RiceMultimodalX-ray / CTRootMorphology / geometry measurementPhysiological trait estimationGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、根の発達と根圏酸化を時系列・個体別に定量化しており、表現型取得手法が研究の中心である。
abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' RG2DO-Root analysis program together with sample optode and CT images (the paper's phenotyping inputs) in a public GitHub repository, matching the allowed URL.Code · publicing
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This work was supported by project JPNP18016, commissioned by the New Energy and
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Industrial Technology Development Organization (NEDO), JST CREST (JPMJCR17O1),
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and JST ALCA-Next (JPMJAN23D3).
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Data availability
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The source code and sample data (optode and CT images) are available from the
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GitHub repository (https://github.com/tsubasa-kawai28/RG2DO-Root).15
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References
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Aguilar EA et al. 2003. Oxygen distribution and movement, respiration and nutrient
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loading in banana roots (Musa spp. L.) subjected to aerated and oxygen-depleted
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environments. Plant Soil. 253:91–102. https://doi.org/10.1023/A:1024598319404.20
Armstrong W, Wright EJ. 1975. Radial oxygen loss fromOpen asset ↗https://github.com/tsubasa-kawai28/RG2DO-Root · RG2DO-Rootpdf-raw-page:19 lines:1-82Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Background Understanding the structure of plant seeds cultivated for human consumption and food manufacturing is vital to provide sustainable products as well as to investigate early growth stages. This includes structural variation between different plant species, varieties and cultivars depending on genetic setup, as well as structural modifications upon germination, aging and storing or seed treatment during processing. For plant seeds as multi-component biological materials, structural characterization must extend across multiple length scales, from molecular organization to cellular architecture. Results We apply scanning Small- and Wide-Angle X-ray Scattering (SWAXS) and X-ray Fluorescence (XRF) on yellow pea seeds to combine local structural information on the molecular scale with imaging of cellular structures on the micrometer scale, enabling a comprehensive analysis of hierarchical organization. To identify and characterize heterogeneous regions within the pea seeds, we implement a fitting-free, data-driven segmentation and analysis workflow based on machine learning tools. This approach allows for classification of structurally distinct domains and enables quantitative comparison across samples without relying on predefined models. Furthermore, we incorporate multi-modal analysis by combining structural imaging with complementary elemental information obtained from XRF. The integration of compositional and structural data provides deeper insight into structure-composition relationships. Conclusions This multi-scale, multi-modal approach opens new possibilities for investigating hierarchical structures and their development under diverse conditions and enables systematic comparison between different species or seeds at different developmental stages or exposed to different processing steps. The approach is broadly applicable to various kinds of samples and other hierarchically organized biological materials, which makes it a valuable technique for plant science as well as plant-based food science.
Why it matches plant phenotyping methods種子の構造・細胞領域をX線散乱／蛍光イメージングと機械学習ベースのセグメンテーションで定量解析する手法が研究の中心であり、植物器官の構造形質を抽出するため採用。
abstractWe apply scanning Small- and Wide-Angle X-ray Scattering (SWAXS) and X-ray Fluorescence (XRF) on yellow pea seeds to combine local structural information on the molecular scale with imaging of cellular structures on the micrometer scale
Alfalfa / lucerneX-ray / CTRoot2D/3D reconstructionVisualization / data managementGrowth / development / phenology
The symbiotic relationship between the legume Medicago sativa and the soil bacteria Sinorhizobium meliloti results in the formation of nitrogen-fixing root nodules. Traditional destructive methods, including paraffin sectioning, vibratome sectioning, and cryosectioning, have been applied to visualize how bacteria occupy the nodule, making it extremely difficult to obtain reliable three-dimensional information. These approaches are often combined with fluorescent labeling or staining, which can introduce additional stress affecting plant growth and nodule formation. MicroCT has emerged as a relatively quick, easy, and robust tool for plant biology that can non-destructively visualize plant histological features in three dimensions (3D), thereby avoiding destructive artifacts during sample preparation and ensuring high-fidelity 3D reconstruction. While microCT has been applied to legume root nodules, a detailed established protocol that documents the process from plant harvest and sample preparation to scanning and software visualization is lacking. In this study, we show a step-by-step microCT workflow using Medicago sativa as a model. The protocol includes nodule excision from roots, fixation, contrast enhancement, mounting, scanning, and three-dimensional reconstruction. Critical parameters affecting elements such as image quality, tissue preservation, and contrast are highlighted. Using this approach, it is possible to visualize the overall tissue organization, bacteroid-infected cells, and vascular bundles in three dimensions without physically sectioning the nodules. The pipeline described here provides a reproducible method for non-destructive, high-resolution imaging of native root nodules and is likely adaptable to other legume species, offering researchers a practical tool for studying nodule structure and bacterial organization within nodules in 3D.
Why it matches plant phenotyping methods根粒の組織構造と感染細胞を3Dで取得するMicroCT撮像・再構成プロトコルが研究の中心であり、植物器官の形態状態を測定する実質的なフェノタイピング手法である。
abstractIn this study, we show a step-by-step microCT workflow using Medicago sativa as a model.
Plant secondary metabolites are mainly synthesized and stored in secretory tissues. Secretory canal development has been mainly characterized in Apiaceae. The secretory canals of Peucedanum praeruptorum contain pharmacologically active coumarins, but their organ-specific distribution and developmental dynamics remain poorly understood. This study integrated light microscopy (LM), transmission electron microscopy (TEM), X-ray microcomputed tomography (µ-CT), and high-performance liquid chromatography (HPLC) to investigate canal development, distribution, ultrastructure, 3D architecture, and coumarin accumulation in P. praeruptorum roots. Histological analysis showed that canals adjacent to the periderm originate from pericycle cells, whereas those in secondary phloem arise from parenchyma differentiation; both develop schizogenously. Canal quantity and dimensions varied temporally. Canals located in phloem showed density increasing toward the cambial zone, where cross-sectional areas were smaller. The canal density index increased from September to November, peaking on November 15, then declined. HPLC revealed dynamic accumulation of five major coumarins: content increased from September, peaked on November 15, then gradually decreased. TEM showed that epithelial cells surrounding the canal lumen were rich in Golgi, ER, mitochondria, plastids, starch grains, and osmiophilic droplets. µ-CT volumetric analysis and segmentation generated detailed 3D models, revealing spatial organization and enabling size-based grouping of canals (1000-3000 μm). These dimensional characteristics aligned with developmental progression. This study characterizes the ontogeny, distribution, ultrastructure, and 3D architecture of secretory canals, providing a structural foundation for investigating correlations between secretory tissues and compound synthesis.
Why it matches plant phenotyping methods根の分泌道の密度・寸法・3D構造という植物器官形質を、µ-CTの体積解析・セグメンテーションと顕微鏡法で取得・抽出しており、画像計測が研究の中心的要素である。
abstractµ-CT volumetric analysis and segmentation generated detailed 3D models, revealing spatial organization and enabling size-based grouping of canals (1000-3000 μm).
Continuous X-ray computed tomography (XCT) combined with digital volume correlation (DVC) is presented to quantify internal three-dimensional strain and failure dynamics in apple cortex tissue during compression, revealing how the cellular microstructure governs its mechanical response. We introduce a dimensionless number Mi that is a function of the average interfacial contact area of cells, cell wall thickness, the average cell volume and tissue porosity, to describe tissue microstructure over different development stages. Mechanical softening during maturation aligned strongly with decreasing Mi, linking microstructure to effective Young's modulus, peak stress, and toughness. DVC revealed distinctive strain-distribution signatures: in young, low-porosity tissue, strain was initially diffuse with early-onset localization indicating progressive failure, whereas mature, high-porosity tissue exhibited sharply peaked strain distributions and highly localized fracture planes indicative of brittle collapse. These findings demonstrate how pore evolution, anisotropy, and cell packing jointly determine macroscopic deformation, establishing XCT-DVC as a powerful framework for connecting plant tissue architecture to mechanical function.
Why it matches plant phenotyping methods4D XCTとDVCを用いてリンゴ組織の内部三次元ひずみ、微細構造、破壊状態を定量化する手法が研究の中心であり、植物組織の構造・力学的状態を抽出しているため。
abstractContinuous X-ray computed tomography (XCT) combined with digital volume correlation (DVC) is presented to quantify internal three-dimensional strain and failure dynamics in apple cortex tissue during compression
Field / plotMRI / PETMultispectral / hyperspectralX-ray / CTRoot2D/3D reconstruction
Mycorrhizal fungi form essential symbiotic relationships with plant roots, facilitating nutrient exchange and promoting plant health. Understanding their interactions can benefit from advanced imaging techniques capable of visualizing nutrient exchange and structural colonization at subcellular resolution across large sample sizes. This review explores novel imaging approaches that are revolutionizing our understanding of root-mycorrhizal fungal symbioses. Several techniques can now visualize and characterize mycorrhizal fungi and associated root structures non-destructively and in three dimensions, for example X-ray computed tomography (micro-CT), X-ray fluorescence (XRF), and X-ray absorption near edge structure (XANES) spectroscopy. Metabolic processes and nutrient exchange can be tracked through positron emission tomography (PET), fluorescent nanoparticles (FNPs), and the monitoring of electrical signalling. Artificial intelligence (AI)-powered image processing software is enabling high-throughput analysis of complex images generated from a range of sources. Mycorrhiza systems are also able to be tracked in-field at multiple scales: hyperspectral imaging can detect mycorrhizal associations at the kilometre scale, while portable MRI imagers can detect changes at the tissue scale. These converging technologies enable the direct, continuous measurement of structural and metabolic root-mycorrhizal fungi interactions, paving the way for a mechanistic understanding of these vital symbiotic partnerships and their impact on plant health and ecosystem functioning.
Why it matches plant phenotyping methods植物根と菌根の構造・代謝・栄養交換を画像およびセンサーで直接測定する手法を扱うレビューであり、植物状態の取得技術が中心である。
abstractThis review explores novel imaging approaches that are revolutionizing our understanding of root-mycorrhizal fungal symbioses.
Endosperm cavities within maize kernels influence quality traits such as kernel plumpness and hardness, serving as a key phenotypic indicator for assessing maize yield and quality. Research on endosperm cavities remains relatively scarce due to the small size of maize kernels and limitations in technical approaches. This study employed X-ray micro-computed tomography (μCT) three-dimensional reconstruction technology to extract morphological parameters and spatial configurations of endosperm cavities in multiple maize varieties, enabling visualisation and quantification of endosperm cavities within maize kernels. Endosperm cavities exhibit spatial heterogeneity within the kernels: embryo-adjacent cavities (EACs) are distributed in a conical pattern around the embryo, whereas internal endosperm cavities (IECs) are located in the floury endosperm at the tip region of the kernel and exhibit a boat-shaped morphology. The volume ratio of EACs to IECs is approximately 5:1. A coordinate system was established with the kernel length axis perpendicular to the horizontal plane, revealing the spatial positions of IECs (x = 3.5 mm, y = 2.1 mm, z = 1.1 mm) and EACs (x = 2.5 mm, y = 2.3 mm, z = 7.1 mm). Significant differences in endosperm cavity characteristics were observed among the different varieties. The average volume of the endosperm cavities was 4.1 mm 3 , with kernel porosities ranging from 0.4% to 3.3%. These parameters exhibited highly significant positive correlations with kernel volume, kernel thickness, cavity surface density, etc. Although manual sectioning methods cannot capture the 3D features of endosperm cavities, their operational simplicity and rapid data extraction allow them to reflect, to some extent, the characteristics of endosperm cavities across different maize varieties, as confirmed by this study. This study elucidates the morphology and spatial distribution of endosperm cavities, revealing significant varietal differences in cavity characteristics that correlate with grain morphological traits. These findings lay the groundwork for research into maize grain digital characterisation and the relationship between grain structure and function.
Why it matches plant phenotyping methodsトウモロコシ種子内の内胚乳空洞をX線マイクロCTで3次元可視化し、形態・空間配置・体積などの表現型を抽出・定量化することが研究の中心である。
abstractThis study employed X-ray micro-computed tomography (μCT) three-dimensional reconstruction technology to extract morphological parameters and spatial configurations of endosperm cavities in multiple maize varieties, enabling visualisation and quantification of endosperm cavities within maize kernels.
Main conclusion This review highlights that integrating physiological, molecular, imaging, and AI-based approaches enables early and reliable detection of graft incompatibility, improving rootstock-scion selection, orchard sustainability, fruit productivity, and long-term tree performance. One of the most serious problems in fruit growing is the breaking, weakening, or dying of the tree at the graft union, either within a short period of time or after 10-15 years. This condition is often triggered by environmental factors; however, it is certainly not solely caused by environmental conditions. This problem is defined as graft incompatibility. Graft incompatibility refers to the failure of successful anatomical and physiological integration between a rootstock and a scion, primarily due to biochemical, molecular, and genetic mismatches that impair vascular reconnection and long-term stability of the graft union. Graft incompatibility remains a significant constraint in fruit tree production, resulting in reduced longevity, yield, and quality of orchards. This review integrates recent advancements in physiological, molecular, and technological approaches for the early detection of graft incompatibility, with special emphasis on Prunus species such as sweet cherry. Physiological and biochemical markers, including phenolic accumulation, antioxidant enzyme activities, and isozyme patterns, serve as early indicators of incompatibility. At the molecular level, transcriptomic, metabolomic, and epigenetic analyses have revealed differentially expressed genes (DEGs) and post-translational modifications associated with stress signaling, vascular reconnection, and callus formation. Imaging-based non-destructive technologies such as micro-CT, MRI, terahertz, and hyperspectral imaging now allow real-time visualization of graft-union structures without damaging plant tissues. The integration of artificial intelligence and machine learning with multi-omics datasets and imaging tools offers unprecedented potential for predictive diagnosis and compatibility assessment. Collectively, these multidisciplinary advances are reshaping the detection and management of graft incompatibility, enabling faster, more reliable, and sustainable rootstock-scion selection in fruit tree breeding.
Why it matches plant phenotyping methods果樹の接ぎ木不親和性という植物状態の早期検出法を、画像・生理・分子・AI技術の観点から体系的にレビューしており、フェノタイピング手法が中心である。
abstractThis review integrates recent advancements in physiological, molecular, and technological approaches for the early detection of graft incompatibility
Abstract Moringa oleifera is widely used in dry tropical and subtropical regions due to its rapid growth and high nutritional value, yet its internal tissue organization has primarily been described using two-dimensional anatomical approaches. Here, we present a three-dimensional micro–X-ray computed tomography (micro-XCT) characterization of lumen space in stem, branch, and outer tissues (bark region) from a single M. oleifera individual. Samples were oven-dried prior to imaging; therefore, the quantified void fraction represents apparent lumen/void space in dried material and should not be interpreted as in vivo porosity. Micro-XCT datasets were segmented to quantify cross-sectional lumen area distributions and apparent void fraction from pooled reconstructed slices. All data originate from a single individual; reported metrics represent structural descriptors of pooled cross-sections and not replicated biological measurements. The stem dataset exhibited a dense arrangement of small lumen features and a high number of segmented objects, consistent with a compact woody tissue organization in the scanned region. The branch dataset showed a larger proportion of void space and a strongly right-skewed size distribution with a minority of large lumen features. The outer tissue dataset displayed heterogeneous void space organization, which likely reflects a mixture of cell lumens, intercellular spaces, and drying-related cracks, and therefore is reported descriptively without assigning xylem-vessel identity. This study provides a conservative 3D structural dataset and an image-analysis workflow for quantifying lumen space in dried M. oleifera tissues, complementing published anatomical descriptions. The results highlight strong within-plant heterogeneity across tissue types and underscore the importance of sample preparation and histological validation when interpreting micro-XCT measurements in woody plants.
Why it matches plant phenotyping methods乾燥植物組織の3D micro-XCT画像から管腔・空隙の構造形質を抽出する画像解析ワークフローとデータセットが研究の中心であり、単なる生物学的測定ではない。
abstractMicro-XCT datasets were segmented to quantify cross-sectional lumen area distributions and apparent void fraction from pooled reconstructed slices.
Computed tomography enables non-destructive phenotyping of fruit internal structure but traditionally requires hundreds of projections, limiting throughput. Under sparse-view conditions, conventional and learning-based methods both suffer from streaking artifacts and regional distortions that degrade trait quantification. This study present CitrusGS, an integrated framework that achieves high-fidelity 3D reconstruction and precise morphological phenotyping of citrus fruit from only 15 projections using radiative 3D Gaussian splatting. Our method employs sparse-point initialization, optimized loss composite, and dual-stage pruning to suppress artifacts while preserving anatomically critical details with significantly higer convergence efficiency. In the citrus fruit datasets, CitrusGS achieves 29.78 dB PSNR and 0.870 SSIM, outperforming corresponding baseline method by 1.58 dB and 0.067 in SSIM, and enables automated extraction of ten external and internal phenotypic traits with R 2 larger than 0.944. Moreover, the framework shows initial zero-shot transferability across pathological citrus samples and additional horticultural specimens without retraining. By reconciling acquisition efficiency with anatomical fidelity using low-cost X-ray hardware, CitrusGS provides a promising framework for high-throughput, non-destructive phenotyping in breeding and grading applications.
Why it matches plant phenotyping methods柑橘果実の疎視野CT再構成法を開発・検証し、内部・外部形質を自動抽出するフェノタイピングが中心である。
abstractThis study present CitrusGS, an integrated framework that achieves high-fidelity 3D reconstruction and precise morphological phenotyping of citrus fruit from only 15 projections using radiative 3D Gaussian splatting.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicOur codes are available at https://github.com/Petrichoror/CitrusGS .Open asset ↗Petrichoror/CitrusGSlines:325-387Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Arbuscular mycorrhizal fungi (AMF) contribute to plant nutrient and water uptake via their extraradical hyphal networks. However, in situ methodologies to quantify architectural and morphological traits of these networks in soil are largely lacking, limiting our understanding of AMF-mediated resource transport. Using synchrotron-based X-ray computed microtomography (micro-CT), we established a workflow to cultivate, noninvasively image, and quantitatively analyze AMF hyphosphere and rhizosphere structures in the interaggregate space across two soil textures and biological contexts. We developed a pipeline for quantitative three-dimensional (3D) assessment of key architectural and morphological traits including structure counts, hyphal length, branching frequency, volume, and surface area. Our method further permits (1) measurement of AMF-soil and AMF-root interface areas and (2) microscale quantification of pore space occupancy by AMF. Micro-CT offers a tool for noninvasively visualizing AMF in air-filled soil pore space. We outline how such quantitative 3D information can be incorporated into image-based and functional-structural soil-plant models, thereby supporting a better mechanistic understanding of AMF-mediated processes in soils and plants.
Why it matches plant phenotyping methods土壌中のAMF構造を対象に、micro-CT撮像と3D画像解析パイプラインを開発し、菌糸の形態・構造形質を定量化する方法が研究の中心であるため。
abstractin situ methodologies to quantify architectural and morphological traits of these networks in soil are largely lacking
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
X-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
A dual-substrate X-ray CT platform using high-contrast media and modular scanning overcomes traditional resolution and size limits to enable large-scale, high-resolution 3D in situ root phenotyping.
Why it matches plant phenotyping methods作物根系のin situ表現型測定を目的とするX線CTプラットフォームの開発であり、取得・解析手法が研究の中心である。
abstractA dual-substrate X-ray CT platform using high-contrast media and modular scanning overcomes traditional resolution and size limits to enable large-scale, high-resolution 3D in situ root phenotyping.
Understanding elemental distributions in plants is critical for agricultural productivity, nutritional quality, and limiting the transfer of toxic elements into the food chain. Conventional elemental mapping techniques typically require thin sectioning or complex tomographic reconstructions, making three-dimensional analysis labor-intensive and destructive. Here, we present a non-destructive approach for three-dimensional elemental mapping in intact plant tissues using confocal X-ray fluorescence (XRF) microscopy with a collimating channel array (CCA) [1]. This method defines a localized 3D detection volume within the sample, enabling the generation of elemental virtual cross-sections without physical sectioning while preserving native spatial relationships. We demonstrate the capability of this technique by mapping Fe distributions in carrot (Daucus carota) roots and shoots and Cd distributions in root tips of near-isogenic wheat (Triticum aestivum) lines. Integration of multiple virtual sections enabled three-dimensional reconstructions that reveal distinct Cd translocation pathways between accumulating and non-accumulating wheat lines, tracing elemental movement from the epidermis through cortical layers into vascular tissues. The method is applicable to diverse plant morphologies, including cylindrical roots and irregular leaf and stem tissues. This approach enables high-sensitivity, non-destructive 3D elemental imaging, providing a powerful tool for studying elemental transport in plants with direct relevance to crop breeding, food safety, and agricultural sustainability [2].
Why it matches plant phenotyping methods植物組織内の元素分布という生理状態を、非破壊3D XRFで取得・再構成する手法の開発と植物試料での実証が中心である。
abstractHere, we present a non-destructive approach for three-dimensional elemental mapping in intact plant tissues using confocal X-ray fluorescence (XRF) microscopy with a collimating channel array (CCA) [1].
SUMMARY Plants display complex structural tissue arrangements and cell shapes that are intimately related to their functionality and whose precise geometry influences the metabolic and physical processes performed by different organs. Analyzing these structure–function relationships requires accurate information on the complex 3D anatomy and its changes over time at meaningful spatial resolution. A non‐invasive approach, micro‐CT imaging, can produce such 3D or 4D datasets and can be leveraged for finite element (FE) simulations of mechanical and physical processes. This combination of techniques has been employed to study biomechanical properties, gaseous diffusion, light propagation, hydraulics, and thermodynamic processes in plant organs. A deep understanding of structure–function relationships also paves the way to design bio‐inspired structures using plant anatomy as a reference. Here, we illustrate how the combination of micro‐CT‐based imaging and FE modeling can be leveraged in plant science for advanced investigation of structure–function relationships.
Why it matches plant phenotyping methods植物器官の3D/4D構造をmicro-CTで取得し、有限要素モデルと組み合わせて構造・機能特性を解析する方法を中心に扱うレビューであり、植物フェノタイピング手法に該当する。
titleQuantification of plant structure–function relationships through micro‐ CT imaging‐based finite element modeling
Xylem tissue enables efficient long-distance water transport but is a primary target for vascular pathogens. This study investigates how systemic invasion by Verticillium dahliae impairs the hydraulic function of pepper (Capsicum annuum) plants, focussing on xylem colonisation and its anatomical and physiological effects. Real-time sap flow was continuously monitored with custom-built ExoBeat sensors, while periodic stem water potential measurements allowed calculation of changes in stem hydraulic conductance as an additional indicator of xylem performance. Fungal colonisation was assessed by quantitative polymerase chain reaction, and vessel occlusions and embolised conduits were visualised using scanning electron microscopy and micro-computed tomography, complemented by direct hydraulic conductivity measurements. By 14 d post inoculation, V. dahliae had progressed from roots to aboveground tissues, coinciding with a marked decrease in sap flow, water potential, and soil-to-stem hydraulic conductance, alongside the onset of dwarfing. Direct fungal blockage and anatomical changes were the primary contributors to hydraulic dysfunction. Vessel occlusion by tyloses, gels, and air embolisms played a negligible role. This study reveals how V. dahliae progressively impairs pepper hydraulics through systemic xylem colonisation, highlighting the value of real-time sap flow monitoring. Our integrative, multidisciplinary approach offers a powerful framework to unravel the complexity of dynamic plant-fungal vascular interactions.
Why it matches plant phenotyping methodsカスタムセンサーによるリアルタイム・サップフロー測定を中心に、植物の水理機能・病原体による機能低下を定量化しており、単なる生物学的測定にとどまらない実質的なフェノタイピング手法の適用である。
abstractReal-time sap flow was continuously monitored with custom-built ExoBeat sensors
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
L.) is increasingly being recognized for the production of functional food ingredients and nutraceutical products with broad applications in human nutrition. Its nutrient-rich seeds are of particular interest for their nutritional profile. Moreover, its inflorescences and trichomes provide sources of nutrient-rich proteins, bioactive compounds, and functional substances for food formulations. Agronomic practices, environmental factors, and genotype considerably influence the hemp nutritional profile; thus, continued interdisciplinary research is needed to standardize quality across supply chains. X-ray micro-computed tomography (micro-CT) combined with 3D image analysis is an emerging non-destructive technique in high-resolution plant phenotyping. The aim of this work was to show the contribution of X-ray micro-CT to the quantitative characterization of the internal hemp seed structure and of the trichomes. The 3D image analysis approach used allowed us to determine many morphometric traits of the different seed parts and of the trichomes. Among them, volume ratios of the different seed parts and the density and morphological characteristics of the trichomes of two cultivars were accurately quantified. Overall, this work showed the contribution of X-ray micro-CT in 3D morphometric characterization of the hemp achene structure and trichomes. The obtained seed morphometric traits could be correlated in future applications with nutritional and/or physiological properties of different hemp varieties in order to support different aspects of the whole hemp supply chain such as the dehulling process, oil and protein recovery, seed quality evaluation, and genotype screening, to which trichome characterization could also contribute.
Why it matches plant phenotyping methodsX線マイクロCTと3D画像解析による種子内部構造・トライコームの形態形質の定量化が研究の中心であり、植物フェノタイピング手法の実質的な適用に該当する。
abstractX-ray micro-computed tomography (micro-CT) combined with 3D image analysis is an emerging non-destructive technique in high-resolution plant phenotyping.
Determining elemental concentrations in plant tissues is essential for physiological studies on abiotic stress. However, high-throughput routine analysis of light elements (sodium to calcium) in plants is challenging due to the need for complete sample dissolution and expensive and time-consuming inductively coupled plasma-mass-spectrometry (ICP-MS). Ion chromatography and ion-selective electrodes are low-cost methods but suffer from major drawbacks, including limited throughput and time-consuming sample preparation. This study reports on a new methodology for quantitative analysis of light elements in plants using monochromatic X-ray fluorescence (MXRF) analysis. We quantitatively assessed sodium and potassium uptake in Arabidopsis thaliana, Oryza sativa and Lactuca sativa in salinity treatments. The new method provides reliable results from samples as small as 1 mg, making it suitable for analysis at the seedling stage. This is enabled by the high sensitivity of the system and optimized sample preparation that ensures sufficient signal even at low sample masses. We tested the accuracy and precision of the technique for other light elements to demonstrate its broad applicability. The results show that the method delivers rapid, non-destructive, and extraction-free light element analysis on small samples highly correlating with ICP-MS. The monochromatic XRF method provides accurate measurements and reproducible results for studying salinity tolerance ideally suited for investigating elemental composition of early plant developmental stages, offering new possibilities for research into early stimuli responses.
Why it matches plant phenotyping methods植物組織中の元素濃度という生理形質を測定するMXRF法の開発と、ICP-MSとの相関、精度・再現性評価が研究の中心であるため。
abstractThis study reports on a new methodology for quantitative analysis of light elements in plants using monochromatic X-ray fluorescence (MXRF) analysis.
Traditional deep learning-based plant computed tomography (CT) image segmentation methods require a large amount of high-quality manually labeled data for model training specific to each species, leading to substantial labor costs and poor adaptability to new species. These limitations hinder the application of CT imaging in large-scale cross-species plant phenotyping analysis. Therefore, developing annotation-free and training-free plant CT image segmentation methods is of significant research and application value in reducing research costs and promoting the efficiency of cross-species analysis. To achieve this, we introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT. It is a 2D-to-3D framework that first segments all 2D slices and then assembles them in their original order to generate a 3D CT segmentation. For each slice, this framework directly constructs discriminative clustering features by combining the general semantic priors provided by the self-attention layers in a pre-trained stable diffusion model with the intrinsic grayscale distribution of original image, thereby completely avoiding the need for manual annotations. The method ultimately outputs segmentation results solely through unsupervised clustering, achieving zero-shot generalization without any model training or fine-tuning. To evaluate the feasibility of DiffPlantCT in cross-species segmentation, we benchmark the segmentation performance on two public datasets (walnut fruit and barley spike) and two self-collected datasets (wheat spike and rice panicle). The results show that DiffPlantCT achieved the best performance, with a 41.6% improvement in overall mIoU compared to the state-of-the-art unsupervised method. For the first time, we demonstrate annotation-free, training-free segmentation of cross-species plant CT images successfully.
Why it matches plant phenotyping methods植物CT画像から3D形状を抽出する、アノテーション不要・学習不要の分割手法を開発し、複数作物データセットで性能評価しており、表現型取得手法が研究の中心である。
abstractwe introduce an unsupervised zero-shot segmentation framework for cross-species plant CT images, DiffPlantCT.
Reproduction assets foundThe paper open-sources the DiffPlantCT implementation code on GitHub and benchmarks on two public plant CT datasets (walnut fruit via figshare; barley spike via Plant Methods), all with explicit availability statements and matching allowed URLs.Code · publicThe datasets and implementation code of the DiffPlantCT framework are open-sourced on GitHub at https://github.com/WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentation .Open asset ↗WeizhenLiuBioinform/DiffPlantCT_Zero-Shot_Plant_CT_Segmentationlines:220-287Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
Among carnivorous plants, the Venus flytrap (Dionaea muscipula) is known for its rapid (<1 s) trap closure. Although buckling instability, hydrostatic pressure, and hydroelastic coupling have all been proposed to be involved, the nature of this process and the relationship between trap size and curvature remain elusive. Here, we monitored the closure of Venus flytraps and performed micro-CT scanning and 3D reconstruction, revealing that increasing angular velocity was correlated with higher values of a non-dimensional shape index. Based on these experimental data, we constructed a geometric model of the trap that takes leaf orientation into account. We found that leaf curvature is dependent on leaf size, a relationship we denote as a size-curvature constraint. We further propose a curvature design derived from differential deformations of a two-layer model of the leaf, which could be a powerful tool to control the curvatures of soft and bending surface structures in the field of biomimetics.
Why it matches plant phenotyping methodsマイクロCTと3D再構成で葉の閉鎖運動・曲率を定量化し、幾何モデルでサイズ–曲率関係を推定することが研究の中心であり、植物形態・運動状態のフェノタイピング手法に該当する。
abstractHere, we monitored the closure of Venus flytraps and performed micro-CT scanning and 3D reconstruction, revealing that increasing angular velocity was correlated with higher values of a non-dimensional shape index.
Reproduction assets foundThe paper's Data Availability statement points to an authors' GitHub page hosting all data files and related rendering files for the Venus flytrap closure measurements and 3D reconstructions, matching an allowed URL.Dataset · publicAll data files and related rendering files are available from the github ( https://satorutsugawa.github.io/flytrap_geometric_model_datashare/) .Open asset ↗githublines:105-144Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 5 Sept 2026
Abstract Climate change increasingly threatens global agriculture by intensifying abiotic stresses and destabilizing crop productivity, necessitating a deeper understanding of root-mediated traits governing resource acquisition and stress resilience. Here, we synthesize recent advances in root-centred plant phenomics, emphasizing how high-throughput phenotyping enables high-resolution, scalable characterization of complex root traits and robust comparative analysis across diverse genotypes and environments. Innovations in multimodal imaging, notably X-ray computed tomography, MRI, and machine learning-integrated rhizotrons, facilitate detailed reconstruction of root system architecture and its temporal dynamics under both controlled and semi-field conditions. Furthermore, root phenotyping is increasingly interpreted within an integrated whole-plant framework. The integration of organ-specific assessments with physiological phenomics leveraging spectral and thermal data enables the characterization of developmental plasticity and root-mediated processes, including water-use dynamics, nutrient acquisition, and canopy stress responses under heterogeneous field conditions. These approaches link root traits such as rooting depth and spatial distribution to canopy-level physiological responses under stress. Despite these advances, significant bottlenecks persist in data interoperability, analytical scalability, and protocol standardization. Future progress will require integration of root phenomics with genomics, predictive modelling, and digital twin frameworks to improve resource-use efficiency, yield stability, and climate resilience in global cropping systems.
Why it matches plant phenotyping methods根系フェノタイピングの高スループット画像化、計算ツール、機械学習統合、データ標準化を中心に扱う方法論レビューであり、植物形質の取得・解析手法が主題である。
titleAdvances in root phenotyping: high-throughput imaging, computational tools, and integrative approaches for crop improvement.
Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.
Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、イネ根の発達と根圏酸化を時系列・空間的に測定しているため、植物フェノタイピング手法が研究の中心である。
abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.
Accurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy, yet remains challenging in densely packed tissues, irregular nuclear morphologies, and across heterogeneous imaging modalities. Here we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation that combines a residual attention 3D U-Net architecture with a reversible gradient-field representation for robust centroid-aware instance reconstruction. NucVerse3D is trained end to end in 3D using modality-agnostic preprocessing and isotropic scale normalization, enabling deployment across confocal microscopy, two-photon microscopy, light-sheet microscopy, micro-computed tomography, and scanning electron microscopy volumes. We benchmarked NucVerse3D on seven volumetric datasets spanning multiple species and tissues, comprising more than forty thousand manually annotated nuclei, including newly released ground-truth datasets of mouse liver tissue (control and hepatocellular carcinoma) and Drosophila brain glial nuclei. Across datasets, NucVerse3D achieved consistently high precision and competitive recall, resulting in strong F1-scores and average precision across a wide range of imaging conditions. While a modest precision-recall imbalance is observed in certain datasets, favoring high-confidence detections, this behavior reflects a conservative instance reconstruction strategy that prioritizes accurate boundary delineation and reduces false positive segmentation in densely packed and morphologically heterogeneous tissues. A single generalized model trained on pooled data matched the performance of dataset-specific models, and ablation experiments demonstrated that preprocessing and scale normalization substantially contribute to performance under strict intersection-over-union criteria. To demonstrate the biomedical utility of NucVerse3D, we applied it to 3D liver images from a mouse model of hepatocellular carcinoma (HCC) to enable spatially resolved 3D nuclear phenotyping. In healthy liver tissue, nuclear DNA content and nuclear volume exhibited a tightly regulated log-log scaling relationship. In contrast, tumor-adjacent and tumor regions displayed progressive disruption of this coupling, forming spatially coherent domains of nuclear DNA-volume decoupling that are not detectable in conventional two-dimensional histology. We quantify this phenomenon using a Nuclear Decoupling Score (NDS), revealing increased nuclear instability aligned with pathological tissue remodeling highlighting NDS as a potential quantitative biomarker of dysplastic and tumor tissue. Together, NucVerse3D provides a robust and generalizable solution for 3D nuclear instance segmentation and enables quantitative nuclear phenotyping across imaging modalities.
