The document discusses the development of a cost-effective plant phenotyping system, highlighting techniques for image acquisition and data analysis essential for studying plant traits. It covers the historical background, applications, and various growth systems utilized in phenotyping research. The conclusion emphasizes the importance of standardized methodologies for ensuring reproducibility in plant experiments.

1. Towards the development
of cost-effective and
moderate throughput
plant phenotyping system
Michal Slota
Phot.M.Slota

2. Presentation outline Slota M., 2014
Introduction
Plant
cultivation
Image
acquisition
Data
analysis
Concept of plant phenotyping
Definitions and terminology
Historical background
Applications
Plant cultivation systems
Soil-grown cultures
Petri plates cultures
Hydroponics & aeroponics
Image acquisition techniques
Digital photography, optical scanners
IR, NIR, fluorescence imaging
Hyperspectral, 3D imaging
Data analysis process
Noise reduction and filtering
Image segmentation
Measurements and data analysis
Phenotyping system
Design of the system
System maintenance
Preliminary results
System costs and throughput
Proposed
phenotyping
system

3. Slota M., 2014
BIOPHYSICSA/ BIOMECHANICSANATOMY/ HISTOLOGY
PHENOTPYE
PROTEOME/ METABOLOME SIGNALLING
GENOTYPE
[PuigandPeñarrubia2009;Tardieu2012;WikimediaCommons]
Introduction

4. Introduction Slota M., 2014
GENOTYPING PHENOTYPING
[Pennisi2009;WikimediaCommons]

5. Slota M., 2014
PHENOTYPIC VS. GENOTYPIC DATA ACQUISITION
27 000 genes Phenome = gene x environment
34 000 proteins Possible interactions = ?
[Integr8 - A.thaliana Genome Statistics]
(Furbank and Tester, 2011)
Introduction

6. Slota M., 2014
PHENOTYPIC VS. GENOTYPIC DATA ACQUISITION
High throughput techniques:
✓ next generation sequencing,
✓ microarrays,
✓ bioinformatics.
✓ automated image acqusition
✓ robotics/ automated sensoring
✓ bioinformatics/ image analysis
Introduction
PHENOTYPING = HIGH THROUGHPUT PLANT PHYSIOLOGY (?)

7. Introduction Slota M., 2014
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.
(Furbank and Tester, 2011)

8. Slota M., 2014Introduction
Plant phenotyping - the quantitative or qualitative investigation of traits at any
organizational level, in a given genomic expression state and a given environment
Organizational levels
in plant phenotyping
(Dhondt et al. 2013)

9. Slota M., 2014Concept of plant phenotyping
a) Mendel’s garden (Augustinian Abbey, Brno),
b) Mendel’s phenotyping instrument: microscope
Gregor Mendel
(1822-1884)
Pisum sativum
[www.zlgc.seu.edu.cn;www.mendel-museum.com]
Time period: 1856- 1863’ (7 years)
Plant material: 29 000 pea plants (Pisum sativum)
Plot: 2 ha monastery garden
Traits tested: 7 traits (7 different loci, each possesing 2 alleles)
(color and seed smoothness, color of the cotyledons, color of
the flowers, shape of the pods, color of the unripe pods,
position of flowers and pods, height of the plants).(Butler, 2009)

10. Slota M., 2014Phenotyping facilitities
HIGH THROUGHPUT
HIGH RESOLUTION
University of Nottingham;
MicroCT (High Resolution X-ray
micro-Computed Tomograph). »
« National Institute for Agricultural
Research (INRA) in Montpellier;
High Throughput Plant Phenotyping
Platform (PPHD).

