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New remote and proximal sensing methodologies in high throughput field phenotyping

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Remote sensing –Beyond images
Mexico 14-15 December 2013

The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)

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New remote and proximal sensing methodologies in high throughput field phenotyping

  1. 1. New remote and proximal sensing methodologies in high throughput field phenotyping Remote Sensing – Beyond images. Mexico City, 2013 JOSE A. JIMENEZ-BERNI. CSIRO PLANT INDUSTRY. HIGH RESOLUTION PLANT PHENOMICS CENTRE
  2. 2. Why phenotyping? Why high throughput?
  3. 3. The High Resolution Plant Phenomics Centre Director: Robert.Furbank@csiro.au
  4. 4. Breeder’s wish list Table 2. Essential and desirable measurements for phenotyping of yield and other traits across multiple experiments in water-limited and high temperature environments. Essential (core) Timing A Desirable (frequency) Plant establishment counts (plants/m2) Ground cover (%) DC 12-13 (1×) Normalized Difference Vegetation Index Anthesis date DC 12-37 (2×) Canopy temperature (°C) Harvest index Spike number (spikes/m2) Plant height (cm) Thousand grain weight (g) Grain yield (g/m2) Observations and scores (e.g. incomplete plots, temperature damage, disease, lodging, and shattering) DC 12-37 (3×) DC 45-65 (every 3d) DC 35-70 (2×) DC 90 DC 90 Timing A (frequency) Canopy light interception (μmol/m2/s) Normalized Difference Vegetation Index Early biomass/leaf area/tiller number DC 35-60 (2×) Carbon Isotope Discrimination DC 30-32 Anthesis biomass (g/m2) Water soluble carbohydrates Canopy temperature during grain filling (°C) DC 60-65 DC 65-70 (1×) DC 70+ (4×) DC 12-60 (4×) DC 30-32 DC 90 DC 90 DC 90 as required A Timing according to the Zadoks decimal code (DC) for scoring stages of cereal development (Zadoks et al. 1974) (More info: Rebetzke et al 2012, (http://prometheuswiki.publish.csiro.au/tiki-index.php)
  5. 5. Phenomobile -30 30 LMS400 70 Canopy Canopy Canopy • 3x LiDARs (Canopy Structure) • 4x RGB cameras (Stereo reconstruction) • 1x Thermal IR camera (Canopy temperature) • 1x Hyperspectral line scanner (Canopy biochemistry) • 1x Full range spectrometer (Canopy biochemistry) • Removable light banks
  6. 6. LiDAR Height Validation 1400 LiDAR measured canopy height (mm) 1200 1000 800 600 400 y = 0.6624x + 230.77 R² = 0.8619 200 0 0 200 400 600 800 1000 Manually measured canopy height (mm) 1200 1400
  7. 7. LiDAR outputs Non-destructive wish list oFractional cover oCrop height oLAI oCanopy architecture oGrowth rate oEars / m2 oHI oBiomass oCrop yield
  8. 8. Canopy architecture and spike counts
  9. 9. Hyperspectral line scanner and high-res thermal
  10. 10. Phenomobile Lite
  11. 11. Acquisition of airborne thermal images The Airframe Sensor Integration The Flight Controller
  12. 12. Airborne thermal mosaic – ready for plot extraction Legend [deg C] ~600 m “Old way” h2<0.1 “New way” h2>0.6 • Capture 3 images / second • One pass of the field ~10 sec (3 passes required) • Time to image entire field ~4 min • Ideal: Simultaneous measurements at nearly a single point in time
  13. 13. Extraction of plot temperature 100’s samples per plot
  14. 14. Wireless infrared thermometers • Zigbee standard • Selectable sampling interval (5min) • 3G transmission from base station • Real time access from Internet • 100 sensors built in 2011 • 400 sensors built in 2012 • 160 in a single deployment (Narrabri / Cotton)
  15. 15. Canopy temperature data
  16. 16. Canopy conductance modelling
  17. 17. Other sensors and applications fAPAR Hyperspectral Reflectance (%) Soil moisture 60 40 20 0 350 550 750 Wavelengh (nm)
  18. 18. SensorDB user interface
  19. 19. Virtual laboratory concept. Real time data mining and filtering
  20. 20. Take home messages • There are no turnkey solutions • Why use NDVI when you can use LiDAR for direct estimation of ground cover, plant establishment and potentially LAI or biomass? • Use imaging sensors when possible: extract information from the right spot, not an integrated observation • Airborne thermography as an alternative to traditional CT measurements: no changes in environmental conditions and multiple measurements per plot • Wireless sensor networks for dynamic phenotyping applications • Never underestimate the data management component and the requirements for data processing
  21. 21. The Plant Phenomics Team HRPPC / CSIRO PI: Bob Furbank Dave Deery Xavier Sirault Jose Jimenez-Berni (Berni) Tony Condon Scott Chapman & Ed Holland Xueqin Wang Alyssa Weirman Tony Agostino Pablo Rozas-Larraondo Peter Kuffner & Michael Salim Scott Kwasny Dac Nguyen Viri Silva Perez Richard Poire Kath Meacham CSIRO E-health: Jurgen Fripp & Antony Paproki Olivier Salvado CSIRO Informatics: Ali Salehi Doug Palmer & Alex Krumphol David Lovell Pascal Vallotton Changming Sun ANU: Murray Badger Susanne von Caemmerer CIMMYT Matthew Reynolds Team USDA : John Vogel Team
  22. 22. Thank you / Gracias For more information: Jose.jimenez-berni@csiro.au http://hrppc.org.au CSIRO PLANT INDUSTRY / HIGH RESOLUTION PLANT PHENOMICS CENTRE

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