Why it matches plant phenotyping methods3D核セグメンテーション手法を開発し、多様な画像データセットでベンチマーク・検証したうえで、核形態とDNA量の定量的フェノタイピングに応用しており、植物対象ではないため本索引の対象外となる可能性はあるが、提示内容上はフェノタイピング手法研究として中心的である。
abstractAccurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy
Reproduction assets foundThe paper's newly released Zenodo deposit (10.5281/zenodo.18517324) containing raw volumes, annotations, training patches, model weights, and segmentation outputs is not among the allowed URLs, so it cannot be listed. The authors' public analysis/segmentation code repository is explicitly deposited with an authors' URLCode · publicThe source code for training and predicting nuclei segmentation using NucVerse 3D is available from https://github.com/Segovia-lab/3D-Nuclei-segmentation.git .Open asset ↗https://github.com/Segovia-lab/3D-Nuclei-segmentation.gitlines:647-728Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
X-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
Summary Plant–microbe interactions are inherently spatial, yet the physical structure of the soil and rhizosphere is rarely treated as a mechanistic variable in experimental design. X‐ray computed tomography (X‐ray CT) enables nondestructive, three‐dimensional, and time‐resolved imaging of intact root–soil systems, providing direct access to the structural context in which plant–microbe interactions occur. Rather than a secondary imaging technique, X‐ray CT can offer a wealth of data as a primary experimental platform for future plant–microbe research. Here, we highlight key structural traits that X‐ray CT can quantify and discuss how they may shape microbial behaviour, plant immune responses, and disease outcomes. We expand on how X‐ray CT could be employed in future to provide a framework to disentangle direct microbial effects from indirect, structure‐mediated feedbacks. For breeding and management, it could enable selection for root traits and soil practices that engineer favourable microhabitats rather than targeting organisms in isolation. Despite this potential, broader adoption will require overcoming current limitations related to access to instrumentation, analytical expertise, and the integration of structural data with biological measurements. Overall, we suggest that resolving these issues will enable the integration of X‐ray CT‐derived structure with molecular, microbiome, and modelling approaches to enable the development of digital rhizospheres, offering a pathway from descriptive observations to predictive, structure‐aware in silico frameworks in plant–microbe research.
Why it matches plant phenotyping methodsX線CTを用いて根・土壌系の構造形質を定量する方法を、植物・微生物相互作用研究の主要な実験プラットフォームとして論じる方法論レビューであり、植物フェノタイピング手法が中心です。
abstractX‐ray computed tomography (X‐ray CT) enables nondestructive, three‐dimensional, and time‐resolved imaging of intact root–soil systems, providing direct access to the structural context in which plant–microbe interactions occur.
Soil-borne vascular pathogens pose serious threats to agriculture with complex invasion strategies that remain poorly characterized compared to foliar pathogens. While foliar pathogens like Magnaporthe oryzae employ specialized appressoria to penetrate plant surfaces through a combination of mechanical force and enzymatic degradation, the invasion mechanisms of vascular pathogens that lack classical appressoria have remained largely theoretical. The nanoscale processes governing root penetration and colonization by these pathogens are particularly challenging to visualize due to technical limitations of conventional microscopy. Here we show, using phase-contrast X-ray computed microtomography and advanced microscopy, that Fusarium oxysporum (Fo) employs distinct mitogen-activated protein kinase (MAPK) cascades to orchestrate root invasion through unprecedented morphological plasticity. We identify previously undocumented appressoria-like structures that facilitate physical penetration, while demonstrating that Fo exhibits remarkable cellular adaptability, reducing hyphal diameter by more than 20-fold (from 5 μm to 220 nm) to navigate confined plant spaces, a dramatic morphological transition previously thought impossible. By using cellulase-deficient mutants, we demonstrate that cellulolytic activity is dispensable for surface breach and submicrometric hyphal colonization, establishing that mechanical force generation rather than enzymatic degradation is the primary determinant of successful host penetration. Three-dimensional reconstruction reveals a quantitative correlation between fungal proliferation and progressive embolism formation, with distinct MAPK pathways differentially regulating penetration force generation (Fmk1), osmotic adaptation during apoplastic colonization (Hog1), and directional growth toward vascular tissues (Mpk1). These findings provide a mechanistic framework for vascular wilt pathogenesis and reveal potential targets for controlling these economically devastating plant diseases.
Why it matches plant phenotyping methods位相コントラストX線マイクロトモグラフィーと3次元再構成を中核に、根の侵入・菌糸形態・塞栓形成という植物の病態と形態を定量化しており、病理学的機構研究であるものの表現型取得法の適用が実質的です。
titlePhase-contrast microtomography unveils mechanisms of root colonization by a vascular fungal pathogen
Abiotic stresses such as heat waves significantly reduce wheat productivity by altering leaf anatomy and physiology, leading to reduced photosynthetic carbon assimilation and crop yield. Despite the advancement in various imaging technologies at the field, canopy, plant, tissue, cellular, and subcellular levels, phenotyping of imaging-based leaf structural traits (e.g. vein density, stomatal density, and stomatal aperture) for abiotic stresses is still time-consuming and expensive without the aid of artificial intelligence (AI) and machine learning (ML). This review consolidates current knowledge of wheat leaf structural and functional adaptations to heat stress and highlights key advancements in imaging technologies for studying these important phenotypic traits. Recent high-resolution, non-destructive imaging technologies, including confocal laser scanning microscopy, X-ray computed tomography, and optical coherence tomography, have enabled in vivo visualization of plants. Integrating these imaging techniques with AI/ML facilitates high-throughput phenotyping and the modelling of stress responses. We emphasize the potential for future research to leverage these technological advancements in imaging and AI, combining imaging data with physiological and multi-omics studies to deepen the understanding of plant heat tolerance mechanisms. Such multidisciplinary integration in leaf structure phenotyping will accelerate the development of resilient wheat varieties, offering critical insights for crop improvement in the face of climate change.
Why it matches plant phenotyping methods植物の葉構造・機能形質を対象とする画像計測技術とAI/MLによる表現型解析を中心に整理したレビューであり、植物フェノタイピング手法レビューに該当する。
abstractThis review consolidates current knowledge of wheat leaf structural and functional adaptations to heat stress and highlights key advancements in imaging technologies for studying these important phenotypic traits.
Background and AimsClimate gradients influence seed morphology, emergence, and early life-history traits with cumulative impacts to individual fitness. For ex situ seed collections, which represent an invaluable repository of potential trait information for species management and conservation, climate data can guide preservation of adaptive variation and inform deployment strategies for restoration. Here we leverage a range-wide ex situ seed collection of critically endangered black ash seeds (Fraxinus nigra) to evaluate how climatic gradients shape variation in morphology and early life-history. MethodsTo test how climate of origin, seed morphology, and early life-history interact to impact first year fitness, high-throughput X-ray imaging and neural network-based segmentation were used to quantify variation in seed morphology for 701 maternal lineages spanning 76 populations across the range of F. nigra. Following this, a subset of seeds were used to establish a common garden experiment and quantify variation in emergence, early life-history transitions, and their cumulative impact to first-year survival and growth. ResultsOn average, differences within-population explained [~]43% of the variability in seed morphology, while among-population differences explained [~]14%. This suggests that substantial genetic variation exists within populations for natural selection to act upon and differences have evolved among populations. Climate associations indicated warmer and drier environments predicted heavier seeds with faster developmental transitions and increased first-year height. Together, climate of origin, seed mass, and timing of developmental transitions best predicted cumulative fitness, with populations from more continental environments exhibiting greater survival and first-year height accumulation on average. ConclusionsOverall, these results highlight the importance of climate of origin, seed traits, and early developmental transitions to first-year fitness in a perennial tree species. This work demonstrates how ex situ collections can be used to identify climatically structured trait variation and guide conservation strategies aimed at maintaining adaptive potential under environmental change.
Why it matches plant phenotyping methods701系統の種子形態を高スループットX線画像とニューラルネットワーク分割で定量しており、形態表現型の取得・抽出が研究の主要な技術基盤である。
abstracthigh-throughput X-ray imaging and neural network-based segmentation were used to quantify variation in seed morphology for 701 maternal lineages spanning 76 populations across the range of F. nigra
How evolutionary and developmental processes interact to determine axes of neural variation that produce behavioural diversity has been debated for many decades, with alternative hypotheses giving differential emphasis to functional coupling, which favours co-evolution, and developmental constraint, which enforces it. A critical omission is data on the genetic architecture of brain size and structure, which more closely illuminates the shared developmental dependencies between components of an integrated system. Here, we exploit ecological divergence between Astatotilapia calliptera and Aulonocara stuartgranti, two closely related cichlid species from Lake Malawi, to explore the genetic architecture of brain evolution. Using computer vision and machine learning techniques to extract volumetric data from micro-tomographic images, we first demonstrate significant divergence in brain composition between these species. Genomic and micro-tomographic imaging data from a population of hybrids generated between the two species were used to investigate genetic factors shaping this differentiation. We show that the majority of brain components are integrated phenotypically in hybrids, but genetic correlations between them are generally weaker. We further show that variation in multiple brain components is associated with variation in largely structure-specific quantitative trait loci, rather than determined by genetic factors with broad effects across the entire brain. These results suggest a genetic architecture that can facilitate modular changes in brain structure, and imply that individual components are independently evolvable.
Why it matches plant phenotyping methodsマイクロCT画像からコンピュータビジョンと機械学習で脳各部の体積を抽出する手法が、脳形態の遺伝的解析における中心的な表現型取得方法として明示されています。
abstractUsing computer vision and machine learning techniques to extract volumetric data from micro-tomographic images
Abstract Plant phenotyping attempts to objectively measure a plant’s reaction to its environment as encoded by its genotype. It has become an essential tool for deepening our understanding of plant responses to environmental stimuli. Understanding the plant’s reaction to, for example, a warmer climate is crucial to ensure food production for future generations. Breeders and researchers rely on automated high-throughput phenotyping for optimizing crops. Ideally, above- and below-ground traits are observed simultaneously. The newly established controlled environment facility at the Technology Center for Phenotyping of the Fraunhofer IIS in Merkendorf provides several climate chambers with individually controllable conditions for up to 400 individual plants to allow simulation of even extreme climatic conditions all year around. Comprehensive measurement of plant structures using X-ray as well as optical cameras provide highly detailed 2D and 3D information to researchers and breeders worldwide. In combination with automated data pipelines, distinct plant traits can be extracted from the sensor data. By bridging above- and below-ground phenotyping, this facility not only advances plant science but also contributes to the breeding of more resilient and productive crops. Collaborators are welcome to unlock the transformative potential of these unique phenotyping capabilities, exploring traits such as root length, leaf area, biomass, and more.
Why it matches plant phenotyping methodsX線・光学カメラと自動データパイプラインによる地上部・地下部形質の高スループット取得を中核とするフェノタイピング施設・プラットフォームの紹介であり、方法と測定基盤が中心。
abstractComprehensive measurement of plant structures using X-ray as well as optical cameras provide highly detailed 2D and 3D information to researchers and breeders worldwide.
Abstract Root system architecture plays a critical role in water and nutrient acquisition, particularly in semi‐arid environments where drought stress limits crop productivity. Despite advances in three‐dimensional (3D) root phenotyping, no dedicated low‐cost imaging platform currently exists for sorghum ( Sorghum bicolor (L.) Moench) in the United States. The objective of this study was to design and construct an affordable laboratory‐based 3D imaging system for sorghum root phenotyping modeled after the digital imaging of root traits (3D) framework. The system consists of a rotating aluminum frame equipped with eight high‐resolution digital cameras controlled by Raspberry Pi microcomputers, uniform LED lighting, and background reference markers to ensure accurate image alignment. Approximately 2000–3000 overlapping images are captured in under 5 min and processed using structure‐from‐motion algorithms to generate colorized 3D point clouds. The total system cost was approximately $6000, substantially lower than commercial imaging technologies such as computed tomography or magnetic resonance imaging. Initial assembly demonstrated strong geometric alignment and minimal distortion, enabling measurement of key root traits including volume, nodal root angle, and whorl spacing. This platform provides a reproducible and scalable approach for sorghum root phenotyping and addresses a critical gap in crop research tools for semi‐arid production systems. The system also offers educational value by integrating engineering design, programming, and plant science, supporting interdisciplinary training and future genotype‐phenotype studies aimed at improving drought resilience.
Why it matches plant phenotyping methodsソルガム根の形態形質を取得する低コスト3D画像プラットフォームの設計・構築が研究の中心であり、根体積や根角度などの測定法を提供している。
abstractThe objective of this study was to design and construct an affordable laboratory‐based 3D imaging system for sorghum root phenotyping modeled after the digital imaging of root traits (3D) framework.
Here, we profiled a diverse panel of whole-grain rice accessions (n = 384 covering wide range of pigmentation) for in vitro glycemic index (GI), resistant starch (RS), digestible carbohydrate (DC), and debranched starch chain-length distributions (CLD) resolved into fine degree-of-polymerization (DP) intervals. Across the panel, low-GI phenotypes were rare, and GI distributions overlapped substantially across pigmented and non-pigmented groups, indicating starch architecture as the dominant determinant of digestibility. Regression and classification models using DP-resolved predictors achieved robust GI prediction (R 2 = 0.70 for whole grain), and model simplification identified a reduced set of informative DP windows. Notably, DP33-36 emerged as a negative predictor of GI, showing an opposing effect relative to adjacent mid-chain intervals. To provide structural context for interval-specific effects, representative lines with contrasting DP architectures were examined by X-ray diffraction (XRD) and solid-state 13 C NMR. Biophysical analyses supported that glycemic variation is not explained by crystalline polymorph type alone, but by localized microstructural organization within an A-type framework. For polished rice, incorporating RS content further improved the model's explanatory power (R 2 = 0.78). These results establish a DP-resolved structure-function framework for GI variation in rice to accelerate screening and selection of low-GI donors for breeding.
Why it matches plant phenotyping methodsイネ系統のGIという植物由来形質を、DP分解データに基づく回帰・分類モデルで予測し、スクリーニングと育種選抜に用いる構造機能フレームワークを提示しており、形質推定ワークフローが中心的です。
abstractRegression and classification models using DP-resolved predictors achieved robust GI prediction (R 2 = 0.70 for whole grain)
OzBarley is a comprehensive genotype-to-phenotype resource to support research and enhance barley breeding by integrating genotypic and phenotypic data for gene discovery. This publicly available dataset comprises genotypic data from historical and modern elite barley cultivars of significance to Australian barley breeding. The phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology. Users can leverage genome-wide association studies (GWAS) and genomic selection to identify genetic variants associated with agronomically important traits in the OzBarley datasets, thereby accelerating targeted breeding strategies. The dataset is accessible for download under CC-BY 4.0 license and users are invited to contribute new data when using OzBarley plant material in their research. Through its FAIR-compliant design (Findable, Accessible, Interoperable, Reusable), OzBarley represents a resource to protect genotypes of historical relevance, explore the genetic architecture of adaptation to dryland environments, and to enhance knowledge of the resilience, yield, and quality of barley cultivars under diverse environmental conditions, contributing to global food security and agricultural sustainability.
Why it matches plant phenotyping methods高スループット画像およびX線CTによる形質取得を含む、再利用可能な遺伝型・表現型データ資源であり、植物フェノタイピング手法とデータセットが中心です。
abstractThe phenotypic component includes high-throughput imaging and X-ray CT-based spike analysis, enabling trait measurements such as plant growth dynamics and seed morphology.
WheatLaboratory / benchtopX-ray / CTRootMorphology / geometry measurementStress / disease detectionGrowth / time-series analysisRoot system architectureWater status / transpiration
Soil structure creates spatial heterogeneity that shapes ecosystem functions, including water retention and root colonization. Chernozems – soils characterized by exceptionally stable aggregation resulting from millennia of root-soil co-evolution – offer a unique model to investigate how aggregate-scale pore architecture controls plant responses to drought. Using soil microcosms (4 × 10 cm, ~80 g soil) with aggregates from Native Steppe and Arable Chernozems, we established six experimental treatments (3 aggregate sizes × 2 soil types) with three replicates each. Root-soil dynamics were tracked through repeated X-ray computed tomography (Neoscan N80, Belgium) at 16 µm resolution. Imaging was synchronized with plant developmental stages – germination, first leaf, and third leaf stage at permanent wilting point – yielding a total of 54 soil tomograms for analysis.Preliminary processing of the data reveals distinct pore network architectures across aggregate size classes. Small aggregates exhibited low CT-visible porosity (24%) with high solid phase connectivity (6.60 mm⁻³), while medium aggregates showed moderate porosity (39%) with lower connectivity (0.64 mm⁻³), and large aggregates had the highest porosity (49%) but the lowest connectivity (0.51 mm⁻³). This structural gradient directly controlled root colonization: solid phase connectivity showed a strong negative correlation with root volume growth (r = −0.76), suggesting that matrix mechanical cohesion, rather than pore characteristics alone, limits root expansion. Medium aggregates – which naturally dominate in undisturbed steppe soils – provided optimal conditions for root development, with 90% greater root surface expansion compared to small aggregates. Root sphericity decreased 3–4 times more in medium aggregates (−0.14) than in small aggregates (−0.04), indicating greater architectural plasticity critical for water acquisition. Importantly, our preliminary results also show that medium aggregates provided the greatest drought resistance: plants in these microcosms reached the permanent wilting point latest, suggesting that this aggregate fraction optimizes both root development and water availability over time.These findings demonstrate that native Chernozem aggregate structure represents an optimized spatial configuration balancing root accessibility with water retention. The strong coupling between aggregate-scale heterogeneity and root response suggests that tillage-induced disruption of natural aggregate distributions may compromise this evolutionary optimization. Our approach – combining high-resolution CT with growth stage-synchronized imaging – offers a framework for quantifying how spatial heterogeneity translates into ecosystem-relevant soil functions. Data processing is ongoing, and final results will include expanded replication and additional root morphometric parameters.
Why it matches plant phenotyping methods高解像度X線CTを用いて根の体積成長、表面拡大、球形度などの形態形質を反復取得・定量する手法が研究の中心であり、植物フェノタイピングへの実質的応用に該当する。
abstractRoot-soil dynamics were tracked through repeated X-ray computed tomography (Neoscan N80, Belgium) at 16 µm resolution.
Lignin plays a central role in the formation and function of secondary cell walls in vascular plants. However, the structural consequences of lignin modification for cell wall properties and cellular function in grasses remain poorly understood. Here, we investigated how cinnamyl alcohol dehydrogenase (CAD) deficiency alters vascular cell architecture in Sorghum bicolor, using the brown midrib-6 (bmr6) mutant as a model system. Biochemical and histochemical analyses confirmed altered lignin chemistry in bmr6, including increased incorporation of hydroxycinnamaldehyde residues and reduced tricin levels. We applied ptychographic X-ray computed tomography (PXCT) to quantify the cell wall geometry, in three dimensions, at nanometer-scale resolution. PXCT enabled measurements of wall thickness distribution and lumen shape along tracheary elements. Analyses revealed no significant differences in wall thickness between wild-type and bmr6 plants. However, three-dimensional morphometric descriptors indicated reduced lumen convexity in bmr6, suggesting localized modifications not detectable by conventional two-dimensional imaging. Water flow numerical simulations through PXCT-derived images indicated reduced vessel permeability and simulated hydraulic conductivity in bmr6, suggesting that subtle geometric changes may influence performance. These findings highlight the value of three-dimensional imaging for resolving cell wall organization and provide new insight into the architectural resilience of grass xylem in response to targeted lignin modification. HighlightThree-dimensional X-ray nano-imaging reveals alterations in the cell wall architecture that affect simulated hydraulic performance under reduced CAD activity in sorghum.
Why it matches plant phenotyping methods植物の木部細胞壁形状をナノスケール3D画像から定量化するPXCT手法が研究の中心であり、壁厚・内腔形状・形態記述子を抽出しているため、植物表現型計測の実質的な適用に該当する。
abstractWe applied ptychographic X-ray computed tomography (PXCT) to quantify the cell wall geometry, in three dimensions, at nanometer-scale resolution.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Accurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy, yet remains challenging in densely packed tissues, irregular nuclear morphologies, and across heterogeneous imaging modalities. Here we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation that combines a residual attention 3D U-Net architecture with a reversible gradient-field representation for robust centroid-aware instance reconstruction. NucVerse3D is trained end to end in 3D using modality-agnostic preprocessing and isotropic scale normalization, enabling deployment across confocal microscopy, two-photon microscopy, light-sheet microscopy, micro–computed tomography, and scanning electron microscopy volumes. We benchmarked NucVerse3D on seven volumetric datasets spanning multiple species and tissues, comprising more than forty thousand manually annotated nuclei, including newly released ground-truth datasets of mouse liver tissue (control and hepatocellular carcinoma) and Drosophila brain glial nuclei. Across datasets, NucVerse3D achieved consistently high precision, recall, F1-score, and average precision, and outperformed the state-of-the-art methods particularly in dense and irregular settings, while remaining competitive on simpler cases. A single generalized model trained on pooled data matched the performance of dataset-specific models, and ablation experiments demonstrated that preprocessing and scale normalization substantially contribute to performance under strict intersection-over-union criteria. To demonstrate the biomedical utility of NucVerse3D, we applied it to three-dimensional liver images from a mouse model of hepatocellular carcinoma (HCC). High-fidelity, nucleus-by-nucleus segmentation enabled the quantification of the Nuclear Decoupling Score (NDS), which captures deviations in nuclear DNA–volume coupling at the single-nucleus level. NDS analysis revealed a progressive increase in nuclear abnormalities within tumor regions, forming spatially coherent domains of dysregulated nuclei and highlighting NDS as a potential quantitative biomarker of dysplastic and tumor tissue. Together, NucVerse3D provides a robust and generalizable solution for 3D nuclear instance segmentation and enables quantitative nuclear phenotyping across imaging modalities. Highlights - NucVerse3D provides accurate 3D nuclear instance segmentation across modalities - Residual attention and gradient fields enable robust separation of dense nuclei - New 3D annotated datasets of mouse liver and Drosophila brain are released - A generalized model achieves performance comparable to dataset-specific training - 3D nuclear phenotyping reveals spatially organized nuclear abnormalities in HCC
Why it matches plant phenotyping methods3D核インスタンスセグメンテーション手法を開発し、多数のデータセットでベンチマークするとともに、核形態状態の定量的フェノタイピングへ応用しているため。
abstractHere we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation
In this work, we present a flexible and eco-friendly humidity sensor suitable for early plant leaf stress monitoring. The humidity sensor was fabricated using graphene/PEDOT:PSS hybrid ink deposited via drop-casting method on interdigitated electrodes (IDEs) screen printed on a eco-friendly paper substrate. Contact angle measurement, scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) studies were performed to demonstrate hydrophilic nature, surface morphology and elemental analysis, respectively, of the sensing layer. The sensor exhibited excellent sensing performance in the measured relative humidity (%RH) range from 25% RH to 94% RH having a maximum % response of 226.5%. The sensor demonstrated a nearly linear response (adj. R² = 0.99) in the considered range with a slope observed as 3.21%/%RH. Multi-cyclic repeatability and reproducibility analysis further confirmed high reliability and consistent performance of the developed sensor. Furthermore, the capability of the sensor was successfully evaluated through capturing variations in plant physiological health status (under different environmental conditions, such as un-watered, water availability and solar irradiation) via monitoring microclimatic relative humidity (%RH) variations on plant (Epiremnun aureum) leaves. Through establishing the %RH values for healthy crops or plants under normal (well-watered) and stress conditions (un-watered or excessive solar irradiations), sensor seems to demonstrate strong potential for smart agriculture i.e., detecting early plant leaf stress.
Why it matches plant phenotyping methods植物葉のストレス状態を相対湿度センサーで取得するセンサー開発と性能評価が中心であり、植物生理状態の早期モニタリングへ実証適用している。
abstractwe present a flexible and eco-friendly humidity sensor suitable for early plant leaf stress monitoring
Rooting systems of plants perceive environmental stimuli and flexibly regulate their growth. Therefore, understanding stimulus perception and response mechanisms is essential for optimizing cultivation. During the transition from aquatic to terrestrial environments, land plants have acquired mechanisms to adapt to gravitational force on land. Thus, elucidating gravity responses of rhizoids in bryophytes, early diverging land plants, provides important insights into how gravity-response mechanisms were established during land plant evolution. Analyzing rhizoid morphology under microgravity, where gravitational effects are largely eliminated, provides an effective approach to examine the gravity-response mechanisms that evolved after terrestrialization. In this study, to elucidate microgravity effects on rhizoid growth of Physcomitrium patens , we analyzed 3D datasets obtained by refraction-contrast micro-CT using synchrotron radiation after fixation and embedding of samples from the Space Moss experiment conducted on the International Space Station. Because each CT volume contains numerous rhizoids, we optimized a WEKA-based machine-learning segmentation approach by improving preprocessing, training, and postprocessing steps, resulting in a significantly improved segmentation accuracy. Comparison of 3D morphological indices between manually segmented rhizoids and predicted results supported the validity of the proposed method for morphological analysis. Morphological analyses revealed that, compared with both ground and artificial 1 × g conditions, rhizoid elongation and gravitropic responses were suppressed under microgravity, leading to reduced vertical growth. These findings indicate that gravity plays a fundamental role in rhizoid morphogenesis, and their absence affects growth orientation and elongation. This study provides foundational data for research on the rooting systems of bryophytes in space.
Why it matches plant phenotyping methodsマイクロCT画像からコケ植物の根茎の3D形態を抽出する機械学習セグメンテーション法を開発・最適化し、手動セグメンテーションとの比較で妥当性を検証しているため、植物フェノタイピング手法が中心です。
abstractwe optimized a WEKA-based machine-learning segmentation approach by improving preprocessing, training, and postprocessing steps, resulting in a significantly improved segmentation accuracy.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
MRI / PETX-ray / CTRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Strategic optimisation of Root System Architecture (RSA) represents a critical frontier for stabilising crop productivity amid increasingly unpredictable moisture-deficit regimes. Understanding key root traits underlying effective drought response is necessary to harness the genetic diversity associated with root growth patterns and environmental adaptations. Many functionally significant root architectural traits have been reported, and the mechanistic importance of some of the anatomical ideotypes, such as the increased metaxylem vessel diameter to reduce axial hydraulic resistance to maintain leaf water potential and change in root growth angle to promote geotropic deep-soil moisture foraging, are discussed in this review. Despite the identification of these characteristics, the knowledge gap in their integration into predictive breeding frameworks remains. This review addresses this fragmentation by critically evaluating how the bottleneck of the ‘phenotyping’ process is being broken down through non-invasive high-throughput phenotyping modalities. Dynamic root-soil interfaces can be spatio-temporally quantified in situ using non-destructive technologies such as X-ray computed tomography and MRI, which can detect developmental plasticity masked by destructive sampling. Artificial Intelligence (AI), especially Convolutional Neural Networks, enables automated extraction of high-dimensional topological parameters from complex digital rhizograms. Present review integrates recent advances in phenotyping with molecular regulatory mechanisms, bridging two traditionally disparate fields. By focusing on the DRO1/qSOR1 loci and ABA-auxin crosstalk, we establish critical connections between molecular regulation and field-scale architectural performance. The resulting multi-scale roadmap may help in targeted selection of climate-resilient cultivars to maximize resource use efficiency.
Why it matches plant phenotyping methods根系構造の非破壊・ハイスループット表現型解析技術を中心に、X線CT、MRI、AIによる根系形質抽出をレビューしており、植物フェノタイピング手法が中核です。
abstractThis review addresses this fragmentation by critically evaluating how the bottleneck of the ‘phenotyping’ process is being broken down through non-invasive high-throughput phenotyping modalities.
Xanthium sibiricum Patrin ex Widder and Xanthium italicum Moretti are morphologically similar fructus that are frequently misidentified. Xanthium italicum Moretti may possess inherent toxicity, and its adulteration of genuine medicinal materials poses a threat to clinical drug safety. Macroscopic observation and three microscopic techniques including stereo microscope, optical microscope, and 3D X-ray microscope were used for morphological identification of Xanthium sibiricum Patr ex Widder and Xanthium italicum Moretti in this study. 3D X-ray microscopy was applied as a novel tool for non-destructive, high-resolution discrimination of the two taxa. Intact fructus (n = 30 per species) were first screened macroscopically, then examined by stereo microscopy, optical microscopy, and 3D X-ray microscopy (0.3, 0.7, 1.5, 3.5, 18.06, 20.01 μm voxel size, Zeiss Xradia 520 Versa). The results showed that stereo microscopy, optical microscopy, and 3D X-ray microscopy collectively confirm the same conclusion from three distinct physical perspectives: surface topography, internal two-dimensional structure, and internal three-dimensional density distribution. The two Xanthium species differ significantly in burr spine morphology, fructus size and shape, the architecture and distribution of non-glandular and glandular trichomes, cotyledon conformation, and seed-coat cell patterning. In particular, 3D X-ray microscopy clearly resolves internal cotyledon spatial configurations and involucral cavity architectures, which furnishes critical endomorphic characters for taxonomic diagnosis. 3D X-ray microscopy provides unprecedented volumetric contrast of surface spines and internal seed architecture, permitting confident, non-destructive species identification. This study provides a basis for the safe clinical use of Xanthium sibiricum Patrin ex Widder. The frontier of 3D X-ray microscopy in plant systematics offers a novel, rapid, accurate and non-destructive protocol for the discrimination of morphologically elusive species.
Why it matches plant phenotyping methods3D X線顕微鏡を含む複数の画像計測法を用いて果実・種子の形態形質を抽出し、近縁2分類群の非破壊識別プロトコルとして比較・検証しており、植物フェノタイピング手法が中心である。
abstract3D X-ray microscopy was applied as a novel tool for non-destructive, high-resolution discrimination of the two taxa.
Genebanks serve as critical repositories for preserving the genetic diversity of plant species, including crops, forages, and their wild relatives, which is essential for adapting to climate change, enhancing food security, and improving agricultural sustainability. Seed phenotyping, the process of evaluating observable seed traits influenced by genetics and environmental factors, plays a pivotal role in characterizing and utilizing this diversity. Traditional phenotyping methods, however, are labor-intensive and inadequate for the vast collections housed in genebanks. This paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum—from gamma rays to radio waves—to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor. We highlight the integration of advanced imaging systems (e.g., hyperspectral, X-ray, and thermal imaging) to enrich genebank datasets, facilitating trait discovery and crop improvement. Despite challenges like cost, scalability, and data standardization, opportunities arise from collaborative initiatives between genebanks and phenomics facilities through organizations such as International Plant Phenotyping Network. Our conclusions underscore how phenomics can revolutionize genebank operations, ensuring the efficient conservation and deployment of genetic resources to address global agricultural demands.
Why it matches plant phenotyping methods種子形質の高スループット取得に用いるセンサー・イメージング技術を中心に整理したフェノタイピングレビューであり、方法論的役割が明確です。
abstractThis paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum—from gamma rays to radio waves—to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor.
Genebanks serve as critical repositories for preserving the genetic diversity of plant species, including crops, forages, and their wild relatives, which is essential for adapting to climate change, enhancing food security, and improving agricultural sustainability. Seed phenotyping, the process of evaluating observable seed traits influenced by genetics and environmental factors, plays a pivotal role in characterizing and utilizing this diversity. Traditional phenotyping methods, however, are labor-intensive and inadequate for the vast collections housed in genebanks. This paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum—from gamma rays to radio waves—to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor. We highlight the integration of advanced imaging systems (e.g., hyperspectral, X-ray, and thermal imaging) to enrich genebank datasets, facilitating trait discovery and crop improvement. Despite challenges like cost, scalability, and data standardization, opportunities arise from collaborative initiatives between genebanks and phenomics facilities through organizations such as International Plant Phenotyping Network. Our conclusions underscore how phenomics can revolutionize genebank operations, ensuring the efficient conservation and deployment of genetic resources to address global agricultural demands.
Why it matches plant phenotyping methods種子形質を対象とする高スループット画像・センサー型フェノタイピング技術を中心に扱うレビューであり、方法論的役割が明確。
abstractThis paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum—from gamma rays to radio waves—to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor.
Adlay ( Coix lacryma-jobi L.) stands out as a vital health-promoting cereal due to its dual nutritional and medicinal properties; however, it remains significantly underdeveloped compared to major crops. The lack of mechanistic understanding of its caryopsis development and trait formation severely constrains targeted genetic improvement. While transformative technologies, specifically micro-computed tomography (micro-CT) imaging combined with AI-assisted analysis (e.g., Segment Anything Model (SAM)) and multi-omics approaches, have been successfully applied to unravel the structural and physiological complexities of model cereals, their systematic adoption in adlay research remains fragmented. Going beyond a traditional synthesis of these methodologies, this article proposes a novel, multidimensional framework specifically designed for adlay. This forward-looking strategy integrates high-resolution 3D phenotyping with spatial multi-omics data to bridge the gap between macroscopic caryopsis architecture and microscopic metabolic accumulation. By offering a precise digital solution to elucidate adlay's unique developmental mechanisms, the proposed framework aims to accelerate precision breeding and advance the scientific modernization of this promising underutilized crop.
