Plant phenomics is a high-throughput path-breaking area that meets all the requirements for the collection of accurate, rapid and multi-faceted phenotypic data. Traditional phenotyping tools are generally low-throughput, labor-intensive, which limits high efficiency and are prone to human error (Atefi et al. 2021). High throughput phenomics (HTP) technologies are essential to avoid human error and to reduce time consumption while phenotyping large germplasm populations (Pasala and Pandey, 2020). HTP is an emerging area with numerous applications that combines plant biology, sensing technology and robotics aiding crop improvement programs. Plant phenomics is the study of plant growth, performance and composition. (Atefi et al. 2021) 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. Thus in reverse phenomics, we discover mechanisms which make ‘best’ varieties the best (Jitender et al. 2015). High Throughput Plant Phenotyping under three scenarios: greenhouses and growth chambers under strictly controlled conditions; ground-based proximal phenotyping in the field and aerial based platforms (Araus et al 2018). Root system architecture (RSA) phenotyping in situ is challenging, RADIX (a rhizoslide platform used to screen the shoots and roots). Application of plant phenotyping methods as a part of breeding programs has developed into an important research tool that facilitates breeders to develop cultivars with higher adaptability under different environmental conditions. Remote sensing with Unmanned Aerial Vehicles (UAVs ) has emerged as highly efficient and accurate used to determine crop performance and biomass estimation. Current advanced techniques include thermal, near-infrared sensing, fluorescence imaging, 3D scanning, RGB imaging, multispectral and hyperspectral sensing are lucratively used for plant growth and development identifcation, quantification and monitoring; disease monitoring and abiotic stress tolerance. The integration of crop functional structure with remote sensing, geography information systems, GPS technologies, cloud computing, decision support systems will promote the development of digital agriculture and provide technical support for modern agriculture (Song et al. 2021). The robust and user-friendly post-processing and analysis tools for processing and interpreting raw data are urgently needed and should be improved (Yang et al. 2020).

1. Perspectives and Challenges of
Phenotyping in Crop
Improvement
Khushbu (A-2018-40-015)
PhD. Student
Major Advisor: Dr. V.K.Sood
(Department of Genetics and Plant Breeding)
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2. 2
Traditional
Agriculture
Mechanised
Agriculture
Automatic
Agriculture
Smart
Agriculture
Revolutionary Step to Transform Agriculture

3. Wilhelm Johannsen
3
Phenotype Genotype
Activity/ process of
determining, analyzing or
predicting all or part of
organism’s phenotype.
Phenotyping

4. Conventional Method High Throughput Phenotyping
4

5. Current Global Status of High-Throughput Phenotyping
Yang et al. 2018
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6. 6
Need for High Throughput Plant Phenotyping
• To avoid human error and reduce time consumption
while phenotyping large germplasm populations
• To select best performing individuals of plant
species
• Accelerated genomic technology
• Meeting future demand of agricultural foodgrains
production
• Increased population growth
• Changing climate scenarios: Development of
improved cultivars in changing climate

7. Steps involved in High Throughput
Phenotyping
7

8. Procedure of High Throughput Phenotyping
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9. • Discriminate useful germplasm having desirable
traits among a collection of germplasm
• leads to identification of ‘best of best’
germplasm lines
Forward
Phenomics
• Pulling the best varieties apart to discover why
they are BEST !
• Involves mechanisms and genes for traits which
make BEST varieties the best
Reverse
Phenomics
Forward and Reverse Phenomics
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10. Workflow of High Throughput Phenotyping
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11. Phenomics
Artificial
intelligence
Life science
Maths and
engineering
science
Agronomy
Computer
& statistical
models
Information
science
Phenomics: A Multidisciplinary Approach
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12. Phenotyping scale with Spectral Ranges
Araus et al. 2018
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13. 13
High Throughput Plant Phenotyping Technologies
Jangra et al. 2021

14. High Throughput Plant Phenotyping Technologies
Jangra et al. 2021
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15. High Throughput Plant Phenotyping Technologies
Jangra et al. 2021
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16. High Throughput Phenotyping at diffetrent stages in Crops
16

17. PHENOTYPING
PLATFORMS
TYPES
Ground Based Platforms
Aerial Platform
Ground
Phenomobile Ladybird Rainout
Shelter
Aerial
Drones Phenocopter Blimp

