Project

Austrian Plant Phenotyping Network – APPN

Goal: The APPN initiative aims to unite the Austrian plant phenotyping community in order to facilitate research collaborations, development of plant phenotyping infrastructure and methodologies, staff training, staff exchange and networking activity. The goal is to increase the visibility and impact of plant phenotyping and enable cooperation by fostering communication between stakeholders in academia, industry, government, and the general public. Through workshops and symposia, APPN seeks to establish different working groups and distribute all relevant information about plant phenotyping in a web-based platform, including available phenotyping platforms in Austria.

If you want to join the APPN community and receive news on the latest technology developments, events, grant opportunities etc. we invite you kindly to subscribe to the APPN mailing list by writing a short message to appn@vbcf.ac.at

Please visit the webpage www.appn.at for further information.

Date: 13 March 2017

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Boris Rewald
added a research item
Root traits are fundamental for the resilience of plants under stress. Image-based phenotyping can provide relevant datasets for the underlying root traits. However, root phenotyping is still hampered by methodological constrains, in particular extraction of root traits from images taken under semi-natural conditions. In this study, we thus propose a strategy for analyzing root images from rhizoboxes. Utilizing three Vicia faba genotypes and two soil moisture conditions, we applied software tools featuring distinctive types of root descriptors. We determined their accuracy of root length measurement, inference from surface-visible root axes towards total root length, inter-relation among root architectural descriptors and their relevance for plant transpiration. Our results show that different image analysis tools provide similar root length estimates in spite of specific segmentation approaches. Several root architectural descriptors are also inter-comparable. Using a structural equation model, we identified relevant phenotyping root traits for root size and branching driving plant transpiration. We conclude that rhizobox systems are a promising approach for root phenotyping. Future developments in image analysis should overcome needs for manual post-processing (e.g. gap closure) to automatize root architecture measurements improving the throughput and thus the range of rhizobox phenotyping applicability for breeding.
Boris Rewald
added a research item
There are high expectations that plant breeding for improved root systems will substantially advance adaptation of crops to resource‐limited environments and climate change. Image‐based phenotyping technologies provide novel opportunities to characterize root systems and overcome traditional throughput limitation in sampling and analysis. This chapter reveals interfaces between root systems modelling and phenotyping and demonstrates how modelling can contribute to maximizing the usability of phenotyping data for breeding purposes. After discussing different viewpoints and classification approaches for root systems as multivariate plant organs, the model‐based analysis of inter‐trait relations to reveal essential measurement traits for phenotyping is demonstrated. A major challenge for application of root phenotyping data in practical breeding is restrictions due to experimental duration (phenology) and growth environment (artificial substrates). Root models allow upscaling from seedling data towards yield‐relevant phenological stages and estimate performance under field conditions. Hence, modelling improves inference from phenotyping platforms towards conditions in a breeding nursery. Defining relevant target root traits for selection requires an accurate characterization of the predominant environmental constraints and quantitative understanding of the root trait–environment interactions influencing stress resistance. In silico modelling experiments provide root ideotypes with distinct target traits to overcome the predominant environmental limitations. This is discussed for mobile (water and nitrate) and immobile (phosphorus) nutrients as well as mechanical constraints. Novel measurement methods to improve environment characterization (envirotyping) (i) are of high relevance for defining/selecting promising root traits during phenotyping and (ii) critically influence the capability of models to reliably predict the expected performance of improved cultivars. Deeper integration of phenotyping and modelling will strongly contribute to overcoming current platform specificity and enhance phenotyping data usability in the context of plant breeding. This requires common formats between phenotyping outputs and model parameters to optimize joint applications and guide future developments in novel software modules and scaling capabilities.
Boris Rewald
added an update
Dear colleagues and friends,
It is our pleasure to invite you to the 4th Austrian Plant Phenotyping
Network (APPN) Conference featuring “From Cell to Field” with the major focus on Pattern Recognition, Machine Learning and Modelling in the context of Plant Phenotyping.
Joint Organization by the University of Vienna, VBCF and OAGM
The meeting will take place on June 25, 2020 at the Vienna BioCenter
(Free) registration open: https://appn.at/4thappn/
Confirmed speakers (in alphabetical order):
Sven Fahrner, ESFRI-EMPHASIS, DE
Kristina Gruden, NIB, SI
Verena Ibl, Mosys, University of Vienna, AT
Astrid Junker, IPK Gatersleben, DE
Thomas Nägele, LMU München, DE
Peter M. Roth, Graz University of Technology , AT
Wolfram Weckwerth, Mosys, Uni Vienna, AT
Please feel free to share & spread the word!
Looking very much forward to meeting you,
Stefanie WIenkoop, Markus Teige and Jakub Jez
 
