Challenges and opportunities involving high throughput plant Phenotyping of plant and organisms interactions in PPHD

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https://hal.inrae.fr/hal-02805459

Submitted on 6 Jun 2020

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Challenges and opportunities involving high throughput plant Phenotyping of plant and organisms interactions

in PPHD

Christophe Salon, Céline Bernard, Christian Jeudy

To cite this version:

Christophe Salon, Céline Bernard, Christian Jeudy. Challenges and opportunities involving high throughput plant Phenotyping of plant and organisms interactions in PPHD. PhenoDays 2013, Lem- natec., Oct 2013, Vaals, Netherlands. �hal-02805459�

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Christophe SALON

UMR 1347-AgroSup/INRA/uB

17 rue Sully - BP 86510 - 21065 Dijon - France

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Towards sustainable agriculture…

ê need for high nutrient, water, or pesticides inputs.

Sustainable agricultural practices.

- soil, water resource degradation.

Context Approach Phenotypic tools Examples Models Conclusion

Maintain yields in a context of:

- climate change,

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Plant architecture, flowering, senescence, … Pods and seeds

Roots and interactions with micro organisms

Valorise soil beneficial biological interactions

Understand plant response to environmental factors, identify genes.

Select the best performing crops..

..“better” yield, environmental « efficiency ».

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Tools and methods

CONCEPTION of PLANT IDEOTYPES

Genetic variability

Characterizing mechanisms

and molecular basis

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Context

PPHD Fluxomic Rhizotrons

Approach Phenotypic tools Examples Models Conclusion

Growth pouches, hydroponics

Labeling chamber ¹³C/¹⁵N/³⁴S Characterizing root development C, N, S flux measurement

Rhizobox

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High Throughput Phenotyping Platform

Building,

Phenotyping facilities Lemnatec^© greenhouses (240+110m²),

climatic chambers (80m²)

Context

Shoot roots etc…

Facilities

Spielberg’s masterpiece Rhizobox

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100 units/h

VIS NIR FLUO

Aerial architecture

120 units/h

6 units/h 20 units/h

2 units/h

Small plants

Organs (seeds…)

Germination

Plant from the agrosystems

Context

Spielberg’s masterpiece Shoot roots etc…

Facilities Rhizobox

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Context

Approach Phenotypic tools Examples Models Conclusion Facilities

Spielberg’s masterpiece Shoot roots etc…

Rhizobox

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Rhizothrones (EU Patent INRA- Inoviaflow, 1300 units

planed⁾ ^Rotating_device

Traits :

projected rootarea

projected nodule area total nodule number

nodule size classification

T0

T1 Tn RBG Image Context

Rhizobox

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RhizoBox (INRA-Inoviaflow⁾

Context

Rhizobox

Linear Sensor 12MP, 3 LEDs RVB, precision 50µm Camera : BASLER racer (Adapter F-Mount V01)

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Examples Models Conclusion Legumes

Grapevine Context Approach Phenotypic tools

Major problems in viticulture è cryptogamic diseases (oïdium, grey mould) of grapevine

Desease detection Objectives

Detecting desease in viticulture

Approaches:

1) ê number of fongicides

treatments and ê applied quantity (decision tools) + images (thermography IR…)

2) alternative strategies to fongicides

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Examples Models Conclusion Context Approach Phenotypic tools

Microplates, dishes HTS (small biological unit phenotyping)

Detection/evaluation of the intensity of diseases on grapes/berries

Legumes Grapevine

Desease detection Objectives

Colour/texture hybrid spaces

-RGB images in colorimetric spaces that integrates texture,

- object with similar colors may have different textures (so need to practice with biological

objects)

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Examples Legumes Grapevine

Context Approach Phenotypic tools Models Conclusion

But…

Fluctuating yields

•  Symbiotic fixation and durability :

ê  fertilizers, fossil energy, GHG emissions, irrigation

Regulation of symbiotic N₂ fixation and NO₃assimilation: determinism, plasticity constituents, optimal root / nodules for maximizing yield?

Genetic diversity Objectives

Identify a strategy Genotype ranking

Sensitivity of symbiotic N₂ fixation to environmental

conditions

•  Two ways of nitrogen nutrition

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Examples Legumes Grapevine

Tools and methods

Genetic variability

Characterizing mechanisms

and molecular basis

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French national collections of pea, fava and lupin (10000 accessions)

Natural genetic variability

Bourion et al. Annals Bot. 2007

Genetic diversity on root architecture

Identify a strategy Genotype ranking Examples

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Recombinant inbred lnes (1400 RILs)

Bourion et al. TAG 2010

Nodules:

Number Surface Biomass Roots:

Number Length Biomass

Aerial part : Height Biomass Leaf surface area

STRUCTURE FONCTION

C use efficiency

N uptake efficiency

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Induced genetic variability

Number of nodules formed during the first nodulation wave 100 200 300 400 500 600 700 800 900

R²= 0.97 Sym28

Sym29 nod3

Frisson

Nodule number

Wild Hypernodula7ng

Nodule development

Voisin et al. Plant Soil 2010 Salon et al. Agr 2001

Duc et al. 1998

0 2 4 6 8 10 12 14 16

0 200 400 600 800 1000 1200

Somme de dégrés-jours depuis le semis Accumulation de matière sèche (g.plante-1)

Degree days a:er sowing Nitrogen accumula7on (g/plant)

Cazenave et al. Plant Soil 2013

Porceddu et al. BioMed 2008 . KK Sidorova

Wild Long roots Ramiﬁed ++ Nod++

Nod++ x

Root architecture

Medicago truncatula, induced by Tnt1

Pea, induced by EMS

Identify/characterize genes involved in nodulation control or root architecture

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TILLING mutant collections

Research mutant in a target gene, analyze the mutation effect Medicago truncatula Jemalong A17 (9000 M2) and pea (Pisum sativum) var. Caméor (5000 M2)

Le Signor et al. Plant Biotechnol 2009 Dalmais et al. BMC Genome Biol 2008

ü  HTP TILLING platform: ABI 3730 (Contact: lesignor@dijon.inra.fr)

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Tools and methods

Genetic variability

Characterizing mechanisms

and molecular basis

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What is the strategy of legume plants faced to a N constraint ?

