X-ray generation occurs in x-ray in a vacuum tube when accelerated electrons from a cathode filament through the vacuum bombard an anode target material (e.g., tungsten). The applied voltage of the X-ray tube determines the kinetic energy of electrons through the vacuum. The X-ray radiation or photons are generated by two processes (Taina et al., 2008). When the incident electron strikes with the atomic electron clouds of the target, it results in the deflection and deacceleration of incident electrons and subsequent conversion of kinetic energy to electromagnetic radiation. This process generates a continuous spectrum; the highest energy of the photon will be achieved when the total kinetic energy of an electron is converted (Jia et al., 2023). In the second process, when the electron strikes the target, the atoms become excited because of the movement of orbital electrons to the higher energy level. When the excited atom comes back to the ground state, distinctive X-ray spectra are released where the total energy is equal to the difference between the energy of the excited and ground state. The distinctive photon-energy amount is subject to the atomic configuration of the target material (Jia et al., 2023). The produced X-ray spectrum is then passed through a metal filter (e.g., Al, Cu) to remove the low-energy X-ray that is not important for imaging. The typical configuration of an X-ray tube is depicted in Figure 2.

The typical X-ray CT consists of generally 3 parts, namely-the X-ray source, sample manipulation stage and detector (Withers et al., 2021). There are mainly 2 types of X-ray instruments, one is for industrial use and the other one is for medical use. The principle behind the two types of system is the same but the basic difference is that in the case of the industrial CT system, the sample is rotated in front of the X-ray source (Figure 3) whereas, in the case of the medical CT system, both the source and detector are rotated around a static object (Helliwell et al., 2013).

When the X-ray photon passes through the sample, it gets attenuated, or the intensity of the X-ray beam gets reduced. The decrease in radiation intensity is exponentially related to the distance traveled by the radiation. The attenuation of a monochromatic X-ray radiation beam is expressed by Beer–Lambert’s law (Withers et al., 2021). When an x-ray beam passes through a sample of the fixed thickness (x), the transmitted beam intensity (I) and the incident beam intensity (I₀) assuming a constant density of the attenuating medium are related as follows (Eq. (1)): I = I 0 e − μ   ρ x (1) Where ρ is the density of material; μ is the attenuation coefficient per unit mass. The distribution of the attenuation coefficients at discrete points within a material can be determined. Attenuation coefficients are dependent on the incident X-ray energy, thickness, and electron density of the absorbing materials.

X-ray CT can be applied for the visualization of the internal makeup of an object in 2-D and 3-D views, depending upon the attenuation of the X-ray. In a typical CT scan, a series of radiograph images are captured from the sample, at incremental angular sites, typically over 360^o (Helliwell et al., 2013). The linear X-ray attenuation coefficient values obtained from the array of radiographic images is integrated and it forms the basis of tomographic reconstruction. Normally, mathematical filtered back-projection algorithms are used. (Figure 4).

Using a set of projections, the computational framework reconstructs the spatial distribution of the attenuation coefficient slice by slice to create a greyscale image of the object. Since similar quality images can be reconstructed at a lower dosage, image reconstructions that enhance image quality can cause decreased radiation exposure. The Radon transform provides a mathematical explanation of the link between the slices and projections. The transform is the foundation for two major groups of reconstruction algorithms, namely analytical and iterative reconstruction (IR). In commercial CT scanners, filtered back projection (FBP) is the most extensively used analytical reconstruction technique, which employs a 1D filter on the projection data before back projecting (2D/3D) the data onto the image space (Yu et al., 2016). The computational effectiveness and numerical stability of FBP-type methods are the fundamental reasons for the wide acceptance of CT scanners. Unlike analytical reconstruction techniques, image reconstruction using IR is accomplished by iteratively optimising an objective function, which characteristically comprises an edge-preserving regularisation term and a data fidelity term (Thibault et al., 2007). Iterations of forward and back-projection between image space and projection space are used in the optimization process in IR. Images via IR could look different from those utilising FBP reconstruction due to the inherent differences in data handling between FBP and iterative reconstruction (such as noise texture). More crucially, because of the non-linear regularisation term and other factors during the optimization process, the spatial resolution in a local region of IR-reconstructed images is highly reliant on the contrast and noise of the surrounding structures (Fessler and Rogers, 1996). Recently, several advanced algorithms are being used for X-ray CT image reconstruction like sparse-view statistical image reconstruction (Mahmoudi et al., 2019), deep learning (Wang et al., 2020), CNN-based projected gradient descent (Gupta et al., 2018), super-resolution reconstruction techniques (Tan et al., 2021), etc. The mathematical algorithms help to generate the cross-sectional 2D image slices from the radiographic projections based on the X-ray attenuation values. Each non-invasive tomographic image stack (i.e., image volume) consists of discrete voxels (the 3D equivalent of a pixel), which represent the resolution of the image stack.