Why it matches plant phenotyping methods穀粒の3DフェノタイピングとAI画像解析を中核に据えた、方法論的な枠組みを提案するレビュー／展望論文である。
abstractThis forward-looking strategy integrates high-resolution 3D phenotyping with spatial multi-omics data
Modern maize stems possess a well-developed vascular bundle system, which is critical for providing mechanical support and lodging resistance. However, characterization of the microanatomical features of vascular bundles and their functional implications in stem mechanics remains challenging, primarily due to technical limitations in high-throughput microanatomical analysis of stem tissues. We thus constructed data sets consisting of over 500,000 maize stem CT images from a maize diversity panel of 383 inbred lines. We evaluated 32 microanatomical phenotypes of maize basal internodes across two environments in different years. By incorporating engineering mechanics parameters, we calculated novel characteristics of the vascular bundles, including the moment of area (MOA) and the polar moment of inertia (PMOI). Through the high-density phenotypic data set, we identified multiple stem microanatomical phenotypes strongly associated with lodging resistance, particularly of vascular bundle mechanical traits. By integrating population genetic profiling, we discovered and confirmed that ZmLSM2 (U6 small nuclear ribonucleoprotein specific Sm-like 2) serves as a key regulator of stem mechanical strength, might function in RNA processing and maturation within vascular stem cells, identifying novel genetic targets for improving maize lodging resistance. This approach demonstrates the value of combining advanced phenotyping with multi-omics analyses for crop improvement. These discoveries will deepen the understanding of plant stem biomechanical principles and provide novel targets for enhancing lodging resistance in crop breeding programs.
Why it matches plant phenotyping methodsトウモロコシ茎のCT画像から微細構造形質を高スループットに抽出する表現型解析基盤とデータセットが研究の中心であり、単なる生物学的測定ではない。
abstracttechnical limitations in high-throughput microanatomical analysis of stem tissues
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicCT cross‐section images of the third internode from 383 maize inbred lines grown in Beijing and Sanya during two growing seasons can be downloaded via the link: https://pan.baidu.com/s/1CP2kkAmTvy1zi3QJGtKSWQ?pwd=JIPB . Extraction code: JIPB.Open asset ↗lines:204-306Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Understanding the complex interplay between environmental factors and fruit quality development requires sophisticated analytical approaches linking cellular architecture to environmental conditions. This study introduces a novel application of dual-resolution X-ray computed tomography (CT) for the non-destructive characterization of apple internal tissue architecture in relation to fruit growth, thereby advancing beyond traditional methods that are primarily focused on postharvest analysis. By extracting detailed three-dimensional structural parameters, we reveal tissue porosity and heterogeneity influenced by crop load, maturity timing and canopy position, offering insights into internal quality attributes. Employing correlation analysis, Principal Component Analysis, Canonical Correlation Analysis, and Structural Equation Modeling, we identify temperature as the primary environmental driver, particularly during early developmental stages (45 Days After Full Bloom, DAFB), and uncover nonlinear, hierarchical effects of preharvest environmental factors such as vapor pressure deficit, relative humidity, and light on quality traits. Machine learning models (Multiple Linear Regression, Random Forest, XGBoost) achieve high predictive accuracy (R 2 > 0.99 for Multiple Linear Regression), with temperature as the key predictor. These baseline results represent findings from a single growing season and require validation across multiple seasons and cultivars before operational application. Temporal analysis highlights the importance of early-stage environmental conditions. Integrating structural and environmental data through innovative visualization tools, such as anatomy-based radar charts, facilitates comprehensive interpretation of complex interactions. This multidisciplinary framework enhances predictive precision and provides a baseline methodology to support precision orchard management under typical agricultural variability.
Why it matches plant phenotyping methodsリンゴ果実の内部組織構造をX線CTで非破壊・三次元計測し、構造パラメータを抽出する手法が研究の中心であり、環境データとの統合や機械学習による形質推定も行っているため。
abstractThis study introduces a novel application of dual-resolution X-ray computed tomography (CT) for the non-destructive characterization of apple internal tissue architecture in relation to fruit growth
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Genebanks serve as critical repositories for preserving the genetic diversity of plant species, including crops, forages, and their wild relatives, which is essential for adapting to climate change, enhancing food security, and improving agricultural sustainability. Seed phenotyping, the process of evaluating observable seed traits influenced by genetics and environmental factors, plays a pivotal role in characterizing and utilizing this diversity. Traditional phenotyping methods, however, are labor-intensive and inadequate for the vast collections housed in genebanks. This paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum-from gamma rays to radio waves-to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor. We highlight the integration of advanced imaging systems (e.g., hyperspectral, X-ray, and thermal imaging) to enrich genebank datasets, facilitating trait discovery and crop improvement. Despite challenges like cost, scalability, and data standardization, opportunities arise from collaborative initiatives between genebanks and phenomics facilities through organizations such as International Plant Phenotyping Network. Our conclusions underscore how phenomics can revolutionize genebank operations, ensuring the efficient conservation and deployment of genetic resources to address global agricultural demands.
Why it matches plant phenotyping methods種子形質を対象とする高スループットセンサー・画像フェノタイピング技術を総説しており、フェノタイピング手法が中心である。
abstractThis paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum-from gamma rays to radio waves-to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor.
Studying virus-infected phloem is of significant importance, as it not only enhances our understanding of viral pathogenesis but also leverages viruses as tools to expand knowledge about plant phloem physiology. The uneven distribution pattern of phloem-infecting viruses poses methodological challenges for such studies-requiring both large field of view (FOV) and high-resolution imaging. A comprehensive anatomical analysis of the phloem necessitates global visualization, while resolving viral structures demands local high-resolution observation. This chapter describes a method, the X-ray microtomography (μCT)-volume electron microscopy (vEM) correlative imaging technique, which effectively addresses these methodological requirements, where μCT provides the large FOV for identification of regions of interest, followed by vEM acquisition of high-resolution images. It is a six-step protocol, including: (1) sample preparation, (2) flaw detection, (3) overview imaging by μCT, (4) identifying viral infection regions, (5) high-resolution imaging by vEM, and (6) image processing and analysis. In this workflow, the steps of sample preparation and identification of viral infection regions are critical. This protocol was originally established for investigating Southern rice black-streaked dwarf virus (SRBSDV) infection in rice phloem, with parameters optimized for plant reoviruses. We provide advice on how to adapt the approach for studying other viral infections.
Why it matches plant phenotyping methods植物のウイルス感染部位と師部構造をμCT・vEM相関イメージングで取得・解析する6段階プロトコルが中心であり、植物状態の画像ベース計測法に該当する。
abstractThis chapter describes a method, the X-ray microtomography (μCT)-volume electron microscopy (vEM) correlative imaging technique
Stomata are pores in the leaf epidermis that regulate the trade-off between CO2 uptake for photosynthesis and water vapor loss to the atmosphere. Stomatal patterning therefore influences water use efficiency and is a target for engineering to avoid drought stress. However, there is limited understanding of how internal leaf anatomy is coordinated with stomatal development, in part due to the technical challenges of assessing three-dimensional anatomy with sufficient resolution. C4 grasses are understudied, and this is a significant knowledge gap given their file-like stomatal distribution and unique mesophyll organization. In this study, wild-type sorghum and a low-stomatal density transgenic line expressing a synthetic Epidermal Patterning Factor (EPFsyn) were studied. High-resolution microCT was paired with machine learning to characterize three-dimensional traits of mesophyll, epidermis, and airspace, which together determine gias. Sorghum internal leaf airspace is an arrangement of large sub-stomatal airspaces with thin air passageways. Adaxial and abaxial surfaces differed in stomatal patterning relative to mesophyll structures, sub-stomatal crypts and airspace CO2 conductance (gias). Surprisingly, adaxial stomata were consistently located above rather than between vascular bundles. Unexpectedly, gias was not significantly different in wild-type versus EPFsyn. EPFsyn plants had larger crypts and shifts in internal leaf anatomy, indicating a potential compensation mechanism for predicted impacts of reduced stomatal density on gias. These findings provide a new understanding of the interplay between leaf surface specific anatomy and internal structural patterning of the mesophyll in a C4 species, and provides knowledge relevant to engineering water use efficiency in crop species.
Why it matches plant phenotyping methods高解像度microCTと機械学習を組み合わせ、葉の三次元形態・気腔などの植物形質を抽出する手法が研究の主要部分であり、単なる生物学的測定ではない。
abstractHigh-resolution microCT was paired with machine learning to characterize three-dimensional traits of mesophyll, epidermis, and airspace
While the root architecture of potted crop seedlings directly determines subsequent crop productivity and adaptability, these root systems remain challenging to quantify using conventional methods due to their structural complexity. To investigate the microscopic characteristics of the root systems of pepper seedlings within pots, Micro-CT was employed to scan the seedling pots. After three-dimensional (3D) reconstruction was conducted on the data acquired from the pot scans, the 3D model of the root system was segmented and extracted using the watershed algorithm. Vertically, the three-dimensional root model was divided from top to bottom into four equally spaced regions (a, b, c, and d), showing the volumetric distribution characteristics of pepper seedling roots within the pots. The results showed that region a had the largest average root volume proportion (29.72%), primarily due to the substantial volume contribution of the taproot. Region d followed with an average proportion of 27.26%, resulting from root coiling and entanglement at the pot bottom caused by the spatial constraints of the seedling tray. The middle regions of the pot, b and c, showed average root volume proportions of 23.14% and 19.89%, respectively. To further investigate the influence of root system characteristics on root injury during seedling gripping, the seedlings were categorized into three types based on their taproot growth positions. A gripping experiment was conducted on these three seedling types using spatula-equipped needles. The results showed that the greatest root injury (12.67%) was observed in Type 1 seedlings, which had taproots located closest to the needle insertion point. In contrast, the least injury (4.09%) was found in Type 3 seedlings, characterized by centrally positioned taproots. Type 2 seedlings, with their taproots growing on the side (laterally away from the insertion point), sustained intermediate injury (5.45%). This was because their lateral positioning led to an uneven distribution of mechanical stress during gripping compared with Type 3 seedlings. A validation experiment conducted on an automated seedling retrieval platform confirmed the root injury analysis. The experimental results showed maximum root injury in Type 1 seedlings (14.16%), followed by Type 2 (6.03%) and Type 3 (4.82%) seedlings, with a successful retrieval rate of 95.29%. These findings were consistent with the Micro-CT analysis. This study could provide a theoretical foundation for low-injury seedling gripping in fully automated seedling transplanters.
Why it matches plant phenotyping methodsMicro-CT、3D再構成、watershed分割を用いて苗の根系形態を定量化する手法が研究の中心であり、根容積分布と根傷害の評価まで検証している。
abstractMicro-CT was employed to scan the seedling pots. After three-dimensional (3D) reconstruction was conducted on the data acquired from the pot scans, the 3D model of the root system was segmented and extracted using the watershed algorithm.
The objective of this study is to use synchrotron-based X-ray fluorescence imaging (XFI) and bulk analysis to investigate elements (Mn, Fe, Cu, Zn, P, S, K, Ca) distributions and relative concentrations in four cool-season oat varieties (CDC Arborg, CDC Nasser, CDC Haymaker, and Summit) obtained from the same growing location, soil conditions and harvest time at the University of Saskatchewan. XFI at the Canadian Light Source's BioXAS-Imaging beamline (5 μm resolution, 15 keV) revealed that P, K, Mn, and Zn were concentrated in the aleurone layer, scutellum, and embryo, while Ca was only localized in the aleurone layer and scutellum in the four oat varieties. Notably, S and Cu were distributed in all parts of the seed across four varieties, but the intensity was low in the endosperm. Bulk analysis results show that there were significant differences in the relative concentrations of K, Fe and Zn among four oat varieties harvested for three consecutive years (2018, 2019, 2020) at the completely mature stage. CDC Nasser oat had the lowest K and Zn, while CDC Haymaker had the highest Fe among the oat varieties. These findings highlight the impact of variety on nutritional quality and could help inform future biofortification strategies to enhance the micronutrient content for human and animal diets. This work is the first to map the oat mineral distributions across cool-season varieties using high-resolution XFI. Unlike rice, oats showed minimal mineral accumulation in the hull, ensuring nutritional retention post-milling. Overall, this study shows XFI's potential as a non-destructive tool for cereal grain analysis and supports breeding nutrient-dense oat varieties to address global micronutrient deficiencies.
Why it matches plant phenotyping methodsオーツ種子の元素分布・濃度という植物器官形質を高解像度X線蛍光イメージングで取得し、非破壊測定法としての有用性を示すことが中心的です。
abstractThis work is the first to map the oat mineral distributions across cool-season varieties using high-resolution XFI.
Porosity and permeability are critical physical parameters for accurately modelling macroscale heat and mass transfer processes during the cooling, thermal processing, and storage of leafy vegetables. However, existing estimation methods primarily rely on lumped semi-empirical approaches, which overlook the realistic 3D structural information, limiting insights into microscale water transport behaviour. This study utilised low- and high-resolution X-ray computed tomography (CT) combined with advanced cell segmentation techniques to determine the porosity-permeability correlation of spinach, a representative easily dehydrated leafy vegetable. Experiments demonstrated that the Cellpose, dilation, and erosion algorithms effectively segmented adhering cells and generated lamina and petiole slices with varying porosity gradients. Using 3D reconstruction and seepage simulation, the porosity and permeability of representative elementary volumes (REVs) in the lamina and petiole tissues were calculated, and the pressure and flow rate distributions within the intercellular spaces were visualised. The porosity-permeability relationship was fitted using the Kozeny-Carman (KC) formula as κₗ = 4.839 × 10⁻¹¹φ¹.⁷⁰/(1 - φ)⁰.⁷⁰ for lamina REVs and κₚ = 1.128 × 10⁻¹⁰φ².¹⁶/(1 - φ)¹.¹⁶ for petiole REVs. Grayscale-porosity and porosity-permeability correlations were further applied to characterise the heterogeneity of porosity (2.742%–53.30%) and permeability (4.925 × 10⁻¹⁴ - 2.829 × 10⁻¹¹ m²) of intact spinach. The study aims to provide technical and theoretical support for multiscale modelling in the quality control of leafy vegetables.
Why it matches plant phenotyping methodsX線CT、細胞セグメンテーション、3D再構成を中核として、ホウレンソウ組織の空隙率・透過率という構造的な植物形質を定量化する手法を開発・適用しているため。
abstractThis study utilised low- and high-resolution X-ray computed tomography (CT) combined with advanced cell segmentation techniques to determine the porosity-permeability correlation of spinach
BACKGROUND: Poultry is a global industry with laying hens that are genetically optimized for high egg yield. Keel bone fractures can affect up to 80% of laying hens, posing welfare and production problems. Therefore, genetic selection to reduce keel fractures is important. However, the lack of a reliable, automated, and heritable phenotypes for keel bones makes this a challenging task. The aim of this study was to (1) develop automated analyses of radiographic images to phenotype keel bones, and (2) investigate whether the proposed phenotypes are heritable and genetically correlated with the post-dissection scores of keel bone fractures and deviations. A total of 1051 laying hens (Bovans Brown and Lohmann Brown) from a commercial farm were x-rayed, followed by keel bone dissection and scoring for deviations and fractures. Furthermore, blood was sampled for genotyping using 50 K Illumina SNP chips. Keel bones were segmented (with ~ 0.90 accuracy) from the radiographic images using deep learning models, after which the images were automatically measured for general geometry and radiopacity. Multi-trait genomic restricted maximum likelihood was used to estimate genetic parameters. RESULTS: Heritability estimates ranged from 0.28 to 0.30 for both keel deviations and fractures observed post-dissection. The automated phenotypes had heritability estimates ranging from 0.07 to 0.10 for keel radiopacity and from 0.11 to 0.39 for keel geometry. Estimates of genetic correlations of keel geometry with keel deviation and fractures ranged from -0.57 to 0.72. CONCLUSIONS: Automated methods were developed for measuring keel bone radiopacity and geometry. Keel concave area was found to be a reliable and heritable phenotype that breeding companies can use to reduce keel deviations and fractures. These methods can also be adapted to measure other bones (e.g., tibiotarsal) or objects (e.g., eggs), allowing breeders to quickly compute phenotypes for keel, tibia, and egg size from the same radiographic image. The developed methods are well-suited for large-scale studies to assess different housing environments and nutrition strategies aimed at improving keel bone conditions.
Why it matches plant phenotyping methodsニワトリの骨を対象とするが、放射線画像と深層学習による骨形状・放射輝度の自動表現型測定法の開発が中心であり、植物フェノタイピングではないため対象外。
abstractdevelop automated analyses of radiographic images to phenotype keel bones
A methodology for achieving micrometer-scale 4D X-ray lab microscopy of living leaf tissue was developed to overcome challenges associated with delicate tissues, radiation damage, and motion artifacts during in vivo imaging. The study focused on optimizing laboratory based X-ray micro-computed tomography (microCT) parameters to balance high-resolution imaging with minimized physiological stress and radiation dose quantification. Assessing the dose-safe imaging window required comparing vertical and horizontal leaf mounting setups. Results demonstrated that the horizontal setup provided greater stability, preventing tissue degradation and maintaining sample viability during continuous acquisitions lasting up to 22 hours ([~]15600 Gy). MicroCT capacities were clearly able to resolve microstructures at the cellular level, achieving a pixel size down to 1 {micro}m. Furthermore, this optimized methodology confirmed the ability to track the spatiotemporal dynamics of applied compounds such as iohexol and aggregated nanoparticles within the leaf tissue. This work establishes that accessible laboratory based microCT enables the in vivo 4D monitoring of anatomical and physiological changes in living plants.
Why it matches plant phenotyping methods生葉を対象とした高解像度4D X線マイクロCTの撮像条件・線量・試料配置を開発し、生体内の解剖学的・生理学的変化を追跡する方法が研究の中心である。
abstractA methodology for achieving micrometer-scale 4D X-ray lab microscopy of living leaf tissue was developed to overcome challenges associated with delicate tissues, radiation damage, and motion artifacts during in vivo imaging.
Plant seeds are one of the most important food sources for humans. As a result, seed morphology and the concentrations of essential and toxic elements in seeds have important implications not only for seed yield and quality, but also for human health. To identify natural variation in the accumulation of various elements in seeds and in seed morphology, high-throughput phenotyping methods are needed. Here, we employed X-ray fluorescence microscopy (μ-XRF) as a method for rapid and high-throughput phenotyping of seed libraries and developed a computer vision-based algorithmic workflow to automatically the extraction of elemental and morphological data from single seeds. This workflow enables rapid segmentation of individual seeds from a genome-wide association study (GWAS) panel with 1163 A. thaliana accessions, and facilitates the extraction of elemental and morphological traits at the individual seed level from the μ-XRF image. A total of 7 and 10 loci, respectively associated with the morphology and elemental concentration of A. thaliana seeds, were identified. The high-throughput and nondestructive method for automated phenotyping of plant seed libraries developed in this study provides a tool for investigating natural genetic variation controlling the seed mineral accumulation and seed morphogenesis.
Why it matches plant phenotyping methods種子の元素濃度・形態をμ-XRFとコンピュータビジョンで高速・自動取得する手法を開発しており、植物表現型取得が研究の中心である。
abstracthigh-throughput phenotyping methods are needed
Reproduction assets foundThe authors explicitly state that the u-XRF source code and algorithm (the computer vision workflow used for seed segmentation and trait extraction from μ-XRF images) are distributed under the MIT License and publicly available at their GitHub repository, making it a paper-specific, public, actionable code asset.Code · publicThe source code and algorithm of u-XRF are distributed under the MIT License, which permits academic use, distribution, and reproduction subject to the terms of the license ( https://opensource.org/license/MIT/ ), unless otherwise specified. Supporting source code, Web of Science Global Science Publications data, and additional datasets can be accessed at https://github.com/The-Wang-Lab-NAU/u-XRF/ for download and upload.Open asset ↗The-Wang-Lab-NAU/u-XRFlines:291-309Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 6 Sept 2026
Premise Analyzing structural changes along the length of an organ provides insight into its development. However, traditional histological methods are limited by intensive procedures and size restrictions. Micro-computed tomography (microCT) enables non-destructive internal imaging along the length of an organ, but high cost, technical complexity, and limited accessibility hinder widespread application. Here, we describe serial section videography (SSV), a new low-cost technique for generating three-dimensional (3D) reconstructions of internal plant anatomy using serial sectioning and open-source software. Methods and results SSV was applied to four fern rhizomes with varied gross morphology and diverse vascular architectures. Specimens were sectioned using a sliding microtome or a handheld blade, and imaged using either a digital camera or smartphone setup. Images were aligned using Fiji and segmented using 3D Slicer. The SSV method enabled continuous visualization of internal stem anatomy over several centimeters and is adaptable to both laboratory and field settings. Conclusions This protocol offers an alternative to microCT for generating 3D anatomical reconstructions, enabling researchers to examine development and structural variation across organs with minimal equipment and software. This accessible protocol reduces technical and financial barriers and is particularly well-suited for comparative studies of vascular tissues, advancing the study of plant anatomy and development.
Why it matches plant phenotyping methods植物器官内部構造を連続撮像・画像処理して3D形態を再構成する低コスト手法の開発と適用が中心であり、植物形態・解剖状態の取得法として収載対象。
abstractHere, we describe serial section videography (SSV), a new low-cost technique for generating three-dimensional (3D) reconstructions of internal plant anatomy using serial sectioning and open-source software.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract Background and aims Determining elemental concentrations in plant tissues is crucial for any study on plant nutrition, physiology, contamination and food safety. However, existing methodologies based on acid digestion of samples coupled to inductively coupled plasma-atomic emission spectrometry (ICP-AES) or plasma-mass spectrometry (ICP-MS) are time-consuming and expensive. Methods This study introduces an innovative approach for the rapid and reliable analysis of light, transition, and heavy elements in plant samples using a novel monochromatic X-ray fluorescence (MXRF) spectrometer. Results The MXRF method was tested for the detection of 12 different elements, including light elements (K, Ca), transition metals (Mn, Fe, Co, Ni, Cu, Zn), metalloids (As, Se), and post-transition “heavy” elements (Tl, Pb), covering concentrations from 1 to 10,000 mg·kg −1 . The limits of detection and quantification ranged from 1.41 to 4.71 mg·kg −1 . The recovery rates varied from 84.74% to 89.34%, with intraday relative standard deviations (RSD) ≤ 2.31% and inter-day RSD ≤ 4.17%. A method-comparison study using 144 plant samples analysed by both MXRF and ICP-AES showed strong correlations ( R 2 > 0.87) for K, Ca, Mn, Fe, Co, Ni, Cu, Zn, As, Pb, and TI. Conclusions This study demonstrates the reliability of the MXRF technique for the quantification of K, Ca, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Pb, and Tl in plant samples. Given that MXRF can also be applied to the analysis of elemental concentrations in soil and water samples, future research will focus on refining and establishing methodologies for these sample types.
Why it matches plant phenotyping methods植物試料中の元素濃度という植物の生理・状態を測定するMXRF法を新規導入し、検出限界、再現性、回収率、ICP-AESとの比較で技術検証しているため、方法が中心的である。
abstractThis study introduces an innovative approach for the rapid and reliable analysis of light, transition, and heavy elements in plant samples using a novel monochromatic X-ray fluorescence (MXRF) spectrometer.
Background Seed quality analysis using X-rays is increasingly explored due to its non-invasive and rapid nature. Yet, the current absence of reliable and standardised imaging protocols has led to contradictory effects of X-ray exposure in previous studies. Our work systematically investigated the effect of low-energy X-rays (peak energy ≲25 keV) with limited doses ( Results The baseline of three germination categories was established across seven species before the application of low-dose X-ray exposure under controlled standard germination conditions. The high inter-varietal and inter-lot variabilities, in addition to the strong interaction between X-ray exposure with both variety and lot, reinforced the need to consider genetic and seed quality aspects while evaluating the impacts of low-dose, low-energy X-rays ( 2 = 0.82) and their germination outcomes after exposure (p 2 = 0.88). Among all species, fennel with notably low density (0.7 g/cm 3 ) demonstrated the most pronounced gains in germination after exposure (4.6 ± 6.3%) due to the stimulative effect. Conclusion Low-dose X-ray exposure is non-destructive with a beneficial effect on germination, but can be strongly influenced by underlying genetics and the physical quality of the tested seeds. This work addressed important gaps in evaluating X-ray impacts and proposed a robust design and well-examined radiography protocol for a proven non-destructive seed quality analysis.
Why it matches plant phenotyping methods種子品質を非破壊に評価するX線ラジオグラフィーのプロトコルを検討・検証し、種子品質および発芽状態の測定法として方法論的貢献が中心に含まれる。
abstractSeed quality analysis using X-rays is increasingly explored due to its non-invasive and rapid nature.
Published1 Nov 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Understanding wheat grain internal structures is critical for improving quality, pest resistance, and breeding efficiency. While X-ray computed tomography (CT) enables non-destructive 3D imaging, existing segmentation methods rely on manual intervention, introducing inefficiency and subjectivity. This study introduces the Residual Depthwise Separable Convolution and Vision Mamba U-Net (RDVM-UNet), an automated framework combining Depthwise Separable Convolution (DSConv) for efficient local feature extraction and Vision Mamba for global contextual modeling. Trained over 200 iterations, the model achieved a mean Intersection over Union (mIoU) of 95.4 % in segmenting wheat tissues (epidermis, embryo, endosperm). Validation across 10 varieties demonstrated robust generalizability (mIoU is 94.78 %) and rapid processing (9.65 s/grain). The framework generated 3D reconstructions, enabling precise quantification of morphological parameters (volume, surface area) critical for analyzing genetic-environmental-morphological relationships. By establishing a non-destructive, high-throughput pipeline, this work advances precision breeding, functional genomics, and trait optimization in cereal crops. RDVM-UNet bridges computational imaging and agricultural science, offering scalable solutions for crop phenotyping and quality enhancement.
Why it matches plant phenotyping methodsX線CT画像から小麦組織を自動分割・3D再構成し、形態形質を定量化する深層学習パイプラインの開発と品種横断検証が中心であり、植物表現型計測法に該当する。
abstractThis study introduces the Residual Depthwise Separable Convolution and Vision Mamba U-Net (RDVM-UNet), an automated framework combining Depthwise Separable Convolution (DSConv) for efficient local feature extraction and Vision Mamba for global contextual modeling.
Hazelnut (Corylus avellana L.) is one of the most appreciated and cultivated nuts in temperate areas. Producers are now facing an increasing demand and industries need to select high-yielding and fine-quality cultivars. In this context, a challenge to take up is the development of a rapid, non-destructive and high-resolution method to study the growth and differentiation dynamics of floral reproductive organs, to limit yield losses especially in response to climate adaptation. In this study, we scanned mixed buds from the hazelnut cultivar Tonda di Giffoni from anthesis to fruit formation by micro-computed tomography (Micro-CT). We reconstructed in three dimensions (3D) the spatial arrangement of flowers within the glomerulus, characterized the position and configuration of ovules, ovaries and funiculus as well as observed the formation of the embryo during the early developmental stages. The proposed approach enables precise volume measurements of ovaries, ovules, and embryos. It helps identify abortive ovules early and track developmental stages, such as embryo formation. Unlike traditional 2D methods, this approach captures growth patterns more accurately, supporting research on fruit development, crop quality, and genetic studies. Overall, it provides a powerful tool for advancing reproductive biology research of hazelnuts and improving hazelnut cultivation.
Why it matches plant phenotyping methodsヘーゼルナッツの生殖器官をMicro-CTで非破壊・高解像度に3D再構成し、器官体積や発達段階を定量化する手法が研究の中心であるため。
abstractthe development of a rapid, non-destructive and high-resolution method to study the growth and differentiation dynamics of floral reproductive organs
Insect pests in stor ed products cause qualitative and quantitative losses in seed lots, reducing their commercial value by directly compromising the physiological and sanitary quality of the seeds. The objective of this study was to evaluate the physiological quality and perform a proximate analysis of rice seeds infested with Sitophilus zeamais Motschulsky (Coleoptera: Curculionidae), using radiographic images. The X-ray analysis was used to detect and identify the weevil development stages and quantify the percentage of infestation in rice seeds. The physiological quality and the proximate analysis were evaluated after the seeds were subjected to four levels of infestation by S. zeamais: 0%, 2%, 3%, and 5%. The radiographic images enabled efficient detection of infestation levels, identification of the weevil's developmental stages, and assessment of damaged and empty seeds. The following physiological tests were performed: germination test, first germination count test, emergency test, retention capacity of the substrate, emergency speed index, and electrical conductivity test. For the physiological and proximate analysis, the experimental design was completely randomized, with four treatments and four replications. Statistical differences were observed in physiological assessments and proximate analysis across infestation levels, confirming that infestation intensity directly affects seed viability and nutritional value. This emphasizes the importance of effective monitoring methods to mitigate pest damage to stored seeds.
Why it matches plant phenotyping methodsX線画像を用いた種子の害虫侵入・発育段階・損傷状態の検出と侵入率の定量が研究の中心であり、種子の状態・品質という植物形質を画像から評価している。
The objective of this study is to use synchrotron-based X-ray fluorescence imaging (XFI) and bulk analysis to investigate elements (Mn, Fe, Cu, Zn, P, S, K, Ca) distributions and relative concentrations in four cool-season oat varieties (CDC Arborg, CDC Nasser, CDC Haymaker, and Summit) obtained from the same growing location, soil conditions and harvest time at the University of Saskatchewan. XFI at the Canadian Light Source's BioXAS-Imaging beamline (5 μm resolution, 15 keV) revealed that P, K, Mn, and Zn were concentrated in the aleurone layer, scutellum, and embryo, while Ca was only localized in the aleurone layer and scutellum in the four oat varieties. Notably, S and Cu were distributed in all parts of the seed across four varieties, but the intensity was low in the endosperm. Bulk analysis results show that there were significant differences in the relative concentrations of K, Fe and Zn among four oat varieties harvested for three consecutive years (2018, 2019, 2020) at the completely mature stage. CDC Nasser oat had the lowest K and Zn, while CDC Haymaker had the highest Fe among the oat varieties. These findings highlight the impact of variety on nutritional quality and could help inform future biofortification strategies to enhance the micronutrient content for human and animal diets. This work is the first to map the oat mineral distributions across cool-season varieties using high-resolution XFI. Unlike rice, oats showed minimal mineral accumulation in the hull, ensuring nutritional retention post-milling. Overall, this study shows XFI's potential as a non-destructive tool for cereal grain analysis and supports breeding nutrient-dense oat varieties to address global micronutrient deficiencies.
Why it matches plant phenotyping methodsX線蛍光イメージングを用いてオート麦種子の元素分布・相対濃度を高解像度かつ非破壊で取得し、その方法の有用性を主要な成果として示しているため、植物器官の化学的形質測定法として採用。
abstractThis work is the first to map the oat mineral distributions across cool-season varieties using high-resolution XFI.
Field / plotGrowth chamberLiDAR / point cloudMultispectral / hyperspectralX-ray / CTCalibration / preprocessing
This paper presents a systemic framework designed to advance multi-modal plant phenotyping research through the strategic use of high-performance computing (HPC) in agricultural science. Our framework addresses the unique challenges in high-throughput plant phenotyping (HTP) research, which integrate multi-mode imaging sensors such as RGB, hyperspectral, LiDAR, thermal, and X-ray computed tomography (CT) across indoor and outdoor environments. Our primary objective is to transform raw sensor data into research-ready plant phenotypical traits. This directly supports downstream agricultural research, such as in plant breeding, crop management, etc. The framework is structured into two main phases. The initial data processing occurs on non-HPC systems, utilizing operating system and license-specific software due to the specialized algorithms and knowledge required for each imaging data pipeline. The subsequent HPC phase refines these processed datasets into tabular formats, preparing them for statistical analysis in agronomy, plant science, and bioinformatics. The current implementation of the second phase utilizes a hybrid parallelization model across HPC nodes and threads. However, its performance could be significantly enhanced by implementing more efficient algorithms and optimizing resource allocation. We discuss current bottlenecks, including technical challenges related to sensors, imaging platforms, and computational pipelines, and propose immediate solutions.
Why it matches plant phenotyping methodsHPCを用いて多モーダルセンサーデータから植物形質を抽出する計算フレームワークが研究の中心であり、植物フェノタイピング基盤として該当する。
abstractThis paper presents a systemic framework designed to advance multi-modal plant phenotyping research through the strategic use of high-performance computing (HPC) in agricultural science.
