Breakthrough Technologies
phenoSeeder - A Robot System for Automated
Handling and Phenotyping of Individual Seeds
1[OPEN]
Siegfried Jahnke*, Johanna Roussel
2
, Thomas Hombach, Johannes K ochs, Andreas Fischbach,
Gregor Huber, and Hanno Scharr
Forschungszentrum Jülich, Institute of Bio- and Geosciences, IBG-2: Plant Sciences, 52425 Jülich, Germany
ORCID IDs: 0000-0002-4086-2567 (S.J.); 0000-0003-3816-5989 (T.H.); 0000-0002-7020-8726 (A.F.).
The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In
particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning
methods delivering only two-dimensional data, often termed seed size. However, three-dimensional traits, such as the volume
or mass of single seeds, are very rarely determined in routine measurements. Here, we introduce a device named phenoSeeder,
which enables the handling and phenotyping of individual seeds of very different sizes. The system consists of a pick-and-place
robot and a modular setup of sensors that can be versatilely extended. Basic biometric traits detected for individual seeds are
two-dimensional data from projections, three-dimensional data from volumetric measures, and mass, from which seed density is
also calculated. Each seed is tracked by an identifier and, after phenotyping, can be planted, sorted, or individually stored for
further evaluation or processing (e.g. in routine seed-to-plant tracking pipelines). By investigating seeds of Arabidopsis
(Arabidopsis thaliana), rapeseed (Brassica napus), and barley (Hordeum vulgare), we observed that, even for apparently round-
shaped seeds of rapeseed, correlations between the projected area and the mass of seeds were much weaker than between
volume and mass. This indicates that simple projections may not deliver good proxies for seed mass. Although throughput is
limited, we expect that automated seed phenotyping on a single-seed basis can contribute valuable information for applications
in a wide range of wild or crop species, including seed classification, seed sorting, and assessment of seed quality.
Seeds play a major role in keeping continuity be-
tween successive generations (Esau, 1977) and are key
for the distribution and evolution (Moles et al., 2005) of
higher plants. Fertile seeds carry an embryo and may
contain nutrient storage tissues in cotyledons, endo-
sperm, and/or perisperm, supporting germination and
seedling development at early developmental stages.
Although this is true for all seed plants, various traits of
seeds, such as size, shape, weight, and chemical com-
position, can be very different between plant species or
accessions. For example, the Arabidopsis (Arabidopsis
thaliana) accession Cape Verde Islands was reported to
yield on average 40% fewer seeds than Landsberg erecta,
but they are almost twice as heavy (Alonso-Blanco et al.,
1999). Considering today’s p lant species, single-seed
mass may vary over a range of 11.5 orders of magni-
tude (Moles et al., 2005). Seed mass is under strong ge-
netic control, whereas the total number of seeds of a
plant is largely affected by the environment (Paul-Victor
and Turnbull, 2009). It has been demonstrated that the
size, mass, and shape of Arabidopsis seeds may be reg-
ulated by brassinosteroid (Jiang et al., 2013), and it was
shown recently that seed size in rice (Oryza sativa)canbe
influenced by the epiallele Epi-rav6 (Zhang et al., 2015).
The ability of plants to switch between small and larger
seeds may be understood as an adaptation to novel en-
vironments (Igea et al., 2016). However, it is still not fully
understood whether, or to what extent, the variability of
seed traits within plant species or genotypes has an im-
pact on the development and further performance of a
plant.
When comparing biometric seed data of different
dimensions such as length (one-dimensional), projected
area (two-dimensional [2D]), or volume and mass (both
three-dimensional [3D]), one can argue that mass is the
most relevant parameter as a proxy for the amount of
reserves a seed provides for the offspring. This might be
true even when conside ring that the type of reserves,
such as proteins, carbohydrates, or lipids (Rolletschek
et al., 2015), and also different seed tissues, such as seed
coat, embryo, or endosperm, may contribute differently
to seed mass (Alonso-Blan co et al., 1999). While seed
mass and time to germination (radicle protrusion) do
not necessarily correlate (Norden et al., 2009), in par-
ticular under greenhouse conditions, higher seed mass
may be advantageous for seedling establishment under
1
This work was performed within the German Plant Phenotyping
Network funded by the German Federal Ministry of Education and
Research (project identification no. 031A053).
