1. 1. TraitCapture: NextGen Open-Source Software for Scaling from Seeds to Traits to Ecosystems Tim Brown, Research Fellow, Borevitz Lab ARC Centre for Plant Energy Biology, Australian National University Chuong Nguyen, Joel Granados, Kevin D. Murray, Riyan Cheng, Cristopher Brack, Justin Borevitz
2. 2. Terraforming To alter the environment of a planet to make it capable of supporting terrestrial life forms. We are currently unterraforming the earth at an exceptionally fast rate To meet the challenges of the coming century we need to restore and re-engineer the environment to support >7 billion people for the next 100 years in the face of climate change while maintaining biodiversity and ecosystem services These ecological challenges are too hard to be solved with existing data and methods
3. 3. Genotype x Environment = Phenotype The degree to which we can measure all three components is the degree to which we can understand plant and ecosystem function FIELDLAB
4. 4. Outline: Phenomics challenges Lab: Measure phenotypes with high precision across large natural populations in varied growth environments Identify the genetic basis of traits of interest Identify novel, cryptic traits Field: Monitor phenotype and environment at high precision across scales from plant to ecosystem to identify natural variation on the landscape Conservation: Ecosystem stability / plasticity (how should we spend limited conservation $$) Restoration: Using existing plasticity and population genetic variation to select seeds for building climate ready populations (reforestation, etc.)
5. 5. Outline: General challenges (1) Processing and managing big data We used to be primarily limited by data collection (hardware) Now we are increasingly limited by data processing and curation (software) We need excel for big data
6. 6. Outline: General challenges (2) Optimizing the knowledge discovery network Data sharing, open access and open source are of major importance for solving research problems: Research dollars are poorly spent when they produce closed data and firewalled journal articles, yet we all aspire to publish our best work in journals that refuse access to the public. This is not just an academic argument, we have serious problems to solve in this decade: This is a network optimization problem Open source matters! The rate of knowledge discovery is determined by how efficiently we can share data, tools and new knowledge.
7. 7. Lab vs field phenotyping Lab: High precision measurement and control but low realism youtu.be/d3vUwCbpDk0
8. 8. Lab vs field phenotyping Field: Realistic environment but low precision measurements In the field we have real environments but the complexity (and bad lighting!) reduces our ability to measure things with precision youtu.be/gFnXXT1d_7s
9. 9. Borevitz Lab Approach Create more natural Lab conditions in growth chambers Measure more precisely in the Field Suzanne Marselis enviro-net.org
10. 10. Lab phenotyping Normal lab growth conditions arent very natural Kulheim, Agren, and Jansson 2002 Real World Growth Chamber
11. 11. Growth cabinets with dynamic semi-realistic environmental & lighting conditions Grow plants in simulated regional/seasonal conditions & simulate climate Control chamber light intensity, spectra (8/10-bands), Temp/Humidity @ 5min intervals Expose cryptic phenotypes Repeat environmental conditions Between studies and collaborators Simulate live field site climate Lab Solution: SpectralPhenoClimatron (SPC) Spectral response of Heliospectra LEDs. (L4A s20: 10-band)
12. 12. TraitCapture: Open-source phenotyping pipeline Phenotype 2,000 plants (7 Conviron chambers) in real-time 2 DSLR cameras per chamber (controlled by raspberry Pis) 4-12 JPG + RAW images/hr every during daylight Automated Image analysis pipeline: phenotype data from 150,000 pot images a day Automated Phenotypes Area Diurnal movement Color (RGB, Gcc, etc) Perimeter Roundness Compactness Eccentricity Upcoming: Leaf Count Leaf tracking Leaf length/width/petiole Corrected Segmented Original
13. 13. Experiment: Arabidopsis in inland vs coastal conditions Goulburn (inland) vs Wollongong (coastal) conditions Red = Wollongong, NSW: Warmer, coastal conditions Purple = Goulburn, NSW: Cooler, Inland conditions Plot: Area (green pixels) of Col-0 controls (1/tray) (~5 weeks growth)
14. 14. Lots of data to visualize Up to 600 graphs per phenotype per experiment Dead plant
15. 15. NextGen Field Ecology Wheres my PCR? Field ecology is like genetics before PCR and high throughput sequencing Back in the 80s & early 90s people would get a PhD just sequencing a single gene. Genetics -> Genomics -> Phenomics 20 years of technical advances have turned genetics into genomics into Phenomics, yielding the ability to address fundamental, very complex questions