Stefanie Wienkoop
added a research item
Plant phenotyping to date typically comprises morphological and physiological profiling in a high-throughput manner. A powerful method that allows for subcellular characterization of organelle stoichiometric/functional characteristics is still missing. Organelle abundance and crosstalk in cell dynamics and signaling plays an important role for understanding crop growth and stress adaptations. However, microscopy cannot be considered a high-throughput technology. The aim of the present study was to develop an approach that enables the estimation of organelle functional stoichiometry and to determine differential subcellular dynamics within and across cultivars in a high-throughput manner. A combination of subcellular non-aqueous fractionation and liquid chromatography mass spectrometry was applied to assign membrane-marker proteins to cell compartmental abundances and functions of Pisum sativum leaves. Based on specific subcellular affiliation, proteotypic marker peptides of the chloroplast, mitochondria and vacuole membranes were selected and synthesized as heavy isotope labeled standards. The rapid and unbiased Mass Western approach for accurate stoichiometry and targeted absolute protein quantification allowed for a proportional organelle abundances measure linked to their functional properties. A 3D Confocal Laser Scanning Microscopy approach was developed to evaluate the Mass Western. Two P. sativum cultivars of varying morphology and physiology were compared. The Mass Western assay enabled a cultivar specific discrimination of the chloroplast to mitochondria to vacuole relations.
Boris Rewald
added an update
The EU-funded Innovative Training Network FutureArctic (http://www.futurearctic.eu) aims to quantify how much carbon will escape from the Arctic in future climate. How do the multitude of ecosystem processes, driven by plant growth, microbial activities and soil characteristics, interact to determine soil carbon storage capacity? A group of fifteen PhD-students will study the Forhot ecosystem in Iceland, where a natural coincidence has provided us with the exceptional opportunity to actually look into the future.
Given the strong urgency of tackling and managing the climate challenge and the particularly important role herein of (sub)Arctic ecosystems, a rapid assessment of the ecosystem and ambient processes in this natural laboratory is essential. FutureArctic will achieve this challenge by adopting the fast advances made in the field of machine learning and artificial intelligence (AI), unmanned aerial vehicles (UAV) and (remote) sensor technology into environmental research at the ecosystem scale, into a new concept of an ‘ecosystem-of-things’.
ESR12: Smart root imaging technology for root phenological studies.
Host: VSI, co-host: UTARTU. Contact person: l.seehra@vienna-scientific.com or boris.rewald@boku.ac.at Expected start: date: September 2019
Location: Austria. Profile: computational image analysis, data science, bioinformatics
The ESR (Early Stage Researcher, PhD student) will develop a permanently installed, fully automated, and remote-controllable mini-rhizotron (MR) camera system with UHD resolution to facilitate root phenological studies in situ. This system will be tested and validated at the ForHot field site under arctic conditions. The ESR will also identify wavebands beyond the visible spectrum allowing for enhanced segmentation (i.e. separation between roots and soil) and species-specific differentiation of three exemplary arctic root systems via image analyses - using a hyperspectral camera. A data processing pipeline, involving machine learning tools, will be developed for automatic analysis of the gathered RGB and/or hyperspectral root signatures. Finally, strategies how to integrate multi-spectral imaging capacities in future generations of MR camera systems will be developed.
  • Applicants must hold a MSc or equivalent in the field of computational image analysis, data science, bioinformatics or a related discipline incl. biology.
  • Applicants must have a good understanding of mechatronics and programming and foster an interest inroots and ecosystem processes. Skills in Matlab, R, CAD software and a reasonable proficiency in C/C++/Python/... are an asset.
  • Applicants can be of any nationality.
  • Applicants must have an ability to understand and express themselves in both written and spoken English to a level that is sufficiently high for them to derive the full benefit from the network training.
  • Applicants must be eligible to enrol on a PhD programme at BOKU university (https://boku.ac.at/en/studienservices/themen/zulassung/internationale-vorbildung/doktorats-phd-studien).
  • Please see the attached file or linked webpage for additional requirements related to the EU mobility rule.
Please see
for details. Applications can be submitted until: 15th of June 2019. Please send your job application (motivation letter & CV incl. references) to office@vienna-scientific.com
 
