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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  • Broken red line: inclination angle of Maize leafSolid red line: height profile across plotNon-destructive wish listFractional coverCrop heightLAICanopy architectureGrowth rateEars / m2HIBiomassCrop yield
  • The next task is to extract the individual temperatures for Nearly 400 samples per plot
  • SensorDB is an unique system that enables turning big data time series into knowledge.--------------------SensorDB was originally designed for handling sensor data from wireless infrared canopy temperature like those ones in the picture. These sensors monitor continuously, 24h a day and 7 days a week, every 5 minutes, the temperature of wheat varieties in field experiments. Generally speaking, the cooler the crop the more it’s transpiring. That information is very valuable in breeding programs for selecting genotypes more suitable for dryer conditions or in irrigated farms for making decisions on the irrigation schedule. At this time resolution, that is more than 100k points per year per single sensor and we may have 60 or 100 of these sensors in each experiment. In total we have around 500 of these sensors, which makes more than 50M points a year. But we also handle other sources of information into SensorDB like data from airborne themography covering thousands of these experimental plots, or data from ground vehicles like this phenomobile, grain harvesters or just handheld devices or ipads registering data from the field. The result is a torrent of data with information that we need to turn into useful knowledge for plant scientists, breeders and farmers...
  • SensorDB is a multi user system. Each user has his own summary with real time statistics that have been previously calculated on data insertion.SensorDB also has a very fast and dynamic search engine so it’s easy to find, select and aggregate the data that we are looking for. We just type and SensorDB guesses what we are looking for. It’s not just sensors... Excel... We also have a number of methods for importing data manually into SensorDB, like for example importing data from Excel with just copy and paste.But probably the most interesting part of SensorDB for the end user is the analysis page where the user can select from a number of pre-built analysis or just create his own personalised analysis. Visulaisation and analysis through virtual laboratory environment – next slide
  • In the Analysis Page is what we call the personalized virtual laboratory. Here the user can visualize different streams of data like for instance weather summaries with the evolution of the weather during the growing season. It is also possible to select and apply filters to the data so for example when we select two different genotypes and restrict the envorironmental conditions in the analysis we can explore the response of a these genotypes to the changes in relative humidity and therefore select the most suitable crop variety for certain environments. This analysis can be done across multiple users, environments and locations and as the system has more information it is possible to explore more and more scenarios. It is also possible to share the analysis with other users, so this becomes a real collaboration tool.
  • 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:
    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, (
    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: CSIRO PLANT INDUSTRY / HIGH RESOLUTION PLANT PHENOMICS CENTRE