16. 16. The current resolution of field ecology is very limited Low spatial & time resolution data Limited sensors; dont capture local spatial variation Sampling is often manual and subjective Observations not-interoperable or proprietary; little or no data sharing Sample resolution is Forest or field not Tree or Plant Very little data from the 20th century ecology is available for reuse This slows our rate of knowledge discovery
17. 17. The challenge Measure everything all the time How do we go from doing the science at the scale of one point per forest to multilayer data cubes for every tree or leaf? 19/ 20
18. 18. National Arboretum Phenomic & Environmental Sensor Array National Arboretum, Canberra, Australia ANU Major Equipment Grant, 2014 Collaboration with: Cris Brack and Albert Van Dijk (ANU Fenner school); Borevitz Lab
19. 19. National Arboretum Phenomic & Environmental Sensor Array Ideal location 5km from ANU (64 Mbps wifi) and near many research institutions Forest is only ~4 yrs old Chance to monitor it from birth into the future! Great site for testing experimental monitoring systems prior to more remote deployments 21/ 20
20. 20. National Arboretum Sensor Array 20-node Wireless mesh sensor network (10min sample interval) Temp, Humidity Sunlight (PAR) Soil Temp and moisture @ 20cm depth uM resolution denrometers on 20 trees Campbell weather stations (baseline data for verification) Two Gigapixel timelapse cameras: Leaf/growth phenology for > 1,000 trees LIDAR: DWEL / Zebedee UAV overflights (bi-weekly/monthly) Georectified image layers High resolution DEM 3D point cloud of site in time-series Sequence tree genomes Environment Phenotype Genetics
21. 21. Arboretum Video https://www.youtube.com/watch?v=YanOqSlW7yE
22. 22. New high resolution Field phenotyping tools 1. Gigapixel imaging 2. UAVs (drones)
23. 23. - Golfer, 7km distant Monitor daily change in every plant in your field site Gigapixel imaging Usable view area for phenology: ~5,000 Ha
24. 24. 20 gigapixel image of Canberra, Australia from the Black Mountain Telstra Tower Zoom in to the National Arboretum
25. 25. Midsummer Zoom in to the Each forest at the Arboretum
26. 26. Low cost sequencing lets us genotype every individual tree and identify genetic loci that correlate with observed phenotypic differences between trees. We can do this for all trees at the arboretum within view of the camera. Fall Color change shows differing rates of fall senescence in trees Late fall
27. 27. UAVs (drones) for monitoring $2-4K airframe (DJI, Aeronavics) + 10-20MP digital camera (~1kg payload) Processing software ($700 - 2,000 USD: Agisoft; Pix4D) 3D models of field site (cm resolutions) Orthorectified image and map layers LAS / point cloud data Automated pipeline: Tree Height; Volume, foliage density (?) RGB color GPS location DEM of site View 3D model online: http://bit.ly/ARB3Dv1 29/ 20
28. 28. Software outputs DEM and point cloud data Processing script for tree data: GPS, Height, 3D volume, top-down area, RGB phenology data Straight to google maps online
29. 29. 3D Point clouds online: http://Phenocam.org.au Up next, re-sort 3D tree data by provenance, size, etc
30. 30. Ultra-high resolution ground-based laser DWEL (CSIRO); Echidna (handheld; $25K LiDAR) Multiband Lidar with full point returns ~30 million points in a 50m2 area (vs 5-10 pts/m for aerial) Data: Michael.Schaefer@csiro.au
31. 31. 3D trees rendered from LiDAR data Image: Stu Ramsden, ANU Vislab
32. 32. Modernizing data visualization The challenge is no longer to gather the data, the challenge is how we do science with the data once we have it A sample is no longer a data point Example: Soil Moisture 5min intervals @ 20 locations, 6 months of data The spatial variation is what is interesting... Artifact or signal? Soil Moisture @ 20 sensor locations
33. 33. Virtual 3D Arboretum Project Goal: Use modern gaming software to explore new methods for visualizing time-series environmental data Historic and real-time data layers integrated into persistent 3D model of the national arboretum in the Unreal gaming engine Collaboration with ANU Computer Science Dept. TechLauncher students Stuart Ramsden, ANU VISlab
34. 34. Thanks and Contacts Justin Borevitz Lab Leader Lab web page: http://borevitzlab.anu.edu.au Funding: Arboretum ANU Major Equipment Grant ARC Center of Excellence in Planet Energy Biology | ARC Linkage 2014 Arboretum http://bit.ly/PESA2014 Cris Brack, Albert VanDijk, Justin Borevitz (PESA Project PIs) UAV data: Darrell Burkey, ProUAV 3D site modelling: Pix4D.com / Zac Hatfield Dodds / ANUVR team Dendrometers & site infrastructure Darius Culvenor: Environmental Sensing Systems Mesh sensors: EnviroStatus, Alberta, CA ANUVR Team Zena Wolba; Alex Alex Jansons; Isobel Stobo; David Wai TraitCapture: Chuong Nguyen; Joel Granados; Kevin Murray; Gareth Dunstone; Jiri Fajkus Pip Wilson; Keng Rugrat; Borevitz Lab Gareth Dunstone; Jordan Braiuka Contact me: tim.brown@anu.edu.au http://bit.ly/Tim_ANU http://github.com/borevitzlab