Roots Dry Weight (g) 0,04 0,06 0,08 0,10 0,12 0,14 0,16 0,18

Nodules Dry Weight (g)

0,004 0,006 0,008 0,010 0,012 0,014 0,016 0,018 0,020

Number of nodules counted on the scan

0 200 400 600 800

Real number of nodules

0 200 400 600 800 1000

Surface area

20 30 40 50 60 70

Root Dry Weight (g root/plant) 0,04 0,06 0,08 0,10 0,12 0,14 0,16 0,18

y = 0.0106 ln(x) + 0.0369, R≤ = 0.78 y = 0,000041x² - 0,001530x + 0,087432, R≤ = 0,839

y = 1.1645 x - 9.45, R≤ = 0.90

Nodules number and size, appearance

NH4NO3 10 mM

-N KNO3 0.5

mM 14 days after inoculation

Split roots

Ruffel et al. (2008), Plant Physiol. 146: 2020-2035.

Salon et al. (2009), CRAS, 332 :1022-1033.

Jeudy et al. (2010), New Phytol, 185:817-828.

Morphometry versus functional

strategy identifcation

“Low” throughput characterization of nodulated roots

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Binary image and

threshold

Young plant

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Only keep nodule shaped forms, get rid of

unused areas

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Get rid of unused areas

(areaopen)

Young plant

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Smooth the image

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Count nodules with the

« circle » form + area on a approximate circle

Young plant

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Original image + superposed

nodules

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Young plant some days later…

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Enlarged image

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Hybrid space (color + texture)

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RGB Image of identified nodules

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Green band of the RGB image

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Binary image with squared nodules

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Nodules automatically detected

95% detec7on eﬃciency

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Original image + superposed nodules

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Genotype

ranking does not vary

Ranking pea genotypes: Pea core collection Hydroponic versus rhizotron Plant biomass

Phenodays, 10/11/2013

Plant biomass decreases

within

rhizotrons

0,00 0,50 1,00 1,50 2,00 2,50 3,00

Hydroponic Rhizotrons

Dry Matter (g/plant)

Hydroponic and substrate

Rhizotron

y = 0,8747x -‐ 0,2539

0,0 0,5 1,0 1,5 2,0 2,5

0,0 0,5 1,0 1,5 2,0 2,5 3,0

R²=0,87

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Context Approach Phenotypic tools Examples Models Conclusion Legumes

Grapevine

Ranking pea genotypes: Pea core collection Hydroponic versus rhizotron Shoot biomass

Genotype

ranking does not vary

Dry Matter (g/plant)

0,00 0,50 1,00 1,50 2,00 2,50

Rhizotron

y = 0,9052x -‐ 0,3116

0,2 0,4 0,6 0,8 1,0 1,2 1,4 1,6 1,8

R²=0,88

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Nodule biomass

Context Approach Phenotypic tools Examples Models Conclusion Legumes

Grapevine

Ranking pea genotypes: Pea core collection Root biomass

Dry Matter (g/plant) Rhizotron

Hydroponic and substrate 0,00

0,05 0,10 0,15 0,20 0,25 0,30 0,35

0,40 Hydroponic

Rhizotrons

y = 1,1576x -‐ 0,0451

0,0 0,1 0,1 0,2 0,2 0,3 0,3 0,4 0,4

0,0 0,1 0,2 0,3 0,4

0,00 0,02 0,04 0,06 0,08 0,10 0,12 0,14

y = 0,7358x -‐ 0,013

0,0 0,0 0,0 0,0 0,0 0,1 0,1 0,1 0,1

0,0 0,0 0,0 0,1 0,1 0,1 0,1 0,1

R²=0,80 R²=0,95

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Leaf area

Total plant biomass Biological efficiency

Total nitrogen amount

Specific nitrogen acquisition Efficiency of N conversion to

leaf area

Microbial communities Rhizodeposition

Respiration

Stimulating root development Improving soil N availability

Nodulated roots

Root biomass over plant biomass ratio

Root Nodules

Integrative Model: Medicago

Decomposing integraCve variables in physiological processes

Soil acidification Organic carbon Organic acids

CO2

Disolved Inorganic Carbon

Provides characteriza7on of phenotypic traits into models

Models need a reduced number of parameters for phenotyping gene7c variability

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Analytical approach

Modelisation Phenotyping Approach

Combine approaches

Identifying differences among genotypes

Interpreting the detected difference

+ +

Food for thoughts…

Ø  Towards functional phenotyping (NAAS System) Ø  Validate in the field : Pheno Field Platform in Dijon

Context

NO3- NO3- N₂

CO₂

CO2

N

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Starring…

Christian JEUDY

Céline BERNARD

Jean-Claude SIMON

Frédéric COINTAULT

Simeng HAN

Marielle ADRIAN

Christophe BAUSSARD

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Agrosystems Adaptation Team Medicago truncatula team

And also …

PPHD’s addicts

Proteaginous target crop team

Data acquisition and management wizards Ecophysiology team

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Rhizothrones (EU Licence INRA- Inoviaflow, 1300 units

planed⁾

Context

Rhizobox

Roots Membrane 32µm

Substrate

Compressed gaz (20-100mbar)

Valve Before inflating

Plant

After inflating