Sensor based phenotyping for grapevine breeding and genetic analyses … · 2018. 9. 6. · Sensor...
Transcript of Sensor based phenotyping for grapevine breeding and genetic analyses … · 2018. 9. 6. · Sensor...
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www.jki.bund.de
Sensor based
phenotyping for grapevine
breeding and genetic
analyses
Institute for Grapevine Breeding Geilweilerhof
Reinhard Töpfer, Rudolf Eibach, Oliver Trapp,
Katja Herzog, Florian Rist, Robert Richter,
Eva Zyprian, Anna Kicherercv. Calardis Blanc
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downy mildewpowdery mildew
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Plants are cultivated with reduced fungicide application
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Peressotti et al.(2010) BMC Plant Biol. 10: 147
Plasmopara
viticola
(downy mildew)
6 dpi
Durability of resistances
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www.jki.bund.de(nach: Mundt et al. 2014, modifiziert; Originaldaten von Browning und Frey 1969)
Durability of resistances –Classical Boom-and-Bust Cycle
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www.jki.bund.de(Form: Mundt et al. 2014, modified; original data from Browning and Frey 1969)
Durability of resistances –Classical Boom-and-Bust Cycle
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Gra
d d
er
Resis
ten
z
-Rpv1 +Rpv1 -Rpv1 -Rpv1-Rpv3.1 -Rpv3.1 +Rpv3.1 -Rpv3.1-Rpv12 -Rpv12 -Rpv12 +Rpv12
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-Rpv1, -Rpv3, -
Rpv12
+Rpv1, -Rpv3, -
Rpv12
-Rpv1,+Rpv3, -
Rpv12
-Rpv1, -Rpv3,
+Rpv12
+Rpv1,+Rpv3, -
Rpv12
+Rpv1, -Rpv3,
+Rpv12
-Rpv1,+Rpv3,+Rpv12
+Rpv1,+Rpv3,+Rpv12
Gra
d d
er
Resis
ten
z
-Rpv1 +Rpv1 -Rpv1 -Rpv1 +Rpv1 +Rpv1 -Rpv1-Rpv3.1 -Rpv3.1 +Rpv3.1 -Rpv3.1 +Rpv3.1 -Rpv3.1 +Rpv3.1-Rpv12 -Rpv12 -Rpv12 +Rpv12 -Rpv12 +Rpv12 +Rpv12
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-Rpv1, -Rpv3, -
Rpv12
+Rpv1, -Rpv3, -
Rpv12
-Rpv1,+Rpv3, -
Rpv12
-Rpv1, -Rpv3,
+Rpv12
+Rpv1,+Rpv3, -
Rpv12
+Rpv1, -Rpv3,
+Rpv12
-Rpv1,+Rpv3,+Rpv12
+Rpv1,+Rpv3,+Rpv12
deg
ree o
f re
sis
tan
ce
-Rpv1 +Rpv1 -Rpv1 -Rpv1 +Rpv1 +Rpv1 -Rpv1 +Rpv1-Rpv3.1 -Rpv3.1 +Rpv3.1 -Rpv3.1 +Rpv3.1 -Rpv3.1 +Rpv3.1 +Rpv3.1-Rpv12 -Rpv12 -Rpv12 +Rpv12 -Rpv12 +Rpv12 +Rpv12 +Rpv12
Breeders answer: select for stacked R-loci (downy mildew)
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Durability of resistances -Occurrence of Fungicide Resistances
Class of Fungicides
First occurrence of resistance
Years prior to occurrence of resistance
Pathogen
Organic mercury 1964 40 Pyrenophora avenae
Benzimidazole 1970 2 Venturia inaequalis, Botrytis
cinerea
Phenylamide 1980 2 Phytophthora infestans,
Plasmopara viticola
Dicarboximide 1982 5 Botrytis cinerea
DMIs 1982 4 Blumeria graminis
Carboxanilide 1986 14 Ustilago nuda
Morpholine 1994 34 Blumeria graminis
Strobilurine 1998 2 Blumeria graminis f.sp. tritici
(according to HG Hewitt (1998) Fungicides in Crop Protection, modified by Deising et al.)
