Agriculture in the digital era Dr Xavier Sirault Scientific Director (A/g) High Resolution Plant Phenomics Centre (Canberra, Australia) Research Team Leader: Phenomics Informatics and Modelling Tools “Together for better HTP digital phenotyping”

13TH JANUARY 2015 – SAN DIEGO (USA)

Credit: © Shutterstock

Difficulties in characterising canopy photosynthesis

Cell/Organ Organ/Plant Canopy/Community Plant/Canopy Molecular/Cell

Mu

ltip

le t

em

po

ral s

cale

s (s

)

10

-9

10

-3

10

0

10

3

10

6

10

9

Convenient approach – Functional Structural Plant Models and biophysical modelling

Convenient approach – Crop Models

Emergent properties

Time (s)

PAR

(

mo

l.m-2

.s-1

) Transient (not steady-state)

Multiple spatial scales and organisational levels

Intensive phenotyping: answer is in the details

Current

cultivars

Wasteful ti

llers

Current c

ultivars

+ tin

Fewer wasteful t i

llers

Larger e

ars

Larger g

rains

Current

cultivars

Wasteful ti llers

Current cultivars + tin

Fewer w

asteful t illers

Larger ears

Larger grains

Current

cultivars

Wasteful ti llers

Current cultivars + tin

Fewer w

asteful t illers

Larger ears

Larger grains

Cu

rren

t

cu

ltiva

rs

Was

tefu

l ti llers

Cu

rren

t cu

ltivars

+ tin

Fe

wer w

aste

ful t ille

rs

Larg

er e

ars

Larg

er g

rain

s

Current

cultivars

Wasteful ti llers

Current cultivars + tin

Fewer w

asteful t illers

Larger ears

Larger grains

Cu

rren

tcu

ltiva

rs

Was

tefu

l tillers

Cu

rren

t cu

ltivars

+ tin

Fe

wer w

aste

ful tille

rsL

arg

er e

ars

Larg

er g

rain

s

Current

cultivars

Wasteful ti llers

Current cultivars + tin

Fewer w

asteful t illers

Larger ears

Larger grains

Current

cultivars

Wasteful ti llers

Current cultivars + tin

Fewer w

asteful t illers

Larger ears

Larger grains

Current

cultivars

Wasteful ti llers

Current cultivars + tin

Fewer w

asteful t illers

Larger ears

Larger grains

Extensive phenotyping: high throughput screening vs.

Paproki et al. (2012) BMC Plant Biology

Synergistic &

Complementary

Pt = G×Et (×M) with t: time

Translation to canopies: cropatron

IR imaging

LiDAR: Distance and Intensity

multi- and hyper-spectral imaging

λ

Sirault et al. (2013) FSPM Liang et al (2013) IEEE

Real vs. in-silico

Automated workflow

Single plant characterisation and data fusion

3D Architecture to correct remote sensed signals

Photosynthetic efficiency parameters: ФPSII, ETR, light response curves

Time of day

PAM fluorescence and Laser Induced Fluorescent Transient (LIFT)

Measuring rate and pattern of organ formation (structural changes over time)

Paproki et al. (2012) BMC Plant Biology 12:63

Automated mathematical modelling (free form cubic

spline) -> derived parameters e.g. RGR,

ULR = f(Anet)

(amenable to prescriptive modelling using prior

knowledge and Ontologies))

time

Leaf

are

a

Collaboration: P Neveu , X Sirault

Data re-usability and delivery (web portal)

Collaboration: School of Computer Science (ANU)

Radiative Transfer Models and Model inversion

How does one validate model outputs?

Zhu et al 2012 17% difference in Ac with average light level (7% with photo-inhibition)

Collaboration on C4: C Pradal, C Fournier, J Guo, C Godin, F Tardieu, R Furbank, X Sirault Collaboration on C3: Xinguang Zhu , X Sirault

Nested-radiosity model implemented in Caribu – OpenAlea using output from PlantScan

Bottom-up approach

Apply light response curve, e.g rectangular hyperbola to calculate net assimilation

dnet RAI

IAA

max

max

integrates measured optical properties of each leaf individually

ScanAlea: •Middleware (MTG) •Model-assisted segmentation (Adel Model – model inversion (phenology)

Scaling from the leaf to the canopy (Cropatron)

Platform for measuring growth, matter and energy exchanges, dynamics of light environment and proxy

sensing of function in canopies

+ Lysimeter

single plant analysis

Credit: Xinguang Zhu

Collaboration: Robert Coe, Katherine Meacham, Robert Furbank, Paul Quick, X Sirault

Growth of OryzaSNP panel from IRRI

Single plant analysis

Collaboration: Robert Coe, Katherine Meacham, Robert Furbank, Paul Quick, X Sirault

Development of an analysis pipeline at canopy level

Validation (on-going) of volumetric density -> surrogate for Leaf area distribution / biomass ?