2
Present address: FH Aachen, University of Applied Sciences,
Fachbereich 9–Medizintechnik und Technomathematik, 52428 Jülich,
Germany.
* Address correspondence to s.jahnke@fz-juelich.de.
The author responsible for distribution of materials integral to the
findings presented in this article in accordance with the policy de-
scribed in the Instructions for Authors (www.plantphysiol.org) is:
Siegfried Jahnke (s.jahnke@fz-juelich.de).
S.J. and H.S. designed the project; J.R., T.H., J.K., A.F., H.S., and
S.J. performed system construction and software engineering; J.R.
and S.J. performed the experiments; G.H., J.R., and S.J. analyzed data;
S.J., G.H., J.R., and H.S. wrote the article; all authors read and ap-
proved the final article.
[OPEN]
Articles can be viewed without a subscription.
www.plantphysiol.org/cgi/doi/10.1104/pp.16.01122
1358 Plant Physiology
Ò
, November 2016, Vol. 172, pp. 1358–1370, www.plantphysiol.org Ó 2016 American Society of P lant Biologists. All Rights Reserved.
Downloaded from https://academic.oup.com/plphys/article/172/3/1358/6115817 by guest on 20 August 2022
adverse environmental conditions (Moles et al., 2005).
For example, shade-tolerant species showed largely
higher seed masses than cogeneric species growing in
open habitats, indicating that seedlings under low-light
conditions need more reserves than under good light
(Salisbury, 1974). Seedlings of wild radish (Raphanus
raphanistrum) emerged more likely from heavier seeds
than from small seeds under field conditions but not in
the gr eenhouse (Stanton, 1984), and for Arabidopsis,
seed mass was reported to be higher in populations
growing naturally at higher altitudes taken as a proxy
for harsher conditions (Montesinos-Navarro et al., 2011).
Seed mass can be measured individually (Stanton,
1984), but it is generally collected as an average value of
batches of 50 to 1,000 seeds (Jako et al., 2001; Jofuku
et al., 2005; Montesinos-Navarro et al., 2011; Tanabata
et al., 2012). Alternatively, 2D scan s are analyzed to
determine parameters such as seed length, width,
are a, and perimeter length as a measure for seed size
(Tanabata et al., 2012) . This approach can be imple-
mented in high-throughput facilities to obtain projected
areas of seed grains combined with genome-wide as-
sociation studies (Yang et al., 2014). Although projected
seed area can easily be measured with a common office
scanner (Herridge et al., 2011; Tanabata et al., 2012;
Moore et al., 2013), it is not necessarily a precise or re-
liable measure of the true seed size because it may de-
pend on the shape (Alo nso-Blanco et al., 1999) and the
orientation of a seed at scan (see “Results”). These is-
sues also apply when using 2D projections to calcu-
late length-to-width ratios as a simple shape factor
(Tanabata et al., 2012). Projected seed area also has been
used to calculate seed mass, assuming a fixed rela-
tionship between these parameters (de Jong et al., 2011;
Herridge et al., 2011). This may hold with sufficient ac-
curacy when averaging a large number of seeds but might
be misleading when considering individual seeds.
From a physical point of view, volume should be a
much better proxy for mass than 2D traits. Although it
has been stated that for 65 species analyzed seed masses
can be compared easily with seed volumes (Moles et al.,
2005), it is not clear how these seed volumes were de-
termined. Volumes can be assessed using advanced
methods such as x-ray computed tomography (CT) on
fruits (Stuppy et al., 2003) or synchrotron radiation x -ray
tomographic microscopy applied in paleobiological stud-
ies (e.g. on fruits and seed; Friis et al., 2014). Nuclear
magnetic resonance (NMR) methods are used to mea-
sure water uptake in kid ney beans (Phaseolus vulgaris)
and adzuki beans (Vigna angularis; Kikuchi et al., 2006) or
to estimate seed weight and content (Borisjuk et al., 2011;
Rolletschek et al., 2015) rather than volumes. To our best
knowledge, affordable methods to measure seed volumes
directly are not achievable so far. For that reason, we have
set up a volume-carving method for 3D seed shape re-
construction that is described brieflyhereandinmore
detail in a recent publication (Roussel et al., 2016).
