Phenotyping and genotyping is utmost important in plant breeding to which phenomics will be a great study in future prospects of agriculture

2. ANGRAU: S.V. AGRICULTURAL COLLEGE
CREDIT SEMINAR ON
Phenomics In Crop Improvement: Advances
and Prospects
Name : Sukrutha.B
Roll no : TAM/2018-36Course-in charge: Dr. Lokanadha Reddy. D
Course No : GP- 591
Date : 7-12-2019

3. Presentation outline
High throughput field phenotyping platforms
Root phenotyping
Gloss of phenome traits
National & international phenotyping facilities and
networks
Case studies
Conclusion
High throughput controlled phenotyping Platforms
High throughput Phenotyping Strategies
Introduction

5. Water scarcity

6. Population growth

7. Hunger index

8. A
Challenge… To feed
people

10. Plant
breeder’s
response
to global
challenges
through
improving
genetics

11. Basic ingredients…..
Genomics Germplasm Phenotyping Data science
Genome
sequencing,
genotyping
platforms,
markers and
genes
Genome and
phenome
characterization
of germplasm
High-
throughput,
cost effective
& field
relevant
phenotyping
Decision
support tools
& integration
of different
datasets for
better
prediction
Integration of different disciplines for crop improvement

12. Genotype-Phenotype Gap
With the advent of Next Generation Sequencing & Chip genotyping, data on
Genotype(G) is obtained quickly at cheaper cost.
However, the use of Genome-wide information of G (SNP variations) to
establish a genotype-trait relationship (genes/QTLs for traits) is limited due to
the non-availability of deep phenotypic data
Conventional phenotyping is labour, space and time intensive
Hence, fail to capture dynamic G x E interaction on organism wide
scale.
(Same level of yield can be achieved by several combinations of physiological
processes in different genotypes).
Not possible to get more than a few traits measured in mapping population
This led to Genotype-Phenotype bottleneck in analytical breeding.

13. Phenotyping bottleneck
Time consuming
Labour intensive
Spatial and temporal flexibility
Error prone
Not precise
Conventional
phenotyping
The traditional phenotyping is done by visual screening
which cannot be useful for physiological and biochemical
characteristic. Some of the biochemical characterization
includes destructive assay.
These are the reasons of evolving the science of
phenotyping.
Chaudhary et al., 2018. Biomolecule reports

14. Conventional phenotyping (characterization) of plant is a rate
limiting step in analytical breeding for high yield, resource use
efficiency and climate resilience.
Phenotype = G + G x E.
PHENOMICS, the transdisciplinary science, has emerged
recently to bridge the phenotype-genotype gap.

15. Why High Throughput Phenotyping?
 Outdated phenotyping tools
 Phenotyping on whole population
 Low time consuming
 Non destructive phenotyping
 Automation and robotics- increase accuracy
 Dynamic phenotyping
 Accelerated genomic technologies - big gap
between genotype and phenotype

16. Basic terminologies,….
Genotype: Genetic makeup of a cell or organism
Phenotype: the set of structural, physiological , and performance
related traits of a genotype in a given environment.
Phenotyping :
Measurement of a set of
phenotypes.
Genotyping :
process of determining the
genetic makeup of an
individual.

17. PHENE
PHENOME
PHENOMICS
GENE
GENOME
GENOMICS
Refer to organism wide phenotype or full
set of phenotype of an individual
organism/plant.
Complete set of genetic information of an
individual/ organism/plant.
Study of structure and function of
whole genome of organisms
non destructive automated high dimensional
phenotyping of plants at various
developmental stages by using sensors and
image processing computational programmes.
Individual genetically determined
characteristic or trait
Distinct nucleotide sequence of chromosome
which act as unit of heredity

18. Phenomics: Historical Aspects
Initial stage: Concept of phenotype(1911)
and phenomics
Schork
Johannsen
concept of phenomics corresponding to
genomics (1997)
Tuberosa proposed the concept that
‘phenotyping was king and heritability was
queen’

19. Phenomics: Historical Aspects
Advance Development
stage: late 20th century
 Phenotypic research teams & commercial
organizations established.
 High-throughput, high-precision,
automated, semi-automated phenotyping
tools developed.
 Belgium crop design (1998) - First company to develop HTPP for
large scale plant trait evaluation.
 Phenomics: The Australian phenomics facility- first research center
established in 2007.
 Germany Lemna Tec (2016) developed
the first field HTPP platform - Field
Scanalyzer

20. Phenomics : Historical Aspects
Systematic development stage:
 The European Plant Phenotyping Network (EPPN) was
originated from 2012
 In 2013, the concept of next-generation phenotyping was
proposed by Mccoueh
 The International Plant Phenotyping Network (IPPN) was
registered in 2016,
 In 2017, Francois Tardieu and Malcolm Bennett presented
strategies for multi-scale phenomics
Zhao et al.,2019. Front. Plant Sci. 10:714

21. Phenomics: A Multi Disciplinary Approach
CROP
PHENOMICS
Information
Science
Computing&
Statistical
Models
Chunjiang Zhao(2019)

22. Phenomics Strategies: Forward vs Reverse
Phenomics
Forward Phenomics:
Forward phenomics uses phenotyping tools to discriminate the useful
germplasm having desirable traits among a collection of germplasm.
This leads to identification of the ‘best of the best’ germplasm line or
plant variety.
A large number of plants can be screened at seedling and there is no
need to grow plants up to the maturity stage in field.
The pots are labelled with barcodes or radio tags, so that the system
can identify which pots contain plants with interesting traits.
The selected plants can then be grown up to produce seed for further
analysis and breeding.

