A growing world population is expected to cause a "perfect storm" of food, feed, and biofuel. Under the climate change scenario, it is a challenge for agricultural scientists to ensure food and nutritional security for an ever-increasing population with limited and rapidly depleting resources. However, researchers are now observing that conventional breeding methods will not be sufficient to meet projected future demands for foods. To overcome these constraints, plant breeding has evolved over the past two decades towards a much closer integration of high-throughput phenotyping (HTP) tools and technologies. The "phenotyping revolution" targets extremely precise and accurate measurements of very specific traits in large populations in the field. Sorghum breeding is not new to this advancement, which obviously implies significant shifts in the breeding programs. First, it indicates breeders integrate trait assessment with traditional yield and agronomic evaluation, emphasising that breeding programmes are opened up to new or other disciplines. It additionally requires that these new or other disciplines think about and conceptualise their own actions and orientations from the perspective of how they may fit into a breeding methodology. In this instance, the four primary sorghum breeding domains—staying green and transpiration limitation under high vapour pressure deficit (VPD); nodal root angle and depth; grain mineral content (Fe, Zn); and grain and stover quality traits—are tightly correlated with HTP. These ongoing initiatives focus on value of the particular trait and why it is considered by breeders; how it is measured with HTP approaches (method, throughput, cost, simplicity) and finally, how these traits are currently being embedded in the breeding program. Through various research, it became evident there are several other avenues of technology that, although not yet routinely implemented, could bring about a major benefit to the breeding programme’s endeavour to increase the rate of genetic gains. Here, we discuss the use of drone imaging for yield trial quality control and pinpoint plot heterogeneity, the integration of quality analysis into the assessment of agronomic traits in the field, and the use of X-ray spectroscopy to assess grain or crop architecture traits.

1. 1

2. ResearchGuide
Dr. D. K. Zate
Assistant Professor
Department of Genetics and Plant
Breeding
COA, VNMKV, Parbhani-431 402
Master’sSeminar on
“HIGH-THROUGHPUT PHENOTYPING METHODS FOR
ECONOMIC TRAITS and DESIGNER PLANT TYPES: TOOL TO
SUPPORT MODERN BREEDING EFFORTS IN SORGHUM”
Presenter
Kute Komal
Reg. no.- 2021A/138M
M.Sc. (Ag.), 3rd Semester
Department of Genetics and Plant Breeding
COA, VNMKV, Parbhani-431 402 SeminarIncharge
Dr. D. K. Zate
Assistant Professor
Department of Genetics and Plant
Breeding
COA, VNMKV, Parbhani-431 402
GP- 591
VASANTRAO NAIK MARATHWADAKRISHI VIDYAPEETH, PARBHANI
DEPARTMENTOF GENETICSAND PLANT BREEDING(AGRIL. BOTANY)
2

3. 01
02
03
04
05
06
Introduction
(GlobalAgriculture towards2050:Risk & Challenges)
High-Throughput Plant Phenotyping (HTPP)
Breeding product profile (BPP)
FLOW
OF
SEMINAR
(Notionof BPP, Role of CGIAR,Categoriesof Portfolio of traits in BPP)
Ongoing initiatives in sorghum breeding
(High-throughputphenotyping:1. Stay-green& transpiration restriction under high VPD 2.
Nodalroot angle & depth3. Mineral grain content(Fe, Zn) 4. Stover& grain quality traits)
New opportunities
(Drone imaging for yield trial quality control, Quality analysis, Image analysisfor
grain/ Crop Architecture traits)
Research Studies
Conclusion
07
Introduction
(GlobalAgriculture towards2050:Risk & Challenges)
High-Throughput Plant Phenotyping (HTPP)
(Terminology,History, A taxonomyof phenotypes,Strategies:Forward vs Reverse
Growthsystems)
Breeding product profile (BPP)
(Notionof BPP, Role of CGIAR,Categoriesof Portfolio of traits in BPP)
Ongoing initiatives in sorghum breeding
(High-throughputphenotyping:1. Stay-green& transpiration restriction under high VPD 2.
Nodalroot angle & depth3. Mineral grain content(Fe, Zn) 4. Stover& grain quality traits)
New opportunities
(Drone imaging for yield trial quality control, Quality analysis, Image analysisfor
grain/ Crop Architecture traits)
Research Studies
Conclusion
3