While traits derived from scanning procedures can
easily be assigned to individual seeds (Herridge et al.,
2011), further handling and processing of phenotyped
single seeds is not as simple, in particular for tiny ones
like those of Arabidopsis. The aim of this work was to
develop an automated seed-handling system that can
analyze single seeds of very different sizes or shapes,
from Arabidopsis seeds up to barley (Hordeum vulgare)
seeds or even bigger. The phenoSeeder system is de-
signed to pick and place seeds, to achieve basic mor-
phometric traits (one-dimensional and 2D data from
projections, 3D reconstruction data, and mass) of each
individual seed, and to store all analyzed seed traits in a
database. Another goal is to use phenoSeeder for seed-to-
plant tracking approaches and to analyze whether, or
which, particular seed traits have an impact on plant
development and performance under various environ-
mental conditions. We describe the main features of the
phenoSeeder technology and present results obtained
with seeds of three accessions of Arabidopsis, rapeseed
(Brassica napus), and barley, respectively. When analyzing
the data, we focused particularly on correlations between
projected seed area, seed volume, and seed mass, with the
hypothesis that the respective seed volume may better
correlate with mass than the projected area.
RESULTS
Here,wedescribethegeneralconceptofthephenoSeeder
system. More technical details are given in Supplemental
Materials and Methods S1. The terminology used for the
measured seed traits is compiled in Table I. Results of
test measurements relevant for the performance of the
system are presented (Table II), and traits of more than
1,000 seeds (for exact numbers, see Table III) for three
accessions of Arabidopsis, rapeseed, and barley, respec-
tively, were evaluated.
Design and Modules of the phenoSeeder System
The setting of the system enables routine handling
(pick and place) of individual seeds and the measure-
ment of morphometric seed traits. Both hardware and
Table I. List of analyzed seed traits and abbreviations
Seed Trait Symbol Unit
Projected area (or size)
a
A mm
2
Length from projection
a
L
2D
mm
Width from projection
a
W
2D
mm
Volume
b
V mm
3
Length
b
L mm
Width
b
W mm
Height
b
H mm
Mass (or weight) M mg
Volume A
a
(=p/6 A
3/2
) V
A
mm
3
Volume 2D
a
(=p/6 L
2D
W
2D
W
2D
) V
2D
mm
3
Density (=M/V) r mg mm
23
Density A (=M/V
A
) r
A
mg mm
23
Density 2D (=M/V
2D
) r
2D
mg mm
23
a
Traits derived from 2D imaging.
b
Traits derived from 3D recon-
struction.
Plant Physiol. Vol. 172, 2016 1359
Automated Ph enotyping of Individual Seeds
Downloaded from https://academic.oup.com/plphys/article/172/3/1358/6115817 by guest on 20 August 2022
software are modular, allowing easy implementation of
new components. Figure 1A shows the main compo-
nents of phenoSeeder with an industrial robot (details
are given in Supplemental Materials and Methods S1),
an exchangeable seed-handling tool, a tool magazine,
a 2D imaging station, 3D imaging modules, balances, a
seed placement station, a nozzle cleaning tool, and a
through-beam sensor for tool calibration. Figure 1B
shows a schematic of the basic workflow: the cycle
starts at the 2D imaging module (station 1), where
dispersed seeds are recognized by image processing
and a selected seed is picked up; after assigning a
unique identifier to the seed, it is then moved to the 3D
imaging module (station 2) to obtain volumetric data;
thereafter, the seed is placed on a balance (station 3);
finally, the seed is either planted or stored (station 4).