23. The reverse phenomics is used where the best of the best
genotypes having desirable trait(s) is already known.
Now through reverse phenomics, mechanisms and genes
for traits shown to be of value are developed which make
‘best’ varieties the best
Reverse Phenomics:
Phenomics Strategies: Forward vs Reverse
Phenomics
Reverse phenomics is pulling the ‘best’ varieties apart to
discover why they are the best.

24. Phenotyping Strategies in crop improvement
Kumar J et al (2015)

26. HIGH THROUGHPUT PHENOTYPING PLATFORMS
Controlled Environment
Phenotyping(CEP)
Field-based
phenotyping(FBP)
1.Imaging technologies
2.Sensors
3.Automation & robotics
1.Ground based
phenotyping
2.Aerial phenotyping
3.Remote sensing
phenotyping

27. Controlled Environment vs Field Phenotyping
Controlled Environment Field
Better control of soil moisture and
nutrient inputs
Difficult to control input
application
Offseason phenotyping and
imposition of biotic stress possible
Not possible
Accurate data acquisition under
desired lighting using variety of
cameras
Lighting cannot be controlled,
shadow effects and influence of
wind
Cannot imitate natural field
conditions-space constraints
Natural growth environment of the
crop
Two approaches :
• Moving plants along the sensors
and
• Moving sensors along the plants
Single approach :
• Moving sensors along the plants

28. IMAGING TECHNIQUES
Imaging
Technologies
Visible
color
imaging
Near
infrared
imaging
X-ray CT
Chlorophyll
fluorescence
imaging
Hyper
spectral
imaging
Magnetic
resonance
imaging
3D imaging
1) Color imaging (2D
and 3D)
2) Near Infrared (900-
1700nm)& RGB
root imaging
3) Far Infrared
imaging
4) Chlorophyll
Fluorescence
imaging
5) Hyperspectral
imaging (400-
1000nm)
6) MRI Scan
7) X-ray CT (X-ray
computed
tomography)

29. Visual RGB Color imaging (400-700 nm)
 Images captured in visual spectrum are widely used to monitor
plant growth and development
 Peak reflectance around 550nm; low absorbance of pigments in
this wavelength region makes plants green and enable us to detect
plant tissue in RGB(red, green, blue) images.
 Color imaging with high resolution camera from top and sides of
the plants provide info about dimensions, morphological and
geometrical properties, as well as color distribution.
Traits : biomass, vigour, greenness, leaf area, stress levels,
senescence, growth rates, phenology.

30. Three-dimensional (3D) imaging
 Digital photos of the top and sides of plants are
combined into a 3D image.
 Measurements that can be taken using a 3D image
include:
• shoot mass
• leaf number, shape and angle
• leaf colour
• leaf health.

31. Infrared Thermal imaging
Thermal imaging allows for the visualization of infrared radiation,
indicating an object as the temperature across the object’s surface
Lower canopy temperature is related to a high transpiration rate and could
be due to high rooting depth. The genotype (a) under drought is showing
higher canopy temperature while the genotype (b) is showing cooler canopy,
hence more drought tolerant under low moisture condition
Pratap et al., Agronomy 2019, 9, 126;

32. Near infrared (NIR) imaging
• Near-infrared (NIR) cameras study water content and
movement in leaves and soil.
• They use light in the NIR region of the spectrum (900–1550 μm)
 Plants are grown in clear pots so roots
can be photographed while the plant is
growing.
 Soil NIR measurements are used to
calculate:
• how much water the roots remove from
the soil
• where and how much water the plant is
using.

33. Spongy layer present backside of leaves that reflect
lot of light in NIR range, after dehydration or stress
condition this spongy layer gets collapsed hence reflect
less NIR Light but same visible range. Thus, we can
differentiate healthy plant from sick plant
https://midopt.com/healthy-crop/

34. Fluorescence imaging
 Fluorescence imaging is used to study plant health and
photosynthesis.
 Fluorescence occurs when an object absorbs light of one
wavelength and gives off light of a different wavelength.
Chlorophyll fluorescence is used
to study the effect of different
genes or environmental
conditions on the efficiency of
photosynthesis.

35. Chlorophyll fluorescence imaging of chickpea leaf for phenotyping heat tolerance.
Here upper half of the leaves were not heat treated while bottom halves were heat
treated at 46 C for 1 h. High photosynthetic activity was detected by deep blue colour
(higher quantum yield Fv/Fm) while orange,yellow and green or complete black colour
represented the diminishing photosynthetic activity 1 and 2 fall in the range of heat
sensitive ones. 3 and 4 were categorized as heat tolerant as they appeared yellow to
green in colour indicating lower photosynthesis but still active even after heat shock
Pratap et al., Agronomy 2019, 9, 126;

36. Magnetic resonance imaging (MRI)
Magnetic resonance imaging (MRI) is used
to study plant roots.
 MRI uses a magnetic field and radio
waves to take images of roots in the
same way as for imaging body organs
in medicine.
 MRI allows the 3D geometry of roots to be
viewed just as if the plant was growing in the
soil.
MRI image shows the effect of temperature on root
growth. Both the growth rate and formation of lateral
roots are affected at the lower temperature.

37. Hyperspectral Imaging
Spectral reflectance is the fraction of light reflected by a non-
transparent surface.
Researchers can
use spectral
reflectance to tell if
a plant is stressed
by saline soil or
drought, well before
it can be seen by
eye.