4. Introduction : Global agriculture towards 2050
Risk and Challenges
Populationgrowth rate Global Hunger Index (GHI) Climatechange
4

5. ❖ Growing world population is expected to cause a perfect storm of Food, Feed & Biofuel.
The current world population of 7.6 billion is expected to reach 9.8 billion in 2050 & 11.2 billion in 2100. (United Nations,
DESA 2018). In 2022, Global Hunger Index, India ranks 107th out of the 121 countries, with a score of 29.1, India has a
level that is serious.
(GHI, India 2022)
❖ By 2050, it is predicted that global food production must rise by 70%. To ensure food & nutritional security for an ever-
increasing population is serious challenge to plant breeders as current agricultural production growth rate of 1.3% per
annum. (FAO,2018)
❖ Due to climate change, global mean temperature raised by 0.8 oC since the 1850s, which are expected to increase further
by 1.8-4.0 oC by the end of this century. (IPCC, 2022)
❖ Will further impact on future average crop yields which may decline across Africa and South Asia by 8% (declines in
yields about 17% in wheat, 5-16% in maize, 11-15% in sorghum & 10% in millet) Therefore, development of
‘climate-smart’ germplasm would priority to tackle these future challenges of climate change. (Leakey et al. 2009)
5

6. ➢ PHENOME – Gene x Environment or the expression of the genome as traits in a
given environment.
➢ PLANT PHENOMICS - Plant phenomics is the study of plant growth,
performance and composition.
➢ HIGH- THROUGHOUT PLANT PHENOTYPING (HTTP) - It is a non-
destructive and rapid approach of monitoring and measuring multiple phenotypic
traits related to the growth , yield and adaptionto biotic and abiotic stress.
HIGH-THROUGHPUT PLANT PHENOTYPING (HTPP): CONCEPT
6

7. HISTORY OF PHENOTYPING
Time period: 1856-1863(7 years)
Plant material : 29000pea plants(Pisumsativum)
Plot: 2 ha monastery garden
Traits tested: 7 traits (7 different loci, each possessing 2 alleles)
(colorand seed smoothness, colourof the cotyledons, color of the
flower shape of the pods, color of the unripe pods, position of
flowers and pods, height of the plants) (Butler et al. 2009)
Gregor Mendel (1822-1884) Mendel’s phenotyping tool Mendel’s garden (Berno)
7

8. A TAXONOMY OF PHENOTYPES
8

9. STRATEGIES: FORWARD VS REVERSE
FORWARDPHENOMICS REVERSE PHENOMICS
✓ Tool to ‘Sieve’ collections of
germplasm for valuable traits.
✓ The sieve or screen could be high-
throughput and fully automated and
low resolution followed by higher
resolution, lower-throughput
measurements.
✓ Screen might include abiotic or biotic
challenge
✓ Detailed dissection of traits shown to be
of value to reveal mechanistic
understanding and allow mechanism
exploitation.
✓ Involve reduction of physiological trait
to biochemical or biophysical process
and ultimatelya genes.
9

10. GROWTH SYSTEMS
✓ Controlled
conditions
✓ High throughput
✓ Adapted for medium
and large plants
✓ High cost
✓ Amount of data
✓ High capacity
✓ Natural conditions
✓ High costs
✓ Complex imaging
✓ Extremely high
amount of data
✓ Highly controlled
conditions
✓ High throughput
✓ High capacity
✓ Adapted only for
small plants
IN-VITRO CULTURE SCREENING FIELD EXPERIMENTS
GREENHOUSE ASSAYS
NATURAL GROWTH PARAMETERS
SCREENING EFFECTIVENESS 10

