Download Image Analysis Workshop booklet - Centre for Plant Integrative Biology

International Workshop on Image Analysis Methods for
Plant Science
University of Nottingham
Jubilee Campus
September 6th 2012
Acknowledgements
Welcome to the first International Workshop on Image Analysis Methods for Plant
Science.
Interest in automatic image analysis has increased significantly within the plant sciences in recent years,
due to the emergence of the systems approach to biological research and an increasing awareness that
quantitative measurement of the phenotype has fallen behind understanding of the genotype. Fully and
semi-automatic image analysis methods and tools have been developed to assess properties of plant roots,
shoots, leaves, and seeds using a variety of imaging modalities. There is clear evidence of an emerging plant
image analysis community working within the wider plant sciences to develop the required image analysis
techniques. This community is, however, distributed across widely separated and disparate plant science
research groups.
The aim of the workshop is to bring those developing image analysis techniques and tools for use in plant
science together to discuss both promising methods and remaining problems. We hope you will find the
day enjoyable, interesting and productive.
This event would not have been possible without the award of an International Workshop grant from the
UK Biotechnology and Biological Science Research Council (BBSRC) and the generous support of our
sponsors:
-
The European Plant Phenotyping Network (http://www.plant-phenotyping-network.eu/)
The Centre for Plant Integrative Biology (www.cpib.ac.uk)
Syngenta
Stemmer Imaging
Lemnatec
UK Plant Phenomics Network
We would like to thank them all.
Tony Pridmore
Susannah Lydon
Hannah Dee
Andrew French
International Workshop on Image Analysis Methods for Plant Science
University of Nottingham
Programme
9.00
Welcome: Tony Pridmore
9.10-10.45 Session 1: Plant Phenotyping (sponsored by EPPN) - Chair: Roland Pieruschka
9.10
Prof Edgar Spalding, University of Wisconsin, Automated phenotyping of root behavior in
mutant and naturally varying populations
9.55
Dr Norbert Kirchgessner, ETH Zurich, Image processing in field crop phenotyping
10.20
Dr Alexander Bucksch, Georgia Institute of Technology, Automatic plant structure analysis in
the field
10.45-11.15 Coffee
11.15- 12.30 Session 2: Cells and Tissues - Chair: Andrew French
11.15
Prof Christophe Godin, INRIA, Mars-Alt v2: toward a robust imaging platform for studying
multicellular systems
11.40
Dr Michael Pound, University of Nottingham, Extraction and analysis of structured networks of
plant cells from confocal images
12.05
Dr Pedro Quelhas, Instituto de Engenharia Biomedica Porto, Local interest point detectors for
cell detection in digital microscopy images
12.30 - 2.00 Lunch and Posters
2.00 – 3.40 Session 3: Seedlings to Crops - Chair: Hannah Dee
2.00
Dr Rick van de Zedde, Wageningen UR, Rapid seedling reconstruction in 3D and other vision
research projects in the Agrifood industry
2.25
Dr Stijn Dhondt, VIB - Plant Systems Biology, Imaging Arabidopsis shoots: from rosette to cell
2.50
Dr Mark Mueller-Linow, Research Center Juelich, 3D reconstruction of plant canopies –
parameterization, registration and further image analysis of stereo images of sugar beet and
barley
3.15 – 3.45 Tea
3.45 – 5.00 Session 4: Roots - Chair: Tony Pridmore
3.45
Mr Guillaume Lobet, Université catholique de Louvain, SmartRoot: A novel image analysis
toolbox enabling quantitative analysis of root system architecture
4.10
Mr Randy Clark, Cornell University, 2-dimensional and 3-dimensional phenotyping platforms to
facilitate genetic mapping of root traits
4.35
Dr Christopher Topp, Duke University, High-throughput 3D imaging and quantitative genetic
analysis of root system architecture
5.00
Close
Abstracts - Talks
1. Automated phenotyping of root behavior in mutant and naturally varying populations
Prof Edgar Spalding, University of Wisconsin
Plant biologists are justified in pursuing a comprehensive mapping of genotype to phenotype because the
goal is important and the genomic half of the equation in some reference systems is highly developed.
However, methodologies for quantifying phenotypes with the same precision and throughput are needed if
the desired map is to be attained. Unlike genotypes, phenotypes continuously change during development
and differently in different environmental conditions. Automated image acquisition and feature extraction
(machine vision) is increasingly seen as a method for quantitatively describing the 'phenotype space' as
required for producing the ultimate genotype-to-phenotype description. In this presentation, I will describe
our efforts to quantify time-dependent phenotypes displayed by Arabidopsis and maize seedling roots in
genetically structured populations.
I will emphasize technical challenges resulting from the age-old tension between throughput and
resolution, and from the newer problem of coupling automated image acquisition to the computational
resources needed for analysis and subsequent statistical genetic modeling. One example of a completed
study will show how a subtle phenotype resulting from mutation of a glutamate receptor-like gene could be
computationally discovered and described. Another will show how the time axis can be added to a
quantitative trait locus (QTL) map of root gravitropism as a result of the machine vision approach.
2. Image processing in field crop phenotyping
Dr Norbert Kirchgessner, Prof Achim Walter, ETH Zurich
In the crop science group at the Institute of Agricultural Sciences, ETH Zürich, we seek to provide innovative
pathways to identify and generate more versatile and efficiency-oriented crop production systems. Imaging
procedures acting at different spatial and temporal scales are core elements of the toolbox of our
interdisciplinary team. In climate chambers, greenhouses and on field sites, we will apply visual, nearinfrared and thermal imaging to quantify shoot and root architecture, dynamic and short-term growth
processes as well as photosynthesis, gas exchange and compound composition of major and alternative
crops alike. These 'phenotyping' analyses, in concert with a range of approaches from plant ecophysiology,
breeding and molecular analysis, will help elucidating differences between plant genotypes. Moreover they
will facilitate the optimization of crop production systems to regionally differing ecological niches or to an
altering climate. Most importantly, these phenotyping approaches will facilitate an improved
understanding of basic rules governing plant-environment-management interactions. This in turn is a
necessary prerequisite to ameliorate the knowledge transfer between lab and field as well as between
plant biology and agricultural sciences, thereby allowing for improved agricultural plants and practices in
the future.
