Data in brief 29 (2020) 105201
Contents lists available at ScienceDirect
Data in brief
journal homepage: www.elsevier.com/locate/dib
Data Article
High throughput phenotyping dataset related to
seed and seedling traits of sugar beet genotypes
lie Charrier a, Didier Demilly a,
Sylvie Ducournau a, Aure
a
le
ne Wagner , Ghassen Trigui a, Audrey Dupont a,
Marie-He
Sherif Hamdy a, Karima Boudehri-Giresse a,
lique Delanoue a,
Laurence Le Corre a, Laurence Landais a, Ange
e Charruaud b, Karine Henry c, Nicolas Henry c,
Dorothe
Lydie Ledroit d, Carolyne Dürr d, *
a
^le des Vari
Groupe d’Etude et de contro
et
es Et des Semences (GEVES), 25 rue Georges Morel, 49071,
Beaucouz
e, France
INRAE, UMR URGI Unit
e de Recherche G
enomique Info, Route de Saint-Cyr, 78000, Versailles, France
c
Ets Florimond Desprez, BP 41, 59242, Cappelle-en-Pevele, France
d
INRAE, UMR IRHS Institut de Recherche en Horticulture et Semences, 42 rue George Morel, 49071,
Beaucouz
e, France
b
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 2 December 2019
Accepted 20 January 2020
Available online 27 January 2020
Several seed and seedling traits are measured to evaluate germination and emergence potential in relation with environmental
conditions. More generally, these traits are also measured in the
field of ecology as simple traits that can be correlated to other
adaptative traits more difficult to measure on adult plants, as for
example traits of the rooting system. Methods were developed for
deep high throughput phenotyping of hundreds of genotypes from
dry seed to the end of heterotrophic growth. The present dataset
comes from a project on genotyping and phenotyping of populations of genotypes, with different geographic and genetic origins so as to increase genotypic diversity of sugar beet in terms of
germination and early growth traits, evaluated at low temperatures. Data were collected in relation to the creation of the first
sugar beet crop ontology. This dataset corresponds to the first
automated phenotyping of a population of 198 genotypes and 4
commercial control varieties and is hosted on INRAE public depository under the reference number doi.org/10.15,454/AKNF4Q.
The equipment and methods presented here are available on a
Keywords:
Seed
Seedling
Germination
Heterotrophic growth
Automated phenotyping
Sugar beet
Genotypes
* Corresponding author.
E-mail address: carolyne.durr@inra.fr (C. Dürr).
https://doi.org/10.1016/j.dib.2020.105201
2352-3409/© 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://
creativecommons.org/licenses/by/4.0/).
2
S. Ducournau et al. / Data in brief 29 (2020) 105201
phenotyping platform opened to collaborative research and
adaptable for specific services for characterizing thousands of genotypes on different crops or other species. The phenotyping
values can also be linked to genomic information to study the
genetic determinism of the trait values.
© 2020 The Authors. Published by Elsevier Inc. This is an open
access article under the CC BY license (http://creativecommons.
org/licenses/by/4.0/).
Specifications Table
Subject
Specific subject area
Type of data
How data were acquired
Data format
Parameters for data collection
Description of data collection
Data source location
Data accessibility
Agricultural and Biological Sciences (General)
Sugar beet seed morphology, germination and heterotrophic growth
Table
Data were acquired on the phenotyping platform PHENOTIC https://www6.inra.fr/
phenotic/). The laboratory equipment on this platform includes:
- a tomograph NSI X-50 (North Star Imaging, Minnesota, USA)
- automated tools measuring germination (Multicam, ESEO, Angers, France)
- automated equipment measuring heterotrophic seedling growth (Eloncam, ESEO,
Angers, France)
- 3D image processing: using Avizo image processing modules where the scripts were
written in TCL scripting language (Ousterhout, TCL/TK) and MATLAB (The MathWorks,
Inc., Natick, Massachusetts, United States).
- 2D image processing of seed germination: using Fiji image processing package based
on ImageJ, http://fiji.sc/.
Raw
3D X-ray data were acquired on dry seeds. All the measurements for seed germination
and seedling heterotrophic growth were made at low temperatures, which is a main
limiting condition for sugar beet at these early stages.
Laboratory experiment on paper layers, non limiting water, low temperatures,
automated image acquisition for measurements on seeds and seedlings.
