Abstract
Every plant science experiment starts with a design that will be adapted to answer a specific biological question and involves evaluation of phenotypic traits. Plant phenotyping has advanced from manual measurements of physiologically relevant parameters to high-throughput phenotyping platforms that use robotics and imaging sensors. Yet, this game-changing technology has its own challenges, namely data analysis and interpretation. The improved quality of the sensors used in the phenotying experiment provides increased understanding, however the insight provided on the research question is limited by the experimental design. Aspects such as replication or spatial variability are important to consider when designing the experiment conducted in highly controlled environment as well as under field conditions. With wider availability of cameras and other sensors, we are able to record increasing number of plant traits. This results in the phenotypic bottleneck moving from data acquisition to data analysis. Throughout this article, we present practical considerations and potential shortcomings of phenotyping systems and suggest some solutions to the challenges of plant phenotyping through streamlined and reproducible data analysis pipelines.
| Original language | English (US) |
|---|---|
| Title of host publication | eLS |
| Publisher | Wiley |
| Pages | 1-14 |
| Number of pages | 14 |
| ISBN (Print) | 9780470016176 |
| DOIs | |
| State | Published - Mar 10 2020 |
Bibliographical note
KAUST Repository Item: Exported on 2021-03-02Acknowledgements: Sónia Negrão and Magdalena M. Julkowska gratefully acknowledge the financial support of University College Dublin (UCD) and UCD- School of Biology and Environmental Science, and King Abdullah University of Science and Technology (KAUST), respectively. We thank Patrick Langan, a PhD student, for his insightful comments. Some parts of the images were created with Biorender.com.