11. Slota M., 2014Concept of plant phenotyping
Breeding
Agricultural
production
Biodiversity
assessment
Functional
genomics
Horticultural
production
Climate
change
IDENTIFICATION
OF HERITABLE TRAITS
FIELD ENVIRONMENT
PLANT GROWTH
STATUS
(Walter et al., 2009)

12. Slota M., 2014Concept of plant phenotyping
(Reynolds et al., 2020)

13. Slota M., 2014Concept of plant phenotyping
GROWTH SYSTEMS
(organic/soil, hydroponic, aeroponics etc.)
DATA COLLECTING
(digital camera images, high resolution
scanning, microscope imaging)
IMAGE ANALYSIS
(noise reduction, segmentation, filtering
counting/measuring, statistical analysis)
(Furbank and Tester, 2011)

14. Slota M., 2014Growth systems
IN-VITRO CULTURES
SCREENING
GROWTH CHAMBER/
GREENHOUSE ASSAYS
FIELD
EXPERIMENTS
✓ highly controlled
conditions,
✓ high throuput,
✓ high capacity,
✓ easy imaging,
- adapted only for
small plants.
✓ controlled
conditions,
✓ high throuput,
✓ adapted for
medium and
large plants,
- high costs,
- amount of data.
✓ high capacity,
✓ natural
conditions,
- high costs,
- complex imaging,
- extremely high
amount of data.
COMPARISON OF GROWTH SYSTEMS

15. Slota M., 2014Growth systems
IN-VITRO CULTURES
SCREENING
GROWTH CHAMBER/
GREENHOUSE ASSAYS
FIELD
EXPERIMENTS
SCREENING EFFECTIVENESS
NATURAL GROWTH PARAMETERS
COMPARISON OF GROWTH SYSTEMS

16. Slota M., 2014Conclusion
EXPERIMENT REPRODUCIBILITY REQUIRES:
✓ data objectively described in a mathematical, easily digitized and
searchable format (using ontologies),
✓ information on how the experiment was carried out (plant material,
growth conditions),
✓ standardization of the employed phenotyping techniques.
(Furbank and Tester 2011)
(Poorter et al., 2012)
Meta analysis of leaf area of
Arabidopsis thaliana Col. plants
grown with the same protocol, the
same seed stock and the same
soil in growth rooms of nine
different laboratories. »
Experiments reproducibility

17. Slota M., 2014Conclusion
Experiments reproducibility
EXPERIMENT REPRODUCIBILITY REQUIRES:
✓ data objectively described in a mathematical, easily digitized and
searchable format (using ontologies),
✓ information on how the experiment was carried out (plant material,
growth conditions),
✓ standardization of the employed phenotyping techniques.
(Furbank and Tester 2011)
(Poorter et al., 2012)
Venn diagram of the genes that
were differently affected by
transfer from Arabidopsis thaliana
plants from 20-22°C to chilling
temperatures (4-8◦C). Data are
from two independent experiments
from Vogel et al. (2005) and
Usadel et al. (2008). »

18. Slota M., 2014Growth systems
Aeroponics: nutrient solution is sprayed on roots
Petri plates in vitro techniques
In vitro techniques
In vitro techniques of plant cultivation are widely used for plant phenotyping as a quick
and cost-effective screening methods for a large number of plant genotypes.
Petri plates screening of a shoot and root phenotype is most applicable for small plants
(eg. Arabidopsis) for short term experiments.
On the other hand, petri plates assays lack to imitate complex natural conditions and
frequently cause stress on its own.
Rice seedlings, 6th day after plates transfer grown on the surface
(top) within agarose gel (bottom) [Phot. M. Slota]. »
A. thaliana root phenotyping
on vertical agar plates.
http://www.ipk-gatersleben.de/

19. Slota M., 2014Growth systems
Dynamics of plant traits
(Wattetal.2020)

20. Slota M., 2014Data collecting
COLOUR IMAGES (5)
Plant area, volume, mass,
structure, phenology
Senescence, relative chlorophyll
content, pathogenic lesions
Seed yield, agronomic traits
NEAR IR IMAGING (1-4)
Tissue water content
Soil water content
FAR IR IMAGING
Canopy, leaf temperature,
water use, salt tolerance
HYPERSPECTRAL IMAGING (6)
Carbohydrates, pigments and
protein
CARBON ISOTOPE RATIO
Transpiration efficiency,
photosynthetic pathway (TDL/MS)
FTIR IMAGING SPECTROSCOPY
Cellular localisation of metabolites
(sugars, protein, aromatics)
FLUORESCENCE IMAGING (7)
Physiological state of
photosynthetic machinery
(Fioranietal.2012)
SENSORS - Overview