11. Sorghum: Breeding Perspective
1) Grain yield
2) Crop Productivity
Value chain perspective
1) Grain yield
2) Crop productivity
3) Grain quality
4) Stay-green
5) Mineral grain content
(Zn, Fe)
6) Stover quality
Conventional Breeding Modern Breeding
▪ Nutrient content traits, like Zn and Fe, that have been found to be deficient in the diet of
populations in the developing world.
▪ Sorghum crop residues have also grown increasingly important in the sorghum value chain, at
least in India & in Africa, long neglected because of an assumed poor quality of these residues,
their quality appears to still be significant and to vary genetically.
▪ the stay-green trait, the capacity of certain sorghum genotypes to maintain green leaves after the
end of grain filling, has shown to contribute largely to yield increases under terminal water
deficit.
11

12. Breeding Product Profile (BPP)
❑ Notionof Breeding Product Profile:
• Lack of adoption of new varieties over the last two decades has increased pressure on breeding programs to
ensure that bred varieties be better align with the expectation from the end users and should they be direct
or indirect users/consumers/processors. (Asrat et al. 2010)
• The idea of ‘Breeding Product Profile’ taken on board by excellence in breeding platform of the CGIAR
(Consultative Group on International Agriculture Research, 1971) to drive changes in breeding decisions
and to develop varieties that are close-fitting the expectations. (Persley and Anthony2017)
Figure 1. Breeding Product Profile (BPP)
1. Game changers
These are traits that would
make a given trait change
the way a variety is used
dramatically (A resistance
to major disease-pest,
mechanization trait)
2. Must-have traits
These are traits that, if not
in the new variety, would
lead to non-adoption
(Crop duration fitting a
cropping season, color of
grain)
3. Good-to-have traits
These are traits that given
an added value to a variety
if not a premium on price
or acceptance (Biofortified
varieties with Fe & Zn
content)
Portfolio of traits in BPP
12

13. Basic evidence for value of the trait
• The capacity of plants to restrict transpiration rates per unit green leaf area (TR)
under conditions of high evaporative demand has become major research traits that
contributeto drought adaptation. (Zaman-Allah et al. 2011)
• Transpiration Rate is a function of stomatal conductance and VPD, and restricting
TR under high VPD tends to increase transpiration efficiency. Pre-anthesis water
savings from the expression of this trait can increase water availability for grain
filling result into increased post-anthesis water availability. This leads to the
expression of stay-green in sorghum and is strongly associated with increased grain
yield under end-of-season drought stress. Therefore, low TR under high VPD and
associated high TE are potentially important selection targets for breeding programs
targeting adaptation to drought stress. (Vadez et al. 2011)
High-throughput phenotyping for stay-green & transpiration restriction under high VPD
Even if there is
a Season-end
Drought, how
is it Green ?
13

14. ❑ Used to measure the response of TR to environmental
conditions along with parallel measurement of
transpiration rates, leaf area, and VPD, preferably under
(semi) natural conditions in order to use diurnal and daily
variation to maximize the range in VPD conditions
observed.
❑ It is consists of 1500 lysimeters that are each located on
their own load cell to allow continuous measurements of
transpiration rates from the decline in lysimeters weight,
adjusted for soil evaporation. A 3D laser scanner measures
the leaf area of plants in each lysimeter on a two-hourly
basis (12 times/day).
LeasyScan
Fig. (A-C) Set of environment sensors: (A)
temperature, relative humidity, (B) solar radiation,
wind speed, (C) rainfall 14