38. X-ray CT (X-ray computed tomography)
Uses computer-processed X-rays to produce tomographic images of
specific areas of the scanned object and can generate a 3D image of the
inside of an object from a large series of 2D radiographic images taken
around a single axis of rotation
Paya et al. 2015. Front Plant Sci. 6: 274

39. Gehen et al . Current Opinion in Plant Biology 2015, 24:93–99
ultimate goal – To collect images, environmental (water supply, light
intensity, temperature, humidity) and physical (e.g. plant weight) data to
quantify genotype by phenotype by environment interactions.
Overview of image techniques

40. http://phenomix.fr/en/a/integrated-imaging-stations

41. Zhao et al.(2019)
Imaging
technology
Sensors Parameters Applications
Visible light
imaging
Visible light
camera
Whole organs or
organ parts.
Morphologic traits,
digital biomass,
height, etc.
Assess plant growth
status, nutritional status,
and accumulated
biomass
Fluorescence
imaging
Fluorescence
cameras
Multiple chlorophyll
fluorescence
parameters and
multi-spectral
fluorescence
parameters
Photosynthetic status/
quantum yield/seedling
structure/leaf disease,
etc
Infrared
imaging
Thermal
imaging,
Near-infrared
cameras
Leaf area index,
surface
temperature,
canopy and leaf
water status, seed
composition
Measurements of leaf
and canopy
transpiration, heat
dissipation, stomatal
conductance
differences

42. Imaging
technology
Sensors Parameters Applications
Spectral
imaging
Spectrometers,
hyperspectral
cameras
Water content, seed
composition, etc.
Disease severity
assessment/ leaf
and canopy
growth potential
3D imaging
Stereocamera /
Time of flight
camera systems
Plant or organ
morphology,
structure, and color
parameters, time
series at various
resolutions
Shoot structure,
leaf angle,
canopy structure,
etc
MRI
Magnetic
resonance
imagers
Water content,
morphology
parameters
Morphometric
parameters/
water content
Zhao et al.(2019)

43. • Phenopsis
• Phenoscope
• Growscreen
• Phenodyn
• TraitMill
• Plant Scan
• Lemna Tec
• QubitPhenomics
HTP Automation & Robotics platforms

44. PHENOPSIS
 Specific setup for automated phenotyping, allowing a culture
of approximately 200-500 Arabidopsis plants in individual
pots with automatic watering and imaging system

45. PHENOSCOPE
 It is a platform having a unique feature of continuously rotating 735
individual pots over a table, adjusting watering automatically .
 Useful for high throughput acquisition, storage and analysis of
quality phenotypes
e.g., Arabidopsis plants

46. GROWSCREEN
This platform was used to study the plant leaf
growth fluorescence and root architecture from
seedling under control conditions for visual
phenotyping for large plant populations
e.g., Arabidopsis

47. The phenotyping system, GrowScreen-PaGe for non-invasive, high throughput
phenotyping of root systems grown on germination paper: (a) and (b) cultivation
system (for a better visualisation of the system boxes are not shaded on the
pictures), (c) image capturing box, and (d) root analysis by using the image-based
software, GROWSCREEN-Root. The original image of a root system of a rapeseed
plant 18 days after sowing and a color-coded image after evaluation with taproot
in green, first-order branch roots in red, and second-order branch roots in blue.

48. PHENODYN
This platform monitors plant growth and
transpiration rate with stressful environmental
condition
e.g., Maize
and Rice

49. Trait Mill
 High-throughput gene engineering platform developed by
Crop Design.
 This is a highly versatile tool that enables large-scale
transgenesis and automated high resolution phenotypic
plant evaluation.

50. TraitMill: Green house

51. Plant Scan
This is an automated high resolution center which
provides non–invasive analysis of plant structure,
morphology and function by utilizing cutting edge
information technology including high resolution
cameras and 3D reconstruction software

52. LemnaTec
• Visualize and analyze 2D/3D non-destructive high-
throughput plant imaging, monitor plant growth and
behaviour under entirely controlled conditions in a
robotic greenhouse system

53. QubitPhenomics
• Integrated conveyor and robotic high-throughput
plant imaging system for the laboratory, growth
chamber and field phenotypic screening and
phenotyping.

54. Plant screening with Qubit System

55. Controlled environment phenotyping:
Advantages
Stresses (water, nutrient, temperature, salinity, etc) can be
imposed at equal stress levels and phenological stages for a
germplasm and mapping population.
Phenotyping for mapping genes/QTL’s for stress tolerance –is
critical step - resource use efficiency in crops.
Environmental variation is minimium - heritability of traits is high.
Hence gene/QTL mapping is accurate.
Maximizes information from a minimum of replicates
Decreases cost through automation and standardization.
Uniform quality of images can be obtained as the light conditions
required for specific images are maintained when automated
nondestructive phenotyping is performed.
Off-season phenotyping is possible

56. Controlled environment phenotyping:
Limitations
Limited greenhouse space or chamber volumes often limits the
number of genotypes to be phenotyped.
Difficult to maintain environmental conditions (temperature, RH,
light) similar to that of natural environment.
The soil volume that is provided for plants in controlled
environments usually is far less than that available to plants in the
field.
In greenhouses and chambers, solar radiation, wind speed and
evaporation rates typically are lower than under open-air
conditions

57. Field-based Phenotyping (FBP)
 Field-based Phenotyping (FBP) is a critical component of crop
improvement through genetics, as it is the ultimate expression the relative
effects of genetic factors, environmental factors, and their interaction on
critical production traits, such as yield potential and tolerance to abiotic /
biotic stresses
FBP
Ground
based
phenomobile Ladybird
Aerial
phenonet
Pheno-
tower
phenocopter Blimp

58. Controlled conditions are not representative of field environment
– Soil (pots)
– Climate (green house)
– Competition between plants
Large number of μ‐plots to characterize (1000‐2000)
Monitoring the dynamics to access canopy functioning
New measurements methods need to be developed
1. High throughput (quick)
2. Non-destructive (monitoring)
3. Cost effective (per plot)
Importance of high throughput phenotyping in field conditions

59. Phenomobile
The phenomobile is a modified golf buggy that moves through a field
of plants, taking measurements from three rows of plants at the
same time.