Jakub Jez
added an update
Two OPEN POSITIONS at the VBCF Plant Sciences Facility:
DATA SCIENTIST and PHENOTYPING SPECIALIST for High-throughput plant phenotyping.
Exciting times ahead of us Hashtag#APPN Hashtag#PHENOPlant!
Join our team!
 
Jakub Jez
added an update
Dear friends,
It is our pleasure to invite you to the 3rd Austrian Plant Phenotyping Network (APPN) Conference featuring “Field Phenotyping and Remote Sensing”.
Last year we put emphasis on root phenotyping (https://www.appn.at/2ndappn_eppn2020-2/); this year the main focus will be (although not exclusively) on field phenotyping and “big” data.
Other plant phenotyping topics (controlled environments, root phenotyping and others) will be covered by the respective sessions based on the received abstracts.
The meeting will take place on June 13, 2019 at the University of Natural Resources and Life Sciences (BOKU), Vienna, AT.
(Free) registration open: https://www.appn.at/3rdappn/
Confirmed speakers (in alphabetical order):
Opening speech:
Christian Obinger, Vice Rector for Research, University of Natural Resources and Life Sciences (BOKU)
Clement Atzberger, Head of Institute for Surveying, Remote Sensing and Land Information, University of Natural Resources and Life Sciences and GreenSense GmbH, AT
“Determining crop traits through multi-spectral data”
Helge Aasen, ETH Zürich, Dep. of Environmental System Science, CH
“Low-altitude / high-resolution remote sensing for field-phenotyping”
Ittai Herrmann, The Hebrew University of Jerusalem, R.H. Smith Faculty of Agriculture, Food and Environment, IL
“Field crops phenotyping (disease, water status and yield) by spectral remote and proximal sensing”
Jan F. Humplík, Palacky University Olomouc, CZ
“Field plant phenotyping using UAV LiDAR system: current state and first experiences.”
Pieter Clauw, Gregor Mendel Institute of Molecular Plant Biology (GMI), AT
“Combining growth chamber simulations and field experiments to tackle temperature adaptation in Arabidopsis thaliana”
Please feel free to share & spread the word!
Looking very much forward to meeting you,
Gernot Bodner, Boris Rewald (local organizers) & Jakub Jez
 
Jakub Jez
added an update
OPEN POSITION: We are looking for a Bioinformatician/Data Analyst to join our team at the Vienna Biocenter Core Facilities GmbH in Vienna, the most liveable city in the world!
Support the construction of #PHENOPlant and help us build our HT plant phenotyping data analysis pipelines!
Apply till the 31st May 2019:
 
Boris Rewald
added a research item
Faba bean (Vicia faba L.) is an important source of protein but breeding for increased yield stability and stress tolerance is hampered by the scarcity of phenotyping information. Because comparisons of cultivars adapted to different agro-climatic zones improve our understanding of stress tolerance mechanisms, the root architecture and morphology of 16 pan-European faba bean cultivars were studied at maturity. Different machine learning (ML) approaches were tested in their usefulness to analyse trait variations between cultivars. A supervised, i.e. hypothesis-driven, ML approach revealed that cultivars from Portugal feature greater and coarser but less frequent lateral roots at the top of the taproot, potentially enhancing water uptake from deeper soil horizons. Unsupervised clustering revealed that trait differences between Northern and Southern cultivars are not predominant but that two cultivar groups, independently from major and minor types, differ largely in overall root system size. Methodological guidelines on how to use powerful machine learning methods such as random forest models for enhancing the phenotypical exploration of plants are given.
Jakub Jez
added an update
Dear APPN community! Dear friends!
It is my pleasure to inform you about the positive evaluation of our PHENOPlant application to the 2nd FFG Infrastructure call 2018.
Austria will get its first and only state-of-the-art multi sensor HT plant phenotyping facility.
Follow us on Twitter (@VBCF_PlantS) for updates or visit www.appn.at
Merry Christmas and a happy New Year!
Jakub Jez
 
