Harmonisation Pilot


The harmonisation pilot service will evaluate how to establish EMPHASIS as a central point of information on Good Phenotyping Practices developed in close collaboration with the phenotyping community and based on state-of-the-art procedures.

The aim is to develop and communicate with the plant phenotyping community best practices (Good Phenotyping Practice). This will be a prerequisite to allow experimental reproducibility between infrastructures and reusability of the obtained data (e.g., by defining standards).  This will include collecting/ defining standards, minimum requirements and guidelines on experimental procedures and the use of equipment.

The aim is to collect minimum requirements for plant phenotyping experiments under controlled and field conditions and guidelines on use and calibration of sensors.

Two tasks have been formulated in this pilot service in the Implementation phase.

 

1. Outreach

Community exchange on harmonisation

We actively interact with the community via targeted e-mailing of experts, general online outings (social media), workshops and via mailings and webinar announcements.

We are organising workshops and round-table discussions with operators of plant phenotyping facilities and users on Good Phenotyping Practice in all infrastructure categories:

                 i) controlled conditions,

                 ii) intensive field,

                 iii) lean field,

                 iv) data and computational services,

                 v) modelling

 

2. Collect information

Requirements for Good Phenotyping Practice are:
  • Evaluate and define experimental procedures required to enable comparability of experiments with focus on experimental design and analysis for single and multiple installation experiments, environmental monitoring, sensor calibration and application etc. (including work from EPPN/EPPN2020 and enable access for EMPHASIS members)
  • Map, evaluate and offer access to existing repositories hosting plant phenotyping protocols and their accessibility for the user community;
  • Develop an EMPHASIS repository integrating information from existing repositories as well as new protocols and quality measures required for Good Phenotyping practice in plant phenotyping.
  • Establishment of a processes continuing quality assurance for the Operational Phase of EMPHASIS.

 

Examples of collected information


 
Publications on harmonisation

Opinion and general overviews

The following paper discusses the difficulty of defining the stress levels experienced by plants accurately as well as a lack of standardization in the terminology used to describe plant responses to various stresses:  Moshelion M (2020) The dichotomy of yield and drought resistance. EMBO Rep 21:e51598, https://doi.org/10.15252/embr.202051598

Poorter et al (2012) Pot size matters: a meta-analysis of the effects of rooting volume on plant growth. Functional Plant Biology, 39, 839–850    also discussed in this webinar

Watt M et al (2020) Phenotyping: New Windows into the Plant for Breeders. Annual Review of Plant Biology, Vol. 71:689-712, https://doi.org/10.1146/annurev-arplant-042916-041124

Sotirios A T, Scharr H (2018) Sharing the Right Data Right: A Symbiosis with Machine Learning. Scientific Life, Volume 24, Issue 2, p99-102, https://doi.org/10.1016/j.tplants.2018.10.016

Coppens F (2017) Unlocking the potential of plant phenotyping data through integration and data-driven approaches. Current Opinion in Systems Biology, Volume 4, Pages 58-63, https://doi.org/10.1016/j.coisb.2017.07.002

 

RGB image analysis

David, E. et al. (2020) Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods. Plant Phenomics 2020, 1–12. https://doi.org/10.34133/2020/3521852

Fernandez-Gallego, J.A. et al. (2019) Cereal crop ear counting in field conditions using zenithal RGB images. JoVE (Journal of Visualized Experiments) 144 e58695, doi:10.3791/58695

Hund, A. et al. (2019) “Non-Invasive Field Phenotyping of Cereal Development.” In Advances in Breeding Techniques for Cereal Crops, edited by Frank Ordon and Wolfgang Friedt, 249–92. Cambridge, UK: Burleigh Dodds Science Publishing. http://dx.doi.org/10.19103/AS.2019.0051.13

 

Hyperspectral imaging

Polder G,  Gowen A (2020) The hype in spectral imaging. Journal of Spectral Imaging Volume 9 Article ID a4 doi: 10.1255/jsi.2020.a4

John R. Gilchrist, Christopher N. Durell, and David W. Allen (2020) "IEEE P4001: Towards a standard for push-broom hyperspectral imagers", Proc. SPIE 11576, Hyperspectral Imaging and Applications, 115760E; https://doi.org/10.1117/12.2585141

Vergara-Diaz, O., Vatter, T., Kefauver, S.C., Obata, T., Fernie, A., Araus, J.L. (2019) Assessing durum wheat ear and leaf metabolomes in the field through hyperspectral data. The Plant Journal 102(3), 615-630

Aasen, H., Bolten, A. (2018) Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers – From theory to application. Remote Sensing of Environment 205, 374–389. 

 

UAVs and image analysis

Aasen, H. et al. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sensing 10, 1091. 

Roth ,L . et al. (2020). Repeated Multiview Imaging for Estimating Seedling Tiller Counts of Wheat Genotypes Using Drones. Plant Phenomics, https://doi.org/10.34133/2020/3729715.