Understanding the genetic basis of root system architecture (RSA) in crops requires innovative approaches that enable both high-throughput and precise phenotyping in field conditions. In this study, we evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize in three field experiments. We used forward and reverse genetic approaches to evaluate >1700 maize root crowns, including a diversity panel, a biparental mapping population, and maize mutant and wild-type alleles at two known RSA genes, DEEPER ROOTING 1 (DRO1) and Rootless1 (Rt1). We show the utility of increasing the dimensionality of traditional two-dimensional (2D) techniques, referred to as the "2D multi-view" method, to improve the capture of whole root system information for mapping genetic variation influencing RSA. Comparison of univariate and multivariate genome-wide association study (GWAS) approaches revealed that multivariate traits were effective at dissecting complex RSA phenotypes and identifying pleiotropic quantitative trait loci (QTLs). Overall, three-dimensional (3D) root models generated from X-ray computed tomography and digital phenotyping captured a larger proportion of RSA trait variations compared to other methods of root phenotyping, as evidenced by both genome-wide and single-gene analyses. Among the individual root traits, root pulling force emerged as a highly heritable estimate of RSA that identified the largest number of shared QTLs with 3D phenotypes. Our study shows that integrating complementary phenotyping technologies helps to provide a more comprehensive understanding of the genetic architecture of RSA in field-grown maize.
Why it matches plant phenotyping methods根系構造を定量化する複数の表現型解析法を比較・評価し、2Dマルチビュー、X線CT、デジタル表現型などの技術性能を遺伝解析で検証しており、表現型取得法が研究の中心である。
abstractwe evaluated multiple phenotyping and analytical frameworks for quantifying RSA in mature, field-grown maize
Reproduction assets foundThe paper deposits raw phenotypic metadata (root crown/RSA measurements from the field experiments) on Dryad, and uses the authors' public 3D root crown analysis pipeline (RCAP) on GitHub for the XRT feature extraction. Both are paper-specific, public, and actionable. Generic R packages and cited prior work are not.Dataset · publicRaw phenotypic metadata are available on the Dryad Digital Repository ( https://doi.org/10.5061/dryad.z34tmpgq4 , http://datadryad.org/share/HeNYoxNMdN_GrHMyZHFN3rUTN1UiG8OFhU-B107E7mM ).Open asset ↗Dryad Digital Repository · 10.5061/dryad.z34tmpgq4lines:499-731Code · publicreferred to here as the root crown analysis pipeline (RCAP). Detailed descriptions of RCAP trait implementations and related resources are available at: https://github.com/Topp‐Roots‐Lab/3d‐root‐crown‐analysis‐pipeline/ .Open asset ↗GitHub · Topp‐Roots‐Lab/3d‐root‐crown‐analysis‐pipelinelines:162-175Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Accurate olive cultivar identification is critical for ensuring quality control and traceability in the olive oil industry. The International Olive Council (IOC) and the International Union for the Protection of New Varieties of Plants (UPOV) have established standardized protocols for varietal characterization. Over the past two decades, two-dimensional image analysis techniques have been increasingly employed for olive variety identification, utilizing various morphological parameters and machine learning approaches. This study investigates olive varietal classification through three-dimensional morphological analysis of fruits and stones using X-ray microtomography. The research evaluates the discriminative power of different trait combinations using both Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) algorithms to contribute to an optimized protocol for cultivar identification. Five autochthonous olive cultivars from the Campania region (Southern Italy) were analyzed. A preliminary comparison of classification performance between continuous and discrete morphological olive data revealed superior effectiveness of the continuous ones. Integrating quantitative morphometric traits with selected visual discrete UPOV characteristics yielded optimal overall classification accuracy of 88.41% using LDA with 84.4% for Ravece, 81.5% for Ortice, 100% for Frantoio, 81.3% for Rotondella, and 90.9% for Minucciola olive varieties. The best variety prediction rates, based on an olive sample not used for training, were provided by SVM, obtaining 70.0% for Ravece, 87.5% for Ortice, 54.5% for Frantoio, 60.0% for Rotondella, and 66.7% for Minucciola. Quantification of varietal overlap through Bhattacharyya coefficients identified Ortice and Ravece as the most phenotypically similar varieties, while Rotondella and Minucciola exhibited the most distinctive fruit morphology. Notably, all varieties showed at least one misclassification with the Frantoio variety. Morphological analysis demonstrated that endocarp surface traits provided the most discriminative power, and internal cavity characteristics also contributed significantly to varietal differentiation. These findings suggest two key implications: potential updates of UPOV guidelines for distinctness evaluation protocols and promising applications in authenticity verification for high-quality olive products.
Why it matches plant phenotyping methodsX線マイクロトモグラフィーによる果実・核の3次元形態計測と、形態形質を用いた分類性能評価が研究の中心であり、品種識別用の植物表現型取得・解析手法に該当する。
abstractThis study investigates olive varietal classification through three-dimensional morphological analysis of fruits and stones using X-ray microtomography.
Deep understanding of the structural composition and growth of biological specimens is becoming increasingly important for the development of bio-based and sustainable material systems. Full-field nano-computed tomography is particularly suitable for this purpose as it allows for non-destructive 3D imaging at high spatial resolution. However, most biological samples are functionalized by water and respond sensitively to any changes in climate conditions, specifically relative humidity, by adjusting their material moisture content. To date, only a limited number of tomography instruments offer an in situ climate control option to users. These, however, are limited either by the range of relative humidity states, the long times required to change the climate state, or obstruction or attenuation of the beam. Here, the first fully automatized climate cell for in situ full-field nanotomography is presented. It has been designed, built and integrated at the nanotomography station at the P05 imaging beamline, operated by Hereon at the DESY storage ring PETRA III, Germany. The highly flexible and windowless design allows the humidity dependent swelling and shrinking of lignified plant cell walls to be studied in situ, using phase contrast nanotomography. The concept of this climate chamber can easily be integrated into other setups. It operates in the relative humidity range of 0-90% and can be controlled in a temperature range of 10-50°C. Climate conditions can be adjusted at any time, remotely from the control hutch by using a humidity generator. Results show that the developed setup maintains a stable climate during the entire duration of a tomographic scan at different humidities and does not obstruct the sample or hinder the imaging conditions. During the tomographic investigation the sample remains stable in the flow of the air stream and shows typical cell wall swelling and shrinking behaviour depending on the equilibrium moisture content. This new climate cell is now available to all users of the P05 nanotomography instrument for conditioning samples, serving a wide range of scientific applications.
Why it matches plant phenotyping methods植物細胞壁の膨潤・収縮を非破壊に取得するナノトモグラフィー用の自動環境制御セルを開発・統合し、安定性と撮像性能を検証しているため、植物表現型取得法が中心である。
abstractHere, the first fully automatized climate cell for in situ full-field nanotomography is presented.
An accurate assessment of wheat quality is crucial for minimizing economic losses and providing high-quality products to consumers. The quality determinants conventionally assessed in wheat include sprout damage, insect damage, vitreousness/hardness, and nutritional composition. Recent advancements in microstructural analysis techniques offer efficient, accurate, and non-destructive alternatives to traditional wheat kernel assessment methods that are often time-consuming, labour-intensive, and subjective. This chapter provides a comprehensive overview of optical techniques for assessing wheat quality, detailing their principles, advantages and limitations, and potential applications in wheat quality determination. Most of these methods rely on objective morphometric parameters ranging from surface examination to in-depth internal structure analysis. Among these techniques, X-ray micro-computed tomography (X-ray micro-CT) and hyperspectral imaging have demonstrated significant promise for wheat quality assessment. Additionally, advancements from other grains present opportunities to enhance existing evaluation methodologies. Adoption of these optical techniques can lead to more precise and non-destructive wheat quality control, ultimately improving product quality and reducing economic losses.
Why it matches plant phenotyping methods小麦粒の品質属性を非破壊・光学的に評価する手法を体系的にレビューしており、形態計測、X線マイクロCT、ハイパースペクトル画像などの表現型取得法が中心である。
titleNon-destructive optical methods for evaluating quality attributes of wheat kernels.
Abstract Correlative imaging is a powerful tool for revealing information on cell-type structures and their biochemistry, with the potential to inform healthier food choices and improved dietary recommendations. Determination of plant structures and their structural biochemistry advances our understanding of specific structures designed to store different biomolecules within cells and tissues. Compared to the classical biochemical separation techniques, the key advantage of sequential correlative imaging techniques is in relating spatial plant (micro)structures to their biochemistry in a nondestructive manner. Sequential imaging reported here comprises six methodologies on a single sample, a cross-section of a Tartary buckwheat (Fagopyrum tataricum) grain, namely, bright-field and autofluorescence microscopy, fluorescence microspectroscopy, MeV-secondary ion mass spectrometry, micro-particle-induced X-ray emission, scanning electron microscopy coupled with energy dispersive X-ray spectroscopy, and laser ablation-inductively coupled plasma-mass spectrometry. Results confirm that the stepwise addition of the desired information across several classes of biomolecules and several spatial scales informs the quality and safety of plant-based produce across scales. Therefore, a viable workflow is proposed, enabling sequential spatial analysis of grain and highlighting plant structures' in situ specificity. The advantages and disadvantages of the selected methodologies were critically evaluated.
Why it matches plant phenotyping methods植物粒の構造とその化学的特徴を複数の相関イメージング法で取得する再利用可能なワークフローを提案し、各手法の長短も評価しているため、表現型取得法が中心である。
abstractTherefore, a viable workflow is proposed, enabling sequential spatial analysis of grain and highlighting plant structures' in situ specificity.
Reproduction assets foundThe paper explicitly points to a public Zenodo deposit containing the correlative imaging data (SEM, micro-PIXE, MeV-SIMS maps) used in its analyses, with instructions for reproducing image fusion in Wolfram Mathematica or ImageJ.Dataset · publicsed to reveal the allocation of K
to cotyledons (Supplementary Fused Image 1). Similarly, on
the same SEM image, MeV-SIMS distribution maps under the
selected peak were overlaid (Supplementary Fused Image 2).
Custom combinations can be done in the Wolfram
Mathematica program or in ImageJ (Merge Channels) using
data available at https://doi.org/10.5281/zenodo.14628251, fol
lowing the instructions in the Materials and Methods.
Conclusions
The low emission properties of fluorescence biomolecules,
when excited with 405 nm light, inherently limit the informa
tion acquired using fluorescence imaging. At this excitation
wavelength, catechin may be the primary fluorophore in
Tartary buckwheat cotOpen asset ↗zenodo · 10.5281/zenodo.14628251pdf-raw-page:13 lines:1-89Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
This study aims to utilise natural variation in pea seed composition from NordGen collections to identify key traits for optimized plant-based ingredients functionality while minimizing refined extraction processes. Given the impracticality of chemically analysing 1942 accessions, an algorithm-assisted approach was employed, using image-derived features and datasets to pre-select 51 accessions. Protein content, thousand kernel weight, perimeter, and G-value were determined as primary criteria via PCA, capturing variations in protein composition and other key components. Protein and starch content ranged from 21.2 to 36.9 % and 21.0-48.1 %, respectively. Image analysis linked geometry to composition, aiding pea selection and application. X-ray scattering differentiates peas based on starch structure. Proteomic profiling revealed that legumin and vicilin varied most, with legumin dominant in smooth peas and vicilin in wrinkled ones, enabling control of their ratio through selection. This study highlights the potential of using natural variation of seed composition for less-refined plant-based ingredients for various applications.
Why it matches plant phenotyping methods画像由来特徴量とアルゴリズムを用いて多数のエンドウ遺伝資源から種子形質・組成を推定し、化学分析対象を選抜するワークフローが研究の中心であるため、植物フェノタイピング手法の実質的応用と判断します。
abstractGiven the impracticality of chemically analysing 1942 accessions, an algorithm-assisted approach was employed, using image-derived features and datasets to pre-select 51 accessions.
Background Agricultural systems are under extreme pressure to meet the global food demand, hence necessitating faster crop improvement. Rapid evaluation of the crops using novel imaging technologies coupled with robust image analysis could accelerate crops research and improvement. This proof-of-concept study investigated the feasibility of using X-ray imaging for non-destructive evaluation of rice grain traits. By analyzing 2D X-ray images of paddy grains, we aimed to approximate their key physical Traits (T) important for rice production and breeding: (1) T 1 chaffiness, (2) T 2 chalky rice kernel percentage (CRK%), and (3) T 3 head rice recovery percentage (HRR%). In the future, the integration of X-ray imaging and data analysis into the rice research and breeding process could accelerate the improvement of global agricultural productivity. Results The study indicated, computer-vision based methods (X-ray image segmentation, features-based multi-linear models and thresholding) can predict the physical rice traits (chaffiness, CRK%, HRR%). We showed the feasibility to predict all three traits with reasonable accuracy (chaffiness: R 2 = 0.9987, RMSE = 1.302; CRK%: R 2 = 0.9397, RMSE = 8.91; HRR%: R 2 = 0.7613, RMSE = 6.83) using X-ray radiography and image-based analytics via PCA based prediction models on individual grains. Conclusions Our study demonstrated the feasibility to predict multiple key physical grain traits important in rice research and breeding (such as chaffiness, CRK%, and HRR%) from single 2D X-ray images of whole paddy grains. Such a non-destructive rice grain trait inference is expected to improve the robustness of paddy rice evaluation, as well as to reduce time and possibly costs for rice grain trait analysis. Furthermore, the described approach can also be transferred and adapted to other grain crops.
Why it matches plant phenotyping methodsX線画像と画像解析を用いてイネ籾の複数の物理形質を非破壊推定する方法の実証であり、形質取得・推定手法が研究の中心です。
abstractThis proof-of-concept study investigated the feasibility of using X-ray imaging for non-destructive evaluation of rice grain traits.
Metabolic processes in plant organs involving transport of water, metabolic gasses, and nutrients depend on the three-dimensional (3D) microscopic tissue morphology. However, imaging and quantifying this microstructure, including the spatial layout of parenchyma cells, pores, vascular bundles and special features such as stone cell clusters (brachysclereids), is challenging. To address this, a 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images. In addition, various training datasets and data augmentation techniques, including synthetic data, were explored to enhance segmentation quality. The 3D panoptic segmentation achieved an Aggregated Jaccard Index of 0.89 and 0.77 for apple and pear tissue, respectively, outperforming both the previously designed 2D instance segmentation model and a marker-based watershed segmentation benchmark. The model successfully labeled vascular bundles with a Dice Similarity Coefficient (DSC) of 0.51 in apple tissue and 0.79 in pear tissue, although thin vasculature in apple remained more challenging to segment. The 3D panoptic segmentation model achieved a DSC of 0.81 and effectively segmented stone cell clusters in pear tissue. Despite evaluating different methods to enhance segmentation quality, none improved test performance beyond that of the model trained on the standard dataset. The proposed 3D panoptic segmentation model offers the most complete automated protocol to date for plant tissue labelling and morphometric quantification from native X-ray micro-CT images, without extensive sample preparation such as contrast labelling. The developed method, if not replaces, drastically accelerates conventional human-in-the-loop analysis of such images.
Why it matches plant phenotyping methods植物組織の3DマイクロCT画像から微細構造を自動セグメンテーションし、形態計測する手法の開発・比較検証が中心であるため。
abstracta 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images.
Plant roots influence various soil physical properties by altering the soil structure and pore configuration; however, a detailed understanding of these effects remains limited. In this study, we applied a relatively simple approach for segmenting plant roots and soil constituents using X-ray computed tomography (CT) images to evaluate root-induced changes in soil structure. The method combines manual initialization with a layer-wise automated region-growing approach, enabling the extraction of the root systems of soybean, Italian ryegrass, and Guinea grass. The method utilizes freely available software with a simple interface and does not require advanced image analysis skills, making it accessible to a wide range of researchers. The soil particles, pore water, and pore air were segmented using a Kriging-based thresholding technique. The segmented four-phase images allowed for the quantification of the volume fractions of soil constituents, pore size distributions, and coordination numbers. Furthermore, by separating the rhizosphere and bulk soil, we found that the root presence significantly reduced solid fractions and increased water content, particularly in the upper soil layers. Macropores and fine pores were observed near the roots, highlighting the complex structural impacts of root growth. While further validation is needed to assess the method’s applicability across different soil types and imaging conditions, it provides a practical basis for visualizing and quantifying root–soil interactions, and could contribute to advancing our understanding of how plant roots influence key soil hydraulic and thermal properties.
Why it matches plant phenotyping methodsX線CT画像から植物根系を抽出・定量するセグメンテーション手法の開発が中心であり、根系という植物形態形質の取得に直接関係するため。
abstractwe applied a relatively simple approach for segmenting plant roots and soil constituents using X-ray computed tomography (CT) images to evaluate root-induced changes in soil structure.
Laboratory / benchtopX-ray / CTRootMorphology / geometry measurementRoot system architecture
Soil compaction and escalating global drought increase soil strength and stiffness. It remains unclear which plant root biomechanical mechanisms/traits enable growth in these harsh conditions. Here, we combine synchrotron X-ray computed tomography with spatially resolved X-ray diffraction to characterize the biomechanics of a replica root-soil system. We map the strain field around the root tip analog, finding strong agreement with finite element simulations, thereby demonstrating a promising new in vivo measurement protocol.
Why it matches plant phenotyping methods根の生育に関わる土壌内の力学的状態・ひずみを、X線CTと回折で可視化・定量する新しいin vivo測定プロトコルを開発・検証しており、植物表現型取得法が中心です。
abstractWe map the strain field around the root tip analog, finding strong agreement with finite element simulations, thereby demonstrating a promising new in vivo measurement protocol.
Reproduction assets foundThe paper's Data availability and Code availability statements both deposit the study's XCT/XRD imaging and diffraction data and the processing scripts in the University of Southampton Pure repository (DOI 10.5258/SOTON/D3309), which is an allowed URL. These are paper-specific, publicly declared assets directly reproduCode · publicAll scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309 .Open asset ↗Pure · 10.5258/SOTON/D3309lines:209-251Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
3D phenotyping of the external and internal structures is important to breed new fruit species. As manual phenotyping is error-prone and time-consuming, developing high-throughput solutions with enhanced precision and low costs is necessary. This study presents CitrusGAN, a generative adversarial network-based method to reconstruct 3D citrus CT models from sparse-view X-ray images. The input X-rays are arranged in orthogonal pairs to provide additional information, and customized loss functions enable more effective learning of the mapping from 2D X-ray features to 3D CT volumes. Experimental results show that 6 views can generate high-quality citrus CT volumes, with a structural similarity index of 92.1 % and a peak signal-to-noise ratio of 26.374 dB compared with the real CT models. Moreover, the morphology of the generated model can be conveniently measured in the 3D space, facilitating the extraction of phenotypic traits including fruit length, width, height, volume, surface area, peel thickness, number of segments, and edible rate with high precision. As X-rays can be obtained using low-cost X-ray machines with high efficiency, the proposed method can be potentially developed into high-throughput equipment for fruit production lines or portable devices to realize in-field phenotyping.
Why it matches plant phenotyping methods柑橘の疎視野X線から3D CTモデルを再構成し、形態形質を抽出する手法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractThis study presents CitrusGAN, a generative adversarial network-based method to reconstruct 3D citrus CT models from sparse-view X-ray images.
Reproduction assets foundThe paper's Data availability statement explicitly states that the code and datasets (the citrus X-ray/CT phenotyping dataset and CitrusGAN analysis code) are publicly available at the authors' GitHub repository.Code · publicData availability
The code and datasets are available at https://github.com/Petrichoror/CitrusGAN . Other data will be made available on request.Open asset ↗Petrichoror/CitrusGANlines:244-347Code / dataset availability confirmedCrossref · Europe PMC · checked 15 Sept 2026
The integration of nanotechnology in agriculture allows for more precise nutrient delivery through nanoparticles (NPs), particularly via foliar application. To mature this technology for enhancing fertilizer efficiency, it is essential to shed new light on the transport and dissolution of NPs in plants. Available analytical methods struggle to address this challenge in a direct manner. We introduce correlative X-ray imaging as a novel analytical tool capable of tracking NP pathways, dissolution and hence nutrient release in plants. By utilizing three complementary X-ray techniques, we offer a unique insight into the plant processes associated with foliar fertilization. We demonstrate that small-angle X-ray scattering enables the characterization of NP size and concentration, while X-ray fluorescence imaging, maps the distribution of elements within the sample. Finally, micro-computed tomography integrates these findings into a complete three-dimensional digital representation of the plant’s microstructure, revealing regions of apparent densification associated with NP accumulation. Using freeze-dried barley plants infiltrated with nano-hydroxyapatite (nHAP), we observed rapid dissolution of NPs, and we are able to associate time and space attributes to the translocation process of nutrients up to three days following foliar application of NPs. With the first pilot study of applying correlative X-ray imaging to live plants, we sought to indicate the potential of this new analytical approach for future nano-enabled agricultural research.
Why it matches plant phenotyping methods植物内のナノ粒子経路・溶解・栄養輸送を可視化する相関X線イメージング手法の導入と実証が中心であり、植物の状態・生理過程を測定する方法論的研究である。
abstractWe introduce correlative X-ray imaging as a novel analytical tool capable of tracking NP pathways, dissolution and hence nutrient release in plants.
Reproduction assets foundThe article's data availability statement points to a public figshare repository hosting the study's datasets (X-ray imaging/phenotyping measurements). No author analysis code with explicit public deposit language was identified; Dragonfly is a commercial visualization tool, not a paper-specific asset.Dataset · publicng Wan , Hainan University, China
Zhansheng Li , Chinese Academy of Agricultural Sciences, China
Firozeh Solimani , Politecnico di Bari, Italy
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://figshare.com/s/33ee8388a36600fe98d5 .
Author contributionsOpen asset ↗figsharelines:191-206Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Identification of the phenotypes of fruits is critical for understanding complex genetic traits. Computed tomography (CT) imaging technology enables the noninvasive acquisition of three-dimensional images of fruit interiors, thus providing a robust data foundation for phenotypic analysis. Accurate segmentation of internal fruit tissues is essential, as it directly influences the accuracy and reliability of the results. Current methods are not optimized for the unique features of plant fruit images. This study introduces XFruitSeg, which is a general deep learning model for segmenting plant fruit CT images. The model uses a U-shaped encoder-decoder architecture and integrates multitask learning. A large convolutional kernel network, RepLKNet, expands the receptive field for feature extraction. Multiscale skip connections and a deep supervision mechanism improve the model's capacity to learn features of various sizes, and a contour feature learning branch specifically targets the interorganizational boundaries. An optimized composite loss function enhances the model's robustness when applied to imbalanced categories. Additionally, a dataset named XrayFruitData was established, which contains high-resolution images of twelve plant fruit varieties, with accurate annotations for orange, mangosteen, and durian fruits for model evaluation. Compared with four mainstream advanced models, XFruitSeg achieved superior segmentation performance on the orange, mangosteen, and durian datasets, with mean Dice coefficients of 95.21 %, 93.24 %, and 94.70 % and mean intersection over union (mIoU) scores of 91.09 %, 87.91 %, and 90.35 %, respectively. The results of extensive ablation experiments demonstrate the effectiveness of each component. Therefore, the proposed XFruitSeg model has been proven to be beneficial for high-precision analysis of internal fruit phenotyping traits.
Why it matches plant phenotyping methods果実CT画像から内部組織を分割し、表現型解析を可能にする深層学習モデルと評価用データセットを開発・検証しており、植物フェノタイピング手法が中心である。
abstractThis study introduces XFruitSeg, which is a general deep learning model for segmenting plant fruit CT images.
Reproduction assets foundThe paper's CT fruit segmentation dataset (XrayFruitData), model weights, and source code are publicly available on the authors' GitHub repository, explicitly stated in the Data availability section and dataset description.Code · publicSome of the raw data, model weights and source codes are accessible at https://github.com/BME-PhenoTeam/Xray4Plant-FruitOpen asset ↗BME-PhenoTeam/Xray4Plant-Fruitlines:530-585Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Identification of the phenotypes of fruits is critical for understanding complex genetic traits. Computed tomography (CT) imaging technology enables the noninvasive acquisition of three-dimensional images of fruit interiors, thus providing a robust data foundation for phenotypic analysis. Accurate segmentation of internal fruit tissues is essential, as it directly influences the accuracy and reliability of the results. Current methods are not optimized for the unique features of plant fruit images. This study introduces XFruitSeg, which is a general deep learning model for segmenting plant fruit CT images. The model uses a U-shaped encoder–decoder architecture and integrates multitask learning. A large convolutional kernel network, RepLKNet, expands the receptive field for feature extraction. Multiscale skip connections and a deep supervision mechanism improve the model's capacity to learn features of various sizes, and a contour feature learning branch specifically targets the interorganizational boundaries. An optimized composite loss function enhances the model's robustness when applied to imbalanced categories. Additionally, a dataset named XrayFruitData was established, which contains high-resolution images of twelve plant fruit varieties, with accurate annotations for orange, mangosteen, and durian fruits for model evaluation. Compared with four mainstream advanced models, XFruitSeg achieved superior segmentation performance on the orange, mangosteen, and durian datasets, with mean Dice coefficients of 95.21 %, 93.24 %, and 94.70 % and mean intersection over union (mIoU) scores of 91.09 %, 87.91 %, and 90.35 %, respectively. The results of extensive ablation experiments demonstrate the effectiveness of each component. Therefore, the proposed XFruitSeg model has been proven to be beneficial for high-precision analysis of internal fruit phenotyping traits.
Why it matches plant phenotyping methods果実CT画像から内部組織を抽出するセグメンテーション手法を開発し、データセット構築と性能比較・検証を行っており、植物表現型取得の方法が中心である。
abstractThis study introduces XFruitSeg, which is a general deep learning model for segmenting plant fruit CT images.
Abstract Background Seed quality analysis using X-rays is increasingly explored due to its invasive and rapid nature. Yet, the current absence of reliable and standardised imaging protocols has led to contradictory effects of X-ray exposure in previous studies. Our work systematically investigated the effect of soft X-rays on a wide range of plant materials. Results The baseline of three germination categories was established across seven species before the application of soft X-ray exposure under controlled standard germination conditions. The high inter-varietal and inter-lot variabilities, in addition to the strong interaction between X-ray exposure with variety and lot, reinforced the need to consider genetic and seed quality aspects while evaluating the impacts of X-rays. A slight stimulative effect was observed on most of the species (bean, carrot, fennel, maize, radish, and ryegrass), notably, with a repeated reduction in ungerminated seeds. Intrinsic physical quality holds a crucial value where the minor negative impact observed in soybean originated from its degraded physical quality and not from X-ray exposure, hence, no destructive effects were detected. To understand whether seed size plays a significant role in a seed's response to exposure, linear regression models were built to predict 3D seed traits (volume) from 2D X-ray images. Yet, seed size did not explain the variation in responses to soft X-rays. However, the average density of the seven species explained both their natural germination ( p p Conclusion Soft X-ray exposure is non-destructive with a beneficial effect on germination but can be strongly influenced by underlying genetics and the physical quality of the tested seeds. This study adopted internationally-standardised germination procedures and tested the effect of soft X-rays across diverse botanical, genetic and seed quality profiles. This work addressed important gaps in evaluating X-ray impacts and proposed a robust design and well-examined radiography protocol for a proven non-destructive seed quality analysis.
Why it matches plant phenotyping methods軟X線ラジオグラフィーによる非破壊的な種子品質・3D形質推定プロトコルの検討と検証が中心的に含まれており、単なる発芽試験ではない。
abstractSeed quality analysis using X-rays is increasingly explored due to its invasive and rapid nature.
Producing food is one of the challenges in space exploration due to limited storage capacity and long travel duration. Extreme environmental conditions such as microgravity, elevated CO2 levels, irregular light exposure, and fluctuating air temperatures pose significant challenges to conventional plant growth and make it susceptible to stress, particularly in root systems, which struggle to absorb water and nutrients efficiently. This study will focus on root phenotyping of the plants (wheat and lettuce) grown in a near-space environment, and the impact of environmental stressors on the plants using image-based technology will be carried out. A specialized growth chamber is designed, incorporating three automated multi-modal imaging systems (MIS): Visible and Near-Infrared (VNIR) wavelength range (400-1000 nm), Micro CT Scan, and RGB cameras used to observe the impact of stress on microgravity on plants. Machine learning and deep learning techniques were also employed to optimize the discriminant classifier within the multi-modal imaging system. Through comparative analysis of these imaging techniques coupled with artificial intelligence techniques, this study aims to deepen our understanding of how microgravity and other space-induced factors affect root systems. This work will also present the challenges and potential faced that can contribute valuable insights for plant growth under space conditions.
Why it matches plant phenotyping methods根の画像ベース表現型計測システムを開発・比較し、機械学習による解析も行うことが中心であるため、植物フェノタイピング手法論文として含める。
abstractThis study will focus on root phenotyping of the plants (wheat and lettuce) grown in a near-space environment
Root system architecture (RSA) underpins plant access to water and nutrients, making its characterization critical for improving crop performance in environments with limited soil fertility. However, current methods for quantifying root features face several challenges. They may rely on 2D images that suffer from occlusion, use expensive sensing technologies like X-ray computed tomography, or depend on 3D modeling approaches with assumptions about branching that make them difficult to generalize. To address these challenges, we introduce an open-source Python framework for quantifying RSA samples from 3D point clouds generated from low-cost photogrammetry. Critically, this method incorporates no assumptions about taxon-specific branching orientation, making it both well-suited for modeling naturally grown annual dicots such as soybean and generalizable across species. Using field-grown soybean as a test case, we demonstrate the utility of this framework to extract biologically meaningful 3D features of divergent root systems sampled across developmental stages and soil environments, and enable new analyses not possible with 2D approaches, such as modeling metabolic scaling relationships. Results indicate that, in our soybean samples, while certain individual features like taproot tortuosity are potentially influenced by the soil environment, and while roots in sandy loam exhibited greater feature plasticity, fundamental scaling properties remain consistent. By combining low-cost photogrammetry with 3D reconstruction of root systems from point clouds, this approach provides the plant science community with new opportunities for more comprehensive root studies.
Why it matches plant phenotyping methods植物根系構造を3D点群から定量化するオープンソース手法の開発が中心であり、低コスト写真測量と3D再構成による形態形質抽出を実証している。
abstractwe introduce an open-source Python framework for quantifying RSA samples from 3D point clouds generated from low-cost photogrammetry.
The discovery of novel plant fertilization strategies heavily relies on our capabilities to probe physiological processes in living plants with sub-cellular precision. State-of-the-art microscopy techniques are in general limited to surface investigation or they require elaborated tissue preparation and often destruction. X-ray microscopy has the potential to resolve some of these limitations by generating micro-to nanometer-scale 3D images deep into the tissue. We introduce experimental designs and the quantitative analysis methodologies, pioneering nanoscale ({approx}150 nm resolution) in-vivo 3D microscopy of plant tissue. We show the first direct in-vivo visualization of foliar-applied untagged nanoparticulate fertilizers deep under the leaf surface, not accessible by other microscopy methods. Ultimately, our approach provides the means for a direct observation of nanoparticle transport and dissolution in living plant tissue, a step critical for developing sustainable plant fertilization approaches.
Why it matches plant phenotyping methods植物組織を対象とする生体内3D X線イメージングと定量解析手法の開発が中心で、ナノ粒子の組織内移動・溶解という植物の生理状態を直接可視化している。
abstractWe introduce experimental designs and the quantitative analysis methodologies, pioneering nanoscale ({approx}150 nm resolution) in-vivo 3D microscopy of plant tissue.
Understanding the regeneration niche is of critical importance for the conservation of rare plants, yet species-specific information is often lacking for key components of the plant life cycle such as seed dormancy and germination. We conducted a detailed study of the regeneration niche for Ivesia webberi , a U.S. federally threatened forb that is endemic to the Great Basin Desert. Using seeds collected from 11 populations across a span of years, we investigated seed storage behavior, embryo morphology, and interannual and interpopulation seed viability, while testing the efficacy of alternative nondestructive methods to assess seed viability. We also studied the effects of various pre-incubation and incubation treatments on germination rates, speed, and synchrony. An examination of x-ray images showed that I. webberi have non-endospermic seeds with spatulate embryos. We observed a significant reduction in seed viability over three years, suggesting a recalcitrant storage behavior. Seed viability exhibited significant interannual, but not interpopulation, variation across 11 I. webberi populations. Both the x-ray and multispectral imaging are promising nondestructive methods that can replace the widely used, but destructive, tetrazolium test. Across all 68 germination treatments, seed germination was higher, faster, and more synchronized under warmer cold-stratified incubation temperatures. Seed germination was significantly increased by pre-incubation chilling and reduced by pre-incubation heat treatments, while pre-incubation and incubation light exposures had no effect. Both the seed embryo morphology and germination experiments suggest physiological dormancy in I. webberi . Results suggest that warmer and shorter winters, such as are consistent with predicted climate change, could increase germination of I. webberi seeds.
Why it matches plant phenotyping methods種子生存性を評価する非破壊X線・マルチスペクトル画像法の有効性を検討しており、植物状態の取得法の検証が明示されているため。
abstractwhile testing the efficacy of alternative nondestructive methods to assess seed viability
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Accurate identification of maize kernel morphology is crucial for breeding and quality improvement. Traditional manual methods are limited in dealing with complex structures and cannot fully capture kernel characteristics from a phenome perspective. To address this, our study aims to develop a high-throughput 3D phenotypic analysis method for maize kernels using Micro-CT-based point cloud data, thereby enhancing both accuracy and efficiency. We introduced new phenotypic indicators and developed a kernel phenome interaction network to better characterize the diversity and variability of kernel traits. Using a natural population of maize, high-resolution 2D slice data from Micro-CT scans were converted into 3D point cloud models for detailed analysis. This process led to the proposal of five new indicators, such as the endosperm density uniformity index (ENDUI) and endosperm integrity index (ENII), and the construction of their corresponding phenome interaction network. The study identified 27 3D morphological feature parameters, significantly improving the accuracy of kernel phenotypic analysis. These new indicators enable a more comprehensive evaluation of trait differences between subgroups. Results show that ENDUI and ENII are central to the phenome interaction networks, revealing synergistic relationships and environmental adaptation strategies during kernel growth. Additionally, it was found that length traits significantly impact the volumes of the embryo and endosperm, with linear regression coefficients of 0.599 and 0.502, respectively. This study not only advances maize kernel morphology research but also offers a novel method for phenotypic analysis. By enriching the phenotypic diversity of maize kernels, it contributes to breeding programs and grain processing improvements, ultimately enhancing the quality, and utilization value of maize kernels.