Crucial to a refined understanding of plant performance in the field is a more detailed understanding of
dynamic growth processes. In the past, a focal point of some of our team members was hence the
refinement and application of optical-flow-based growth analysis techniques, which also required the
establishment of physiologial coordinate systems of plant organs. In the future, a focus of our lab
phenotyping activities will be put on non-destructive growth analyses of roots via CT. On our experimental
field site, we will establish a rigged camera system (field phenotyping platform FIP) equipped with multiple
sensors.
3. Automatic plant structure analysis in the field
Dr Alexander Bucksch, Georgia Institute of Technology
Quantitative description of branching structures in plants is important for creating and validating
mathematical growth models, estimating the plants interactions with the environment and to describe
phenotypic traits of the plants. An additional challenge arises when the extraction of such branching
structures is carried out under field conditions. 2D digital photography and emerging 3D technologies like
terrestrial laser scanning enable us to capture the branching hierarchy in a short time. However, there are
many challenges to decipher the resulting 2D or 3D data and to extract the captured branching structure.
We give 2 examples of automatic parameter extraction under field conditions:
1.) Tree canopies measured with terrestrial laser scanners and represented as 3D point clouds. We analyze
the point clouds by means of optimal network theory.
2.) Bean roots represented as a 2D digital photograph to extract phenotyping parameters in the field
Both examples rely on (distinct) skeletonization algorithms to represent and analyze the branching
structure. We validate both methods against manually collected field data. For the tree canopies the
branching hierarchy and branch lengths were measured in the field. Based on these two quantities we
discriminate two growing conditions for a test set of six trees, by analyzing the side-branch-statistics and
the internode length. In case of the roots we followed the Shovelomics protocol for manual root
phenotyping in the field. We extracted basal root number and angles from the 2D images as typical
parameters for this protocol.
4. Mars-Alt v2: toward a robust imaging platform for studying multicellular systems
Prof Christophe Godin, INRIA
MARS-ALT is a pipeline of algorithms that has been recently developed to segment cells and to track cell
lineages in multicellular organisms observed with laser microscopy. In this talk I will present the recent
developments made to improve the robustness of the system in addressing a greater number of biological
systems, using different laser imaging protocols, making the system available to a wider number of users
and developers.
5. Extraction and analysis of structured networks of plant cells from confocal images
Dr Michael Pound, University of Nottingham
It is increasingly important in life sciences that many cell-scale and tissue-scale measurements are
quantified from confocal microscope images. However, extracting and analyzing large-scale confocal image
data sets represents a major bottleneck for researchers. To aid this process, CellSeT software has been
developed, which utilizes tissue-scale structure to help segment individual cells. We provide examples of
how the CellSeT software can be used to quantify fluorescence of hormone-responsive nuclear reporters,
determine membrane protein polarity, extract cell and tissue geometry for use in later modeling, and take
many additional biologically relevant measures using an extensible plug-in toolset. Application of CellSeT
promises to remove subjectivity from the resulting data sets and facilitate higher-throughput, quantitative
approaches to plant cell research
6. Local interest point detectors for cell detection in digital microscopy images
Dr Pedro Quelhas, Instituto de Engenharia Biomedica Porto
Automatic image analysis approaches used in microscopy cell image analysis have enabled the objective
analysis of large scale biology research image data, removing a large workload from the biology researcher.
However, most of the approaches used in this automated analysis systems are based on classic automatic
image segmentation, which is neither trivial to apply nor robust to image quality variations.
A way to analyze digital microscopy images with increased robustness and performance is through the use
of local interest point detectors. This has gained recent popularity due to the performance in high noise low
contrast situations where automatic image segmentation fails. Local interest point detectors are designed
to have high response in specific locations in the image where certain shapes are visible. By relying in prior
knowledge of the shape of cells in microscopy images under analysis their application for this task becomes
trivial. Additionally, the parameters needed in these methods are much more intuitive as they relate
directly to the shape and size of the cells.
7. Rapid seedling reconstruction in 3D and other vision research projects in the Agrifood industry
Dr Rick van de Zedde, Wageningen UR
Rick van de Zedde is a senior scientist at Wageningen UR in The Netherlands, with an M. Sc degree in
Artificial Intelligence. His specialism is computer vision/ robotics. He is the projectmanager of several
computer vision related research projects, and is the coordinator of the centre of expertise for computer
vision in Wageningen UR – GreenVision (greenvision.wur.nl). Together with various machine builders,
Wageningen UR - GreenVision is developing novel sensor modules for diverse machine-automation
solutions in the Agrifood industry.
Several research projects will be presented. One interesting project is about automating the manual
grading of seedlings at nurseries. Very labour-intensive, time consuming and expensive. We have
developed an automated system that uses 3D-vision techniques to grade seedlings according to their
quality. In our system the 3D plant model is created based on the information of 10 cameras. The current
version processes 18.500 seedlings on a single line conveyer belt, the software reconstructs 3D model
within 30ms.