X-ray CT is an influential technique to visualise and quantify the architecture of soil particles, pores, root systems, or any other object of interest given that the object can sufficiently attenuate X-rays. This technique is increasingly adopted in soil, plant, and ecological sciences. The greatest opportunity it holds is the non-invasive visualization of the internal organisation of an opaque substance like soil. Looking through the soil also provides an opportunity to visualise the porous structure through which root grows, fluid flows and microbes thrive. Applications of X-ray microtomography are discussed in the following sections. Here some of the practical aspects of using X-ray microtomography is discussed to consider before designing research on quantitative analysis of the soil-plant system. Application of X-ray CT in different areas of soil science is summarised in Table 1.

The pore structure and distribution regulate the distribution of micro-organisms, moisture, and air in the soil (Helliwell et al., 2013; Ghosh et al., 2020a) and thus have a potential role in controlling soil organic matter decomposition (Rabbi et al., 2016). The quantification of the internal soil structure is an important key for understanding different physico-chemical and biological processes occurring in the soil. Recently X-ray CT gives a novel way to study soil pore structure (Pires et al., 2020; Liu et al., 2021). The characterization of soil structure is significantly influenced by the representation of X-ray CT data as grayscale images and the appropriate choice of segmentation techniques to binarize it (Wang et al., 2011; Torre et al., 2020). The application of X-ray CT for the evaluation of soil porosity has become a research tool for the investigation of soil porosity and pore size distribution (Rab et al., 2014; Ghosh et al., 2022). Yang et al. (2018) used different soil amendments like straw mulch, superabsorbent polymer (SAP), and organic fertilizers for improving the soil structure and porosity. They used X-ray CT to determine the number, size, location, and morphology of soil pores. They reported that the combined application of amendments improved the soil pore structure more effectively as compared to individual applications. They also found that the application of both straw mulch and organic manure improved the soil porosity and soil structure more effectively as compared to other combinations of amendments. Pires et al. (2019) employed the X-ray CT to evaluate the effect of three different tillage systems (ZT-zero tillage; RT-reduced tillage and CT-conventional tillage) in the soil porous system of an Oxisol. They used the undisturbed core of 0–10 cm soil depth for scanning through X-ray CT. The results showed that the soil under ZT had the smallest porosity when compared with the other management practices. The largest porosity and the most connected pores were seen in conventionally tilled soil. Young & Ritz, (2000) stated in their review that macropore continuity is meaningfully improved in the soil profile of the ZT system. X-ray CT images of macropore network (>1 mm) of soil cores (10 cm diameter) collected from (a) ZT and (b) CT plots. Macropores continuity is significantly improved in the soil of the ZT plot.

It has long been understood how significant soil macropores are as preferred channels for the flow of chemicals, air, and water in various soils. However, it is difficult to quantify complicated macropore structures and how they relate to different soil types and land uses. Luo et al. (2010) quantified 3-D macropore networks in unbroken soil core under contrasting soil types and land uses by X-ray CT. For every soil typeand land use grouping, intact soil columns of 102 mm diameter and about 350 mm length were scanned at energy levels of 130 kV and 100 mA and voxel resolution was 0.234 mm × 0.234 mm × 2.000 mm. After image reconstruction, the features of the macropore networks, such as the incessant change in macroporosity along the depth, the distribution of macropore sizes, the density of the network, the length density, the surface area, the length distribution, the mean hydraulic radius, the tortuosity, the inclination angle, and connectivity, were quantified. This study offers an enhanced quantitative assessment of several soil macropore characteristics that have substantial consequences for field soil modeling of chemical transport and non-equilibrium flow prediction. A study was done by Beraldo et al. (2014) for the quantification of soil porosity using X-ray CT under CT, ZT, and native forest. In this study, they acquired the images by a system of X-ray CT (Model 1,172 of SkyScan), consisting of a microfocus X-rays tube with a 100 kV high voltage source, the detector made of a CCD camera of 10 MP connected to the computer for image acquisition. The images had a resolution of 34.81 µm and it enables the detection of microporosity having pore diameter >0.05 mm) and a narrow range of micropores with diameter <0.05 mm. As a result, they reported that there was a change in soil porosity under different management practices, and the difference was more significant in conventional and no-tillage compared to the area under forest. Hamamoto et al. (2016) used X-ray computed tomography for obtaining information regarding pore networks under different compaction levels for the column of sand and glass beads which represents different shapes and sizes of particles. Luo et al. (2008) quantified soil structure and preferential flow in intact soil of 10 cm diameter and 30 cm height using X-ray CT (OMNI-X model, BioImaging Research, Lincolnshire, IL). The CT system had a 225 kV microfocus x-ray generator and a 225-mm-diameter image intensifier. The maximum resolution of ∼5 μm was attained by the microfocus system and the sample was rotated 360° to obtain the polychromatic x-ray beam. Image analysis was done by AMIRA software (TGS Inc., San Diego, CA), and the data obtained in this experiment was analyzed using ImageJ (Rasband, 2002). Their results showed that different types of macropores having dissimilar morphologies (like earthworm burrows and root channels, and interaggregate macropores) and their diverse roles in the solute transport process were detected, which changed in different soil depths and horizons. They observed that few biogenetic macropores which are tremendously continuous are responsible for solute transport in the deeper soil layers. Luo et al. (2008) reported that data from real-time solute transport can generate voxel-based breakthrough curves which are useful in getting solute dispersion parameters of small areas, estimating water movement and solute transport in varied soils and it is helpful to test existing dual-permeability models.