Why it matches plant phenotyping methodsMicro-CT画像からトウモロコシ粒の3D形態形質を抽出する高スループット表現型解析法の開発が研究の中心であり、新規指標と解析手法を提示している。
abstractour study aims to develop a high-throughput 3D phenotypic analysis method for maize kernels using Micro-CT-based point cloud data
Identifying and segmenting the vitreous and starchy endosperm of maize kernels is essential for texture analysis. However, the complex internal structure of maize kernels presents several challenges. In CT (computed tomography) images, the pixel intensity differences between the vitreous and starchy endosperm regions in maize kernel CT images are not distinct, potentially leading to low segmentation accuracy or oversegmentation. Moreover, the blurred edges between the vitreous and starchy endosperm make segmentation difficult, often resulting in jagged segmentation outcomes. We propose a deep learning-based CT image analysis pipeline to examine the internal structure of maize seeds. First, CT images are acquired using a multislice CT scanner. To improve the efficiency of maize kernel CT imaging, a batch scanning method is used. Individual kernels are accurately segmented from batch-scanned CT images using the Canny algorithm. Second, we modify the conventional architecture for high-quality segmentation of the vitreous and starchy endosperm in maize kernels. The conventional U-Net is modified by integrating the CBAM (convolutional block attention module) mechanism in the encoder and the SE (squeeze-and-excitation attention) mechanism in the decoder, as well as by using the focal-Tversky loss function instead of the Dice loss, and the boundary smoothing term is weighted as an additional loss term, named CSFTU-Net. The experimental results show that the CSFTU-Net model significantly improves the ability of segmenting vitreous and starchy endosperm. Finally, a segmented mask-based method is proposed to extract phenotype parameters of maize kernel texture, including the volume of the kernel (V), volume of the vitreous endosperm (VV), volume of starchy endosperm (SV), and ratios over their respective total kernel volumes (VV/V and SV/V). The proposed pipeline facilitates the nondestructive quantification of the internal structure of maize kernels, offering valuable insights for maize breeding and processing.
Why it matches plant phenotyping methodsマイクロCT画像と深層学習によるセグメンテーションから、トウモロコシ粒の内部構造・体積形質を抽出するパイプラインが研究の中心である。
abstractWe propose a deep learning-based CT image analysis pipeline to examine the internal structure of maize seeds.
Introduction Quantitative wood anatomy is critical for establishing climate reconstruction proxies, understanding tree hydraulics, and quantifying carbon allocation. Its accuracy depends upon the image acquisition methods, which allows for the identification of the number and dimensions of vessels, fibres, and tracheids within a tree ring. Angiosperm wood is analysed with a variety of different image acquisition methods, including surface pictures, wood anatomical micro-sections, or X-ray computed micro-tomography. Despite known advantages and disadvantages, the quantitative impact of method selection on wood anatomical parameters is not well understood. Methods In this study, we present a systematic uncertainty analysis of the impact of the image acquisition method on commonly used anatomical parameters. We analysed four wood samples, representing a range of wood porosity, using surface pictures, micro-CT scans, and wood anatomical micro-sections. Inter-annual patterns were analysed and compared between methods from the five most frequently used parameters, namely mean lumen area ( MLA ), vessel density ( VD ), number of vessels ( VN ), mean hydraulic diameter ( D h ), and relative conductive area ( RCA ). A novel sectorial approach was applied on the wood samples to obtain intra-annual profiles of the lumen area ( A l ), specific theoretical hydraulic conductivity ( K s ), and wood density ( ρ ). Results Our quantitative vessel mapping revealed that values obtained for hydraulic wood anatomical parameters are comparable across different methods, supporting the use of easily applicable surface picture methods for ring-porous and specific diffuse-porous tree species. While intra-annual variability is well captured by the different methods across species, wood density ( ρ ) is overestimated due to the lack of fibre lumen area detection. Discussion Our study highlights the potential and limitations of different image acquisition methods for extracting wood anatomical parameters. Moreover, we present a standardized workflow for assessing radial tree ring profiles. These findings encourage the compilation of all studies using wood anatomical parameters and further research to refine these methods, ultimately enhancing the accuracy, replication, and spatial representation of wood anatomical studies.
Why it matches plant phenotyping methods木材解剖学的形質を抽出する画像取得法を比較・不確実性分析し、標準化ワークフローも提示しており、植物フェノタイピング手法が中心です。
abstractwe present a systematic uncertainty analysis of the impact of the image acquisition method on commonly used anatomical parameters.
Rice is a staple food for nearly half the global population and, with rising living standards, the demand for high-quality grain is increasing. Chalkiness, a key determinant of appearance quality, requires accurate detection for effective quality evaluation. While traditional 2D imaging has been used for chalkiness detection, its inherent inability to capture complete 3D morphology limits its suitability for precision agriculture and breeding. Although micro-CT has shown promise in 3D chalk phenotype analysis, high-throughput automated 3D detection for multiple grains remains a challenge, hindering practical applications. To address this, we propose a high-throughput 3D chalkiness detection method using micro-CT and VSE-UNet. Our method begins with non-destructive 3D imaging of grains using micro-CT. For the accurate segmentation of kernels and chalky regions, we propose VSE-UNet, an improved VGG-UNet with an SE attention mechanism for enhanced feature learning. Through comprehensive training optimization strategies, including the Dice focal loss function and dropout technique, the model achieves robust and accurate segmentation of both kernels and chalky regions in continuous CT slices. To enable high-throughput 3D analysis, we developed a unified 3D detection framework integrating isosurface extraction, point cloud conversion, DBSCAN clustering, and Poisson reconstruction. This framework overcomes the limitations of single-grain analysis, enabling simultaneous multi-grain detection. Finally, 3D morphological indicators of chalkiness are calculated using triangular mesh techniques. Experimental results demonstrate significant improvements in both 2D segmentation (7.31% improvement in chalkiness IoU, 2.54% in mIoU, 2.80% in mPA) and 3D phenotypic measurements, with VSE-UNet achieving more accurate volume and dimensional measurements compared with the baseline. These improvements provide a reliable foundation for studying chalkiness formation and enable high-throughput phenotyping.
Why it matches plant phenotyping methodsマイクロCT画像とVSE-UNet、3D再構成を統合したコメ粒の着色・形態形質検出法を開発し、精度を評価しているため、フェノタイピング手法が研究の中心である。
abstractTo address this, we propose a high-throughput 3D chalkiness detection method using micro-CT and VSE-UNet.
Abstract Background Agricultural systems are under extreme pressure to meet the global food demand, hence necessitating faster crop improvement. Rapid evaluation of the crops using novel imaging technologies coupled with robust image analysis could accelerate these improvements. This study assesses the potential of X-ray imaging for non-destructive evaluation of rice grain traits. By analyzing 2D X-ray images of paddy grains, we aim to approximate their key physical grain traits important in rice breeding: (1) T 1 chaffiness, (2) T 2 chalky rice kernel percentage (CRK%), and (3) T 3 head rice recovery percentage (HRR%). Successful integration of X-ray imaging and data analysis into the breeding process could prospectively revolutionize rice breeding and improve global agricultural productivity. Results The study aimed to predict the key rice traits (chaffiness, CRK%, HRR%) using 2D radiographs obtained from high resolution X-ray imaging systems. The accuracy of trait inference algorithms was evaluated by comparing the predicted values with ground-truth measurements. We showed that all three traits can be predicted with reasonable accuracy (chaffiness: R 2 = 0.9987, RMSE = 1.302; CRK%: R 2 = 0.9397, RMSE = 8.91; HRR%: R 2 = 0.7613, RMSE = 6.83). Conclusions Our study demonstrated that multiple key physical grain traits important in rice breeding (chaffiness, CRK%, and HRR%) can be inferred from single 2D X-ray images of whole paddy grains. Such a non-destructive rice grain trait inference is expected to improve the robustness of paddy rice evaluation, as well as to reduce time and possibly costs for rice grain trait analysis. Furthermore, the described approach can also be transferred and adapted to other grain crops.
Why it matches plant phenotyping methodsX線画像からイネ籾の品質形質を非破壊推定する画像解析手法を開発・精度評価しており、表現型取得が研究の中心である。
abstractThis study assesses the potential of X-ray imaging for non-destructive evaluation of rice grain traits.
X-ray fluorescence (XRF) is a well-established technique for elemental determination. This study evaluates the ability of XRF to quantify soybean protein content based on elemental composition, particularly sulfur emission. Univariate linear regression, multiple linear regression, and partial least squares regression (PLS) were compared. Two scenarios were considered: scenario A used 108 soybean samples for calibration and 54 for validation; scenario B expanded the protein content range of scenario A, including 32 new samples of soybean mixed with concentrates. PLS showed the best performance in validation, with R 2 of 0.73 and 0.89 in scenarios A and B, respectively. The results indicate that protein quantification by XRF has relative prediction errors below 3.1 %. The developed methods provide an alternative for monitoring soybean protein content, suitable for screening applications such as integrating XRF sensors on soybean harvesters.
Why it matches plant phenotyping methods大豆子実のタンパク質含量という植物形質をXRFで推定する手法を開発・検証しており、検量・独立検証・予測性能評価が研究の中心である。
abstractThis study evaluates the ability of XRF to quantify soybean protein content based on elemental composition, particularly sulfur emission.
Understanding the regeneration niche is of critical importance for the conservation of rare plants, yet species-specific information is often lacking for key components of the plant life cycle such as seed dormancy and germination. We conducted a detailed study of the regeneration niche for Ivesia webberi, a U.S. federally threatened forb that is endemic to the Great Basin Desert. Using seeds collected from 11 populations across a span of years, we investigated seed storage behavior, embryo morphology, and interannual and interpopulation seed viability, while testing the efficacy of alternative nondestructive methods to assess seed viability. We also studied the effects of various pre-incubation and incubation treatments on germination rates, speed, and synchrony. An examination of x-ray images showed that I. webberi have non-endospermic seeds with spatulate embryos. We observed a significant reduction in seed viability over three years, suggesting a recalcitrant storage behavior. Seed viability exhibited significant interannual, but not interpopulation, variation across 11 I. webberi populations. Both the x-ray and multispectral imaging are promising nondestructive methods that can replace the widely used, but destructive, tetrazolium test. Across all 68 germination treatments, seed germination was higher, faster, and more synchronized under warmer cold-stratified incubation temperatures. Seed germination was significantly increased by pre-incubation chilling and reduced by pre-incubation heat treatments, while pre-incubation and incubation light exposures had no effect. Both the seed embryo morphology and germination experiments suggest physiological dormancy in I. webberi. Results suggest that warmer and shorter winter, such as are consistent with predicted climate change, could increase germination but also lead to shifts in regeneration phenology that increase vulnerability of seedlings to frost.
Why it matches plant phenotyping methods種子生存性の評価について、X線およびマルチスペクトル画像という非破壊的な表現型取得法の有効性を検証しており、単なる生物学的測定ではなく手法評価が明示されています。
abstractwhile testing the efficacy of alternative nondestructive methods to assess seed viability.
X-ray / CTRootMorphology / geometry measurementSegmentationRoot system architecture
Soil, a critical Earth resource, sustains ecosystems and global food production, serving as a habitat, regulating water, sequestering carbon, and supplying nutrients. Roots play a crucial role in the composition and health of soil. Soil properties and root distribution data provide essential information for land management and agriculture. In this study, we propose an innovative approach combining x-ray computed tomography (CT) scanning, machine learning-based root segmentation, and traditional root analysis methods to investigate plant root distribution comprehensively. Intact soil cores with plant roots were CT scanned to visualize root systems in their natural soil environment. Utilizing a UNET transformer (UNETR) machine learning framework, we achieved automated root segmentation, extracting and differentiating roots from the surrounding soil. Validation against traditional analysis with WinRHIZO and RhizoVision Explorer for root trait measurement showed a strong positive correlation (up to 0.78 Pearson coefficient), affirming the precision of our machine learning method in quantifying root characteristics. This integration of CT scanning and machine learning-based root segmentation provides a non-destructive and efficient method for studying root architecture and distribution. Our research highlights the potential of combining advanced imaging techniques with AI to enhance the understanding of root dynamics and their role in supporting plant growth. The proposed methodology offers a promising toolset for automated root analysis, reducing manual processing time and effort. By shedding light on root-soil interactions, our study contributes to the field of plant root phenotyping and provides valuable insight into the complex world of below-ground plant systems, aligning with scalable and cost-effective monitoring techniques and innovations in remote-sensing-based soil monitoring frameworks
Why it matches plant phenotyping methodsCT画像と機械学習による根の分割・形質定量法を開発し、既存手法と比較検証しているため、植物フェノタイピング手法が中心である。
abstractcombining x-ray computed tomography (CT) scanning, machine learning-based root segmentation, and traditional root analysis methods
Background Poultry is a global industry with laying hens that are genetically optimized for high egg yield. Keel bone fractures can affect up to 80% of laying hens, posing welfare and production problems. Therefore, genetic selection to reduce keel fractures is important. However, the lack of a reliable, automated, and heritable phenotypes for keel bones makes this a challenging task. The aim of this study was to (1) develop automated analyses of radiographic images to phenotype keel bones, and (2) investigate whether the proposed phenotypes are heritable and genetically correlated with the post-dissection scores of keel bone fractures and deviations. A total of 1051 laying hens (Bovans Brown and Lohmann Brown) from a commercial farm were x-rayed, followed by keel bone dissection and scoring for deviations and fractures. Furthermore, blood was sampled for genotyping using 50 K Illumina SNP chips. Keel bones were segmented (with similar to 0.90 accuracy) from the radiographic images using deep learning models, after which the images were automatically measured for general geometry and radiopacity. Multi-trait genomic restricted maximum likelihood was used to estimate genetic parameters.Results Heritability estimates ranged from 0.28 to 0.30 for both keel deviations and fractures observed post-dissection. The automated phenotypes had heritability estimates ranging from 0.07 to 0.10 for keel radiopacity and from 0.11 to 0.39 for keel geometry. Estimates of genetic correlations of keel geometry with keel deviation and fractures ranged from -0.57 to 0.72.Conclusions Automated methods were developed for measuring keel bone radiopacity and geometry. Keel concave area was found to be a reliable and heritable phenotype that breeding companies can use to reduce keel deviations and fractures. These methods can also be adapted to measure other bones (e.g., tibiotarsal) or objects (e.g., eggs), allowing breeders to quickly compute phenotypes for keel, tibia, and egg size from the same radiographic image. The developed methods are well-suited for large-scale studies to assess different housing environments and nutrition strategies aimed at improving keel bone conditions.
Why it matches plant phenotyping methodsニワトリの生体画像から骨形状・放射線不透過性を自動抽出する深層学習ベースの表現型測定法を開発しており、方法開発が研究の中心である。
abstractThe aim of this study was to (1) develop automated analyses of radiographic images to phenotype keel bones
3D phenotyping of the external and internal structures is important to breed new fruit species. As manual phenotyping is error-prone and time-consuming, developing high-throughput solutions with enhanced precision and low costs is necessary. This study presents CitrusGAN, a generative adversarial network-based method to reconstruct 3D citrus CT models from sparse-view X-ray images. The input X-rays are arranged in orthogonal pairs to provide additional information, and customized loss functions enable more effective learning of the mapping from 2D X-ray features to 3D CT volumes. Experimental results show that 6 views can generate high-quality citrus CT volumes, with a structural similarity index of 92.1% and a peak signal-to-noise ratio of 26.374 dB compared with the real CT models. Moreover, the morphology of the generated model can be conveniently measured in the 3D space, facilitating the extraction of phenotypic traits including fruit length, width, height, volume, surface area, peel thickness, number of segments, and edible rate with high precision. As X-rays can be obtained using low-cost X-ray machines with high efficiency, the proposed method can be potentially developed into high-throughput equipment for fruit production lines or portable devices to realize in-field phenotyping.
Why it matches plant phenotyping methods柑橘の疎視野X線から3D CTモデルを再構成し、形態形質を抽出する手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractThis study presents CitrusGAN, a generative adversarial network-based method to reconstruct 3D citrus CT models from sparse-view X-ray images.
Machine learning (ML) models for image segmentation typically require a significant amount of accurately annotated data for training, which is rarely readily available in plant and soil science datasets due to the high time and monetary costs of manually labelling the images. Training datasets can be augmented with synthetically generated images that aim to match the visual features and biological properties of the original dataset. Segmentation masks can be created automatically during the synthetic image generation process, removing the need for tedious manual annotation and ensuring high accuracy of the labels. We present an adaptable semi‐automatic pipeline for creating annotated synthetic micro‐computed tomography (micro‐CT) volumes at scale using the 3D modelling tool Blender, and we demonstrate our method using a dataset of micro‐CT images of tomato plant roots embedded in sieved soil columns. First, the foreground is generated using a mathematical L‐system model to give a 3D model of the target sample. Then, the surrounding material is created and textured to simulate the relative density of the materials in which the object is embedded. The final stage is to render the images by slicing the volume at defined regular intervals, generating both the synthetic micro‐CT image and the corresponding labels at each slice. We use our synthetically generated images alongside real data to create augmented datasets to train a U‐Net‐based segmentation model. Our results demonstrate that when there is a small amount of real annotated data available, using synthetic data in the training dataset can improve the segmentation accuracy, and we show the impact of varying the texturing process.
Why it matches plant phenotyping methods植物根のマイクロCT画像を対象に、合成データ生成とセグメンテーション精度改善のパイプラインを開発・評価しており、根形態の抽出手法が研究の中心である。
abstractWe present an adaptable semi‐automatic pipeline for creating annotated synthetic micro‐computed tomography (micro‐CT) volumes at scale using the 3D modelling tool Blender
Metabolic processes in plant organs involving transport of water, metabolic gasses, and nutrients depend on the three-dimensional (3D) microscopic tissue morphology. However, imaging and quantifying this microstructure, including the spatial layout of parenchyma cells, pores, vascular bundles and special features such as stone cell clusters (brachysclereids), is challenging. To address this, a 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images. In addition, various training datasets and data augmentation techniques, including synthetic data, were explored to enhance segmentation quality. The 3D panoptic segmentation achieved an Aggregated Jaccard Index of 0.89 and 0.77 for apple and pear tissue, respectively, outperforming both the previously designed 2D instance segmentation model and a marker-based watershed segmentation benchmark. The model successfully labelled vascular bundles with a Dice Similarity Coefficient (DSC) of 0.51 in apple tissue and 0.79 in pear tissue, although thin vasculature in apple remained more challenging to segment. The 3D panoptic segmentation model achieved a DSC of 0.81 and effectively segmented stone cell clusters in pear tissue. Despite evaluating different methods to enhance segmentation quality, none improved test performance beyond that of the model trained on the standard dataset. The proposed 3D panoptic segmentation model offers the most complete automated protocol to date for plant tissue labelling and morphometric quantification from native X-ray micro-CT images, without extensive sample preparation such as contrast labelling. The developed method, if not replaces, drastically accelerates conventional human-in-the-loop analysis of such images.
Why it matches plant phenotyping methods植物果実組織の3D微細構造をマイクロCT画像から自動抽出・定量化する深層学習手法を開発し、既存手法およびベンチマークと性能比較しているため、植物フェノタイピング手法が研究の中心です。
abstracta 3D deep learning-based panoptic segmentation model, combining semantic and instance segmentation, was developed to accelerate and improve microstructure characterization of apple and pear fruit tissue in X-ray micro-computed tomography (CT) images.
This study aims to utilise natural variation in pea seed composition from NordGen collections to identify key traits for optimized plant-based ingredients functionality while minimizing refined extraction processes. Given the impracticality of chemically analysing 1942 accessions, an algorithm-assisted approach was employed, using image-derived features and datasets to pre-select 51 accessions. Protein content, thousand kernel weight, perimeter, and G-value were determined as primary criteria via PCA, capturing variations in protein composition and other key components. Protein and starch content ranged from 21.2 to 36.9 % and 21.0–48.1 %, respectively. Image analysis linked geometry to composition, aiding pea selection and application. X-ray scattering differentiates peas based on starch structure. Proteomic profiling revealed that legumin and vicilin varied most, with legumin dominant in smooth peas and vicilin in wrinkled ones, enabling control of their ratio through selection. This study highlights the potential of using natural variation of seed composition for less-refined plant-based ingredients for various applications.
Why it matches plant phenotyping methods画像由来特徴量とアルゴリズムを用いて、化学分析対象のエンドウ種子アクセッションを事前選抜し、画像形状と組成の関係を評価するワークフローが研究の主要手法です。
abstractan algorithm-assisted approach was employed, using image-derived features and datasets to pre-select 51 accessions
The hidden lifestyle of the holoparasitic perennial Lathraea squamaria L. (common toothwort), which parasitizes the roots of deciduous trees in forests and woodlands, has led to significant knowledge gaps about the mechanisms underlying this plant symbiotic interaction. Here we present the first detailed structural examination of the interface between L. squamaria and its host root belowground using non-destructive micro-computed tomography (micro-CT). To clarify the physiology of seed germination in this elusive species, we demonstrated efficient in vitro germination of L. squamaria seeds. The terminology for the gross morphology and micromorphology of L. squamaria has been revised, and clear microscopic evidence for several previously described structures (e.g., prehaustoria, haustoria, seeds, elaiosomes, nectaries, and pollen) has been provided. Generally, this research aims to deepen our understanding of parasitic interactions, phenology, as well as the ecological significance of L. squamaria and its biotic associates (e.g., feeders and pollinators) within forest ecosystems.
Why it matches plant phenotyping methods非破壊マイクロCTを用いて寄生植物と宿主根の界面構造を詳細に可視化しており、植物の形態・構造状態の取得が研究の中心的手法の一つである。
abstractHere we present the first detailed structural examination of the interface between L. squamaria and its host root belowground using non-destructive micro-computed tomography (micro-CT).
Field / plotLaboratory / benchtopX-ray / CTRootWhole plant / canopy / plot / field
Soil compaction and escalating global drought increase soil strength and stiffness. It remains unclear which plant root biomechanical mechanisms/traits enable growth in these harsh conditions. Here, we combine synchrotron X-ray computed tomography with spatially resolved X-ray diffraction to characterize the biomechanics of a replica root-soil system. We map the strain field around the root tip, finding strong agreement with finite element simulations, thereby demonstrating a promising new in-vivo measurement protocol.
Why it matches plant phenotyping methods根周辺のひずみ場という根の力学的形質を、X線CT・回折と有限要素解析で測定・検証する新規プロトコルが研究の中心である。
abstractWe map the strain field around the root tip, finding strong agreement with finite element simulations, thereby demonstrating a promising new in-vivo measurement protocol.
Reproduction assets foundThe paper deposits its X-ray diffraction and X-ray imaging (XCT) measurements in the Southampton Pure repository (DOI 10.5258/SOTON/D3309.274) and its processing scripts in a companion deposit (DOI 10.5258/SOTON/D3309.276), both with explicit availability statements and public URLs.Dataset · public∇uT ), F(σ′) > 0,x ∈ Ω
σ′ = Cep
: (∇u+∇uT ), F(σ′) = 0,x ∈ Ω
u·ê1 = 0, x ∈ ΓAxis
u = 0, x ∈ ΓC,Top
u = [0,wstep]T , x ∈ Γbot ∪Γout
n̂·∇u = 0, x ∈ Γtop ∪ΓC,tip
n̂·σ = ppen, x ∈ (Γtop ∩Ω∩Ωc)∪(ΓC,tip ∩Ω∩Ωc)
. (29)
Data Records
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All X-ray diffraction and X-ray imaging data used in this study can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.274
Code availability
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All scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.276
References
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1. Lee, H. et al. Ipcc, 2023: Climate change 2023: Synthesis report, summary for policymakers. contribution of working
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groups i, ii and iii to the sixth assessment report ofOpen asset ↗Pure repository · 10.5258/SOTON/D3309.274pdf-raw-page:9 lines:1-96Code · publicu = 0, x ∈ Γtop ∪ΓC,tip
n̂·σ = ppen, x ∈ (Γtop ∩Ω∩Ωc)∪(ΓC,tip ∩Ω∩Ωc)
. (29)
Data Records
273
All X-ray diffraction and X-ray imaging data used in this study can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.274
Code availability
275
All scripts used to process the data can be found in the Pure repository: https://doi.org/10.5258/SOTON/D3309.276
References
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1. Lee, H. et al. Ipcc, 2023: Climate change 2023: Synthesis report, summary for policymakers. contribution of working
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groups i, ii and iii to the sixth assessment report of the intergovernmental panel on climate change [core writing team, h.
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lee and j. romero (eds.)]. ipcc, geneva, switzerland. (2023).
2Open asset ↗Pure repository · 10.5258/SOTON/D3309.276pdf-raw-page:9 lines:1-96Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Dec 2024International journal of food science & technology
The intricate task of achieving three‐dimensional (3D) visual reconstruction of wheat kernels represents a notable challenge within the domain of digital grain analysis, playing a pivotal role in the realms of grain storage, processing, and breeding. However, existing investigations focused on individual kernels predominantly encompass dimensions such as length, width, height, and epidermal texture features, with a tendency to be invasive to the internal organisational structure of the kernel. Non‐local mean filtering algorithm is proposed to segment various tissues, and the 2D grey scale images are extracted from X‐ray micro‐computed tomography (μCT) to reconstruct a meticulous 3D visualisation model of individual wheat grains. Furthermore, building upon this foundation, an exhaustive assessment of morphological and structural parameters pertaining to each facet of the internal organisation of the wheat seed grain is conducted. Notably, these calculated parameters align with data generated by prior researchers，with 80% of the volume of the endosperm, 12% of the pericarp, about 2% of the endosperm and scutellum, and 4% of the pores. The established parameters and resultant 3D visual models serve as foundational components for subsequent in‐depth examinations into various physicochemical properties, including quality characteristics, heat, and mass transfer attributes, as well as variations in its morphological structure during breeding, which are pertinent to individual grains. This research contributes valuable insights and methodologies that can propel the advancement of studies in wheat kernel analysis.
Why it matches plant phenotyping methodsX線μCT画像から単一コムギ粒の3D構造を再構成し、内部組織の形態・構造パラメータを抽出する方法が研究の中心であるため、植物表現型計測法として含める。
abstractNon‐local mean filtering algorithm is proposed to segment various tissues, and the 2D grey scale images are extracted from X‐ray micro‐computed tomography (μCT) to reconstruct a meticulous 3D visualisation model of individual wheat grains.
SoybeanX-ray / CTRootMorphology / geometry measurementRoot system architecture
Typically, root system architecture (RSA) is not visible, and realistically, high-throughput methods for RSA trait phenotyping should capture key features of developing root systems in solid substrates in 3D. In a published 2-D study using thin rhizoboxes, vermiculite as a growing medium, and photography for imaging, triplicates of 137 soybean cultivars were phenotyped for their RSA. In the transition to 3-D work using X-ray computed tomography (CT) scanning and mineral soil, two research questions are addressed: (1) how different is the soybean RSA characterization between the two phenotyping systems; and (2) is a direct comparison of the results reliable? Prior to a full-scale study in 3D, we grew, in pots filled with sand, triplicates of the Casino and OAC Woodstock cultivars that had shown the most contrasting RSAs in the 2-D study, and CT scanned them at the V1 vegetative stage of development of the shoots. Differences between soybean cultivars in RSA traits, such as total root length and fractal dimension (FD), observed in 2D, can change in 3D. In particular, in 2D, the mean FD values are 1.48 ± 0.16 (OAC Woodstock) vs. 1.31 ± 0.16 (Casino), whereas in 3D, they are 1.52 ± 0.14 (OAC Woodstock) vs. 1.24 ± 0.13 (Casino), indicating variations in RSA complexity.
Why it matches plant phenotyping methods2D写真法と3D X線CT法という根系表現型計測システムを比較・評価し、RSA形質の測定結果の信頼性を検討しているため、フェノタイピング手法が中心である。
abstracthigh-throughput methods for RSA trait phenotyping should capture key features of developing root systems in solid substrates in 3D
Reproduction assets foundThe paper's own supplement (MDPI S1) contains two videos produced in MATLAB from skeletal 3-D images of the root systems reconstructed from this study's CT scanning data — paper-specific phenotyping outputs made publicly available. The figshare links are explicitly described as 'soybean genomic data' from the prior GWASupplement · publicTwo videos (.AVI files), one per soybean cultivar, were produced in MATLAB (MathWorks, Natick, MA, USA) from skeletal 3-D images of the root systems, and are made available as a supplement to the graphical results presented for the 3-D phenotyping system in Figure 2 in the manuscript.Open asset ↗lines:100-116Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
RiceField / plotX-ray / CTRootMorphology / geometry measurementSegmentationRoot system architecture
SUMMARY Plant roots are essential for water and nutrient uptake, as well as resistance to abiotic stresses. While measuring root systems under field conditions is labor‐intensive, most quantitative trait loci (QTLs) related to root traits have been detected under artificial conditions. However, QTLs identified under artificial conditions may not always manifest the expected effects that are observed under field conditions. To address this issue, we developed RSApaddy3D, a rapid phenotyping method for rice root systems, using X‐ray computed tomography (CT) volumes of soil blocks collected from paddies. RSApaddy3D employs 2‐dimensional kernel filters tailored to extract disk‐shaped fragments from the CT volumes. Tubular root fragments are expected to exhibit disk‐shaped cross‐sections along the x ‐, y ‐, or z ‐axes. By applying these filters along all three axes and integrating the results, 3‐dimensional root fragments can be accurately extracted. Furthermore, vectorizing the root system enables geometrical removal of the roots of neighboring individuals. We conducted a genome‐wide association study (GWAS) of root diameter, number, and growth angle in 133 Japanese rice varieties and detected three QTLs ( qNCR1 , qNCR2 , and qRGA1 ) that were associated with each trait. This process was completed within 10 person‐days from soil monolith collection in the paddy to the GWAS. Without RSApaddy3D, roots would need to be washed from the soil monolith and measured, which is estimated to require >500 person‐days. Therefore, RSApaddy3D was approximately 50× more labor‐saving. In summary, we have demonstrated that RSApaddy3D is an efficient method for phenotyping rice root systems under field conditions.
Why it matches plant phenotyping methods圃場土壌のX線CT画像からイネ根系形質を抽出するRSApaddy3Dを開発し、根径・根数・成長角を測定して検証・適用した研究であり、表現型取得法が中心的です。
abstractwe developed RSApaddy3D, a rapid phenotyping method for rice root systems, using X‐ray computed tomography (CT) volumes of soil blocks collected from paddies.
Foxtail millet is an important minor cereal crop rich in nutrients. Due to the small size of its seeds, there is little information on the diversity of its seed structure among germplasms, limiting the identification of genes controlling seed development and germination. This paper utilized X-ray computed tomography (CT) scanning technology and deep learning models to reveal the microstructure of foxtail millet seeds, gaining insights into their internal features, distribution, and composition. A total of 100 foxtail millet varieties were scanned with X-ray computed tomography to obtain 3D reconstruction images and slices. Pre-processing steps were adopted to improve image segmentation accuracy, including noise reduction, rotation, contrast enhancement, and brightness enhancement. The experiment revealed that traditional OpenCV image processing methods failed to achieve precise segmentation, whereas deep learning models exhibited outstanding performance in segmenting seed CT slice images. We compared UNet, PSPNet, and DeepLabV3 models, selected different backbones and optimizers based on the dataset, and continuously adjusted learning rates and maximum training epochs to train the models. Results demonstrated that VGG16-UNet achieved an accuracy of 99.19% on the foxtail millet seed CT slice image dataset, outperforming PSPNet and DeepLabV3 models. Compared to ResNet-UNet, VGG16-UNet shows an improvement of approximately 3.18% in accuracy, demonstrating superior performance in accurately segmenting the inner glume, outer glume, embryo, and endosperm under various adhesion conditions. Accurate segmentation of foxtail millet CT images enables analysis of embryo size, endosperm size, and glume thickness, which impact germination, growth, and nutrition. This study fills a gap in small grain structure research, offering insights to optimize agriculture and molecular breeding for improved yield and quality.
Why it matches plant phenotyping methodsフォックステイルミレット種子のCT画像から内部器官を分割し、形態形質を抽出する画像解析手法を開発・比較検証しており、植物フェノタイピング手法が中心である。
abstractThis paper utilized X-ray computed tomography (CT) scanning technology and deep learning models to reveal the microstructure of foxtail millet seeds
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Spectral imaging technique has been widely applied in plant phenotype analysis to improve plant trait selection and genetic advantages. The latest developments and applications of various optical imaging techniques in plant phenotypes were reviewed, and their advantages and applicability were compared. X-ray computed tomography (X-ray CT) and light detection and ranging (LiDAR) are more suitable for the three-dimensional reconstruction of plant surfaces, tissues, and organs. Chlorophyll fluorescence imaging (ChlF) and thermal imaging (TI) can be used to measure the physiological phenotype characteristics of plants. Specific symptoms caused by nutrient deficiency can be detected by hyperspectral and multispectral imaging, LiDAR, and ChlF. Future plant phenotype research based on spectral imaging can be more closely integrated with plant physiological processes. It can more effectively support the research in related disciplines, such as metabolomics and genomics, and focus on micro-scale activities, such as oxygen transport and intercellular chlorophyll transmission.
