Innovationer för hållbar växtodling Partnermöte 20-21 juni 2017, Mariestad-Töreboda, Sverige 3d. Underlag för ett webbaserat beslutsstödssystem för smart växtodling 4a. Webbaserat beslutsstödssystem för smart växtodling Lena Engström, Kristin Piikki, Mats Söderström och Bo Stenberg. Sveriges lantbruksuniversitet (SLU), Inst. för Mark och miljö, Precisionsodling och pedometri, Skara, Sverige.
3d. Underlag för ett webbaserat beslutsstödssystem för smart växtodling Syfte och mål med delaktiviteten: utveckla och anpassa algoritmer som översätter tillgängliga gröd- och markdata till platsanpassade åtgärdsrekommendationer. Med hjälp av fjärranalys/sensorer : bestämma biomassa, N-innehåll och skörd vid kompletteringsgödsling i höstvete med hjälp av sensorer. bestämma kväveinnehåll på hösten i höstraps för optimal beräkning av vårgivan. bestämma näringsinnehåll i vall för optimal skördetidsprognos.
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 (www.r-project.org). K. Piikki
Data från satelliter: 10x10, 20x20, 30x30 m raster/pixel = 1 värde 10 m 10 m
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 = 15-16 kg N/ha (drone). No good prediction with satellite-pictures since sampling area (1 m 2 ) did not properly represent the area of 10-30 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 Calibrated Cross-validated r 2 RMSEC r 2 RMSECV rpd UAV- Micasense, indices NDVI all 0,43 30 0,38 33 1,2 not 2 high 0,68 14 0,64 15 1,6 val. gårdar 0,68 14 0,58 16 1,5 MSAVI2 all 0,43 30 0,37 33 1,2 not 2 high 0,66 14 0,61 16 1,5 val. fields 0,66 14 0,52 17 1,4 SAVI all 0,43 30 0,38 33 1,2 not 2 high 0,68 14 0,64 15 1,6 val. fields 0,68 14 0,58 16 1,6 UAV - Micasense, wavebands (B, G, R, Red edge, NIR) not Entorp all 0,47 29 0,24 37 1,0 ej 2 höga 0,80 11 0,69 14 1,7 val. fields 0,80 11 0,30 21 1,1 UAV Sony (R, G, B) alla 0,44 30 0,16 38 1,1 not 2 high 0,85 9 0,77 12 2,1 val. fields 0,85 9 0,71 13 1,9 Handheld GreenSeeker NDVI all 0,63 24 0,57 28 1,5 Not 2 high 0,80 11 0,78 12 2,1 val. fields 0,80 11 0,68 14 1,8
Results: UAV - Sony (R,G, B) Cross validated model (4 sites), R 2 = 0,71, RMSECV = 13) 120 RGB: PLS calibration on respons variable kg N per hectar 110 Entorp 100 Lanna 90 80 70 Entorp Bjertorp21 Lanna Predicted Y (kg N/ha, Factor-3) 60 50 40 30 20 10 0 Lanna Bjertorp21 Bjertorp21 Entorp Bjertorp21 Entorp Lanna Bjertorp31 Bjertorp31 Bjertorp21 Reference Y (kg N/ha, Factor-3) 0 10 20 30 40 50 60 70 80 90 100 110 120 Bjertorp31 Bjertorp31 Lanna
Plans for 2017: 50-70 crop samples at 4-6 sites 2017 10-20 samples with high biomass 100-200 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!
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. Differences between treatments in field trials were well described by e.g. NDVI 8/7 2016 (UAV) and IR/R och Si1 15/6 2017 (handheld N-sensor)! NDVI, timotej-försök 2016 9 Index: IR/G, timotejförsök 2017 0,82 0,80 0,78 0,76 0,74 0,72 0,70 70 100 130 160 190 130 160 160 190 220 Index-värde 8 7 6 5 4 3 2 1 0 70 100 130 160 190 130 160 160 190 220 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 2 givr 3 givor 2 givor 3 givor
Plans for 2017: Measurements with UAV, satellite, handheld multispectral sensor in one field trial with Timothy grass ley. Various N-rates, 70-220 kg N/ha and strategies (40 plots 3 dates, n= 120). Crop sampling (cutting by hand) at three harvest dates (15 June, 30 Juni and 10 July).
