Plant Phenotyping


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.

Key Concepts

  • Plant phenotypes are complex, resulting from the interaction between genotype and environment.
  • The phenotype can be divided into traits, for example, biomass can be dissected into leaf area, branches/tillers, fruits.
  • The relationship between traits depends on the environment, genotype and treatment.
  • Each phenotyping method is optimised to answer a specific research question.
  • Exploring the relationships between phenotypes and their changes across genotypes/treatments increases our understanding of the underlying physiological processes.
  • Experimental design should include an optimal number of replicates and sample randomisation to ensure a successful interpretation of phenotypic results.
  • Phenotyping results require detailed statistical analysis to be adequately interpreted.

Keywords: plant phenotype; trait; experimental design; plant growth; variability; high‐throughput phenotyping; data analysis

Figure 1. The array of the most prevalent phenotyping systems for answering a research question. The control over the uniformity of conditions for the experiments decreases with the increasing agronomic relevance. While one has the highest control of the conditions in experimental systems using, for example, agar plates and hydroponics, these experimental setups are carrying little relevance to growth conditions typically used in agricultural settings. The control of conditions, including humidity, light, temperature and nutrient availability increases with darker shades of yellow, whereas the agronomic relevance of measured traits such as plant growth, morphometry, photosynthesis and yield increases with darker shades of green.
Figure 2. Guidelines for choosing the growing system to address your biological question. The most commonly used phenotypes are listed in green boxes, while the phenotyping systems are indicated in the yellow boxes.
Figure 3. Imaging of plant size throughout time using RGB imaging. (a) Critical factors to consider when taking RGB pictures, namely contrasting background (typically blue gives the best contrast with plants), size reference, reference for white balance and positioning of light to prevent shade casting. (b) For an accurate estimation of plant size with 3D architecture, two side view images (typically at a 90° angle from each other) and one top view image are required. (c) Increase of area over time can be plotted for individual plants using image‐based phenotyping. The increase in area for individual plants is represented by separate grey curves. Depending on the developmental stage used for the experiment, the growth of the plant can be divided into early stage, where the plants grow exponentially, followed by mid‐stage where linear growth is more prevalent, and late stage, where the growth slows down. Dependent on the experimental interest, the differences between the genotypes/treatments can be compared across the entire growth period or individual intervals. (d) Estimation of plant growth by fitting, for example, linear or exponential functions to the increase of area over time. The graphs represent an observed area at specific time points with green dots, while the fitted function is indicated by the blue dashed line. The formulas for individual functions are presented above the graphs, where LGR stands for Linear Growth Rate, and RGR for Relative Growth Rate, as the projected increase in area at one time point (t), using an exponential model is relative to the area at the previous time point (t−1).


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Negrão, Sónia, and Julkowska, Magdalena M(Mar 2020) Plant Phenotyping. In: eLS. John Wiley & Sons Ltd, Chichester. [doi: 10.1002/9780470015902.a0028894]