Scanalyzer 3D tomato phenotyping - LemnaTec

LemnaTec High-Content Screening
TOMATO PHENOTYPING
LemnaTec GmbH
Matthias Eberius
Schumanstr. 18
52146 Würselen, Germany
Tel. +49 (0) 2405 / 4126-0
Fax +49 (0) 2405 / 4126-26
matthias.eberius@lemnatec.com
www.lemnatec.de

Why image-based high-content phenotyping?
With the steady multiplication of genetic research data, it is getting increasingly
interesting to find correlations between genotypes and phenotypes. Such correlations
make rapid and focussed conventional breeding and validation of genes for efficient
molecular reproduction possible.
To distinguish efficiently between inter- and intra-cultivar variability, the testing of a
greater number of plants is important. But only comprehensive quantitative phenotyping
provides a sufficient database for biostatistics.
As breeding programs and screenings need to include hundreds or thousands of plants
often grown in different seasons and at places far apart, a reliable and reproducible
measurement of morphological parameters is of utmost importance to produce valid,
comparable and comprehensive data. Image-based phenotyping using imaging under
defined conditions provides data with exactly this high standard needed for data mining.
Even if specific, particularly interesting observation parameters are discovered only years
after the start of a certain project or after it has been terminated, the images stored in the
database allow reanalysis of all plants ever used in this assignment.
Each plant that has spent a whole life cycle in a research or breeding greenhouse has in
the end a total value of up to $ 800. Any technical device to extract additional
information from images already stored and available for reanalysis — instead of
performing new tests — can reduce research costs by several $ 100,000 and
simultaneously accelerate breeding of new cultivars.
While all these factors can already be applied to directed breeding research, they have an
even greater importance for culture collections. Here only small amounts of seeds are
available for the routine regeneration of seed quality, and researchers interested in
specific phenotypes are almost by definition scattered all over the world. And they have
usually never seen the plant they are interested in when requesting seed samples.
Comprehensive high-content phenotyping may not only include the analysis of the visual
results of regularly grown plants, but can also provide dynamic information, e. g. about
opening and closing dynamics of stomata (based on IR-imaging), as well as showing
reactions under specific stressors (heat, drought, pests etc.).
Plants on conveyor systems are constantly moved in the greenhouse during the day,
minimising hotspots of heat or temperature for growth conditions, so that they can be
completely randomised. This new option immensely reduces plant variability due to
different growth conditions and produces an extremely homogeneous set of background
values. Taking appropriate statistics (mean values, median values, significance tests like ttest
etc.) even allows the detection of small effects attributed to a potentially different
genetic background of a plant. Having such advanced datasets, QTL-methods, for
example, become a very powerful tool to identify the relation between genes and any kind
of development or phenotype trait.
In addition, water usage and watering schemes can be completely and efficiently
controlled by LemnaTec watering stations, thus adding another important parameter
(tool?) to comprehensive phenotyping.
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Phenotyping with the LemnaTec Scanalyzer 3-D and Scanalyzer 3-D
Conveyor Technology
LemnaTec provides a modular family of plant phenotyping units easily adjustable to
customer requirements. The basic principle of all units is to image each plant under
reproducible conditions in a closed imaging box, thus providing vastly comparable images.
Plants are generally imaged from the top and two sides by automatically turning the pot
by 90°. If needed, more than two side images can be taken during each sequence. Plants
are identified automatically by barcode or RFID-technology. Thus a high throughput is
combined with the absolute certainty of not mixing up plants.
Depending on the throughput needed and on greenhouse organisation, LemnaTec provides
different types of imaging units. As a rule, plants are imaged several times during their
growth phase to quantify growth and development of important traits in time.
Fig. 1: Every LemnaTec Plant Scanalyzer 3-D consists of an imaging unit for top and side
images. It can be manually fitted with plants (so richtig?) and is most suitable if only one
imaging mode (mostly VIS imaging) is to be performed at medium or low throughput.
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Fig. 2: LemnaTec Scanalyzer 3-D conveyor units include at least a short conveyor belt
that can be loaded with the plants to be imaged. This allows for higher throughput and
sequential imaging in various imaging modes (e. g. VIS and NIR or more). Depending on
the method of application the unit may include automatic doors or shadowing canals.
