Field phenotyping: affordable alternatives. J.L. Araus, S C. Kefauver, O. Vergara, S. Yousfi, A.K. Elazab, M.D. Serret, J. Bort

Transcription

1 Field phenotyping: affordable alternatives J.L. Araus, S C. Kefauver, O. Vergara, S. Yousfi, A.K. Elazab, M.D. Serret, J. Bort

2 Context Field phenotyping Proximal sensing & imaging: affordable alternatives RGB indices RGB vs Spectroradiometrical vegetation indices Discussion Other uses of RGB images

3 Outline Climate Change Need for crops more resilient to the increasing adverse conditions!!! Temperature Changes precipitation pattern Abiotic and biotic stresses

4 Phenotyping process Breeding advances

5 After Passioura 2006 Funct. Plant Biol. 33,

6 Different categories of traits Araus & Cairns 2014 Trends Plant Sci.

7 Visual scores: e.g. canopy senescence Measurement: - score from 0-10, divide the % of estimated total leaf area that is dead by 10 - initiation & rate of canopy senescence 1 (10%) 3 (30%) 5 (50%) 7 (70%) 9 (90%) M. Banzinguer - CIMMYT

8 Other visual scores anthesis-silking interval leaf rolling ear aspect and grain setting M. Banzinguer - CIMMYT

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11 Control Reflectance N-deficient Wavelength, nm Spectroradiometrical Indices Different levels of assessment: - Canopy - Seedlings - Leaves

12 Background Some indices for remote sensing of crop status. Physiological parameter Leaf area, [Chl], Green Biomass, etc. Chl degradation Car/Chl PRUE Water Content Radiometric Index RNIR R NDVI R R SR SAVI R NIR NIR R R red NIR Re d red R Re d R R L NIR (where L=0.5 for most crops) NPQI SIPI PRI WI R R R R R 900 R R R R R red R R R R ( 1 L)

13 GreenSeeker SPAD

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16 Flir tau 640 IR camera Mini Tetracam

17 RGB cameras: have shown promise for phenotyping development but have yet to be tested in a wide range of stressor conditions

18 RGB image processing Numerical representation of color RGB: Red, Green and Blue related with color reproduction by computer screens, etc. HIS Hue, Intensity, Saturation Practical for image analysis Hue wheel: CIE-lab ~ sensitivity of human visual system Consistent distance practical for arithmetics

19 RGB image processing Hue HIS color space Green Area (GA) (% pixels with 60º < Hue < 120º) Greener Area (GAA) (% pixels with 80º < Hue < 120º)

20 RGB image processing CIE-Luv (u* & v*) CIE-Lab (a* & b*)

21 Normalized Green Red Difference Index (NGRDI) NGRDI = [(Green Red)] / (Green + Red)] Tucker, C.J., Remote Sensing of Environment 8 Gitelson et al Remote Sensing of Environment 80

22 Specific: Test the performarce of the Vegetation Indexes at: aerial, ground and leaf scales Test their performance under a wide range of stressor conditions Test their performance with different crop species (wheat, maize)

23 Plant material & Growing conditions Biotic stress conditions Yellow rust in Wheat 16 genotypes Natural spread Lethal necrosis in Maize 270 genotypes Inoculated disease

24 Plant material & Growing conditions Abiotic stress conditions Nitrogen stress in Maize 10 hybrids 5N fertilization treatments (0, 10, 20, 80 and 160 kg ha -1 NH 4 NO 3 ) Water stress in Wheat 24 genotypes Rainfed and Irrigation treatmets

25 Plant material & Growing conditions Abiotic stress conditions Combined Water stress and Nitrogen fertilization in Wheat 10 genotypes x 3 fertilization levels Nitrogen fertilizaition and heat stress in Maize 1 genotype x 5 nitrogen fertilization levels and two heat stresses

26 RGB Images: different scales Leaf scale Aerial scale Ground scale

27 Spectroradiometrical measurements Single leaf Ground scale Aerial scale

28 Maize Lethal Necrosis Evaluation in Kenya ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, not significant

29 N fertilization treatments in Maize in Zimbabwe CIMMYT s South Africa regional station, Harare

30 N fertilization treatments in Maize in Zimbabwe Zaman-Allah et al Plant Methods Vergara et al. submitted Vegetation Indexes ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, not significant

31 Wheat - yellow rust in Spain

32 Yellow rust Wheat Yellow rust Puccinia striformis f. sp. tritici. A very virulent new strain in Europe named Warrior/Ambition, first cited in England in Started mid-april 2013.

