Imaging-based plant growth analysis





Achim Walter and Michael Mielewczik


Growth is defined as an irreversible increase in volume or substance which is bound to the living cell (Strasburger 1998). Even in the single plant cell, growth is a complex and dynamic process. In multicellular tissues, organs and organisms, growth of a single cell has to be mediated with growth of neighbouring cells that often fulfil different functions. Hence, plant growthis highly regulated and depends on a network of factors. In contrast to most animals, plants grow throughout their entire life. While growth processes in animals are largely deterministic, they are more flexible in plants. This flexibility is an important feature allowing plants to dynamically adjust their performance to fluctuating environmental conditions (Walter 2000; Walter, Silk, Schurr 2009). The dynamic growth potential of plants can be utilised on demand during acclimation processes to environmental perturbations. When plants are exposed to shade, for example, they can selectively elongate their shoot axis to increase light interception, while decreasing growth rates of other organs to save energy and growth substrates for the success of shoot elongation. Another well-known example for the flexibility of growth distributions in plants is the adjustment of the ratio between shoot and root growth to mineral nutrient availability: In nutrient-limited situations plants can increase root-shoot ratio in order to maximize nutrient uptake from the soil while overall saving energy and growth substrates by keeping total plant growth rates low compared to plants at higher nutrient availability. Although some regulatory processes giving rise to the before mentioned – and other – growth phenomena are well understood, plant science is far from providing functional models for gene x environment interactions with which plant growth can be described at various scales that would be relevant for a range of research questions.


Plants depend on a continuous gain in energy via light input, they need to take up water, mineral nutrients, oxygen and CO2to sustain growth. All of the necessary uptake processes take place across plant interfaces that are exposed to the environment: They are either occurring via the leaf or via the root surface. Because of this fact, the analysis of leaf and root surface expansion processes is a good proxy for a general performance analysis of plants in their environment. Vice versa, plant growth analysis of a range of genotypes which are exposed to defined environmental conditions allows for selection of the most interesting genotypes in breeding programs and allows studying basic ecophysiological rules that govern the relation between plant growth and environmental conditions which can often fluctuate dynamically. While growth of roots and of leaves of monocotyledonous plants is very directly affected by alterations of environmental conditions, leaf growth of dicot plants is connected to environmental changes in a nonlinear manner that is modulated by the circadian clock (Walter et al., 2009; Poiré et al. 2010).


Growth analysis of a plant, organ or tissue region requires knowledge of the size of the investigated system at least at two consecutive points in time. Destructive methods do not allow studying growth processes with high resolution. Due to the high variability between different individuals, size differences that are reached within minutes or hours cannot be determined with statistical significance by comparing two populations on the basis of destructive measurements. The same argument applies to the destructive analysis of growth differences of spatially neighbouring tissue regions. Hence, high spatial or temporal resolution of growth analysis can only be achieved by utilising non-invasive methods that determine surface or volume of the investigated organ. Often, special cultivation or imaging systems are necessary to ensure an exact quantification of plant organ growth and to control environmental parameters with appropriate accuracy. Also, high-throughput analyses of large plant populations are often only feasible when special imaging equipment is used or when specialised growth facilities are realised that can include for example conveyor belts transporting plants to cameras.


Throughout the last years, a number of quantitative methods based on single image analysis or image sequence analysis have been developed. Some of these allow rapid quantification of leaf size or of the size of total leaf area, while others allow precise analysis of short-term growth variations and small-scale growth heterogeneities. Rapid, single-image based growth methods are typically based on red-green-blue colour images. Growth analysis methods for root growth and for continuous recording of leaf growth at day and night are based on image acquisition in the near infrared spectral region (wavelength around 900 nm) allowing image acquisition without applying wavelengths disturbing the plant behaviour. On top of classical, (direct measurement of organ dimensions, part a) below), there are three further classes of approaches: b) morphometric, c) particle tracking and d) optical flow. The morphometric approach allows for precise shape analysis of organs or canopies (related content is currently in development).

a) Direct measurement of organ dimensions

The oldest and easiest method to record growth is to use a ruler. This is still an appropriate method for easy and rapid investigation of growth processes within plant populations (Walter and Schurr 1999). True leaf area can be calculated from the product of leaf length and leaf width, multiplied by a shape factor which has to be determined for each plant species. Often, these shape factors are scale-invariant, which means that the same factor can be used for small and large leaves as well as for young and full-grown leaves although their proportions might differ. It can be determined by an easy calibration procedure: The outline of several leaves comprising the full range of sizes and developmental stages to be determined has to be sketched on paper of a known density (or the leaves have to be copied). By weighing the cut paper blueprints, the true area can be plotted against the area of the product of length X width of the lamina. The inclination of the fit line equals the species-specific shape factor. In the case of tobacco, cultivar Samsun, this is e.g. 0.75; for Arabidopsis thaliana, factors are between 0.6 and 0.75 (depending on ecotype). To increase throughput of direct measurements, it is useful to use a dictating machine or any other kind of voice recording when dimensions of successive leaves are read.