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Anti Resistence Strategy 2017 Against Fungicids
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Category Commercial Product Active Compound Group of Active Compound
Maximum 3 applications per season for all fungiciides labled by the same letter and color; Category D: only one application per year
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Maximum 3 applications per season for all fungiciides labled by the same letter and color; Category D: only one application per year
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Category Commercial Product Active Compound Group of Active Compound
Anti Resistence Strategy 2018 Against Fungicids
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Säu
re i
n g
/l
Acidity of Grapevine Must on September 20th
Riesling
GF.GA-47-42
Trend:Riesling: 11,2 g/l in 33 years (0,34 g/l per year)Gf.Ga-47-42: 5,0 g/l in 33 years (0,15 g/l per year)
Climate Change: Decrease of Acidity
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Breeders answer: select for late ripening
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A new cultivar needs
• resistance …
− downy mildew
− powdery mildew
− Botrytis
− Phylloxera
− other pests & diseases
− abiotic stress factors
• vigor
• yield
• wood maturation
• phenological adaptation
• quality
• …
• a name
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A new cultivar is
always a
compromise on
the timeline
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A new cultivar is
always a
compromise on
the timeline
Calardis Blanc
upright growth, late ripening,
small berries, loose cluster
wine style: decent aromas of Muskateller Ren3Rpv3-2Rpv3-1 BR BotrytisRen9
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Downy mildew Powdery mildew
Rpv12 Ren3 Ren4Rpv3-2Rpv1 Rpv3-1 Rpv10 Run1 Ren1
Ren3
Rpv1 Rpv3-1 Rpv10
Run1 Ren1
Rpv12
Ren3
Rpv3-2Rpv1
Run1
Ren4
1 …………..……..further combinations……………………40
Type of resistance: 3+3
Stacking of resistances:
Possibilities in elite genetic background
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Limitations in Grapevine Breeding
• Combination of resistance and quality
• Combinations with other traits
� Seedling numbers need to be high
• Limitation in space in greenhouse and field
• Limiting number of markers for MAS
• Limiting phenotypic possibilities
1 hectare(5000 plants) per year
Mainlymarkers forPM and DM
Tools for fast traitevaluation
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E1
E2
P1
P�
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Demand in seedlings –
idealized crossing scheme
1/256
F1-offspring
i.e. 25,600 seedlings to get 100 desired plants for further selection
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E1 E1
E2
P1
P�
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Demand in seedlings –
idealized crossing scheme
all
E2
P1
PT
LSH-lines
F1-offspring
i.e. one can produce thousands of desired plants for further selection
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� increasingnumber of
plants createa strong demand
for MAS andHT- phenotyping
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e.g. JKI in Siebeldingen
– Breeding (~ 6 ha)
– Breeding Research (~ 0.5 ha)
– Genetic Repository (~ 3 ha)
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Demand in phenotyping
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• Plants need to be screened (for several
traits) from the side not from top.
Challenges in Grapevine Phenotyping
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Traits of interest
Breeding
� Screening of seedlings � once a year
– Resistance, i.e. downy and powdery mildew
– Yield
– Phenology, ripening
– Wine Quality Parameters � Seedling selection
–
Greenhouse Field Wine taste
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Breeding Research/Genetic Resources
� E.g. Mapping populations � several times during the year
– Aim: Identification of new trait-related Loci
• E.g. resistance, root and bunch architecture
� Development of molecular markers for MAS
Plasmopara viticola
on leaf disks
Root architecture in
Rhizotrones Bunch architecture
Lab Greenhouse Field
Traits of interest
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• Objective and precise phenotypic data
• Reduced error variation, retro perspective
analysis
• High throughput (field) phenotyping
• Increased efficiency of grapevine breeding
Automated acquisition of the grapevine phenotype
Aim
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Various Sensors Options
Impedance
Cameras
3D Scanner
Computer-based3D reconstruction
� Spectroscopy
Hyperspectral
VIS-NIR Spectroscopy
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Phenotyping
for traits for
bunch rot
resilience
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The gray mold disease
Source: JKI
Botrytis cinerea
No active defense response in Vitis vinfera
Compact bunches: high damage potential
Breeding: Focus on physical barrierse.g. loose bunch architecture
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Phenotyping of bunch architecture as a trait
• Bunch Traits- length and width
- number of berries• Berry Traits
- diameter and volume• Stem Traits
- number and length of internodes
- length of pedicels
OIV 204: 1- 3 - 5 - 7 - 9
loose dense
Density
Robert Richter, Eva Zyprian et al.
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Labor intense approach for objective phenotyping
• Bunch Traits- length and width
- number of berries• Berry Traits
- diameter and volume• Stem Traits
- number and length of internodes
-- length of pedicels
Image based Methods- Cluster-Analysis-Tool (CAT)- Berry-Analysis-Tool (BAT)- ImageJ
time intense
labor intense
determined by on-going season
Workflow- Harvesting- Image of the bunch- Destemming- Berries on plate- Image of the berries- Image of the stem skeleton
Robert Richter, Eva Zyprian et al.
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Establishment of a 3D-based high throughputphenotyping pipeline
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Easy to handle user interface
Florian Rist, Katja Herzog et al.
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Validation of the sensor
• Validation on 4 varieties, BBCH87
Dornfelder Pinot Noir Calardis Blanc Riesling
Class 1 Class 3 Class 5 Class 7
• Application on segregating population GF.GA-47-42 X Villard Blanc, BBCH89
• highly variable in morphotype
Florian Rist, Katja Herzog et al.
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Artec Spider is applicable for analysis of grape cluster determining parameters
High correlation of berry parameters
Lower value for width
Massive time saving compared to 2D (> 10x) Florian Rist, Katja Herzog et al.
