f
t
he
ex
peri
m
ent
a
l plo
t
s
T
h
e
ex
p
er
im
e
n
tal
p
lo
ts
wer
e
im
p
lem
en
ted
in
th
e
San
tag
u
ed
a
V
illag
e,
m
u
n
icip
ality
o
f
Palest
in
a
(
C
o
lo
m
b
ia)
.
T
h
e
ch
ar
ac
ter
is
ti
cs
o
f
th
e
ar
ea
a
r
e,
av
e
r
ag
e
t
em
p
er
atu
r
e
o
f
2
5
.
8
°C
,
altitu
d
e
o
f
1
,
0
1
0
m
eter
s
ab
o
v
e
s
ea
lev
el,
a
n
n
u
al
av
er
a
g
e
p
r
ec
ip
itatio
n
o
f
2
,
2
0
0
m
m
a
n
d
r
elativ
e
h
u
m
id
ity
o
f
7
6
%.
A
h
o
m
o
g
en
e
o
u
s
lo
t
o
f
ap
p
r
o
x
im
ately
o
n
e
h
ec
tar
e
was
u
s
ed
.
Yello
w
m
aize
h
y
b
r
id
AT
L
2
0
0
p
lan
ts
wer
e
s
o
wn
at
8
0
cm
b
etwe
en
r
o
ws,
with
7
s
ee
d
s
p
er
s
q
u
ar
e
m
eter
.
An
in
teg
r
ated
p
est
an
d
d
is
ea
s
e
m
an
ag
em
en
t
was
ca
r
r
ied
o
u
t
to
elim
in
ate
ex
ter
n
al
f
ac
to
r
s
in
th
e
ex
p
er
im
en
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
4
,
No
.
6
,
Dec
em
b
er
2
0
2
5
:
4
7
2
2
-
4
7
3
0
4724
2
.
2
.
Sp
ec
t
ra
l sig
na
t
ures c
a
pt
ure
T
h
e
FLAM
E
-
T
-
VI
S
-
NI
R
Sp
ec
tr
o
m
eter
with
a
r
an
g
e
o
f
3
5
0
to
1
0
0
0
n
m
an
d
an
o
p
tical
r
eso
lu
tio
n
o
f
1
.
5
n
m
was
u
s
ed
to
tak
e
s
p
e
ctr
al
s
ig
n
atu
r
es.
C
alib
r
atio
n
was
p
er
f
o
r
m
ed
with
a
ca
lib
r
atio
n
tar
g
et
o
f
9
9
%
r
ef
lecta
n
ce
an
d
a
d
a
r
k
o
b
j
ec
t
o
f
m
in
im
u
m
r
e
f
lecta
n
ce
,
to
r
ed
u
ce
n
o
is
e
in
th
e
r
ef
lecta
n
ce
o
f
ea
ch
m
ea
s
u
r
em
en
t.
Su
b
s
eq
u
e
n
tly
,
s
am
p
lin
g
p
lan
ts
wer
e
id
en
tifie
d
,
an
d
th
eir
s
p
ec
tr
al
s
ig
n
atu
r
e
was
tak
en
at
a
4
5
º
an
g
le,
av
o
id
i
n
g
s
h
ad
o
w
f
o
r
m
atio
n
,
th
at
is
,
allo
win
g
d
ir
ec
t
s
u
n
lig
h
t
to
s
h
in
e
o
n
th
e
s
u
r
f
ac
e
o
f
th
e
leav
es.
B
etwe
en
6
an
d
1
0
s
p
ec
tr
al
s
ig
n
atu
r
es we
r
e
tak
en
f
r
o
m
ea
ch
s
am
p
le,
d
is
tr
ib
u
ted
eq
u
ally
b
e
twee
n
lo
w
an
d
h
ig
h
leav
es,
an
d
th
en
av
er
a
g
ed
f
o
r
ea
ch
ty
p
e
o
f
s
am
p
le.
2
.
3
.
T
a
r
s
po
t
s
ev
er
it
y
ev
a
lua
t
io
n
T
h
e
d
iag
r
am
m
atic
s
ca
le
p
r
o
p
o
s
ed
b
y
[
1
3
]
was
u
s
ed
f
o
r
th
e
ev
alu
atio
n
o
f
d
is
ea
s
e
s
ev
er
ity
o
f
th
e
tar
s
p
o
t
d
is
ea
s
e.
B
ased
o
n
th
e
ar
ea
af
f
ec
ted
b
y
th
e
p
ath
o
g
e
n
o
n
th
e
leav
es,
an
d
b
y
v
is
u
al
c
o
m
p
ar
is
o
n
with
th
e
s
ca
le,
th
e
s
ev
er
ity
clas
s
was
d
ef
in
ed
in
th
e
leav
es
o
f
th
e
p
la
n
t
(
s
ee
Fig
u
r
e
1
)
.
T
h
e
o
b
jectiv
e
o
f
th
is
s
tu
d
y
is
to
d
eter
m
in
e
t
h
e
m
o
m
en
t
wh
e
n
th
e
cr
o
p
is
a
f
f
ec
ted
b
y
th
e
p
ath
o
g
en
.
I
n
t
h
is
s
en
s
e,
th
e
class
if
icatio
n
r
eq
u
ir
em
e
n
ts
f
o
cu
s
o
n
estab
lis
h
in
g
th
e
ea
r
lies
t
m
o
m
en
t
o
f
co
n
tag
io
n
(
class
1
)
to
in
itiate
tr
ea
tm
en
t
an
d
av
o
id
lo
s
s
es in
y
ield
.
Fig
u
r
e
1
.
Diag
r
a
m
m
atic
s
ev
er
i
ty
s
ca
le
f
o
r
th
e
tar
s
tain
co
m
p
l
ex
in
m
aize
[
1
3
]
2
.
4
.
Da
t
a
a
na
ly
s
is
On
ce
s
p
ec
tr
al
s
ig
n
atu
r
es
wer
e
co
llected
,
th
e
d
ata
was
ex
tr
ac
ted
an
d
lo
a
d
ed
i
n
E
x
ce
l
(
Mic
r
o
s
o
f
t®
E
x
ce
l
f
o
r
MA
C
v
1
6
.
6
3
)
f
o
r
p
r
ep
r
o
ce
s
s
in
g
o
f
th
e
s
p
ec
tr
al
s
ig
n
atu
r
es.
I
n
itially
,
th
e
ed
g
es
o
f
th
e
s
ig
n
atu
r
es
wer
e
r
em
o
v
e
d
,
r
esu
ltin
g
in
s
i
g
n
atu
r
es
b
etwe
en
4
0
0
to
9
0
0
n
m
.
Su
b
s
eq
u
e
n
tly
,
th
e
s
ig
n
atu
r
es
wer
e
s
m
o
o
th
ed
an
d
n
o
r
m
alize
d
to
elim
in
ate
n
o
is
e,
an
d
f
in
ally
,
th
e
s
ig
n
atu
r
e
s
o
f
th
e
s
am
e
s
ev
er
ity
we
r
e
a
v
er
ag
ed
to
o
b
tain
a
s
in
g
le
r
ef
er
e
n
ce
s
ig
n
atu
r
e
p
e
r
s
ev
er
ity
.
T
h
e
n
,
with
class
es
0
an
d
1
,
m
ac
h
in
e
lear
n
in
g
m
o
d
e
ls
wer
e
ap
p
lied
to
d
ef
in
e
th
e
b
est
class
if
icatio
n
m
o
d
el
th
at
allo
ws
f
o
r
ea
r
ly
id
en
tific
atio
n
o
f
th
e
d
is
ea
s
e
b
ef
o
r
e
it
ca
n
ca
u
s
e
ir
r
ep
ar
ab
le
d
am
a
g
e
to
cr
o
p
y
i
eld
s
.
Ad
d
itio
n
ally
,
a
lo
g
is
tic
r
eg
r
ess
io
n
m
o
d
el
was
ev
alu
ated
f
o
r
class
if
icatio
n
,
co
n
s
id
er
in
g
t
h
at
a
d
ich
o
to
m
o
u
s
ca
teg
o
r
ical
v
ar
iab
le
is
to
b
e
class
if
ied
.
2
.
5
.
M
a
chine le
a
rning
cla
s
s
i
f
ica
t
io
n a
lg
o
rit
hm
s
T
o
d
eter
m
in
e
th
e
b
est
m
ac
h
in
e
lear
n
in
g
class
if
icatio
n
m
o
d
el
th
at
r
esp
o
n
d
e
d
to
t
h
e
r
esear
ch
o
b
jectiv
e,
eig
h
t
class
if
icatio
n
alg
o
r
ith
m
s
wer
e
ex
ec
u
te
d
:
b
o
o
s
tin
g
class
if
icatio
n
(
B
C
)
,
d
ec
is
io
n
tr
ee
class
if
icatio
n
(
DT
C
)
,
k
-
n
ea
r
e
s
t
n
eig
h
b
o
r
s
class
if
icatio
n
(
K
NNC),
lin
ea
r
d
is
cr
im
in
an
t
cl
ass
if
icatio
n
(
L
DC
)
,
n
eu
r
al
n
etwo
r
k
class
if
icatio
n
(
NNC
)
,
r
a
n
d
o
m
f
o
r
est
class
if
icatio
n
(
R
FC
)
,
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
cl
ass
if
icatio
n
(
SVMC)
[
2
1
]
‒
[
2
3
]
.
Ad
d
itio
n
ally
,
th
e
lo
g
is
tic
r
eg
r
ess
io
n
m
o
d
el
was
an
aly
z
ed
,
co
n
s
id
er
in
g
th
e
n
ee
d
to
class
if
y
two
p
o
s
s
ib
ilit
ies
o
f
th
e
p
lan
t'
s
s
tate
(
class
0
an
d
class
1
)
.
