Innovationer för hållbar växtodling 3d. Underlag för ett webbaserat beslutsstödssystem för precisionsodling

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1 Innovationer för hållbar växtodling 3d. Underlag för ett webbaserat beslutsstödssystem för precisionsodling Lena Engström, Kristin Piikki, Mats Söderström och Bo Stenberg. Sveriges lantbruksuniversitet (SLU), Inst. för Mark och miljö, Precisionsodling och pedometri, Skara, Sverige.

2 3d. Underlag för ett webbaserat beslutsstödssystem för precisionsodling Syfte och mål med delaktiviteten: utveckla och anpassa algoritmer som översätter tillgängliga gröd- och markdata till platsanpassade åtgärdsrekommendationer.

3 3d. Underlag för ett webbaserat beslutsstödssystem för precisionsodling Med hjälp av olika verktyg som handburna sensorer, drönare och satelliter: I höstvete bestämma skörd vid kompletteringsgödsling. I höstraps bestämma kväveinnehåll på hösten för optimal beräkning av vårgivan. I gräsvall bestämma näringsinnehåll för optimal skördetidsprognos.

4 Data från drönare: Röda polygonen visar jordbruksblocket 5m Fotografering med RGB-kamera (RX-100, Sony) Fotografering med multispectral kamera (RedEdge, Micasense) Skapande av mosaik (SOLVI) Skapande av mosaik (Micasense ATLAS) Georeferering Mosaikerna georefererades med hjälp av Jordbruksverkets blockdatabas. Ofta 5-10 m fel innan georeferering. (ArcGIS, ESRI). Dataextraktion Ett medelvärde för alla celler inom 5 m radie från inmätta punkten beräknades Detta gjordes band för band ( K. Piikki

5 Data från drönare i försök: Mosaikbildens pixlar 8 x 8 cm, medianvärdet i varje ruta tas fram för de fem våglängdsbanden

6 Data från satelliter: 10x10, 20x20, 30x30 m raster/pixel = 1 värde 10 m 10 m

7 Winter oilseed rape Decision support for calculating the optimal N rate in spring to winter oilseed rape by estimation of N- uptake in autumn by means of remote sensing. Four fields, crop sampling (1 m 2 ) at five positions in each field in late autumn, n= 25. RGB-image 13/ , Entorp, Skara, kg N/ha kg N/ha kg N/ha kg N/ha kg N/ha kg N/ha

8 Winter oilseed rape Lanna, skifte 13, kg N/ha kg N/ha kg N/ha kg N/ha kg N/ha kg N/ha

9 Winter oilseed rape Bjertorp skifte 31, kg N/ha kg N/ha kg N/ha kg N/ha kg N/ha kg N/ha

10 Results 2016: Modelling the autumn N-uptake of winter oilseed Best prediction of N-uptake (11-90 kg N/ha, 4 sites, n= 20 ) with RGB-pictures (drone) and handheld GreenSeeker (NDVI), RMSECV = 12 kg N/ha. Good prediction models also with all wavebands (RMSEC = 14 kg N/ha) or single indices, RMSECV = kg N/ha (drone). No good prediction with satellite-pictures since sampling area (1 m 2 ) did not properly represent the area of m pixels in the satellites. Regression models (PLS and SLR) Response: N-uptake (kg N/ha). Predictors: All wave bands (B, G, R, Rededge, NIR ) Indices (NDVI, MSAVI, SAVI) RGB Regression models Cross-validated r 2 RMSECV rpd UAV- Micasense, indices NDVI all 0, ,2 not 2 high 0, ,6 val. gårdar 0, ,5 MSAVI2 all 0, ,2 not 2 high 0, ,5 val. fields 0, ,4 SAVI all 0, ,2 not 2 high 0, ,6 val. fields 0, ,6 UAV - Micasense, wavebands (B, G, R, Red edge, NIR) not Entorp all 0, ,0 ej 2 höga 0, ,7 val. fields 0, ,1 UAV Sony (R, G, B) all 0, ,1 not 2 high 0, ,1 val. fields 0, ,9 Handheld GreenSeeker NDVI all 0, ,5 Not 2 high 0, ,1 val. fields 0, ,8

11 Results: UAV - Sony (R,G, B) Cross validated model (4 sites), R 2 = 0,71, RMSECV = 13) 120 RGB: PLS calibration on respons variable N-uptake (kg N per hectar) Lanna Entorp Entorp Bjertorp21 Lanna Predicted Y (kg N/ha, Factor-3) Lanna Bjertorp21 Bjertorp21 Entorp Bjertorp21 Entorp Lanna Bjertorp31 Bjertorp31 Bjertorp21 Reference Y (kg N/ha, Factor-3) Bjertorp31 Bjertorp31 Lanna

12 Plans for 2017: crop samples at 4-6 sites samples with high biomass, kg N/ha. Improve georeferencing by using 2-3 markers around each sampling plot, instead of only one (2016). Adapt sampling strategi for satellite pictures. Sample in larger homogenious areas!