If necessary, the nozzle gets cleane d (station 5) before
a new cycle starts. A more detailed description of the
phenoSeeder workflow is presented in Supplemental
Materials and Methods S1. The sequence of the different
steps is illustrated in Supplemental Movie S1. All in-
formation of measured traits and the actual location of
the seed are stored in a distributed database system
(Schmidt et al., 2013). Seed location can be used in sub-
sequent experiments to reidentify the seed or seedling.
Seed-Handling Tools and Pneumatic System
The robot arm is equipped with a tool-changing head
to which seed-handling tools or grippers can be plug-
ged or unplugged in an automated fashion (Fig. 2A). A
seed-handling tool is equipped with a dedicated nozzle
depending on seed size or shape to which either vac-
uum is provided for sucking or slight overpressure for
releasing the seed. The actual air pressure at the nozzle
is measured by a pressure sensor, P3 (Fig. 2A), and is
fully controlled by a pneumatic system (Supplemental
Fig. S1) described in more detail in Supplemental
Materials and Methods S1. The air pressure at P3 indi-
cates whether a seed is sucked at the nozzle, whether a
seed has been properly released, or whether the nozzle
is clogged. The actual pressure values may depend on
seed properties and specifications of the seed-handling
tool including the nozzle. A typical change in air pres-
sure at P3 is shown in Figure 2B f or an Arabidopsis
seed handled wi th a 0.15-mm nozzle (Supplemental
Materials and Methods S1): when the nozzle orifice is
open with no seed at the nozzle (phase 1), the pressure
is about 215 mbar; as soon as a seed is sucked (arrow
facing down), the pressure decreases and reaches a
new plateau of approximately 180 mbar (phase 2) after
about 200 ms; within the first 20 to 30 ms of the pressure
drop, it is decided whether a seed has been sucked or
not by simple thresholding; the s eed is released by
providing a slight overpressure to the nozzle (ph ase 3;
arrow facing up) for about 150 ms, and after that short
time, air pressure is switched back to vacu um. Evacu-
ation of the system takes a little longer than pressuriz-
ing but, after around 320 ms, phase 1 is reestablished
and the system is ready for a new cycle. Seeds of Ara-
bidopsis, rapeseed, and barley sucked at a nozzle are
shown in Figure 2, C, D, and E, respectively, demon-
strating that seeds of very different sizes and shapes can
be handled.
2D Imaging Station, Seed Segmentation, and Selection
Seeds are dispersed on an optical glass filter equip-
ped with a vibration device to separate seeds touching
each other (Fig. 3A). A light-emitting diode ring light
for illumination and a calibrated camera with a macro
lens are mounted underneath the glass filter (for details,
see Supplemental Fig. S2; Supplemental Materials and
Methods S1). From the seeds on the glass filter, photo-
graphs are taken as demonstrated for Arabidopsis (Fig.
3B), rapeseed (Fig. 3D), and barley (Fig. 3F), for which
enlarged areas are shown in Figure 3, C, E, and G, re-
spectively. Such images are acquired repeatedly during
each cycle of the workflow to detect and segment in-
dividual seeds. The procedure use d for seed segmen-
tation is explained in more detail in Supplemental
Materials and Methods S1. For each segmented seed,
the projected area (A) is determined, both length (L
2D
)
and width ( W
2D
) are calculated from fitting an ellipse to
the seed, and red, green, and blue (RGB) color statistics
are obtained. The x and y positions of each seed’s center
Table II. Variance of seed parameters when single seeds were measured repeatedly
Mean values 6 relative standard deviation (RSD); RSDs are given in percentages. Lag2-2, Accession
Lagodechi; n, number of repetitions; No., seed number.
Species and Accession No. nA V M
mm
2
mm
3
mg
Rapeseed, Wotan 1 60 2.80 6 5.4 2.73 6 0.23 3.13 6 0.13
2 60 1.70 6 3.8 1.47 6 0.45 1.61 6 0.29
3 60 2.68 6 5.5 3.08 6 0.20 3.42 6 0.13
Barley, Barke 1 63 24.3 6 4.1 40.1 6 1.3 54.4 6 0.03
2 60 19.3 6 6.4 26.9 6 1.3 45.6 6 0.02
3 60 23.3 6 7.8 34.5 6 1.2 44.7 6 0.02
Arabidopsis, Lag2-2 1 60 0.123 6 5.1 0.0144 6 3.7 0.0169 6 14.5
2 60 0.103 6 6.1 0.0098 6 5.4 0.0099 6 29.0
3 60 0.131 6 4.7 0.0155 6 1.8 0.0184 6 11.8
1360 Plant Physiol. Vol. 172, 2016
Jahnke et al.