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3D bunch trait data show similar QTL regionscompared to reference data
Number of Berries
Chr 18
green reference data
red / blue 3D data
Total Volume
Chr 18
Grape Length/
Rhachis Length
Chr 9
Berry Volume
Chr 17
Massive time saving compared to 2D (up to 10x)
Florian Rist et al., 2018
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Extended application in the field
For a more intense mapping/association approach: extensive phenotyping
Increasing number of plants
Multi cultivar screening
non invasive phenotyping
Florian Rist et al., 2018
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Artec Spider is usable for field application
• Use of Artec Spider directly in the field
• 47 clusters of 4 varieties (Riesling, Calardis Blanc, Pinot Noir, Dornfelder)
• Analysis of variance of `field-`, `front-`, 360°-Scan
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Partial point clouds sufficient for detection of berry diameter, volume and cluster length
Fluctuations for width and convex hullFlorian Rist, Katja Herzog et al.
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• Berry surface = cuticle andepicuticular wax layer
Thickness, uniformity andintactness of the cuticle andepicuticular wax is correlated toresilience towards Botrytisbunch rot
BERRY SURFACE AND BOTRYTIS BUNCH ROT
Herzog et al. 2015Barré, Herzog et al., submitted
assumption
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• Measurement of electrical impedance described as indicator for thicknessand permeability of cuticle/epic. wax
• Improved, simple-to-handle sensor
• 450 berries per hour• One point measurements/ berry• Inexpensive sensor technique• Small memory usage
OBJECTIVE PHENOTYPING DUE TO IMPEDANCE
Herzog et al. 2015
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REDUCED RISK FOR BOTRYTIS BUNCH ROT
Seibel 7511
Sauvignon Blanc
Orion
Re
lative
im
pe
da
nce
of cu
ticle
an
de
pic
. w
ax
23.8 19.4 17.1
Brix when up to 5% of berries were infested withBotrytis bunch rot
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IRZ
Florian RistKatja HerzogPheno Team
Robert RichterEva Zyprian
University of BonnComputer Science 4
Volker SteinhageJenny Mack
Actors to dissect Botrytis Resilience
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Development of
field phenotyping
platforms
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Multi-sensor field phenotyping platform: PHENObot*
Automated data recording on
single vine level:
• Georeference with high precision
• PHENObot stops at each vine
• Plant ID is connected with the image
FIELD PHENOTYPING PLATFORM
* Kicherer, Herzog, Pflanz, Wieland, Rüger, Kecke, Kuhlmann, Töpfer (2015) Sensors, 15(3), 4823-4836.
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Multi-sensor field phenotyping platform: PHENObot*
SENSORS FOR OBJECTIVE FIELD PHENOTYPING
* Kicherer, Herzog, Pflanz, Wieland, Rüger, Kecke, Kuhlmann, Töpfer (2015) Sensors, 15(3), 4823-4836.
Plant ID
=
allocation of
single vineU V W
X YZ [ U
\ ] ^ _ ` a ` a b c d e f g h i j k l m ` n o p a o
Synchronic
image
acquisition15 s / vine
20 images/min
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Post-processed
• Automated data management
AUTOMATED PHENOTYPING – DATA MANAGEMENT
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Post-processed
• Automated data
management and
data analysis
AUTOMATED PHENOTYPING – DATA ANALYSIS
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AUTOMATED PHENOTYPING – BIVCOLOR
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PROOF OF CONCEPT:
Acquisition of RGB images and extraction of traits
• Utilization of PHENObot and BIVcolor in the genetic repository
• 2700 vines (~970 Accessions) within 12 hours
• berry size and -color
Kicherer et al. 2015a, Sensors
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Post-processed
• Automated data management
• Automated data analysis
AUTOMATED PHENOTYPING
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Multi-sensor field phenotyping platform: PHENObot*
Disadvantages:
� slow speed
� image acquisition at night to ensure standardized lightconditions
SENSORS FOR OBJECTIVE FIELD PHENOTYPING
* Kicherer, Herzog, Pflanz, Wieland, Rüger, Kecke, Kuhlmann, Töpfer (2015) Sensors, 15(3), 4823-4836.
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Improved multi-sensor field phenotyping platform:
Phenoliner*
• Automated, synchronic image
capture with high-throughput
• Georeference with high precision
* Kicherer, Herzog, Bendel, Klück, Backhaus, Wieland, Rose, Klingbeil, Läbe, Kohl, Petry, Kuhlmann, Seiffert, Töpfer(2017) Sensors,
SENSORS FOR OBJECTIVE FIELD PHENOTYPING
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UAV FOR MANAGEMENT APPROACHES
• Inventory of vines growing in the
genetic repository
– GPS position of single vines
– RTK-GPS for adequate precision
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UAV FOR MANAGEMENT APPROACHES
• Inventory of vines growing in the
genetic repository
– GPS position of single vines
– RTK-GPS for adequate precision
• Detection of missing vines �
e.g. maintaining of grapevine
accessions (3 plants)
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MANY THANKS TO
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www.jki.bund.de
* Naming according to:
Gailhardsswilre =
Calardiswilre =
Geilwilre =
Geilweiler =
Geilweilerhof
Calardis Blanc*(Gf.1993-22-6)
variety protection 2018
Many Thanks to
• Breeding teamRudi EibachOliver Trapp et al.
• Botrytis teamKatja HerzogRobert RichterFlorian RistEva Zyprian et al.