8. Imaging Arabidopsis shoots: from rosette to cell
Dr Stijn Dhondt, Dirk Inzé, VIB - Plant Systems Biology
Phenotyping is widely recognized as the most laborious and technically challenging part in the process of
understanding the genomic code and implementing this knowledge towards applications, making it costly
and time consuming. Nevertheless, this ‘phenotyping bottleneck’ can now be addressed by combining
novel image capturing technologies, robotics, image analysis, and data integration. We developed a series
of imaging setups and data analysis pipelines to increase the throughput of a number of phenotypic
studies, both on the organismal, organ, and cellular level. To cover these different levels, our image analysis
algorithms handle a variety of input images. Visible and NIR imaging is applied to extract rosette growth
rates, dark field imaging is employed to segment venation patterns in leaves, and differential interference
contrast imaging is utilized in a proof of concept to extract the cellular content of the leaf's epidermis.
Furthermore, we investigate the possibility to use X-ray computed tomography to study overall plant
morphology and cellular organization in three dimensions. Such imaging tools can ensure a fast and precise
phenotypic description of biological structures, limiting laborious, costly, and often repetitive manual
intervention. Furthermore, in comparison to manual screening experiments, often more phenotypic traits
are recorded, increasing the throughput and output of phenotypic analyses.
9. 3D reconstruction of plant canopies – parameterization, registration and further image analysis of
stereo images of sugar beet and barley
Dr Mark Mueller-Linow, Research Center Juelich
Three-dimensional canopies form complex architectures with spatially and temporally changing
distributions of leaf orientations which serve as important indicators for canopy function and plant state.
The 3-d reconstruction from stereo images - taken by ordinary SLR-cameras - is a first step to analyze these
structural properties. Our methodology comprises several automated steps including camera calibration,
image rectification, area of interest selection, correlation-based block methods to solve the
correspondence problem and the implementation of several filters for image post-processing, e.g. the
detection of occlusions, outliers and non-plant background. Further image analysis of the 3-d stereos uses
image segmentation techniques as a prerequisite in order to derive the leaf angle distribution at the leaf
and at the pixel level. These imaging processing techniques have been applied on stereo images of sugar
beet and barley which have been recorded at our outdoor research site in Klein-Altendorf (University of
Bonn). At the same time hyperspectral images were co-registered for later matching and parametrization
of hyperspectral data by the 3-d canopy structure . The methodology will be overviewed and first results on
sugar beet and barley will be given.
10. SmartRoot: A novel image analysis toolbox enabling quantitative analysis of root system architecture
Mr Guillaume Lobet, Université catholique de Louvain
SmartRoot is a novel, semi-automated image analysis software to stream- line the quantitative analysis of
root growth and architecture of complex root systems. The software combines a vectorial representation of
root objects with a powerful tracing algorithm which accommodates to a wide range of image sources and
quality.
The root system is treated as a collection of roots (possibly connected) that are individually represented as
parsimonious sets of connected segments. Pixel coordinates and grey level are therefore turned into
intuitive biological attributes such as segment diameter and orientation, distance to any other segment or
topological position. As a consequence, user interaction and data analysis directly operate on biological
entities (roots) and are not hampered by the spatially discrete, pixel-based nature of the original image.
The software supports a sampling-based analysis of root system images, in which detailed information is
collected on a limited number of roots selected by the user according to speciifc research requirements.
SmartRoot, is an operating system independent freeware based on ImageJ and relies on cross-platform
standards for communication with data analysis softwares.
See Figure 1 below.
11. 2-dimensional and 3-dimensional phenotyping platforms to facilitate genetic mapping of root traits
Mr Randy Clark, Cornell University
The efficient quantification of root traits remains a critical factor in the effective utilization of genomic
resources during the study of root system development and function. For our research, two highthroughput 2-dimensional (2D) and 3-dimensional (3D) root phenotyping platforms were developed to take
advantage of publicly available germplasm and genotypic information. These platforms take advantage of
modular growth and imaging designs that are adaptable to a wide range root phenotyping studies using
diverse growth systems (hydroponics, paper pouches, gel and soil) involving several plant species,
including, but not limited to rice, maize, sorghum, tomato and Arabidopsis. Two complementary software
tools (RootReader2D and RootReader3D) were developed to process and analyze 2D and 3D root images
and were designed with both user-guided and automated features that increase flexibility and enhance
efficiency when selecting and measuring root growth traits from specific roots or entire root systems during
large-scale phenotyping studies. Using our phenotyping tools in combination with publically available
germplasm and genetic resources, we are beginning to explore and identify genomic regions that relate to
root system architecture (RSA) characteristics in rice (Oryza sativa) through linkage and association
mapping studies.
See Figure 2 below
12. High-throughput 3D imaging and quantitative genetic analysis of root system architecture
Christopher N Topp; Anjali S Iyer-Pascuzzi; Jill T Anderson; Cheng-Ruei Lee; Paul R Zurek; Olga Symonova;
Ying Zheng; Alexander Bucksch; Yuriy Milyeko; Taras Galkovskyi; Brad Moore; John Harer; Herbert
Edelsbrunner; Thomas Mitchell Olds; Joshua S Weitz; Philip N Benfey, Duke University
Root system architecture (RSA) describes the spatial organization of the root system, which is critical to
plant productivity in challenging environments, such as saline, dry, acidic and otherwise marginal soils.
Modern genomics would allow us to exploit both natural and engineered variation for breeding more
efficient crops, but the lack of parallel advances in plant phenomics is widely considered to be a primary
hindrance to developing ‘next-generation’ agriculture. Root imaging and analysis has been particularly
intractable, as decades of phenotyping efforts have failed to identify even one gene controlling a
quantitative RSA trait in a crop species. Thus, innovations in the ability to take high-throughput and
accurate measurements of RSA through time are required. We demonstrate here the use of a semiautomated 3D imaging and phenotyping system to reveal the genetic basis of root architecture in rice. The
integrated system leverages prior advances by our group and our collaborators in the areas of hardware,
imaging, software and analysis. We combined these methods to automatically reconstruct and phenotype a
well-studied rice mapping population during four days of growth in a gellan gum media. We identified 99
QTL at fourteen hotspots for a suite of 29 RSA traits that control the extent, shape, distribution, and surface
size of root networks. Several hotspots correspond to major QTLs previously identified under field and
greenhouse conditions, while several others are novel. We developed a multivariate-composite QTL
approach to home in on central RSA phenotypes and identify five large effect QTL (25-37%) that control
multiple root traits. The combination of our multi-trait phenotyping with high throughput sequencing
technologies will be a powerful approach to cloning and characterizing genes of importance to plant
productivity. We will also discuss efforts to automatically capture and quantify the growth of individual
roots in 3D. These data will help us to understand how local growth decisions contribute to the emergent
properties of root networks, as well as the fine-scale response of root systems to environmental challenges.