T
h
e
R
(
R
4
.
2
.
2
GUI
1
.
7
9
B
ig
Su
r
AR
M
b
u
ild
(
8
1
6
0
)
an
d
R
Stu
d
io
v
2
0
2
3
.
0
3
.
0
+3
8
6
(
2
0
2
3
.
0
3
.
0
+3
8
6
)
)
[
2
4
]
a
n
d
J
ASP
(
v
0
.
1
7
.
1
Ap
p
le
Sil
ico
n
)
[
2
5
]
s
o
f
twar
e
was
u
s
ed
to
e
x
e
cu
te
th
e
m
o
d
els.
T
h
e
h
y
p
er
p
a
r
am
eter
s
f
o
r
th
e
m
ac
h
in
e
lear
n
in
g
m
o
d
els
in
th
e
C
l
a
s
s
0
S
e
v
e
r
i
t
y
0
%
C
l
a
s
s
1
S
e
v
e
r
i
t
y
1
-
6
%
C
l
a
s
s
2
S
e
v
e
r
i
t
y
7
-
2
2
%
C
l
a
s
s
3
S
e
v
e
r
i
t
y
2
3
-
5
5
%
C
l
a
s
s
4
S
e
v
e
r
i
t
y
5
6
-
8
4
%
C
l
a
s
s
5
S
e
v
e
r
i
t
y
8
5
-
9
5
%
C
l
a
s
s
6
S
e
v
e
r
i
t
y
9
6
-
1
0
0
%
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
E
a
r
ly
d
etec
tio
n
o
f ta
r
s
p
o
t
d
is
ea
s
e
in
Zea
ma
ys u
s
in
g
… (
C
l
a
u
d
ia
N
o
h
emy
Mo
n
to
ya
-
E
s
tr
a
d
a
)
4725
J
ASP
s
o
f
twar
e
wer
e
au
to
m
at
ically
ad
ju
s
ted
b
y
th
e
s
o
f
twar
e
in
its
p
r
o
g
r
am
m
i
n
g
.
Ass
u
m
p
tio
n
s
a
n
d
d
ata
in
teg
r
ity
wer
e
ch
ec
k
ed
in
R
s
o
f
twar
e
u
s
in
g
An
d
e
r
s
o
n
Dar
lin
g
test
s
.
2
.
6
.
M
o
del per
f
o
rma
nce
Prin
cip
al
co
m
p
o
n
en
t
an
aly
s
is
(
PC
A
)
was
p
er
f
o
r
m
ed
o
n
th
e
co
m
p
lete
s
p
ec
tr
al
s
ig
n
atu
r
e
o
b
tain
ed
f
r
o
m
ea
ch
p
lan
t
to
d
eter
m
i
n
e
th
e
m
o
s
t
s
u
itab
le
wav
elen
g
t
h
s
f
o
r
tr
ain
in
g
th
e
m
ac
h
in
e
l
ea
r
n
in
g
alg
o
r
ith
m
s
.
T
h
e
f
ilter
ed
d
ata
b
ase
co
n
tain
in
g
th
e
PC
A
wav
elen
g
t
h
s
was r
an
d
o
m
l
y
d
iv
id
ed
in
to
8
0
%
tr
ain
in
g
d
ata
a
n
d
2
0
%
test
d
ata.
Sev
er
al
p
er
f
o
r
m
a
n
c
e
cr
iter
ia
wer
e
s
elec
ted
:
ac
cu
r
ac
y
,
p
r
ec
is
io
n
,
r
ec
all,
F1
-
s
co
r
e
,
an
d
ar
ea
u
n
d
e
r
cu
r
v
e
(
AUC).
Fu
r
th
er
m
o
r
e,
t
h
e
m
o
d
el'
s
p
er
f
o
r
m
an
ce
was
ev
alu
ated
u
s
in
g
r
ec
eiv
er
o
p
e
r
atin
g
ch
ar
ac
ter
is
tics
(
R
OC
)
cu
r
v
es
as
a
cr
iter
io
n
.
R
OC
cu
r
v
es
d
ep
ict
th
e
r
elatio
n
s
h
ip
b
etwe
en
th
e
tr
u
e
p
o
s
itiv
e
r
ate
(
T
PR
)
an
d
th
e
f
alse
p
o
s
itiv
e
r
ate
(
FP
R
)
,
wh
ich
r
ef
lects
th
e
s
en
s
itiv
ity
an
d
s
p
ec
if
icity
.
As
s
en
s
itiv
ity
in
cr
ea
s
es,
s
p
ec
if
icity
d
ec
r
ea
s
es
an
d
v
ice
v
er
s
a.
A
c
u
r
v
e
clo
s
er
to
th
e
u
p
p
er
lef
t
c
o
r
n
er
o
f
th
e
g
r
a
p
h
in
d
icate
s
h
i
g
h
er
ac
cu
r
ac
y
o
f
t
h
e
alg
o
r
ith
m
.
C
o
n
v
er
s
ely
,
a
cu
r
v
e
clo
s
er
to
th
e
d
iag
o
n
al
(
4
5
°)
i
n
d
icate
s
a
p
er
f
o
r
m
an
ce
ac
c
u
r
a
cy
th
at
is
n
o
b
etter
th
an
g
u
ess
in
g
.
Fo
r
t
h
e
lo
g
is
tic
r
eg
r
ess
io
n
m
o
d
el,
u
s
in
g
th
e
s
am
e
wav
elen
g
th
s
d
eter
m
i
n
ed
with
PC
A,
th
e
b
est
m
o
d
el
is
ca
lcu
lated
u
s
in
g
t
h
e
b
ac
k
war
d
elim
in
atio
n
m
eth
o
d
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
r
e
s
u
l
ts
a
l
l
o
w
u
s
t
o
d
e
t
e
r
m
in
e
t
h
a
t
t
h
e
b
e
s
t
a
l
g
o
r
i
t
h
m
f
o
r
d
e
t
e
c
t
i
n
g
t
h
e
s
e
c
o
n
d
l
e
v
e
l
o
f
i
n
f
e
s
t
at
i
o
n
i
s
K
N
NC
f
o
l
l
o
we
d
b
y
S
V
MC
in
t
e
r
m
s
o
f
a
c
c
u
r
a
c
y
,
p
r
e
ci
s
i
o
n
,
a
n
d
r
e
c
a
l
l
.
I
n
t
e
r
m
s
o
f
A
UC
t
h
e
b
es
t
r
es
u
l
t
is
o
b
t
a
i
n
e
d
w
i
t
h
R
FC
,
w
h
i
l
e
t
h
e
b
e
s
t
F
1
is
o
b
t
a
i
n
e
d
w
i
t
h
t
h
e
KN
N
C
al
g
o
r
i
t
h
m
(
T
a
b
l
e
1
)
.
T
h
e
r
e
s
u
l
ts
i
n
T
a
b
l
e
1
c
a
n
b
e
c
o
r
r
o
b
o
r
a
t
e
d
w
i
t
h
t
h
e
R
OC
c
u
r
v
e
s
i
n
F
i
g
u
r
e
2
.
I
n
F
i
g
u
r
e
2
(
f
)
t
h
e
b
e
s
t
p
e
r
f
o
r
m
i
n
g
c
u
r
v
e
s
a
r
e
R
F
C
,
a
n
d
F
i
g
u
r
e
2
(
c
)
K
N
N
C
a
s
t
h
e
y
a
r
e
t
h
e
c
l
o
s
e
s
t
t
o
t
h
e
u
p
p
e
r
l
e
f
t
p
a
r
t
o
f
t
h
e
p
l
o
t
.
T
h
e
w
e
a
k
e
s
t
p
e
r
f
o
r
m
a
n
c
e
a
s
a
f
u
n
c
t
i
o
n
o
f
R
O
C
c
u
r
v
e
s
a
n
d
A
U
C
w
a
s
f
o
r
t
h
e
N
N
C
,
S
V
M
C
,
a
n
d
D
T
C
a
l
g
o
r
i
t
h
m
s
(
s
e
e
F
i
g
u
r
e
s
2
(
e
)
,
2
(
g
)
,
a
n
d
2
(
b
)
r
e
s
p
e
c
t
i
v
el
y
)
.
I
n
F
i
g
u
r
e
s
2
(
a
)
an
d
2
(
d
)
a
r
e
t
h
e
c
a
s
e
o
f
B
C
a
n
d
L
D
C
al
g
o
r
i
t
h
m
s
,
t
h
e
r
e
s
p
o
n
s
e
is
i
n
t
e
r
m
e
d
ia
t
e
.
T
ab
le
1
.
Mo
d
el
p
er
f
o
r
m
a
n
ce
o
f
th
e
s
ev
en
class
if
ier
s
M
o
d
e
l
A
c
c
u
r
a
c
y
P
r
e
c
i
s
i
o
n
R
e
c
a
l
l
AUC
F1
BC
0
.
6
8
0
0
.
6
2
7
0
.
6
8
0
0
.
8
0
2
0
.
5
8
3
D
TC
0
.
7
6
0
0
.
7
5
1
0
.
7
6
0
0
.
6
7
5
0
.
7
4
2
K
N
N
C
0
.
7
8
0
0
.
7
7
3
0
.
7
8
0
0
.
8
1
2
0
.
7
6
7
LD
C
0
.
7
6
0
0
.
7
5
1
0
.
7
6
0
0
.
8
0
5
0
.
7
4
2
NNC
0
.
7
2
0
0
.
7
0
2
0
.
7
2
0
0
.
5
9
5
0
.
6
8
8
R
F
C
0
.