The genotype population was obtained from a cross between an elite commercial
genotype used in European sugar beet (Beta vulgaris L.) grown area and an exotic
accession of Beta vulgaris maritima from Denmark. Institution: Florimond Desprez; City/
v e
le; Country: France. Latitude and longitude for collected
Town/Region: Cappelle-en Pe
samples 50.5167; 3.1667
Repository name: URGI Plant and Fungi Dataverse
Data identification number: https://doi.org/10.15454/AKNF4Q
Direct URL to data: https://doi.org/10.15454/AKNF4Q
Value of the Data
Seed and seedling traits are increasingly measured in the field of ecology as simple traits that can be used to describe
species diversity. A deeper phenotyping of genetic diversity in crops is also necessary to better understand their tolerance
to varied environmental conditions and determine more precise breeding traits. Measurements on seed or seedling traits
are easier than on an adult plant. They can be correlated to more general adaptative traits to environmental conditions
[1,2]. This is the case, for instance, for the temperature or water potential responses [3] or traits of the young root system
[4,5].
Several methods were developed for the deep high throughput phenotyping of hundreds of genotypes from dry seed to
the end of heterotrophic growth [6]. This is a first example of automated measurements from dry seed to young seedling
carried out on a progeny (elite X exotic) of 198 sugar beet genotypes (plus 4 standards as references). Such set of data can
be reproduced for other species in order to characterize seed and seedlings in their early developmental stages.
The methods presented here can now be routinely used for the phenotyping of several thousands of genotypes and other
species on a platform accessible to users [7].
The dataset was collected in relation to the sugar beet crop ontology which was also created within the same project [8].
The data set provides the range of values measured for each variable on a large population of genotypes. The ontology
defines a general international framework to collect and compare variables' values. The range of values measured here
could be compared with measurements on other genotypes at intra- and inter-species level to determine the range of
variation of these traits' values and provide a better understanding of adaptation of genotypes or species to different agroor ecosystems [9,10].
S. Ducournau et al. / Data in brief 29 (2020) 105201
3
1. Data description
The dataset contains 28 variables measured for each genotype. Data is represented by three groups
of variables (Table 1) measured on a population of 198 sugar beet genotypes plus 4 standard commercial varieties. This data was obtained at high throughput on automated equipment. The first group
of variables describes the dry seed. An innovative tool (X-ray microtomography) was used to investigate the internal anatomy of sugar beet seeds. This technology generates high-resolution images of
internal seed structures. An automated image processing protocol was developed to extract data on
traits of interest. The obtained data provide precise measurements of volume, surface area and shape of
seed embryo, perisperm and seed coat. The second group describes seed germination speed measured
as mean germination time and time to reach 50% and 70% germination, and final germination rate at
three temperatures 5 C, 10 C and 20 C, obtained by automated acquisition and analyzes of images.
The third group describes individual seedling elongation rate measured 7 days after seed germination
at 10 C by automated image acquisition under inactinic lights. These variables are consistent with the
sugar beet international crop ontology.
2. Experimental design, materials, and methods
An exotic accession of Beta vulgaris maritima from Denmark was crossed with a sugar beet elite
pollinator (Beta vulgaris L.) from the Florimond Desprez company. Two successive backcrosses with
another elite pollinator, from the same seed company, were completed, leading to 198 individuals that
Table 1
Measured variables on sugar beet genotypes with the PHENOTIC platform equipments.
Phenotyping
device
Variable identification
code in the ontology
Variable name
Variable
Unit
Tomograph
CO_333:1000327
CO_333:1000324
CO_333:1000323
CO_333:1000389
CO_333:1000390
CO_333:1000320
CO_333:1000319
CO_333:1000391
CO_333:1000392
CO_333:1000325
CO_333:1000317
CO_333:1000393
CO_333:1000394
Germination at 5 C
CO_333:1000311
CO_333:1000330
CO_333:1000330
CO_333:1000388
CO_333:1000321
Germination at 10 C
CO_333:1000311
CO_333:1000330
CO_333:1000330
CO_333:1000321
Germination at 20 C
CO_333:1000311
CO_333:1000330
CO_333:1000330
CO_333:1000321
CO_333:1000386
CO_333:1000387
SeedMass
PerispVol
PerispSurfArea
PerispShapeVA3d
PerispSpDiameter
EmbVol
EmbSurfArea
EmbShapeVA3d
EmbSpDiameter
SeedCoatVol
CoatSurfArea
SeedCoatShapeVA3d
SeedCoatSpDiameter
Seed Mass
Perispersm Volume
Perispersm Surface Area
Perispersm Shape 3d
Perispersm Spherical Diameter
Embryo Volume
Embryo Surface Area