60. Ladybird
Ladybird was an
autonomous unmanned
ground-vehicle robot for
row-crop phenotyping,
which was also coupled
with a data processing
framework
Underwood et al. 2017

61. Phenonet sensor network
Sensors include:
• far infrared thermometer
• weather station
• soil moisture sensor
• thermistor (soil temperature)
A network of data loggers collects information
from a field of crops and sends it through the mobile
phone network back to researchers at the lab.

62. PHENOTOWER
• The phenotower is a cherry picker
used to take images of crops 16 m
above the ground.
The left smaller image show the
field under visible light, and the
right is taken using an infrared
camera

63. Phenocopter
 The multicopter can take images of a field from a few
centimetres above the ground to a height of up to 100 metres.
 Multicopter will be equipped with a computer, a GPS, and
colour and infrared cameras.
 The infrared and colour images can be used to identify the
relative differences in canopy temperature. It indicates plant water
use most efficiently.

64. BLIMP
• The blimp can take images of whole fields from 30 to 100 m
above the ground. This allows many plants to be measured
at the same time-point.
• Researchers attach a video camera to the blimp. The blimp
is held in place by a rope

66. pros and cons of UAVs and UGVs
 UGV (phenomobile): Access to mm² observations: 200 μ‐plots/hour
• Few hours between first and last μ‐plots sampled
• Need active measurements
• Access to the detailed structure (3D)
• Access to the organ level biochemical constituents (Chlorophyll)
 UAV (multicopter): access to m² observations: 10000 μ‐plots/ hour
• Few minutes between first and last plots sampled
• Easy way to monitor the dynamics
• Access to canopy macro‐structure
• Difficulty to access the detailed structure and organ level
biochemical content
• Possible confounding effects: retrievals may be cultivar specific
• Access to instantaneous stress status: thermal IR, fluorescence

67. Field phenotyping: Advantages
• Large number of genotypes can be evaluated
• Phenotyping is done in conditions near to the farmers field.
Hence directly applicable when input conditions are
standardized/performance oriented research trials (ACRIP yield
trials).
• Genotype × environment interaction effect can be estimated

68. Field Phenotyping: Limitations
• Field and environmental variability results in requirement of
multilocation and multi-year experiments to obtain reliable results
• Under field conditions the inability to obtain standardized and
consistent drought stress/nutrient deficiency contributes to a loss
in heritability and presents a challenge for both selection and
mapping experiments
• Not possible to impose similar levels of stress at defined
phenological stages for a mapping population and germplasm
lines

69. Overview of high-throughput phenomics
Rahman et al.2015

70. Tissue Software Purpose and design Reference
Roots WinRhizo Tron Morphological descriptions of root area, volume, length, etc http://www.regent.qc.ca/products/rhizo/RHIZOTron.html
KineRoot 2D analysis of root growth and curvature Basu et al. (2007)
PlaRoM Platform for measuring root extension and growth traits Yazdanbakhsh and Fisahn (2009)
EZ-Rhizo 2D analysis of root system architecture Armengaud et al. (2009)
RootTrace Counting and measuring root morphology Naeem et al. (2011), French et al. (2009)
DART 2D analysis of root system architecture Le Bot et al. (2010)
SmartRoot ImageJ plugin for the quantification of growth and architecture Lobet et al. (2011)
RootReader3D 3D analysis of root system architecture Clark et al. (2011)
RootReader2D 2D analysis of root system architecture Clark et al. (2012)
Gia-Roots 2D analysis of root system architecture Galkovskyi et al. (2012)
Shoots/leaves WinFolia Morphological measurements of broad leaves http://www.regent.qc.ca/products/folia/WinFOLIA.html
TraitMill Platform for measuring various agronomic characteristics Reuzeau et al. (2006)
PHENOPSIS Automated measurement of water deficit-related traits Granier et al. (2006)
Leaf Analyser Rapid analysis of leaf shape variation Weight et al. (2007)
LAMINA Quantification of leaf size and shape Bylesjo et al. (2008)
HYPOTrace Analysis if hypocotyl growth and shape Wang et al. (2009)
LEAFPROCESSOR Analysis of leaf shape Backhaus et al. (2010)
Lamina2Shape Analysis of lamina shape Dornbusch and Andrieu (2010)
HTPheno ImageJ plugin for morphological shoot measurements Hartmann et al. (2011)
LEAF-GUI Analysis of leaf vein structure Price et al. (2011)
LemnaTec 3D
Scanalyzer
Comprehensive platform for analysis of color, shape, size, and
architecture
Golzarian et al. (2011), http://www.lemnatec.com/
Seeds/grain WinSEEDLE Volume and surface area measurements of seeds and needles http://www.regent.qc.ca/products/needle/WinSEEDLE.html
SHAPE Quantitative evaluation of shape parameters Iwata and Ukai (2002), Iwata et al. (2010)
ImageJ General image analysis software for area, size, and shape;
applied to grain
Herridge et al. (2011), http://rsb.info.nih.gov/ij/
GROWSCREEN-
Rhizo
Simultaneous analysis of growth rate, leaf area, and root
growth
Nagel et al. (2012)
SmartGrain High-throughput measurement of seed shape Tanabata et al. (2012)
Table : Selected image analysis software programs and phenotyping platforms available for high-throughput phenotyping