7
4
0
0
.
7
3
5
0
.
7
4
0
0
.
8
1
4
0
.
7
0
4
S
V
M
C
0
.
7
6
0
0
.
7
5
8
0
.
7
9
0
0
.
6
5
8
0
.
7
7
3
Fo
r
th
e
lo
g
is
tic
r
eg
r
ess
io
n
m
o
d
el,
b
ased
o
n
th
e
b
ac
k
war
d
m
o
d
el,
s
ix
wav
elen
g
th
s
wer
e
estab
lis
h
ed
as th
e
id
ea
l o
n
es (
s
ee
th
e
f
ir
s
t
co
lu
m
n
o
f
T
ab
le
2
)
.
W
ith
th
ese
ch
ar
ac
ter
is
tics
f
o
r
th
e
lo
g
is
ti
c
r
eg
r
ess
io
n
m
o
d
el,
th
e
co
n
f
u
s
io
n
m
atr
ix
s
h
o
ws
t
h
e
r
esu
lts
s
u
m
m
ar
ize
d
in
T
ab
le
3
.
T
ab
le
3
p
r
esen
ts
th
e
r
esu
lts
in
th
e
f
o
r
m
o
f
a
co
n
f
u
s
io
n
m
atr
i
x
with
an
ac
cu
r
ac
y
o
f
8
3
.
4
6
%,
th
is
m
etr
ic
r
ef
er
s
to
th
e
d
is
p
er
s
io
n
o
f
th
e
s
et
o
f
v
alu
es o
b
tain
ed
f
r
o
m
r
ep
ea
ted
m
ea
s
u
r
em
e
n
ts
o
f
a
m
ag
n
itu
d
e.
T
h
e
r
ec
all
o
r
s
en
s
itiv
ity
an
d
s
p
ec
if
icity
a
r
e
two
v
al
u
es
th
at
in
d
icate
th
e
ca
p
ac
ity
o
f
o
u
r
es
tim
ato
r
to
d
is
cr
im
in
ate
th
e
p
o
s
itiv
e
ca
s
es
f
r
o
m
th
e
n
eg
ativ
e
o
n
es,
in
th
e
ca
s
e
o
f
th
e
lo
g
is
tic
r
eg
r
ess
io
n
m
o
d
el
th
e
v
alu
es
o
b
tain
ed
ar
e
9
1
.
3
9
%
an
d
6
1
.
7
6
%
r
esp
ec
tiv
ely
.
T
h
e
p
er
f
o
r
m
a
n
ce
m
etr
ics
o
f
t
h
e
lo
g
is
tic
r
eg
r
es
s
io
n
m
o
d
el
ar
e
p
r
esen
te
d
in
T
ab
le
4
,
wh
ile
t
h
e
esti
m
ates
p
lo
ts
ar
e
s
h
o
wn
i
n
Fig
u
r
e
3
with
a
9
5
%
co
n
f
id
e
n
ce
in
ter
v
al.
Fig
u
r
e
4
illu
s
tr
ates
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
l
o
g
is
tic
r
eg
r
ess
io
n
m
o
d
el
f
r
o
m
th
e
R
OC
cu
r
v
e
with
s
atis
f
ac
to
r
y
r
esu
lts
f
o
r
th
e
class
if
icatio
n
o
f
th
e
f
ir
s
t two
p
est in
f
es
tatio
n
s
tag
es.
T
ab
le
2
.
L
o
g
is
tic
r
eg
r
ess
io
n
m
o
d
el
s
u
m
m
ar
y
P
a
r
a
me
t
e
r
Est
i
m
a
t
e
S
t
a
n
d
a
r
d
e
r
r
o
r
z
W
a
l
d
t
e
st
W
a
l
d
s
t
a
t
i
st
i
c
df
p
(
I
n
t
e
r
c
e
p
t
)
2
0
.
2
7
1
5
.
7
3
6
3
.
5
3
4
1
2
.
4
9
0
1
<
.
0
0
1
*
*
*
6
2
5
.
1
2
5
4
.
5
9
5
1
4
.
5
4
7
3
.
7
5
3
1
4
.
0
8
4
1
<
.
0
0
1
*
*
*
6
9
8
.
8
1
9
9
5
.
2
1
8
3
6
.
0
3
7
2
.
6
4
2
6
.
9
8
1
1
0
.
0
0
8
*
*
7
0
3
.
0
8
3
-
1
5
6
.
0
3
4
3
7
.
5
6
9
-
4
.
1
5
3
1
7
.
2
5
0
1
<
.
0
0
1
*
*
*
7
2
2
.
7
4
9
5
3
.
6
3
7
9
.
1
8
7
5
.
8
3
8
3
4
.
0
8
5
1
<
.
0
0
1
*
*
*
7
6
2
.
4
9
6
-
2
1
.
4
8
6
3
.
7
8
9
-
5
.
6
7
0
3
2
.
1
5
2
1
<
.
0
0
1
*
*
*
8
8
9
.
6
8
-
1
6
.
5
1
8
6
.
4
0
4
-
2
.
5
7
9
6
.
6
5
2
1
0
.
0
1
0
*
*
(
I
n
t
e
r
c
e
p
t
)
2
0
.
2
7
1
5
.
7
3
6
3
.
5
3
4
1
2
.
4
9
0
1
<
.
0
0
1
*
*
*
N
o
t
e
.
S
EV
E
R
I
TY
l
e
v
e
l
'3
%'
c
o
d
e
d
a
s
c
l
a
ss
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
4
,
No
.
6
,
Dec
em
b
er
2
0
2
5
:
4
7
2
2
-
4
7
3
0
4726
T
ab
le
3
.
C
o
n
f
u
s
io
n
m
atr
ix
f
o
r
th
e
lo
g
is
tic
r
eg
r
ess
io
n
m
o
d
el
O
b
serv
e
d
P
r
e
d
i
c
t
e
d
%
C
o
r
r
e
c
t
C
l
a
s
s
0
C
l
a
s
s
1
C
l
a
s
s
0
42
26
6
1
.
7
6
5
C
l
a
s
s
1
16
1
7
0
9
1
.
3
9
8
O
v
e
r
a
l
l
%
c
o
r
r
e
c
t
8
3
.
4
6
5
N
o
t
e
.
T
h
e
c
u
t
-
o
f
f
v
a
l
u
e
i
s s
e
t
t
o
0
.
5
T
ab
le
4
.
L
o
g
is
tic
r
eg
r
ess
io
n
m
o
d
el
s
u
m
m
ar
y
P
e
r
f
o
r
ma
n
c
e
me
t
r
i
c
V
a
l
u
e
A
c
c
u
r
a
c
y
0
.
8
3
5
AUC
0
.
9
0
2
S
e
n
s
i
t
i
v
i
t
y
0
.
9
1
4
S
p
e
c
i
f
i
c
i
t
y
0
.
6
1
8
P
r
e
c
i
s
i
o
n
0
.
8
6
7
F
-
mea
su
r
e
0
.
8
9
0
B
r
i
e
r
s
c
o
r
e
0
.
1
1
3
H
-
mea
su
r
e
0
.
5
1
5
(
a)
(
b
)
(
c)
(
d
)
(
e)
(f)
(
g
)
Fig
u
r
e
2
.
R
OC
cu
r
v
es p
lo
ts
f
o
r
th
e
s
ev
en
class
if
ier
s
:
(
a)
B
C
,
(
b
)
DT
C
,
(
c)
KNNC,
(
d
)
L
DC
,
(
e)
NNC,
(
f
)
R
FC
,
an
d
(
g
)
SVMC
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
E
a
r
ly
d
etec
tio
n
o
f ta
r
s
p
o
t
d
is
ea
s
e
in
Zea
ma
ys u
s
in
g
… (
C
l
a
u
d
ia
N
o
h
emy
Mo
n
to
ya
-
E
s
tr
a
d
a
)
4727
Fig
u
r
e
3
.
C
o
n
d
itio
n
al
esti
m
ates p
lo
ts
with
a
9
5
% c
o
n
f
id
e
n
ce
in
ter
v
al
f
o
r
th
e
lo
g
is
tic
r
eg
r
ess
io
n
Fig
u
r
e
4
.
Per
f
o
r
m
an
c
e
o
f
t
h
e
l
o
g
is
tic
r
eg
r
ess
io
n
m
o
d
el
I
n
th
e
liter
atu
r
e
r
ev
iew
c
o
n
d
u
cted
f
o
r
th
is
r
esear
ch
,
it
h
a
s
b
ee
n
o
b
s
er
v
ed
th
at
t
h
e
d
et
ec
tio
n
an
d
ch
ar
ac
ter
izatio
n
o
f
ta
r
s
p
o
t
in
f
ec
tio
n
lev
els
ar
e
s
till
ca
r
r
ied
o
u
t
u
s
in
g
v
is
u
al
tech
n
iq
u
es
[
1
3
]
,
wh
ich
r
eq
u
ir
e
h
ig
h
ly
tr
ai
n
ed
p
er
s
o
n
n
el
an
d
tim
e
-
co
n
s
u
m
in
g
f
ield
wo
r
k
f
o
r
s
am
p
lin
g
.
T
h
e
v
is
u
al
d
etec
tio
n
m
o
d
el
f
o
r
tar
s
p
o
t
p
r
o
p
o
s
es
7
lev
els
r
a
n
g
in
g
f
r
o
m
"c
lass
0
"
with
n
o
p
r
ese
n
ce
o
f
th
e
d
is
ea
s
e
to
"c
lass
6
"
in
d
icatin
g
to
tal
v
eg
etativ
e
lo
s
s
o
f
th
e
p
lan
t
[
1
3
]
.