The soil strength in terms of compaction is a serious threat to agriculture, especially in conventional agriculture. Traditionally soil strength can be determined by soil penetrometer or as indirect determination from bulk density but they are unable to recognise a spatial pattern of soil strength. X-ray CT is an effective tool to characterised soil compaction. Kim et al. (2010) used intact soil core to show how 3D macropore geometry is affected by soil compaction. By using X-ray CT, they reported that the total numbers of macro- and mesopore increased when the surface compaction reduced by 69% and 64%, respectively.

X-ray CT images showed a consistently decreasing continuity of macropores as soil BD increased. The average Euler number increased to 0.999 in 1.8 Mg m^-3 soil BD from 0.677 in 1.3 Mg m^-3 soil BD (Feng et al., 2020). They also reported a negative correlation between macropore throat number and thickness, which indicated the loss of pore connectivity with the increase in the soil compaction. Previously, X-ray CT was used for soil BD measurements where gray level values were calibrated by measuring the attenuation of penetrating X-ray CT (Pires et al., 2019). Crestana et al. (1985) developed a soil mini-scanner to determine X-ray attenuation coefficients for estimating soil BD in the field. Previous studies had mainly used medical X-ray CT when laboratory-based fine-resolution X-ray CT was not widely available. There are numerous studies, investigating soil BD using laboratory-based X-ray CT, while they have been emphasising measuring X-ray attenuation as a function of soil density (Anderson et al., 1988). This technology has a unique ability to measure local BD along the soil core, and also visual assessment of density variation is possible, which adds a novel dimension to measuring BD (Le Roux et al., 2019). Le Roux et al. (2019) acquired 2000 to 3,000 images while the sample was rotated at 360° to get a highly magnified image. These images were reconstructed by 3D model, using Graphics VG Studio Max 3.0 (Du Plessis et al., 2016) software, which provides total volume and internal features of the sample. To determine the soil BD, the instrument specification used for the field-moist and wax-coated oven-dry clay soil clods were scanned at 200 kV and 140 μΑ at 100, 50 and 25 μm resolution whereas the sandy soil core was scanned at 150 kV and 100 μΑ with 50 μm resolution. Elias et al. (2021) used X-ray CT in the determination of the BD of cracking soils, they scanned intact clods at 120kv energy level and the pixel densities determined ranged from −1,500 to 3,000 Hounsfield materials, the soil clods were scanned horizontally having 1 mm thickness and a slice distance of 2 mm. The BD was measured by the relation given by Rogasik et al. (1999).

Destructive methods and 2D images, together with thin sections and electron microscopy, are used conventionally to understand soil structure but they are unable to study the spatial arrangements of undisturbed soil (Young et al., 2001). X-ray CT has a great potential to detect spatial arrangement of different constituents of the soil sample. With technological advancement, it is now used to interpret the finer soil fraction than sand with a voxel size of <15 microns (Feeney et al., 2006; Wang et al., 2012). Further improvement in image resolution enables the detection of clay fraction, nutrient liberation, and stage of weathering, etc (Helliwell et al., 2013). More X-ray attenuation at smaller energy levels helps to discriminate different minerals of comparable mass (Taina et al., 2008). But it is a challenge to isolate or quantify the soil particles, water volume, and air-filled porosity. Rogasik et al. (1999) used a medical X-ray CT (Siemens Somatom Plus-CT scanner, Siemens, Erlangen, Germany) with 80 kV (190 mA; 760 mAs) and 120 kV (85 mA; 340 mAs) energy levels to compute water and air content, and phase composition of two silt loam soils of chernozem (FAO classification), coherent subsoil Ck horizons. The resolution of the reconstruction matrix was 512 by 512 pixels. The results suggested that in low resolution, the gravimetric soil water content was inversely related to soil BD and no definite relation was found between water content and BD in high resolution. However, voxels encompassing solid and air portion occur mostly in the broader dry BD values.