13 Results 2016: Modelling yield in winter winter wheat Using 6 fields with 20 samples in each (n= 120): No good prediction of yield (or N-uptake and biomass) was achieved with the models tested (multispectral images from drones and satellites). No good prediction due to sampling area (0,5 x 0,5 m) did not properly represent the area from the satellite and UAV pictures. Using 4 field trials from 2015 (n= 225) and a handheld sensor (multispectral): Good prediction of yield was achieved with all wavebands, NDVI+Rededge and MSAVI+Rededge. Regression models (PLS and SLR) Response: Yield (kg/ha) Predictors: All wave bands ( ) Indices (NDVI, MSAVI, SAVI..) n= 225 Korsvalidering Validering gårdsvis r2 rmsecv rpd valid. r2 rmsecv rpd valid. Alla våglängder 0, ,3 0, ,6 utan 0N 0, ,4 Index NDVI 0, ,7 MSAVI 1, ,4 SAVI 0, ,2 Rededge (NDRE) 0, ,9 Clor.index 0, ,9 Rededge+Cl.index 0, ,0 NDVI+Rededge 0, ,0 0, ,8 MSAVI+Rededge 0, ,0 0, ,5

14 NDVI+Rededge, cross validation with sites (4) Predicted vs. Reference Elements: Slope: Offset: Correlation: R2(Pearson): R-Square: RMSECV: SECV: Bias: 225 0, ,5637e+03 0, , , ,1872e+03 1,1899e+03 9, Ba 56 2Ba43 2BbvIII BbIII 2BbvI BbII 57 2Ab 332BbvII 15 2Ba 2Ba BbII 19 2Ab 2BbvIII BbvII 2BbII 2Aa 5 2BbIII 7 2BbI 48 2Aa 54 1Bb 21 2BbvI 51 1AaIII 46 1Aav26 2Ab 27 2BbI 25 2BbIII 45 1Ba 49 1Ab 55 1AaI 16 2BbII 34271Ab 2BbI 40 1AaII 4 2Aa Aa 19 2BbvIII 39 2BbIII 47 2BbII 25 2BbIII 1 2BbvI 52Ba 2BbIII 25 2BbIII 520 2BbIII 139 1Ab 2Ab 17 2BbvII 26 2Ab 15 2Ba 37 2BbvIII 18 1AaII 9 2Ab7 32BbI Aa 501AaIII 28 2BbvI 36 1AaI 57 2Aa27 2BbvII 2BbI Aa43 2BbvIII 7 2BbI 13 1Ab Ab Ba Ba 2Ba 2BbvII 1Bb16 1Aav 28 2BbII29 1AaIII 1AaI 2BbI 35 2BbvII 36 2Aa 46501Aav 2BbvI 2 1AaI AaII 1 2BbvI Aav Ba 6 1AaII Ab 1Ab 2BbvII 54 2BbII 3 1Aav19 392BbvIII 2BbIII BbII 2Ab 56 2Ba 43 2BbvIII 2BbvIII 24 1Bb 8 1AaIII 1Bb 18 1Ba 45 1Ba Ba 1AaII AaI AaIII 1Ab 1Aav 512BbvI 1AaI 40 1AaII 21 2BbvI BbII 1Aav 1AaIII 24 1Bb 24 1Bb 1 2BbvI Ab 1Bb20 1AaII 645 1Aav 1AaII 1Ba 55511AaI 1AaIII Ab1Bb 18 1Ba 23 1Aav 19172BbvIII 27 2BbI 22 2BbII 30 1Ba BbvIII Aa 2BbvII 39 2BbIII 21 2BbvI 29 1AaIII Ab 2Ba 29 1AaIII Ba2BbII 4 2Aa34 1Ab 5 2BbIII Ab2BbIII 33 2Ba 24 1Bb 28 1AaI 36 2Aa BbI 2Ba 2BbvII AaIII7 2BbvIII 53 2Ab 6 1AaII 20 1AaII 9 2Ab16112BbII 1Bb 13 1Ab 20 1AaII 4021AaII 3 1Aav 45 1Ba 23 1Aav 46 1Aav54551Bb 18 1Ba 49 1Ab 51 1AaIII 8500 Predicted Y (Skörd kg/ha, Factor-1) AaII 44 0AaII 42 0AaI 14 0AaI 32 0AaII 12 0AaII 44 0AaII AaIII 10 0AaIII 12 0AaII 42 0AaI 42 0AaI AaIII 38 0AaIII 41 0AaIII 14 0AaI 31 0AaI 32 0AaII 12 0AaII 31 0AaI 10 0AaIII 10 0AaIII 32 0AaII 14 0AaI 38 0AaIII 10 0AaIII AaII 0AaI 31 0AaI 32 0AaII 31 0AaI 3000 Reference Y (Skörd kg/ha, Factor-1)