21. Fluorescence
Infrared imaging Near Infrared imaging
Hyperspectral sensoring
Visible light
3D reconstruction
Slota M., 2014Data collecting
Imaging device
Sensors Visualized object
[http://www.lemnatec.de/products/imaging-modules]

22. Slota M., 2014Data collecting
(Clarketal.2020)

23. Slota M., 2014Phenotyping facilitities
RGB imaging consists of the construction of growth
profiles of plant shoot by acquiring time series using
cameras sensitive to the visible light range (400-700 nm).
The RGB imaging characteristics:
▪ projected shoot area is extracted following image
preprocessing and segmentation either in the RGB
space or in the HSV (hue, saturation, value) space,
▪ imaging setups vary widely depending on the
cultivation format,
▪ for cereals, multiple view angles created by rotating
the plants are generally used to reduce image
occlusions.
The calibration of projected shoot area (the projected
area of a 3D object onto a plane) based on total leaf area
and fresh and dry shoot mass measured destructively
throughout the growth trajectory was first performed in
Arabidopsis, barley, and tobacco leading to highly
significant linear or polynomial correlations.
(Fiorani and Schurr, 2013)
SENSORS - Visible light RGB imaging
Time-lapse images for
different cereals.
http://www.lemnatec.de/

24. Slota M., 2014Data collecting
Chlorophyll fluorescence is commonly used from lab to field scales. It offers
a rapid way to probe photosystem II status in vivo . Active fluorescence protocols
exploiting pulse amplitude modulation of commercial instruments can measure
the potential and effective quantum efficiency of photosystem II, the electron
transport rate, and the extent of nonphotochemical quenching.
Several possible uses of chlorophyll fluorescence have been recently proposed
for diagnosing early stress responses to abiotic and biotic factors before a decline
in growth can be measure.
(Fiorani and Schurr, 2013)
SENSORS - Chlorophyl fluorescence
Segmentation of various
stages of the symptom
development of 2 weeks-
old bean plants
inoculated with mock.
A: visible image obtained
by scanning.
B: Fv/Fm image obtained
by chlorophyll
fluorescence imaging.
(Rousseau et al. 2013)

25. Slota M., 2014Data collecting
Measurements of leaf and canopy temperature by thermal imaging (3–14
μm spectral range) have been introduced in the lab and in the field to evaluate
leaf water status.
Canopy temperature depression (the temperature difference between the canopy
and the surrounding air) is currently used in cereal breeding programs as a
selection trait for drought resistance in dry environments. Direct selection for
canopy temperature depression has contributed to yield gains.
(Fiorani and Schurr, 2013)
SENSORS - Infrared imaging (IR)
Thermal images of control and drought-stressed barley plants [A] and a wheat field [B].
(FurbankandTester,2011)
(SlotaM.2014)

26. Slota M., 2014Data collecting
Near-infrared spectroscopy (NIR) is a
spectroscopic method that uses the near-infrared
region of the electromagnetic spectrum (800-2500 nm).
Specific bands of the NIR to mid-infrared region due to
the changes in reflectance or transmittance to a range
of water content can be applied to estimate tissue
water status noninvasively and design screening
protocols for plant differential responses to drought.
(Fiorani and Schurr, 2013)
SENSORS - Near Infrared imaging (NIR)
(CIMMYT,2013)
NIR images of maize field [A] and wheat drought-stressed plant at elevated temperature [B].
http://www.lemnatec.de/

