Challenges and opportunities for quantifying roots and rhizosphere interactions through imaging and image analysis

The morphology of roots and root systems influences the efficiency by which plants acquire nutrients and water, anchor themselves and provide stability to the surrounding soil. Plant genotype and the biotic and abiotic environment significantly influence root morphology, growth and ultimately crop yield. The challenge for researchers interested in phenotyping root systems is, therefore, not just to measure roots and link their phenotype to the plant genotype, but also to understand how the growth of roots is influenced by their environment. This review discusses progress in quantifying root system parameters (e.g. in terms of size, shape and dynamics) using imaging and image analysis technologies and also discusses their potential for providing a better understanding of root:soil interactions. Significant progress has been made in image acquisition techniques, however trade-offs exist between sample throughput, sample size, image resolution and information gained. All of these factors impact on downstream image analysis processes. While there have been significant advances in computation power, limitations still exist in statistical processes involved in image analysis. Utilizing and combining different imaging systems, integrating measurements and image analysis where possible, and amalgamating data will allow researchers to gain a better understanding of root:soil interactions

X-ray microtomography to evaluate the efficacy of paraffin wax coating for soil bulk density evaluation

The paraffin-coated method is a well-used approach to measure the soil bulk density (BD). BD is a physical property of great importance for studies of soil quality and health. Therefore, representative measurements of this property are highly valued. Resin and paraffin wax are utilized to coat soil samples; however, if these materials ingress into the sample it could affect the representativeness of BD evaluation. The advance in three-dimensional (3D) image analysis techniques such as X-ray microtomography (μCT) offers a great opportunity to visualize and quantify the possible penetration of paraffin wax into clod samples. In this paper we investigated porous system morphological properties of soil samples coated with paraffin wax. The morphological properties of the pores filled with paraffin wax inside the samples were also studied. We observed qualitatively that samples with large pores close to their borders were more susceptible to the penetration of paraffin wax. Samples with pores >10 mm3 had the highest amount of paraffin wax into them. Triaxial shaped and complexly pores also offered less resistance to the ingress of paraffin wax. Positive relations between the amount of paraffin wax inside the samples and the volume of pores measured, pore tortuosity and degree of anisotropy were found. Conversely, the soil pore connectivity was not correlated with the penetration of paraffin wax into the samples, at least for the region of interest (≈27.3 cm3) studied. Finally, an analysis of the impact of paraffin wax ingress inside the samples in measured BD showed increments of ≈0.09 and ≈0.11 g cm−3 in this property when the paraffin wax penetrates into the large pores

X-ray microtomography and linear discriminant analysis enable detection of embolism-related acoustic emissions

Background: Acoustic emission (AE) sensing is in use since the late 1960s in drought-induced embolism research as a non-invasive and continuous method. It is very well suited to assess a plant's vulnerability to dehydration. Over the last couple of years, AE sensing has further improved due to progress in AE sensors, data acquisition methods and analysis systems. Despite these recent advances, it is still challenging to detect drought-induced embolism events in the AE sources registered by the sensors during dehydration, which sometimes questions the quantitative potential of AE sensing. Results: In quest of a method to separate embolism-related AE signals from other dehydration-related signals, a 2-year-old potted Fraxinus excelsior L. tree was subjected to a drought experiment. Embolism formation was acoustically measured with two broadband point-contact AE sensors while simultaneously being visualized by X-ray computed microtomography (mu CT). A machine learning method was used to link visually detected embolism formation by mu CT with corresponding AE signals. Specifically, applying linear discriminant analysis (LDA) on the six AE waveform parameters amplitude, counts, duration, signal strength, absolute energy and partial power in the range 100-200 kHz resulted in an embolism-related acoustic vulnerability curve (VCAE-E) better resembling the standard mu CTVC(VCCT), both in time and in absolute number of embolized vessels. Interestingly, the unfiltered acoustic vulnerability curve (VCAE) also closely resembled VCCT, indicating that VCs constructed from all registered AE signals did not compromise the quantitative interpretation of the species' vulnerability to drought-induced embolism formation. Conclusion: Although machine learning could detect similar numbers of embolism-related AE as mu CT, there still is insufficient model-based evidence to conclusively attribute these signals to embolism events. Future research should therefore focus on similar experiments with more in-depth analysis of acoustic waveforms, as well as explore the possibility of Fast Fourier transformation (FFT) to remove non-embolism-related AE signals