See Figure 3 below
Figures for talk abstracts
Fig 1: Root tracing with SmartRoot
Fig 2: Depiction of the 3-dimensional imaging platform and a reconstructed rice root system with labeled
root system components.
Fig 3: Sixteen day old rice lines imaged in gellan gum and accompanying 3D reconstructions used for root
architecture QTL analysis.
Abstracts - Posters
1. A high-throughput root phenotyping (HTP) system for Brassica rapa
Michael Adu1,2*, Malcolm Bennett2, Philip White1, Lionel Dupuy1, Martin Broadley2
1 The James Hutton Institute, Invergowrie, Dundee UK;
2 School of Biosciences, University of Nottingham, Sutton Bonington, UK
*E-mail: [email protected] Address: The James Hutton Institute, Invergowrie, Dundee, DD2 5DA
Scotland UK.
Abstract
To reduce phosphorus (P) fertilizer input, the P use efficiency of crops (PUE) must be improved. PUE is the
product of root P-uptake efficiency (PUpE) and physiological P-utilisation efficiency (PUtE). Differences
between crop genotypes in their yield responses to P fertilization are often correlated to PUpE but not
PUtE. Altering root system architecture (RSA) through breeding could improve PUpE. RSAs are however
difficult to quantify. Emerging 2D and 3D imaging are promising techniques, but still have limitations due to
cost, reproducibility and throughput. Here, we present the development and data from a low cost, highthroughput 2D root imaging system. At current capacity, the phenotyping system has been developed for
recordings of root growth of up to 72 seedlings for 14d, with high spatial and temporal resolution. Data
from the system indicated significant variation in root traits between Brassica rapa genotypes and inbred
lines and that total and primary roots of 14d old seedlings follow a non-linear growth function. We also
used the system to investigate root extension profiles of different B rapa genotypes in various [P]ext
growth conditions and observed an increase in lateral rooting at low [P]ext. The phenotyping system will
assist efforts aimed at breeding for varieties with enhanced P use efficiency.
2. Investigating protein dynamics in the Arabidopsis mapk4 pathway by means of fluorescent protein
tagging c
Monica Agarwal
Heriot-Watt University
[email protected]
Abstract
The Arabidopsis signal transduction pathway composed of MEKK1, MKK1/MKK2 and MPK4 is a key
regulator of basal resistance, systemic acquired resistance and abiotic stress resistance. This pathway has
been well studied at the genetic and biochemical level, but relatively little is known about the dynamics and
interactions of the proteins within the cell, and what differentiates a biotic from an abiotic response in
terms of protein localization and interaction. We present our experimental approach and data obtained by
observing fluorescently tagged proteins in vivo. The four genes comprising the pathway have been fused at
N or C terminal with derivatives of GFP (mcherry, YFP, GFP) and cloned into binary vectors. Agrobacterium
tumefaciens has been used to carry out both transient and stable transformations of Arabidopsis, and the
intracellular location of the proteins before and after biotic or abiotic stress has been determined by
confocal microscopy using advanced techniques such as FLIM. Cellular organelles have also been identified
by means of specific dyes such as Draq5 nuclear stain. Protein-protein interaction will also be investigated
in plants bearing multiple transgenes with complementary excitation and emission wavelengths by means
of FRET analysis. These experiments illuminate the complexities of MAP Kinase signalling in plants.
3. A three-dimensional modelling approach for data integration and statistical analysis of spatial
distributions in cellular imaging
Philippe Andrey
INRA
[email protected]
Abstract
Protein intracellular localization and trafficking is essential to many biological processes in plant cells.
Cellular imaging techniques such as confocal microscopy are tools of choice to visualize within the three-
dimensional (3D) cellular space the distribution of vesicles transporting labelled proteins. However, the
comparison and quantitative analysis of spatial distributions of vesicles in different physiological, biological
and experimental conditions requires integrating imaging data from different cells into common coordinate
frames and representations. This represents a challenging task because of the morphological fluctuations
across individual cells. We present here the spatial modelling strategy we have designed to address this
issue. We describe the algorithmic suite we have developed for reconstructing graphical 3D models from
image stacks, for registering and averaging individual models, for spatially normalizing individual data into
average models and for generating and comparing statistical 3D density maps. We also present the latest
version of Free-D (http://free-d.versailles.inra.fr), an integrated, multi-platform, and freely distributed 3D
modelling software we develop to make these methods practical and available for biologist users. The
methodology is illustrated here with the spatial normalization and averaging of imaging data over more
than 100 root cells in Arabidopsis thaliana. Since they are generic, the described methodology and
software are of interest for quantitative imaging studies in plant science, and more generally for statistically
analysing the spatial distributions of punctuate structures at multiple scales (cells, vesicles, etc.) in
biological systems.