Why it matches plant phenotyping methods植物表現型に用いるスペクトル画像技術を体系的にレビューし、各手法の適用性や比較を扱っているため、方法論レビューとして中心的です。
abstractThe latest developments and applications of various optical imaging techniques in plant phenotypes were reviewed, and their advantages and applicability were compared.
Decay management is crucial in the citrus industry due to the rapid spread of infections through wounds. Despite the urgency, effective methodologies for screening citrus rind micro-wounds are lacking. This study presents a preliminary investigation into real-time detection of citrus rind micro-wounds using contrast-enhanced X-ray imaging. This method highlights and magnifies rind wounds in X-ray images. The process involves immersing fruit in a contrast solution, capturing three sequential X-ray images, and then washing off residual contrast. Satsuma mandarins were used, with potassium iodide (KI) as the contrast agent duo to its distinct contrast properties on rind wounds coupled with high safety levels. A Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model with multi-head attention mechanisms was developed, achieving a detection accuracy of 97.19 %. Post-radiography assessments showed minimal effects on the fruit's external appearance and internal quality, though a slight weight loss was observed. These results demonstrate the proposed method's effectiveness in detecting citrus rind micro-wounds, offering a promising approach for enhancing decay management in the citrus industry.
Why it matches plant phenotyping methods柑橘果皮の微小創傷という植物器官の状態を、造影X線画像とCNN-LSTMでリアルタイム検出する手法の開発が中心であり、植物病害・損傷状態のフェノタイピングに該当する。
abstractThis study presents a preliminary investigation into real-time detection of citrus rind micro-wounds using contrast-enhanced X-ray imaging.
Stalk lodging in the monocot Zea mays is an important agricultural issue that requires the development of a genome-to-phenome framework, mechanistically linking intermediate and high-level phenotypes. As part of that effort, tools are needed to enable better mechanistic understanding of the microstructure in herbaceous plants. A method was therefore developed to create finite element models using CT scan data for Zea mays. This method represents a pipeline for processing the image stacks and developing the finite element models. 2-dimensional finite element models, 3-dimensional watertight models, and 3-dimensional voxel-based finite element models were developed. The finite element models contain both the cell and cell wall structures that can be tested in silico for phenotypes such as structural stiffness and predicted tissue strength. This approach was shown to be successful, and a number of example analyses were presented to demonstrate its usefulness and versatility. This pipeline is important for two reasons: (1) it helps inform which microstructure phenotypes should be investigated to breed for more lodging-resistant stalks, and (2) represents an essential step in the development of a mechanistic hierarchical framework for the genome-to-phenome modeling of herbaceous plant stalk lodging.
Why it matches plant phenotyping methodsCT画像スタックからトウモロコシ組織の有限要素モデルを構築する画像処理・計算パイプラインが研究の中心であり、構造剛性や組織強度という植物表現型の推定・解析に用いられているため。
abstractA method was therefore developed to create finite element models using CT scan data for Zea mays.
ABSTRACT Accurately determining the elemental composition of plant tissues is essential for physiological studies on plant stress, including salinity tolerance. However, high-throughput routine analysis of light elements (range of sodium to calcium) in plant samples is challenging due to the need for complete sample dissolution and expensive inductively coupled plasma-mass-spectrometry (ICP-MS) analysis. Lower costs method (ion chromatography, ion selective electrodes) exists, but also require sample dissolution and lack sensitivity for very small samples (<10 mg). This study reports on a new method for the quantitative analysis of light elements in plant tissues using monochromatic X-ray fluorescence (XRF) instrumentation and innovative sample preparation and mounting. We used this approach to assess elemental uptake, distribution, and accumulation in Arabidopsis thaliana and Oryza sativa plants subjected to salt stress. The method can be used on samples as small as 1 mg making it suitable for small Arabidopsis thaliana plants. We systematically evaluated different sample preparations methods, repeatability, and measurement times to confirm the robustness of the technique. The results show that the monochromatic XRF method delivers rapid, non-destructive, and extraction-free analysis, strongly correlating with ICP-MS acquired data. As such, the monochromatic XRF method is a reliable and efficient alternative for studying salinity tolerance ideally suited for investigating elemental composition of early plant developmental stages, offering new possibilities for research into early stimuli sensing, perception and nutrient efficiency.
Why it matches plant phenotyping methods植物組織の元素状態を測定するXRF法の開発と、試料調製・反復性・測定時間・ICP-MSとの相関による検証が中心であり、植物の生理状態を定量するフェノタイピング手法に該当する。
abstractThis study reports on a new method for the quantitative analysis of light elements in plant tissues using monochromatic X-ray fluorescence (XRF) instrumentation and innovative sample preparation and mounting.
ABSTRACT The global population is placing unprecedented demand on food systems, which can be met only through a complex interplay of technology, sustainable food production intensification methods and climate resilience. To address such compounded requirements, developing high‐yielding crop varieties using precise plant breeding methods bolstered with efficient and nondestructive plant trait documentation approaches is vital. High‐throughput crop phenotyping (HTCP) platforms have prominently emerged as a mainstream approach for reducing the phenotyping bottleneck in breeding programmes. HTCP has the potential to provide detailed quantitative information of large plant populations under different growth stages across diverse environmental regimes, facilitating accelerated plant breeding strategies. New imaging platforms also enable nondestructive characterization of a wide range of above and below‐ground crop parameters. The specificity in use of sensors, automation of data collection, large‐scale data handling systems and accurate analytical tools have a substantial role in dynamic crop monitoring and big data interpretation. HTCP platforms are capable of making precise measurements of a wide range of physiological, morphological, biochemical and stress responses in plants. Developments of sensors with improved precision, intervention of unmanned aerial vehicles, robotics, computed tomography and machine learning have given a dramatic developmental leap to precise and large‐scale crop phenotyping. This review provides an avenue for understanding various high‐throughput phenotyping platforms, working principles, current developments and contributions to high‐throughput phenotyping of various crops under laboratory and field conditions. A detailed comparative idea on the advantages and pitfalls of these available platforms can help researchers in choosing the right technology suiting specific practical requirements. Furthermore, the review aims to provide novel future prospects and developmental requirements that can potentially widen the application and utilization of these HTCP technologies in agriculture.
Why it matches plant phenotyping methods植物のハイスループット表現型解析プラットフォーム、センサー、画像化、データ解析を中心に比較・整理するレビューであり、方法論が主題である。
abstractThis review provides an avenue for understanding various high‐throughput phenotyping platforms, working principles, current developments and contributions to high‐throughput phenotyping of various crops under laboratory and field conditions.
Soybean ( Glycine max L.) is an important leguminous plant, in which pests trigger significant damage every year. Important members of this community are insects with piercing-sucking mouthpart, especially the southern green stinkbug, Nezara viridula L.. This insect with its extraoral digestion causes visible alterations (morphological and color changes) in the seeds. We aimed to obtain precise information about the extent and nature of damage in soybeans caused by N. viridula using nondestructive imaging methods. Two infestation conditions were applied: one with controlled numbers of pests (six insects/15 pods) and another with naturally occurring pests (samples collected from the apical part of the plant and samples from whole plants). An intact control group was also included, resulting in four treatment groups. Seed samples were analyzed by computed tomography (CT) and image color analysis under laboratory conditions. According to our CT findings, the damage caused by N. viridula changed the radiodensity, volume, and shape (Solidity) of the soybean seeds during the pod-filling and maturing period. Radiodensity was significantly reduced in all three damaged categories compared to the intact sample; the mean radiodensity reduction range was 49-412 HU. The seed volume also decreased significantly (25%-80% decrease), with a threefold reduction for samples exposed to regulated damage compared to natural ones. The samples exposed to natural damage showed significant but minor reduction in solidity, while samples exposed to regulated damage showed a prominent decrease (~12%). Image color analysis showed that the damaged samples were well distinguishable, and the differences were statistically verifiable. The achieved data derived from our external and internal imaging approaches contribute to a better understanding of the internal chemical processes, and CT analysis helps to understand the alteration trends of the hidden structure of seeds caused by a pest. Our results can contribute to the development of a practically applicable system based on image analysis, which can identify lots damaged by insects.
Why it matches plant phenotyping methodsCTと画像色解析を用いて、害虫被害を受けたダイズ種子の内部構造・形態・色の変化を定量化しており、植物状態の取得手法が研究の中心である。
abstractWe aimed to obtain precise information about the extent and nature of damage in soybeans caused by N. viridula using nondestructive imaging methods.
Abstract The intricate task of achieving three-dimensional (3D) visual reconstruction of wheat kernels represents a notable challenge within the domain of digital grain analysis, playing a pivotal role in the realms of grain storage, processing, and breeding. However, existing investigations focused on individual kernels predominantly encompass dimensions such as length, width, height, and epidermal texture features, with a tendency to be invasive to the internal organisational structure of the kernel. Non-local mean filtering algorithm is proposed to segment various tissues, and the 2D grey scale images are extracted from X-ray micro-computed tomography (μCT) to reconstruct a meticulous 3D visualisation model of individual wheat grains. Furthermore, building upon this foundation, an exhaustive assessment of morphological and structural parameters pertaining to each facet of the internal organisation of the wheat seed grain is conducted. Notably, these calculated parameters align with data generated by prior researchers，with 80% of the volume of the endosperm, 12% of the pericarp, about 2% of the endosperm and scutellum, and 4% of the pores. The established parameters and resultant 3D visual models serve as foundational components for subsequent in-depth examinations into various physicochemical properties, including quality characteristics, heat, and mass transfer attributes, as well as variations in its morphological structure during breeding, which are pertinent to individual grains. This research contributes valuable insights and methodologies that can propel the advancement of studies in wheat kernel analysis.
Why it matches plant phenotyping methodsX線μCT画像から小麦粒の3D形状・内部組織を再構成し、形態・構造パラメータを抽出する手法が研究の中心であり、植物器官形質の取得方法を開発・評価している。
abstractNon-local mean filtering algorithm is proposed to segment various tissues, and the 2D grey scale images are extracted from X-ray micro-computed tomography (μCT) to reconstruct a meticulous 3D visualisation model of individual wheat grains.
Background and aims Root system architecture (RSA) plays a key role in plant adaptation to drought, because deep rooting enables better water uptake than shallow rooting under terminal drought. Understanding RSA during early plant development is essential for improving crop yields, because early drought can affect subsequent shoot growth. Herein, we demonstrate that root distribution in the topsoil significantly impacts shoot growth during the early stages of rice (Oryza sativa) development under drought, as assessed through three-dimensional image analysis. Methods We used 109 F12 recombinant inbred lines obtained from a cross between shallow-rooting lowland rice and deep-rooting upland rice, representing a population with diverse RSA. We applied a moderate drought during the early development of rice grown in a plant pot (25 cm in height) by stopping irrigation 14 days after sowing. Time-series RSA at 14, 21 and 28 days after sowing was visualized by X-ray computed tomography and, subsequently, compared between drought and well-watered conditions. After this analysis, we investigated drought-avoidant RSA further by testing 20 randomly selected recombinant inbred lines in drought conditions. Key results We inferred the root location that most influences shoot growth using a hierarchical Bayes approach: the root segment depth that impacted shoot growth positively ranged between 1.7 and 3.4 cm in drought conditions and between 0.0 and 1.7 cm in well-watered conditions. Drought-avoidant recombinant inbred lines had a higher root density in the lower layers of the topsoil compared with the others. Conclusions Fine classification of soil layers using three-dimensional image analysis revealed that increasing root density in the lower layers of the topsoil, rather than in the subsoil, is advantageous for drought avoidance during the early growth stage of rice.
Why it matches plant phenotyping methodsX線CTと三次元画像解析による時系列の根系構造・根密度の取得と細分類が研究の中心であり、植物形質を抽出する方法の実質的な適用に該当する。
abstractTime-series RSA at 14, 21 and 28 days after sowing was visualized by X-ray computed tomography and, subsequently, compared between drought and well-watered conditions.
X-ray / CTFruitMorphology / geometry measurementGrowth / development / phenology
Lignans play a crucial role in maintaining plant growth, development, metabolism and stress resistance. Computed tomography (CT) imaging technology can be used to explore the internal structure and morphology of plants, and understanding the correlation between the two is highly significant. In this study, the content of lignan metabolites in coconut water was determined using liquid chromatography. The internal structure data of coconut fruit was obtained by CT scanning, and the relationship between lignan metabolites and CT image data at different developmental stages was evaluated using partial least square (PLS) regression. The results showed that the total lignan content in coconut water initially decreased, then increased, and gradually decreased after the maturity stage. The Wenye No. 5 variety exhibited higher levels of Epiturinol, Turbinol, Isobarinin-9'-o-glucoside, 5'-methoxy-rohanoside, Rohan rosin-4,4'-di-o-glucoside, turbinol-4-O-glucoside, cycloisoperinolin-4-O-glucoside compared to local coconuts. Coconut meat had the greatest effect on Rohan rosin-4,4'-di-o-glucoside, coconut water on Daphne, and coconut shell and coconut fiber on Larinin-4'-o-glucoside. The data from different parts of coconut fruit's images showed a significant correlation with the content of lignan metabolites. This study has preliminarily explored the correlation between non-destructive testing of coconut fruit and its development process of coconut fruit, providing a new approach and method for further research on non-destructive testing of coconut fruit development.
Why it matches plant phenotyping methodsココナツ果実の内部構造・発達状態をCT画像から非破壊的に評価し、PLS回帰で代謝物との関連を解析する手法が研究の中心であり、植物器官の表現型取得法に該当する。
abstractComputed tomography (CT) imaging technology can be used to explore the internal structure and morphology of plants
Background Grains make up a large proportion of both human and animal diets. With threats to food production, such as climate change, growing sustainable and successful crops is essential to food security in the future. Germination is one of the most important stages in a plant's lifecycle and is key to the success of the resulting plant as the grain undergoes morphological changes and the development of specific organs. Micro-computed tomography is a non-destructive imaging technique based on the differing x-ray attenuations of materials which we have applied for the accurate analysis of grain morphology during the germination phase. Results Micro Computed Tomography conditions and parameters were tested to establish an optimal protocol for the 3-dimensional analysis of barley grains. When comparing optimal scanning conditions, it was established that no filter, 0.4 degrees rotation step, 5 average frames, and 2016 × 1344 camera binning is optimal for imaging germinating grains. It was determined that the optimal protocol for scanning during the germination timeline was to scan individual grains at 0 h after imbibition (HAI) and then the same grain again at set time points (1, 3, 6, 24 HAI) to avoid any negative effects from X-ray radiation or disruption to growing conditions. Conclusion Here we sought to develop a method for the accurate analysis of grain morphology without the negative effects of possible radiation exposure. Several factors have been considered, such as the scanning conditions, reconstruction, and possible effects of X-ray radiation on the growth rate of the grains. The parameters chosen in this study give effective and reliable results for the 3-dimensional analysis of macro structures within barley grains while causing minimal disruption to grain development.
Why it matches plant phenotyping methods大麦の発芽中の穀粒形態を対象に、マイクロCTの撮像条件・再構成・放射線影響を検討し、3次元形態解析プロトコルを開発・検証しているため。
abstractMicro-computed tomography is a non-destructive imaging technique based on the differing x-ray attenuations of materials which we have applied for the accurate analysis of grain morphology during the germination phase.
A complete three-dimensional reconstruction of the internal damage (oviposition holes, entry and exit galleries, cavities caused by fungal infection) of three destructive pests of olive fruit was obtained using micro-computed tomography. In the case of the olive fruit fly (Bactrocera oleae), complete reconstruction of the galleries was achieved. The galleries were colour-coded according to the size of the internal lumens produced by larval instars. In the case of the olive moth (Prays oleae), we confirmed that the larvae only consume olive stones, leaving pulp tissue intact. This study revealed the evolutionary defensive adaptation developed by larvae, creating entrance/exit gallery in the form of a zigzag with alternating angles to avoid the action of possible parasitoids. In the case of olive fruit rot, caused by fungal infection transmitted by the midge (Lasioptera berlesiana), microtomography revealed the infection cavity, which was delimited by a protective layer of tissue produced by the plant to isolate the infection zone, which contained fungal hyphae and reproductive organs of the fungus. Two ovoid cavities were observed below a single external orifice in the concave necrotic depression. These results were interpreted as successive ovipositions of B. oleae, followed by the parasitoid L. berlesiana. High-resolution 3D rendered images are included as well as supplementary videos that could be useful tools for future research and teaching aids.
Why it matches plant phenotyping methodsマイクロCTを用いてオリーブ果実内部の食害・感染損傷を3次元的に取得・可視化する手法が研究の中心であり、植物の損傷状態を測定している。
abstractA complete three-dimensional reconstruction of the internal damage (oviposition holes, entry and exit galleries, cavities caused by fungal infection) of three destructive pests of olive fruit was obtained using micro-computed tomography.
Ginkgo (Ginkgo biloba L.) is one of the earliest extant species in seed plant phylogeny. Embryo development patterns can provide fundamental evidence for the origin, evolution, and adaptation of seeds. However, the architectural and morphological dynamics during embryogenesis in G. biloba remain elusive. Herein, we obtained over 2,200 visual slices from 3 stages of embryo development using micro-computed tomography imaging with improved staining methods. Based on 3-dimensional (3D) spatiotemporal pattern analysis, we found that a shoot apical meristem with 7 highly differentiated leaf primordia, including apical and axillary leaf buds, is present in mature Ginkgo embryos. 3D rendering from the front, top, and side views showed 2 separate transport systems of tracheids located in the hypocotyl and cotyledon, representing a unique pattern of embryogenesis. Furthermore, the morphological dynamic analysis of secretory cavities indicated their strong association with cotyledons during development. In addition, we identified genes GbLBD25a (lateral organ boundaries domain 25a), GbCESA2a (cellulose synthase 2a), GbMYB74c (myeloblastosis 74c), GbPIN2 (PIN-FORMED 2) associated with vascular development regulation, and GbWRKY1 (WRKYGOK 1), GbbHLH12a (basic helix-loop-helix 12a), and GbJAZ4 (jasmonate zim-domain 4) potentially involved in the formation of secretory cavities. Moreover, we found that flavonoid accumulation in mature embryos could enhance postgerminative growth and seedling establishment in harsh environments. Our 3D spatial reconstruction technique combined with multiomics analysis opens avenues for investigating developmental architecture and molecular mechanisms during embryogenesis and lays the foundation for evolutionary studies of embryo development and maturation.
Why it matches plant phenotyping methods胚の形態・発生構造をマイクロCT画像から3D再構成・解析する手法が研究の中心であり、植物器官の表現型取得・解析に該当する。
abstractwe obtained over 2,200 visual slices from 3 stages of embryo development using micro-computed tomography imaging with improved staining methods.
Microcomputed tomography (µCT) is a nondestructive X-ray imaging method used in plant physiology to visualize in situ plant tissues that enables assessments of embolized xylem vessels. Whereas evidence for X-ray-induced cellular damage has been reported, the impact on plant physiological processes such as carbon (C) uptake, transport, and use is unknown. Yet, these damages could be particularly relevant for studies that track embolism and C fluxes over time. We examined the physiological consequences of µCT scanning for xylem embolism over 3 mo by monitoring net photosynthesis (Anet), diameter growth, chlorophyll (Chl) concentration, and foliar nonstructural carbohydrate (NSC) content in 4 deciduous tree species: hedge maple (Acer campestre), ash (Fraxinus excelsior), European hornbeam (Carpinus betulus), and sessile oak (Quercus petraea). C transport from the canopy to the roots was also assessed through 13C labeling. Our results show that monthly X-ray application did not impact foliar Anet, Chl, NSC content, and C transport. Although X-ray effects did not vary between species, the most pronounced impact was observed in sessile oak, marked by stopped growth and stem deformations around the irradiated area. The absence of adverse impacts on plant physiology for all the tested treatments indicates that laboratory-based µCT systems can be used with different beam energy levels and doses without threatening the integrity of plant physiology within the range of tested parameters. However, the impacts of repetitive µCT on the stem radial growth at the irradiated zone leading to deformations in sessile oak might have lasting implications for studies tracking plant embolism in the longer-term.
Why it matches plant phenotyping methods植物の木部エンボリズムを可視化するµCTについて、反復スキャンが生理・成長へ及ぼす影響を評価しており、測定法の妥当性・安全性検証が中心である。
abstractMicrocomputed tomography (µCT) is a nondestructive X-ray imaging method used in plant physiology to visualize in situ plant tissues that enables assessments of embolized xylem vessels.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
MaizeField / plotX-ray / CTRootMorphology / geometry measurementRoot system architecture
BACKGROUND: The use of 3D imaging techniques, such as X-ray CT, in root phenotyping has become more widespread in recent years. However, due to the complexity of the root structure, analyzing the resulting 3D volumes to obtain detailed architectural root traits remains a challenging computational problem. When it comes to image-based phenotyping of excavated maize root crowns, two types of root features that are notably missing from existing methods are the whorls and soil line. Whorls refer to the distinct areas located at the base of each stem node from which roots sprout in a circular pattern (Liu S, Barrow CS, Hanlon M, Lynch JP, Bucksch A. Dirt/3D: 3D root phenotyping for field-grown maize (zea mays). Plant Physiol. 2021;187(2):739-57. https://doi.org/10.1093/plphys/kiab311 .). The soil line is where the root stem meets the ground. Knowledge of these features would give biologists deeper insights into the root system architecture (RSA) and the below- and above-ground root properties. RESULTS: We developed TopoRoot+, a computational pipeline that produces architectural traits from 3D X-ray CT volumes of excavated maize root crowns. Building upon the TopoRoot software (Zeng D, Li M, Jiang N, Ju Y, Schreiber H, Chambers E, et al. Toporoot: A method for computing hierarchy and fine-grained traits of maize roots from 3D imaging. Plant Methods. 2021;17(1). https://doi.org/10.1186/s13007-021-00829-z .) for computing fine-grained root traits, TopoRoot + adds the capability to detect whorls, identify nodal roots at each whorl, and compute the soil line location. The new algorithms in TopoRoot + offer an additional set of fine-grained traits beyond those provided by TopoRoot. The addition includes internode distances, root traits at every hierarchy level associated with a whorl, and root traits specific to above or below the ground. TopoRoot + is validated on a diverse collection of field-grown maize root crowns consisting of nine genotypes and spanning across three years. TopoRoot + runs in minutes for a typical volume size of [Formula: see text] on a desktop workstation. Our software and test dataset are freely distributed on Github. CONCLUSIONS: TopoRoot + advances the state-of-the-art in image-based phenotyping of excavated maize root crowns by offering more detailed architectural traits related to whorls and soil lines. The efficiency of TopoRoot + makes it well-suited for high-throughput image-based root phenotyping.
Why it matches plant phenotyping methods3D X線CT画像からトウモロコシ根冠の構造形質を抽出する計算パイプラインを開発し、多様な圃場試料で検証した研究であり、植物フェノタイピング手法が中心である。
abstractWe developed TopoRoot+, a computational pipeline that produces architectural traits from 3D X-ray CT volumes of excavated maize root crowns.
To study plant organs, it is necessary to investigate the three-dimensional (3D) structures of plants. In recent years, non-destructive measurements through computed tomography (CT) have been used to understand the 3D structures of plants. In this study, we use the Chrysanthemum seticuspe capitulum inflorescence as an example and focus on contact points between the receptacles and florets within the 3D capitulum inflorescence bud structure to investigate the 3D arrangement of the florets on the receptacle. To determine the 3D order of the contact points, we constructed slice images from the CT volume data and detected the receptacles and florets in the image. However, because each CT sample comprises hundreds of slice images to be processed and each C. seticuspe capitulum inflorescence comprises several florets, manually detecting the receptacles and florets is labor-intensive. Therefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques. The proposed method improves the accuracy of contact point detection using prior knowledge that contact points exist only around the receptacle. In addition, the integration of the detection results enables the estimation of the 3D position of the contact points. According to the experimental results, we confirmed that the proposed method can detect contacts on slice images with high accuracy and estimate their 3D positions through clustering. Additionally, the sample-independent experiments showed that the proposed method achieved the same detection accuracy as sample-dependent experiments.
Why it matches plant phenotyping methodsCT画像から花序内の小花と花托の接触点を自動検出し、3D位置を推定する手法の開発・精度評価が研究の中心であるため、植物フェノタイピング手法に該当する。
abstractTherefore, we propose an automatic contact point detection method based on CT slice images using image recognition techniques.
Reproduction assets foundThe authors publicly deposited the labeled CT slice-image dataset (contact point annotations and receptacle segmentation labels) on Figshare, and a 3D visualization video of the contact point estimation results is available on YouTube. Raw CT volumes are only available on request. No author analysis code repository is.Dataset · publicre task is to automate the clustering parameters, which are currently determined manually. We also plan to develop a mathematical model of the position of the contact point between the receptacle and florets based on the estimation results.
Data availability statement
The labeled data for this study can be found in the Figshare https://doi.org/10.6084/m9.figshare.25388434 . The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributionsOpen asset ↗Figshare · 10.6084/m9.figshare.25388434lines:513-540Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
The efficiency of N 2 -fixation in legume-rhizobia symbiosis is a function of root nodule activity. Nodules consist of 2 functionally important tissues: (a) a central infected zone (CIZ), colonized by rhizobia bacteria, which serves as the site of N 2 -fixation, and (b) vascular bundles (VBs), serving as conduits for the transport of water, nutrients, and fixed nitrogen compounds between the nodules and plant. A quantitative evaluation of these tissues is essential to unravel their functional importance in N 2 -fixation. Employing synchrotron-based x-ray microcomputed tomography (SR-μCT) at submicron resolutions, we obtained high-quality tomograms of fresh soybean root nodules in a non-invasive manner. A semi-automated segmentation algorithm was employed to generate 3-dimensional (3D) models of the internal root nodule structure of the CIZ and VBs, and their volumes were quantified based on the reconstructed 3D structures. Furthermore, synchrotron x-ray fluorescence imaging revealed a distinctive localization of Fe within CIZ tissue and Zn within VBs, allowing for their visualization in 2 dimensions. This study represents a pioneer application of the SR-μCT technique for volumetric quantification of CIZ and VB tissues in fresh, intact soybean root nodules. The proposed methods enable the exploitation of root nodule's anatomical features as novel traits in breeding, aiming to enhance N 2 -fixation through improved root nodule activity.
Why it matches plant phenotyping methodsSR-μCTと半自動3Dセグメンテーションを用いて根粒内部組織を可視化・体積定量する手法が中心であり、育種に利用可能な新規植物形質を抽出している。
abstractA semi-automated segmentation algorithm was employed to generate 3-dimensional (3D) models of the internal root nodule structure of the CIZ and VBs, and their volumes were quantified based on the reconstructed 3D structures.
Manual segmentation of the petals of flower computed tomography (CT) images is time-consuming and labor-intensive because the flower has many petals. In this study, we aim to obtain a three-dimensional (3D) structure of Camellia japonica flowers and propose a petal segmentation method using computer vision techniques. Petal segmentation on the slice images fails by simply applying the segmentation methods because the shape of the petals in CT images differs from that of the objects targeted by the latest instance segmentation methods. To overcome these challenges, we crop two-dimensional (2D) long rectangles from each slice image and apply the segmentation method to segment the petals on the images. Thanks to cropping, it is easier to segment the shape of the petals in the cropped images using the segmentation methods. We can also use the latest segmentation method for the task because the number of images used for training is augmented by cropping. Subsequently, the results are integrated into 3D to obtain 3D segmentation volume data. The experimental results show that the proposed method can segment petals on slice images with higher accuracy than the method without cropping. The 3D segmentation results were also obtained and visualized successfully.
Why it matches plant phenotyping methods花弁のCT画像から3D構造を抽出する画像セグメンテーション手法の開発と精度比較が中心であり、植物形態フェノタイピングに該当する。
abstractThe experimental results show that the proposed method can segment petals on slice images with higher accuracy than the method without cropping.
Reproduction assets foundThe paper's CT volume data of Camellia japonica flowers (with ground-truth annotations) is publicly deposited on Figshare, and the authors' segmentation/integration code is publicly available on GitHub, both with explicit availability statements.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.6084/m9.figshare.25264774.v1Open asset ↗figshare · 10.6084/m9.figshare.25264774.v1lines:447-494Code · publicThe code implementing the proposed method is available at https://github.com/yu-NK/petal_ct_crop_seg.gitOpen asset ↗github · yu-NK/petal_ct_crop_seglines:447-494Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Background Taxonomic identification of wood specimens provides vital information for a wide variety of academic (e.g. paleoecology, cultural heritage studies) and commercial (e.g. wood trade) purposes. It is generally accomplished through the observation of key anatomical features. Classic methodologies mostly require destructive sub-sampling, which is not always acceptable. X-ray computed micro-tomography (µCT) is a promising non-destructive alternative since it allows a detailed non-invasive visualization of the internal wood structure. There is, however, no standardized approach that determines the required resolution for proper wood identification using X-ray µCT. Here we compared X-ray µCT scans of 17 African wood species at four resolutions (1 µm, 3 µm, 8 µm and 15 µm). The species were selected from the Xylarium of the Royal Museum for Central Africa, Belgium, and represent a wide variety of wood-anatomical features. Results For each resolution, we determined which standardized anatomical features can be distinguished or measured, using the anatomical descriptions and microscopic photographs on the Inside Wood Online Database as a reference. We show that small-scale features (e.g. pits and fibres) can be best distinguished at high resolution (especially 1 µm voxel size). In contrast, large-scale features (e.g. vessel porosity or arrangement) can be best observed at low resolution due to a larger field of view. Intermediate resolutions are optimal (especially 3 µm voxel size), allowing recognition of most small- and large-scale features. While the potential for wood identification is thus highest at 3 µm, the scans at 1 µm and 8 µm were successful in more than half of the studied cases, and even the 15 µm resolution showed a high potential for 40% of the samples. Conclusions The results show the potential of X-ray µCT for non-destructive wood identification. Each of the four studied resolutions proved to contain information on the anatomical features and has the potential to lead to an identification. The dataset of 17 scanned species is made available online and serves as the first step towards a reference database of scanned wood species, facilitating and encouraging more systematic use of X-ray µCT for the identification of wood species.
Why it matches plant phenotyping methodsX線µCTによる木材内部の解剖学的特徴の非破壊取得について、解像度を比較・評価し、参照データベース用データセットも提供しているため、植物形質取得法が中心です。
abstractX-ray computed micro-tomography (µCT) is a promising non-destructive alternative since it allows a detailed non-invasive visualization of the internal wood structure.
RiceMesh / voxelX-ray / CTRootObject detectionSegmentationRoot system architecture
Abstract Background X-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice ( Oryza sativa ) root segments in CT volumes. Results VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data. Conclusions A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating.
Why it matches plant phenotyping methods植物根のCT画像から根をセグメンテーションし、根成長の定量化に用いる手法の開発・技術評価が中心であるため、植物フェノタイピング方法論に該当する。
abstractX-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil.
Reproduction assets foundThe paper's own training/prediction scripts for the 3D semantic segmentation model (SStrainer3D) are publicly available on GitHub with explicit availability language. The CT volume datasets are only available upon request. RSAvis3D and RSAtrace3D are cited prior-work tools, not paper-specific assets.Code · publicThe scripts for the training and prediction of the 3D semantic segmentation are available at the GitHub repository ( https://github.com/st707311g/SStrainer3D/ , branch 1.0).Open asset ↗st707311g/SStrainer3Dlines:156-186Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Post-harvest losses during cereal grain storage are a big concern in both developing and developed countries, where spring and durum wheat are staple food grains. Varieties under these classes behave differently under storage, which affects their end storage life. High resolution imaging data of dry as well as spoiled seed are not available for any class of wheat; therefore, an attempt was made to generate 3D data for better understanding of seed structure and changes due to spoilage. Six wheat varieties (3 varieties for each class of wheat) were stored for 5 week at 17% moisture content (wb) before scanning. Seeds were also stored in a freezer (-18 °C) for further scanning to determine if any changes occur in the structure of seeds due to freezing. Spring varieties of wheat performed better than durum varieties and freezing did not affect seed structure. Data could also help plant breeders to develop varieties that do not easily spoil, adjust grain processing techniques, and develop post-harvest recommendations for other wheat varieties.
Why it matches plant phenotyping methods小麦種子の構造変化を非破壊3D画像で取得・評価することが研究の中心であり、植物器官の形態状態を測定する画像ベース手法の応用に該当する。
abstractHigh resolution imaging data of dry as well as spoiled seed are not available for any class of wheat; therefore, an attempt was made to generate 3D data for better understanding of seed structure and changes due to spoilage.
