4PMI: Plant Phenotyping Platform for Plant and Microorganisms Interactions Phenotyping innovations, opportunities and challenges

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4PMI: Plant Phenotyping Platform for Plant and Microorganisms Interactions Phenotyping innovations,

opportunities and challenges

Christophe Salon, Céline Bernard, Mickaël Lamboeuf, Christian Jeudy

To cite this version:

Christophe Salon, Céline Bernard, Mickaël Lamboeuf, Christian Jeudy. 4PMI: Plant Phenotyping

Platform for Plant and Microorganisms Interactions Phenotyping innovations, opportunities and chal-

lenges. Green Agricultural project China Agricultural University, Dec 2017, Nanjing, China. �hal-

02733820�

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4PMI: Plant Phenotyping Platform for Plant and Microorganisms Interactions

Phenotyping innovations, opportunities and challenges

(Christophe Salon, Céline Bernard, Mickael Lamboeuf, Christian Jeudy,

UMR Agroécologie, INRA, Dijon, France)

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“Manual” image

4PMI image

0 0,02 0,04 0,06 0,08 0,1 0,12 0,14

0,00 1,00 2,00 3,00 4,00

S h o o t f re sh w e it gh (g)

Projected area (cm²)

Shoot Fresh weight

“Manual” weight

y = 0,00575187x

²

+ 1,37710288x R² = 0,97425900 0

10 20 30 40 50 60 70 80

0 20 40

Mes ur ed Lea f a rea (c m ²)

Projected area (cm²) Shoot leaf area

Choice of best image acquisition model !

Mean of 3 lateral views

Shoot phenotyping: basics

Slide 3 over 2586

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L e a f

F l o w e r s Segmentation

Original image

Leaf Flowers Green fruit Orange fruits Red fruits

Tomato in pots

Shoot phenotyping: 1st example

Slide 4 over 2586

Micro tom phenotyping, coll. C Rothan (BFP Bordeaux, France)

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0 50 100 150 200 250 300

0 5 10 15 20 25 30 35 40 45 50

0 200 400

Ve ge tat ive o rgan s pr oj ec te d ar ea (c m

2

)

Re p ro d u ct iv e o rg an s p ro je ct ed a re a (cm ²)

Degree days sum - base 12

fleurs fruits non murs fruits rouges feuilles tiges

y = 0,3142x + 0,5182 R² = 0,8923

0 5 10 15 20 25

0,00 20,00 40,00 60,00 80,00

Re d f ru it b io m as s (g)

Mature fruit projected area (cm

²

)

MS fruits oranges /rouges

• Fruit detection realized = f(image analysis).

• Phenology and fruit maturing followed non destructively from image analysis.

Shoot phenotyping: 2nd example

Slide 5 over 2586

Micro tom phenotyping, coll. C Rothan (BFP Bordeaux, France)

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Algorithms to identify symptoms on 300 genotypes

23% 20% 32% 25%

01% 52% 40% 7%

100%

Chlorosis Sensible

Tolerant

PEA plants

Shoot phenotyping: 3rd example

1800 plants capacity !

(I know…, that’s less than Trevor’s PF..)

Slide 6 over 2586

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Ø Crop breeding programmes: root traits rarely used as

selection criteria, a focus on adaptation to high-input systems,

Root phenotyping: why go into trouble ?

Ø Technical difficulties:

• Access to roots ,

• Root diversity,

• Plasticity of RSA (abiotic and biotic factors including plant and microorganisms interactions) in order to enhance its efficiency.

Improve crop resource-use efficiency through:

(i) physiological utilization of acquired resources, (ii) resource acquisition

Slide 7 over 2586

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We wish:

- To visualize (harvest) roots, at high resolution, dynamically and non destructively, for a large number of biological units, various species.