Fig. 3: With LemnaTec Scanalyzer 3-D conveyor units and greenhouse management
systems, either the whole or at least part of the greenhouse is additionally equipped with
conveyor belts. Plants stay on the belts for their whole life cycle, being randomised in the
greenhouse when they are not imaged. Such units are particularly suitable for large
numbers of plants in bigger pots and in cases where watering and spraying is
programmed as well.
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Watering
All types of conveyor belts can be fitted with a watering and weighing station where each
plant is weighed in passing and data of water loss are recorded in the database.
Depending on the programming mode and database entry, water will be added individually
up to a certain total weight to keep the soil water content as constant as possible, or to a
reduced level to induce controlled stress.
VIS imaging
The basic approach to plant phenotyping is imaging in visual light. Homogeneous cloudy
day illumination from top and side forms the basis for taking images later to be compared
within and between plants over longer periods (images that can later be compared to find
differences either between or within the plants over extended time spans).
The use of certain high quality colour cameras and zoom lens systems according to
industrial standard, which are completely controlled by configurations stored in the
database, allows highly reproducible imaging. Getting this kind of high reproducibility is of
major importance for the assessment of developments within one plant and the detection
of differences between larger sets of plants.
Cameras have a high sensitivity and customisable special modules can be provided, e. g.
to filter images for continuous chlorophyll fluorescence or GFP (or related fluorescence.).
If multi-spectral images are needed, hyper-spectral filters can also be implemented.
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Near Infrared Imaging
Near Infrared (NIR) imaging can be used, for example, to get detailed information on the
watering status of plant leaves and their reaction to limited water availability. The
following image shows how a corn plant reacts to drought stress over a period of 8 h.
Corn 0 h
Corn 8 h
Fig. 4: The images show how a corn plant under moderate water stress loses water over
a period of 8 h (top, bottom left). While dark green areas signify a maximum absorption of
NIR light by water (high water content), light areas represented as yellow or red show the
minimum amount of water. The graphs show the absolute development of projected leaf
area (top right) and relative changes over time (bottom right).
While the plant area is still showing growth during the day (but the growth rate is already
dropping towards the end), water content of the leave is significantly decreasing. This
shows that the plant is subject to water deficiency stress, and that this parameter can
efficiently be monitored quantitatively by the LemnaTec Scanalyzer system.
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Results of phenotyping
Plant phenotyping
Plants will be imaged from the top and at least from two sides. For tomato plants imaging
from all four sides is recommended as each may yield different information and tomatoes
have no preferred (main, principal) symmetry axis. Depending on the parameter analysed
it can make sense either to calculate average or median values from all four side images
or to add up information in order to get a comprehensive dataset for each plant.
For the sake of better illustration the following images show only one-sided images.
In the first step after imaging, all plant area is segmented from background, sticks and
pot.
The figure below shows the result of such an analysis for a set of morphologically
different plants. The different visible height of the plants is due to different growth as all
were grown in pots of the same size.
1 2 3 4 5 6
Fig. 5: Projected leaf areas of 6 tomato plants (1 to 6 from the left) on a side image.
Such images can be analysed for a set of quantitative parameters which in combination
characterise a phenotype, as shown in the following table.
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Tab. 1: Morphological parameters, derived from a set of test plants, characterising
different phenotypes
Plant 1 2 3 4 5 6
Moment ratio 2,62 1,47 1,24 3,62 3,02 3,04
Plant height total 220 135 188 236 198 137
Height over pot 212 128 142 192 158 116
Height hanging down 8 7 46 44 40 21
Plant width 146 135 221 147 143 109
Projected plant area 9.414 6.843 19.384 15.614 9.269 5.374
Rotational moment 0,404 0,312 0,248 0,287 0,272 0,337
Roundness 1.308 772 837 944 488 282
Compactness 60,3 63,6 72,4 70,8 74,5 76,1
Compactness as quantitatively described above is a parameter that may be used to
quantify the density of leaves. It could be important for optimum light yield or shadowing
between leaves of one plant. Another application could be to describe how fast a plant
can dry up its leaves in the wind, thus avoiding fungal growth caused by high moisture .