18. PHENOTYPING
PLATFORMS
(GB-HTP)
TYPICAL SENSORS
TYPES
PASSIVE SENSORS
1) Red-green-blue
2) Hyperspectral
3)Fluorescence
4) Thermal
ACTIVE SENSORS
1) Ultrasonic sonar
2) LiDAR laser scanner
Gebremedhin et al. 2019
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19. Sensors used during High Throughput Phenotyping
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20. Spectral Signature of Vegetation
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21. 21
Spectral Reflectance of Cotton
Leafhopper damage
(Prabhakar et al. 2011)

22. Ladybird
Autonomous unmanned ground
vehicle robot for row crop
phenotyping coupled with data
processing framework
Phenomobile
A modified golf buggy that
moves through a field,
taking measurements from
three rows at the same time
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23. Growscreen rhizo
• Greenhouse-based
phenotyping platform
that takes high-resolution
images of plant roots and
shoots
• Root system architecture
• It images soil-filled
rhizotrons with a
throughput of 60
rhizotrons per hour
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24. Field Scanalyzer
The sensors installed in this platform
include visible imaging (monitoring
growth, development, plant and canopy
responses to stress), Infrared imaging,
hyperspectral imaging, fluorescence
analysis and laser imaging
They are generally used to monitor
canopy height, plant geometry, growth
biomass, vegetation indices and
chlorophyll fluorescence
Greenhouse Scanalyzer
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25. Rainout Shelters
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1) Vital phenotyping tools in water stress research as they:
• exclude untimely rain events
• require for field validation of traits
• understanding the crop responses to different agroecological
conditions
2) Equipped with portable solar system, full drainage system, a
surveillance camera
3) Range from simple fixed structures to complex retractable
designs

26. 26
Vinobot Robotanist Thorvald
Bonirob Ladybird FlexRo

27. Unmanned aerial vehicle (UAV)
 An unmanned aerial vehicle,
commonly known as a drone, is an
aircraft without any human pilot,
crew or passengers on board
 UAVs are a component of an
unmanned aircraft system, which
includes adding a ground-based
controller and a system of
communications with the UAV
 Drone used for agriculture purpose is
known as agriculture drone
 Agriculture drones are mostly small
drones weighing more than 250 g but
less than 25 kg, including payload
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28. Different types of UAV models
Fixed
wing Single
rotor Quad copter
Octa copter
Hexa copter
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29. Commercial Agriculture Drones
UAV Lance (Mapping and
surveying in agriculture)
Parrot Blueglass (crop
scouting)
Delair UX11Ag (Large scale
surveying in Agriculture &
Forestry)
NLA 610 (Agriculture Plant
Protection)
QuanticMapper
Aeroenviornment
DJI Phantom 4-
multispectral (Crop
Inspection and
monitoring)
The Hindu 23.02.2022 29

30. Drones in Pest management
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31. Updated Drone Rules, 2021
• A person owning an unmanned aircraft system make an
application to register and obtain a unique identification
number for his unmanned aircraft system and provide requisite
details online in digital sky platform for flight permission
• All drone training and testing to be carried out by an
authorized drone school
• DGCA shall prescribe training requirements, oversee drone
schools and provide pilot licenses online
• No flight permission required up to 400 feet in green zones
and up to 200 feet in the area between 8 and 12 km from the
airport perimeter
• No pilot license required for micro drones (for non commercial
use), nano drone and for R&D organizations
(Ministry of Civil Aviation, GOI July 15, 2021)

32. Phenocopter
• Can take images of a field from a
few centimeters above the ground
to a height of upto 100m meters
• Equiped with computer, GPS,
color and infrared cameras, used
to identify canopy temperature
Blimp
• It can images of a complete
field from 30m to 100m
above the ground. Measure
many plants at same point
• Camera attached and blimp
held by a rope

33. Applications of Crop Phenotyping
Abiotic stress tolerance
Biotic stress resistance
Yield estimation
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34. Phenotyping for Biotic stress related traits
Jangra et al. 2021
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35. Phenotyping for Abiotic Stress related traits
Jangra et al. 2021, Phenomics
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36. Case Study
36