Image Segmentation for Quantification of Air-Water Interface in Micro-CT Soil Images

Soils are complex environments comprising various biological (roots, water, air etc) and physical constituents (minerals, aggregates, etc). Synchrotron radiation based X-ray microtomography (XMT) is widely used in extracting qualitative and quantitative information regarding spatial distribution of biological and physical soil constituents. Segmentation of these micro-CT soil images is of interest to geologists, hydrologists, civil and petroleum engineers and soil scientists. In this present work, we study and implement segmentation algorithms for microhydrology studies, specifically for soil water conductivity. Three well-known image segmentation algorithms are studied for evaluating their performance for the task. We demonstrate the problems and ways to segment XMT images and extract data for evaluating the air pressure in the soil pores to promote soil hydrology studies. To this end we take the recommended in the literature approach to differentiate textures and segment images using Fuzzy C-means Clustering (FCM). Secondly, we demonstrate the performance of two state-of-the-art level-set based active contours methods followed by curve fitting for radii detection and air pressure calculation

Detection of pore space in CT soil images using artificial neural networks

Computed Tomography (CT) images provide a non-invasive alternative for observing soil structures, particularly pore space. Pore space in soil data indicates empty or free space in the sense that no material is present there except fluids such as air, water, and gas. Fluid transport depends on where pore spaces are located in the soil, and for this reason, it is important to identify pore zones. The low contrast between soil and pore space in CT images presents a problem with respect to pore quantification. In this paper, we present a methodology that integrates image processing, clustering techniques and artificial neural networks, in order to classify pore space in soil images. Image processing was used for the feature extraction of images. Three clustering algorithms were implemented (K-means, Fuzzy C-means, and Self Organising Maps) to segment images. The objective of clustering process is to find pixel groups of a similar grey level intensity and to organise them into more or less homogeneous groups. The segmented images are used for test a classifier. An Artificial Neural Network is characterised by a great degree of modularity and flexibility, and it is very efficient for large-scale and generic pattern recognition applications. For these reasons, an Artificial Neural Network was used to classify soil images into two classes (pore space and solid soil). Our methodology shows an alternative way to detect solid soil and pore space in CT images. The percentages of correct classifications of pore space of the total number of classifications among the tested images were 97.01%, 96.47% and 96.12%

MEASURING GEOMETRICAL TORTUOSITY OF POROUS MEDIA FROM 3D COMPUTED TOMOGRAPHY IMAGES

Tortuosity is an important parameter that has a significant impact on many environmental processes and applications. Flow in porous media, diffusion of gases in complex pore structures, and transmembrane flux in water desalination are examples of the application of the micro-scale parameter. The main objectives of this thesis are to develop functional relationships that relate tortuosity to geometrical and topological parameters of porous media using three-dimensional (3D) computed tomography images, and select the best model that has the best capability to predict geometrical tortuosity. The objectives were achieved by implementing Random Paths MATLAB code that was developed in this work and compared with available Tort3D MATLAB code using high resolution 3D synchrotron computed tomography images of representative porous media. Tortuosity factors were computed from random tortuous paths of connected voxels (Random Paths Code) and tortuous paths derived from 3D medial surface of void space (Tort3D Code). Tortuosity factors were related to geometrical and topological parameters including porosity (∅), median grain diameter (d50), uniformity coefficient (Cu), coefficient of gradation (Cc), sphericity index (Si), roundness index (Ri), and specific surface area (SSA). Tort3D code was validated by comparing measured with predicted tortuosity factors from models reported in the literature. The two codes measured geometrical tortuosity of different sand systems effectively. However, they provided different tortuosity values, since they were developed using different concepts. Models were developed and predicted tortuosity values were compared with measured tortuosity values. Good agreement was found between predicted and measured tortuosity values with low error (less than 20%). Model 3 that considers ∅, d50, Cu, and Cc has best capability to predict tortuosity compared with other developed models