Ho
wev
e
r
,
late
d
etec
tio
n
o
f
in
f
estatio
n
ca
n
lea
d
to
s
ig
n
if
ican
t
ec
o
n
o
m
ic
lo
s
s
es
in
th
e
cr
o
p
,
as
th
e
d
is
ea
s
e
p
r
o
g
r
ess
es
r
ap
id
ly
af
t
er
"c
lass
2
"
in
f
estatio
n
,
m
a
k
in
g
co
n
tr
o
l
m
o
r
e
ch
allen
g
in
g
.
I
t
s
h
o
u
ld
also
b
e
n
o
ted
t
h
at
v
is
u
al
d
etec
tio
n
is
a
s
am
p
lin
g
p
r
o
ce
s
s
,
an
d
i
n
lar
g
e
c
r
o
p
a
r
ea
s
,
in
co
r
r
ec
t
d
etec
tio
n
s
ca
n
o
cc
u
r
as
it
is
im
p
o
s
s
ib
le
to
v
is
u
alize
th
e
en
tire
p
la
n
t
p
o
p
u
latio
n
.
I
n
th
is
r
eg
ar
d
,
th
e
d
etec
tio
n
o
f
th
e
i
n
itial "c
lass
1
"
s
tag
e
o
f
in
f
estatio
n
is
o
f
u
tm
o
s
t im
p
o
r
tan
ce
f
o
r
th
e
cr
o
p
m
an
ag
em
en
t p
r
o
ce
s
s
,
b
u
t,
d
etec
tin
g
th
e
f
i
r
s
t
s
tag
es
o
f
tar
s
p
o
t
in
f
estatio
n
b
y
P
h
ylla
ch
o
r
a
ma
yd
is
v
is
u
ally
c
an
b
e
a
s
u
b
jectiv
e
p
r
o
ce
s
s
f
o
r
an
u
n
tr
ai
n
ed
o
r
in
ex
p
er
ien
ce
d
e
y
e
[
1
2
]
.
T
h
er
ef
o
r
e,
p
r
o
p
o
s
in
g
r
em
o
te
s
en
s
in
g
-
b
ased
s
o
lu
tio
n
s
th
at
allo
w
f
o
r
ea
r
ly
an
d
ea
s
y
d
etec
t
io
n
o
f
i
n
f
estatio
n
is
cr
u
cial
f
o
r
th
e
p
r
o
f
itab
ilit
y
o
f
m
aize
cu
ltiv
atio
n
.
T
h
e
ML
m
eth
o
d
s
p
r
o
p
o
s
ed
in
th
is
s
tu
d
y
allo
w
f
o
r
th
e
class
if
icatio
n
o
f
th
e
f
ir
s
t
two
lev
els
(
class
0
an
d
class
1
)
with
an
a
cc
u
r
ac
y
b
etwe
en
6
8
%
an
d
7
8
%,
with
KNNC
an
d
R
FC
m
o
d
els
s
h
o
win
g
th
e
b
est
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
4
,
No
.
6
,
Dec
em
b
er
2
0
2
5
:
4
7
2
2
-
4
7
3
0
4728
AUC.
T
h
e
lo
g
is
tic
r
eg
r
ess
io
n
m
eth
o
d
b
ased
o
n
s
p
ec
tr
al
s
ig
n
atu
r
es
allo
ws
f
o
r
th
e
class
if
icatio
n
o
f
th
e
f
ir
s
t
lev
el
o
f
in
f
estatio
n
(
class
1
)
with
a
T
PR
(
s
en
s
itiv
ity
)
o
f
9
1
.
4
%
an
d
an
o
v
e
r
all
ac
cu
r
ac
y
o
f
8
3
.
5
%,
m
ak
in
g
it
th
e
b
est
m
o
d
el
f
o
r
p
r
e
d
ictio
n
s
in
th
is
ex
p
e
r
im
en
t.
T
h
ese
r
es
u
lts
en
ab
le
ef
f
ec
tiv
e
in
f
estatio
n
co
n
tr
o
l
ac
tio
n
s
to
b
e
tak
en
at
a
tim
e
w
h
en
class
if
icatio
n
s
th
at
co
u
ld
n
o
t b
e
e
f
f
ic
ien
tly
d
etec
ted
v
is
u
ally
(
class
1
)
ar
e
id
e
n
tifie
d
.
T
h
e
p
r
esen
t
s
tu
d
y
s
u
cc
ess
f
u
lly
estab
lis
h
es
a
p
r
o
ce
s
s
in
g
p
ip
elin
e
f
o
r
h
y
p
er
s
p
ec
tr
al
r
ef
lecta
n
ce
s
ig
n
atu
r
es
to
ac
cu
r
ately
d
etec
t
tar
s
p
o
t
d
is
ea
s
e
in
f
ield
tr
ials
.
T
h
e
ad
v
an
ta
g
es
an
d
th
e
n
e
ed
f
o
r
a
f
ir
s
t
-
lev
el
in
f
estatio
n
d
eter
m
i
n
atio
n
m
o
d
el
wer
e
d
is
cu
s
s
ed
u
s
in
g
t
wo
m
ac
h
in
e
lear
n
in
g
m
o
d
el
s
an
d
o
n
e
l
o
g
is
tic
r
eg
r
ess
io
n
m
o
d
el,
co
m
p
ar
in
g
th
eir
p
er
f
o
r
m
a
n
ce
m
etr
ics.
T
h
e
lo
g
is
tic
r
eg
r
ess
io
n
m
o
d
e
l
s
h
o
wed
th
e
b
est
p
er
f
o
r
m
an
ce
f
o
r
d
etec
tin
g
th
e
f
ir
s
t
lev
el
o
f
in
f
estatio
n
"c
lass
1
"
wh
ile
h
av
in
g
lo
wer
co
m
p
u
tatio
n
al
co
s
t
co
m
p
ar
ed
to
th
e
m
ac
h
i
n
e
lear
n
in
g
m
o
d
els.
T
h
e
p
er
f
o
r
m
an
ce
m
etr
ics
o
f
th
e
th
r
ee
p
r
o
p
o
s
ed
m
o
d
els
wer
e
ca
lcu
lated
f
o
llo
win
g
th
e
r
ec
o
m
m
en
d
atio
n
s
o
f
[
2
1
]
.
I
m
p
r
o
v
em
en
ts
an
d
ad
v
a
n
ce
m
en
ts
in
in
f
estatio
n
d
etec
tio
n
ca
n
b
e
ac
h
iev
ed
u
s
in
g
air
b
o
r
n
e
m
u
ltis
p
ec
tr
al
s
en
s
o
r
s
.
Fin
a
lly
,
it
is
im
p
o
r
tan
t
t
o
h
ig
h
lig
h
t
th
at
th
e
p
r
o
p
o
s
ed
m
eth
o
d
is
o
b
jectiv
e
a
n
d
elim
in
ates
th
e
s
u
b
jectiv
ity
o
f
th
e
ev
alu
ato
r
an
d
th
e
n
ee
d
f
o
r
e
x
ten
s
iv
e
p
r
io
r
f
ield
ex
p
er
ien
ce
.
C
o
n
s
id
er
in
g
th
e
ar
g
u
m
en
ts
p
r
esen
ted
,
we
c
o
n
clu
d
e
th
at
tar
s
p
o
t
d
etec
tio
n
u
s
in
g
m
u
ltis
p
ec
tr
al
m
eth
o
d
s
s
u
p
p
o
r
ted
b
y
n
u
m
er
i
ca
l c
alcu
latio
n
s
is
a
p
o
ten
tial t
o
o
l f
o
r
d
eter
m
in
in
g
th
e
f
i
r
s
t le
v
el
o
f
in
f
estatio
n
in
f
ield
co
n
d
itio
n
s
.
I
t
h
as
b
ee
n
d
em
o
n
s
tr
ated
t
h
at
th
is
tech
n
o
lo
g
y
ca
n
r
ep
lace
th
e
lab
o
r
i
o
u
s
wo
r
k
o
f
v
is
u
al
s
co
r
in
g
b
y
p
r
o
v
id
in
g
r
eliab
le
p
ar
am
eter
s
ef
f
icien
tly
.
T
h
e
p
r
esen
ted
ap
p
r
o
ac
h
ca
n
b
e
tr
an
s
f
er
r
ed
to
a
g
r
icu
ltu
r
al
p
r
ac
tice
f
o
r
d
ec
is
io
n
-
m
ak
i
n
g
i
n
in
teg
r
ated
p
est m
an
ag
em
e
n
t.
4.
CO
NCLU
SI
O
N
Dete
ctin
g
th
e
f
ir
s
t
s
tag
e
o
f
ta
r
s
p
o
t
in
f
estatio
n
b
y
P
h
ylla
ch
o
r
a
ma
yd
is
v
is
u
ally
ca
n
b
e
a
s
u
b
jectiv
e
p
r
o
ce
s
s
f
o
r
a
n
u
n
tr
ain
ed
o
r
i
n
ex
p
er
ien
ce
d
ey
e
.
T
h
e
r
ef
o
r
e
,
p
r
o
p
o
s
in
g
r
e
m
o
te
s
en
s
in
g
-
b
ased
s
o
lu
tio
n
s
th
at
allo
w
f
o
r
ea
r
ly
an
d
ea
s
y
d
etec
tio
n
o
f
in
f
estatio
n
is
cr
u
cial
f
o
r
th
e
p
r
o
f
itab
ilit
y
o
f
m
aize
cu
ltiv
atio
n
.