71. PHENOTYPING THE HIDDEN HALF

72.  Root system architecture can strongly affect
yield
 Sustainable plant production requires root
systems optimized for growing conditions in the
field
 Many of the traits required in future crops are
tightly linked to root properties:
abiotic/biotic stress tolerance
water and nutrient use efficiency
yield...
Phenotyping the hidden half of plants – Why??
However, root phenotyping is a challenging
task, mainly because of the hidden nature of
this plant organ

73. 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.

74. 2-D Phenotyping of root

75. 3-D PHENOTYPING
A, Diagram of 3D digital imaging system
used for capturing image sequences;
B, Growth cylinder containing gellan gum
and a 10-day old Azucena rice seedling.
C, Representative single 2D root system
image from an image sequence captured
with the 3D imaging system.
D, 3D reconstruction of the rice root
system showing the five root types:
primary (pr), embryonic crown (ecr),
postembryonic crown (pecr), large lateral
(llr), and small lateral (slr) roots. The
primary, crown, and large and small lateral
roots can be visually distinguished from
one another.
Clark, R. T. et al.,[2011]. Plant Physiology 156: 455–465.

76. Combines hydroponics and rhizotrons. System is made of a
nylon fabric supported by an aluminum frame. The set-up is
immersed in a tank filled with liquid media. Allows non-
destructive, 2-D imaging of root architecture while
simultaneously sampling shoots.
Garcia et al. 2015. Plants, 4, 334-355
Rhizoponics

77. Uses transparent pots filled with soil or other potting media. Seeds
are planted close to the pot wall to enable high-throughput
imaging of roots along the clear pot wall. To prevent light
exposure, the clear pot is placed in black pots while roots are
developing
Garcia et al. 2015. Plants, 4, 334-355

78. Garcia et al. 2015. Plants, 4, 334-355
Minirhizotrons
Minirhizotrons
A transparent observation tube
permanently inserted in the soil.
Images of roots growing
alongthe minirhizotron wall at
particular locations in the soil
profile can be captured over time

79. Shovelomics
Involves manual excavation of plants and separating roots
from the shoots. Washed roots are then placed on a
phenotyping board for root trait quantification. New
algorithms allow extraction of several root traits in a high
throughput manner.
Garcia et al. 2015. Plants, 4, 334-355

80. Uses a tractor-mounted, hydraulic soil corer to drive
steel alloy sampling tubes into the soil. When combined
with novel planting configurations (e.g., hill plots), this
method allows for phenotyping deep rooted crop varieties
Garcia et al. 2015. Plants, 4, 334-355
Soil coring

81. Strategies and Approaches for growing plants prior
to root phenotyping
Approach Growth
conditions
Advantages Disadvantages
Laboratory
methods
Highly
controlled
• Evaluate root growth in
real time
• Large space for plant
growth is not required
• Easy to handle
• Sterile conditions for
evaluation excludes effect of
possible interaction with
beneficial microbes
• Physiological relevance of root
phenes are further evaluated
Greenhouse
methods
Moderately
controlled
• Intermediate system b/n
lab and field
• Enables control of
certain conditions
• Labour intensive to process
and clean bigger roots
• Plants could be exposed to
some disease/insect pressure
Field
methods
Less
controlled
Physiological and practical
relevance
• Labour and time intensive
• Intensive root cleanup
• Challenges due to variability in
field particularly soil conditions

82. Softwares and imaging analysis platforms for root
phenotyping
Software Parameters measured Reference
WinRHIZO Tron Measures root length, diameter,
area, surface area, and root
volume
http://www.regent.qc.ca/produ
cts/
rhizo/RHIZOTron.html
KineRoot Measures root growth and
curvature
Basu et al. ( 2007 )
GiA Roots 2D analysis of root system
architecture
Galkovskyi et al. ( 2012 )
GROWSCREEN-
Rhizo
Root architecture parameters in
2D and shoot biomass evaluation
Nagel et al. ( 2012 )
DART 2D analysis of root system
architecture
Le Bot et al. ( 2010 )
RooTrak 3D plant root architecture of
plant grown in soil
Mairhofer et al. ( 2012 )
RootReader3D 3D analysis of root system
Architecture
Clark et al. (2011 )
H. Rahman et al., 2015

87. TRAITS ESTIMATED THROUGH HIGH-
THROUGHPUT PHENOTYPING
• Plant height
• Plant 3D structure
• Internode length
• Leaf area index
• Leaf angle
• Biomass
• Canopy width
• Rate of growth
• Rate of senescence
• Root volume
• Root length
• Root 3D structure
• Secondary root
structures
• Leaf vegetation Index
• Relative chlorophyll
content
• Rate of temperature
range
• Tissue water content
• Soil water content
• Plant water content
• Efficiency of
photosynthesis
• Yield prediction
• Grain filling status
• Grain quality
• Chemical composition
• Moisture content
• Pathogen infestation