Jakub Jez
added an update
Join us for the ESFRI EMPHASIS Plant Phenotyping Forum by the APPN taking place the 13th September 2018 at the Vienna Biocenter Core Facilities (VBCF) GmbH.
The Forum will address the plant phenotyping landscape and role of Austria, Czech Republic and Slovakia within the context of European plant phenotyping initiatives such as EMPHASIS and EPPN2020 with the goal to advance basic plant research, plant breeding and foster sustainable agriculture.
Confirmed speakers include: Ulrich Schurr, Roland Pieruschka, Marian Brestic, Lukáš Spíchal, Herman Buerstmayr, Pavel Hauptvogel, Ivan Ingelbrecht, Gabriela Pastori and Dr. Andreas Walter.
Looking forward to meeting you,
Jakub Jez and Roland Pieruschka
 
Jakub Jez
added a research item
There is a need for flexible and affordable plant phenotyping solutions for basic research and plant breeding. We demonstrate our open source plant imaging and processing solution ('PhenoBox'/'PhenoPipe') and provide construction plans, source code and documentation to rebuild the system. Use of the PhenoBox is exemplified by studying infection of the model grass Brachypodium distachyon by the head smut fungus Ustilago bromivora, comparing phenotypic responses of maize to infection with a solopathogenic Ustilago maydis (corn smut) strain and effector deletion strains, and studying salt stress response in Nicotiana benthamiana. In U. bromivora-infected grass, phenotypic differences between infected and uninfected plants were detectable weeks before qualitative head smut symptoms. Based on this, we could predict the infection outcome for individual plants with high accuracy. Using a PhenoPipe module for calculation of multi-dimensional distances from phenotyping data, we observe a time after infection-dependent impact of U. maydis effector deletion strains on phenotypic response in maize. The PhenoBox/PhenoPipe system is able to detect established salt stress responses in N. benthamiana. We have developed an affordable, automated, open source imaging and data processing solution that can be adapted to various phenotyping applications in plant biology and beyond.
Boris Rewald
added a research item
Spectral imaging makes use of different wavelength to infer on plant properties and processes. In the context of plant phenotyping, spectral imaging mostly uses multispectral sensors with defined broad- band wavelength in then VIS (400-700 nm), NIR (700-1100 nm) and SWIR (1100-2500 nm) regions. Hyperspectral imaging on the contrary captures the entire spectrum with up to several hundred narrow-band channels. It is expected that the comprehensive spectral signature obtained from hyperspectral imaging can provide deeper insights into plant properties of potential use for structural-functional phenotyping. On the other hand the resulting spatial-spectral datasets are substantially larger compared to multi-spectral images, targeting defined wavelength to obtain spectral indices (e.g. NDVI), and require adequate methods to extract information from the data cloud. Here we present the application of hyperspectral imaging to the root zone of plants grown in soil filled rhizoboxes. Essential steps in processing the hyperspectral datasets to obtain structural and functional information on the root system are demonstrated and implications for phenotyping application are discussed. Plants of Triticum durum are grown in soil (silty loam topsoil, 2 mm sieve-size) filled rhizoboxes (30 x 1000 x 1 cm) at optimum moisture (field capacity) for imaging via a transparent mineral glass side. Spectral images are taken via a spectral scanner (1000-1700 nm, 222 bands, spatial resolution 0.1 mm). Different pre-processing, dimensionality reduction and segmentation algorithms for separating root foreground and soil background pixels are discussed. Chemometric analysis of the segmented root images is exemplified for spectral distinction of root regions. Results demonstrate that pre-processing of spectral images is the most important step for classification of root vs. soil pixels. Thereby the heterogeneity of the root axes as well as the soil background (water content, surface morphology) can be significantly reduced. As an example, polynomial de-trending with subsequent scatter correction via standard normal variate increases the Bhattacharyya distance between pixel histograms of root vs. soil from 0.47 for raw data to 2.76 for pre-processed data, with maximum distinction at a wavelength of 1462 nm. Based on pre-processed images and identification of most distinctive wavelength, segmentation (e.g. via fuzzy clustering) provides accurate binary images of the root system that can be further analysed with different chemometic approaches. This is exemplified by identifying central vs. boarder regions on the root axes showing different spectral signature. It is hypothesized that these spectral feature represents the distinction between parts belonging to the central cylinder with water conducting xylem (lower reflectance at water sensitive bands) vs. the cortex region. First results demonstrate that hyperspectral imaging can provide novel insights into the root zone via distinctive spectral characteristics of different domains. Due to the heterogeneous biophysical and biochemical nature of the root zone, a key requirement for successful application of hyperspectral imaging to plant phenotyping is the use of efficient image processing tools in order to extract features of interest capturing root structure and functionality.
Boris Rewald
added an update
Please find enclosed the final programme. See you soon in Vienna.
 
