Presentations text content in Estimating the content of clover and grass in the
Slide1
Estimating the content of clover and grass in the sward using a consumer camera and image processing
A. K. Mortensen
1
, H. Karstoft
1
, K. Søegaard
2
and R. N. Jørgensen
1
1
Department of Engineering, Aarhus University
2
Department of
Agroecology
, Aarhus University
Slide2Outline
Motivation
Test bed and data acquisition
Methodology
Results
Conclusion and future work
Slide3Motivation
Clover and grass is sown as a catch crop and as a feed crop
However,
it
is unknown how the distribution is in the field
The distribution and total dry matter determines
the N-uptake in the field
As feed for dairy cows
intake
milk yield
Current method is destructive analysis
Labour intensive
Cumbersome
Slide4Test bed and data acquisition
Plot experiment (2013) near Research
Center
Foulum
, Denmark
Two different seed mixtures:
“Blanding 35”:
perennial ryegrass and white clover
“Blanding 45”:
perennial ryegrass,
festulolium
, red clover and white clover
Photographed and cut on 4 different occasions
Cuts made within 3 days after photograph
Photographed from above
45 images
Dry matter analysis in laboratory
Dry matter: 195 kg/ha
6111 kg/ha
Clover: 10-72%
Grass: 26-90%
Weed: 0-3%
Slide5Test bed and data acquisition
Slide6Test bed and data acquisition
Slide7Methodology
Slide8Methodology
Illumination classification
Direct and indirect sun lightHistogram of pixel intensitiesCross correlation used for classification
Slide9Methodology
Coverage estimationRemove background:Soil, dead plant material and deep shadowsExtract clover leafs:Inverted edge image + erosionGrass:RemainingTrained on patches
Slide10MEthodology
Transformation of coverage to dry matter distribution
Slide11Results
Coverage estimation
Direct light
Indirect light
Sh
*
So*
G*
C*
F*
W*
∑
Sh
*
So*
G*
C*
F*
W*
∑
Sh
66%
1%
25%
8%
0%
0%
2955
Sh
52%
11%
29%
9%
0%
0%
3934
So
0%
0%
0%
0%
0%
0%
0
So
11%
87%
2%
1%
0%
0%
2422
G
9%
0%
73%
17%
0%
0%
4745
G
11%
1%
65%
23%
0%
0%
6757
C
13%
0%
39%
48%
0%
0%
6565
C
3%
0%
29%
68%
0%
0%
13491
F
30%
20%
46%
4%
0%
0%
254
F
18%
0%
77%
5%
0%
0%
79
W
24%
0%
71%
4%
0%
0%
181
W
7%
0%
52%
41%
0%
0%
617
∑*
3361
98
6986
4255
0
0
-
∑*
3541
2654
9795
11310
0
0
-
Slide12Results
Dry matter distributionNo correlation between error and mixture, clover dry matter or total dry matter.
Error (%-points) Absolute error (%-points) MeanStd.MaxMin MeanStd.MaxMinTest set: Clover-2.19.819.1-15.6 7.95.819.10.0 Grass2.610.417.4-18.7 8.85.718.71.3Training set: Clover0.012.918.9-50.3 8.69.450.30.2 Grass0.013.551.0-20.7 9.29.751.00.2
Slide13Conclusion and future work
It is possible with a reasonable accuracy (8-9%-points)
Greatest source of error
Coverage estimation
Room for improvement
Better estimation of coverage
Texture analysis
Illumination invariant model
Include growth models
Time since last harvest
Temperature sum
Available water
Slide14Slide15Slide16Slide17Slide18Slide19Slide20
Estimating the content of clover and grass in the
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