88. Databases containing information on
phenotypic characteristics of crop plants
H. Rahman et al., 2015

89. NATIONAL & INTERNATIONAL
PLANT PHENOTYPING FACILITIES
AND NETWORKS

90. Field Phenotyping system-ICRISAT, Hyderabad
The LeasyScan platform uses the PlantEye®
scanner, a camera with a 45 degree angle that
captures 3-D images. Extracts leaf area, leaf
angle, plant height, leaf area

91. Phenomics Initiatives at ICAR
ICAR-CRIDA- 1 JULY 2014 ICAR-IIHR- 1st November 2015
ICAR- NIASM - 23rd October 2016

92. Phenomics Facility at IARI
Weighing and water stationsSide Imaging

96. Objective :
To illustrate how high-throughput automated phenotyping can
accelerate breeding for quantitative disease resistance.
CASE STUDY - 1

97. Materials and Methods :
 335 European winter wheat cultivars were grown in 1:1 × 1:4m
plots replicated twice
 Automated image analysis
 The plots received full agrochemical inputs typically associated
with intensive wheat cultivation
 An unusual feature of this experiment is that all STB infection
was natural, with the epidemic caused by a highly diverse Z. tritici
population that immigrated into the experimental plots via
windborne ascospores coming from nearby wheat fields that were
treated with fungicides

98. Steps involved in the acquisition and analysis of phenotype data

99. • The automated analysis pipeline generated phenotypes for 21,420 leaves,
with an average of 30 leaves sampled per plot across the two time points.
• Nearly 37m2 of leaf area was analyzed, with approx. 11m2 scored as
damaged by STB. The mean analyzed area for an individual leaf was
17cm2.
• The percentage of leaf area covered by STB lesions (PLACL) ranged from
0 to 99 with a mean value of 32.
• The number of pycnidia found on a leaf ranged from 0 to 4,034, with a
mean value of 127.
• The density of pycnidia within STB lesions (ρlesion) ranged from 0 to 256
per cm2 of lesion, with a mean value of 24.
• The automatically generated phenotypes included 21,420 measures each of
PLACL and ρlesion and 2.7 million measures each of pycnidia size and
pycnidia melanization, yielding a total of >5.44 million automatically
measured phenotypes that were not prone to human scoring error

100. Manhattan plots showing significant SNP markers associated with each trait
Results
14 SNP
4 on 5D-10.3%
51 SNP
15 on 6B-9.3%

101. Manhattan plots showing significant SNP markers associated with each trait
3 SNP
18 on 2B-5.9%
36 SNPs
23 on 4D-10.6%

102. Chromosomal intervals defined by 26 significant GWAS associations
PLACL = percentage of leaf area covered by lesions; PDL = pycnidia density within lesions
(ρlesion); MPA= mean pycnidia area (size);PGV = pycnidia gray value; NA = no associations

103. Positions on the Chinese Spring reference genome of 26 significant GWAS
marker-trait associations across four resistance traits compared to positions of
previously mapped STB resistance genes

104. Conclusion
Automated image analysis for STB enabled rapid acquisition of large
datasets, including millions of phenotype data points that were highly
informative under both greenhouse and field conditions
Many of the genes found in the intervals identified in the GWAS are
plausible candidates to explain the observed phenotypes associated
with STB resistance
Recombining the SNP markers associated with the STB resistance
intervals identified in this experiment accelerate breeding efforts aimed
at increasing quantitative resistance to STB

105. Objective: To develop a high-throughput rice phenotyping facility
(HRPF) to monitor agronomic traits during the rice growth period
No. of genotypes studied: 533
CASE STUDY - 2

106. .
Figure: Combination of the HRPF (RAP and YTS) and GWAS : To automatically screen the rice- core
germplasm resource throughout the growth period (a), the entire HRPF was designed with two main
elements: a rice automatic plant phenotyping device (RAP, b) and a YTS (c). These novel phenotyping
tools were able to extract not only the traditional agronomic traits but also several novel phenotypic traits
(such as plant compactness and grain-projected area). After the rice phenotypic traits (d) were extracted
with the RAP and YTS, new loci were dissected using GWAS

107. Imaging techniques used in RAP and YTS

108. Comparison among GWAS results using three phenotyping methods for shoot fresh weight, shoot dry
weight and green leaf area. The three phenotyping methods included manual measurement, RAP
measurement and raw measurement. The RAP measurement is the predicted value calculated by the
raw features and the selected models. The raw measurement is the projected area calculated by the
number of foreground pixels, which is easily extracted without modelling. Manhattan plots for shoot
fresh weight (a), shoot dry weight (b) and green leaf area (d) Blue bars indicate associated loci detected
by manual measurement. Red bars and green bars indicate specific loci detected by RAP
measurement and raw measurement, respectively.

109. Comparison of rice accessions exhibiting different plant compactness
values and grain-projected areas
7 new loci
4 loci
24 loci

110. Performance evaluation of the RAP and YTS
• when continuously operated (24 h per day), the total throughput of the RAP
was 1,920 pot grown rice plants out of a total greenhouse capacity of 5,472
pots.
• Considering the time required to feed spikelets and to retrieve the filled
spikelets, the efficiency of the YTS is 1 min per plant
Results:
• HRPF was developed to monitor 13 traditional agronomic traits and 2 newly
defined traits during the rice growth period.
• Using GWAS of the 15 traits, they identify 141 associated loci, 25 of which
contain known genes such as the Green Revolution semi-dwarf gene, SD1.
Conclusion:
• Based on a performance evaluation of the HRPF and GWAS results, high-
throughput phenotyping has the potential to replace traditional phenotyping
techniques and can provide valuable gene identification information.
• The combination of the multifunctional phenotyping tools HRPF and GWAS
provides deep insights into the genetic architecture of important traits.

