This document summarizes a seminar presentation on high-throughput plant phenotyping. It discusses various imaging technologies used for plant phenotyping like 3D imaging, near infrared imaging, fluorescence imaging etc. It explains how these technologies are used to phenotype traits like growth, architecture, abiotic and biotic stress responses. The document also discusses the importance of phenotyping for plant breeding and genetics research. It highlights challenges in data management for large phenotyping datasets and the need for developing suitable analysis tools and sharing resources.

1. M.Sc course seminar
On the topic
Supervisor : Prof. J. P Shahi Name : Kumari Shikha
I.D. NO. G-15096
M.Sc (Ag) ,final year
Department of Genetics and plant breeding
Banasars Hindu University
Varanasi-2210055/24/2017 1

2. CONTENTS
 Introduction
 Importance
 Imaging technology
 Gloss of modern technologies
 Traits phenotyped
 Relevance
 Data management
 conclusion
3-D Imaging
Far –infrared imaging
NIR Imaging
Fluorescence imaging
Automated watering and spraying
Hyperspectral imaging
Visible imaging
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3. Plant High-throughput Phenotyping
• Phenomics term given by Gerlai, 2002.
• The term phenomic refers to sum total of phenotypes
at various levels ranging from molecules to organs
and the whole organism.
• Study of plant growth ,architecture, performance and
composition using high throughput methods of data
acquisition and analysis.
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4. Continue..
• Phenotyping is essential for –
functional analysis of specific genes
forward and reverse genetic analysis
production of elite plants
• High throughput is essential for phenotyping in
different growth conditions of many different lines.
mutant populations
mapping population
breeding population
germplasm collection5/24/2017 4

5. Forward phenomics vs Reverse phenomics
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6. Why phenotyping needed?
Need more food
By 2050, 9.1 billion people Efficient breeding technique
required
Tilman, et al5/24/2017 6

7. Accelerating Plant Phenomics
 High-throughput screens
 Multiple camera units
 Non-destructive measurements
 Quantitative analysis
Monitor gowth dynamics
Stress assessment
Link to genomics
Opening new prospects
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8. Automated Facilities for HTP
Australian plant phenotyping
networking
ICAR-IIHR
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9. Imaging Technique
 3-D imaging
 Near infrared imaging
 Far infrared imaging
 Flourescence imaging
 Visible light scanning
 Automated spraying and watering
High throughput Plant
Phenotyping1486203666711
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10. 3-Dimensional imaging
SIDE VIEW SIDE VIEW TOP VIEW
After 90 degree
rotation
Digital photos of the top and side view of a plant
combined into 3D image.
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11. Measurements taken using 3-D
• Shoot mass
• Leaf number, shape and angle
• Leaf colour
• Leaf health
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12. Far-infrared Imaging
• Measure temperature difference between leaves
and plants.
• Temperature ranges between 15-1000 micrometer.
• Cooler plants absorb more water
• Can be used for single plant to whole plant
• Temperature differences used to measure:-
Photosynthetic activity
 Salinity tolerance
 Effective water use efficiency
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13. Near- infrared imaging
• More the presence of chlorophyll more will be
reflectance in NIR range.
• Facilitates estimation of
Water content and movement within leaves
and soil
Carbohydrate content of leaves
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14. Spongy layer present backside of leaves that reflects lot of light in NIR range,
after dehydration or stress condition this spongy layer get collapsed hence
reflect less NIR light but same visible range. Thus , we can differentiate healthy
plant from sickly plant.
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15. Fluorescence imaging
Measure photosynthesis rate
biotic and abiotic stress responses
Chlorophyll content.
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16. Visible light scanning (VIS)
• Colour information give estimation of the degree
of senescence.
• Senescence of older leaves during drought suggest
an escape or avoidance.
• Genotypes with stay-green type can be identified
that would be able to continue photosynthesis
under water stress
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17. Continued..
Measure aspects of plant architecture such as
• Image-based projected biomass, leaf area, colour,
growth dynamics, seedling vigour, seed morphology,
• Root architecture, leaf disease severity assessments,
yield, and fruit number and distribution.
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18. Leaf growth decreases in response to drought even before
a decline in stomatal conductance or photosynthesis.
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19. Hyperspectral Imaging
• Spectral reflectance is the fraction of light reflected
by non transparent surface.
• Researchers use this spectral reflectance for
detection of plants stressed by saline soil or
drought, well before it can be eye.
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20. Positron emission tomography(PET)
• Nuclear imaging system
• Produces 3-D image of a functional process
• While consumption of co2 , transport of
radiolabelled carbon imaged in 3-D by PET.
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21. Root Phenotyping
• Different procedure-
Visualization of excavated root system.
Analysis by camera systems which are introduced into
soil through small tubes made up of Plexiglass (changes in
electrical properties of soil due to water uptake by soil).
2-D and 3-D analysis
Phenotyping platforms using aeroponic or hydroponic
culture systems for direct visualization and imaging of roots.
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22. Steps used in the analysis of
root morphology.
Step 1: soil coring;
Step 2: dividing soil samples
Step 3: washing of roots
Step 4: separating roots from
soil
Step 5: preserving roots in 25%
ethanol
Step 6: scanning roots using root
scanner Epson Perfection
step 7 analyzing the scanned
root images using Winrhizo
software.
2-D Phenotyping of root
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23. 3-D PHENOTYPING
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24. Rhizoponics is an innovative design of hydroponic rhizotrons
adapted to Arabidopsis thaliana. The setup allows to
simultaneously characterize the RSA and shoot development from
seedling to adult stages, i.e. from seed to seed. This system offers
the advantages of hydroponics such as control of root
environment and easy access to the roots for measurements or
sampling. Being completely movable and low cost, it can be used
in controlled cabinets.
Hydroponic
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25. Measurement of leaf area
• Morphometric method
• Optical flow method
• Particle /marker tracking method
Plant biomass estimation
• 3-digital imaging technique
• Hyperspectral imaging
• Non-optical method (electrical determination of water
content of plant , portable nuclear magnetic resonance
device)
Seed and fruit phenotyping
• 3-D Laser –scanning technology
• Visual imaging
• NIR Spectroscopy
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26. Analysis of root system
• Rootreader 2D
• Smartreader
• Rootreader 3D
Analysis of shoot system
• Hyperspectral imaging
• Visible imaging
Analysis of chemical content
• Mass spectrometry and Gas chromatography (amino
acids present in fresh plant material )
• Liquid chromatography
• Flow cytometry
• NIR Spectroscopy5/24/2017 26