27. Slota M., 2014Data collecting
Hyperspectral imaging is focused on a smaller range of electromagnetic
spectrum (e.g. 400–1000 nm), but takes images at a spectral resolution between 1
and 10 nm. Multispectral and hyperspectral cameras capable of scanning
wavebands of interest at high resolutions, in particular around the peak of green
reflectance at 550 nm and the water absorption bands in the near-infrared (NIR).
Several indices have been introduced in both field research and breeding programs
for large-scale phenotyping and dynamic estimation of biomass, greenness,
nitrogen content, pigment composition, photosynthetic status, and water content.
(Fiorani and Schurr, 2013)
SENSORS – Spectral imaging
(ChapmanS.,CSIRO)
Plant height data collected by camera on the phenocopter [A] and image acquisition principle [B].
http://www.lemnatec.de/

28. Slota M., 2014Data collecting
It is anticipated that plant phenotyping research will eventually address the
need for 3D reconstructions at different scales, from individual leaves to entire
shoots and canopies. It is difficult to precisely estimate the potential impact of high-
precision 3D reconstructions of shoot phenotyping for screening purposes, but
these approaches will certainly be invaluable for modeling purpose. However,
recent work has demonstrated the first applications of stereo camera systems and
the simultaneous use of multiple sensors to enable 3D canopy reconstruction.
(Fiorani and Schurr, 2013)
SENSORS – 3D reconstruction
(IPKGatersleben)
Software for automatic 3-D model generation by analyzing the structure of object rotation pictures
[A] and a 3-D visualisation of maize using X-ray computed tomography [B].
(CPIBNottingham)

29. Slota M., 2014Image analysis
Object
parameters
quantification
▪ Object
measurements
▪ Object tracking
Raw image
acquisition
Data analysisImage
segmentation
▪ Format conversion
▪ Noise reduction
▪ Brightness, color
tresholding
IMAGE ANALYSIS PROCESS
(Phot. M. Slota)

30. Slota M., 2014Image analysis
Image segmentation process
http://prian.lab.imtlucca.it/
Top: time-lapse images of Arabidopsis
seedlings; bottom: corresponding
segmentation masks obtained manually.
Segmentation is the process dividing an
image into regions with similar properties.
Image segmentation can be carried by using
HSV-Segmentation. Images were converted
from RGB- to HSV color space and
segmented by using a minimum and
maximum threshold value for each single
channel.
Based on the segmented images, the
following parameters can be measured:
▪ Projected Leaf Area: The number of
pixels A belonging to the plant in the
image, given by segmentation.
▪ Plant Height: Height of the plant without
pot in pixel or cm in images from side
camera.
▪ Area Coverage: Relation of the plant area
to the area of their convex hull.
Software: FIJI (http://fiji.sc/Fiji)

31. Slota M., 2014Image analysis
Analysis software
Phenotyping methods require
new software solutions for data
extraction and treatment. These
solutions are instrumental in
supporting various research
pipelines, ranging from the
localisation of cellular compounds to
the quantification of tree canopies.
Number of available systems varies
very much with plant organs. In
particular, a large proportion of the
tools are dedicated to individual
leaves, then to the analysis of roots
(either root systems or single roots)
and cells. Distribution of the tools presented in the plant-
image-analysis.org website.
A. Number of software by plant organ type.
B. Proportion of operating systems by organ type.
C. Proportion of license type by organ type.
D. Proportion of automation levels by organ type.
(Lobet et al. 2013)

32. Slota M., 2014Image analysis
Analysis software
Main search page of the www.plant-image-analysis.org website. Users can browse through the
software solutions (A), make a free search (B), or use pre-defined search criteria (C). Here, the list
of software was restricted by the application of a filter on the organ type root-system(D).
(Lobetetal.2013)

33. Slota M., 2014Phenotyping facilitities
(Clarketal.2020)

34. Slota M., 2014Phenotyping facilitities
[http://www.lemnatec.com/]
Worldwide distribution of large-scale phenotyping facilities

35. Slota M., 2014Phenotyping facilitities
Phenoarch, Lemnatec
Installation for: medium/large plants
Environmental monitoring: temperature, humidity, light, soil water content
Parameters: growth, transpiration, growth rate, leaf area
transpiration, biomass, 3D architecture
Capacity: 1680 plants
Experiment duration: 90 days
(Neumann et al., 2012)