37. Material and Methods
• 20 Wild Type plants and 20 osphyb plants were composed to normal and drought
stress conditions
• Image Based Parameters and Analyzing Tools
i. RGB Imaging and extracting image based parameters (Plant Leaf area, colour
and compactness)
ii. NIR Imaging for Plant water content
iii. IR Imaging to assess Plant Temperature
iv. Fluorescence Imaging for Photosynthetic Efficiency 37

38. • The results indicated that
these methods can detect
the difference between
tolerant and susceptible
plants
• A new imaging platform
was constructed to
analyze drought-tolerant
traits of rice. Rice was
used to quantify drought
phenotypes through
image-based parameters
and analyzing tools
RGB image of WT and osphyb plants
Results
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39. • In Kashmir, an experiment was
conducted for monitoring the
larvae of Pieris brassicae in Cole
crops and mapping of foliage
damage index using GIS
• They used different GIS maps for
showing hotspots to assess the
foliage damage by cabbage
butterfly
• By employing geo-referenced maps,
they target cabbage butterfly
breeding and damage areas
Map showing damage assessment scale on the basis of
foliage damage index (FDI) of Pieris brassicae in
Kashmir Valley on Cabbage
(Hussain et al. 2018)
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40. Automatic Monitoring of Insect Pest Species
Insects Sensors Automatic Detection Technique Efficacy References
Aphids Digital camera,
images of leaves
Convolutional neural networks
(CNNs)
>80% Chen et al.
2018
Fruit
Flies
Modified traps with
Fresnel lenses and
associated
wingbeat stereo
recording device
Linear support vector classifier,
radial basis function support
vector machine, CNNs
98–99% Potamitis et
al. 2018
Sucking
pests
Humidity and
temperature
Sensor, Ambient
light sensor
RGB-to-LUV color model
conversion, extraction of the V-
channel color component, static
thresholding for image
segmentation.
90–96% Rustia et al.
2020
Psyllids Camera Raspberry
Pi V.2
Insects trap and automatic
image collection and storage in
a server.
90% Blasco et al.
2019
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National Image Base for Plant
Protection (NIBPP)

42. • RGB imaging to estimate
individual plants base area
and tiller number
• Multispectral and
hyperspectral imaging in
describing the crop canopy
and plant biomass yield
• Ultrasonic Sonar Height for
plant height estimation
• Li-DAR to estimate biomass
• UASs have the potential to
rapidly measure ground
cover, plant height, biomass
and leaf area index
Material and Methods
Phenorover Unmanned Aerial Satellites
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43. Image processing
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44. Phenomics facilities in India
44

45. Rainout Shelter and Lysimeter Facility
• Screening of crop germplasm for drought
tolerance
• The lysimeter approach provides a
bridge between field-based and
laboratory-based research
• It enables the collection of precise data
on water consumption (quantities,
timing) which relates to grain yield and
provides information on traits that
contribute to yield increase
ICRISAT High Throughput Plant Phenotyping Facilities
Leasy Scan Phenotyping Platform
• Uses the PlantEye® scanner, a camera that
captures 3-D images
• The high-throughput scanning equipment
can scan between 3200 to 4800 plots per
2 hours
• Several algorithms then operate to extract
leaf area, leaf angle and plant height that
are the focus for plant drought adaptation
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46. HTPP (ICAR-CRIDA, ICAR-NEH, IIHR,IARI)
Facilities at (CRIDA, ICAR-NEH, IIHR,IARI) Temprature Gradient
Tunnel (NRCAF)
46

47. Indian Plant Phenomics Facilities
Salinity screening phenotyping facility ICAR-IIWBR 47

48. CSKHPKV, Palampur
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49. Challenges
• Phenotyping robots are designed for specific
crops and field layouts
• Durability and stability of these robotic systems
under harsh outside environment
• Cost of phenotyping is prohibitely high in most
cases
• Data collection efficiency is very low for large
fields
• Navigation in cluttered environments is
challenging such as under canopy
• Dynamic nature of plants and agricultural
environments
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50. Conclusion
Phenotyping is necessary to improve the selection
efficiency in molecular breeding populations
Field based phenotyping is a critical component as it is
ultimate expression of the relative effects of the
genetic factors, environmental factors and their
interaction on complex traits
Automation and robotics, new sensors and imaging
technologies have provided an opportunity for HTPP
platforms
Overcoming the challenges, combine the HTPP with
modern technologies can help rapid gain of complex
traits in crop improvement.
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