15. ❑ Lysimeters are large trays (40 X 60 cm and 30 cm depth) in which plants are grown at a
planting density that mimics the field conditions. The platform being located outdoors,
growing conditions are close to the field. Combined with continuous records of
environmental conditions, the response of TR to VPD and radiation can be calculated
without harvesting plants under fully irrigated conditions. (Vadez et al. 2015)
❑ A similar automated lysimeters setup, consisting of a platform with 128 large (50 L)
lysimeters and one with 560 small (4 L) lysimeters. that setup currently lacks a
capability for leaf area imaging, high-throughput measurement of the response of TR to
VPD is difficult to operate, and the platform with 560 small lysimeters is best suited to
high-throughput phenotyping of TE. However, the size of the large lysimeters allows
unrestricted plant and root growth until maturity, making that lysimeter platform ideally
suited for detailed studies on trait dissection of TE. (Chenu et al. 2018)
Fig. Schema of a lysimeter station
15

16. Integration in the Breeding Process
❑ Phenotyping of structured mapping populations, such as backcross-nested association mapping
(BCNAM) populations in diverse sorghum genetic backgrounds, allows combining of phenotypic and
genotype information to identify QTL and genetic markers for these traits using genome-wide
association studies (GWAS). By screening large breeding populations early in the breeding cycle,
germplasm with the desired transpiration restriction, together with crop vigor attributes, could be
selected. This could significantly reduce the number of entries that eventuallygo into breeding trials.
❑ The cost of running one replication in the LeasyScan platform is between 10 and 15 US$ for the trait
assessment over a 4–5 weeks period. One replication represents a micro-plot of 0.25 m2 which usually
accommodates four to eight plants, dependingon the recommended density.
16

17. Basic evidence for value of the trait
• Sorghum grain generally contains 79–83% starch, 7–14% protein, and
1–7% fat, but this percentage can differ within species and
interspecies. The baseline for Fe is 30 ppm and Zn 20 ppm. (Rhodes
et al. 2017)
• Sorghum is staple food for more than 300 million people, supplies
more than 50% of micronutrient in the semi-arid tropics in Asia and
Africa. Major cause of micronutrient malnutrition globally is
consumptionof inadequate amount of iron (Fe), Zinc (Zn) & others
• It is feasible to enhance the grain Fe and Zn concentration in sorghum
by genetic means without concomitant increase in grain phytate
content(556.52-606.07mg/100g DW). (Ashok et al. 2015)
High-throughput phenotyping for Grain Mineral Content (Iron & Zinc)
The cultivar PARBHANI SHAKTI was
a selection from grain sorghum
Caudatum landrace accession (IS 26962)
that originated in India. One of the
selected lines from this effort was
sorghum restorer line ICSR 14001.
which has a high protein content of 12%
and a low phytate content (4.14%) with
high Fe (47 ppm) and Zn (32 ppm) 17

18. ❑ In sorghum, Efficient phenotyping methods are use
for the identification of mineral composition (Zn &
Fe) in order to understandits nutritionalvalue.
❑ Both Fe and Zn concentrations exhibit significant
positive association (r2 = 0.6–0.8), and it is feasible
to improve both the traits at the same time.
❑ Additive gene action plays significant role in
conditioning the grain Zn concentration, while both
non-additive and additive gene actions condition
the grain Fe concentration.(Kumar et al.2012)
High-throughput phenotyping platform for analysis of grain mineral content
TECHNIQUES USED TO ASSESS
GRAIN FE & ZN CONCENTRATION
Perls Prussianblue & diphenyl
thiocarbazone-baseddithizone (DTZ)
AtomicAbsorptionSpectrometer(AAS)
InductivelyCoupledPlasma-Optical
EmissionSpectrometer(ICP-OES)
X-ray fluorescencespectrometer(XRF)
Near-InfraredReflectance
Spectrophotometer(NIRS)
elemental distributionmaps secondary ion
mass spectrometry(NanoSIMS)
synchrotronX-ray
fluorescencespectroscopy, and micro X-
ray fluorescencespectroscopy(μ-XRF) 18

