1. Australian Plant Phenomics Facility
Mark Tester

2. Phenotyping – the new bottleneck in plant science
Genomics is accelerating gene discovery but how do we capitalise on these resources to establish gene function and development of new genotypes?
Physiological characterization of plants is still time consuming and labor intensive

3. High throughput phenotyping
Phenotyping is essential for
functional analysis of specific genes
forward and reverse genetic analyses
production of new plants with beneficial characteristics
High throughput is essential for phenotyping
in different growth conditions (e.g. watering regimes)
of many different lines
mutant populations
mapping populations
breeding populations
germplasm collections

4. The technological opportunity
Relieve phenotyping bottleneck with robotics, noninvasive imaging and analysis using powerful computing
Provide “whole of lifecycle”, quantitative measurements of plant performance from the growth cabinet to the field
Help deliver genomics advances to all plant science - e.g. model systems, cereals, grapevines, natural ecosystems
Accelerate transfer of IP from gene discovery to trait discovery and release of innovative new varieties

5. Australian Plant Phenomics Facility
Established with NCRIS award of $15.2m to relieve the phenotyping bottleneck Total package = $53m
Aim: To provide infrastructure based on automated image analysis to enable the phenotypic characterisation of plants
- National facility, at the international forefront
- Robotics, non-invasive imaging, analysis using powerful computing
- ‘Whole lifecycle’ quantitative measurements of plant performance
from the growth cabinet to the field
- Ontology-based storage of phenomics data - Research collaborations, international profile and engagement

6. Australian Plant Phenomics Facility – two nodes
High Resolution Plant Phenomics Centre
Canberra
Bob Furbank
The Plant Accelerator™
Adelaide
Mark Tester 6

7. Australian Plant Phenomics Facility
The Plant Accelerator™
Mark Tester

8. The Plant Accelerator™
ACPFG

10. The Plant AcceleratorTM
High throughput phenotyping of plant populations
4,485 m2 building, 2,340 m2 of greenhouses, 250 m2 for growth chambers
Grow >100,000 plants annually in a range of conditions
4 x 140 m2 fully automated ‘Smarthouses’
Plants delivered on 1.2 km of conveyors to five sets of cameras
High capacity state-of-the-art image capture and analysis equipment
Regular, non-destructive measurements of growth, development, physiology
First public sector facility of this type and scale in the world
Owned by University of Adelaide, opened 29 Jan 2010
National facility to support Australian plant research
Full GM and quarantine status
UniSA and ACPFG established a Chair and Assoc Prof in Plant Phenomics and Bioinformatics ($1.5m)

14. Measuring techniques relevant for drought research
Colour imaging
biomass, structure, phenology
leaf health (chlorosis, necrosis)
Near infrared imaging
tissue water content
soil water content
Far infrared imaging
canopy/leaf temperature
Fluorescence imaging
physiological state of photosynthetic machinery
Automated weighing and watering
water usage, control of drought conditions

15. Image acquisition modes
Top View Side View Side View 90°
Technical Details:
Camera: x 960 Pixel
Optic: 17 mm technical optic

16. Plant skeleton analysis Key to growth dynamics and morphology
separation of stem and leaves
information about nodes, length of leaves
morphology
plant growth phase

17. Color classification of leaves
Color classification of leaves
User defined color classification e.g. to characterise plant fitness under optimum or draught conditions or to distinguish herbicide/genetically modified from other plants

18. Quantitative morphology to characterise plants
Areas
Node distances
Leaf-stem angle
Height
width
Fingerprinting of morphological data

19. Plant colour classification
Key to plant health

20. Identification of active male flower
LemnaTec 2004

21. But wheat is not as neat….

22. Growth measurements – counting pixels
mean±SE;n=8

23. Estimation of shoot biomass
The projected shoot area of the RBG images gives a good correlation with shoot biomass
Tested for various plant species
wheat, barley
rice
cotton
Arabidopsis …
5wk old barley plants, 8 cultivars

24. Estimation of shoot biomass
But control and salt stressed plants have different area-weight ratios
20d old barley

25. Estimation of shoot biomass
Improved estimate of biomass when age of the plant is taken into account
Y = a0 + a1×(G+B+Y)+ a2×(G+B+Y)×H
(H = number of days after seed preparation date)
(Correction for leaf colour did not greatly improve weight estimates)
(Cross validation run 10x)
Predicted shoot dry weight [g]
Measured shoot dry weight [g]
Golzarian et al. (2010) IEEE Proceedings Signal Processing, in review

26. Use of colour information e.g. boron toxicity screen
Original image
Colour classified image
Treated with
100 mM GeO2, 8 d
Line
Green area
Necrosis area
% Necrosis
Sahara
30739
4232
12%
Clipper
11640
15321
57%
Julie Hayes, Margie Pallotta and Tim Sutton, ACPFG