4. Sorting of the immune receptor FLAGELLIN SENSING 2 to different endosomal compartments is
depending on its activation status
Martina Beck
The Sainsbury Laboratory
[email protected]
Abstract
Perception of pathogen-associated molecular pattern (PAMPs) at the cell surface is the first line of defence
in plant immunity. FLAGELLIN SENSING 2 (FLS2), the receptor for bacterial flagellin (flg22), resides at the
plasma membrane and undergoes ligand-dependent internalization into vesicles within minutes (Robatzek,
2006). The nature of these FLS2 containing vesicles, their formation and their roles in plant immunity still
remains poorly understood. To resolve FLS2 trafficking, we exploited quantitative confocal imaging for colocalisation studies and chemical interference. We developed EndomembraneQuantifier and
EndomembranetCoLocQuantifier, two algorithms and software implementations for quantifying and
identifying co-localised spot-like objects.This high-throughput imaging system coupled with the
development of fully automated images analyses procedures allowed us to quantitatively dissect the
endocytic route of FLS2 over time.
In this study we defined the endocytic trafficking pathway of the A. thaliana FLS2 receptor in the absence of
flg22 ligand and upon flg22-induced activation.FLS2 localises to bona-fide endosomes via two distinct
endocytic trafficking routes depending on its activation status: FLS2 receptors constitutively recycle in a
Brefeldin A (BFA)-sensitive manner while flg22-activated receptors traffic via ARA7/Rab F2b- and ARA6/Rab
F1-positive endosomes, insensitive to BFA. In addition, flg22-induced FLS2 endosomal numbers increased
by Concanamycin A (ConcA) treatment but reduced by Wortmannin (Wm) indicating that activated FLS2
receptors are targeted to late endosomal compartments.
Our data revealed the endocytic pathway of FLS2 in an activation-state and time-dependent manner, and
demonstrates the usefulness of quantitative high throughput confocal imaging and computational
algorithms for deciphering trafficking of membrane compartments.
5. Modelling colour variation in Arabidopsis rosettes with a low-cost capture device
Hannah Dee
Aberystwyth University
[email protected]
Abstract
This poster describes preliminary results on the characterisation of colour variation in Arabidopsis over
time using consumer-grade imaging hardware. Two varieties of Arabidopsis (one wild-type, one mutant)
were planted in a chessboard pattern in plant trays. These trays were then placed in a controlled growing
environment, and a standard consumer webcam (Logitech) was positioned above the trays, connected to a
low-spec Windows desktop computer. Freeware video-surveillance software was used to capture an image
from the webcam every 15 minutes. The capture ran for just over three months, recording the plants from
seedling stage through to flowering. The challenges in handling this dataset are many: plant trays are
occasionally moved (when watered); the camera is uncalibrated; the images are jpg files and therefore
already compressed; colour is influenced by artificial lighting; and with the later images in the dataset there
are significant occlusion problems as the plants spill over the edges of their tray cells and overlap.
Nonetheless, we are able to segment the plants from the background in the first month of growth
(approximately 2,000 images), and therefore begin to characterise them in terms of size, shape and colour.
Each image is 1600x1200 pixels, and contains around 20 individual plants of each type. We have handsegmented ground truth images, in groups of 10 images at various points during the growing phase, and
these are used in a 9:1 training:testing split to build statistical models of the colour of the plants as they
grow. This poster will show that even with uncalibrated, compressed images, in artificial light, we can
model variation in colour during active growth. To demonstrate that we can relate this to the underlying
genetics, we also show preliminary results on differentiation between mutant and wild-type based on
colour features.
6. Image processing software for the analysis of the meristem
Manuel Guillermo Forero Vargas
Cardiff University
[email protected]
Abstract
We present a suite of new tools developed for the analysis of the meristem, and also useful for the analysis
of root. They allows to extract several parameters to be extracted from 2D slices and projections, requiring
only one single angle acquisition. Segmentation of cells is automated, but an additional new tool allows
false edges to be correct by hand. Using the segmented image cell areas can be extracted and other
parameters such as perimeter, eccentricity, circularity, statistical moments can easily be calculated. An
additional program has been written to allow the intensity of a second fluorophore to be measured within
the detected boundaries of the cells. As an extension of the 2D method, an improved 3D method has being
developed in order to segment and analyze single view samples.
7. A segmentation procedure using colour features applied to images of Arabidopsis thaliana
Ruben Ispiryan* Anton R. Schäffner and Igor Grigoriev
Helmholtz Zentrum München - German Research Centre for Environmental Health
Department of Scientific Computing, Institute of Biochemical Plant Pathology.
* Corresponding author. [email protected], Tel.: +49 89 3187 1226
Abstract
In studies of environmental effects on plant growth the images of plants are being used as non-destructive
measurements in phenotyping. In this work a computational procedure has been developed to segment
images of plants allowing an improved separation of different parts of plants and background. The
proposed procedure is based on colour analysis and image morphology. The RGB values are being
transformed to a colour space as ratios of R, G and B vs. the sum of R, B, and G channels. We introduce an
approach to render the training set of pixels on an Excel two-dimensional graph and a technique to
determine regions of pixel classes. The important feature of the regions, being used are discriminates, is
that they may have curved boundaries providing a flexible way of separation even into more than two
classes. We demonstrate that such a pixel classification followed by a series of morphological operations
provides a robust technique to be applied in segmentation of plants. The procedure is applied on images of
Arabidopsis thaliana and geometrical features such as area and solidity are extracted.
8. Physical localization of 1.5kb TaSTOP1 loci in bread wheat (Triticum aestivum L.) using Tyr-FISH
technique
Ana Luisa Oliveira
Trás os Montes and Alto Douro University, Portugal
[email protected]
Abstract
Physical mapping of agronomically important genes shown to be a powerful tool in genetics and plant
breeding that could be used in crop improvement programme. Through in situ hybridization it’s possible to
detect small DNA sequences that enable its chromosomal recognition. Several genes are engaged in the
control of Al3+ resistance, most shown, until now, belonging to ALMT and MATE families. By mutational
analysis in Arabidopsis, a transcription factor namely zinc finger family protein member STOP1 is found to
control the expression of wide range of genes involved in Al3+ tolerance mechanisms. Recently, we isolated
the STOP gene corresponding to distinct genomes (AA, BB and DD) in bread wheat. In present investigation,
physical localization of TaSTOP1 was performed by in situ hybridization. Probe was prepared by PCR
amplification of the 1.5 kb genomic region of TaSTOP1 from ‘Barbela 7/72/92’ wheat genotype.