19. ❑ The Fe and Zn concentrations can be precisely estimated using
Atomic Absorption Spectrometry and inductively coupled
plasma-opticalemission spectrometry (ICP-OES).
❑ The ICP-OES method is precise but destructive, laborious, and
more expensive to adopt on large scale. Use for assessing the
germplasm, fixed breeding lines, and cultivars for Fe and Zn in
sorghum to aid in the biofortification.
❑ The standardized X-ray fluorescence spectrometer (XRF) method
is low-cost, robust, and nondestructive phenotyping method for
assessing Fe and Zn in sorghum, highly suitable for discarding
poorgenotypes from large population.
Fig. XRF – low-cost, robust, and
nondestructive phenotyping method for
assessing Fe and Zn at ICRISAT
19

20. Integration in the Breeding Process
❑ The deployment of Fe and Zn in final products by crossing parents with high Fe and Zn,
improving the Fe and Zn in parents by making elite X elite crosses, crossing the high Fe and Zn
landraces with elite parents, crossing post-rainy sorghum landrace cultivars with high Fe and Zn
germplasm lines, and identification of QTLs controlling grain Fe and Zn that be transferred to
elite lines, based on the targeted adaptation and cultivar choice.
❑ At ICRISAT, the baselines for Fe and Zn were established by assessing the entire spectrum of
commercial cultivars (66) grown for food use in India. Recombination and selection supported
by efficient phenotypingimproved the Fe and Zn concentration.
❑ Biofortified sorghum hybrid ICSH 14002 was developed which not only has high grain Fe and
Zn (Fe 52 and Zn 30 ppm) but also has high grain yield (4.5 t ha-1 ). Both parents of the hybrid,
ICSA 101 and ICSR 196 recorded >40 ppm Fe and >24 ppm Zn. (Ashok et al. 2015)
20

21. High-throughput phenotyping for Grain & Stover Quality Traits
Countries Purposeof Sorghum grain Qualitytrait relation
Australia for animal feed, with a small
portion for biofuel and to
cater for the ethanol
production. Gluten-free
productproductionis
marginal.
retained are starch, protein,
phytochemicals(phenolic
acids, flavonoids,
carotenoids, tannins), which
are then used in the
transformation industry
Ethiopia to prepare the traditionalflat
bread injera
relate to the fermentation
process (size of air bubbles,
taste, texture).
India used to bake flatbreads to the easiness to knit the
dough, taste, and shelf life
of the flour
Nigeria or
Burkina Faso
beer brewing industry, for
poultryfeed as a substituteto
maize.
Malting & milling
Basic evidence for value of the trait
21

22. ❑ Sorghum cultivated under the stress conditions of the post-
rainy season of India usually achieves low harvest indices for
which the total value of the stover in the sorghum value chain
is higher than the value of the grain. Therefore, it has become
now a necessity to have sorghum productivity as a must-have
trait in the post-rainysorghum varieties.
❑ Stover quality is another factor that also merits a lot of
attention. Although long considered to be a low-grade feed,
crop residues vary largely in several factors that characterize
their quality, such as the N content, the in vitro digestibility, or
the extractable metabolic energy. (Kholová et al. 2014)
22

23. ❑ by which quality traits are assessed in the grain or sorghum stover
residues. For that, sorghum grain or stover samples are dried and
grounded to a particle grade below 1 mm for stover and to flour grade
for grain. For grain, whole grain can also be analyzed by NIRS
without grinding, although the thickness of the pericarp can be a
confoundingfactor. (Guindo et al. 2016)
❑ A set quantity of samples is then placed inside a cuvette that fits the
NIRS equipment, and the reflectance of the sample across the NIR
spectrum (780–2500 nm) is measured. Up to 500 samples can be
processed in a day.
Near-Infrared Spectroscopy (NIRS) for grain & stover quality analysis
Transmission: used for clear liquids,
slightly scattering samples like grain
23