Societal Impact Statement Parasitic plants that deprive crops of water and nutrients are an increasingly concerning food security issue, affecting the livelihood of millions of subsistence, small‐ and mid‐scale farmers. An in‐depth understanding of parasite–host interactions is required to develop species‐specific and ecologically sustainable parasite management methods. The non‐invasive visualization of herbaceous contact zones, applicable to diverse parasite–host pathosystems presented in this study, brings methodological advance to the research of biotic interactions between crops and plant parasites belonging to the most devastating parasitic plant family (Orobanchaceae). This work also provides first insights into how the parasites' feeding organ displaces host tissue beyond the direct parasite–host interface. Summary High‐resolution X‐ray computed tomography (HRXCT) enables sectioning‐free two‐dimensional imaging of biological structures and reconstruction of three‐dimensional objects. Although its application is common in many areas of biomedicine and despite its flexibility regarding resolution levels, the technology remains underutilized in the plant sciences. Here, we explored HRXCT for the study of parasitic plant–plant interactions by developing protocols to access soft‐tissue host–parasite contact zones at cell‐level resolution. We tested various sample preparation methods and contrast stains for their efficiency to improve the imaging of haustorium samples. In doing so, we achieved cellular resolution with the visible cellular organization of haustorial structures, especially of the vascular system. Fresh stained and dehydrated sample preparation of soft haustoria enables the highest spatial resolution with fine‐cellular discrimination of haustorium versus host cells. Application of cell‐level resolved HRXCT to five pathosystems: Alectra ‐cowpea, Phelipanche ‐tomato, Phtheirospermum ‐tomato, Rhamphicarpa ‐tomato, and Striga ‐sorghum highlighted a life history‐specific organization and uncovered an as yet undescribed internal displacement of host tissue at parasite–host interfaces. Following image‐based training, our HRXCT approach could invoke AI‐based cell recognition for automated parasite cell–host cell differentiation. Superseding extensive microsectioning for 3D imaging, the newly established HRXCT protocol for 2D‐ and 3D‐visualization of herbaceous plant–plant contact zones and the first insights gained from it, is useful for mid‐throughput, comparative studies of parasitic plant–host interactions.
Why it matches plant phenotyping methodsHRXCTによる植物組織の2D・3D画像取得プロトコルを開発し、試料調製・染色を比較検証したうえで、寄生植物と宿主の接触領域を細胞レベルで可視化する方法が中心である。
abstractHere, we explored HRXCT for the study of parasitic plant–plant interactions by developing protocols to access soft‐tissue host–parasite contact zones at cell‐level resolution.
Background The detection of internal defects in seeds via non-destructive imaging techniques is a topic of high interest to optimize the quality of seed lots. In this context, X-ray imaging is especially suited. Recent studies have shown the feasibility of defect detection via deep learning models in 3D tomography images. We demonstrate the possibility of performing such deep learning-based analysis on 2D X-ray radiography for a faster yet robust method via the X-Robustifier pipeline proposed in this article. Results 2D X-ray images of both defective and defect-free seeds were acquired. A deep learning model based on state-of-the-art object detection neural networks is proposed. Specific data augmentation techniques are introduced to compensate for the low ratio of defects and increase the robustness to variation of the physical parameters of the X-ray imaging systems. The seed defects were accurately detected (F1-score >90%), surpassing human performance in computation time and error rates. The robustness of these models against the principal distortions commonly found in actual agro-industrial conditions is demonstrated, in particular, the robustness to physical noise, dimensionality reduction and the presence of seed coating. Conclusion This work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects.
Why it matches plant phenotyping methods種子内部欠陥という植物器官の状態を2D X線画像から自動抽出する深層学習パイプラインを開発・検証しており、表現型取得手法が中心である。
abstractThis work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging.
Quantitative analysis of vessel characteristics at the cellular scale is of great significance for understan-ding plant adaptation strategies to environment. The direct grinding combined with stereo-microscope imaging is one of the main approaches to examine the anatomical structure of xylem (conifer tracheid and hardwood vessel) wood structure, which inevitably damages xylem cells, hindering the accurate understanding of anatomical structures. In this study, we applied X-ray micro-computed tomography (μCT) and stereo-microscope technology to quantitatively measure the diameter and area of vessels of seven Canadian broadleaved tree species ( Acer saccharum , Betula papyrifera , Fraxinus americana , Ostrya virginiana , Populus grandidentata , Quercus rubra , and Carya cordiformis ). We fitted the results by linear model and tested the feasibility of μCT technology in quantifying the vessel size of broadleaved species. We found that the results of the two methods for measuring vessel size were highly similar ( R 2 =0.98). The goodness of fit of the vessel diameter results measured by the two methods for the ring-porous wood species ( C. cordiformis , R 2 =0.98; F. americana , R 2 =0.96; Q. rubra , R 2 =0.99) was higher than that of the diffuse-porous wood species ( B. papyrifera , R 2 =0.88; O. virginiana , R 2 =0.73; A. saccharum , R 2 =0.68; P. grandiden-tata , R 2 =0.88). The goodness of fit of small vessels (diameter≤200 μm, R 2 =0.94) measured by the two methods was higher than that of large vessels (diameter>200 μm, R 2 =0.92). Thus, the μCT technique provided a new non-destructive detection method for quantifying xylem vessels of broadleaved tree species.
Why it matches plant phenotyping methodsμCTを用いた木部道管サイズ測定法をステレオ顕微鏡法と比較検証し、非破壊的な植物形質取得法として実証しているため。
abstractWe fitted the results by linear model and tested the feasibility of μCT technology in quantifying the vessel size of broadleaved species.
The detection of seed viability is an important step in seed production, as well as in conservation and restoration practice. Due to random natural events, the quality and viability of seeds of wild flowering species vary substantially, and hence a quick and reliable method for seed viability assessment is desirable. X-rays provide information about the internal structures of a seed and therefore show promise for detection of viability and even germination capacity. Seeds of 207 accessions of 176 wild flowering plant species were x-rayed and the viability results were compared with combined germination-TZ test results. Of special interest was whether there are certain plant families for which x-ray is an appropriate method for viability detection, considering correlations with seed internal morphology, seed mass and/or shape. The comparison revealed a strong correspondence between viability determination by combined germination-TZ tests and by x-ray analysis. According to taxonomy and seed type, two main groups could be distinguished, that differed significantly in viability detection by x-ray and combined germination-TZ test. Whereas the evaluation of little/non-endospermic seeds gave approximately identical results, there was greater discrepancy for of endospermic seeds. Seeds of different sizes and shapes were evaluated similarly with both methods. Especially for little/non-endospermic seeds, x-ray can provide a useful and quick tool for viability detection, whereas for endospermic seeds, further research is needed. For the commercial seed industry, viability detection via x-ray should be the first step before further vigour testing is performed.
Why it matches plant phenotyping methods種子の生存性をX線で推定する方法を、207アクセッション・176種で発芽/TZ試験と比較検証しており、植物状態の取得手法が研究の中心である。
abstractSeeds of 207 accessions of 176 wild flowering plant species were x-rayed and the viability results were compared with combined germination-TZ test results.
Abstract A complete three-dimensional reconstruction of the internal damage (oviposition holes, entry and exit galleries, cavities caused by fungal infection) of three destructive pests of olive fruit was obtained by micro-computed tomography. In the case of the olive fruit fly ( Bactrocera oleae ), a complete reconstruction of the galleries was obtained. The galleries were colour-coded according to the internal lumen, corresponding to the size of the larval instars. In the case of the olive moth ( Prays oleae ), it was confirmed that the larvae only consume olive stones, leaving the pulp tissue intact. This study revealed the evolutionary defensive adaptation that the larva has developed by making the entrance/exit gallery in the form of a zigzag with alternating angles to avoid the action of possible parasitoids. In the case of olive fruit rot, caused by a fungal infection transmitted by the midge ( Lasioptera berlesiana ), microtomography revealed the infection cavity, delimited by a protective layer of tissue produced by the plant to isolate the infection zone, full of fungal hyphae and the reproductive organs of the fungus. Below and near the single external orifice present in the concave necrotic depression, two ovoid cavities were observed. These results were interpreted as successive ovipositions of B. oleae and its parasitoid L. berlesiana . High-resolution 3D rendered images are included as well as supplementary videos that could be a useful tool for future research and a valuable teaching aid.
Why it matches plant phenotyping methodsマイクロCTによるオリーブ果実内部の損傷・感染状態の3D取得と再構成が研究の中心であり、植物器官の病害・食害状態を画像化する実質的な表現型計測である。
abstractA complete three-dimensional reconstruction of the internal damage (oviposition holes, entry and exit galleries, cavities caused by fungal infection) of three destructive pests of olive fruit was obtained by micro-computed tomography.
The bacterial plant pathogen Xylella fastidiosa causes disease in several globally important crops. However, some cultivars harbour reduced bacterial loads and express few symptoms. Evidence considering plant species in isolation suggests xylem structure influences cultivar susceptibility to X. fastidiosa. We test this theory more broadly by analysing high‐resolution synchrotron X‐ray computed tomography of healthy and infected plant vasculature from two taxonomic groups containing susceptible and resistant varieties: two citrus cultivars (sweet orange cv. Pera, tangor cv. Murcott) and two olive cultivars (Koroneiki, Leccino). Results found the susceptible plants had more vessels than resistant ones, which could promote within‐host pathogen spread. However, features associated with resistance were not shared by citrus and olive. While xylem vessels in resistant citrus stems had comparable diameters to those in susceptible plants, resistant olives had narrower vessels that could limit biofilm spread. And while differences among olive cultivars were not detected, results suggest greater vascular connectivity in resistant compared to susceptible citrus plants. We hypothesize that this provides alternate flow paths for sustaining hydraulic functionality under infection. In summary, this work elucidates different physiological resistance mechanisms between two taxonomic groups, while supporting the existence of an intertaxonomical metric that could speed up the identification of candidate‐resistant plants.
Why it matches plant phenotyping methods高解像度X線CT画像から植物の木部血管形態・連結性を抽出し、耐病性候補指標として比較することが研究の中心であり、植物状態の画像ベース表現型解析に該当する。
titleA high‐throughput analysis of high‐resolution X‐ray CT images of stems of olive and citrus plants resistant and susceptible to Xylella fastidiosa
The peels of lime, lemon, pomelo and citron are investigated at macroscopic and microscopic level. The structural composition of the peels is compared and properties such as peel thickness, proportion of flavedo, density and proportion of intercellular spaces are determined. μCT images are used to visualize vascular bundles and oil glands. SEM images provide information about the appearance of the cellular tissue in the outer flavedo and inner albedo. The proportion of intercellular spaces is quantitatively determined by manual and software-assisted analysis (ilastik). While there are macroscopic differences in the fruits, they differ only slightly in the orientation of the vascular bundles and the arrangement of the oil glands. However, in peel thickness and flavedo thickness, the fruit peels differ significantly from each other. There are no significant differences between the two analysis methods used, although the use of ilastik is preferred due to time reduction of up to 70%. The large amount of intercellular spaces in the albedo but also the denser flavedo both have a mechanical protective function to prevent damage to the fruit. In addition, the entire peel structure is mechanically reinforced by vascular bundles. This combination of penetration protection (flavedo) and energy dissipation (albedo) makes Citrus spp. peels a promising inspiration for technical material systems.
Why it matches plant phenotyping methods柑橘果皮の構造形質を光学・SEM・μCT画像から取得し、手動法とソフトウェア支援セグメンテーションを比較・検証しているため、植物形質取得法が中心である。
abstractproperties such as peel thickness, proportion of flavedo, density and proportion of intercellular spaces are determined.
Abstract Background Taxonomic identification of wood specimens provides vital information for a wide variety of academic (e.g. paleoecology, cultural heritage studies) and commercial (e.g. wood trade) purposes. It is generally accomplished through the observation of key anatomical features. Classic methodologies mostly require destructive sub-sampling, which is not always acceptable. X-ray computed micro-tomography (µCT) is a promising non-destructive alternative since it allows a detailed non-invasive visualization of the internal wood structure. There is, however, no standardized approach that determines the required resolution for proper wood identification using X-ray µCT. Here we compared X-ray µCT scans of 17 African wood species at four resolutions (1µm, 3µm, 8µm and 15µm). The species were selected from the Xylarium of the Royal Museum for Central Africa, Belgium, and represent a wide variety of wood-anatomical features. Results For each resolution, we determined which standardized anatomical features can be distinguished or measured, using the anatomical descriptions and microscopic photographs on the Inside Wood Online Database as a reference. We show that small-scale features (e.g. pits and fibres) can be best distinguished at high resolution (especially 1µm voxel size). In contrast, large-scale features (e.g. vessel porosity or arrangement) can be best observed at low resolution due to a larger field of view. Intermediate resolutions are optimal (especially 3 µm voxel size), allowing recognition of most small- and large-scale features. While the potential for wood identification is thus highest at 3µm, the scans at 1µm and 8µm were successful in more than half of the studied cases, and even the 15µm resolution showed a high potential for 40% of the samples. Conclusions The results show the potential of X-ray µCT for non-destructive wood identification. Each of the four studied resolutions proved to contain information on the anatomical features and has the potential to lead to an identification. The dataset of 17 scanned species is made available online and serves as the first step towards a reference database of scanned wood species, facilitating and encouraging more systematic use of X-ray µCT for the identification of wood species.
Why it matches plant phenotyping methods植物木材の内部形態をX線µCTで取得し、解像度ごとの解剖学的特徴の識別・測定性能を比較検証した研究であり、植物形質取得法が中心です。
abstractX-ray computed micro-tomography (µCT) is a promising non-destructive alternative since it allows a detailed non-invasive visualization of the internal wood structure.
The growth and development of embryophytes is deeply influenced by environmental stimuli, such as light, temperature and soil nutrients. Understanding the mechanisms underlying the growth response of plants to environmental stimuli is crucial for agriculture. In this study, we examined the morphology of a flowering plant, Arabidopsis thaliana, using microfocus X-ray computed tomography (µCT), which enables non-destructive analysis of the external and internal structures of plants. Three-dimensional (3D) images of the plant, which were reconstructed from X-ray scanned data, clearly showed the shapes of its leaves, stems, and buds from any angle. At a higher magnification, the mCT also revealed the small hair-like structures called trichomes on the Arabidopsis leaf epidermis. However, motion artifacts found in the 3D-reconstructed images indicated that plant's growth rate was faster than scanning speed. Thus, scan parameters must be accordingly optimized. Additionally, CT-based 3D printing can be used to design micro devices that can be further used to monitor plant growth. These results suggest that µCT is a useful technique for analyzing morphology of growing plants.
Why it matches plant phenotyping methods植物の外部・内部形態を非破壊かつ3Dで取得するマイクロフォーカスX線CTを中心に、成長モニタリングへの適用とスキャン条件・モーションアーティファクトの評価を行っており、表現型取得法が中核です。
abstractwe examined the morphology of a flowering plant, Arabidopsis thaliana, using microfocus X-ray computed tomography (µCT), which enables non-destructive analysis of the external and internal structures of plants.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo without harming the plant is extremely challenging. New solutions are needed in ligneous and perennial species, for which the sustainability of plantations is crucial. To tackle this challenge, we developed a novel approach based on multimodal 3D imaging and artificial intelligence-based image processing that allowed a non-destructive diagnosis of inner tissues in living plants. The method was successfully applied to the grapevine ( Vitis vinifera L.). Vineyard’s sustainability is threatened by trunk diseases, while the sanitary status of vines cannot be ascertained without injuring the plants. By combining MRI and X-ray CT 3D imaging with an automatic voxel classification, we could discriminate intact, degraded, and white rot tissues with a mean global accuracy of over 91%. Each imaging modality contribution to tissue detection was evaluated, and we identified quantitative structural and physiological markers characterizing wood degradation steps. The combined study of inner tissue distribution versus external foliar symptom history demonstrated that white rot and intact tissue contents are key-measurements in evaluating vines’ sanitary status. We finally proposed a model for an accurate trunk disease diagnosis in grapevine. This work opens new routes for precision agriculture and in-situ monitoring of tissue quality and plant health across plant species.
Why it matches plant phenotyping methodsブドウ樹内部組織の非破壊的な表現型取得・診断を目的に、MRI・X線CT・自動ボクセル分類を開発し、精度評価と組織状態の定量化を行っており、フェノタイピング手法が中心である。
abstractwe developed a novel approach based on multimodal 3D imaging and artificial intelligence-based image processing that allowed a non-destructive diagnosis of inner tissues in living plants.
RadishRiceX-ray / CTRoot2D/3D reconstructionGrowth / time-series analysisRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent the direct visualization of plant roots, thus posing a challenge to effective phenotyping. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We show that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. Additionally, we also developed computational models to visualize the roots of tuber crops and monocotyledons and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device's groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系の発達を対象に、分布型光ファイバーセンサー、信号処理、根の可視化モデルを開発し、X線CTとの比較検証まで行う、植物フェノタイピング手法が中心の研究です。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe authors publicly provide MATLAB code for virtual root reconstruction and the sample datasets used in the study via their GitHub repository Fiber-RADGET, with explicit availability statements in the Methods and Data availability sections.Code · publicThe custom code for the virtual root reconstruction in MATLAB (MathWorks, Massachusetts, USA) is available at https://github.com/mtei1/Fiber-RADGET.git .Open asset ↗mtei1/Fiber-RADGETlines:126-223Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Introduction In contemporary agronomic research, the focus has increasingly shifted towards non-destructive imaging and precise phenotypic characterization. A photon-counting micro-CT system has been developed, which is capable of imaging lychee fruit at the micrometer level and capturing a full energy spectrum, thanks to its advanced photon-counting detectors. Methods For automatic measurement of phenotypic traits, seven CNN-based deep learning models including AttentionUNet, DeeplabV3+, SegNet, TransUNet, UNet, UNet++, and UNet3+ were developed. Machine learning techniques tailored for small-sample training were employed to identify key characteristics of various lychee species. Results These models demonstrate outstanding performance with Dice, Recall, and Precision indices predominantly ranging between 0.90 and 0.99. The Mean Intersection over Union (MIoU) consistently falls between 0.88 and 0.98. This approach served both as a feature selection process and a means of classification, significantly enhancing the study's ability to discern and categorize distinct lychee varieties. Discussion This research not only contributes to the advancement of non-destructive plant analysis but also opens new avenues for exploring the intricate phenotypic variations within plant species.
Why it matches plant phenotyping methodsライチ果実のマイクロCT画像から表現型形質を自動抽出する深層学習モデルを開発・評価しており、フェノタイピング手法が研究の中心です。
abstractFor automatic measurement of phenotypic traits, seven CNN-based deep learning models including AttentionUNet, DeeplabV3+, SegNet, TransUNet, UNet, UNet++, and UNet3+ were developed.
Abstract Persian walnuts (Juglans regia L.) are the second most produced and consumed tree nut, with over 2.6 million metric tons produced in the 2022–2023 harvest cycle alone. The United States is the second largest producer, accounting for 25% of the total global supply. Nonetheless, producers face an ever‐growing demand in a more uncertain climate landscape, which requires effective and efficient walnut selection and breeding of new cultivars with increased kernel content and easy‐to‐open shells. Past and current efforts select for these traits using hand‐held calipers and eye‐based evaluations. Yet there is plenty of morphology that meets the eye but goes unmeasured, such as the volume of inner air or the convexity of the kernel. Here, we study the shape of walnut fruits based on X‐ray computed tomography three‐dimensional reconstructions. We compute 49 different morphological phenotypes for 1264 individual nuts comprising 149 accessions. These phenotypes are complemented by traits of breeding interest such as ease of kernel removal and kernel‐to‐nut weight ratio. Through allometric relationships, relative growth of one tissue to another, we identify possible biophysical constraints at play during development. We explore multiple correlations between all morphological and commercial traits and identify which morphological traits can explain the most variability of commercial traits. We show that using only volume‐ and thickness‐based traits, especially inner air content, we can successfully encode several of the commercial traits.
Why it matches plant phenotyping methodsX線CTの3次元再構成を用いてクルミ個体の49種類の形態表現型を算出しており、植物形質の取得・抽出が研究の中心です。
abstractHere, we study the shape of walnut fruits based on X‐ray computed tomography three‐dimensional reconstructions.
Characterizing dispersal structures is crucial for species identification and selecting diverse germplasm. This study aimed to investigate the morphology of Spondias tuberosa dispersion units and assess the efficacy of x-ray imaging in characterizing their internal morphology. X-ray imaging successfully revealed the internal structures, enabling the identification of filled, translucent, malformed, and empty seeds. The morphological analysis provided valuable insights into the dispersal units and presented a non-destructive and efficient method for future germplasm research.
Why it matches plant phenotyping methodsX線画像による種子・散布単位の内部形態の非破壊評価法を中心に、その有効性を検討しており、植物形質の取得方法として中核的である。
abstractassess the efficacy of x-ray imaging in characterizing their internal morphology
Background High quality 3D information of the microscopic plant tissue morphology-the spatial organization of cells and intercellular spaces in tissues-helps in understanding physiological processes in a wide variety of plants and tissues. X-ray micro-CT is a valuable tool that is becoming increasingly available in plant research to obtain 3D microstructural information of the intercellular pore space and individual pore sizes and shapes of tissues. However, individual cell morphology is difficult to retrieve from micro-CT as cells cannot be segmented properly due to negligible density differences at cell-to-cell interfaces. To address this, deep learning-based models were trained and tested to segment individual cells using X-ray micro-CT images of parenchyma tissue samples from apple and pear fruit with different cell and porosity characteristics. Results The best segmentation model achieved an Aggregated Jaccard Index (AJI) of 0.86 and 0.73 for apple and pear tissue, respectively, which is an improvement over the current benchmark method that achieved AJIs of 0.73 and 0.67. Furthermore, the neural network was able to detect other plant tissue structures such as vascular bundles and stone cell clusters (brachysclereids), of which the latter were shown to strongly influence the spatial organization of pear cells. Based on the AJIs, apple tissue was found to be easier to segment, as the porosity and specific surface area of the pore space are higher and lower, respectively, compared to pear tissue. Moreover, samples with lower pore network connectivity, proved very difficult to segment. Conclusions The proposed method can be used to automatically quantify 3D cell morphology of plant tissue from micro-CT instead of opting for laborious manual annotations or less accurate segmentation approaches. In case fruit tissue porosity or pore network connectivity is too low or the specific surface area of the pore space too high, native X-ray micro-CT is unable to provide proper marker points of cell outlines, and one should rely on more elaborate contrast-enhancing scan protocols.
Why it matches plant phenotyping methodsX線マイクロCT画像から植物組織の個別細胞形態を3D定量化する深層学習セグメンテーション手法を開発・ベンチマークしており、植物表現型取得が研究の中心です。
abstractdeep learning-based models were trained and tested to segment individual cells using X-ray micro-CT images of parenchyma tissue samples from apple and pear fruit
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Introduction In the past years, it has been observed that the breeding of plants has become more challenging, as the visible difference in phenotypic data is much smaller than decades ago. With the ongoing climate change, it is necessary to breed crops that can cope with shifting climatic conditions. To select good breeding candidates for the future, phenotypic experiments can be conducted under climate-controlled conditions. Above-ground traits can be assessed with different optical sensors, but for the root growth, access to non-destructively measured traits is much more challenging. Even though MRI or CT imaging techniques have been established in the past years, they rely on an adequate infrastructure for the automatic handling of the pots as well as the controlled climate. Methods To address both challenges simultaneously, the non-destructive imaging of plant roots combined with a highly automated and standardized mid-throughput approach, we developed a workflow and an integrated scanning facility to study root growth. Our “ chamber #8 ” contains a climate chamber, a material flow control, an irrigation system, an X-ray system, a database for automatic data collection, and post-processing. The goals of this approach are to reduce the human interaction with the various components of the facility to a minimum on one hand, and to automate and standardize the complete process from plant care via measurements to root trait calculation on the other. The user receives standardized phenotypic traits and properties that were collected objectively. Results The proposed holistic approach allows us to study root growth of plants in a field-like substrate non-destructively over a defined period and to calculate phenotypic traits of root architecture. For different crops, genotypic differences can be observed in response to climatic conditions which have already been applied to a wide variety of root structures, such as potatoes, cassava, or corn. Discussion It enables breeders and scientists non-destructive access to root traits. Additionally, due to the non-destructive nature of X-ray computed tomography, the analysis of time series for root growing experiments is possible and enables the observation of kinetic traits. Furthermore, using this automation scheme for simultaneously controlled plant breeding and non-destructive testing reduces the involvement of human resources.
Why it matches plant phenotyping methods植物根系の非破壊X線イメージング、施設自動化、データ処理、根形態形質計算を統合したフェノタイピング手法・プラットフォームの開発が中心である。
abstractwe developed a workflow and an integrated scanning facility to study root growth.
The current investigation on image recognition and internal crack detection of maize seeds primarily relies on visible light imaging. However, due to the low transmissivity of plant cells, even with image enhancement measures, the clarity of internal cracks in the images and the subsequent feature extraction process can be a trade-off. Soft X-rays possess exceptional penetration capability and offer better safety and convenience compared to hard X-rays, making them highly suitable for visualizing internal structures within plant tissues like maize seeds. In this paper, a non-invasive Imaging Technique for Image Enhancement is proposed, combining wavelet thresholding denoising, image standardization, bilateral filtering, and laplacian sharpening. This method is based on soft X-rays and successfully achieves image recognition of cracks present inside Zhengdan 958 maize seeds using an optimized YOLOv8 model. It effectively addresses challenges related to the limited light transmission of maize seeds, difficulty in crack localization, and algorithm generalization issues. The optimized YOLOv8 model demonstrates an average precision (AP) value that is 3.1% higher than that of the original model. Furthermore, by applying image enhancement, the AP value increases by 1.8%. The proposed method exhibits an average recognition accuracy of 99.66% for intact or broken seeds, an average precision of 99.87%, an average recognition recall of 99.48%, and an average single-frame image detection time of 7.49 ms in the single seed detection experiment.
Why it matches plant phenotyping methodsトウモロコシ種子内部の亀裂という植物器官の状態を、軟X線画像強調と最適化YOLOv8で検出・分類する手法開発が研究の中心である。
abstractIn this paper, a non-invasive Imaging Technique for Image Enhancement is proposed, combining wavelet thresholding denoising, image standardization, bilateral filtering, and laplacian sharpening.
Here, high-throughput tomography (HiTT), a fast and versatile phase-contrast imaging platform for life-science samples on the EMBL beamline P14 at DESY in Hamburg, Germany, is presented. A high-photon-flux undulator beamline is used to perform tomographic phase-contrast acquisition in about two minutes which is linked to an automated data processing pipeline that delivers a 3D reconstructed data set less than a minute and a half after the completion of the X-ray scan. Combining this workflow with a sophisticated robotic sample changer enables the streamlined collection and reconstruction of X-ray imaging data from potentially hundreds of samples during a beam-time shift. HiTT permits optimal data collection for many different samples and makes possible the imaging of large sample cohorts thus allowing population studies to be attempted. The successful application of HiTT on various soft tissue samples in both liquid (hydrated and also dehydrated) and paraffin-embedded preparations is demonstrated. Furthermore, the feasibility of HiTT to be used as a targeting tool for volume electron microscopy, as well as using HiTT to study plant morphology, is demonstrated. It is also shown how the high-throughput nature of the work has allowed large numbers of `identical' samples to be imaged to enable statistically relevant sample volumes to be studied.
Why it matches plant phenotyping methods高速X線トモグラフィー、ロボット試料交換、自動再構成を統合した高スループット画像化プラットフォームが主題であり、植物形態の画像計測への適用も明示されている。
abstracthigh-throughput tomography (HiTT), a fast and versatile phase-contrast imaging platform for life-science samples
Puffiness is one of the dominant postharvest disorders in easy-peeling citrus cultivars. In this study, the structural changes between healthy and puffy Satsuma mandarin were investigated and the potential of using optical methods for disorder detection was explored. To gain more insight in this disorder, the external appearance and internal quality attributes were first compared between healthy and puffy Iwasaki Satsuma mandarins at three harvest times. Although no consistent differences were observed in the appearance of fruits, the soluble solids content and Brix minus acid values in puffy mandarin were found to be higher compared to the corresponding healthy fruit. The structural properties of the flavedo and albedo tissue layer in the peel were quantified from X-ray CT scans. Whilst no differences were observed in the size of the oil glands in the flavedo, the pore size in the albedo of puffy mandarin was found to be larger with later harvest. The bulk optical properties of the intact fruit were estimated from laser scatter images with a metamodel calibrated on optical phantoms. The reduced scattering coefficient (μₛ') for the intact fruit was found to be lower in puffy mandarin relative to healthy fruit. The distinction between healthy and puffy mandarin based on μₛ' was further validated on Goku Wase Satsuma mandarin. The results obtained indicate that healthy and puffy mandarin can be separated well based on their μₛ' at all the selected wavelengths. This provides a basis for the non-destructive optical detection of puffing disorder at an early stage.
Why it matches plant phenotyping methods柑橘果実の生理・構造状態（puffiness）を光学計測とX線CTで非破壊検出する方法を開発・検証しており、植物フェノタイピング手法が中心である。
abstractthe potential of using optical methods for disorder detection was explored
Premise Most studies of the movement of orchid fruits and roots during plant development have focused on morphological observations; however, further genetic analysis is required to understand the molecular mechanisms underlying this phenomenon. A precise tool is required to observe these movements and harvest tissue at the correct position and time for transcriptomics research. Methods We utilized three-dimensional (3D) micro-computed tomography (CT) scans to capture the movement of fast-growing Erycina pusilla roots, and built an integrated bioinformatics pipeline to process 3D images into 3D time-lapse videos. To record the movement of slowly developing E. pusilla and Phalaenopsis equestris fruits, two-dimensional (2D) photographs were used. Results The E. pusilla roots twisted and resupinated multiple times from early development. The first period occurred in the early developmental stage (77-84 days after germination [DAG]) and the subsequent period occurred later in development (140-154 DAG). While E. pusilla fruits twisted 45° from 56-63 days after pollination (DAP), the fruits of P. equestris only began to resupinate a week before dehiscence (133 DAP) and ended a week after dehiscence (161 DAP). Discussion Our methods revealed that each orchid root and fruit had an independent direction and degree of torsion from the initial to the final position. Our innovative approaches produced detailed spatial and temporal information on the resupination of roots and fruits during orchid development.
Why it matches plant phenotyping methods3DマイクロCT、2D画像、画像処理パイプラインを用いてランの根・果実のねじれ運動を時空間的に抽出する手法が研究の中心であり、植物形態表現型の取得に該当する。
abstractWe utilized three-dimensional (3D) micro-computed tomography (CT) scans to capture the movement of fast-growing Erycina pusilla roots, and built an integrated bioinformatics pipeline to process 3D images into 3D time-lapse videos.
Reproduction assets foundThe paper's authors publicly released their custom 3D time-lapse pipeline code on GitHub and the micro-CT reconstruction data (young and mature E. pusilla plants) on Figshare, both explicitly cited in the Data Availability Statement.Code · publicThe scripts of the newly built 3D time‐lapse pipeline are available from GitHub ( https://github.com/LMVaskimo/3D-Lapse-Pipeline ).Open asset ↗LMVaskimo/3D-Lapse-Pipelinelines:238-443Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Abstract Deeper rooted crops are an avenue to increase plant water and nitrogen uptake under limiting conditions and increase long‐term soil carbon storage. Measuring rooting depth, however, is challenging due to the destructive, laborious, or imprecise methods that are currently available. Here, we present LEADER (Leaf Element Accumulation from DEep Roots) as a method to estimate in‐field root depth of maize plants. We use both X‐ray fluorescence (XRF) spectroscopy and ICP‐OES (inductively coupled plasma optical emission spectroscopy) to measure leaf elemental content and relate this to metrics of root depth. Principal components of leaf elemental content correlate with measures of root length in four genotypes ( R 2 = 0.8 for total root length), and we use linear discriminant analysis to classify plants as having different metrics related to root depth across four field sites in the United States. We can correctly classify the plots with the longest root length at depth (deeper than 30 or 40 cm) with high accuracy (accuracy >0.6) at two of our field sites (Hancock, WI and Rock Spring, PA). We also use strontium (Sr) as a tracer element in both greenhouse and field studies, showing that elemental accumulation of Sr in leaf tissue can be measured with XRF and can estimate root depth. We propose the adoption of LEADER as a tool for measuring root depth in different plant species and soils. LEADER is faster and easier than any other methods that currently exist and could allow for extensive study and understanding of deep rooting.
Why it matches plant phenotyping methods葉の元素をXRF等で測定し、根長・根深度という植物形質を非破壊推定するLEADERプラットフォームの開発であり、表現型取得法が研究の中心です。
abstractWe propose the adoption of LEADER as a tool for measuring root depth in different plant species and soils.
The vasculature along conifer needles is fundamentally different from that in angiosperm leaves as it contains a unique transfusion tissue inside the bundle sheath. In this study, we used specific tracers to identify the pathway of photoassimilates from mesophyll to phloem, and the opposing pathway of nutrients from xylem to mesophyll. For symplasmic transport we applied esculin to the tip of attached pine needles and followed its movement down the phloem. For apoplasmic transport we let detached needles take up a membrane-impermeable contrast agent and used micro-X-ray computed tomography to map critical water exchange interfaces and domain borders. Microscopy and segmentation of the X-ray data enabled us to render and quantify the functional 3D structure of the water-filled apoplasm and the complementary symplasmic domain. The transfusion tracheid system formed a sponge-like apoplasmic domain that was blocked at the bundle sheath. Transfusion parenchyma cell chains bridged this domain as tortuous symplasmic pathways with strong local anisotropy which, as evidenced by the accumulation of esculin, pointed to the phloem flanks as the preferred phloem-loading path. Simple estimates supported a pivotal role of the bundle sheath, showing that a bidirectional movement of nutrient ions and assimilates is feasible and emphasizing the role of the bundle sheath in nutrient and assimilate exchange.
