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.
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:
ii) intensive field,
iii) lean field,
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
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
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
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
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. (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.
Machine learning and artificial intelligence
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
Methods and protocols
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
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
Initiatives towards harmonisation
(FAIR) data repositories/information system
Also see publication:
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