Downloaded from https://academic.oup.com/plphys/article/172/3/1358/6115817 by guest on 20 August 2022
Table III. Selected seed traits of rapeseed, barley, and Arabidopsis
Bn1, Wotan; Bn2, Expert; Bn3, Pirola; Hv1, Barke; Hv2, HOR13719; Hv3, HOR9707; At1, Col-0; At2, Lag2-2; At3, Aguaron (Agu-1); n, number of repetitions. Mean values 6 RSD; RSDs are
given in percentages. Abbreviations of traits are as listed in Table I. All pairwise differences between the means of the different accessions of a plant species were statistically significant (P , 0.01)
except for L (Hv1 versus Hv2), r
A
(Bn1 versus Bn2), r
2D
(Bn1 versus Bn2), and r
2D
(Hv1 versus Hv3), denoted by lowercase a, b, c, and d, respectively.
Trait Rapeseed Barley Arabidopsis
Bn1 Bn2 Bn3 Hv1 Hv2 Hv3 At1 At2 At3
n 1,077 1,009 1,019 1,047 1,112 1,058 1,006 1,009 1,008
M (mg) 1.88 6 24.5 2.31 6 32.3 1.32 6 40.0 46.6 6 15.1 51.3 6 17.5 56.8 6 19.4 0.0210 6 15.7 0.0129 6 26.4 0.0236 6 22.9
A (mm
2
) 2.12 6 15.4 2.42 6 20.6 1.97 6 22.5 21.3 6 11.7 21.7 6 12.6 27.6 6 13.5 0.140 6 9.36 0.109 6 12.8 0.159 6 12.7
V (mm
3
) 1.62 6 25.0 2.04 6 33.5 1.19 6 40.9 37.1 6 13.9 39.6 6 17.6 49.3 6 17.0 0.0200 6 14.2 0.0112 6 22.2 0.0215 6 20.6
V
A
(mm
3
) 2.35 6 23.1 2.88 6 30.9 2.12 6 34.0 74.3 6 17.0 76.5 6 18.4 109.6 6 19.9 0.0395 6 14.0 0.0274 6 19.2 0.0483 6 18.8
V
2D
(mm
3
) 2.17 6 24.1 2.71 6 31.9 1.98 6 36.2 52.3 6 18.1 54.5 6 19.5 64.8 6 21.1 0.0325 6 13.5 0.0223 6 18.6 0.0384 6 19.4
L (mm) 1.77 6 8.07 1.86 6 11.1 1.72 6 11.6 7.16 6 6.31 a 7.20 6 6.77 a 9.82 6 8.01 0.488 6 7.04 0.433 6 9.79 0.542 6 8.19
L
2D
(mm) 1.81 6 7.48 1.90 6 10.5 1.74 6 9.94 7.76 6 6.30 7.54 6 6.30 10.62 6 8.76 0.527 6 6.27 0.477 6 8.31 0.583 6 7.21
W (mm) 1.45 6 9.13 1.61 6 11.2 1.41 6 14.2 3.66 6 5.54 3.73 6 6.77 3.56 6 6.97 0.311 6 4.97 0.256 6 7.28 0.318 6 8.05
W
2D
(mm) 1.50 6 8.97 1.62 6 11.8 1.44 6 13.9 3.57 6 8.24 3.69 6 8.63 3.39 6 8.81 0.343 6 5.12 0.297 6 7.00 0.353 6 8.09
H (mm) 1.19 6 12.6 1.29 6 15.6 0.97 6 19.6 2.75 6 6.83 2.83 6 8.27 2.78 6 8.84 0.264 6 6.11 0.210 6 8.28 0.249 6 10.2
r (mg mm
23
) 1.17 6 2.49 1.14 6 2.44 1.12 6 7.96 1.25 6 4.80 1.29 6 3.39 1.15 6 6.99 1.05 6 12.5 1.16 6 20.4 1.10 6 13.0
r
A
(mg mm
23
) 0.80 6 13.0 b 0.81 6 16.4 b 0.62 6 19.0 0.64 6 15.6 0.68 6 12.3 0.53 6 17.7 0.53 6 14.3 0.47 6 22.6 0.49 6 17.2
r
2D
(mg mm
23
) 0.87 6 13.8 c 0.86 6 17.9 c 0.67 6 21.0 0.91 6 18.1 d 0.95 6 14.9 0.89 6 18.6 d 0.65 6 14.5 0.58 6 23.0 0.62 6 17.4
r
2
(M versus V) 0.991 0.994 0.977 0.930 0.964 0.888 0.492 0.537 0.713
r
2
(M versus A) 0.669 0.691 0.692 0.531 0.678 0.504 0.352 0.389 0.480
r
2
(M versus V
2D
) 0.680 0.706 0.798 0.370 0.566 0.327 0.275 0.385 0.471
Plant Physiol. Vol. 172, 2016 1361
Automated Ph enotyping of Individual Seeds
Downloaded from https://academic.oup.com/plphys/article/172/3/1358/6115817 by guest on 20 August 2022