33 Durum wheat - yellow rust Vergara et el The Crop Journal

34 Bread wheat - yellow rust 9/09/15 Zhou et al Comp. Elect. Agric.

35 Wheat water treatments in Spain ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, not significant

36 Wheat water treatments in Spain ***, P < 0.001; **, P < 0.01; *, P < 0.05; ns, not significant

37 Wheat N & Water regimes in Algeria Yousfi et al Agric. Water Manag.

38 Wheat N & Water regimes in Algeria First crop season ( ) Heading Grain filling 0,8 (a) r = 0.74** (T. durum) (d) r = 0.56** (T. durum) 0,6 NDVI 0,4 r = 0.36** (T.aestivum) 0,2 Durum wheat Bread wheat r = 0.49** (T.aestivum) Green Area 0,0 1,2 0,8 0,4 (b) r = 0.58** (T. durum) r = 0.37* (T.aestivum) (e) r = 0.77** (T.durum) 0,0 r = 0.44** (T.aestivum) -0,4 Greener Area 1,0 0,5 0,0 (c) r = 0.52** (T.durum) r = 0.38** (T.aestivum) (f) r = 0.73** (T.durum) r = 0.20 ns (T.aestivum) -0, Grain yield (T/ha) Grain yield (T/ha) Yousfi et al Agric. Water Manag.

39 Maize nitrogen and heat stress in Maize in Spain Elazab et al Europ. J. Agron.

40 Elazab et al Eur. J. Agron.

41 In most of these studies, RGB indexes outperformed NDVI RGB indexes have a more limited spectral range but they have an excellent spatial resolution NDVI may be more affected by: Canopy architecture Crop density Spikes and soil because of their effect on the reflectance at longer wavelengths.

42 Advantages of RGB-VIs Very low sampling cost and high resolution Sampling [almost] not conditioned by weather Calculation of RGB-VIs can be automated (a trial with hundreds of plots can be sampled and processed in the same day) Good repeatability and representability (taking several pictures per plot allows accounting for its spatial variability) Validated as Vegetation Indices Casadesus pers. comm.

43 Limitations of RGB-VIs As other VI, they get saturated at high LAI (e.g. at stages with much green biomass, under irrigated conditions) As other VI, they get disturbed after anthesis by the structure of the canopy Effect of spikes / panicles Vertical distribution of green biomass Casadesus pers. comm.

44 Implications for breeding and crop management RGB-indexes at different levels may be employed in wheat and maize for precise crop management and breeding programs aimed to improve cereal crop performance under a wide range of conditions. Even the more affordable digital cameras used at the canopy level may serve as a reliable and robust high throughput field phenotyping tool to predict crop yield and disease conditions. For more information see * Shawn Kefauver *General Public License under active development

45 3D images from 2D pictures. Deery et al Agronomy 5

46 3D images from 2D pictures. Other information derived from RGB images: 3D reconstructions:

47 9/09/15 3D modelling done in Agisoft: the blue 3D mesh (left) and the combination of the 3d model and the color photos creates the color 3D image (right).

48 9/09/15

49 Other information derived from RGB images: ear counting :

50 Weat ear counting Strategy Acquisition and image processing - natural light Color space transformations Techniques for segmentation Techniques Zenithal Image. Color standardization Color space - CIE L*a*b K-means clustering segmentation

51 Wheat ear counting Image input RGB Color space transformation CIE L*a*b K-means clustering Techniques RGB CIE L*a*b 3 axis L a b Yellow Leaves and soil Blue Awns Red Ears

52 Wheat head counting Image Input RGB Color Space Transformation CIE L*a*b K-means clustering Problems Scene Illumination variability Wheat head superposed or lost Segmentation mistakes. Awns instead of ears

53 Many thanks Acknowledgements AGL R Maria Teresa Nieto-Taladriz Jesús Vega Nieves Aparicio