1. Ændr 2. linje i overskriften til AU Passata Light 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU 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

2. 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular OUTLINE  Motivation  Test bed and data acquisition  Methodology  Results  Conclusion and future work

3. 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular 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

4. 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular 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%

5. 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular TEST BED AND DATA ACQUISITION

6. 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular TEST BED AND DATA ACQUISITION

7. 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular METHODOLOGY

8. 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular METHODOLOGY  Illumination classification  Direct and indirect sun light  Histogram of pixel intensities  Cross correlation used for classification

9. 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular METHODOLOGY  Coverage estimation  Remove background: › Soil, dead plant material and deep shadows  Extract clover leafs: › Inverted edge image + erosion  Grass: › Remaining  Trained on patches

10. 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular METHODOLOGY  Transformation of coverage to dry matter distribution

11. 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular 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% 2955 Sh 52%11%29%9%0% 3934 So 0% 0 So 11%87%2%1%0% 2422 G 9%0%73%17%0% 4745 G 11%1%65%23%0% 6757 C 13%0%39%48%0% 6565 C 3%0%29%68%0% 13491 F 30%20%46%4%0% 254 F 18%0%77%5%0% 79 W 24%0%71%4%0% 181 W 7%0%52%41%0% 617 ∑*3361986986425500- ∑* 3541265497951131000-

12. 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular RESULTS  Dry matter distribution  No correlation between error and mixture, clover dry matter or total dry matter. Error (%-points) Absolute error (%-points) MeanStd.MaxMin MeanStd.MaxMin Test set: Clover -2.19.819.1-15.6 7.95.819.10.0 Grass 2.610.417.4-18.7 8.85.718.71.3 Training set: Clover 0.012.918.9-50.3 8.69.450.30.2 Grass0.013.551.0-20.7 9.29.751.00.2

13. 23 NOVEMBER 2014 NJF AARHUS UNIVERSITY AU Overskrift én linje Bold eller Regular 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

14. AARHUS UNIVERSITY AU