27. QTL for Ge tolerance identified using colour imaging overlaps QTL for B tolerance (1999)
B toxicity - leaf symptoms
Ge toxicity - leaf symptoms
Jefferies et al TAG 98,
Hayes et al., unpubl., using LemnaTec

28. Salinity tolerance - trait dissection
osmotic
tolerance
Breeding for overall salt tolerance difficult due to low heritability
Dissection into individual traits suitable for forward genetics approach
Use of The Plant AcceleratorTM to perform high throughput phenotyping
Na+
exclusion
tissue
tolerance
Munns & Tester (2008) Annu Rev Plant Biol 59:

29. Screening for osmotic tolerance
mean±SE;n=8

30. Screening for osmotic tolerance
Rajendran et al. (2009) Plant Cell Environ 32,

31. Osmotic tolerance screen in bread wheat
(day-1)
Berkut
Krichauff
Mapping population of Berkut x Krichauff
Berkut – CIMMYT
Krichauff – Australian cultivar
Berkut higher overall tolerance despite higher tissue [Na+]
Parents
Berkut – 0.65
Krichauff – 0.33
Range of progeny
0.13 to 0.96
Karthika Rajendran

32. QTL mapping of osmotic tolerance
Chromosome 1D
Significant QTL on chromosome 1D
QTL1D.9 explains 21% of phenotypic variation in the population
Favourable allele comes from Berkut
Karthika Rajendran

33. Hardware purchased Currently Expansion, room for TPA acknowledges
IBM BladeCenter Chassis
3 x HS21 blade servers
6 x HS22 blade servers
2 x DS4700 storage controller
8 x DS4000 storage expansion units
140 x 1TB hard drives
$510K (2008 & 2009)
Virtualisation with VMware
Expansion, room for
5 additional servers
20 additional hard disks
TPA acknowledges
Lachlan Tailby (ACPFG)
Picked up by IBM’s Smarter Planet campaign

34. LemnaTec Data System James Eddes
FLUO
1392 x 1040
RGB
2056 x 2454 IR
320 x 256
NIR
Snapshot
Smarthouse operations
Imaging configurations
Conveyor tasks
Watering tasks
Single database stores acquired data, SmartHouse operation configurations and tasks and analysis results
No project level data management
Backup, archive, delete
Access control
Around 30MB per snapshot
72 GB per day, 0.5 TB per week
Smarthouse
database
Analysis results
James Eddes

35. Data flow / management James Eddes Plant Accelerator servers
LemnaMiner
LemnaLauncher
Daemon
Daemon
DATA PROCESSING & MINING
Plant Accelerator Project DBs
Project 1
Project 2
Project 3
Project 4
Project 5
Project 6
MIDDLE
LemnaTec Production DB
buffered transfer
buffered transfer
SH1
SH2
OPERATION & ACQUISITION
Smarthouse 1 (South)
Smarthouse 2 (North)
SH1
SH2
LemnaLauncher
LemnaLauncher
James Eddes

36. Data management issues
Building databases, managing export of data from LemnaTec, returning data to LemnaTec for further analyses
Image analyses – LemnaTec image processing grids, quality control, basic statistics
Data service – image directories, processing, analysis spreadsheets, metadata, PODD
Data dissemination
Embargo
Offsite back-up
James Eddes

37. Wider computational issues
Data acquisition
Data management
Image analysis
Counting pixels
3-D modelling – computer vision, machine intelligence
Statistical analyses
Modeling and biological interpretation
Plugging numbers back in to the plant
Genetics – aligning phenomics data with genomics data to allow quantitative genetics

38. Plans to address issues
Raise money, hire people, collaborate
NCRIS ALA (Bogdan!) - Systems manager, feeding PODD
NCRIS ANDS data architects for 1 yr, feeding PODD
EIF programming - Image analysis
- Computer vision (Anton Vandenhengel)
ARC Linkage (LemnaTec) - Image analysis, computer vision
HFSP Computer vision
- Machine intelligence
Collaboration with - PODD, ALA, etc within IBS
- UniSA node of ACPFG
- Desmond Lun
- Computer vision group of UniAdl
- Anton Vandenhengel

39. The Plant Accelerator™ team to date
Mark Tester
Geoff Fincher
Helli Meinecke – business manager
Bettina Berger – postdoctoral scientist
James Eddes, Bogdan Masznicz, Jianfeng Li – computer programmers
Robin Hosking – horticulturalist
Richard Norrish – electrical engineer
Lidia Mischis, A.N. Other – technicians
Karthika Rajendran – PhD student
Brett Harris – Honours student
Desmond Lun, Irene Hudson, Mahmood Golzarian
– UniSA /ACPFG maths, stats
Anton van den Hengel – UA computer vision
+ three programmers in UQ to construct the database repository