Boris Rewald
added an update
2nd Conference of the Austrian Plant Phenotyping Network
New sensors and algorithms are increasing the possibilities of plant traits accessible to phenotyping. Data availability on different scales – from single plants to ecosystems – is rapidly increasing. Expectations are thus high that phenotyping will open new ways for plant breeding, agricultural management and environmental protection. A key for advance is the interaction between plant, technical and mathematical sciences. The conference will link researchers, developers and users, in order to present the state-of-the art and discuss future chances and requirements to plant phenotyping.
Technical workshop APPN/EPPN2020 on root phenotyping
Visualizing the “hidden half” of plants is a big opportunity for phenotyping. This workshop will compare experimental approaches, sensors and image analysis algorithms for the root zone currently used among the European phenotyping community. Key questions to address are the influence of experimental environments on root trait expression, added-value of complex “deep phenotyping” approaches and feasibility of extrapolation towards field environments. Examples of root imaging approaches at the BOKU site in Tulln and root excavations in the field will be visited.
Date: April 17th & 18th, 2018
Venue April 17th, 9:00 – 17:00: Festsaal BOKU (3rd floor), Gregor Mendel Haus (BOKU main building) Gregor-Mendel-Straße 33, 1180 Vienna
Venue April 18th, 9:00 – 15:00: Seminarraum 15, BOKU UFT Tulln, Konrad-Lorenz-Str. 24, 3430 Tulln a. d. Donau
Deadline for registration: April 6th, 2018 (VERY SOON!)
Please visit http://www.appn.at/2ndappn_eppn2020/ for speakers and abstracts. Keynote speaker: Michelle Watt, Forschungszentrum Jülich GmbH
For contact details, please refer to:
Gernot Bodner Univ. of Natural Resources and Life Sciences Division of Agronomy gernot.bodner@boku.ac.at +43 1 476549 5115
Stefanie Koemeda Vienna Biocenter Core Facilities Plant Sciences stefanie.koemeda@vbcf.ac.at +43 1 7962324 7258
See you soon at BOKU!
 
Boris Rewald
added an update
This is a reminder and a call for submissions of abstracts for oral presentations.The deadline for abstract submission is this Thursday 1st of March, 2018. In case you want to attend the APPN/EPPN2020 meeting and workshop please note that the online registration is free but mandatory! Access to the meeting, coffee breaks and lunch will only be possible with a badge!
Visit http://www.appn.at/2ndappn_eppn2020/ for Registration and Abstract Submission.
 
Boris Rewald
added an update
The short movie illustrates the need for root phenotyping and the activities at the University of Natural Resources and Life Sciences Vienna (BOKU). The Movies was developed as part of the dissemination activities of the FP7 project EUROLEGUME (www.eurolegume.eu).
Watch the Movie here:
 
Boris Rewald
added an update
The 2nd Austrian Plant Phenotyping Network (APPN) Meeting which will take place on the 17th & 18th of April 2018 at the BOKU Vienna and Tulln. This year we will be joined by the European Plant Phenotyping Network (EPPN2020).
The meeting will give you the unique opportunity to present and discuss your research in a highly multidisciplinary and international environment. We also offer a technical workshop on root phenotyping, including a field visit. We intend to bring together biologists, breeders, technology developer, imaging experts, statisticians and bioinformaticians. The keynote lecture will be given by Michelle Watt, Scientific Director of the Institute of Bio- and Geosciences, Plant Sciences (IBG-2) Jülich, followed by EPPN2020 Speakers, selected talks and a poster session in the Festsaal at BOKU Vienna.On the second day we offer a technical workshop on root phenotyping, taking place at BOKU Tulln. Key dates:
Registration link (free): www.appn.at/2ndappn_eppn2020
Abstract submission: 1 March 18
Registration deadline: 6 April 18
 