24. StoverQuality Measurement:
1. Drying and grinding a subsample for NIRS analysis.
2. NIRS reading and data processing to extract quality index values from the calibration
equations.
Limitations:I. One person can grind about 200 samples/day
II. One person can read about 500 samples/day.
III. Grinding being the labor intensive.
From breeding prospective, it would be needed to include the measurement of stover
productivity and qualityat the time of yield assessment.
(1) The harvesting, weighing, and grinding of plot stover residues.
(2) The subsequentdetermination of the water content to have a dry weight equivalent.
(3) The subsequentdrying of a subsample.
(4) NIRS assessment of the subsample.
Integration in the Breeding Process
24

25. • The sorghum root system: a) Single primary root that originates from the embryo b) Number of
nodal roots that appear from stem nodes. Significant genotypic differences in the angle at which
the first flush of nodal roots grows have been observed for sorghum and this has been linked to
the root system architecture of mature plants. (Singh et al. 2012)
• For plants with a narrow angle (relative to a vertical plane), the root system of mature plants tends
to explore soil below the plant depth relatively well vs the mature root system of plants with a
wide angle for the first flush of nodal rootstends to explore the inter-row space better.
• As a consequence, a narrow root angle is well suited to crops with high plant density that are
grown on deep soils, whereas the wide root angle is better suited to skip row systems. (Hammer
et al. 2009)
High-throughput phenotyping for Root angle & Root Depth
Basic evidence for value of the trait
25

26. • The angle of the first flush of nodal roots, in sorghum which appears when around 5 leaves have fully
expanded, phenotyping can only be done around 2–3 weeks after germination. This puts restrictions
on the design of the phenotyping platform, and makes non-soil-based platforms developed for crop
like maize and wheat, for which root angle can be measured a few days after germination, but it is
difficult to implement for sorghum. (Joshi et al. 2017)
26

27. Soil-based phenotyping platform for screening genetic variation for nodal root angle
A) Set of small root chambers:
Each root chamber consists of two 6 mm thick transparent
Perspex sheets of 50 cm high, 45 cm wide and 3 mm thick that
where separated on three sides (2 long sides and one of the short
sides) by 3 mm thick rubber and held in place by three metal clamps.
a) Purpose built root chamber filled
with soil
b) Metal tubes containing root
chambers
c) Polycarbonate sheet covering the
top of the root chambers to exclude
light
d) Nodal root visible through the
transparent wall of Perspex sheets
at 6th leaf stage.
27

28. B) Imaging setup:
High-throughput imaging box with components:
a) ( C, Camera; BM, Ball Mount; LB, Light
Box; L, Lid)
b) b) Imaging of nodal roots after harvest (RC,
Root Chamber)
c) c) For each image , the left (aL) and the
right (aR) angle between the first pair of
nodal roots and the vertical plane was
measured using the software package
28

29. ➢ Phenotyping of structured populations (BCNAM) and elite hybrids can provide insights into the
genetic control of the nodal root angle.
➢ The use of probe genotypes is essential for efficient capture of the effects of both spatial and
temporal environmental variation on the expression of nodal root angle.
➢ However, the high genetic correlation between pairs of experiments with overlapping genotypes,
as well as the high broad-sense heritability (H2 , 77–95% for BCNAM and 94–96% for advanced
hybrids), based on spatially adjusted experiments using 500 plants and partial replication,
indicates that in this platform, variation associated with random factors was much smaller than
genotypic variation. The platform is relatively easy and cheap to set up and maintain, which
allows implementation even in breeding programs with limited resources. (Joshi et al. 2017)
Integration in the Breeding Process
29

30. CASE STUDIES
30

31. RESEARCH
STUDY
1
Field-Based High-Throughput Phenotyping of Plant Height in
Sorghum Using Different Sensing Technologies
• Author: Wang, X., Singh, D., Marla, S., Morris, G., & Poland,J.
• Year: 2018
• Publishedin: Plant methods, 14:53
• Objective: To evaluate the performance of five different sensing technologies for field-based
high throughput plant phenotyping(HTPP) of sorghum [Sorghumbicolor (L.) Moench] height.
Wang, X., Singh, D., Marla, S., Morris, G., & Poland, J. (2018). Field-based high-
throughput phenotyping of plant height in sorghum using different sensing
technologies. Plant methods, 14, 53. https://doi.org/10.1186/s13007-018-0324-5
31