- To estimate structural (and functional?) traits, avoid shading roots, oxygen shortage and pH, nutrient unregulated conditions

- To study plant-plant and plant-microorganism interactions

Christmas wishes

First priority

Root projected area Nodule projected area Nodule number

Total root length

Root depth, prospection

Tap root, first order, second order

…

Also structures arising from plants and microorganisms : nodules, mycorrhiza then

Main and lateral root length

Number of lateral roots, of secondary roots on lateral roots

Number and position of nodules on each root Apical diameter of roots

Notes:

Number: total, by segment-segment length Projected area: individual, by class

Position: individual, by class

Nodule efficiency: individual, by class Estimated biovolume: a root ≠ cylinder Biomass estimation: calibration

…and access various descriptors of RSA:

Slide 8 over … a lot

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Many opportunities

Nagel et al. 2009.

Plant and Soil. 316:285-297.

Soil and Rhizotrons

Downie et al. 2012

DOI: 10.1371/journal.pone.0044276

Artificial transparent soil

Trachsel et al. 2011.

Plant and Soil. 341 :75-87.

Soil

Gellan gum, in tubes

Clark et al. 2011. Plant Phys. 156:455-465.

Gro wth po uch es

Hun d et al. 2009. Plan t an d S oil. 325: 335 –349.

Fang

et al. 2009 . Plant

J. 20 09; 6

0:109 6-1108.

Vois in et

al. 2013 Ag ron Sus

t De v. 20 13; 3

3:829 -838 Clark

et al. 2013 . Plant

Cell Env

. 201 3; 36

:454- 466.

Hydr opon ic

Plate

Tube Pot

Agar pla

tes, petr i dische s

Hargrea ves et a l. 2009.

Plant and

Soil. 31 6:285- 297.

Yazda

nbakhs h 2009 Func Plant B

iol., 36, 938–

946 Mo rad i e t a l. P lan t S oil (2 00 9) 31 8:2 43 –255 Mo on ey et al. 20 12 . P lan t S oil , 3 52 :1– 22

X R ay to mo gra ph y

R. M etzn er C. Wi

ndt

MR I

I give up counting slides…

(11)

1 to 6 plants

Shading

« shell »

Base for conveyors

Jeudy et al. Plant Methods (2016) 12:31 DOI 10.1186/s13007-016-0131-9

METHODOLOGY

RhizoTubes as a new tool for high

throughput imaging of plant root development and architecture: test, comparison with pot grown plants and validation

Christian Jeudy

¹

, Marielle Adrian

¹

, Christophe Baussard

²

, Céline Bernard

¹

, Eric Bernaud

¹

, Virginie Bourion

¹

, Hughes Busset

¹

, Llorenç Cabrera-Bosquet

³

, Frédéric Cointault

¹

, Simeng Han

¹

, Mickael Lamboeuf

¹

,

Delphine Moreau

¹

, Barbara Pivato

¹

, Marion Prudent

¹

, Sophie Trouvelot

¹

, Hoai Nam Truong

¹

, Vanessa Vernoud

¹

, Anne-Sophie Voisin

¹

, Daniel Wipf

¹

and Christophe Salon

^1*

Abstract

Background: In order to maintain high yields while saving water and preserving non-renewable resources and thus limiting the use of chemical fertilizer, it is crucial to select plants with more eﬃcient root systems. This could be achieved through an optimization of both root architecture and root uptake ability and/or through the improvement of positive plant interactions with microorganisms in the rhizosphere. The development of devices suitable for high- throughput phenotyping of root structures remains a major bottleneck.

Results: Rhizotrons suitable for plant growth in controlled conditions and non-invasive image acquisition of plant shoot and root systems (RhizoTubes) are described. These RhizoTubes allow growing one to six plants simultaneously, having a maximum height of 1.1 m, up to 8 weeks, depending on plant species. Both shoot and root compartment can be imaged automatically and non-destructively throughout the experiment thanks to an imaging cabin (Rhizo- Cab). RhizoCab contains robots and imaging equipment for obtaining high-resolution pictures of plant roots. Using this versatile experimental setup, we illustrate how some morphometric root traits can be determined for various species including model (Medicago truncatula), crops (Pisum sativum, Brassica napus, Vitis vinifera, Triticum aestivum) and weed (Vulpia myuros) species grown under non-limiting conditions or submitted to various abiotic and biotic constraints. The measurement of the root phenotypic traits using this system was compared to that obtained using

“classic” growth conditions in pots.