b) Morphometric approach

In the morphometric approach, images of a growing -object’ (root, leaf or shoot) are taken in relevant time steps (daily, weekly) and the borderline of the growing object is automatically -segmented’ from the background, based on the different colour or brightness of object and background. Segmentation can be performed with different software packages, such as Photoshop or the freeware Image J, which is designed for analysis of segmented object dimensions. Threshold values for colour or brightness can be defined in the software and can be applied to multiple images as long as imaging situations (incident light, magnification of the object etc.) do not vary between images. Area, shape and colour of the segmented object can be quantified thereafter. If a lot of objects are quantified and if their growth rates are to be calculated throughout a longer time period, including several imaging events, proper data management becomes a crucial bottleneck of the applicability of the procedure. There are companies that provide custom-made solutions for automated plant imaging, segmentation and data management and there are several research groups that have set up automated platforms to analyse total leaf area and a number of other plant properties at the same time (Leister et al. 1999, Granier et al. 2006, Walter et al. 2007, Jansen et al. 2009). Such procedures have successfully been applied to calculate e.g. root elongation and root system architecture (Armengaud et al. 2009; Nagel et al. 2009; Hund et al. 2009), root gravitropic curvature (Miller et al. 2007), the projected area of single leaves (Granier et al. 2006, Taylor et al. 2003) or of total leaf area (Barbagallo et al. 2003; El Lithy et al. 2004; Granier et al. 2006; Leister et al. 1999; Walter et al. 2007) and parameters of leaf or rosette shape (Jansen et al. 2009). Such approaches can beneficially be applied for high-throughput phenotyping, but do not reveal spatial distributions of growth within organs. Moreover, diel growth patterns are hard to extract with such methods as e.g. in dicot leaves, movements of organ borderlines out of the focal plane of the optical system have to be prevented or corrected by more sophisticated, three-dimensional image analysis methods.

c) Particle tracking approach

In the particle tracking approach, a discrete number of particles is applied (e.g. graphite particles) or selected (e.g. vein crossings, characteristic cell walls) on the object (Ishikawa and Evans 1997; Basu et al. 2007; Beemster and Baskion 1998). The location of these particles is registered and the particles are recognized in successive images via recognition of the local neighbourhood of these particles. With this approach, spatial distributions of growth rate can be calculated.

If substance concentration patterns are determined – usually by destructive means – it is possible to calculate fluxes and deposition rates of substances of interest within the growth zone, (e.g. Kavanova et al. 2006; Silk et al. 1986) thereby allowing for deeper insight into the biochemistry of growth processes. This approach can only be applied, if the imaged organ surface is restricted to stay in the focal plane of the camera throughout the analysis period or if proper three-dimensional reconstructions can be provided. It has mainly been applied to roots growing along transparent walls of growth containers.

d) Optical flow-based approach

Finally, the optical flow approach is based on evaluation of the entire optical structure of the organ that is seen in the image (Barron and Liptay 1997; Schmundt et al. 1998; Supatto et al. 2005; van der Weele et al. 2003, Walter et al. 2002a). Similar to the previously mentioned approaches, experimentalists have to make sure that the organ surface is not strongly moving towards or away from the camera as this would lead to artefacts in calculation of growth rates. Appropriate procedures to keep growing leaves within the focal plane of a camera have been established (e.g. Walter et al. 2002b), but are tedious and have to be adapted to the requirements of each species. Again, three-dimensional visualization might improve the applicability of this approach, but is not realized yet. The advantages of this approach are that it provides high temporal and spatial resolution and can render reliable results even in very long image sequences since the local neighbourhood of any structure needs only to remain steady for a short time frame, on which the calculations are based and which is shifted through the dataset. Borderlines of organs, that can be problematic because of upward or downward movements during the analysis period, can be neglected on demand. Successive images are typically acquired at short time intervals (20 s to 5 min) and are stacked for evaluation. Optical structures of the growing tissue, such as vein crossings, trichomes or – if the optical resolution is high enough – cell walls result in -streaks’ (virtual, inclined objects) within the image stack: If an optical structure is moving in reality, the streak is not parallel to the time axis, but leads to a linear structure with a certain orientation (angles between this structure, the time axis and the two spatial axes of the images). The orientation of these streaks at different locations within the x-y-time stack is calculated sequentially in small, local neighbourhoods with the so-called -structure tensor’ approach (Bigün and Granlund 1987; Haussecker and Spies 1999). The entirety of all spatial and temporal neighbourhoods renders a dense field of velocity for each image of the tissue surface. A systematic calculation of differences of velocities at different locations within the image stack (the so-called -divergence of the velocity field’) reveals quantitative maps of relative growth rate on leaves and roots in optical flow and particle tracking approaches. However, proper pre-processing of the images (reducing noise, smoothing of the images; Scharr et al. 2005) and proper implementation of the algorithms is crucial in both approaches to obtain robust and meaningful results.


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