32. 1 Genetic materials :
2 Localities:
Experimental Setup and Data Collection:
3
➢ Experimental and Control hybrids with a total of
427 hybrids grown in Jimbour and 422 hybrids
grown in Pirrinuan.
➢ Western Downs region in Queensland, northeast
Australia
➢ The UAV image data used for the analysis came
from two hybrid sorghum breedingtrials
➢ Data was collectedvia a Tarot custom-made drone
with 3DR-Px4 flight controllerequippedwith a
Red-Edge multispectral camera (Red-Edge, Mica-
Sense, Seattle, Washington) at seven time points
during the vegetation period in both trials.
Materials and Methods
32

33. Table 01: Summary statistics for the calculated canopy traits and yield (t/ha) at the two locations Jimbour and Pirrinuan
➢ All calculated canopy traits showed moderate to high broad-sense heritabilities in both environments ranging from 37.6 to
92.5.
➢ The heritability and genotypic variation for most of the traits were lower in Jimbour than in Pirrinuan. Phenotypic values of
canopy traits varied similarly across experiments with higher means in Pirrinuan for most traits. 33

34. ➢ BLUPs of yield and LSN values showed a weak but significant negative correlation (-0.18).
➢ In comparison to the post-anthesis parameters, the pre-anthesis parameters SL, AUC-pre, and max-NDVI were weakly
correlated with across-site LSN (0.05, 0.11, and 0.07, respectively).
➢ Moreover, even though max-NDVI itself did not correlate strongly with across-site LSN, it had strong associations with the
other components.
➢ whereas the correlations of max-NDVI with the pre-anthesis parameters were higher than those with the Post-anthesis
parameters. Interestingly, AUC-pre and AUC-post were positively correlated whereas S-pre and S-post were showing the
opposite relationship.
Table02: Correlationtable of the canopy traits (BLUPS) for the averageeffect of all hybrids
34

35. ➢ Within the large set of diverse hybrids observed in this study, it appears that canopy size
before flowering made a relatively small contribution to the expression of a stay-green
phenotype after flowering. However, the effect varied depending on the female tester which
shows the importance of considering genotypic background and other context dependencies
when evaluating traits for the selection of complex traits such as stay-green.
➢ If stay-green is a result of higher water use during grain filling, traits such as water
extraction efficiency and water use efficiency rather than leaf area before flowering may be
the main drivers for the expression of the trait. In conclusion, this study showed that
variation in canopy size before flowering is not a good predictor of stay-green expression in
this set of breeding trials. In contrast, using UAVs to monitor the NDVI decay after
flowering is a suitable method for high-throughput phenotyping of stay-green.
35
Conclusion

36. RESEARCH
STUDY
2
High-Throughput Phenotyping of Dynamic Canopy Traits
Associated with Stay-Green in Grain Sorghum
• Author: J. Liedtke, J. D., Hunt, C. H., George-Jaeggli, B., Laws, K., Watson, J., Potgieter, A.
B., Cruickshank, A., & Jordan, D. R.
• Year: 2020
• Publishedin: Plant phenomics (Washington, D.C.), 4635153.
• Objective: To compare measurements of canopy dynamics obtained from remote sensing on
two sorghum [Sorghum bicolor (L.) Moench] breeding trials to stay-green values obtained
from visual leaf senescence ratings in multi-environment breeding trials to determine which
componentsof canopy development were most closely linked to stay-green phenotype.
J. Liedtke, J. D., Hunt, C. H., George-Jaeggli, B., Laws, K., Watson, J., Potgieter, A. B.,
Cruickshank, A., & Jordan, D. R. (2020). High-Throughput Phenotyping of Dynamic Canopy
Traits Associated with Stay-Green in Grain Sorghum. Plant phenomics (Washington, D.C.),
2020, 4635153. https://doi.org/10.34133/2020/4635153
36