Chromosome spreads were carried from the root tips of ‘Barbela 7/72/92’ seedlings at mitotic metaphase,
followed by probe labelling and hybridization mixture. Detection of hybridization signals was carried out
using the Tyramide Signal Amplification Kit. To identify wheat chromosomes with positive signals, samples
were re-hybridized using the pAS1 repetitive sequence and GAA-satellite sequence as probes. Individual
slides were observed under a Nikon Eclipse 80i microscope (Nikon Instruments Europe BV, UK). Images
were captured with a Nikon CCD camera using the appropriate Nikon 3.0 software and processed with
Photoshop 4.0 software. In conclusion, our results confirmed that TaSTOP1 is localized on long arm of
homoeologous chromosome group 3 (3AL, 3BL and 3DL) in bread wheat (Figure 1 A & B).
9. Measuring and modelling Miscanthus using lasers
Rokas Zmuidzinavicius1, Hannah Dee1, Mark Neal1, Paul Robson2
1 Department of Computer Science, Aberystwyth University, Aberystwyth, CEREDIGION. SY23 3DB, UK
2 Institute of Biological Environmental & Rural Sciences, Aberystwyth University, Plas Gogerddan,
Aberystwyth, CEREDIGION. SY23 3EE, UK.
[email protected]
Abstract
Miscanthus plants are grown for the production of bioenergy due to rapid biomass accumulation and good
net energy production. Harvested yield comprises all above ground biomass and therefore yield is strongly
correlated with above ground morphology, through direct (e.g. stem thickness and stem number) and
indirect (e.g. leaf area for light capture) mechanisms. Therefore to maximise yield in this crop accurate
above ground assessments of plant development are required. In order to acquire quality data when
monitoring growth of Miscanthus crops, we hypothesise that it will be useful to incorporate laser scanner
technology. Current techniques for modelling and measuring plant growth involve accurate but labourintensive measurements. Laser scanning technology could be especially useful when high frequency
monitoring is required for large areas. We describe a laser scanning study of a replicated field trial with 324
Miscanthus plants scanned throughout one season. Laser scans were compared with conventional
measurements on training and testing subsets. For laser-based Miscanthus surveillance a whole field was
scanned from a number of positions, using a static terrestrial laser scanner Leica HDS6200 to capture pointcloud data. Multiple scans were registered using Leica Cyclone software to give a single point cloud for the
entire field, incorporating scans from multiple viewpoints; this is necessary to counteract the effect of
occlusions within the field. The point cloud was then segmented spatially into individual plants. Further
algorithms were applied on each plant's point-cloud to retrieve canopy height and volume. Preliminary
results on the estimation of biomass and segmentation of number of stems will be described. Biomass is of
particular interest as this is a property that it is very hard to measure in a non-destructive fashion using
traditional methods. We will also discuss various issues associated with field-based imaging of large plants.
10. Improving high-throughput 2D and 3D phenotyping of complete crop plants with state-of-the-art
computer vision techniques
Stefan Schwartz
LemnaTec GmbH
[email protected]
Abstract
High-throughput plant phenotyping requires an automated, non-destructive system that extracts desirable
biological and agronomical traits from high numbers of screened plants. This text presents three different
state-of-the-art techniques and explains their value for plant phenotyping, particularly for separating
individual plant organs, with the example of corn. The approaches can also be transferred to other crops
and used for multi- and hyper-spectral imaging.
AdaBoost is a supervised machine-learning algorithm that combines several weak classifiers into a single
strong classifier. This way, a weak classifier is only required to be better than random guessing, being a fast
and simple classifier. It can be proven that HaarLike Feature in combination with AdaBoost can also be used
to detect the leaf edges or overlapping leaves of various plants (e.g. arabidopsis, corn), and thus to
separate and acquire information for individual plant organs.
Space carving is a technique that creates a precise 3D model by using multiple images of the same object
taken with one or more cameras. To do this, a photo-consistency check is performed for each pixel
contained in the captured images. Only eight images are sufficient to create a full 3D model of an entire
plant. The resulting model also contains the spectral information of all underlying frequencies (VIS, NIR, IR,
fluorescence or hyperspectral) present in the datasets.
Thinning out the segmented plant will eventually result in a skeletonized image of the organism. Extracting
information for individual leaves such as leaf length and angle is very simple once the skeleton of the plant
is computed.
If a high imaging throughput is the main aim, it is sufficient to take only two or three images to calculate
the approximate organ sizes of segmented plants. In a first step, the plant is segmented into individual
images by using a colour threshold or a more advanced technique (e.g. Randomwalker). The next step is to
determine the leaf edges by means of the proposed algorithm and to use the skeleton of the plant to
determine length angles, projected sizes and additional spectral and shape parameters.
The methods applied here need a minimum of parameterizing and are suitable for a wide range of plants,
from small, almost 2D Arabidopsis rosettes to mature corn plants, as well as cereals or dicot plants like
tomato, soybean or cotton. This flexibility allows the integration in high-throughput plant phenotyping
systems.