Boris Rewald
added an update
It is a great pleasure to invite you to the APPNWorkshop on Multispectral 3D Plant Imaging which will take place October 12th & 13th 2017 at the Vienna Biocenter.
For the workshop, PHENOSPEX will visit the VBC with their new PlantEye F500 device and perform multispectral 3D scans of your samples.
Your plants will be grown at the VBCF PlantS Facility and scanned one day before the workshop. Therefore, we need to receive your seeds in advance (deadline_1).
Hands-on data analysis will be performed during the workshop - follow-up discussion of derived results will be discussed on the second day in individual meetings.
“PlantEye F500 is a 3D laser scanner combined with multispectral information. It generates 3D point clouds of plants in high resolution and combines it on the fly with a 4-channel multispectral camera in the range of 400-900nm. This unique sensor fusion concept delivers spectral information for each data point and automatically computes a diverse set of validated morphological plant parameters.”
Key dates:
Workshop:  12th Oct. 20017
Discussion of results:  13th Oct. 2017
  • Deadline_1: 1st Sept. 2017 (if you want your samples to be scanned)
  • Deadline_2: 1st Oct. 2017 (general registration deadline)
Please note that there is a limited number of participants – the first come first serve principle applies.
VIENNADphone+mobile e-mail web Twitter
 
Boris Rewald
added a project goal
The APPN initiative aims to unite the Austrian plant phenotyping community in order to facilitate research collaborations, development of plant phenotyping infrastructure and methodologies, staff training, staff exchange and networking activity. The goal is to increase the visibility and impact of plant phenotyping and enable cooperation by fostering communication between stakeholders in academia, industry, government, and the general public. Through workshops and symposia, APPN seeks to establish different working groups and distribute all relevant information about plant phenotyping in a web-based platform, including available phenotyping platforms in Austria.
If you want to join the APPN community and receive news on the latest technology developments, events, grant opportunities etc. we invite you kindly to subscribe to the APPN mailing list by writing a short message to appn@vbcf.ac.at
Please visit the webpage www.appn.at for further information.
 
Boris Rewald
added an update
Project goal
The APPN initiative aims to unite the Austrian plant phenotyping community in order to facilitate research collaborations, development of plant phenotyping infrastructure and methodologies, staff training, staff exchange and networking activity. The goal is to increase the visibility and impact of plant phenotyping and enable cooperation within Austria and Europe by fostering communication between stakeholders in academia, industry, government, and the general public. Through workshops and symposia, APPN seeks to connect different working groups and distribute all relevant information about plant phenotyping in a web-based platform, including a list of available phenotyping platforms in Austria.
If you want to join the APPN community and receive news on the latest technology developments, events, grant opportunities etc. we invite you kindly to subscribe to the APPN mailing list by writing a short message to appn@vbcf.ac.at
Please visit www.appn.at for further information.
Background and motivation
Improving plant productivity is key to address major economic, ecological and societal challenges. A limited number of crops provides the resource for food and feed; estimates indicate that food supplies need to be largely increased to meet the increasing nutritional demand of the growing human (and animal) population. At the same time, plants are increasingly utilized as renewable energy source and as raw material for a new a generation of products. Climate change and scarcity of arable land constitute additional challenges for future scenarios of sustainable agricultural production. It is thus necessary to increase breeding / screening genebanks for varieties with improved performance in agricultural environments.
Integrating approaches across all scales from molecular to field applications are thus necessary to enhance sustainable plant production - targeting higher yield quantities and qualities and using limited resources. While significant progress has been made in molecular and genetic approaches in recent years, the quantitative analysis of plant phenotypes - structure and function of plants - has been identified as major bottleneck. Plant phenotyping is an emerging science that links genomics with plant ecophysiology and agronomy. The functional plant body (Phenotype) is formed during plant growth and development from the dynamic interaction between the genetic background (Genotype) and the environment. These interactions determine plant performance and productivity measured.
The understanding of the link between genotype and phenotype is currently hampered by insufficient capacity (both technical and conceptual) of the plant science community to analyze the existing genetic resources for their interaction with the environment. Advances in plant phenotyping are therefore a key factor for success in modern breeding and basic plant research.