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

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

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

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%

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

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

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

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

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

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

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% 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 ∑* ∑*

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 Grass Training set: Clover Grass

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

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