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 achievd with all wavebands, NDVI+Rededge and MSAVI+Rededge. Regression models (PLS and SLR) Response: Yield (kg/ha) Predictors: All wave bands (400-1000) Indices (NDVI, MSAVI, SAVI..) n= 225 Kalibrerad Korsvalidering Validering gårdsvis r2 rmsec r2 rmsecv rpd valid. r2 rmsecv rpd valid. Alla våglängder 0,83 871 0,82 895 2,3 0,73 1326 1,6 utan 0N 0,5 761 0,45 797 1,4 Index NDVI 0,67 1209 0,66 1219 1,7 MSAVI 0,46 1536 1,45 1549 1,4 SAVI 0,36 1679 0,34 1694 1,2 Rededge (NDRE) 0,74 1070 0,73 1078 1,9 Clor.index 0,73 1090 0,72 1098 1,9 Rededge+Cl.index 0,76 1030 0,75 1043 2,0 NDVI+Rededge 0,76 1024 0,76 1032 2,0 0,68 1187 1,8 MSAVI+Rededge 0,75 1038 0,75 1050 2,0 0,59 1374 1,5
Index: NDVI+Rededge, gårdsvalidering (4 st) Predicted vs. Reference 12500 12000 11500 11000 10500 10000 9500 9000 Elements: Slope: Offset: Correlation: R2(Pearson): R-Square: RMSECV: SECV: Bias: 225 0,7442809 2,5637e+03 0,826647 0,6833452 0,6976857 1,1872e+03 1,1899e+03 9,0038147 33 2Ba 56 2Ba43 2BbvIII 3950 2BbIII 2BbvI 4752 53 2BbII 57 2Ab 332BbvII 15 2Ba 2Ba16 2235 37 19 17 2BbII 19 2Ab 2BbvIII 36 4 2BbvII 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 33 48 2Aa 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 48 29 2Aa 501AaIII 28 2BbvI 36 1AaI 57 2Aa27 2BbvII 2BbI 15 4 2Aa43 2BbvIII 7 2BbI 13 1Ab 11 53 2Ab 56 52 2Ba 5711 1Ba 2Ba 2BbvII 1Bb16 1Aav 28 2BbII29 1AaIII 1AaI 2BbI 35 2BbvII 36 2Aa 46501Aav 2BbvI 2 1AaI 23 40 1AaII 1 2BbvI 5522 1Aav 17 30 1Ba 6 1AaII 26 34352Ab 1Ab 2BbvII 54 2BbII 3 1Aav19 392BbvIII 2BbIII 5247 53 37 2BbII 2Ab 56 2Ba 43 2BbvIII 2BbvIII 24 1Bb 8 1AaIII 1Bb 18 1Ba 45 1Ba 306 2 21Ba 1AaII 28 2 1AaI 49 831AaIII 1Ab 1Aav 512BbvI 1AaI 40 1AaII 21 2BbvI 22 46 2BbII 1Aav 1AaIII 24 1Bb 24 1Bb 1 2BbvI 23 13 111Ab 1Bb20 1AaII 645 1Aav 1AaII 1Ba 55511AaI 1AaIII 49 54 1Ab1Bb 18 1Ba 23 1Aav 19172BbvIII 27 2BbI 22 2BbII 30 1Ba 43 50 2BbvIII 48 57 2Aa 2BbvII 39 2BbIII 21 2BbvI 29 1AaIII 34 561Ab 2Ba 29 1AaIII 3047 1Ba2BbII 4 2Aa34 1Ab 5 2BbIII 2625 2Ab2BbIII 33 2Ba 24 1Bb 28 1AaI 36 2Aa 52 15 352BbI 2Ba 2BbvII 37 8 1AaIII7 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) 8000 7500 7000 6500 6000 5500 5000 4500 4000 3500 44 0AaII 44 0AaII 42 0AaI 14 0AaI 32 0AaII 12 0AaII 44 0AaII 41 380AaIII 10 0AaIII 12 0AaII 42 0AaI 42 0AaI 41 38 0AaIII 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 12 14 0AaII 0AaI 31 0AaI 32 0AaII 31 0AaI 3000 Reference Y (Skörd kg/ha, Factor-1) 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000
