Estimating the content of clover and grass in the PowerPoint Presentation, PPT - DocSlides

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

Outline

Motivation

Test bed and data acquisition

Methodology

Results

Conclusion and future work

Motivation

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

Test 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%

Test bed and data acquisition

Test bed and data acquisition

Methodology

Methodology

Illumination classification

Direct and indirect sun lightHistogram of pixel intensitiesCross correlation used for classification

Methodology

Coverage estimationRemove background:Soil, dead plant material and deep shadowsExtract clover leafs:Inverted edge image + erosionGrass:RemainingTrained on patches

MEthodology

Transformation of coverage to dry matter distribution

Results

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

-

Results

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

Conclusion 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