As soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems, its precise determination provides crucial technical support for assessing soil quality, calculating the potential of terrestrial carbon sinks, and promoting low-carbon agriculture. In recent years, elemental analysis has become the mainstream method for detecting SOC content; however, the pretreatment step for removing inorganic carbon directly affects measurement accuracy. The silver cup and centrifugation methods are two widely used and relatively stable pretreatment approaches. However, existing studies mostly focus on standard soil compounds, neglecting the influence of key factors such as land use types, soil-forming environments, and soil matrix compositions. This narrowness makes it difficult to provide method selection references for the efficient and precise detection of large quantities of various soil types. Therefore, this study selected soils from four typical land use types—agricultural land, grassland, urban green space, and sandy wasteland—to systematically explore their impact on measurement results obtained using the two pretreatment methods, thereby providing a scientific basis and technical support for the efficient and precise detection of organic carbon in different soil types.

The study used a VELP CN802 carbon-nitrogen analyzer to determine the SOC content based on the Dumas dry combustion method. After strictly verifying the instrument’s precision and accuracy, the accuracy and reproducibility of the silver cup and centrifugation methods were evaluated using standard soil compounds. Subsequently, 39 soil samples from multiple provinces (regions), including Heilongjiang, Jilin, Inner Mongolia, and Shaanxi, were selected. For each sample, three replicates were prepared using the same pretreatment methods, and each sample was measured three times consecutively. The average SOC content of each sample was calculated based on the mean of nine measurements, and the relative difference between the values from the two pretreatment methods was compared to assess inter-method consistency and variance.

The results showed that both pretreatment methods had good stability. The silver cup method had higher accuracy, while the centrifugation method was low-cost and convenient for batch operations. Land use types significantly affected assessment consistency by altering the SOC content, component characteristics, and the inorganic carbon state. The relative difference generally followed the order agricultural land < urban green space < grassland < sandy wasteland, and it was significantly positively correlated with soil pH. The conversion equation (y = 1.0191x + 0.0374, R² = 0.9995) based on the data obtained could effectively and correctly measure the centrifugation method data, reducing the relative error to 0.04%–5.15%, meeting industry standards and achieving a balance between detection cost and accuracy.

This study provides referential methods for the large-scale, efficient, and precise determination of SOC in different land use types. However, due to the limited number and coverage of samples, the influence of microscopic factors such as soil texture and SOC components could not be deeply analyzed. Thus, the generalizability of the conclusions and correction models needs verification. In the future, sample type and spatial coverage can be expanded, and deviation mechanisms can be analyzed in combination with microscopic techniques to build a multi-method optimization system and a universal correction database, providing a more reliable technical basis for the precise detection of SOC content.

X-ray computed tomography (X-CT) is an important nondestructive three-dimensional imaging technique for characterizing internal structures across various fields, including geography, geoscience, materials science, biomedicine, and porous media research. Compared with destructive testing and two-dimensional section observation, X-CT enables the digital reconstruction of internal structures, providing a basis for quantitative characterization, geometric measurement, and process-oriented analysis. However, CT images are not direct optical records of a sample’s true structure; rather, they represent three-dimensional attenuation fields reconstructed from multi-angle projection data through mathematical algorithms. The grayscale value of each voxel depends on multiple factors, including material composition, density, X-ray energy spectrum, imaging geometry, detector response, sampling strategy, and reconstruction method. Therefore, grayscale values lack a simple one-to-one correspondence with material categories. Reliable structural interpretation requires a comprehensive processing workflow that transforms raw grayscale volume data into physically meaningful, computable, and comparable quantitative information.

Exploring the full X-CT processing chain, this review systematically summarizes the major methods, technical roles, and applicable limitations of image reconstruction, preprocessing, artifact correction, target segmentation, three-dimensional visualization, structural quantification, parameter extraction, and common software platforms. Image reconstruction determines the quality of the input data for subsequent processing, while preprocessing and artifact correction improve the signal-to-noise ratio, reduce systematic distortions, and enhance structural boundaries. Segmentation is the key step that converts continuous grayscale data into discrete structural categories such as pores, particles, fractures, matrix phases, or defects. Three-dimensional characterization and parameter extraction further generate quantitative indicators, including porosity, specific surface area, connectivity, tortuosity, and pore-network topology. Because these parameters derive from a chain of interdependent procedures, the reliability of CT-based quantitative analysis depends not only on individual algorithms but also on the consistency, transparency, and quality control of the entire workflow.

The review identifies noise, artifacts, insufficient spatial resolution, partial-volume effects, and grayscale overlap as the major factors restricting the accuracy and robustness of CT image analysis. Noise and artifacts may distort grayscale distributions and structural boundaries; insufficient spatial resolution and partial-volume effects may blur interfaces and lead to the omission or underestimation of small-scale pores, throats, and fractures; grayscale overlap in complex multiphase samples often triggers threshold-based segmentation instability. These uncertainties propagate and amplify along the processing chain, impacting the accuracy and comparability of porosity, connectivity, specific surface area, tortuosity, and topological parameters. In addition, differences in preprocessing strategies, segmentation algorithms, region-of-interest selection, parameter definitions, and software implementations may reduce reproducibility across studies. Therefore, CT-derived parameters must be understood as structural estimates under specific imaging conditions and processing workflows rather than absolute representations of the true structure.

