Transcript PURE SPECIES OF GRASS DISCRIMINATION WITH THE HELP OF HYPERSPECTRAL IMAGING NIR Laura Monica DALE,,, Ioan ROTAR1, Anca BOGDAN1, Florin PACURAR1, Andre THEWIS2,

PURE SPECIES OF GRASS
DISCRIMINATION WITH THE HELP
OF HYPERSPECTRAL IMAGING NIR
Laura Monica DALE,,, Ioan ROTAR1, Anca BOGDAN1, Florin PACURAR1, Andre THEWIS2, Juan FERNÁNDEZ PIERNA3, Nicaise
KAYOKA MUKENDI3, Vincent BEATEN3 Department of Grassland and Forage Crops, University of Agricultural Science and
Medicine Veterinary, Cluj Napoca, 3-5, Calea Manaştur, 400372, Cluj Napoca
Animal Science Unit, Gembloux Agro-Bio Tech, University of Liège, 2, Passage des Déportés, 5030 Gembloux, Belgium
Walloon Agricultural Research Centre, Valorisation of Agricultural Products Department, 24 Chaussée de Namur, 5030
Gembloux, Belgium
„Use of NIR HSI to detect the pure samples of a mixture”
Arnica
montana L.
n= 4
(No =16) *
Trifolium
repens L.
n= 4
(No =16) *
Hieracium
aurantiacum L.
n= 4
(No =16) *
Festuca
rubra L.
Agrostis
capillaris L.
n= 4
(No =16) *
n= 4
(No =16) *
- construction of spectral classes ;
- identification of the spectral classes for pure samples;
- discrimination of pure samples of a mixture of several species.
* represents the number of preleveted samples from the category of forages (n x 4 =16)
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Were build 4 classes for each type of pure samples:
- Arnica montana L (AM)
- Trifolium repens L (TR)
- Hieracium aurantiacum L (HA)
- Festuca rubra L(FR)
1000 spectra contein all data base.
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Classes
AM
TR
HA
FR
AM
100
0
0
0
TR
0
100
0
0
HA
0
0
100
0
FR
0
0
0
100
4
Classes
AM
TR
HA
FR
AM
98.40
0.30
1.20
1.000e-01
TR
0
99.90
1.000e-02
0
HA
0.80
0.40
98.80
0
FR
0
0
0
100
5
The external validation model was build from
5 classes (4 pure species wich are in
calibration model + Agrostis capillaris(AC)).
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Samples/Scores Plot of X,c & Xac,
1.5
Y Predicted 2 (Class 2)
1
0.5
0
Y Predicted 2 (Class 2)
Class 0
Class 1
Class 2
Class 3
Class 4
Discrim Y 2
x-axis zero
y-axis zero
-0.5
-1
200
400
600
800
Scores on LV 1 (88.48%)
1000
1200
1400
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Samples/Scores Plot of X,c & Xac,
400
300
Scores on LV 2 (10.71%)
200
100
0
-100
Scores on LV 2 (10.71%)
Class 0
Class 1
Class 2
Class 3
Class 4
Discrim Y 4
x-axis zero
y-axis zero
-200
-300
-400
-0.4
-0.2
0
0.2
0.4
0.6
Y Predicted 4 (Class 4)
0.8
1
1.2
1.4
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Samples/Scores Plot of X,c & Xam,
2
Y Predicted 1 (Class 1)
Class 0
Class 1
Class 2
Class 3
Class 4
Discrim Y 1
x-axis zero
y-axis zero
1.5
Y Predicted 1 (Class 1)
1
0.5
0
-0.5
-1
500
Class AM
AM 97.62
1000
1500
TR
0.79
2000
2500
Sample
HA
1.59
3000
3500
4000
4500
5000
FR
0
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Samples/Scores Plot of X,c & Xam,
150
Scores on LV 3 (0.37%)
Class 0
Class 1
Class 2
Class 3
Class 4
Discrim Y 1
x-axis zero
y-axis zero
100
Scores on LV 3 (0.37%)
50
0
-50
-100
-150
-200
-1
-0.5
0
0.5
Y Predicted 1 (Class 1)
1
1.5
2
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Samples/Scores Plot of X,c & Xfr,
1.4
Y Predicted 4 (Class 4)
Class 0
Class 1
Class 2
Class 3
Class 4
Discrim Y 4
x-axis zero
y-axis zero
1.2
Y Predicted 4 (Class 4)
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
Class
FR
500
AM
0
1000
1500
TR
0
2000
2500
Sample
3000
HA
0.60
11
3500
4000
FR
99.40
4500
5000
Samples/Scores Plot of X,c & Xfr,
400
300
Scores on LV 2 (10.71%)
200
100
0
Scores on LV 2 (10.71%)
Class 0
Class 1
Class 2
Class 3
Class 4
Discrim Y 4
x-axis zero
y-axis zero
-100
-200
-300
-400
-0.4
-0.2
0
0.2
0.4
0.6
Y Predicted 4 (Class 4)
12
0.8
1
1.2
1.4
Samples/Scores Plot of X,c & Xhr,
1.5
Y Predicted 3 (Class 3)
1
Y Predicted 3 (Class 3)
Class 0
Class 1
Class 2
Class 3
Class 4
Discrim Y 3
x-axis zero
y-axis zero
0.5
0
-0.5
-1
Class
HA
500
AM
1.37
1000
1500
TR
0
2000
2500
Sample
3000
HA
98.63
13
3500
FR
0
4000
4500
5000
Samples/Scores Plot of X,c & Xhr,
150
Scores on LV 3 (0.37%)
Class 0
Class 1
Class 2
Class 3
Class 4
Discrim Y 3
x-axis zero
y-axis zero
100
Scores on LV 3 (0.37%)
50
0
-50
-100
-150
-200
-1
-0.5
0
0.5
1
1.5
Y Predicted 3 (Class 3)
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Samples/Scores Plot of X,c & Xtr,
2
1.5
Y Predicted 2 (Class 2)
1
0.5
0
Y Predicted 2 (Class 2)
Class 0
Class 1
Class 2
Class 3
Class 4
Discrim Y 2
x-axis zero
y-axis zero
-0.5
-1
Class
TR
500
AM
2.07
1000
1500
TR
96.73
2000
2500
Sample
HA
1.19
15
3000
3500
FR
0
4000
4500
5000
Samples/Scores Plot of X,c & Xtr,
80
Scores on LV 4 (0.12%)
Class 0
Class 1
Class 2
Class 3
Class 4
Discrim Y 2
x-axis zero
y-axis zero
60
Scores on LV 4 (0.12%)
40
20
0
-20
-40
-60
-80
-1
-0.5
0
0.5
Y Predicted 2 (Class 2)
16
1
1.5
2
Classes AM
AM
97.62
TR
2.07
HA
1.37
FR
0
TR
0.79
96.73
0
0
HA
1.59
1.19
98.63
0.60
FR
0
0
0
99.40
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1. The discrimination or the underline of the pure
samples, of the pure species is made using the Imaging
NIR instrument (Camera NIR) with a very good
standard prediction error SEP.
2. Because we can identify species with the help of the
Near Infrared Hyperspectral Imaging or NIR Camera, it
had been tried to relate the crude protein content with
the organic substance’s digestibility content from
samples of which’s floristical composition is known. So
the calibration model can be used in order to
discriminate species from mixes of two or three
species.
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