Conclusions: This integrated system, to include 1200 Rhizotubes, will allow high-throughput phenotyping of plant shoots and roots under various abiotic and biotic environmental conditions. Our system allows an easy visualization or extraction of roots and measurement of root traits for high-throughput or kinetic analyses. The utility of this system for studying root system architecture will greatly facilitate the identification of genetic and environmental determi- nants of key root traits involved in crop responses to stresses, including interactions with soil microorganisms.

Keywords: High-throughput, Growth, Drought, Nitrogen availability, Plant–microorganism interactions, Image acquisition, Phenotyping, Plant roots, RhizoCab, RhizoTubes

© 2016 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/

publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Open Access

Plant Methods

*Correspondence: christophe.salon@dijon.inra.fr

1 UMR 1347 Agroécologie AgroSup/INRA/uB, 17 Rue Sully, BP 86510, 21065 Dijon Cedex, France

Full list of author information is available at the end of the article

RhizoTube: the concept

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Hand conveyor

Process control

Keyboard RhizoTube

Patent INRA-InoviaFlow-Shakti

Imaging : RhizoCabs

Blue

Red Green

Camera, rotating plate, LEDs

Software

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High Resolution RhizoCab

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10 cm

Maize

100 µm

50 µm

10 µm Nodules

Keyboard

Hyphaes

High Resolution RhizoCab

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High Resolution RhizoCab

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1 6

High Resolution RhizoCab

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1 7

High Resolution RhizoCab

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High Resolution RhizoCab

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1 9

High Resolution RhizoCab

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2 0

High Resolution RhizoCab

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2 1

High Resolution RhizoCab

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2 2

High Resolution RhizoCab

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High Resolution RhizoCab

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High Resolution RhizoCab

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World wide

distribution Trademark

High Throughput RhizoCab

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High throughput:

1000 RT/day, 5s/RT/WL Medium resolution (42 µ m) Medium throughput:

100 RT/Day, 5-60s/day/WL Very high resolution (7 µ m)

RhizoCab HT RhizoCab HR

Nodules

Hyphae

RhizoCabs : summary

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Segmentation software

M. Lamboeuf

(29)

y = 0,0013x R² = 0,8595 0,00

0,10 0,20 0,30 0,40 0,50

0 100 200 300 400

Biovolume / biomass (g/plante)

Projected area (cm

²

) Day

0 20 40 60

Le ngt h mai n ro o t

1 2 3 4

0,00 10,00 20,00

Projected area (cm

²

)

1 2 3 4

Day

10 0 20 30 40 50 60 70 80

1 1001 2001 3001 4001 5001 6001 7001

Di ame te r (p ix el )

Length (pixel)

Dynamic trait characterization

Roots: Length, diameter => projected area => biovolume

Software

Han et al, Machine Vision 2017

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Lat. 1 Lat. 2

Lat. n

Lat. 35

Event

Root 1 Nodule 1

Root n Right

Left

Distance Nodule 2

Nodules and lateral roots detection

Roots, detect events: lateral roots and nodules detection

Software

Han et al, Machine Vision 2017

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Focus on image

Nodules: Number, projected surface, position, color

Software

Han et al, Machine Vision 2017

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Hybrid spaces (color + texture)

(Cointault et al, 2008)

Software

Nodules: Number, projected surface, position, color

Han et al, Machine Vision 2017

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I mage with nodules

Software

Nodules: Number, projected surface, position, color

Han et al, Machine Vision 2017

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Original i mage + superimposed nodules

Software

Nodules: Number, projected surface, position, color

Han et al, Machine Vision 2017

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Nodules detected

95% detection efficiency

Han et al, Machine Vision 2017

Software

Nodules: Number, projected surface, position, color

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Pour faire quoi ?