15 All wave bands, cross validation with sites (4) Predicted Y (Skörd kg/ha, Factor-3) Elements: Slope: Offset: Correlation: R2(Pearson): R-Square: RMSECV: SECV: Bias: 10 0AaIII 14 0AaI 41 0AaIII 12 0AaII 42 0AaI 14 0AaI 14 0AaI 38 0AaIII 41 0AaIII 12 0AaII 10 0AaIII 44 0AaII AaIII 41 0AaIII 38 0AaIII 31 0AaI 32 0AaII 225 0, ,2582e+03 0, , , ,3256e+03 1,6483e , AaII 0AaI 10 0AaIII 31 0AaI 32 0AaII 38 0AaIII 33 2Ba 31 0AaI 32 0AaII 44 0AaII 12 0AaII42 0AaI 44 0AaII 32 0AaII 38 0AaIII Predicted vs. Reference 43 2BbvIII 15 2Ba BbvI 2Ab 4 2Aa 5 2BbIII BbIII 192BbII 2BbvIII 33 2Ba 50 2BbvI53 2Ab 47522BbII 2BbI BbvI 5 2BbIII 36 2Aa BbIII 2BbI 15 92Ba 2Ab BbI 2BbII 443 2Aa 2BbvIII 26 2Ab Ba 2Aa BbII 2BbvIII Aa 2BbI 17 2BbvII 19 1Ba Aa BbvIII 57 2BbvII AaIII 2Ab1AaI Ba 2BbvII 392BbvIII 2BbIII 47 2BbII 21 2BbvI 56 2Ba 21 2BbvI 22 2BbII 34 1Ab 51 1AaIII Aa 1Ab 33 2Ba 25 2BbIII BbII Ab 6 1AaII 1 1AaII 2BbvI 11 1Bb 11 1Bb 26 2Ab 24 1Bb 57 2BbvII 54 1Bb 3 1Aav 3 1Aav 46 1Aav 5 2BbIII 25 2BbIII BbvIII 1Ab 40 1AaII Ba 28 1AaI 15 2Ba Aa 1Aav 1Bb Ab 1Ab Ba 131AaIII Ab Aav 1Ba AaI 511Aav 1AaIII Ab1Bb 1AaIII 1AaII 1Bb49 1Ab 621AaII 17 2BbvII Ba 20 2Ba AaIII 8 1AaIII 302BbvII 1Ba34 1Ab 39 2BbIII 7 2BbI 43 2BbvIII 48 2Aa 23 1Aav Aav AaII 2BbvI 401AaIII BbI 16 2BbII 18 1Ba 531AaI 30 1Ba Ab 1Bb 1AaI 45 1Ba 49 1Ab 54 1Bb Aav 1AaIII 48 2Aa 19 2BbvIII 39 2BbIII 4 2Aa5 2BbIII 9 2Ab 15 2Ba 16 2BbII 2 1AaI 53 2Ab Ba 2BbI 47 2BbII BbvI 7 2BbvII 2BbI 8 3 1AaIII 1Aav Ab 1AaII 11 1Bb BbvIII Aav 2BbvI 57 2BbvII 2BbvIII 40 1AaII 55 1AaI 1Ba 51 1AaIII 54 1Bb Ba 1Ab 35 2BbvII 36 2Aa 22 2BbII 25 2BbIII Bb 2BbvI Ab 2BbI 20 1AaII 23 1Aav 28 1AaI 29 1AaIII 30 1Ba 34 1Ab 31 0AaI 3000 Reference Y (Skörd kg/ha, Factor-3) AaI