111. CASE STUDY- 3
Objective : To identify QTL for plant height in maize through remote
sensing phenotyping using UAV

112. Materials :
 Maize inbred lines- 117 temperate lines
135 tropical lines
 sowing - Each row was 2 m long, and contained 8 plants. Each plot
consisted of three rows, with 65 cm distance between rows
 UAV for capturing images
 Ruler for manual measurement

113. Field high-throughput phenotyping for plant height. (A) Digitally designed images of
maize plants at four growth stages. (B) UAV equipment and plant height extraction process.
(C) Dynamic plant height and quantitative trait loci (QTL) dissection. Whole procedure included
trait variation analysis and genome-wide association study.

114. Linear relationship between plant height estimated from unmanned aerial
vehicle (UAV) data and that measured manually at three growth stages.
Blue solid line shows regression line, grey shadow represents 99%
confidence interval.
Results

115. Correlation coefficient matrix among seven plant height-related traits.
Yellow and blue indicate positive and negative correlations,
respectively. Size of circle is proportional to correlation coefficient
(number).

116. Plant height and related trait variations between temperate (TEM) and
tropical (TST) populations at four growth stages. Blue and red box
represent TEM and TST populations, respectively. Line in box plots
shows median value

117. Genome-wide association study for plant height at four stages among temperate (TEM),
tropical(TST), and both (BOTH) maize groups. Different colours represent different
chromosomes. Dotted line is threshold. Single nucleotide polymorphisms (SNPs) above the
threshold showed significant association

119.  65% of QTL were newly identified including QTLs for traits related to
PH and GRPH
 More QTLs for PH were detected at the flowering stage
 At the R stage, a strong candidate gene was ARFTF4 was detected
in the QTL located at chr2 in the TEM group
 A candidate gene SAUR71 was detected at the early growth stage
(PH 1) in the TST group.
 8 QTLs were identifed which simultaneously controls both PH and
GRPH
 At the late development stage, two QTLs related to PH and GRPH
were identified.–
- one QTL harboured the candidate gene ARFTF4.
- other encoded the auxin-independent growth promoter,
GRMZM2G142664

120. QTLs for PH among the TEM, TST and BOTH groups
38 QTLs identified for PH

121. QTLs for GRPH among the TEM, TST and BOTH groups.
50 QTLs identified for GRPH

122. Unique QTLs for both PH and GRPH traits among the
TEM, TST and BOTH groups
68 unique QTLs identified

124. Conclusion
The accuracy of PH by UAV increased with the growth of maize
plants
The PH data collected by the UAV-HTPP s were credible and the
genetic mapping power was high. Therefore, UAV-HTPPs have
great potential for use in studies on PH.
The application of these high-throughput, automated phenotyping
systems can greatly shorten the phenotypic investigation time,
ensure the accuracy of the phenotype, and allow for the discovery
of phenotypes that could not be discovered using conventional
techniques.
High-throughput platform can identify known genes as well as new
loci, providing increased capacity for gene identification

125. Received: 22 November 2017; Accepted: 8 February 2018; Published: 23 February 2018
CASE STUDY - 4
Objective:
To evaluate the suitability of an aerial imaging methodology for
estimating crop canopy cover and leaf senescence from small plots in
maize field trials.

126. Materials and methods:
 Three breeding trials composed of 50 varieties each were planted in three
replicates
 The three trails were planted on a block that was depleted of nitrogen by
removing all crop residues and without any additional nitrogen fertilizer
 UAV flown at speed of 8 m/s and a height of 80 m above ground for images

127. Aerial image mosaic of a maize hybrid trials with 150 plots each

128. Time sequence aerial images of maize hybrids at three different
developmental stages

129. Results
1. Ground canopy cover
 +ve correlation b/n yellow canopy and grain yield indicates effective
remobilization process

130. 2. Leaf Senescence

131. Conclusion
 UAV-based aerial sensing platforms have great potential for
monitoring the dynamics of crop canopy characteristics like crop vigor
through ground canopy cover and canopy senescence in breeding trial
plots.
UAV enable the generation of data at the high resolutions needed for
accurate crop parameter estimations, and allow in-season dynamic
assessment of the crop
 can be used significantly to assist in improving selection efficiency
through higher precision and accuracy, and the reduced time and cost
of data collection

132. S.
No
Platform /
Technique/Software
Traits recorded / Approach
used
Crop
Growth traits, phonological traits and physiological traits
1. Light Curtain arrays
(LCs)
Rapid determination of plant
height and leaf area
Maize, barley,
rapeseed, tomato
2. “LEAF-E” Analyzing leaf growth parameters Maize, Miscanthus
spp.,Brachypodium
3. Phenovator For photosynthesis and growth Arabidopsis
4. Digital still color camera
under natural light
Chlorophyll content and Leaf
nitrogen concentration
Rice
5. The image-based
method
Flowering (spikelet anthesis) Rice
6. TRiP (Tracking
Rhythms in Plants)
Circadian period Arabidopsis
Details of use of selected phenomics platforms for
trait phenotyping in plants
R.R. Mir et al. 2019