37. Materials and Methods
5
4
3
1
2
Ultrasonic sensor
Digital camera was evaluated on an
unmanned aerial vehicle platform to obtain
the performance baselines to measure the
plant height in the field.
Kinect v2 camera
LIDAR-Lite v2 sensor
Imaging array of four high-resolution cameras
were evaluated on a ground vehicle platform
36

38. ➢ Plot-level height was extracted by averaging different percentiles of elevation
observations within each plot.
➢ Measurements were taken on 80 single-row plots of a US × Chinese sorghum
recombinant inbred line (n=670) generated by crossing chilling-sensitive US
line BTx623 with chilling-tolerant Chinese accessions in 2 replication.
➢ The performance of each sensing technology was also qualitatively evaluated
through comparison of device cost, measurement resolution, and ease and
efficiency of data analysis.
a) Ground based data acquisition system a phenotyping mobile unit
(PheMU) was developed to collect plant phenotypic data.
b) Aerial based image acquisition system low-cost unmanned aerial system
(UAS) was integrated for high throughput phenotyping of large breeding
nurseries. 37

39. ➢ They observed that the plot-level plant height measured by the Ultrasonic sensor was higher than the LL2 sensor. This
is likely due to the sampling rate of the ultrasonic sensor being two times higher than the LL2 sensor, resulting in a
higher spatial resolution.
Fig. 7: Comparisonbetween sensormeasurements and manual observationsat plot 1-4 & 2-4
38

40. Fig. 10: Maximum measurements as plot-levelheight compared with manual measurements
39

41. Fig. 11:Average top 50% measurementsas plot-levelplant height compared with manual measurements
40

42. Fig. 12: Box plots of plant height measurements of 80 selectedplots by different sensingtechnologies
41

43. Result of Figs. 10&11 OR 11&12
➢ The plot-level plant height results were derived by both the maximum
and the averaged top 5% of the measurements.
➢ These two types of plot-level measurements of the 80 selected plots
for each of the sensing technologies were compared with manual
measurements (Fig. 10,11). According to the quantitative comparison
results (Fig. 11,12), the plot-level height values measured by the
averaged top 5% of the proximal DEM values were the closest to the
manual measurements.
42

44. Table 1: Performance comparison of each sensing technique
➢ we compared the performance of each sensing technology, ultimately we can say that, the
heights measured by the ultrasonic sensor, the LIDAR-Lite v2 sensor, the Kinect v2 camera,
and the imaging array had high correlation with the manual measurements (r≥0.90), while the
heights measured by remote imaging had good, but relatively lower correlation to the manual
measurements (r=0.73). 43

45. Conclusion
➢ In above investigation of five different sensing technologies for field-based HTPP of
plant height in sorghum we concluded that, Using the data collection approaches and the
data processing methods introduced in this study, we found the plot-level height values
measured by the ultrasonic sensor, the LL2 sensor, the Kinect camera, and the proximal
imaging by four DSLR cameras were all highly correlated with the manual
measurements. Therefore, each sensing technique could be used for precisely and quickly
measuring large numbers of sorghum genotypes to identify height variance.
44

46. ➢ Precise and accurate measurement of traits plays an important role in the
genetic improvement of the crops, which can help in identification of
either the best genotype having the desired traits or mechanism & genes
that make a genotypes best.
➢ Various techniques have been developed for screening biotic, Abiotic,
physiological and biochemical traits in sorghum crop, have been very
advanced in the era of digital science, make plant physiology in ‘New
Clothes’.
➢ High-throughput phenotyping provides the opportunity to bring to study
previously unexplored areas of plant science, by bringing together
Genetics and physiology to reveal the molecular genetic basis.
NUTSHELL OF SEMINAR
45
Phenotypingfacility at
NIASM, Malegaon

47. Any
Questions ?
“The more we know, the more we realize there is to know.”
- Prof. Jennifer Dounda
46