16 Results 2017: Modelling yield in winter winter wheat Using 2 field trials, Bjertorp (n= 40) and Skofteby (n= 64), and a multispectral sensor equipped drone Bjertorp: good prediction of yield was achieved with all wavebands, RGB, the indices ChII and NDRE. Skofteby: good prediction of yield was achieved with all wavebands Regression models (PLS and SLR) Response: Yield (kg/ha) Predictors: 5 wave bands (B,G,R, Rededge, NIR) Indices (NDVI, ChII, NDRE, MSAVI 2) Cross validation of N-treatments Bjertorp 2017 r2 rmsecv rpd Wave bands (5) 0, ,3 RGB 0, ,4 NIR 0, ,3 Rededge No correlation! Indices: NDVI 0, ,4 ChII 0, ,2 NDRE 0, ,0 MSAVI2 0, ,6 Cross validation of N-treatments Skofteby 2017 r2 rmsecv rpd Wave bands (5 ) 0, ,6 Without 0N 0, ,9 RGB No correlation! NIR 0, ,9 Indices NDVI 0, ,6 ChII 0, ,1 MSAVI2 No correlation! Prediction of Bjertorp: Model: Skofteby Wave bands (5) 0, ,7

17 Bjertorp 2017 All wave bands and cross validation with N-treatments (3) Predicted vs. Reference Elements: Slope: Offset: Correlation: R2(Pearson): R-Square: RMSECV: SECV: Bias: 57 0, , , , , , , , Predicted Y (Skörd, Factor-5) Reference Y (Skörd, Factor-5)

18 Skofteby 2017 All wave bands and cross validation with N-treatments (16) Predicted vs. Reference Elements: Slope: Offset: Correlation: R2(Pearson): R-Square: RMSECV: SECV: Bias: 64 0, , , , , , , , Predicted Y (Skörd, Factor-5) Reference Y (Skörd, Factor-5)

19 Results 2016: Modelling protein, energy and NDF in timothy grass leys No good regression models could be made when using UAV-pictures from 3 fields with 5 samples in each at 3 harvest dates, n= 45 or a field experiment (n= 70). Satellite pictures not analyzed yet. Visual differences between treatments in field trials were well described by e.g. NDVI 8/ (UAV) and IR/R och Si1 15/ (handheld N-sensor)! NDVI, timotej-försök Index: IR/G, timotejförsök 2017 NDVI Fröskörd kg/ha 8 7 0,82 0,80 0,78 0,76 0,74 0,72 0, Index-värde giva vår 2 givor vår 2 givor 3 givor

20 Results 2017: Modelling protein, energy and NDF in timothy grass leys Using a field trial in a timothy grassley with increasing N-rates (n= 40) and crop sampling at three harvest dates (15/6, 27/6, 10/7, n=120). Good prediction of yield was achieved with all wavebands, RGB, the indices GNDVI and ChII (two dates n= 80) The results from the third harvest date have not yet been analysed. Regression models (PLS and SLR) Response: Yield (kg/ha) Predictors: 5 wave bands (B,G,R, Rededge, NIR) Indices (GNDVI, ChII) Two harvest - dates, n=80 Crossvalidation (N-treatments) Protein: r2 RMSECV rpd Wavebands (5 ) 0,89 7,0 3,1 RGB 0,88 7,3 3,0 NIR 0,74 11,1 1,9 Indices: GNDVI 0,79 9,85 2,2 ChII 0,79 9,85 2,2

21 All wavwbands and crossvalidation of N-treatments (10) Predicted vs. Reference Elements: Slope: Offset: Correlation: R2(Pearson): R-Square: RMSECV: SECV: Bias: 80 0, , , , , , , , Predicted Y (RåproteinNIR, Factor-5) Reference Y (RåproteinNIR, Factor-5)

22 GNDVI and cross validation with N-treatments (10) 135 Predicted vs. Reference Elements: Slope: Offset: Correlation: R2(Pearson): R-Square: RMSECV: SECV: Bias: 80 0, , , , , , , , Predicted Y (RåproteinNIR, Factor-1) Reference Y (RåproteinNIR, Factor-1)

23 Projektets finansiärer Förutom Interreg Öresund-Kattegatt-Skagerack har projektet även följande finansiärer: promilleafgiftsfonden för landbrug