Embryo Shape 3d
Embryo Sperical Diameter
Seed Coat Volume
Seed Coat Surface Area
Seed coat Shape 3d
Seed coat Spherical Diameter
mg
mm3
mm2
No unit
mm
mm3
mm2
No unit
mm
mm3
mm2
No unit
mm
MGT 5 C
T50%
T70%
EGermRt 17 days at 5 C
GermFnlRt 28 days at 5 C
Mean Germination Time
Time To Reach 50% Of Germination
Time To Reach 70% Of Germination
Early germination rate
Germination Final Rate
h
h
h
h
%
MGT 10 C
T50% 10 C
T70% 10 C
GermFnlRt 15 days 10 C
Mean Germination Time
Time To Reach 50% Of Germination
Time To Reach 70% Of Germination
Germination Final Rate
h
h
h
%
MGT 20 C
T50% 20 C
T70% 20 C
GermFnlRt 6 days 20 C
HGerm 10 C
RadLg 10 C
Mean Germination Time
Time To Reach 50% Of Germination
Time To Reach 70% Of Germination
Germination Final Rate
Hour of germination at 10 C
Radicle length 7 days after
germination at 10 C
h
h
h
%
h
mm
Multicam for
Germination
ElonCam
4
S. Ducournau et al. / Data in brief 29 (2020) 105201
constituted the (elite X exotic) progeny. This population was completed by four commercial cultivars
considered as controls and thus a total of 202 genotypes were phenotyped. A set of 28 variables were
measured on the seeds of the 202 genotypes: 3D internal morphology of seeds, germination at several
temperatures and automated acquisition of heterotophic seedling growth traits. These measurements
were performed on a phenotyping platform PHENOTIC dedicated to instrumentation and imagery for
seeds, seedlings and plants. The platform's activities are dedicated to both researchers and professionals, for specific services and collaborative research projects. Table 1 presents the list of the
measured variables.
2.1. 3D dry seed measurements
Tomography was performed on 25 seeds per genotype. Images were obtained using an NSI X-50
manufactured by North Star Imaging (Minesotta, USA). The system has a focus tube with a focal spot up
to 1 mm, a flat-panel detector with a resolution of 256 x256 and an adjustable turntable. All seed
samples were scanned at a voltage of 45 kV and a current of 178 mA, a rotation step of 0.5 acquiring
1080 images of the transmitted signal after passing through the object.
The reconstructed data are represented by 2D images combined into one image stack and ordered
by their position in the z-axis of the imaged sample. The 3D volume reconstruction is obtained from
multiple cross-section 2D images of the object, using the software supplied with the machine. The
stack comprises (976 976 x 976) voxels in the (x, y, z) referential. The 3D image can either be visually
interpreted or analyzed using image processing tools in order to extract relevant information.
An automated image processing pipeline (IPP) was developed to extract sugar beet seed features
from raw data. This pipeline was developed using Avizo image processing modules where the scripts
were written in TCL scripting language (Ousterhout, TCL/TK) and MATLAB (The MathWorks, Inc.,
Natick, Massachusetts, United States). The IPP consists of two successive tasks of morphological operations. The first task was designed to individualize the sugar beet seeds in each image Ixyz into
separate objects where each scan has a fixed number of 25 seeds. Some pre-processing steps such as
noise removal and segmentation were applied to separate the voxels that correspond to sugar beet
seeds from the sample holders, and also to generate 3D binary masks Mxyz that represent only the
voxels that belong to the sugar beet seeds according to equation (1). The resulting set of masks was
obtained after the segmentation with a threshold t which was experimentally fixed.
The original image Ixyz was then masked using the generated set of binary masks Mxyz, as shown in
equation (2).
Mxyz ¼
0 if Ixyz t
1 if Ixyz > t
f Ixyz ; Mxyz ¼ i1;xyz ; i2;xyz …i25;xyz
(1)
(2)
The second task in the IPP aims to measure and estimate the traits and characteristics of the
principal structural components (embryo, perisperm and seed coat) of sugar beet seeds. The
mentioned regions of interest (ROI) of the seed's structure were segmented based on the differences in
intensities induced by X-ray attenuation (Fig. 1). The marker-controlled watershed segmentation was
then performed to separate seed's structure [11]. The markers used as inputs of the watershed operation were determined using the succession of morphological erosion and dilation of the binary image.
Once separated, several features for each individual structure were measured.
The image processing workflow comprises also a filtering step to eliminate morphologically distorted measurements corresponding to dead, malformed or empty seeds. Multiple analyzis and features extraction of the embryo, perisperm and seed coat were carried out on the scanned images like
estimating the volume, surface area, shape factor, filling factor and the spherical diameter. A total
number of 14 variables were extracted for each component of the seed. The outputs of the procedure
give directly a statistical summary (mean and standard deviation for 25 seeds) of each of the features.