T
h
e
m
ac
h
in
e
lear
n
in
g
m
eth
o
d
s
p
r
o
p
o
s
ed
in
th
is
s
tu
d
y
all
o
w
f
o
r
th
e
class
if
icatio
n
o
f
t
h
e
f
ir
s
t
two
lev
els
(
class
0
an
d
class
1
)
with
an
a
cc
u
r
ac
y
b
etwe
en
6
8
%
an
d
7
8
%,
with
KNNC
an
d
R
FC
m
o
d
els
s
h
o
win
g
th
e
b
est
AUC.
T
h
e
lo
g
is
tic
r
eg
r
ess
io
n
m
eth
o
d
b
ased
o
n
s
p
ec
tr
al
s
ig
n
atu
r
es
allo
ws
f
o
r
th
e
class
if
icatio
n
o
f
th
e
f
ir
s
t
lev
el
o
f
in
f
estatio
n
(
class
1
)
with
a
T
PR
(
s
en
s
itiv
ity
)
o
f
9
1
.
4
%
an
d
an
o
v
e
r
all
ac
cu
r
ac
y
o
f
8
3
.
5
%,
m
ak
in
g
it
th
e
b
est
m
o
d
el
f
o
r
p
r
e
d
ictio
n
s
in
th
is
ex
p
e
r
im
en
t.
T
h
ese
r
es
u
lts
en
ab
le
ef
f
ec
tiv
e
in
f
estatio
n
co
n
tr
o
l
ac
tio
n
s
to
b
e
tak
en
at
a
tim
e
wh
en
class
if
icatio
n
s
th
at
co
u
ld
n
o
t
b
e
ef
f
i
cien
tly
d
etec
ted
v
is
u
ally
(
class
1
)
ar
e
id
en
tifie
d
.
Fu
tu
r
e
wo
r
k
wo
u
ld
allo
w
th
e
o
p
tim
izatio
n
o
f
class
if
icatio
n
alg
o
r
ith
m
s
b
ased
o
n
cr
o
s
s
-
v
al
id
atio
n
m
o
d
els
an
d
th
e
u
s
e
o
f
h
i
g
h
-
r
eso
lu
tio
n
im
a
g
er
y
as r
em
o
te
s
en
s
in
g
to
o
ls
t
o
im
p
r
o
v
e
in
f
estatio
n
class
if
icatio
n
p
r
o
ce
s
s
es.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
Au
th
o
r
s
s
tate
n
o
f
u
n
d
in
g
in
v
o
lv
ed
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
C
lau
d
ia
No
h
em
y
Mo
n
to
y
a
-
E
s
tr
ad
a
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Oscar
C
ar
d
o
n
a
-
Mo
r
ales
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Oscar
L
ó
p
ez
-
Nar
an
jo
✓
✓
✓
✓
✓
✓
Fre
d
d
y
E
lis
eo
Her
n
an
d
ez
-
J
o
r
g
e
✓
✓
✓
✓
✓
✓
✓
Yeiso
n
Alb
er
to
Gar
cé
s
-
Gó
m
ez
✓
✓
✓
✓
✓
✓
✓
✓
✓
C
:
C
o
n
c
e
p
t
u
a
l
i
z
a
t
i
o
n
M
:
M
e
t
h
o
d
o
l
o
g
y
So
:
So
f
t
w
a
r
e
Va
:
Va
l
i
d
a
t
i
o
n
Fo
:
Fo
r
mal
a
n
a
l
y
s
i
s
I
:
I
n
v
e
s
t
i
g
a
t
i
o
n
R
:
R
e
so
u
r
c
e
s
D
:
D
a
t
a
C
u
r
a
t
i
o
n
O
:
W
r
i
t
i
n
g
-
O
r
i
g
i
n
a
l
D
r
a
f
t
E
:
W
r
i
t
i
n
g
-
R
e
v
i
e
w
&
E
d
i
t
i
n
g
Vi
:
Vi
su
a
l
i
z
a
t
i
o
n
Su
:
Su
p
e
r
v
i
s
i
o
n
P
:
P
r
o
j
e
c
t
a
d
mi
n
i
st
r
a
t
i
o
n
Fu
:
Fu
n
d
i
n
g
a
c
q
u
i
si
t
i
o
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
E
a
r
ly
d
etec
tio
n
o
f ta
r
s
p
o
t
d
is
ea
s
e
in
Zea
ma
ys u
s
in
g
… (
C
l
a
u
d
ia
N
o
h
emy
Mo
n
to
ya
-
E
s
tr
a
d
a
)
4729
CO
NF
L
I
C
T
O
F
I
N
T
E
R
E
S
T
ST
A
T
E
M
E
NT
Au
th
o
r
s
s
tate
n
o
co
n
f
lict o
f
in
t
er
est.
DATA AV
AI
L
AB
I
L
I
T
Y
T
h
e
d
ata
th
at
s
u
p
p
o
r
t
th
e
f
i
n
d
i
n
g
s
o
f
th
is
s
tu
d
y
ar
e
a
v
ailab
le
f
r
o
m
th
e
c
o
r
r
esp
o
n
d
in
g
au
th
o
r
,
[
YAG]
,
u
p
o
n
r
ea
s
o
n
ab
le
r
eq
u
est.
RE
F
E
R
E
NC
E
S
[
1
]
L.
S
e
l
f
o
r
s,
P
.
W
e
r
t
s,
a
n
d
T.
G
r
e
e
n
,
“
D
e
t
e
c
t
i
n
g
a
n
d
m
a
n
a
g
i
n
g
n
e
w
f
o
l
i
a
r
c
o
r
n
d
i
s
e
a
se
s
i
n
t
h
e
U
.
S
.
,
”
C
r
o
p
s
&
S
o
i
l
s
,
v
o
l
.
5
1
,
n
o
.
4
,
p
p
.
3
2
–
5
9
,
2
0
1
8
,
d
o
i
:
1
0
.
2
1
3
4
/
c
s
2
0
1
8
.
5
1
.
0
4
0
6
.
[
2
]
G
.
M
a
h
u
k
u
e
t
a
l
.
,
“
C
o
mb
i
n
e
d
l
i
n
k
a
g
e
a
n
d
a
sso
c
i
a
t
i
o
n
ma
p
p
i
n
g
i
d
e
n
t
i
f
i
e
s
a
m
a
j
o
r
Q
TL
(
q
R
t
sc
8
-
1
)
,
c
o
n
f
e
r
r
i
n
g
t
a
r
s
p
o
t
c
o
m
p
l
e
x
r
e
si
st
a
n
c
e
i
n
m
a
i
z
e
,
”
T
h
e
o
re
t
i
c
a
l
a
n
d
A
p
p
l
i
e
d
G
e
n
e
t
i
c
s
,
v
o
l
.
1
2
9
,
n
o
.
6
,
p
p
.
1
2
1
7
–
1
2
2
9
,
M
a
r
.
2
0
1
6
,
d
o
i
:
1
0
.
1
0
0
7
/
s0
0
1
2
2
-
016
-
2
6
9
8
-
y.
[
3
]
C
.
R
.
D
.
S
i
l
v
a
e
t
a
l
.
,
“
R
e
c
o
v
e
r
y
p
l
a
n
f
o
r
t
a
r
s
p
o
t
o
f
c
o
r
n
,
c
a
u
s
e
d
b
y
p
h
y
l
l
a
c
h
o
ra
m
a
y
d
i
s
,
”
Pl
a
n
t
H
e
a
l
t
h
Pr
o
g
r
e
s
s
,
v
o
l
.
2
2
,
n
o
.
4
,
p
p
.
5
9
6
–
6
1
6
,
J
a
n
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
9
4
/
P
H
P
-
04
-
21
-
0
0
7
4
-
R
P
.
[
4
]
N
.
M
.
K
l
e
c
z
e
w
sk
i
a
n
d
N
.
D
.
B
o
w
m
a
n
,
“
A
n
o
b
s
e
r
v
a
t
i
o
n
o
f
c
o
r
n
t
a
r
s
p
o
t
d
i
s
p
e
r
s
a
l
f
r
o
m
a
g
r
i
c
u
l
t
u
r
a
l
f
i
e
l
d
s
t
o
a
n
i
so
l
a
t
e
d
u
r
b
a
n
p
l
o
t
,
”
Pl
a
n
t
H
e
a
l
t
h
Pr
o
g
ress
,
v
o
l
.
2
2
,
n
o
.
1
,
p
p
.
6
9
–
7
1
,
J
a
n
.
2
0
2
1
,
d
o
i
:
1
0
.
1
0
9
4
/
P
H
P
-
10
-
20
-
0
0
8
2
-
B
R
.
[
5
]
N
.
M
.
K
l
e
c
z
e
w
sk
i
e
t
a
l
.
,
“
D
o
c
u
me
n
t
i
n
g
t
h
e
e
s
t
a
b
l
i
sh
me
n
t
,
sp
r
e
a
d
,
a
n
d
se
v
e
r
i
t
y
o
f
p
h
y
l
l
a
c
h
o
r
a
ma
y
d
i
s
o
n
c
o
r
n
,
i
n
t
h
e
U
n
i
t
e
d
S
t
a
t
e
s,”
J
o
u
rn
a
l
o
f
I
n
t
e
g
r
a
t
e
d
P
e
st
M
a
n
a
g
e
m
e
n
t
,
v
o
l
.
1
1
,
n
o
.
1
,
Ja
n
.
2
0
2
0
,
d
o
i
:
1
0
.
1
0
9
3
/
j
i
p
m
/
p
m
a
a
0
1
2
.
[
6
]
J.
H
o
c
k
,
J.
K
r
a
n
z
,
a
n
d
B
.
L.