The following figure shows a graphic representation of the measured data. Biologically
interesting dimensions of compactness (looking for smaller or bigger areas between leaves
depending on plant morphology) can be parameterised within the software.
Fig. 6: Relation between projected plant area (red) and areas (grey) here parameterised as
being shadowed or being within the plant.
As another approach to plant morphology, it could be interesting to explore at which
height over ground the plant develops which amount of biomass. Leaf areas lower than
pot level may be a pure artefact of growth in pots or a sure sign that certain plants
produce leaves that would – under field conditions – decay on the soil.
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Fig. 7: The image above shows plants analysed
for projected plant area depending on height over
ground (green to orange). The blue areas show
leaves hanging down below the top level of the
soil.
The graphs shown on the right describe height
profiles for the 6 plants with the same colours as
in the image above.
If it proves interesting, this data type can be
linked to the colour classification of the plant,
quantifying leaf and fruit colour in correlation to
height over ground (not shown).
Leaf and branch phenotyping
The same phenotyping parameters that are used for complete plants (and even more) can
be quantified for cut leaves or branches.
This may reveal additional important traits, as branch morphology is strongly dependent
on the genetic background of the plant.
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Fruit phenotyping
Another important option with the LemnaTec Scanalyzer is fruit phenotyping, which could
prove very important to achieve a constantly high fruit quality (e. g. to minimise the
amount of fruits that have to be discarded due to size, shape or coloration).
The following figures show some examples for parameters that can be derived from fruits.
As an example, tomatoes are classified based on certain parameters.
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Fig. 8: Colour classification of tomatoes imaged from the side. Colours were chosen to
permit maximum separation of important differences. Tomatoes are sorted in rows A, B,
C and columns 1 to 6. While the left graph shows the absolute amount of area per colour,
the right one shows the percentages.
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Fig. 9: Based on a similar colour scheme as shown in the figure above, tomatoes were
grouped in different classes. Such schemes allow users the interpretation of quantitative
results and separation of objects into self-defined, biologically relevant classes. The
classes are: red, dark orange, orange, yellow, yellow green and red green.
Fig. 10: The figure shows a shape classification by side view, based on grouping of the
first eight factors of an elliptic Fourier transformation of each fruit outline. Such
classifications can provide highly unbiased and differentiated groupings of various objects.
Plant growth dynamics – dynamic phenotyping
All the parameters shown above and even more can be quantified in time series,
generating dynamic phenotyping data for each plant. From such data growth rates, stay-
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green periods, flowering periods, ripening dynamics, reactions to stressors (heat, drought,
pests) or nutrients and much more can be extracted.
Data storage and data mining
Having taken all images and analysed them with many different observation parameters,
the resulting data will remain in the database completely linked. This enables users to
perform all kinds of image reanalyses for complete tests at any point of time in the future,
and it even allows the research of completely new aspects. So the whole procedure has
two major advantages over making a range of completely new experiments: It is
dramatically faster as it takes only minutes or hours to get valuable results. And it is
noticeably cheaper, considering that the total costs of a plant having completed a full life
cycle in a greenhouse may range up to $ 800.
The whole database is stored on a speedily accessible array of hard drives on a server
which may be designed to comply with the specific data safety needs of the user (Raid
system, backup strategies).
All data and related images are available for easy data mining with the LemnaMiner. This
interface was specially designed to grant easy access to databases for experts interested
in the results. Staff do not need any specific database skills, thus making them
independent of IT specialist services.
Conclusion
The LemnaTec Scanalyzer is a comprehensive phenotyping platform highly suitable to
quantify morphological traits of tomatoes (and any other plant) over their whole life cycle.
The examples above just give a first impression of the full capabilities of the system for
quantitatively characterising tomatoes (and other plants). All results are reproducible
based on biologically relevant parameters.
The LemnaTec systems can be customised for various applications and diverse
throughput, depending on the customers’ needs.
For further information please do not hesitate to contact
Matthias Eberius
LemnaTec GmbH
Schumanstr. 18
52146 Würselen, Germany
Tel. +49 (0) 2405 / 4126-0
Fax +49 (0) 2405 / 4126-26
matthias.eberius@lemnatec.de
www.lemnatec.com
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