Food security has been identified as one of the largest challenges being faced in the world today [36]. Globally we have become increasingly dependent on a select handful of plant species and as a result an increasing importance is being put on research of these crops [37]. In many crops yield is dependent on the stability and uniformity in grains (shape, size and yield) and this has been the target of breeding programmes. The current challenge is to develop methods able to measure grain traits on a large scale in a quick and robust manner.

In this study we demonstrate that X-ray micro-computed tomography (μCT) can provide non-destructive, quantitative data on the environmental impact of stress on grain traits within their normal developmental context. Moreover this can be done quickly, accurately, and is scalable to large sample sizes with minimal user intervention.

μCT as the method of choice for spike and grain trait analysis

There is a scarcity of organ-level imaging approaches that lend themselves to rapid quantitative measurements suitable for in-depth physiological or genetic dissection and modelling. Light and electron microscopy are widely used but they provide limited information and tend to be labour-intensive to produce [38]. Other techniques using conventional cameras that rotate around the subject can also generate accurate 3D model but do not provide information about the internal structure of plant material [39, 40]. As the organs of interest are often embedded within other tissues, the techniques described above require the manual removal of the surrounding tissue. This can be time-consuming and spatial/developmental information is easily lost. Methods have been designed to allow automated removal of grains from the spike while retaining positional information, but these are highly specialized and expensive instruments [41].

These limitations can be largely overcome by μCT. μCT has traditionally been used, to great effect, in medical imaging, and its applications in plant science have increased over the last few years [17,18,19,20,21,22,23,24,25]. Methodologies developed in the medical field have been applied to wider biological studies, for example techniques used to model regions of the human heart [42, 43] have more recently been used to examine seed anatomy [18] and methods used to study metamorphosis in insects [44], modified to track root development in soil and non-destructive floral staging [19, 45].

One of the critical advantages of μCT imaging is that positional information of organs and tissues is preserved and can be analysed. This is extremely important when looking at changes throughout development and variation in grain traits within each spikelet or along the length of a spike is a good example. Imaging of internal tissues and organs without dissection is also possible, although this will require scanning at higher resolutions. Thus non-destructive imaging of the bran layer and the embryo, both of which are economically important traits, could be further developed and scaled for breeding and quality control applications. Finally, detailed study of specific 3D grain parameters such as circularity, surface area and crease volume which are agronomically relevant are also made possible by this method.

Constraints of the scanning and image analysis methodology

Underlying the increased use of μCT in plant biology has been the development of more affordable small, and even benchtop, μCT scanners with sample loading carousels more suitable for larger sample numbers. However, their use necessitates a number of trade-offs between sample number, size and data quality. For example, the loading carousel imposes physical limitations on the size of individual samples and we had to divide many spikes. To re-integrate measurements taken from separate portions of the same spike, we identified conjoining points along the rachis of each spike and rejoining images was introduced as an additional processing step. Further issues can emerge from the use of a fixed X-ray beam which rotates the subject to obtain a 360° image. This gives opportunity for movement during scanning resulting in minor image distortion. To limit movement, scanning material was held in place using thermoplastic starch that, although visible in the scan, can easily be removed by the application of morphological filters during image processing. The time required to produce and reconstruct high resolution scans represents, perhaps, the most serious bottleneck for routine grain analyses. For a wheat spike this can take several hours using typical hardware. To address this, the scans were performed at the lower resolution of 0.2 megapixels (512 × 512) rather than much higher resolutions used in previous studies, for example 5 megapixels (2048 × 2048) and larger is often used [19]. This also reduced the output file size on average by a factor of 16. The trade-off for this lower resolution was the decrease in spatial accuracy resulting in the incorrect joining of juxtaposed objects; this was rectified during the segmentation process.

Development of a robust computer vision pipeline

During our initial attempts to analyse the data produced through μCT we discovered that there was a lack of software that could handle the volume of the data and implement modern computer vision algorithms easily and was well-suited to high-throughput automation. VGStudio Max, a commercially licensed software package, and BoneJ, a free and open source software package, are often used in biological and medical science for image analysis and visualisation [19, 46, 47]. However they require human interaction on a per image basis. While this level of interaction is justifiable for high value subjects (i.e. in a medical context), the scale required for crop biology demands minimal intervention.

This prompted us to design and create a new computer vision based methodology. Our aim was to develop an entirely adaptable system which we could build upon in future, and robust enough to work with a multitude of grain shapes and sizes. The MATLAB [26] scientific programming language and environment provided a widely available professional platform that has closely related open-source alternatives (Octave [48]) that can be used to implement our method, albeit with reduced functionality (some of the watershedding techniques are not yet implemented in Octave).

Suitability for grain trait analysis

As a proof of principle, the developed methodology was used to study the effect of temperature and water regime on spike development and grain traits on a population of wheat plants. We found that temperature differentially affects grain formation along the spike with the middle of the spike being more sensitive to the stresses. Recent studies have shown that there are two discrete developmental stages where the spike is more sensitive to temperature: early booting when meiosis is occurring and anthesis [8,9,10]. Floret development along the spike is asynchronous [12] it is thus tempting to speculate that the florets in the middle were at a temperature-sensitive stage when the stress was applied. In agreement with previous reports [9] we also found an inverse relationship between grain number and grain volume across treatments. While high temperature and high water regime caused a decrease in the number of grains per spike, the average volume of grains increased, partially compensating for grain loss. It should be noted that the low water plants were slightly ahead in terms of spike development when the heat stress was applied and this could explain why in these plants’ temperature has a less detrimental effect on grain number per spike. Despite suggestions that grain height, width and depth are affected by independent sets of genes [49], our data indicates that the response of these traits to different growth conditions are highly correlated. It will be informative to extend these studies to diversity and mapping populations to explore how changes in spike architecture and grain traits in response to multiple stresses are genetically controlled.

Finally, to demonstrate the wider applicability of the method, we examined different species (foxtail millet, oats, darnel ryegrass and ryegrass) that illustrate a diversity of inflorescence and grain morphologies, from the dispersed open panicle structure of oats to the very congested structure of millet which has numerous small grains packed together (Fig. 8). In all cases, simply by adjusting two parameters (the structuring element size, and minimum grain size) our method identified the grains and performed grain feature extraction accurately (Additional file 7: Table S4).

Fig. 8 Evaluating methodological versatility: 3D reconstructions of µCT images. a, b Foxtail millet (Setaria italica), c, d oats (Avena sativa), e, f darnel ryegrass (Lolium temulentum) and g, h ryegrass (Lolium perenne). a, c, e, g Pre-segmentation images and b, d, f, h post-segmentation images Full size image

Challenges and future perspectives

Grain uniformity is of economic value in many cereals and is an active breeding target. Grain size changes not only along the spike but also within each spikelet. Relating the position of an organ in physical space to its position in developmental space is a wider challenge, well-illustrated by the contrast between oats and millet but also applicable to other grasses. Besides the grain morphometric traits, the use of μCT may also provide a handle on more difficult to measure traits like crease volume and thickness of the bran layer. Both of these traits are commercially important and determine grading of grains for the milling industry, but are extremely difficult to measure. Embryo size in the seed is also thought to be important in determining seedling growth and final biomass of the plant, but again this is difficult to measure in a non-destructive way [50].

The challenge now is to develop more advanced computing methods that are able to detect and measure these highly complex and variable traits. Recent developments in computer vision methods and machine learning modelling should prove to be very useful for this purpose.