R
e
n
f
r
o
,
“
S
t
u
d
i
e
s
o
n
t
h
e
e
p
i
d
e
mi
o
l
o
g
y
o
f
t
h
e
t
a
r
s
p
o
t
d
i
sea
s
e
c
o
m
p
l
e
x
o
f
ma
i
z
e
i
n
M
e
x
i
c
o
,
”
Pl
a
n
t
Pa
t
h
o
l
o
g
y
,
v
o
l
.
4
4
,
n
o
.
3
,
p
p
.
4
9
0
–
5
0
2
,
1
9
9
5
,
d
o
i
:
1
0
.
1
1
1
1
/
j
.
1
3
6
5
-
3
0
5
9
.
1
9
9
5
.
t
b
0
1
6
7
1
.
x
.
[
7
]
J.
V
.
-
T
o
r
r
e
s
e
t
a
l
.
,
“
Ta
r
sp
o
t
:
a
n
u
n
d
e
r
st
u
d
i
e
d
d
i
s
e
a
se
t
h
r
e
a
t
e
n
i
n
g
c
o
r
n
p
r
o
d
u
c
t
i
o
n
i
n
t
h
e
A
mer
i
c
a
s,”
P
l
a
n
t
D
i
sea
s
e
,
v
o
l
.
1
0
4
,
n
o
.
1
0
,
p
p
.
2
5
4
1
–
2
5
5
0
,
O
c
t
.
2
0
2
0
,
d
o
i
:
1
0
.
1
0
9
4
/
P
D
I
S
-
02
-
20
-
0
4
4
9
-
F
E.
[
8
]
K
.
A
.
M
o
t
t
a
l
e
b
,
A
.
Lo
l
a
d
z
e
,
K
.
S
o
n
d
e
r
,
G
.
K
r
u
s
e
ma
n
,
a
n
d
F
.
S
.
V
i
c
e
n
t
e
,
“
Th
r
e
a
t
s
o
f
t
a
r
s
p
o
t
c
o
m
p
l
e
x
d
i
se
a
s
e
o
f
mai
z
e
i
n
t
h
e
U
n
i
t
e
d
S
t
a
t
e
s
o
f
A
mer
i
c
a
a
n
d
i
t
s
g
l
o
b
a
l
c
o
n
se
q
u
e
n
c
e
s
,
”
M
i
t
i
g
a
t
i
o
n
a
n
d
A
d
a
p
t
a
t
i
o
n
S
t
r
a
t
e
g
i
e
s
f
o
r
G
l
o
b
a
l
C
h
a
n
g
e
,
v
o
l
.
2
4
,
n
o
.
2
,
p
p
.
2
8
1
–
3
0
0
,
F
e
b
.
2
0
1
9
,
d
o
i
:
1
0
.
1
0
0
7
/
s1
1
0
2
7
-
0
1
8
-
9
8
1
2
-
1.
[
9
]
S
.
O
h
e
t
a
l
.
,
“
Est
i
m
a
t
i
o
n
o
f
v
i
s
u
a
l
r
a
t
i
n
g
o
f
TA
R
sp
o
t
d
i
s
e
a
s
e
o
f
c
o
r
n
u
s
i
n
g
u
n
m
a
n
n
e
d
a
e
r
i
a
l
sy
s
t
e
m
s
(
U
A
S
)
d
a
t
a
a
n
d
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
t
e
c
h
n
i
q
u
e
s
,
”
i
n
I
G
AR
S
S
2
0
2
0
-
2
0
2
0
I
EEE
I
n
t
e
rn
a
t
i
o
n
a
l
G
e
o
s
c
i
e
n
c
e
a
n
d
Re
m
o
t
e
S
e
n
s
i
n
g
S
y
m
p
o
s
i
u
m
,
W
a
i
k
o
l
o
a
,
U
S
A
:
I
EEE,
2
0
2
0
,
p
p
.
4
8
8
2
–
4
8
8
5
.
d
o
i
:
1
0
.
1
1
0
9
/
I
G
A
R
S
S
3
9
0
8
4
.
2
0
2
0
.
9
3
2
4
3
3
1
.
[
1
0
]
N
.
M
.
K
l
e
c
z
e
w
s
k
i
,
J.
D
o
n
n
e
l
l
y
,
a
n
d
R
.
H
i
g
g
i
n
s,
“
P
h
y
l
l
a
c
h
o
ra
m
a
y
d
i
s
,
c
a
u
s
a
l
a
g
e
n
t
o
f
t
a
r
sp
o
t
o
n
c
o
r
n
,
c
a
n
o
v
e
r
w
i
n
t
e
r
i
n
n
o
r
t
h
e
r
n
i
l
l
i
n
o
i
s,
”
P
l
a
n
t
H
e
a
l
t
h
Pr
o
g
r
e
ss
,
v
o
l
.
2
0
,
n
o
.
3
,
p
p
.
1
7
8
–
1
7
8
,
Ja
n
.
2
0
1
9
,
d
o
i
:
1
0
.
1
0
9
4
/
P
H
P
-
04
-
19
-
0
0
3
0
-
B
R
.
[
1
1
]
E.
N
.
R
.
H
e
r
r
e
r
a
,
Y
.
M
.
O
.
F
u
e
n
t
e
s,
E
.
C
.
C
h
á
v
e
z
,
J.
L
.
F
l
o
r
e
s,
M
.
C
.
S
i
l
l
e
r
,
a
n
d
R
.
R
.
G
u
e
r
r
a
,
“
F
u
n
g
i
a
sso
c
i
a
t
e
d
w
i
t
h
t
h
e
t
a
r
s
p
o
t
i
n
ma
i
z
e
c
u
l
t
i
v
a
t
i
o
n
i
n
M
e
x
i
c
o
,
”
R
e
v
i
s
t
a
Me
x
i
c
a
n
a
d
e
C
i
e
n
c
i
a
s
A
g
rí
c
o
l
a
s
,
v
o
l
.
8
,
n
o
.
2
,
p
p
.
4
5
7
–
4
6
2
,
A
u
g
.
2
0
1
7
,
d
o
i
:
1
0
.
2
9
3
1
2
/
r
e
me
x
c
a
.
v
8
i
2
.
6
5
.
[
1
2
]
C
.
Zh
a
n
g
e
t
a
l
.
,
“
M
o
n
i
t
o
r
i
n
g
t
a
r
sp
o
t
d
i
se
a
se
i
n
c
o
r
n
a
t
d
i
f
f
e
r
e
n
t
c
a
n
o
p
y
a
n
d
t
e
m
p
o
r
a
l
l
e
v
e
l
s
u
si
n
g
a
e
r
i
a
l
m
u
l
t
i
sp
e
c
t
r
a
l
i
ma
g
i
n
g
a
n
d
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
,
”
F
ro
n
t
i
e
rs
i
n
P
l
a
n
t
S
c
i
e
n
c
e
,
v
o
l
.
1
3
,
J
a
n
.
2
0
2
3
,
d
o
i
:
1
0
.
3
3
8
9
/
f
p
l
s.
2
0
2
2
.
1
0
7
7
4
0
3
.
[
1
3
]
L.
H
.
R
a
m
o
s
a
n
d
J
.
S
.
S
.
I
sl
a
s
,
“
D
i
a
g
r
a
mm
a
t
i
c
sc
a
l
e
se
v
e
r
i
t
y
f
o
r
t
a
r
sp
o
t
c
o
mp
l
e
x
i
n
ma
i
z
e
,
”
R
e
v
i
st
a
Me
x
i
c
a
n
a
d
e
F
i
t
o
p
a
t
o
l
o
g
í
a
,
v
o
l
.
3
3
,
n
o
.
1
,
p
p
.
9
5
–
1
0
3
,
2
0
2
5
.
[
1
4
]
C
.
H
.
B
o
c
k
,
G
.
H
.
P
o
o
l
e
,
P
.
E
.
P
a
r
k
e
r
,
a
n
d
T
.
R
.
G
o
t
t
w
a
l
d
,
“
P
l
a
n
t
d
i
sea
se
s
e
v
e
r
i
t
y
e
s
t
i
mat
e
d
v
i
su
a
l
l
y
,
b
y
d
i
g
i
t
a
l
p
h
o
t
o
g
r
a
p
h
y
a
n
d
i
ma
g
e
a
n
a
l
y
si
s
,
a
n
d
b
y
h
y
p
e
r
sp
e
c
t
r
a
l
i
ma
g
i
n
g
,
”
C
r
i
t
i
c
a
l
R
e
v
i
e
w
s
i
n
P
l
a
n
t
S
c
i
e
n
c
e
s
,
v
o
l
.
2
9
,
n
o
.
2
,
p
p
.
5
9
–
1
0
7
,
M
a
r
.
2
0
1
0
,
d
o
i
:
1
0
.
1
0
8
0
/
0
7
3
5
2
6
8
1
0
0
3
6
1
7
2
8
5
.
[
1
5
]
C
.
A
.
R
.
-
G
a
r
c
í
a
,
L.
J.
M
.
-
M
a
r
t
í
n
e
z
,
a
n
d
J.
H
.
B
.
-
R
i
o
b
o
,
“
Es
t
i
m
a
t
i
n
g
c
h
l
o
r
o
p
h
y
l
l
a
n
d
n
i
t
r
o
g
e
n
c
o
n
t
e
n
t
s
i
n
m
a
i
z
e
l
e
a
v
e
s
(
Z
e
a
may
s
L.
)
w
i
t
h
s
p
e
c
t
r
o
sc
o
p
i
c
a
n
a
l
y
si
s
,
”
R
e
v
i
st
a
C
o
l
o
m
b
i
a
n
a
d
e
C
i
e
n
c
i
a
s
H
o
rt
í
c
o
l
a
s
,
v
o
l
.
1
6
,
n
o
.
1
,
Jan
.
2
0
2
2
,
d
o
i
:
1
0
.