11. Phenotypic analysis of root system development in Hordeum vulgare L. mutants for the identification
of new cell cycle associated genes involved in plant root morphogenesis
Michal Slota*, Miroslaw Maluszynski, Iwona Szarejko (Department of Genetics, Faculty of Biology and
Environmental Protection, University of Silesia; Poland)
[email protected]
Abstract
The principal aim of the project is to reach more comprehensive understanding of the cell cycle genes
involvement in the root system development in monocots and perform the identification of new alleles of
genes determining the root morphology. Spring barley (Hordeum vulgare L.) is a model species selected for
the research. During the study it will be used for the detection of new mutations and functional analyses of
target genes. In the course of initial bioinformatics analysis several genes involved in cell cycle progression
involved in parallel in the root system morphogenesis were identified.Selected genes analysis in terms of
single-nucleotide mutations with the use of TILLING (Targeting Induced Local lesions IN Genomes) strategy
has been intended. The identification of mutations within analysed genes will be carried on the genetic
material of plants originating from the HorTILLUS population obtained in the Department of Genetics,
University of Silesia after the sodium azide (NaN3) and consisting N-methyl-N-nitrosourea (MNU) combined
mutagenic treatment. Obtained lines will be subjected to detailed molecular and phenotypic analyses to
assess the particular influence of mutations on root system development.Designed research will require an
optimization of a root architecture phenotyping method adequate for accurate and efficient analysis.
Subsequently derived mutant lines with present changes in the root phenotype will be subjected to a
further detailed analysis over the experiment conducted with the use of 50 cm acrylic tubes filled with
vermiculite or soda lime glass-beads alternatively. After about a 2-week growth period plants will be
removed from the tubes, washed, separated and scanned using a specially designed scanners. WinRHIZO
image analysis system will be applied to obtain general results. Desirable root traits will comprise of
seminal root growth dynamics alteration, the degree of root branching and a number of formed lateral
roots.
12. Towards Automated Large-Scale Plant Phenotyping by Image Analysis
Y. Song*1, C.A. Glasbey1, G.W. Horgan1, G. Polder2, J.A. Dieleman2, G.W.A.M. van der Heijden2
1 Biomathematics and Statistics Scotland, United Kingdom
2 Wageningen University and Research Centre, Netherlands
*Presenting author [email protected]
Abstract
Most high-throughput systems for automated plant phenotyping in greenhouses involve a fixed recording
cabinet to which plants are transported. However, important greenhouse plants like tomato and pepper
are too tall to be transported. In the EU-KP7-project SPICY (Smart tools for the Prediction and Improvement
of Crop Yield; www.spicyweb.eu), we developed a system known as “SPYSEE” to automatically measure
plant characteristics while they are growing in the greenhouse. With a device equipped with multiple
colour and range cameras, two types of features are extracted: (1) features from a 3D reconstruction of the
plant canopy and (2) statistical features derived directly from RGB images.
The first type of features requires accurately counting and measuring plant parts like leaves and fruits in
3D. We used a combination of a range camera and stereo vision to obtain a 3D reconstruction of the
canopy, and plant parts are then segmented and measured. We will demonstrate how we quantify surface
leaf area and leaf angle. The second type of features is to extract statistical features from the images
without segmenting individual plant parts from a background. The aim in the latter approach is to find
features with high heritability, i.e., reproducible differences between genotypes, and/or strong genetic
correlation with yield or its components.
Our experiment comprised 151 genotypes of a recombinant inbred population of pepper, to examine the
heritability and QTLs of the features. The dataset contained over 85,000 images captured from 2500 plants.
Our results show that both approaches can provide heritable features for which QTL can be found. We will
also outline challenges associated with automatic phenotyping by image analysis and future research
directions.
13. Barley transformation at the James Hutton Institute
Jennifer Stephens (JHI, UK)*, Jackie Lyon (JHI, UK), Diane Davidson (JHI, UK), Jimmy Dessoly (JHI, UK)
[email protected], The James Hutton Institute, Errol Road, Invergowrie, Dundee, Scotland,
UK, DD2 5DA
Abstract
At the James Hutton Institute we collaborate with a number of labs to elucidate the function of genes in
plant species such as barley, potato and tobacco. Our approach uses Agrobacterium-mediated methods to
up- or down-regulate expression of genes to produce transgenic lines with altered phenotype. Some of the
genes we are investigating underlie traits that are involved in nitrogen use efficiency, improved biofuels
production and water use efficiency.
Characterization of these plants involves many techniques including molecular, biochemical and cell
biological approaches. This presentation focuses on one of our experiments in barley cultivar Golden
Promise that is over-expressing the beta-glucuronidase (GUS) gene.
14. Natural leaf shape variation in Arabidopsis
Joe Vaughan* (University of York), Richard Waites (University of York)
[email protected]
Abstract
Naturally occurring Arabidopsis accessions exhibit a variety of leaf shapes. Use of computational
approaches allows this variation to be captured and analysed. We aim to understand the genetic and
environmental basis of leaf shape variation, using mapping populations and common garden experiments.
Here, leaf shape is recorded using equally spaced landmarks around the margin of the leaf. These
landmarks are Procrustes fitted to separate shape and size. Principal Component (PC) analysis on this data
then defines correlated variation in these landmarks as PCs, which we use as phenotypic traits. Along with
these PCs, we also score for margin serration, and estimate self shading within the rosette. Using these
metrics to score a variety of mapping populations has allowed us to pick out candidate genes potentially
responsible for natural shape variation. These genes have a variety of functional roles, and we aim to
understand the mechanism of their impact on leaf shape. We have also phenotyped a collection of
accessions, and aim to explore the link between natural allelic variants of our candidate genes, and leaf
shape variation in this collection. To understand the significance of leaf shape variation, we are working to
record the changes in leaf shape induced by seasonal changes and individual environmental variables such
as light intensity.
15. High-throughput Image Analysis Algorithms
Ji Zhou
The Sainsbury Laboratory
[email protected]
Abstract
At the Sainsbury Laboratory, we are developing high-throughput image processing algorithms to detect and
quantify plant cells and cellular objects in a whole leaf tissue, on the basis of images captured by highcontent screening systems. We use Acapella (based on C and C#) as our main development platform. Our
algorithms can now precisely detect fluorescently labelled plant cellular objects such as the plasma
membrane, cell outlines, stomata, plasmodesmata (PDs), and various endomembrane compartments.