0 0,05 0,1 0,15 0,2 0,25

01 .2 8 01 .2 9

02 .0 1 02 .0 2

02 .0 3 02 .0 4

02 .0 5 02 .0 8

02 .0 9 02 .1 0

02 .1 1 02 .1 2

02 .1 5 02 .1 6

02 .1 7

bi om as se (g )

Projected area from images (cm²)

Root biomass

0 0,005 0,01 0,015 0,02 0,025 0,03 0,035

01 .2 8 01 .2 9

02 .0 1 02 .0 2

02 .0 3 02 .0 4

02 .0 5 02 .0 8

02 .0 9 02 .1 0

02 .1 1 02 .1 2

02 .1 5 02 .1 6

02 .1 7

bi om as s (g )

Projected area from images (cm²)

Nodule biomass

Software

A simple example: root and nodule dynamics

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Which species?

Pea Faba Lupin Bean

Vesce Commune Brassica Weeds Maize

Wheat Medicago

Tomato Brachypodium

Vesce of Narbonne

Grape Soybean

… or in association

Alone…

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Nutrient efficiency Drought

Nut Effic Drought x

Throughput (rhizotube number)

500 1000

Biotic stress x Drought

Archirac

Wheat

SOLACE Wheat

EuCLEG

Alfalfa

Pea

Perspeacase

Pea, faba Soybean

OptiPhen

Heat x Drought

Pea, Medicago

GRASP

Pea

Wheat

Pea MIRGA

Maize

Increasing throughput…

Eauptic

Pea

All

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y = 0,0031x + 0,0157 R² = 0,8975

0 0,05 0,1 0,15 0,2 0,25 0,3 0,35 0,4 0,45 0,5

0 50 100 150

Ro o t b io m a ss ( g )

Projected area from images (cm²)

Nodules

Genotypes with contrasted architectures: pea

y = 0,014x + 0,0038 R² = 0,8289

0 0,01 0,02 0,03 0,04 0,05 0,06 0,07 0,08 0,09 0,1

0 2 4 6

No du le bi om as s (g)

Projected area from images (cm²) James

Myster Balltrap Safran Kayane Spencer

Geronim o Dexter Isard

Roots

Some results: Pea

Bourrion & Prudent, unpublished

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

Caméor - pot

Caméor - rhizotron

Kayanne - pot

Kayanne - rhizotron

Ra tio v al ue

Shoot/root biomass

0,0E+00 5,0E-05 1,0E-04 1,5E-04 2,0E-04 2,5E-04 3,0E-04 3,5E-04

Caméor -

pot Caméor -

rhizotron Kayanne -

pot Kayanne - rhizotron

Bi om as s (g /pl ant e)

Mean nodule biomass

Similar traits in pots and RhizoTubes: pea

0 0,1 0,2 0,3 0,4 0,5 0,6

Caméor -

pot Caméor -

rhizotron Kayanne -

pot Kayanne - rhizotron

Biom ass (g /plan te )

Plant biomass

Pot

0 1 1 2 2 3 3 4

0 20 40 60

N b n o du le s m a in r oot

0 20 40 60 80 100 120 140

0 20 40 60

N b n o du le s o n la ter als

Distance from stem base (cm) RhizoTube

Same distribution profile!

RhizoTubes vs Pots ?

Distance from stem base (cm) Pot

RhizoTube

C. Jeudy

Jeudy et al, Plant Methods 2016

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18th June 20th June 22nd June 23rd June 24th June 25th June

Some results: Maize (MIRGA)

0 2 4 6 8 10 12 14

0 200 400 600 800 1000

Shoot biom ass (g )

Projected area (cm2)

WW WS

Projected area vs shoot biomass

y = 7E-06x

²

+ 0,0082x + 0,0192

R² = 0,94996

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Some results: Maize (MIRGA)

y = 1E-06x

²

+ 0,0022x + 0,0052

R² = 0,8843

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

0 200 400 600

B io m a ss e pa rt ie s a é ri e n n e s ( g)

Projected area (cm2)

WS+WW

18th June 20th June 22nd June 23rd June 24th June 25th June

Projected area vs root biomass

0 2 4 6 8 10 12 14

0 200 400 600 800 1000

Shoot biom ass (g )

Projected area (cm2)