Architectural information at canopy level

Cross section (pixel index)

Cross section (pixel index)

Leaf angle

Deery et al. (2014)

Leaf angle

Valade, Paproki , Sirault ,Fripp in prep

Top-down approach to identify individual plants in canopies

Bradley et al 2013, SIGGRAPH (Disney Research)

Preliminary validation of thermal imaging for estimation of conductance

35°C

20°C 12:00 09:00 17:00

y = 0.972 x -0.654, R2 = 0.7924

Diurnal Canopy Temperature

Time (Day)

800 1000 1200 1400 1600 1800

Tem

per

atu

re (

°C)

16

18

20

22

24

26

28

30

32

34

36

Relationship between canopy tempertaure measured with the FLIR and leaf temperature measured with thermocouples

Temperature FLIR (°C)

20 25 30 35 40

Tem

per

atu

re M

easu

red (

°C)

16

18

20

22

24

26

28

30

32

34

36

Spectral imaging for leaf and vegetation properties

Transmission (τλ)

Reflection (ρλ) Absorption (αλ)

Hyperspectral Image

R² = 0.8024

0

100

200

300

400

500

600

700

800

0 1 2 3 4 5 6

Leaf

ch

loro

ph

yll c

on

ten

t (m

g m

2)

Index value

CI590 (N880/VIS590)-1

Leaf 5 Leaf 6 Leaf 7 Leaf 8

Coe, Meacham, Furbank, Sirault (unpublished)

Leaf characteristics

PROSPECT

Jacquemoud et al 1995: remote sensing of environment 52:163-172

Parameters Leaf mesophyll structure parameters N

Chlorophyll content Cab (g/cm2) Carotenoid content Ccx (g/cm2)

Brown pigments Cbp (r.u.) Equivalent water thickness Cw (cm)

Dry matter content Cdm (g/cm2)

Jacquemoud & Baret (1990), Remote Sensing of Environment, 34:75 -91 Féret et al. (2008), Remote Sensing of Environment 112:3030-3043

Direct mode Inverse mode

Coupling canopy functioning and radiative transfer models (physical) for remote sensing data assimilation

Leaf Reflectance/Transmittance Canopy structure

(LAI, l, s) Soil reflectance

SAIL

Canopy reflectance

Measurement characteristics view and sun

geometries Diffuse fraction (450-

2500nm)

canopy bidirectional reflectance model

Inverting involves minimising the mismatch between observed reflectance and predicted reflectance under some loss function

SAIL: Scattering by Arbitrarily Inclined Leaves

Perspectives Using advanced imaging technologies and computer vision provide: - a suite of comprehensive tools for phenotyping architectural and functional traits in crops - a way of measuring correlated phenotypes through time

These computational approaches are essential - to assessing the effect of changes to photosynthetic properties on crop growth; - to developing models which predict the effects of future climate change related to CO2, temperature

and water inputs; - to understanding the influence of leaf structural traits and their adaptation for use in altering

photosynthetic performance; - to linking phenotypic trait to gene in target crops wheat, rice and sorghum.

Main Challenges:

- Developing data assimilation frameworks to derive traits from remote-sensed data (e.g. conductance): inversion of complex physical or eco-physiological models to quantify traits at lower hierarchical levels (which methods to use? Neural networks, Look-up table, Marcov Chain Monte Carlo; non-unique solution)

- Linking and distributing information captured at multiple spatio-temporal scale: which method? RDF triples (Phenomics Ontology Driven Data Repository)

Currently HRPPC is developing a virtual laboratory environment running on HPC cluster and cloud to make the developed methods available to the scientific community (access based on our current business model: ie cost recovery).

Acknowledgements

Robert Furbank (Agriculture flagship) Jurgen Fripp (DP&S flagship)

Helen Daily (Agriculture) Peter Kuffner (Agriculture) Peter Ansell (Agriculture)

Julio Hernandez-Zaragoza (Agriculture) Dac Nguyen (Agriculture)

Robert Coe (IRRI) Chuong Nguyen (ANU)

Anthony Paproki (DP&S) Anne Bernhart (TelecomParis)

Jose Berni-Jimenez (Agriculture) David Deery (Agriculture)

(lots of students…)

Christophe Pradal (INRIA, CIRAD) Christian Fournier (INRA)

Christophe Godin (INRIA, CIRAD) Francois Tardieu (INRA) Frederic Baret (INRA) Pascal Neveu (INRA)

Barry Osmond (ANU)

Graham Farquhar (ANU) Graeme Hammer (UQ)

Paul Quick (IRRI) Xinguang Zhu (Plant Systems Biology / CAS)

Justin Borevitz (ANU)