Earlier fluid flow studies in the porous soil used to be done by mathematically solving Darcy’s law using the fundamental Navier-Stokes equation. X-ray CT has great potential to detect water content and its movement within the soil matrix (Schlüter et al., 2020). The ability of X-ray CT to reproduce 3D images allows for the identification of interconnected void spaces (Perret et al., 2000; Mooney, 2002; Wildenschild et al., 2005). With recent advancements in X-ray CT and robust modeling techniques, pore-scale fluid flow can now be easily modeled using the Navier-Stokes equation (Fourie et al., 2007). The model provides data on the soil’s isotropy, tortuosity, and dispersivity in all directions. Rezanezhad et al. (2009) carried out a study to examine how the flow of water through peat soil was affected as the size and shape of the pores changed. They used X-ray CT at 45 µm resolution to detect pore size and configuration under changing water regimes. The study suggested that HC of peat soils was greatly regulated by a pore shape coefficient which was generally supposed to contain pore characteristics like PSD, path tortuosity, roundness, and sphericity of pores. A pore channel model developed with different pore segments showed that flow in water varied with the pore shape in 2D images whereas tortuosity is prominent in 3D images (Choi et al., 2020). For the characterization of all the factors required for pore geometry, X-ray micro-CT equipment was used with a voxel size ranging from 3 to 10 μm and commercial Avizo software was used to visualize, and analyse the stacked image. Stokes and Brinkman equations were used to capture the relation of flow pattern with apparent and indistinct pore geometry. There was a good agreement between the predicted permeability gained from the pore channel model and experimentally observed permeability. They reported that the pore channel model made the complex pore geometry study simpler and very much suitable for fluid flow study in water at the pore level. Grayling et al. (2018) used the Phoenix Nanotom^® X-ray μCT scanner (GE Measurement and Control Solutions, Wunstorf, Germany) for tracing the movement of a particle in the soil. They used decabromodiphenyl ether (DBDE) as a proxy solute material of 1–2 μm sized particles. Scanning of discrete regions of interest (ROI) was done at 120 kV and 90 μA using a 0.1 mm copper filter. After reconstruction, Volume Graphics (VG) StudioMAX^® 2.0 software was used to analyse the image (10 mm × 23 mm diameter). DBDE concentration in the leachate accumulated at the bottom of each soil column was determined after every scan using gas chromatography (GC) and a chemical breakthrough curve was made. The findings showed that at the beginning, the movement of the tracer material was very fast in the upper soil layer and a robust relationship between DBDE concentration and X-ray attenuation (R ² = 0.98) was obtained which confirmed that with an increase in concentration, the colour intensity of the tracer material in each tube became lighter. Clausnitzer and Hopmans (2000) and Anderson et al. (2003) used iodide (NaI or KI) or bromide (CaBr2) tracers for the study of solute transport in the soil materials due to their high contrasting X-ray attenuation. They proposed that the porosity can be estimated from CT-derived solute breakthrough curve. The CT-derived porosity showed a good agreement with laboratory-measured porosity by the differences varied from −4.5% to 4.2%. Low-resolution X-ray CT can quantify soil moisture content in the root rhizosphere and bulk soil; however, a linear calibration between X-ray attenuation and water content is required. (Hamza et al., 2001). As a single wavelength of X-ray CT will not be able to readily make a differentiation between changes in BD and water content, a uniform BD could be assumed (Moradi et al., 2011). Daly et al. (2015) utilized X-ray CT to visualize the soil water in-situ in 3D at successive increments in matric suction in bulk and rhizosphere soil, the study provides a complete picture of unsaturated HC and hydraulic properties with the help of numerical modeling. Similarly, Tracy et al. (2015) used high-contrast X-ray CT images to compute the water distribution in soil systems under sequential reductive drying in contrasting soil textures.