WW WS

Projected area vs shoot biomass

y = 7E-06x

²

+ 0,0082x + 0,0192

R² = 0,94996 0

1 2 3 4 5 6 7

51 1- 1 00 51 1- 1 11 51 1- 1 24 51 1- 1 3 51 1- 1 36 51 1- 1 43 51 1- 1 52 51 1- 1 64 51 1- 1 72 51 1- 1 85 51 1- 1 93 51 1- 2 51 1- 2 05 51 1- 2 15 51 1- 2 28 51 1- 2 35 51 1- 2 41 51 1- 2 50 51 1- 2 59 51 1- 2 67 51 1- 2 77 51 1- 2 82 51 1- 2 9 51 1- 2 97 51 1- 3 01 51 1- 3 07 51 1- 3 15 51 1- 3 25 51 1- 3 31 51 1- 3 40 51 1- 3 47 51 1- 3 53 51 1- 3 59 51 1- 3 64 51 1- 3 7 51 1- 3 77 51 1- 3 89 51 1- 3 95 51 1- 4 03 51 1- 4 11 51 1- 4 20 51 1- 4 27 51 1- 4 36 51 1- 4 43 51 1- 4 50 51 1- 4 59 51 1- 4 68 51 1- 4 74 51 1- 4 8 51 1- 5 3 51 1- 6 1 51 1- 7 51 1- 7 7 51 1- 8 5 51 1- 9 3 51 1- 9 9 51 3- 1 1 51 3- 1 8 51 3- 2 7

Bi o m a ss e Pa rt . Ae r. ( g /p la n te )

Large panels (e.g 400, up to 1200) of genotypes

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Projected area vs root biomass and length: wheat

y = 2E-05x + 0,0063 R² = 0,9511

0 0,05 0,1 0,15 0,2 0,25 0,3 0,35

0 5000 10000 15000 20000

Me su re d Root s dr y w ei ght (g)

Projected area from images (mm²)

Root dry matter

y = 1,01E+00x R² = 9,70E-01

0 100 200 300 400 500 600 700

100 200 300 400 500

Me su re d R o o ts le n g h t ( mm)

Projected area from images (mm²)

Root length

Some results: Maize (EPPN)

EPPN Project, Josh Klein AARO Volcani Israel

Josh Klein

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Christmas wishes…and what you’ll get under the tree

Automatic trait quantification Done Nearly done … soon

Root projected area X

Total root length X

Root convex hull X

Root exploration dynamics (H and V) X

Root density X

Root number (incl. typology) X

Root angle X

Root diameter X

Nodule projected area X

Nodule number (inc.typology) X

Nodule biovolume (inc. classes) X

Nodule number, position on each root X

Nodule efficiency X

Mycorhizes, hyphae X

Germination checkup X

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Empoting Mounting

Fast root recuperation (allows ‘omics)

Upstream and dowstream

(46)

Tram Hydroponic RhizoTubes

Others

Germination chamber

Tutors

Energy base

Buubling pump

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NO₃^- NO₃^- N₂

CO₂

N

Thèse Delphine Moreau – 13 avril 2007 QUANTITE TOTALE D’AZOTE SURFACE FOLIAIRE

PROJETEE

BIOMASSE SOUTERRAINE

BIOMASSE TOTALE

Coefficients d’ajustement Efficience biologique

Date de début de fixation de N₂

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

0 10 20 30 40

PAR (mol m^-2jour^-1)

0 1 2 3 4

0 10 20 30 40

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

0 10 20 30 40

(

1500 2250 3000 3750 4500

0 10 20 30 40

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

0 2 4 6 8 10

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

0 2 4 6 810

PAR (mol m^-2jour^-1) PAR (mol m^-2jour^-1)

PAR (mol m^-2jour^-1)

[NO₃^-] (mM)

[NO₃^-] (mM) [NO₃^-] (mM)

0 400 800 1200

0 2 4 6 8 10

Efficience de conversion de N en surface projetée

Prélèvement de N spécifique AVANT la date de début de fixation de N₂

Prélèvement de N spécifique APRES la date de début de fixation de N₂