36. Slota M., 2014Phenotyping facilitities
Scanalyzer Field
Installation for: multitude of crops, small trees
Environmental monitoring: CO2, humidity, wind, light, temperature
Parameters: plant heigh, coverage, leaf area index, N
content, transpiration (FIR)
Capacity: 10 x 40 m field
Experiment duration: up to 6 months
Field growth
monitoring
instalation in
Jülich.
(Sirault et al., 2009)

37. Slota M., 2014
AIM OF STUDIES: Screening for osmotic component of salinity tolerance in
cereals using infrared thermography.
PLANT MATERIAL: durum wheat commercial varieties
PLATFORM: field trial (5m x 2m plots per genotype)
Imaging: ThermaCAM SC660 IR camera
(Sirault et al., 2009)
Scanalyzer Field
Phenotyping facilitities

38. Slota M., 2014Phenotyping facilitities - Roots
Shoot growth
dynamics imaging
Root architecture
analysis with a use of
semihydoponics system
University of
Nottingham;
MicroCT image.
UC Louvain aeroponics.
NIR root water
content imaging
LemnaTec scanalyzer.
Gel-based growth platform, GIT Atlanta.

39. Slota M., 2014References
▪ Butler, J.M. 2009. Fundamentals of Forensic DNA Typing, Elsevier Academic Press, Burlington
Clark, N. M., Van den Broeck, L., Guichard, M., Stager, A., Tanner, H. G., Blilou, I., & Sozzani,
R. (2020). Novel Imaging Modalities Shedding Light on Plant Biology: Start Small and Grow
Big. Annual Review of Plant Biology, 71.
▪ Fiorani, F. , U. Rascher and S. Jahnke. 2012. Imaging plants dynamics in heterogenic
environments. Current opinion in biotechnology 23: 227-235
▪ Fullerton-Smith, J. 2007. The Truth About Food. Bloomsbury Publishing, London.
▪ Furbank, R.T. and M. Tester. 2011. Phenomics--technologies to relieve the phenotyping
bottleneck. Trends Plant Sci. 16(12): 635-44
▪ Karsch-Mizrachi, I., Y. Nakamura, G. Cochrane. 2012. The International Nucleotide Sequence
Database Collaboration. Nucleic Acids Res. 40: D33–D37
▪ Nagel, K.A., B. Kastenholz, S. Jahnke, D. van Dusschoten and T. Aach. 2009. Temperature
responses of roots: impact on growth, root system architecture and implications for
phenotyping. Functional Plant Biology 36: 947-959
▪ Neumann, K., N. Stein, A. Graner, C. Klukas, A. Entzian and B. Kilian. 2012. Non-destructive
phenotyping using the high-throughput LemnaTec-Scanalyzer 3D platform to investigate
drought tolerance in barley, European Cereals Genetics Co-operative Newsletter 158-160.
▪ Poorter, H., F. Fiorani, M. Stitt, U. Schurr, A. Finck, Y. Gibon, B. Usadel, R. Munns, O. Atkin, F.
Tardieu and T.L. Pons . 2012. The art of growing plants for experimental purposes: a practical
guide for the plant biologist. Funct. Plant Biol. 39(11) 821-838
▪ Reynolds, M., Chapman, S., Crespo-Herrera, L., Molero, G., Mondal, S., Pequeno, D. N., ... &
Saint Pierre, C. (2020). Breeder friendly phenotyping. Plant Science, 110396.
▪ Sirault, X.R.R., R.A. James and R.T. Furbank. 2009. A new screening method for osmotic
component of salinity tolerance in cereals using infrared thermography. Functional Plant
Biology 36: 970-977
▪ Watt, M., Fiorani, F., Usadel, B., Rascher, U., Muller, O., & Schurr, U. (2020). Phenotyping: New
Windows into the Plant for Breeders. Annual review of plant biology, 71.

40. Slota M., 2014
Michal Slota
University of Silesia, Faculty of Biology and Environmental Protection,
28 Jagiellońska Street, 40-032 Katowice, Poland