1
7
5
8
4
/
r
c
c
h
.
2
0
2
2
v
1
6
i
1
.
1
3
3
9
8
.
[
1
6
]
A.
-
K
.
M
a
h
l
e
i
n
,
“
P
l
a
n
t
d
i
se
a
se
d
e
t
e
c
t
i
o
n
b
y
i
ma
g
i
n
g
s
e
n
s
o
r
s
–
p
a
r
a
l
l
e
l
s
a
n
d
s
p
e
c
i
f
i
c
d
e
m
a
n
d
s
f
o
r
p
r
e
c
i
si
o
n
a
g
r
i
c
u
l
t
u
r
e
a
n
d
p
l
a
n
t
p
h
e
n
o
t
y
p
i
n
g
,
”
Pl
a
n
t
D
i
se
a
s
e
,
v
o
l
.
1
0
0
,
n
o
.
2
,
p
p
.
2
4
1
–
2
5
1
,
F
e
b
.
2
0
1
6
,
d
o
i
:
1
0
.
1
0
9
4
/
P
D
I
S
-
03
-
15
-
0
3
4
0
-
F
E
.
[
1
7
]
A
.
Jo
h
a
n
n
e
s
e
t
a
l
.
,
“
A
u
t
o
ma
t
i
c
p
l
a
n
t
d
i
se
a
se
d
i
a
g
n
o
si
s
u
si
n
g
mo
b
i
l
e
c
a
p
t
u
r
e
d
e
v
i
c
e
s,
a
p
p
l
i
e
d
o
n
a
w
h
e
a
t
u
s
e
c
a
se
,
”
C
o
m
p
u
t
e
rs
a
n
d
El
e
c
t
r
o
n
i
c
s i
n
Ag
r
i
c
u
l
t
u
re
,
v
o
l
.
1
3
8
,
p
p
.
2
0
0
–
2
0
9
,
2
0
1
7
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
c
o
mp
a
g
.
2
0
1
7
.
0
4
.
0
1
3
.
[
1
8
]
A
.
L
o
l
a
d
z
e
,
F
.
A
.
R
o
d
r
i
g
u
e
s
,
F
.
T
o
l
e
d
o
,
F
.
S
.
V
i
c
e
n
t
e
,
B
.
G
é
r
a
r
d
,
a
n
d
M
.
P
.
B
o
d
d
u
p
a
l
l
i
,
“
A
p
p
l
i
c
a
t
i
o
n
o
f
r
e
mo
t
e
se
n
s
i
n
g
f
o
r
p
h
e
n
o
t
y
p
i
n
g
t
a
r
sp
o
t
c
o
mp
l
e
x
r
e
si
st
a
n
c
e
i
n
m
a
i
z
e
,
”
Fro
n
t
i
e
rs
i
n
P
l
a
n
t
S
c
i
e
n
c
e
,
v
o
l
.
1
0
,
p
p
.
1
-
1
0
,
A
p
r
.
2
0
1
9
,
d
o
i
:
1
0
.
3
3
8
9
/
f
p
l
s.
2
0
1
9
.
0
0
5
5
2
.
[
1
9
]
S
.
O
h
e
t
a
l
.
,
“
T
a
r
s
p
o
t
d
i
se
a
se
q
u
a
n
t
i
f
i
c
a
t
i
o
n
u
s
i
n
g
u
n
ma
n
n
e
d
a
i
r
c
r
a
f
t
sy
st
e
ms
(
U
A
S
)
d
a
t
a
,
”
Re
m
o
t
e
S
e
n
s
i
n
g
,
v
o
l
.
1
3
,
n
o
.
1
3
,
Ju
n
.
2
0
2
1
,
d
o
i
:
1
0
.
3
3
9
0
/
r
s1
3
1
3
2
5
6
7
.
[
2
0
]
D.
-
Y
.
Le
e
e
t
a
l
.
,
“
C
o
n
t
o
u
r
-
b
a
se
d
d
e
t
e
c
t
i
o
n
a
n
d
q
u
a
n
t
i
f
i
c
a
t
i
o
n
o
f
t
a
r
s
p
o
t
s
t
r
o
m
a
t
a
u
s
i
n
g
r
e
d
-
g
r
e
e
n
-
b
l
u
e
(
R
G
B
)
i
ma
g
e
r
y
,
”
Fro
n
t
i
e
rs
i
n
P
l
a
n
t
S
c
i
e
n
c
e
,
v
o
l
.
1
2
,
O
c
t
.
2
0
2
1
,
d
o
i
:
1
0
.
3
3
8
9
/
f
p
l
s
.
2
0
2
1
.
6
7
5
9
7
5
.
[
2
1
]
G
.
Jam
e
s,
D
.
W
i
t
t
e
n
,
T.
H
a
st
i
e
,
a
n
d
R
.
Ti
b
s
h
i
r
a
n
i
,
An
i
n
t
r
o
d
u
c
t
i
o
n
t
o
s
t
a
t
i
st
i
c
a
l
l
e
a
r
n
i
n
g
:
w
i
t
h
a
p
p
l
i
c
a
t
i
o
n
s
i
n
R
,
N
e
w
Y
o
r
k
:
S
p
r
i
n
g
e
r
,
2
0
1
7
,
d
o
i
:
1
0
.
1
0
0
7
/
9
7
8
-
1
-
0
7
1
6
-
1
4
1
8
-
1
.
[
2
2
]
A
.
E
.
M
a
x
w
e
l
l
,
T.
A
.
W
a
r
n
e
r
,
a
n
d
F
.
F
a
n
g
,
“
I
mp
l
e
me
n
t
a
t
i
o
n
o
f
ma
c
h
i
n
e
-
l
e
a
r
n
i
n
g
c
l
a
ssi
f
i
c
a
t
i
o
n
i
n
r
e
mo
t
e
se
n
si
n
g
:
a
n
a
p
p
l
i
e
d
r
e
v
i
e
w
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
R
e
m
o
t
e
S
e
n
s
i
n
g
,
v
o
l
.
3
9
,
n
o
.
9
,
p
p
.
2
7
8
4
–
2
8
1
7
,
M
a
y
2
0
1
8
,
d
o
i
:
1
0
.
1
0
8
0
/
0
1
4
3
1
1
6
1
.
2
0
1
8
.
1
4
3
3
3
4
3
.
[
2
3
]
F
.
B
i
c
a
k
l
i
,
G
.
K
a
p
l
a
n
,
a
n
d
A
.
S
.
A
l
q
a
semi
,
“
C
a
n
n
a
b
i
s
s
a
t
i
v
a
L
.
S
p
e
c
t
r
a
l
d
i
scri
m
i
n
a
t
i
o
n
a
n
d
c
l
a
ss
i
f
i
c
a
t
i
o
n
u
s
i
n
g
sat
e
l
l
i
t
e
i
m
a
g
e
r
y
a
n
d
m
a
c
h
i
n
e
l
e
a
r
n
i
n
g
,
”
A
g
r
i
c
u
l
t
u
r
e
,
v
o
l
.
1
2
,
n
o
.
6
,
J
u
n
.
2
0
2
2
,
d
o
i
:
1
0
.
3
3
9
0
/
a
g
r
i
c
u
l
t
u
r
e
1
2
0
6
0
8
4
2
.
[
2
4
]
B
.
D
.
R
i
p
l
e
y
,
“
T
h
e
R
p
r
o
j
e
c
t
i
n
st
a
t
i
st
i
c
a
l
c
o
mp
u
t
i
n
g
,
”
M
S
O
R
C
o
n
n
e
c
t
i
o
n
s
,
v
o
l
.
1
,
n
o
.
1
,
p
p
.
2
3
–
2
5
,
F
e
b
.
2
0
0
1
,
d
o
i
:
1
0
.
1
1
1
2
0
/
mso
r
.
2
0
0
1
.
0
1
0
1
0
0
2
3
.
[
2
5
]
J.
L
o
v
e
e
t
a
l
.
,
“
JA
S
P
:
g
r
a
p
h
i
c
a
l
st
a
t
i
st
i
c
a
l
s
o
f
t
w
a
r
e
f
o
r
c
o
mm
o
n
st
a
t
i
st
i
c
a
l
d
e
si
g
n
s
,
”
J
o
u
r
n
a
l
o
f
S
t
a
t
i
s
t
i
c
a
l
S
o
f
t
w
a
r
e
,
v
o
l
.
8
8
,
n
o
.
2
,
2
0
1
9
,
d
o
i
:
1
0
.
1
8
6
3
7
/
j
ss.
v
0
8
8
.
i
0
2
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
1
4
,
No
.
6
,
Dec
em
b
er
2
0
2
5
:
4
7
2
2
-
4
7
3
0
4730
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
Cla
u
d
i
a
No
h
e
m
y
M
o
n
to
y
a
-
Est
r
a
d
a
re
c
e
iv
e
d
h
e
r
B
.
Sc
.
in
M
icro
b
i
o
l
o
g
y
fro
m
th
e
Ca
th
o
l
ic
Un
iv
e
rsit
y
o
f
M
a
n
iza
les
,
2
0
0
6
,
Co
l
o
m
b
ia.
S
h
e
o
b
tain
e
d
h
e
r
M
.
Sc
.
in
P
lan
t
P
a
th
o
l
o
g
y
fro
m
t
h
e
Un
i
v
e
rsity
o
f
Ca
ld
a
s,
2
0
1
1
,
Co
l
o
m
b
ia
a
n
d
h
e
r
P
h
.
D.
i
n
P
la
n
t
P
a
th
o
l
o
g
y
fro
m
th
e
F
e
d
e
ra
l
U
n
iv
e
rsit
y
o
f
Vic
o
sa
,
2
0
1
8
,
Bra
z
il
.