of mass (white dots in Fig. 3, C, E, and G) are used for
picking up the seed by the robot. For further processing,
a seed is chosen either randomly or by a criterion (e.g.
projected area) defined by the user. As soon as a seed is
selected and successfully sucked by the robot, the seed
gets an identifier and its database record is created.
3D Imaging and Weighing Stations
Two 3D imaging modules are set up differing in their
optical layout suited for different seed sizes; the one for
small seeds is shown in Figure 4A. When a seed is
picked up by the robot, it is positioned in front of the
cameralenswhilehangingatthenozzle(Fig.4A),
stepwise turned by 360°, and at each step of, for ex-
ample, 10°, an image is acquired. To achieve 3D recon-
struction of a seed (Fig. 4B), a volume-carving technique
is used to determine the seed volume (V) and to calculate
the length (L), width (W), and height (H) of the seed
employing an ellipsoid fit. Both the imaging modules
and the 3D reconstruction have been described in a
previous publication (Roussel et al., 2016), and more
information is given in Supplemental Materials and
Methods S1. For small seeds (like those of Arabidopsis),
a high-resolution balance is used with a custom-made
Figure 1. Overview of phenoSeeder. A, Photograph of the setup located in a security cage. The system is composed of modules
that can be placed at various positions in the cruising range of the industrial pick-and-place robot (a), equipped with a seed-
handling tool (b) and a tool magazine (c). Default modules are a 2D imaging station (1), 3D imaging stations (2), balances (3), a
seed-placement station (4), and a nozzle-cleaning station (5). B, Schematic illustrating the main workflow along the different
modules. At the 2D imaging station (1), dispersed seeds are detected, 2D traits are measured, and one seed is picked up by the
seed-handling tool; at the 3D imaging stations (2), the volumetric data of the seed are measured; the mass of the seed is taken at a
balance (3); the seed is planted or stored (4); and the nozzle gets cleaned if needed (5).
Figure 2. Seed handling and seeds of the
three species investigated here. A, Tool
change head (TCH) at the robot arm to
which, by the aid of a tool change adapter
(TCA), an exchangeable seed-handling tool
(EXT) is fixed. A pressure sensor (P3), sole-
noid valve (V2), and dedicated nozzle (Nz)
also are shown. B, Screen shot of temporal
pressure changes near the nozzle as mea-
sured by P3. The denoted phases 1 to 3 and
the arrows are described in detail in the
text. C to E, Seeds sucked at the nozzle are
shown for Arabidopsis (C), rapeseed (D),
and barley (E). Bars = 1 mm.
1362 Plant Physiol. Vol. 172, 2016
Jahnke et al.
Downloaded from https://academic.oup.com/plphys/article/172/3/1358/6115817 by guest on 20 August 2022