Combined application of the Lattice Boltzmann (LB) model and X-ray CT has been reported to simulate pore-scale water flow and chemical transport in a porous medium (Zhang et al., 2016). Pores can be quantified from X-ray CT data that are larger than the resolution of the data collected. Due to this limitation, X-ray CT obtained solid phase may be a grey phase in realism and contain considerably associated pores responsible for fluids and solute transport in soil. Single-relaxation time approach is used in the LB model and a modified partial bounce-back scheme is used to assess the resistance imposed by the permeable solid phase to chemical transport (Wang and Zhu, 2018). Zhang et al. (2016) applied the LB model to X-ray CT acquired images having 30 μm resolution to study how solute transport through the solid phase affects the average solute transport behaviour. They showed that the pore geometry has a pivotal role in water flow and solute transport in vadose zones of the soil and the pore geometry is extremely inconstant in cultivated lands where different management practices such as crop rotation, tillage and trafficking, and soil modifications, have been done. Filipović et al. (2020) combinedly applied laboratory and numerical methods with dye staining and X-ray CT to evaluate the porous system and preferential flow paths using an intact soil core of a vineyard. They analysed 600 images with Image J software and found that the largest pore diameter ranged from 5.2 to 10.6 mm, indicating that the pore source is earthworm burrows and plant roots known as biopores contributing to the preferential flow. Müller et al. (2018) characterized the hydraulic characteristics of macropores from the X-ray CT images captured from three horizons of an Andosol and a Gleysol. For the Andosol, images of soil cores were taken using a 160 kV X-ray source at 150 μA and 1,250 angular projections through isotropic voxel size of 0.0734 mm in each scan. Results indicated that imaged-limited microporosity could best predict K_unsat and a less total macroporosity along with high macropore density, showed the richness of smaller macropores, important to homogeneous matrix flux.

The characterization of organic constituents like decomposing crop residue and plant root identification is a more challenging task than characterisation of soil void space. As a result, little research has been conducted in this area, with the majority of studies focusing on characterising plant roots. (Mooney et al., 2006; Tracy et al., 2012b). Quinton et al. (2009) used X-ray CT (GE Medical CT, model MS8X-130) in highly organic soil like peat and found that a single large pore represented more than 99 percent of the active porosity, whereas 2D analysis revealed a relatively steady conversion of small pores to large ones. They faced a challenge to discriminate organic matter and soil water because being more porous, organic matter produced a similar kind of attenuation of X-ray when the soil was saturated with water. To avoid this problem, negligible values of the volumetric linear attenuation coefficient (LAC) of organic matter was taken for nearly saturated soil samples (0 to −40 cm). Lately, Elyeznasni et al. (2012) was able to detect coarse-sized organic matter (>40 µm) content from X-ray CT technique, and the grey level values of the scanned image, also found that 3D segmented images are well associated well measured porosity. Chenu and Plante (2006) used transmission electron microscopy (TEM), and Kinyangi et al. (2006), used synchrotron-based scanning transmission X-ray microscopy (SEM), reported that small organic particles are materially obstructed in micropores, emphasizing the role of microscale soil structures. Peth et al. (2014) used a synchrotron-based -µCT scanning with a voxel resolution of 9.77 µm and osmium as the staining agent to detect the presence of SOM soil aggregates under in-situ conditions. They showed that the relationship between osmium signal intensity and organic matter concentration is strong, but the relationship is not so variable among the samples and only the relative concentration of SOM may be measured. Their findings can be useful in geometry-based mechanistic modeling approaches to systematically study PSD on carbon decomposition (Peth et al., 2014) as such models need explicitly spatial explicit inputs stating from pore architecture geometry, and the spatial microbial distribution and SOM as decomposable substrates. A study of soil microbial functioning through X-ray CT analysis would be a commanding tool in understanding the role of soil pore geometry on SOM degradation as the tomographic technique is not harmful to the microbial community (Bouckaert et al., 2013). However, many researchers (De Gryze et al., 2006; Kaestner et al., 2006) reported that phase separation/segmentation became very difficult as the X-ray attenuation coefficients of SOM become in-between air/water-filled pores and mineral matrix. Consequently, in-situ measurement of low concentrations of SOM in cultivable mineral soils (i.e., 5 % wt. basis) is restricted due to the weak X-ray attenuation coefficient of SOM as compared to the other complexes and SOM has a very near association with the surfaces of clay mineral.