Roth, L. et al. (2018). “Extracting Leaf Area Index Using Viewing Geometry Effects—A New Perspective on High-Resolution Unmanned Aerial System Photography.” ISPRS Journal of Photogrammetry and Remote Sensing 141 (July): 161–75. https://doi.org/10.1016/j.isprsjprs.2018.04.012.

Tmušić G et al (2020). Current Practices in UAS-based Environmental Monitoring. Remote Sensing 12, 1001, https://doi.org/10.3390/rs12061001

 

Machine learning and artificial intelligence

Fernandez-Gallego, J.A. et al. (2020) Automatic wheat ear counting using machine learning based on RGB UAV imagery. The Plant Journal: https://doi.org/10.1111/tpj.14799

Rosenqvist, E. et al. (2019) The phenotyping dilemma – the challenges of a diversified phenotyping community. Frontiers in Plant science 10, 163, https://doi.org/10.3389/fpls.2019.00163

Tardieu et al. (2017) Plant Phenomics, From Sensors to Knowledge, Volume 27, Issue 15, PR770-R783, https://doi.org/10.1016/j.cub.2017.05.055

 

Trait assessment

Poorter, H. et al. (2019) A meta‐analysis of plant responses to light intensity for 70 traits ranging from molecules to whole plant performance. New Phytologist, Volume 223, Issue 3, Pages 1073-1105, https://doi.org/10.1111/nph.15754

 

Environmental simulation and field monitoring

Freschet, G. et al. (2020) A starting guide to root ecology: strengthening ecological concepts and standardizing root classification, sampling, processing and trait measurements, HAL Id: hal-02918834

Halbritter, A. et al. (2018) The handbook for standardised field measurements in terrestrial global change experiments,  10.13140/RG.2.2.23234.22729.

Reynolds, M.P. et al. (2012) Physiological Breeding I: Interdisciplinary Approaches to Improve Crop Adaptation. CIMMYT

Pask, A. et al. (2012) Physiological Breeding II: A field Guide to Wheat Phenotyping. CIMMYT

 

Methods and protocols

Covshoff, S. (editor) (2018) Photosynthesis, Methods and Protocols (book). Springer Protocols: https://doi.org/10.1007/978-1-4939-7786-4

Recommended chapters:

Survey of Tools for Measuring In Vivo Photosynthesis

Photosynthetic Gas Exchange in Land Plants at the Leaf Level

Light-Response Curves in Land Plants

Pérez-Harguindeguy et al. (2013) New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany

Poorter et al. (2012) The art of growing plants for experimental purposes: a practical guide for the plant biologist. Functional Plant Biology, 2012, 39, 821–838

Poorter, H., et al.  (2016). Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field. New Phytologist 212: 838-855

 

Multi-site experiments/comparison studies

Massonnet C et al (2010) Probing the Reproducibility of Leaf Growth and Molecular Phenotypes: A Comparison of Three Arabidopsis Accessions Cultivated in Ten Laboratories. Plant Physiology, Vol. 152, pp. 2142–2157

 

Bioinformatics, data management and metadata

Arend, D. et al. (2020) The on-premise data sharing infrastructure e!DAL: Foster FAIR data for faster data acquisition. GigaScience, Volume 9, Issue 10, giaa107, doi.org:10.1093/gigascience/giaa107

Papoutsoglou, E. on behalf of The MIAPPE Team (2020) Agricultural Ontologies in Use: The MIAPPE Standard, CGIAR Platform for Big Data in Agriculture (blog post)

Papoutsoglou et al. (2020) Enabling reusability of plant phenomic datasets with MIAPPE 1.1. New Phytologist 227: 260–273, doi:10.1111/nph.16544

Jonquet, C., et al. (2018) Computers and Electronics in Agriculture, Volume 144, Pages 126-143, https://doi.org/10.1016/j.compag.2017.10.012

Jonquet, C. et al. (2018) Harnessing the power of unified metadata in an ontology repository: the case of AgroPortal, Journal on Data Semantics volume 7, pages191–221, https://doi.org/10.1007/s13740-018-0091-5

Ćwiek‑Kupczyńska et al (2016) Measures for interoperability of phenotypic data: minimum information requirements and formatting. Plant Methods 12:44

Krajewski et al. (2015) Towards recommendations for metadata and data handling in plant phenotyping. Journal of Experimental Botany, 66, 5417–5427

Lobet, G. et al. (2015) Root System Markup Language: Toward a Unified Root Architecture Description Language. Plant Physiology, Vol. 167, pp. 617–627, DOI: https://doi.org/10.1104/pp.114.253625

Volk, G.M. (2010) Advantages for the Use of Standardized Phenotyping in Databases. HortScience, Volume 45: Issue 9, https://doi.org/10.21273/HORTSCI.45.9.1310

Hollebecq, J.-E. et al. (2019). Identification of objects with Uniform Resource Identifier (URI): recommendations for application in plant phenotyping. 10.5281/zenodo.3560629


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If you have yet unpublished protocols that you would like to share with the phenotyping community, please forward this via email.