Moreover, by overlapping fluorophores captured by multiple cameras, we can detect co-localisation
between multi-channel cellular objects. After image processing, a range of cellular attributes (e.g., size,
roundness, width, length, and fluorescence signal intensity) can be stored in spreadsheets and used for
further statistical analysis. Due to restricted functions of Acapella, we recently advanced our algorithms to
batch process TIFF files obtained by conventional epi-fluorescence microscopy (wide field) and confocal
laser scanning microscopy (LSM). This new application has successfully applied to experiments such as
detecting callose deposition, pathogen infection, coloured spot objects, plasmodesmata, cell wall, and
stomata.
16. Why size matters: Grain shape analysis in Avena sativa L.
Irene Griffiths, Alexander Cowan, Alan Gay, Catherine Howarth.
IBERS, Aberystwyth University, Gogerddan, Aberystwyth, SY23 3EE
[email protected]
Grain shape and size are critical factors in determining milling quality and thus the suitability of oat
varieties for commercial exploitation. Oat grain develop on a many branched panicle with each spikelet
containing between two and three florets. All of these florets can mature to produce grain. The
architecture of both the panicle and spikelet directly influences both quality and yield. For example, the
arrangement of grain within the spikelet results in a bimodal distribution in grain size.
Non- destructive image analysis tools have been developed at IBERS to accurately quantify grain and kernel
size parameters. These have been used to characterise both varietal differences and also within panicle
differences in grain size, shape and weight. A range of oat cultivars were selected for detailed panicle
analysis from two field seasons. Spikelets from each whorl were separated into groups based on the
number of mature grains within the spikelet. Primary and secondary grains were then separated and
weighed prior to image analysis. Kernel content was determined from bulked primary and secondary grain
samples and the size and shape of the de-hulled groat also determined.
The techniques developed in the above study were used to identify differences between grain lots from
field trials. Control varieties grown over a range of locations were compared for grain size and shape. The
resulting distribution in grain shape is discussed. Results from the genetic mapping of grain shape and size
are also presented.
Participants
Michael Adu
[email protected]
Monica Agarwal
The University of Nottingham and
The James Hutton Institute
Heriot-Watt University
Philippe Andrey
INRA
[email protected]
Martina Beck
The Sainsbury Laboratory
[email protected]
Christoph Briese
IBG-2, Forschungszentrum Jülich
[email protected]
Alexander Bucksch
Georgia Institute of Technology
[email protected]
Anyela Valentina
Camargo-Rodriguez
Randy Clark
Aberystwyth Uiversity
[email protected]
Cornell University
[email protected]
Hannah Dee
Aberystwyth University
[email protected]
Stijn Dhondt
VIB - Plant Systems Biology
[email protected]
Xavier Draye
Université catholique de Louvain
[email protected]
Manuel Guillermo
Forero Vargas
Andrew French
School of Biosiences-Cardiff
University
CPIB, Nottingham
[email protected]
Christophe Godin
INRA
[email protected]
Lea Hallik
Estonian University of Life Sciences
[email protected]
Paul Hand
Harper Adams University College
[email protected]
Stephen Harper
Germains Seed Technology
[email protected]
Brandon Hurr
Syngenta
[email protected]
Mohammad Sayedul
Islam
Ruben Ispiryan
University Of Aberdeen
[email protected]
[email protected]
James Johnson
Helmholtz Zentrum München German Research Centre for
Environmental Health
The University Of Nottingham
Kim Kenobi
CPIB, Nottingham
[email protected]
Norbert Kirchgessner
ETH Zurich
[email protected]
Jacob Lage
KWS UK LTD
[email protected]
Tracy Lawson
University of Essex
[email protected]
Xiaoqing Li
Lancaster University
[email protected]
Robert Lind
Syngenta
[email protected]
Guillaume Lobet
[email protected]
Stefan Mairhofer
Earth and Life Institute, Université
catholique de Louvain
CPIB, Nottingham
Nathan Miller
University of Wisconsin
[email protected]
Mark Mueller-Linow
Research Center Juelich
[email protected]
Candida Nibau
IBERS, Aberystwyth University
[email protected]
Ana Luisa Oliveira
Trás os Montes and Alto Douro
University, Portugal
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
Michael Pound
CPIB, Nottingham
[email protected]
Tony Pridmore
University of Nottingham
[email protected]
Pedro Quelhas
[email protected]
Paul Robson
INEB - Instituto de Engenharia
Biomedica Porto, Portugal, FEUP Faculdade de Engenharia da
Universidade do Porto
Aberystwyth University
Pieruschka Roland
Forschungszentrum Jülich
[email protected]
Stefan Schwartz
LemnaTec GmbH
[email protected]
Michal Slota
[email protected]
Edgar Spalding
Department of Genetics, Faculty of
Biology and Environmental
Protection, University of Silesia
Biomathematics and Statistics
Scotland
University of Wisconsin
Catherine Taylor
Sainsbury laboratory
[email protected]
Christopher Topp
Duke University
[email protected]
Karel Van De Velde
Bayer CropScience NV
[email protected]
Rick Van De Zedde
Wageningen
[email protected]
Peter van Loon
[email protected]
Joseph Vaughan
Rijk Zwaan Zaadteelt en
Zaadhandel B.V.
University of York
Darren Wells
CPIB, Nottingham
[email protected]
Michael Wilson
CPIB, Nottingham
[email protected]
Nathalie Wuyts
VIB Department of Plant Systems
Biology, Ghent University
The Sainsbury Laboratory
[email protected]
Yu Song
Ji Zhou
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]