The rhizosphere is the region/volume of soil which is influenced by a plant root (Rabbi et al., 2016) and very vital for plant growth and nutrient uptake. Compared to bulk soil, rhizosphere soil has different physical and chemical characteristics. Chemical alterations have not, however, been thoroughly measured in-situ in pore-scale level aside from root-induced physical changes. The different hydraulic properties of soil are highly influenced by soil pore geometry and its modification (i.e., alteration in porosity and diameters of the pore) root-microbe interactions and rhizodeposition (i.e., mucilage, exudates) at root rhizosphere (Mendes et al., 2013). Most commonly used methods to characterise the soil biotic properties like plant roots, movement of water to the roots and soil micro or macro fauna and flora, are destructive in nature, but X-ray CT has remarkably capable to characterize these biological characteristics in opaque and composite medium (Gregory et al., 2003; Young and Crawford, 2004). Van Veelen et al. (2020) wanted to understand the chemical changes brought about by plant roots, so they combine structural data formerly gained using synchrotron X-ray CT with synchrotron X-ray fluorescence microscopy (SR-XRF) and X-ray absorption near-edge structure (XANES). To directly identify different elements in biological and geological samples, XANES in conjunction with SR-XRF mapping has proven to be an effective non-destructive technique. This combined approach can be distinguished into 3 zones: the close rhizosphere with improved porosity, the compaction zone, and bulk soil. The primary processes, like reduction, microbial alteration, and surface-moderated reactions occurs in the first two zones. This zone also has substantially greater rates of species interactions and transformations. Helliwell et al. (2017) used high-resolution X-ray CT to investigate the influence of root growth on soil structural alteration at scales pertinent to discrete micropores and aggregates. Using a Phoenix Nanotom 180NF X-ray micro-CT scanner (GE Sensing and Inspection Technologies, Wunstorf, Germany), all samples were scanned, with X-ray energy of 115 kV, 110 µA current, and 750 µs exposure time resultant imaged voxel size of 12 µm. After tomographic reconstruction, all images were cropped 3 mm from the column border to prevent any possible edge effects and then processed using the VG StudioMax^® 2.1 software. They reported that soil porosity around the roots in the rhizosphere initially rose in contrasting soils, nevertheless reduced in sandy loam soil while improved in clay loam soil. With the help of X-ray CT, it was possible to determine the precise changes in the soil structure caused by roots both close to and at some distances from the root-soil boundary. Helliwell et al. (2019) spatially quantified the variations in soil texture and compaction caused by three commonly occurring but different plant species (like pea (Cicer arietinum L), tomato (Solanum lycopersicum), and wheat (Triticum aestivum)). Initial scans of all samples were performed using a Phoenix Nanotom 180NF X-ray micro-CT scanner (GE Sensing and Inspection Technologies, Wunstorf, Germany). The source had a 3 m focused point, and since the sample’s centre was 5.4 cm from the X-ray source, the imaged voxel size was 12 μm. Utilizing the program VG StudioMax^® 2.2, images were processed, and to reduce noise and preserve the structural boundaries, a median filter with a radius of 3 pixels was used before segmenting the soil, root, and pore phases. Plant species significantly impacted the zone of impact of the root (i.e., the spatial grade of any variation in porosity away from the bulk soil) in the order of wheat (Triticum aestivum) > pea (Cicer arietinum L) > tomato (Solanum lycopersicum) (average of 694.7,483.9, and 21.2 mm³, respectively). The use of X-ray CT also provides the means to immediately apply numerical models describing the diffusion of nutrients to the observed geometries, in addition, to direct in-situ visualization of plant-soil interaction (Daly et al., 2015; Tracy et al., 2015). Daly et al. (2016) used X-ray CT to understand how root hairs contribute to nutrient uptake at various soil water contents, to construct an image-based modeling approach that applied to root hairs surrounded by a vast or infinite amount of soil, and to calculate the impacts of root-hair growth on nutrient uptake. G. Wang et al. (2020) analysed the effects of water and salinity stresses on maize (both separately and in combination) (Zea mays L.). To collect the aggregates clinging to the roots, they removed the roots and used X-ray CT to scan them. All aggregates’ porosity and PSD were determined from pore-scale simulations of water and solute movement in the void area, while their permeability and tortuosity were measured from segmented pictures. Aggregates from the unstressed control were used for comparisons. With an electron acceleration energy of 85 keV and 100 A current, the samples were scanned with a spatial resolution of 4 μm, requiring about 30 min for each sample. Chapman et al. (2020) investigated how auxin affects root growth in Arabidopsis thaliana and Medicago truncatula using X-ray CT in conjunction with minirhizotrons and molecular biology techniques. It was discovered that the RSA, auxin levels, and rootward auxin transport are all under the control of these two-plant species’ soil-grown receptor mutants. It is claimed that the CEP signaling and CEP receptor downstream targets can be altered to disturb RSA. To define the dynamics of nutrient cycling and exudate release, Van Veelen et al. (2020) developed data-intensive modeling of the rhizospheric process. In their research, X-ray CT was used to evaluate the soil pore geometry. while MRI was used to determine the water distribution and plant exudates. Finally, it is noteworthy that Ganther et al. (2020) evaluated the rhizosphere’s reaction to X-ray exposure to determine the effect that X-ray CT analysis itself has on biological systems. According to these authors, the rhizosphere’s bacterial microbiome and microbial growth characteristics were not affected by the X-ray CT study, and a 24-h delay was enough to undo any effects on the system that had been noticed. Hamza et al. (2001) observed that transpiration rates change with the variations in root diameters. They also reported that the high-resolution X-ray CT image can acquire spatial information on the microstructure of the root rhizospheres and which would be helpful for modeling water movement in association with root axial expansion. Aravena et al. (2011) reported that with a increase in soil compaction in the root rhizosphere, the contact between the soil and plant roots increased, which leads to more unsaturated hydraulic conductivity between aggregates, and finally it causes a net positive effect on the accessibility and transportation of water to the root. A new method was developed by Schindelin et al. (2012) for the quantification of root-to-soil contact using X-ray CT images, they segmented the plant root and soil separately and were able to obtain detailed information on plant roots and soils under water stress in loamy sand and vermiculite compound. Hu et al. (2019) used X-ray CT to quantify the soil macropores and root architecture, and revealed the larger degree of macroporosity under shrubs was due to the greater root network density.

