Pitfalls and potential of high-throughput plant phenotyping platforms

Hendrik Poorter; Grégoire M Hummel; Kerstin A Nagel; Fabio Fiorani; Philipp von Gillhaussen; Olivia Virnich; Ulrich Schurr; Johannes A Postma; Rick van de Zedde; Anika Wiese-Klinkenberg

doi:10.3389/fpls.2023.1233794

Pitfalls and potential of high-throughput plant phenotyping platforms

Front Plant Sci. 2023 Aug 23:14:1233794. doi: 10.3389/fpls.2023.1233794. eCollection 2023.

Authors

Affiliations

¹ Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany.
² Department of Natural Sciences, Macquarie University, North Ryde, NSW, Australia.
³ Phenospex, Heerlen, Netherlands.
⁴ International Plant Phenotyping Network e.V. (IPPN), Jülich, Germany.
⁵ Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands.
⁶ Bioinformatics (IBG-4), Forschungszentrum Jülich GmbH, Jülich, Germany.

Abstract

Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r² (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved.

Keywords: calibration curve; digital biomass; high-throughput plant phenotyping; leaf mass per area; sensors.

Grants and funding

This paper was partially funded by the Helmholtz Association of Germany and the European Union Grant agreement No. 101059784 (CROPINNO). Open access was partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491111487.