Tomography in Soil Science: From the First Experiments to Modern Methods (A Review)

Абросимов, К. Н.; Gerke, Kirill M.; Fomin, Dmitry; Романенко, К. А.; Корост, Д. В.

doi:10.1134/s1064229321090027

Cited by 10 publications

(7 citation statements)

References 93 publications

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“…Perret et al (2000) studied the application of CT scanning technology in determining the distribution of soil macropores, and pointed out that this method can more clearly show the flow path of dye tracer through soil macropores. In 1987, Sasov used computed tomography to study the microstructure of soil, but on the computer at that time, it took seven minutes to reconstruct a slice (Abrosimov et al 2021). The low computational power of slice reconstruction, phase segmentation and volume calculation is a serious problem in the early development of tomography.…”

Section: Direct Methodsmentioning

confidence: 99%

“…The type of CT has also gradually developed from X-ray CT (X-CT) to nuclear magnetic resonance CT (NMR-CT), positron CT (PET), ultrasonic CT (U-CT), etc. Especially around 2010, a microtomography scanner capable of scanning entire fragments with a diameter of 10 cm and a height of 15-20 cm at a resolution of about 10 μm appeared on the market, opening up new possibilities for studying soil pore structure at the microscopic level (Abrosimov et al 2021).…”

Section: Recent Studies On Soil Porementioning

confidence: 99%

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Soil pore structure and its research methods: A review

Wang,

Zhang

2024

Soil Water Res.

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Soil pore is an important part of soil structure. According to the causes of formation, soil pores can be divided into biological pores formed by animal movement and plant root development and non-biological pores formed by dry-wet and freeze-thaw alternation or artificial tillage. The soil pore structure affects the migration of water, gas, nutrients and so on in the soil, especially the macropores can also produce water or solute preferential migration. Studying soil pores is of great significance for predicting soil hydraulic properties, reducing groundwater pollution and soil nutrient loss. Based on previous studies on soil pore structure, this paper systematically summarized the role of soil pores, influencing factors and the advantages and disadvantages of various research methods. This paper not only introduces traditional methods (including direct and indirect methods), but also summarizes the new research on soil pores combined with computed tomography (CT) technology and other science and technology in recent years. Finally, the prospect and development trend of soil pore research in the future were predicted, so as to provide reference for further research on soil pore structure.

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Section: Direct Methodsmentioning

confidence: 99%

Section: Recent Studies On Soil Porementioning

confidence: 99%

Soil pore structure and its research methods: A review

Wang,

Zhang

2024

Soil Water Res.

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“…In the realm of biomaterials, porosity analysis informs the design and performance estimation of implants and scaffolds, directly influencing their biocompatibility and mechanical behavior [6]. Furthermore, environmental sciences benefit from porosity quantification in understanding soil water retention and contaminant transport within subsurface environments [7,8].…”

Section: Introductionmentioning

confidence: 99%

Multiple Cubes Growth Algorithms for Simple Representative Elementary Volume Determination on 3D Binary Images

Kadyrov

2024

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Porosity analysis is fundamental for understanding various material properties and transport phenomena in scientific and engineering disciplines. This study delves into the challenging task of determining the representative elementary volume (REV) in porous media, crucial for accurate analyses. Two novel algorithms, Center-Corner Cubes Growing (3CG) and Random Cubes Growing (RCG), were proposed and tested on synthetic body-centered cubic (BCC) sphere packing and natural porous structures of Berea sandstone and Indiana limestone, obtained using µCT. First algorithm (3CG) operates by analyzing porosity within cubes growing from each of the eight corners and a central region of a 3D binarized stack. In contrast, the Random Cube Growing (RCG) algorithm randomly selects seed points within the 3D stack and grows cubic regions around them. Both algorithms systematically compute porosity for various cube sizes, determining the average porosity and standard deviation for each extent. These visual analytics tools contribute to identifying the specific size ranges where porosity curves converge and stabilize, indicating potential REV within the material. While 3CG simplifies the approach by focusing on a limited number of curves, RCG provides a broader view, capturing diverse porosity patterns. The absence of consistent local minima in certain cases indicates high porosity heterogeneity and the impossibility of achieving REV in certain sample sizes.

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“…The increasing availability of X‐ray computed tomography (X‐ray CT) makes this approach a feasible technique for assessing soil health. However, this will require automation, which is currently impossible due to soil image segmentation (Abrosimov et al., 2021; Schlüter et al., 2014). Image segmentation is one of the main steps of the image processing, which results in the separation of images into two (binarization, e.g., soil pores and solids) or more phases.…”

Section: Introductionmentioning

confidence: 99%

“…Errors consisting of the misclassification of voxels were removed manually. Therefore, the methods described above require significant labor input in image processing (Abrosimov et al., 2021; Baveye et al., 2010; Iassonov et al., 2009). To solve this problem, the automation of image processing steps began to be applied, for example, based on neural networks.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of plant residues on soil X‐ray CT images using neural network

Valdes‐Korovkin,

Fomin,

Yudina

2023

Agronomy Journal

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In soil, plant residues have low contrast making them difficult to detect using X‐ray computed tomography (CT). In this work, we tested a convolutional neural network (U‐Net) for its ability to improve the identification of crop residues in soil samples assembled from aggregates of different size fractions (small, large, water‐stable aggregates, and average aggregate composition). Soil CT images were obtained using a 244 μm resolution. About 2500 soil images were annotated to train the neural network, of which only 631 images were selected for the training data set. Intersection over Union (IoU) was used as a measure of success of segmentation by neural network, which takes values from 0 to 1. In the validation data set, IoU of background was 0.93, IoU of solid phase was 0.95, IoU of pore space was 0.77, and IoU of plant residues was 0.40. However, IoU of plant residues in the total data set increased to 0.7. Soil structure influences the quality of multiphase segmentation of soil CT images. The poorest segmentation of plant residues was in the soil samples composed of average aggregate size composition. The quality of pore space segmentation increased with increasing porosity of the soil sample . The model tends to generalize the large areas occupied by plant residues and overlooks the smaller ones. The low values of the IoU metric for plant residues in the training data set can also be related to insufficient quality of annotation of the original images.This article is protected by copyright. All rights reserved

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Tomography in Soil Science: From the First Experiments to Modern Methods (A Review)

Cited by 10 publications

References 93 publications

Soil pore structure and its research methods: A review

Soil pore structure and its research methods: A review

Multiple Cubes Growth Algorithms for Simple Representative Elementary Volume Determination on 3D Binary Images

Segmentation of plant residues on soil X‐ray CT images using neural network

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