133. S.
No
Platform / Technique /
Software
Traits recorded / Approach
used
Crop
Biotic and Abiotic stresses (drought, heat, cold tolerance, salinity,
nutrient-starving, UV light; low N-stress).
1. automatic RGB image
analysis
Cold-tolerance Pea
2. Phenoplant Chlorophyll fluorescence Arabidopsis
3. Dual-mode microwave
resonator
Water content of leaves and
the ionic conductivity of the
leaf
Potato, maize,
canola and wheat
4. Unmanned aerial
platforms (UAP)
Low nitrogen (low-N) stress
tolerance
Maize
5. Automated video tracking
platform
Resistance to aphids and
other piercing-sucking insects
Arabidopsis and
lettuce
6. Hyperspectral imaging
(HSI)
Changes on the leaf and
cellular level in plants during
resistance reactions
Barley
7. Hyper spectral absorption-
reflectance-transmittance
imaging (HyperART)
Leaf traits (like disease
severity)
Maize, barley,
rapeseed, tomato

134. Future challenges and prospects
The single and individual phenotypic information cannot satisfy the
association analysis in the new era called ‘-omics,’ and the systematic
and complete phenomics information will be the foundation of future
research.
In response to emerging challenges, new methods and techniques based
on artificial intelligence shall be introduced to advance image-based
phenotyping
Modeling is a powerful tool to understand G × E × M interactions,
identify key traits of interest for target environments. By this, we can
speed up the gene-to-phenotype journey through modeling to develop the
required agricultural outputs and sustainable environments
Coppens et al. (2017) - the future of plant phenotyping lies in synergism
at the national and international levels.

135. Take home message
Need to Combine the HTPP and large-scale QTL or GWAS analysis and
genomic selection for the rapid gain of complex traits in crop
improvement
Phenotyping is necessary to improve the selection efficiency in large
molecular breeding populations.
Automation and robotics, new sensors and imaging technologies have
provided an opportunity for high throughput plant phenotyping platforms.
Field-based phenotyping (FBP) is a critical component of crop
improvement as it is the ultimate expression of the relative effects of
genetic factors, environmental factors, and their interaction on complex
traits( yield and abiotic/biotic stresses).
Conventional phenotyping often based on visual scorings, labor and
time-intensive, expensive, destructive and not objective- using HTPP more
convenient and approachable.
Just like genomics, HTPP will be a key valuable tool to assist selection in
plant breeding.

136. References
Jitendra Kumar, Aditya Pratap ShivKumar. 2015. Phenomics in Crop Plants:
Trends,Options and Limitation. springer. DOI 10.1007/978-81-322-2226-2
Aditya Pratap et al., 2019.Using Plant Phenomics to Exploit the Gains of
Genomics .Agronomy . 9, 126; doi:10.3390/agronomy903012
Zhao, C., Zhang ,Y., Du, J., Guo, X., Wen, W., Gu, S., Wang, J and Fa,n J.2019.
Crop Phenomics:Current Status and Perspectives. Front. Plant Sci. 10:714.
Chaudhari,N., Bhoyar, P.I., and Sandeep, R.2018. Phenomics :The Future of
Phenotyping . Biomolecule Reports- An International eNewsletter
Zhang,Y and Zhang,N.2018.Imaging technologies for plant high-throughput
phenotyping:a review. Front. Agr. Sci. Eng. 2018, 5(4): 406–419
Rahman.H , Ramanathan.V ,Jagadeeshselvam,N., Ramasamy ,Rajendran,S.,
Ramachandran, Sudheer,D.V.N., Sushma Chauhan, Senthil Natesan and
Raveendran Muthurajan.2015. Phenomics: Technologies and Applications in Plant
and Agriculture.

137. Tanger,P., Klassen,S., Julius, P., John, T., Lovell, Brook, T., Moyers, Baraoidan,M,
Maria Elizabeth B. Naredo., Kenneth, L., McNally, Jesse Poland, Daniel R. Bush,
Hei Leung, Jan E. Leach. and John K.McKay.2017. Field-based high throughput
phenotyping rapidly identifies genomic regions controlling yield components in rice.
scientific reports | 7:42839
Jonathan A Atkinson.,Pound, Malcolm, J., Bennett and Darren M Wells.2019.
Uncovering the hidden half of plants using new advances in root phenotyping.
Current Opinion in Biotechnology. 55:1–8
Fabio Fiorani and Ulrich Schurr. 2013. Future Scenarios for Plant Phenotyping .
Annu. Rev. Plant Biol. 64:267–91
Malia A Gehan ,Ivan Baxter and Noah Fahlgren .2015. Lights, camera, action:
high-throughput plant phenotyping is ready for a close-up. Current Opinion in Plant
Biology. 24:93–99
Nadia Al-Tamimi, Chris Brien, Helena Oakey, Bettina Berger, Stephanie Saade,
Yung Shwen Ho,Sandra M. Schmokel, Mark Tester and Sonia Negra.2016. Salinity
tolerance loci revealed in rice using high-throughput non-invasive phenotyping.
nature communications. DOI: 10.1038/ncomms13342
References

138. Wang, Zhang, R., Song, W., Han, L., Liu ,L., Sun, X., Luo, M., Chen, K., Yang, H.,
Yang,G ., Zhao, Y and Zhao,J.2019.Dynamic plant height QTL revealed in maize
through remote sensing phenotyping using a high throughput unmanned aerial
vehicle (UAV). Scientific Reports. 9:3458
Makanza, R.,,Zaman-Allah , Cairns, E., Magorokosho , Tarekegne, A., Olsen, M.,
and Prasanna,M. 2018. High-Throughput Phenotyping of Canopy Cover and
Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging. Remote
Sens. 2018, 10, 330;
References

139. THANK YOU
At Nanaji Deshmukh Plant Phenomics Centre, IARI , NewDelhi.