Recent advances in artificial intelligence, three-dimensional quantitative analysis, multimodal fusion, and reproducible workflows are transforming X-CT image processing. Machine learning and deep learning methods provide new opportunities for denoising, artifact correction, and complex-structure segmentation, especially in low-contrast, noisy, or large-scale three-dimensional datasets. Nevertheless, model performance depends strongly on training-data representativeness, annotation quality, imaging conditions, generalization, and physical interpretability. By pairing X-CT with complementary techniques, such as FIB-SEM, EDS, EBSD, NMRI, or spectral imaging, multimodal fusion integrates structural, compositional, and functional data across scales, thereby extending CT analysis from morphological description to structure–composition–function interpretation. At the same time, standardized reporting, open data, open-source workflows, benchmark datasets, and uncertainty assessment are essential for improving reproducibility and cross-study comparability. Future X-CT image processing should not simply pursue algorithmic complexity or full automation. Instead, it must balance algorithm robustness, workflow transparency, and result verifiability, thereby enhancing the credibility of quantitative analysis of complex samples and strengthening the role of X-CT in cross-scale structural characterization and multidisciplinary research.

Traditional plant phenotyping methods are characterized by low efficiency and high subjectivity, which severely constrain the pace of genetic improvement and the understanding of plant responses to environmental stresses. This review systematically evaluates multiple imaging techniques for addressing key biological questions concerning plant responses to drought, nutrient deficiency, and salt stress, with the aim of providing a technical roadmap for high-throughput, high-precision, and cost-effective phenotyping that can support smart breeding and precision agriculture.

More than 210 peer-reviewed studies published between 2015 and 2026 are analyzed, and the techniques are compared across three functional dimensions: internal structure imaging, physiological and biochemical imaging, and three-dimensional structural imaging. For each technique, the measurement principles, principal areas of applicability, and current limitations are systematically assessed. Optical imaging constitutes the primary modality for analysis of surface morphology and biochemical composition, stress diagnosis, and three-dimensional structural reconstruction. Non-optical imaging techniques offer unique and irreplaceable complementary value for internal structure analysis, water dynamics monitoring, and spatial visualization of metabolite distribution, although their application in plant phenotyping remains more limited than that of optical methods. The performance of deep learning and swarm intelligence algorithms in noise reduction, dimensionality reduction, image segmentation, feature extraction, and phenotype–environment association modeling is also evaluated. Multimodal data fusion reduces prediction errors relative to single-sensor approaches, reflecting the fact that no single imaging technique can simultaneously satisfy the dual requirements of high throughput and high precision. The technical evolution from single-modality measurement to multimodal fusion analysis, from laboratory settings to field applications, and from traditional machine learning to deep learning and swarm intelligence optimization is reviewed systematically from three perspectives: optical–non-optical data synergy, multimodal fusion architectures based on deep learning and swarm intelligence, and cross-level integration of phenomics and genomics.

Future advances should focus on three directions: the development of low-cost, lightweight imaging sensors suitable for high-throughput field phenotyping platforms while retaining high precision; the design of cross-scale registration algorithms capable of aligning data acquired by different sensors and at different spatial scales; and the construction of interpretable phenotype–genotype association models that integrate plant physiological data with deep learning and swarm intelligence algorithms. Standardized quantitative evaluation systems for assessing fusion effectiveness are also urgently required. Realization of these goals will furnish critical technical support for smart breeding and precision agriculture, and will ultimately accelerate understanding of plant responses to drought, nutrient stress, salt stress, and other key biological challenges.

Intensive management is a crucial measure for optimizing the allocation and sharing of scientific research equipment in universities. Exploring the practical difficulties in its implementation and overcoming management bottlenecks based on advanced concepts are crucial for enhancing the scientific competitiveness of university research. Small high-temperature equipment, as an essential infrastructure for experimental teaching and scientific research, is widely applied in chemistry, geography, and life sciences. However, during its operation, it encounters multiple safety hazards such as high-temperature burns, fire explosions, and electrical short circuits. Under the intensive management model, as the scope of equipment sharing expands and the usage frequency increases, the contradiction between the increase in the safety risk index and the demand for sharing, the contradiction between frequent use and maintenance guarantee, and the contradiction between differentiated experimental needs and instrument function adaptation have become increasingly prominent, severely affecting the safe and stable operation of the laboratory and the improvement of research quality. Therefore, a systematic solution needs to be urgently developed.

This study used the State Key Laboratory of Earth Surface Processes and Disaster Risk Reduction with a fishbone diagram as an example to overcome the existing drawbacks of the intensive management of small high-temperature equipment and deeply analyze the causes of these drawbacks. The Six Major White Hats method from enterprise lean production management was introduced, and its feasibility and adaptability for intensive management of small high-temperature equipment were demonstrated from the perspectives of concept-scenario compatibility and multi-concept synergy. Based on the feasibility analysis, the following targeted implementation paths were further proposed: “planning white hat”: to coordinate resource layout and optimize the allocation system, “standard white hat”: to standardize the entire process operation and strengthen safety control, “training white hat”: to enhance personnel safety literacy and lay a solid foundation for standardized operation, “inspection white hat”: to improve the risk prevention network and reduce potential equipment hazards, “emergency white hat”: to establish a response plan system and enhance the ability to respond to emergencies, and “improvement white hat”: to dynamically iterate the management mechanism and promote continuous upgrading of the model, ultimately building a lifecycle management system centered on “preventing risks, standardized operation, resource optimization allocation, and continuous improvement.”

The implementation proved that this innovative management system, with its refined, standardized, and collaborative management logic, not only specifically resolved the inherent mechanism defects of the intensive management model but also effectively balanced the adverse impacts of the external environment, thereby effectively promoting the transformation and upgradation of small high-temperature equipment management from passive response to active optimization and demonstrating outstanding application effects for strengthening safety control, promoting the release of equipment usage efficiency, and supporting scientific research breakthroughs.

This study proposes a scientific and efficient operational model for the intensive management of small-scale equipment in universities and provides new insights into balancing safety and efficiency in shared research equipment systems. Moreover, the cross-disciplinary application of this management tool offers a practical reference for advancing refined management in university research laboratories.