S. Ducournau et al. / Data in brief 29 (2020) 105201
5
Fig. 1. Raw data and image segmentation results of a beet seed. (I,IV) 3D volume rendering showing the structural components of
the seed namely EMB for embryo, PER for perisperm, COA for seed coat, and C1 for internal empty space and with respectively
transverse (II, V) and longitudinal (III, VI) cross-section views. (bar ¼ 1mm).
The last version of the pipeline is a fully automated process that was intensively optimized and
improved compared to the previous versions to reach 32 minutes to perform the IPP with an estimated
phenotyping time of approximately 2.36 minutes per seed.
2.2. Germination at several temperatures
Germination time courses were recorded at 5, 10 and 20 C using PHENOTIC automated germination
tools [12]. Two pieces of equipment were used to phenotype seed germination at 5 C, with a
maximum of 1600 seeds each. Two other automated tools were used for 10 and 20 C seed germination
phenotyping. The temperature accuracy at the seedbed level was ±0.5 C. Two replicates of 25 seeds
were randomly sown on top of blotter (GE Heathcare 3644) continuously supplied with demineralised
water in a Jacobsen tank system. Seeds were germinated in the dark except when images were taken
every 2 hours (at 10 and 20 C) and every 4 hours (at 5 C) with less than 1 min of light. Seed
germination was detected automatically by image analyzis. The individual seed germination time was
determined by seed movement and radicle protrusion. The mean germination time [13] was calculated
for the data in each experiment as well as percentile germination parameters (time to reach 50% and
70% of germination, see Table 1) that were extracted from germination curves. Early counts after 17
days at 5 C was measured as an additional evaluation of seed vigor. Final germination rates were also
reported after 28, 15 and 6 days, respectively, at 5 C, 10 C and 20 C.
2.3. Heterotrophic seedling growth, radicle elongation
A growth chamber was specifically equipped for measurements of seedling heterotrophic growth. A
method commonly used for mimicking darkness for plants is to observe them under green light, as the
plant photoreceptors are assumed to be not activated under wave-lengths in the 515e550 nm range
and are sensitive only either to red/far red or blue wavelengths expositions respectively [14,15]. Green
LEDs, with narrow light spectrum in the range 515e550 nm were used to light the seeds or seedlings
during image acquisition. A color camera (Gigabit Ethernet camera Prosilica GC2450C, 5.0 Megapixel
camera with Sony ICX625 CCD sensor, 2448 2050 pixels) was used with a Fujinon C-Mount 5MP 2/300
16mm f/1.6 lens, allowing a resolution of 9pixels/mm at seed and seedlings locations. The image
6
S. Ducournau et al. / Data in brief 29 (2020) 105201
acquisition by this camera generally lasts 0.1 second and is synchronized with an on/off switch of light.
Images are taken at intervals chosen by the operator, starting at a given time after sowing, with a limit
of at least 1 h between two images. Interval between images were chosen according to the growth
temperature and to limit the number of light flashes received by seedlings. Two rails with 20 plastic
boxes (15 15 cm) loaded on each one with precisely determined positions, were available to grow
seedlings. The back of the boxes was covered with a blue paper saturated with demineralised water. In
each box, ten seeds were sown and covered with a second sheet of blue paper saturated with water, for
seed imbibition and also mechanical support to prevent seedlings from falling during their growth.
Thus, seedlings were grown almost vertically (with an inclination of 10 ) and follow their natural
gravitropism during their growth. A plate (30 30 cm) equipped with LED green lights and the camera
were arranged on a backlight mode, with the box placed between the camera and the LEDs. LEDs were
switched on in synchronization with image capture. Two cameras and plates of LEDs (one for each rail
with plastic boxes) moved together and precisely stopped at the 20 box positions to take images of
these boxes. The camera positioning was very precisely controlled, with one pixel precision, by the
software piloting image acquisition. The entire system was placed in a growth chamber, with
controlled temperature (range 10e30 C). Green channels of images were saved and analyzed at the
end of experiment with ImageJ software. The time for germination of each seed was determined as the
hour at which a radicle becomes visible on the images. Starting time from each individual seed
germination, individual seedling length of each organ (shoot and radicle) can then be measured and
elongation curves can be drawn for each seedling, starting from its own germination time. The different
seedlings parts were separated by visual inspection based on changes in grey levels and/or angles
between organs. The possibility to look at several successive images also helps in separating plant
parts. For the experiment described here, temperature was 10 C. Images were taken every 4 h for 21
days, leading to a total of 20,160 images. The germination time of each seed was determined (Hgerm)
and measurement of the seedling radicle length (RadLg) was performed exactly seven days (148 hours)
after individual seed germination time.
Acknowledgments
This work received support from the French Government supervised by the “Agence Nationale de la
Recherche” in the framework of the program Investissements d’Avenir under Reference ANR-11-BTBR0007 (AKER program).
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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