Alla våglängder, gårdsvalidering (4 st) Predicted Y (Skörd kg/ha, Factor-3) 13500 13000 12500 12000 11500 11000 10500 10000 9500 9000 8500 8000 7500 7000 6500 6000 5500 5000 4500 4000 3500 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 10 420AaIII 41 0AaIII 38 0AaIII 31 0AaI 32 0AaII 225 0,930308 1,2582e+03 0,8527921 0,7272543 0,6247552 1,3256e+03 1,6483e+03 561,99744 12 14 0AaII 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 2Ba16 19 17 2BbvI 2Ab 4 2Aa 5 2BbIII 7 39 2BbIII 192BbII 2BbvIII 33 2Ba 50 2BbvI53 2Ab 47522BbII 2BbI 37 1 2BbvI 5 2BbIII 36 2Aa 2527 2BbIII 2BbI 15 92Ba 2Ab 33 7 16 2BbI 2BbII 443 2Aa 2BbvIII 26 2Ab 30 362Ba 2Aa 22 35 35 37 2BbII 2BbvIII 36 272Aa 2BbI 17 2BbvII 19 1Ba29 48 2Aa 5037 28 2BbvIII 57 2BbvII 5253 1AaIII 2Ab1AaI 5635 2Ba 2BbvII 392BbvIII 2BbIII 47 2BbII 21 2BbvI 56 2Ba 21 2BbvI 22 2BbII 34 1Ab 51 1AaIII 48 132Aa 1Ab 33 2Ba 25 2BbIII 22 472BbII 40 26 2Ab 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 1913 62BbvIII 1Ab 40 1AaII 8 2 18 1Ba 28 1AaI 15 2Ba 23 424 2Aa 1Aav 1Bb 34 9 2Ab 1Ab 27 45 1Ba 131AaIII 820 11 1Ab 23 45 1Aav 1Ba 55 46 1AaI 511Aav 1AaIII 49 54 1Ab1Bb 1AaIII 1AaII 1Bb49 1Ab 621AaII 17 2BbvII 1856 1Ba 20 2Ba57 29 21 1AaIII 8 1AaIII 302BbvII 1Ba34 1Ab 39 2BbIII 7 2BbI 43 2BbvIII 48 2Aa 23 1Aav 29 2 3 1Aav 2050 1AaII 2BbvI 401AaIII 2852 2BbI 16 2BbII 18 1Ba 531AaI 30 1Ba 24 55 2Ab 1Bb 1AaI 45 1Ba 49 1Ab 54 1Bb 46 51 1Aav 1AaIII 48 2Aa 19 2BbvIII 39 2BbIII 4 2Aa5 2BbIII 9 2Ab 15 2Ba 16 2BbII 2 1AaI 53 2Ab 56 52 2Ba 2BbI 47 2BbII 17 1 2BbvI 7 2BbvII 2BbI 8 3 1AaIII 1Aav 13 6 1Ab 1AaII 11 1Bb 4650 43 2BbvIII 3745 1Aav 2BbvI 57 2BbvII 2BbvIII 40 1AaII 55 1AaI 1Ba 51 1AaIII 54 1Bb 18 49 1Ba 1Ab 35 2BbvII 36 2Aa 22 2BbII 25 2BbIII 24 21 1Bb 2BbvI 26 27 2Ab 2BbI 20 1AaII 23 1Aav 28 1AaI 29 1AaIII 30 1Ba 34 1Ab 31 0AaI 3000 Reference Y (Skörd kg/ha, Factor-3) 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 55 1AaI
Plans for 2017: UAV, satellite images and handheld sensor measures from 3 field experiments with increasing N-rates/strategies. 20 m 2 harvested. Add data for 2016 and 2017 to modell with 3 N- rates (n= 225). Continue to modell yield and protein content.