Plant roots play a vital role in the growth and development of plants as roots can acquire and deliver water and nutrients to the above-ground plant (Perret et al., 2007). Previously, the commonly used destructive methods of root analysis such as root washing could not deliver thorough information on root architecture, as well as branching features and extension rate, which are integrally connected to environments within the soil profile (Tracy et al., 2010). Rhizotrons are one of the oldest non-destructive techniques to study root growth inside the soil. Rhizotrons covered underground walkways with clear windows on one or both sides are used to study the roots and their rhizosphere organisms detected at the soil-wall boundary. Rhizotrons typically are costly to build and work on (Taylor and Brar, 1991). With the advancement of technology, the X-ray CT allows us to envisage root system architecture (RSA) under the soil, non-destructively and in a 3D arrangement. Though, CT imaging, reconstruction procedures, and root separation from X-ray CT volumes, consume substantial time. Mooney et al. (2006) used X-ray CT for the first time to study lodging roots in the soil and they reported that the root lodging formed a substantial rise in porosity and size of the pore at the macroscale. Perret et al. (2007) & Hamza et al. (2007) have done remarkable contributions to root length imaging using X-ray CT. Perret et al. (2007) also developed a protocol for the quantification of chickpea (Cicer arietinum L) roots after 21 days of growth and they also determined some important parameters of roots like the number of lateral roots, their volume, length, wall area, tortuosity and orientation. They reported that X-ray CT analytically undervalued root length compared to the destructive method. Hamza et al. (2007) tried to investigate the effect of salt stress on root growth of lupin (Lupinus angustifolius L) and radish (Raphanus sativus L.) in −2.0 MPa to −0.10 MPa range by using X-ray CT. Han et al. (2009) studied the effect of scan-inducing bacterium density on potato root growth. Daly et al. (2015) did an image-based modeling study using X-ray CT to understand and visualize the spatial distribution of water around the roots, and also calculated the water flux into the root. They quantified the effect of root architecture and its geometry on water uptake by doing a comparison between image-based models and spatially average models. They revealed that the spatially averaged models did better when compared to the image-based models and the observed difference in uptake was <2% Different plant root studies using X-ray CT are listed in Table 2.