27. Analysis of physiological parameters
• SPAD chlorophyll meter
• Fluorescent imaging (chlorophyll fluorescence show negative
co-relation with photosynthetic activity)
Assessment of water use
• CID (carbon isotope discrimination technique used in wheat)
• Leaf and canopy temperature ( higher in case of decreased
transpiration rate)
• SPAD chlorophyll meter
Assessment of soil water content
• Mobile NIR
• Visual spectrophotometer
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28. Relevance of HTP
• Identification of stress.
• Rapid and efficient screening for mutants.
• Detection and monitoring of disease epidemics in field.
• Detection of root attack by pathogens.
• Facilitate screening of germplasm.
• Study of various physiological processes.
• Modelling of biomass production.
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29. continued..
Facilitate selection of superior genotypes
from breeding population.
Allow huge genomic information to be
reliably related to specific phenotypes.
Permit systematic study of pleiotropic effects
of the genes.
Crop improvement .
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30. Data management
Phenomic data management involves three critical
components:
ALGORITHM AND
PROGRAM
PHENOTYPIC
INFORMATION
SENSORY DATA
MODEL
DEVELOPMENT
GENOTYPE AND
PHENOTYPE
INTERACTIONS
UNDERSTAND
MANAGEMENT
DATABASES
RESOURCE DEVELOPMENT
AND RESOURCE SHARING
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31. Continued
Some suggestion :-
Creating complete and
accurate metadata
Deposition of data into primary
repository
Easily Accessible to every
researchers
Development of open source
community database
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32. Software companies
• Some of these companies encourage co-
development (system customization, software
development for computation) as a process of
improving their current product and product
utilization such as Lemnashare and Lemna
launcher etc.
PHENOSPEX
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33. Case study
• GiNA platform developed for phenotyping of horticultural
crops. For small fruits such as grapes, cranberries, or
cherries a picture of 40 fruits can be taken every minute (or
less). Therefore, in an hour, at least 20 different parameters
for 2400 fruits can be accurately measured from 60 images.
LENGTH , WIDTH , PERIMETER AREA ,
COLOR ESTIMATED
POTATO AND CHERRY FRUIT
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34. Access genotypic adaptation
to water stress
Thermography
Leaf temperature detection by
infrared thermometers has
been used to detect water
stress, which results in
stomatal closure and an
increase in temperature
through decreased adiabatic
cooling
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35. a): Excavated maize root stocks which were split
lengthwise
b): imaged root stock under constant
illumination
c): root top angle
d): gap size distribution
e): cluster thickness distribution Colombi et al.2015
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36. This high-throughput phenotyping
system developed at the USDA Arid-
Land Agricultural Research Center in
Maricopa, AZ is being used to collect
plant height, canopy temperature, and
canopy reflectance data from cotton
plants.
Researchers at CSIRO using a
remote-controlled gas-powered
model helicopter called the
“phenocopter” to measure plant
height, canopy cover, lodging, and
temperature throughout a day.
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37. Future of HTP
• Current phenotyping is largely extensive hence need for
intensive approach.
• Available methods are not satisfactory , hence urgent need
to develop suitable statistical models.
• Software developed for statistical analysis should permit
automated data analysis.
• Integration of all phenomic related research as did for
genomic efforts.
• Phenomics teams must be transdisciplinary.
Bilder et al. 2009
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38. Phenotyping
Conclusion
Identifying differences among genotypes and
interpretation of detected differences.
(Fiorani and Schurr, 2013; Klukas
et al., 2014)
Analytical approach
Management
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39. Thankyou
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