S
h
e
c
o
m
p
l
e
ted
h
e
r
p
o
st
d
o
c
t
o
ra
l
sta
y
a
t
th
e
Au
t
o
n
o
m
o
u
s
Un
iv
e
rsity
o
f
Ch
a
p
in
g
o
a
n
d
a
t
th
e
Na
ti
o
n
a
l
S
e
rv
ice
o
f
Ag
ri
-
F
o
o
d
He
a
lt
h
,
S
a
fe
ty
a
n
d
Q
u
a
li
ty
in
M
e
x
ico
,
2
0
2
0
.
S
h
e
is
a
n
As
so
c
iate
P
ro
fe
ss
o
r
a
t
th
e
In
sti
tu
te
o
f
Re
se
a
rc
h
in
M
icro
b
i
o
l
o
g
y
a
n
d
A
g
ro
i
n
d
u
st
rial
Bio
tec
h
n
o
l
o
g
y
a
t
th
e
Ca
th
o
li
c
Un
iv
e
rsit
y
o
f
M
a
n
iza
les
.
S
h
e
h
a
s
e
x
p
e
rien
c
e
d
e
v
e
lo
p
in
g
i
m
m
u
n
o
lo
g
ica
l
tes
ts
f
o
r
th
e
d
e
tec
ti
o
n
o
f
b
a
c
teria
i
n
t
h
e
fiel
d
,
g
e
n
e
ti
c
d
i
v
e
rsity
a
n
d
a
g
g
re
ss
iv
e
n
e
ss
o
f
p
a
th
o
g
e
n
s,
a
n
d
q
u
a
n
ti
f
y
i
n
g
d
a
m
a
g
e
c
a
u
se
d
b
y
b
a
c
teria
,
fu
n
g
i,
a
n
d
n
e
m
a
to
d
e
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
c
m
o
n
to
y
a
@u
c
m
.
e
d
u
.
c
o
.
Fre
d
d
y
Eli
se
o
H
e
r
n
a
n
d
e
z
-
J
o
r
g
e
is
m
a
ste
r
'
s
d
e
g
re
e
in
Ag
ricu
lt
u
ra
l
P
ro
d
u
c
ti
o
n
S
y
ste
m
s,
Un
iv
e
rsity
o
f
Ca
ld
a
s,
De
p
a
rtme
n
t
o
f
Ag
ricu
lt
u
ra
l
P
ro
d
u
c
ti
o
n
,
Ag
ric
u
lt
u
ra
l
P
ro
d
u
c
ti
o
n
Re
se
a
rc
h
a
n
d
P
r
o
j
e
c
ti
o
n
G
ro
u
p
-
G
IP
P
A.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
e
li
se
o
.
h
e
rn
a
n
d
e
z
@u
c
a
ld
a
s.e
d
u
.
c
o
.
O
sc
a
r
Ca
r
d
o
n
a
-
M
o
r
a
les
re
c
e
iv
e
d
h
is
B.
S
c
.
in
E
lec
tro
n
ic
E
n
g
i
n
e
e
rin
g
,
f
o
c
u
sin
g
o
n
c
o
n
tro
l
sy
ste
m
s
a
n
d
d
i
g
it
a
l
sig
n
a
l
p
ro
c
e
ss
in
g
fro
m
th
e
Un
iv
e
rsi
d
a
d
Na
c
io
n
a
l
d
e
C
o
lo
m
b
ia,
M
a
n
iza
les
,
in
2
0
0
9
.
He
o
b
tain
e
d
h
is
M
.
S
c
.
in
In
d
u
strial
En
g
in
e
e
ri
n
g
a
n
d
A
u
to
m
a
ti
o
n
i
n
2
0
1
1
a
n
d
h
is
P
h
.
D.
i
n
En
g
in
e
e
rin
g
–
A
u
to
m
a
ti
c
in
2
0
1
6
fr
o
m
th
e
sa
m
e
in
stit
u
t
io
n
.
He
se
rv
e
s
a
s
th
e
h
e
a
d
o
f
t
h
e
A
u
to
m
a
ti
c
Re
se
a
rc
h
G
ro
u
p
a
t
th
e
Au
t
o
n
o
m
o
u
s
Un
i
v
e
rsity
o
f
M
a
n
iza
les
,
w
h
e
re
h
e
a
lso
c
o
o
rd
i
n
a
tes
th
e
De
p
a
rtme
n
t
o
f
El
e
c
tro
n
ics
a
n
d
Au
t
o
m
a
ti
o
n
a
n
d
th
e
S
p
e
c
ializa
ti
o
n
in
Artifi
c
ial
In
tell
ig
e
n
c
e
.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
si
g
n
a
l
p
r
o
c
e
ss
in
g
,
tele
c
o
m
m
u
n
ica
ti
o
n
s,
re
m
o
te
se
n
sin
g
,
a
n
d
i
n
telli
g
e
n
t
in
d
u
strial
a
n
d
e
n
v
iro
n
m
e
n
tal
m
o
n
it
o
rin
g
sy
ste
m
s.
His
a
c
a
d
e
m
ic
c
o
n
tri
b
u
ti
o
n
s
e
n
c
o
m
p
a
ss
d
e
v
e
l
o
p
i
n
g
m
o
n
it
o
ri
n
g
sy
ste
m
s
fo
r
p
re
d
icti
v
e
m
a
in
ten
a
n
c
e
a
n
d
a
p
p
l
y
i
n
g
re
m
o
te
se
n
sin
g
tec
h
n
o
lo
g
ies
in
a
g
ricu
lt
u
re
a
n
d
e
n
v
i
ro
n
m
e
n
tal
m
a
n
a
g
e
m
e
n
t.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
o
sc
a
r.
c
a
rd
o
n
a
m
@a
u
to
n
o
m
a
.
e
d
u
.
c
o
.
O
sc
a
r
Ló
p
e
z
-
Na
r
a
n
j
o
is
m
a
ste
r'
s
d
e
g
re
e
i
n
Re
m
o
te
S
e
n
s
in
g
,
Un
iv
e
rsity
o
f
Ca
ld
a
s,
a
n
d
G
e
o
lo
g
i
ts
fr
o
m
t
h
e
Un
iv
e
rsi
d
a
d
d
e
Ca
l
d
a
s.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
o
slo
n
a
ra
n
jo
@g
m
a
il
.
c
o
m
.
Ye
iso
n
Alb
e
r
to
G
a
r
c
é
s
-
G
ó
m
e
z
re
c
e
iv
e
d
b
a
c
h
e
lo
r
’s
d
e
g
r
e
e
in
El
e
c
tro
n
ic
En
g
i
n
e
e
rin
g
,
a
n
d
m
a
ste
r’s
d
e
g
re
e
s
a
n
d
P
h
.
D.
in
E
n
g
i
n
e
e
rin
g
fr
o
m
De
p
a
rtme
n
t
o
f
E
lec
tri
c
a
l,
El
e
c
tro
n
ic
a
n
d
C
o
m
p
u
ter
E
n
g
i
n
e
e
rin
g
,
Un
i
v
e
rsid
a
d
Na
c
io
n
a
l
d
e
Co
l
o
m
b
ia,
M
a
n
iza
les
,
Co
lo
m
b
ia,
i
n
2
0
0
9
,
2
0
1
1
a
n
d
2
0
1
5
,
re
sp
e
c
ti
v
e
ly
.
He
is F
u
ll
P
ro
fe
ss
o
r
a
t
th
e
Ac
a
d
e
m
ic Un
it
fo
r
Train
in
g
i
n
Na
tu
ra
l
S
c
ien
c
e
s
a
n
d
M
a
t
h
e
m
a
ti
c
s,
Un
iv
e
rsid
a
d
C
a
tó
li
c
a
d
e
M
a
n
iza
les
,
a
n
d
tea
c
h
e
s
se
v
e
ra
l
c
o
u
rse
s
su
c
h
a
s
e
x
p
e
rime
n
tal
d
e
sig
n
,
sta
ti
stics
,
a
n
d
p
h
y
sic
s
.
His
m
a
in
re
se
a
rc
h
fo
c
u
s
is
o
n
a
p
p
li
e
d
tec
h
n
o
l
o
g
ies
,
e
m
b
e
d
d
e
d
s
y
ste
m
,
p
o
we
r
e
lec
tro
n
ics
,
p
o
we
r
q
u
a
li
t
y
,
b
u
t
a
lso
m
a
n
y
o
t
h
e
r
a
re
a
s
o
f
e
lec
tro
n
ics
,
sig
n
a
l
p
r
o
c
e
ss
in
g
a
n
d
d
id
a
c
ti
c
s.
He
p
u
b
li
s
h
e
d
m
o
re
t
h
a
n
3
0
sc
ien
ti
fic
a
n
d
re
se
a
rc
h
p
u
b
li
c
a
ti
o
n
s,
a
m
o
n
g
th
e
m
m
o
re
t
h
a
n
1
0
j
o
u
rn
a
l
p
a
p
e
rs.
He
wo
r
k
e
d
a
s
p
rin
c
i
p
a
l
re
se
a
rc
h
e
r
o
n
c
o
m
m
e
rc
ial
p
r
o
jec
ts
a
n
d
p
ro
jec
ts
b
y
th
e
M
i
n
istry
o
f
S
c
ien
c
e
,
Tec
h
a
n
d
In
n
o
v
a
ti
o
n
,
Re
p
u
b
li
c
o
f
Co
l
o
m
b
i
a
.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
y
g
a
rc
e
s@
u
c
m
.
e
d
u
.
c
o
.
Evaluation Warning : The document was created with Spire.PDF for Python.