X-ray CT performs a leading role as a modern research tool to understand root architecture, interaction with soil, root development, etc. Using X-ray CT, it is possible to study the same distinct root growth over time and to observe root development procedures in pot study, also it offers the chance to discover the unchanged conformation of the 3D root architecture intermingling with a real field soil matrix. X-ray CT-based phenotyping techniques i.e., the characterization of realistic root structures of plant establishments in the field (Araus and Cairns, 2014) allow the spatio-temporal quantification of roots, aids to recognize the novel adaptive root architectural changes to the abiotic and biotic stresses (Mairhofer et al., 2017). Hu et al. (2019) reported more root development in porous soil and revealed a strong positive relationship between root volume, length, surface area and the solid surface/solid volume ratio. Gao et al. (2019) showed that X-ray CT could give a higher root length as compared to the root length gained by root washing along with 2D light scanning. Almost all land plants depend on their root systems to survive, and agricultural species must improve their ability to withstand abiotic stress, accumulate nutrients, and produce more, which shows the importance of root phenotyping. The classical destructive method of root phenotyping is where roots are removed from soil, washed, scanned then analysed using either commercially available (such as WinRHIZO, Regent Instruments Inc., Sainte-Foy, Quebec, Canada) or specially designed software (such as the so-called “shovelomics” approaches (Trachsel et al., 2011; Colombi et al., 2015). In contrast, X-ray CT is a more powerful tool for root phenotyping having several advantages, like, in pot studies, the development and growth of a single root can be tracked over time. Furthermore, the 3D root system architecture (RSA), in its unaltered configuration, can be investigated as it interacts with a real field soil matrix (Pfeifer et al., 2015). Using the variations in attenuation brought on by X-rays between different materials, X-ray CT can see roots in the soil. For X-ray CT imaging, densitometric slice pictures (reconstructions) are computed using signal data from multi-angle projections. These reconstructions are then stacked to create 3-D volumes (Heeraman et al., 1997). However, the techniques involved in X-ray CT scanning, reconstruction, and root separation from the voluminous images are time-consuming. Teramoto et al. (2020) have created a high-throughput process flow for the three-dimensional imagining of rice (Oryza sativa) RSA, which consists of radicle and crown roots. The procedure involves using calcined clay with even particle size to lessen the likelihood of seeing non-root sections, using a greater tube voltage and current in the X-ray CT scanning to boost root-to-soil distinction, and a 3-D median filter and edge detection algorithm were used to isolate root parts. To accomplish this procedure, 10 min of scanning and reconstruction, and 2 min of image processing were required, they named the technique for RSA visualisation as “RSAvis3D” and were assured enough that it would provide a possibly effective use for 3-D RSA phenotyping of different plant species. Herrero-Huerta et al. (2021) created a spatial-temporal root architecture modeling technique based on 4D information from X-ray CT. This innovative method takes into account the precision and robustness of the results as well as the cost-effective time required to process the data for high-throughput phenotyping. They used X-ray CT with an energy level of 200 kV and 3.5 mA with a pixel pitch of 90 μm, after the root reconstruction skeletonization was done for temporal analysis, and the root system architecture model was used for spatial analysis. Therefore, significant root architectural traits are mined from the model relying on the digital twin, which includes root elongation rate, number, length, growth angle, height, diameter, branching map, axial volume and lateral roots. Rogers et al. (2016) used an X-ray CT system to measure how rice’s RSA altered due to the changes in physical characteristics of the growth media, they discovered that the interactions between a plant’s genotype and its environment are what cause the RSA that is seen in that particular plant. The contrast among the roots, soil, and SOM is typically low, although that X-ray CT has been employed regularly to examine roots. Keyes et al. (2017) proposed using iodinated contrast media (ICM) to enhance phase contrast in root system studies, particularly for root vascular structures and rhizobial nodules.

Presently available X-ray CT are unable to detect microorganisms in the soil as X-ray has a high attenuating nature in the soil in contrast to minimum attenuation from microbes (O’Donnell et al., 2007). Despite the inability to envisage microbial communities directly, X-ray CT offers a chance to perceive their habitat and the structural dynamics within the soil. Different studies on soil microorganisms using X-ray CT are listed in Table 3. X-ray CT used combined with additional methods like thin sectioning and molecular biology methods offer very valuable information regarding habitat structure, resource flow, microbial populations (size and structure) and spatial distributions (Crawford et al., 2012). X-ray CT is a very sophisticated tool for qualitative and quantitative characterisation of invertebrate burrows in soil and also helps to visualize with time, ultimately providing useful information for soil physical properties. Capowiez et al. (2011) employed X-ray CT for the discrimination of burrows between anecic and endozoic species. They reported that the endozoic burrows were narrower and in more numbersand branched and less perpendicular. Once the earthworm density folded twice, the volume and length of the endozoic burrow system improved while no increase was detected for anecic earthworms. Ma et al. (2021) used X-ray CT to evaluate the influence of anecic earthworm Metaphire guillelmi on the properties of macropores and water content of the soil. They also reported that earthworms enhanced the soil macropores and microporosity, and the soil water content (SWC) linearly declined with growing earthworm density. Balseiro-Romero et al. (2020) employed X-ray CT to assess the earthworm behaviour in heterogeneously multi-polluted soils and they noticed that the earthworms sidestepped the polluted regions differently. Booth et al. (2020) applied X-ray CT to characterise and quantify wireworms, their tunnel arrangements and the rooting characteristics of two distinct crop types (barley-Hordeum vulgare L and maize-Zea mays L) in a pot experiment, samples were imaged at different times gap. They reported that the burrow systems were set up rapidly and persisted at a comparable volume. A significant difference was there in the burrow systems volume formed by wireworms amongst the two crop types which suggested the variations in wireworm behaviour caused by different crop types. Bailey et al. (2015) tried to determine the effect of mole cricket (Scapteriscus spp) tunnel on soil hydraulic conductivity, they revealed that the biopore formed by mole crickets led to the preferential flow hence, enhanced the soil water movements. Figure 5 illustrates some examples of X-ray CT images under different applications.