Why it matches plant phenotyping methodsマイクロX線CT、画像セグメンテーション、3D構造の可視化・定量が、トレーサー輸送と針葉の機能構造解析の中心的手法であるため。
abstractused micro-X-ray computed tomography to map critical water exchange interfaces and domain borders
Current phenotyping technologies, whether based on cameras, LIDAR or hyperspectral imaging, are capturing mainly 3D surfaces and are limited, to penetrate into growth media and internal tissue and structures. 3D X-ray computed tomography (CT) alleviates many of these shortfalls by enabling a non-destructiv visualization of optically inaccessible plant structures, allowing for the 3D reconstruction and measurement of objects at high resolution and high throughput. We will present a range of fully automated, industrially validated 3D X-ray CT based technologies, that enable to visually and quantitively follow up in 4D the entire plant development cycle from flowers/ears to seeds to germinating seedling in filter paper to plants and root structures in soil. We will emphasize the non-destructive fully-automated 3D phenotyping of seeds and the resulting germinating seedlings including their internal organs in filter paper across time, i.e. in 4D, at a current throughput of 200 seeds/min and 25 seedlings/min, respectively. The presented technologies, being universally applicable across plant and crop species, allow for the quantitative, objective and reproducible assessment of morphological seed and seedling traits in 4D. They provide powerful tools to investigate any influence, whether genetic, environmental or treatment-related on seed quality and the germination capacity, vigor and 3D phenotype of the resulting seedling over large samples as big data. We will present the technologies and data on traits such as seed quality, seedling development, degree of abnormalities, germination capacity and vigor across different crop types.
Why it matches plant phenotyping methodsX線CTを用いた種子・幼植物の非破壊3D/4D形質計測技術の開発・自動化・高スループット化が中心であり、植物フェノタイピング手法として明確に適格です。
abstract3D X-ray computed tomography (CT) alleviates many of these shortfalls by enabling a non-destructiv visualization of optically inaccessible plant structures, allowing for the 3D reconstruction and measurement of objects at high resolution and high throughput.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Background : The use of 3D imaging techniques, such as X-ray CT, in root phenotyping has become more widespread in recent years. However, due to the complexity of root structure, analyzing the resulting 3D volumes to obtain detailed architectural traits of the root system remains a challenging computational problem. Two types of root features that are notably missing from existing computational image-based phenotyping methods are the whorls of a nodal root system and soil line in an excavated root crown. Knowledge of these features would give biologists deeper insights into the structure of nodal roots and the below- and above-ground root properties. Results : We developed TopoRoot+, a computational pipeline that computes architectural traits from 3D X-ray CT volumes of excavated maize root crowns. TopoRoot+ builds upon the TopoRoot software [1], which computes a skeleton representation of the root system and produces a suite of fine-grained traits including the number, geometry, connectivity, and hierarchy level of individual roots. TopoRoot+ adds new algorithms on top of TopoRoot to detect whorls, their associated nodal roots, and the soil line location. These algorithms offer a new set of traits related to whorls and soil lines, such as internode distances, root traits at every hierarchy level associated with a whorl, and aggregate root traits above or below the ground. TopoRoot+ is validated on a diverse collection of field-grown maize root crowns consisting of nine genotypes and spanning across three years, and it exhibits reasonable accuracy against manual measurements for both whorl and soil line detection. TopoRoot+ runs in minutes for a typical downsampled volume size of 400 3 on a desktop workstation. Our software and test dataset are freely distributed on Github. Conclusions : TopoRoot+ advances the state-of-the-art in image-based root phenotyping by offering more detailed architectural traits related to whorls and soil lines. The efficiency of TopoRoot+ makes it well-suited for high-throughput image-based root phenotyping.
Why it matches plant phenotyping methodsCT画像からトウモロコシ根系の形態形質を抽出する計算パイプラインを開発し、手動測定および多様な圃場試料で検証した、中心的な画像ベース植物フェノタイピング研究。
abstractWe developed TopoRoot+, a computational pipeline that computes architectural traits from 3D X-ray CT volumes of excavated maize root crowns.
BACKGROUND: Spike is the grain-bearing organ in cereal crops, which is a key proxy indicator determining the grain yield and quality. Machine learning methods for image analysis of spike-related phenotypic traits not only hold the promise for high-throughput estimating grain production and quality, but also lay the foundation for better dissection of the genetic basis for spike development. Barley (Hordeum vulgare L.) is one of the most important crops globally, ranking as the fourth largest cereal crop in terms of cultivated area and total yield. However, image analysis of spike-related traits in barley, especially based on CT-scanning, remains elusive at present. RESULTS: In this study, we developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet). Firstly, the spikes of 11 barley accessions, including 2 wild barley, 3 landraces and 6 cultivars were used for X-ray CT scanning to obtain the tomographic images. And then, an optimized 3D image processing method was used to point cloud data to generate the 3D point cloud images of spike, namely 'virtual' spike, which is then used to investigate internal structures and morphological traits of barley spikes. Furthermore, the virtual spike-related traits, such as spike length, grain number per spike, grain volume, grain surface area, grain length and grain width as well as grain thickness were efficiently and non-destructively quantified. The virtual values of these traits were highly consistent with the actual value using manual measurement, demonstrating the accuracy and reliability of the developed model. The reconstruction process took 15 min approximately, 10 min for CT scanning and 5 min for imaging and features extraction, respectively. CONCLUSIONS: This study provides an efficient, non-invasive and useful tool for dissecting barley spike architecture, which will contribute to high-throughput phenotyping and breeding for high yield in barley and other crops.
Why it matches plant phenotyping methodsX線CT、深層学習、3D画像処理を組み合わせ、オオムギ穂の形態・構造形質を定量化する高スループット表現型計測法を開発し、手動測定で検証しているため。
abstractwe developed a non-invasive, high-throughput approach to quantitatively measuring the multitude of spike architectural traits in barley through combining X-ray computed tomography (CT) and a deep learning model (UNet).
Reproduction assets foundThe authors explicitly state that all code and datasets for deep learning segmentation, prediction, and barley spike trait extraction are open-sourced on GitHub at the authors' repository, which is an allowed URL.Code · publicAll code and datasets pertaining to deep learning segmentation training, predicting and barley spike traits extraction is open-sourced on Github at https://github.com/zerosky010/CT_barley_spike_detection .Open asset ↗zerosky010/CT_barley_spike_detectionlines:160-268Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Walnuts are the second most produced and consumed tree nut, with over 2.6 million metric tons produced in the 2022-23 harvest cycle alone. The United States is the second largest producer, accounting for 25% of the total global supply. Nonetheless, producers face an ever-growing demand in a more uncertain climate landscape, which requires effective and efficient walnut selection and breeding of new cultivars with increased kernel content and easy-to-open shells. Past and current efforts select for these traits using hand-held calipers and eye-based evaluations. Yet there is plenty of morphology that meets the eye but goes unmeasured, such as the volume of inner air or the convexity of the kernel. Here, we study the shape of walnut fruits based on X-ray CT (Computed Tomography) 3D reconstructions. We compute 49 different morphological phenotypes for 1264 individuals comprising 149 accessions. These phenotypes are complemented by traits of breeding interest such as ease of kernel removal and kernel weight. Through allometric relationships —relative growth of one tissue to another—, we identify possible biophysical constraints at play during development. We explore multiple correlations between all morphological and commercial traits, and identify which morphological traits can explain the most variability of commercial traits. We show that using only volume and thickness-based traits, especially inner air content, we can successfully encode several of the commercial traits.
Why it matches plant phenotyping methodsクルミ果実を対象にX線CT 3D再構成から49種類の形態表現型を抽出し、育種関連形質との関係を評価しており、画像ベースの表現型取得・解析が研究の中心です。
abstractHere, we study the shape of walnut fruits based on X-ray CT (Computed Tomography) 3D reconstructions.
Cotton ( Gossypium hirsutum L.) seed morphological structure has a significant impact on the germination, growth and quality formation. However, the wide variation of cotton seed morphology makes it difficult to achieve quantitative analysis using traditional phenotype acquisition methods. In recent years, the application of micro-CT technology has made it possible to analyze the three-dimensional morphological structure of seeds, and has shown technical advantages in accurate identification of seed phenotypes. In this study, we reconstructed the seed morphological structure based on micro-CT technology, deep neural network Unet-3D model, and threshold segmentation methods, extracted 11 basics phenotypes traits, and constructed three new phenotype traits of seed coat specific surface area, seed coat thickness ratio and seed density ratio, using 102 cotton germplasm resources with clear year characteristics. Our results show that there is a significant positive correlation ( P P < 0.001). Comparison of changes in Chinese self-bred varieties showed that seed volume, seed surface area, seed coat volume, cavity volume and seed coat thickness increased by 11.39%, 10.10%, 18.67%, 115.76% and 7.95%, respectively, while seed kernel volume, seed kernel surface area and seed fullness decreased by 7.01%, 0.72% and 16.25%. Combining with the results of cluster analysis, during the hundred-year cultivation history of cotton in China, it showed that the specific surface area of seed structure decreased by 1.27%, the relative thickness of seed coat increased by 8.70%, and the compactness of seed structure increased by 50.17%. Furthermore, the new indicators developed based on micro-CT technology can fully consider the three-dimensional morphological structure and cross-sectional characteristics among the indicators and reflect technical advantages. In this study, we constructed a microscopic phenotype research system for cotton seeds, revealing the morphological changes of cotton seeds with the year in China and providing a theoretical basis for the quantitative analysis and evaluation of seed morphology.
Why it matches plant phenotyping methodsマイクロCT、3Dセグメンテーション、深層学習を用いて綿実の形態形質を抽出し、新規指標を開発した研究であり、表現型取得・解析法が中心である。
Walnuts are the second most produced and consumed tree nut, with over 2.6 million metric tons produced in the 2022-23 harvest cycle alone. The United States is the second largest producer, accounting for 25% of the total global supply. Nonetheless, producers face an ever-growing demand in a more uncertain climate landscape, which requires effective and efficient walnut selection and breeding of new cultivars with increased kernel content and easy-to-open shells. Past and current efforts select for these traits using hand-held calipers and eye-based evaluations. Yet there is plenty of morphology that meets the eye but goes unmeasured, such as the volume of inner air or the convexity of the kernel. Here, we study the shape of walnut fruits based on X-ray CT (Computed Tomography) 3D reconstructions. We compute 49 different morphological phenotypes for 1264 individuals comprising 149 accessions. These phenotypes are complemented by traits of breeding interest such as ease of kernel removal and kernel weight. Through allometric relationships —relative growth of one tissue to another—, we identify possible biophysical constraints at play during development. We explore multiple correlations between all morphological and commercial traits, and identify which morphological traits can explain the most variability of commercial traits. We show that using only volume and thickness-based traits, especially inner air content, we can successfully encode several of the commercial traits.
Why it matches plant phenotyping methodsX線CTによる3D再構成を用いて、クルミ個体から49種類の形態表現型を抽出することが研究の中心であり、育種関連形質との関連も評価している。
abstractHere, we study the shape of walnut fruits based on X-ray CT (Computed Tomography) 3D reconstructions.
Walnuts are the second most produced and consumed tree nut, with over 2.6 million metric tons produced in the 2022-23 harvest cycle alone. The United States is the second largest producer, accounting for 25% of the total global supply. Nonetheless, producers face an ever-growing demand in a more uncertain climate landscape, which requires effective and efficient walnut selection and breeding of new cultivars with increased kernel content and easy-to-open shells. Past and current efforts select for these traits using hand-held calipers and eye-based evaluations. Yet there is plenty of morphology that meets the eye but goes unmeasured, such as the volume of inner air or the convexity of the kernel. Here, we study the shape of walnut fruits based on X-ray CT (Computed Tomography) 3D reconstructions. We compute 49 different morphological phenotypes for 1264 individuals comprising 149 accessions. These phenotypes are complemented by traits of breeding interest such as ease of kernel removal and kernel weight. Through allometric relationships —relative growth of one tissue to another—, we identify possible biophysical constraints at play during development. We explore multiple correlations between all morphological and commercial traits, and identify which morphological traits can explain the most variability of commercial traits. We show that using only volume and thickness-based traits, especially inner air content, we can successfully encode several of the commercial traits.
Why it matches plant phenotyping methodsX線CTによる3D再構成を用いて多数のクルミ果実形態表現型を抽出しており、表現型取得・解析が研究の中心です。
abstractHere, we study the shape of walnut fruits based on X-ray CT (Computed Tomography) 3D reconstructions.
Understanding silique and seed morphology is essential to developmental biology. Arabidopsis thaliana is one of the best-studied plant models for understanding the genetic determinants of seed count and size. However, the small size of its seeds, and their encasement in a pod known as silique, makes investigating their numbers and morphology both time consuming and tedious. Researchers often report bulk seed weights as an indicator of average seed size, but this overlooks individual seed details. Removal of the seeds and subsequent image analysis is possible, but automated counts are often impossible due to seed pigmentation and shadowing. Traditional ways of analyzing seed count and size, without their removal from the silique, involve lengthy histological processing (24-48 h) and the use of toxic organic solvents. We developed a method that is non-invasive, requires minimal sample processing, and obtains data in a short period of time (1-2 h). This method uses a custom X-ray imaging system to visualize Arabidopsis siliques at different stages of their growth. We show that this process can be successfully used to analyze the overall topology of Arabidopsis siliques and seed size and count. This new method can be easily adapted for other plant models. Key features • No requirement for organic solvents for imaging siliques. • Easy image capture and rapid turnaround time for obtaining data. • Protocol may be easily adapted for other plant models.
Why it matches plant phenotyping methodsアラビドプシスの莢をX線で撮像し、種子のサイズ・数および莢の形態を非侵襲的に測定する新規画像法の開発であり、フェノタイピング手法が研究の中心である。
abstractWe developed a method that is non-invasive, requires minimal sample processing, and obtains data in a short period of time (1-2 h).
PoplarLaboratory / benchtopX-ray / CTLeafTissuePhysiological trait estimationWater status / transpiration
Plant hydraulic traits related to leaf drought tolerance, like the water potential at turgor loss point (TLP) and the water potential inducing 50% loss of hydraulic conductance (P50), are extremely useful to predict the potential impacts of drought on plants. While novel techniques have allowed the inclusion of TLP in studies targeting a large group of species, fast and reliable protocols to measure leaf P50 are still lacking. Recently, the optical method coupled with the gas injection (GI) technique has been proposed as a possibility to speed up the P50 estimation. Here, we present a comparison of leaf optical vulnerability curves (OVcs) measured in three woody species, namely Acer campestre (Ac), Ostrya carpinifolia (Oc) and Populus nigra (Pn), based on bench dehydration (BD) or GI of detached branches. For Pn, we also compared optical data with direct micro-computed tomography (micro-CT) imaging in both intact saplings and cut shoots subjected to BD. Based on the BD procedure, Ac, Oc and Pn had P50 values of -2.87, -2.47 and -2.11 MPa, respectively, while the GI procedure overestimated the leaf vulnerability (-2.68, -2.04 and -1.54 MPa for Ac, Oc and Pn, respectively). The overestimation was higher for Oc and Pn than for Ac, likely reflecting the species-specific vessel lengths. According to micro-CT observations performed on Pn, the leaf midrib showed none or very few embolized conduits at -1.2 MPa, consistent with the OVcs obtained with the BD procedure but at odds with that derived on the basis of GI. Overall, our data suggest that coupling the optical method with GI might not be a reliable technique to quantify leaf hydraulic vulnerability since it could be affected by the 'open-vessel' artifact. Accurate detection of xylem embolism in the leaf vein network should be based on BD, preferably of intact up-rooted plants.
Why it matches plant phenotyping methods葉の木部エンボリズム脆弱性を測定する光学法・ガス注入法を比較し、マイクロCTで検証しており、植物生理形質の取得法の技術評価が中心である。
abstractfast and reliable protocols to measure leaf P50 are still lacking.
Abstract Walnuts are the second most produced and consumed tree nut, with over 2.6 million metric tons produced in the 2022-23 harvest cycle alone. The United States is the second largest producer, accounting for 25% of the total global supply. Nonetheless, producers face an ever-growing demand in a more uncertain climate landscape, which requires effective and efficient walnut selection and breeding of new cultivars with increased kernel content and easy-to-open shells. Past and current efforts select for these traits using hand-held calipers and eye-based evaluations. Yet there is plenty of morphology that meets the eye but goes unmeasured, such as the volume of inner air or the convexity of the kernel. Here, we study the shape of walnut fruits based on X-ray CT (Computed Tomography) 3D reconstructions. We compute 49 different morphological phenotypes for 1264 individuals comprising 149 accessions. These phenotypes are complemented by traits of breeding interest such as ease of kernel removal and kernel weight. Through allometric relationships —relative growth of one tissue to another—, we identify possible biophysical constraints at play during development. We explore multiple correlations between all morphological and commercial traits, and identify which morphological traits can explain the most variability of commercial traits. We show that using only volume and thickness-based traits, especially inner air content, we can successfully encode several of the commercial traits. Core Ideas X-ray Computed Tomography (CT) imaging is used to compute a broad array of morpho-logical phenotypes in walnuts. These morphological traits suggest biophysical constraints at play during walnut development. Relative inner air, shell, and packing tissue volumes are significantly correlated to the rest of shape phenotypes. These volumes produce the best prediction models for traits of commercial interest such as shell strength. Inexpensive phenotyping platforms that focus solely on volume measurement would enable better walnut breeding.
Why it matches plant phenotyping methodsクルミ果実を対象にX線CT 3D画像から49種類の形態表現型を抽出する手法を中心に扱い、育種形質への応用・予測も評価しているため。
abstractHere, we study the shape of walnut fruits based on X-ray CT (Computed Tomography) 3D reconstructions. We compute 49 different morphological phenotypes for 1264 individuals comprising 149 accessions.
Here we present High-Throughput Tomography (HiTT), a fast and versatile phase-contrast imaging platform for life-science samples on the EMBL beamline P14 at DESY in Hamburg, Germany. We use a high photon flux undulator beamline to perform tomographic phase contrast acquisition in about two minutes which is linked to an automated data processing pipeline that delivers a 3D reconstructed data set less than a minute and a half after the completion of the X-ray scan. Combining this workflow with a sophisticated robotic sample changer enables the streamlined collection and reconstruction of X-ray imaging data from potentially hundreds of samples during a beamtime shift. HiTT permits optimal data collection for many different samples and makes possible the imaging of large sample cohorts thus allowing population studies to be attempted. We demonstrate the successful application of HiTT on various soft tissue samples in both liquid (hydrated and also dehydrated) and paraffin embedded preparations. Furthermore, we demonstrate the feasibility of HiTT to be used as a targeting tool for volume electron microscopy (vEM), as well as using HiTT to study plant morphology. We also show how the high throughput nature of the work has allowed large numbers of “identical” samples to be imaged to enable statistically relevant sample volumes to be studied. Synopsis We present HiTT – high throughput tomography – a propagation based phase contrast X-ray imaging technique which can visualise 1 mm 3 biological samples of various types at high resolution. The 3D reconstructions of the imaged volumes are calculated automatically once data collection is complete. The entire process from pressing start on data collection to viewing the final data takes less than 3 minutes. This speed in combination with the use of the automated sample changer to exchange the samples truly enables high throughput X-ray imaging for the first time.
Why it matches plant phenotyping methods高速・自動3D X線トモグラフィー基盤の開発と、植物形態の画像化への適用が中心であり、植物表現型計測手法として適格です。
abstractHere we present High-Throughput Tomography (HiTT), a fast and versatile phase-contrast imaging platform for life-science samples on the EMBL beamline P14 at DESY in Hamburg, Germany.
Synchrotron-based micro-X-ray fluorescence analysis (µXRF) is a nondestructive and highly sensitive technique. However, element mapping of rare earth elements (REEs) under standard conditions requires care, since energy-dispersive detectors are not able to differentiate accurately between REEs L-shell X-ray emission lines overlapping with K-shell X-ray emission lines of common transition elements of high concentrations. We aim to test REE element mapping with high-energy interference-free excitation of the REE K-lines on hyperaccumulator plant tissues and compare with measurements with REE L-shell excitation at the microprobe experiment of beamline P06 (PETRA III, DESY). A combination of compound refractive lens optics (CRLs) was used to obtain a micrometer-sized focused incident beam with an energy of 44 keV and an extra-thick silicon drift detector optimized for high-energy X-ray detection to detect the K-lines of yttrium (Y), lanthanum (La), cerium (Ce), praseodymium (Pr), and neodymium (Nd) without any interferences due to line overlaps. High-energy excitation from La to Nd in the hyperaccumulator organs was successful but compared to L-line excitation less efficient and therefore slow (∼10-fold slower than similar maps at lower incident energy) due to lower flux and detection efficiency. However, REE K-lines do not suffer significantly from self-absorption, which makes XRF tomography of millimeter-sized frozen-hydrated plant samples possible. The K-line excitation of REEs at the P06 CRL setup has scope for application in samples that are particularly prone to REE interfering elements, such as soil samples with high concomitant Ti, Cr, Fe, Mn, and Ni concentrations.
Why it matches plant phenotyping methods植物組織中の希土類元素分布を測定するµXRF法の技術開発・比較検証が中心であり、植物の元素蓄積状態を直接マッピングする方法論研究である。
abstractWe aim to test REE element mapping with high-energy interference-free excitation of the REE K-lines on hyperaccumulator plant tissues and compare with measurements with REE L-shell excitation
Field / plotLaboratory / benchtopX-ray / CTLeafRootTissueWhole plant / canopy / plot / field2D/3D reconstructionSegmentationVisualization / data management
Studies visualizing plant tissues and organs in three-dimension (3D) using micro-computed tomography (CT) published since approximately 2015 are reviewed. In this period, the number of publications in the field of plant sciences dealing with micro-CT has increased along with the development of high-performance lab-based micro-CT systems as well as the continuous development of cutting-edge technologies at synchrotron radiation facilities. The widespread use of commercially available lab-based micro-CT systems enabling phase-contrast imaging technique, which is suitable for the visualization of biological specimens composed of light elements, appears to have facilitated these studies. Unique features of the plant body, which are particularly utilized for the imaging of plant organs and tissues by micro-CT, are having functional air spaces and specialized cell walls, such as lignified ones. In this review, we briefly describe the basis of micro-CT technology first and then get down into details of its application in 3D visualization in plant sciences, which are categorized as follows: imaging of various organs, caryopses, seeds, other organs (reproductive organs, leaves, stems and petioles), various tissues (leaf venations, xylems, air-filled tissues, cell boundaries, cell walls), embolisms and root systems, hoping that wide users of microscopes and other imaging technologies will be interested also in micro-CT and obtain some hints for a deeper understanding of the structure of plant tissues and organs in 3D. Majority of the current morphological studies using micro-CT still appear to be at a qualitative level. Development of methodology for accurate 3D segmentation is needed for the transition of the studies from a qualitative level to a quantitative level in the future.
Why it matches plant phenotyping methods植物組織・器官の3D形態を取得するマイクロCT技術を中心に扱い、定量化に向けたセグメンテーション手法の必要性も論じる方法レビューである。
titleThree-dimensional visualization of plant tissues and organs by X-ray micro–computed tomography
RadishRiceX-ray / CTRootObject detection2D/3D reconstructionGrowth / time-series analysisVisualization / data managementRoot system architecture
Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent direct visualization. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We demonstrate that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. We also developed computational models to visualize the roots of root crops and monocotyledons, and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device’s groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground.
Why it matches plant phenotyping methods地下根系を対象とする分布型光ファイバーセンサーと計算モデルを開発し、根系フェノタイピングへの適用・比較検証まで行うことが中心であるため。
abstractwe demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development
Reproduction assets foundThe paper's custom MATLAB code for virtual root reconstruction from fiber-optic strain data is explicitly stated to be publicly available on the authors' GitHub repository (Fiber-RADGET). No separate public phenotype dataset deposit is mentioned; the supplementary movie is not a qualifying dataset URL.Code · publicThe
custom code for the virtual root reconstruction in MATLAB
(MathWorks, Massachusetts, USA) is available at
https://github.com/mtei1/Fiber-RADGET.git.Open asset ↗mtei1/Fiber-RADGETpdf-page:12 lines:1-24Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Abstract Characterizing dispersal structures is crucial for species identification and selecting diverse germplasm. This study aimed to investigate the morphology of Spondias tuberosa dispersion units and assess the efficacy of x-ray imaging in characterizing their internal morphology. X-ray imaging successfully revealed the internal structures, enabling the identification of filled, translucent, malformed, and empty seeds. The morphological analysis provided valuable insights into the dispersal units and presented a non-destructive and efficient method for future germplasm studies.
Why it matches plant phenotyping methodsウンブ樹の種子・散布単位の内部形態をX線画像で評価し、識別性能と非破壊的手法としての有効性を検討しており、植物形質取得法が中心である。
abstractassess the efficacy of x-ray imaging in characterizing their internal morphology
The potential applications of microCT scanning in the field of archaeobotany are only just beginning to be explored. The imaging technique can extract new archaeobotanical information from existing archaeobotanical collections as well as create new archaeobotanical assemblages within ancient ceramics and other artefact types. The technique could aid in answering archaeobotanical questions about the early histories of some of the world's most important food crops from geographical regions with amongst the poorest rates of archaeobotanical preservation and where ancient plant exploitation remains poorly understood. This paper reviews current uses of microCT imaging in the investigation of archaeobotanical questions, as well as in cognate fields of geosciences, geoarchaeology, botany and palaeobotany. The technique has to date been used in a small number of novel methodological studies to extract internal anatomical morphologies and three-dimensional quantitative data from a range of food crops, which includes sexually-propagated cereals and legumes, and asexually-propagated underground storage organs (USOs). The large three-dimensional, digital datasets produced by microCT scanning have been shown to aid in taxonomic identification of archaeobotanical specimens, as well as robustly assess domestication status. In the future, as scanning technology, computer processing power and data storage capacities continue to improve, the possible applications of microCT scanning to archaeobotanical studies will only increase with the development of machine and deep learning networks enabling the automation of analyses of large archaeobotanical assemblages.
Why it matches plant phenotyping methods植物試料の内部形態と三次元量的形質をmicroCTで取得する方法を扱うレビューであり、植物形質の計測・抽出法が中心です。
abstractThis paper reviews current uses of microCT imaging in the investigation of archaeobotanical questions, as well as in cognate fields of geosciences, geoarchaeology, botany and palaeobotany.
Background: The cereal spike is the main harvested plant organ determining the grain yield and quality, and its dissection provides the basis to estimate yield- and quality-related traits, such as grain number per spike and kernel weight. Phenotypic detection of spike architecture has potential for genetic improvement of yield and quality. However, manual collection and analysis of phenotypic data is laborious, time-consuming, low-throughput and destructive. Results We used a barley model to develop a non-invasive, high-throughput approach through combining X-ray computed tomography (CT) and deep learning model (UNet) to phenotype spike architectural traits. We used an optimized 3D image processing methods by point cloud for analyzing internal structure and quantifying morphological traits of barley spikes. The volume and surface area of grains per spike can be determined efficiently, which is hard to be measured manually. The UNet model was trained based on two types of spikes (wheat cultivar D3 and two-row barley variety S17350), and the best model accurately predicted grain characteristics from CT images. The spikes of ten barley varieties were analyzed and classified into three categories, namely wild barley, barley cultivars and barley landraces. The results showed that modern cultivated barley has shorter but thicker grains with larger volume and higher yield compared to wild barley. The X-ray CT reconstruction and phenotype extraction pipeline needed only 5 minutes per spike for imaging and traits extracting. Conclusions The combination of X-ray CT scans and a deep learning model could be a useful tool in breeding for high yield in cereal crops, and optimized 3D image processing methods could be valuable means of phenotypic traits calculation.
Why it matches plant phenotyping methodsX線CT、深層学習、3D画像処理を組み合わせ、オオムギ穂の内部形態・粒形質を非破壊かつ高スループットに抽出する手法を開発しており、フェノタイピング手法が研究の中心である。
abstractWe used a barley model to develop a non-invasive, high-throughput approach through combining X-ray computed tomography (CT) and deep learning model (UNet) to phenotype spike architectural traits.
Reproduction assets foundThe authors explicitly state that all code and datasets for deep learning segmentation, prediction, and barley spike trait extraction are open-sourced on GitHub at the allowed URL.Code · publicAvailability of data and materials: All code and datasets pertaining to deep learning segmentation training, predicting and barley spike traits extraction is open-sourced on Github at https://github.com/zerosky010/CT_detection_barley_spike_python.Open asset ↗zerosky010/CT_detection_barley_spike_pythonlines:99-131Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
MaizeX-ray / CTRootPhysiological trait estimation2D/3D reconstructionRoot system architectureStress response / toleranceWater status / transpiration
Root hairs, tubular protrusions of epidermal root cells, are considered a key rhizosphere feature: by substantially increasing the contact area between roots and soil, they enhance the ability of plants to capture soil resources. Hence, they are considered a breeding target for improving drought tolerance and yield stability of crops. While their pivotal role in the uptake of immobile nutrients such as phosphorus is well accepted, their effect on root water uptake remains controversial as it varies across plant species. By means of image-based modelling, our objective was to identify environmental conditions (e.g. soil water content) and hair traits (e.g. root hair length and density) that determine the effectiveness of root hairs in root water uptake. Furthermore, we investigated the effect of drought stress-induced root hair shrinkage on root water uptake.We scanned root compartments of 8 days old maize seedlings (Zea Mays L.) grown in a loamy soil using synchrotron radiation X-ray CT. Based on the collected image-data, we implemented a 3D root water uptake model. By solving Richards equation numerically, we computed the propagation of water potential gradients across the root-soil continuum which allowed to quantify root water uptake. The high spatial resolution of the acquired images enabled us to explicitly take rhizosphere features, such as root hairs and root-soil matrix contact into account. We determined the key parameters governing the effectiveness of root hairs in water uptake by comparing a set of six maize root compartments before and after digitally removing their hairs. The quantification of root hair turgor-loss in response to progressive soil drying allowed us to implement hair shrinkage within our model.We found that the effect of root hairs in root water uptake is governed by 1) the root hair induced increase in root soil contact and 2) root hair length. Furthermore, our results suggest that root hairs potentially facilitate root water uptake under dry soil conditions (
Why it matches plant phenotyping methods画像ベースの3DモデルとX線CT画像を中核に、根毛形質と根の水吸収を定量化しており、植物表現型の取得・推定手法が実質的に中心である。
abstractBy means of image-based modelling, our objective was to identify environmental conditions (e.g. soil water content) and hair traits (e.g. root hair length and density) that determine the effectiveness of root hairs in root water uptake.
MicroscopyX-ray / CTLeafTissueCalibration / preprocessingVisualization / data management
Abstract Leaves of the majority of plants contain calcium oxalate (CaOx) crystals or druses which often occur in spectacular distribution patterns. Numerous studies on CaOx in plant tissues across many different plant groups have been published, since it can be visualised readily under a light microscope (LM). However, there is surprisingly limited knowledge on the actual, precise distribution of CaOx in the leaves of quite ordinary plants such as common native and exotic trees. Traditional sample preparation for the documentation of the distribution of CaOx crystals in a given sample – including overall distribution – requires time‐consuming clearing procedures. Here we present a refined fast preparation method to visualise the overall CaOx complement in a sample: The plant material is ashed and the ash viewed under the polarising microscope. This is a rapid method which overcomes many shortcomings of other methods and permits the visualisation of the entire CaOx content in most leaf samples. Pros and cons in comparison with the conventional clearing technique are discussed. Further aspects for CaOx investigations by micro‐CT and scanning electron microscopy are discussed.
Why it matches plant phenotyping methods葉中のシュウ酸カルシウム結晶の分布を可視化する試料調製法を開発・改良し、従来法と比較しているため、植物形質取得法が中心である。
abstractHere we present a refined fast preparation method to visualise the overall CaOx complement in a sample: The plant material is ashed and the ash viewed under the polarising microscope.
Background The non-invasive 3D-imaging and successive 3D-segmentation of plant root systems has gained interest within fundamental plant research and selectively breeding resilient crops. Currently the state of the art consists of computed tomography (CT) scans and reconstruction followed by an adequate 3D-segmentation process. Challenge Generating an exact 3D-segmentation of the roots becomes challenging due to inhomogeneous soil composition, as well as high scale variance in the root structures themselves. Approach (1) We address the challenge by combining deep convolutional neural networks (DCNNs) with a weakly supervised learning paradigm. Furthermore, (2) we apply a spatial pyramid pooling (SPP) layer to cope with the scale variance of roots. (3) We generate a fine-tuned training data set with a specialized sub-labeling technique. (4) Finally, to yield fast and high-quality segmentations, we propose a specialized iterative inference algorithm, which locally adapts the field of view (FoV) for the network. Experiments We compare our segmentation results against an analytical reference algorithm for root segmentation ( RootForce ) on a set of roots from Cassava plants and show qualitatively that an increased amount of root voxels and root branches can be segmented. Results Our findings show that with the proposed DCNN approach combined with the dynamic inference, much more, and especially fine, root structures can be detected than with a classical analytical reference method. Conclusion We show that the application of the proposed DCNN approach leads to better and more robust root segmentation, especially for very small and thin roots.
Why it matches plant phenotyping methods植物根系の3D画像セグメンテーション手法を開発し、既存手法と比較検証しているため、根系形態の表現型取得が研究の中心です。
abstractWe address the challenge by combining deep convolutional neural networks (DCNNs) with a weakly supervised learning paradigm.
Internal structural parameters of rice seedling stems are of great significance for rice growth detection, rice selection, breeding, and damage examination. Aiming at the problems of non-repeatability and low detection accuracy in the existing plant internal structure phenotypic traits detection methods, this paper presents a non-destructive segmentation method for examining the internal structure of rice seedling stems based on deep learning. We use a standard X-ray CT imaging technology to obtain non-destructive tomographic images of rice seedling stems and then design a class attention-enhanced multi-scale segmentation model (MCC-Net), where UNet is used as the backbone network. Specifically, the proposed MCC-Net mainly consists of three core components: multi-scale convolutional block (MCB), coordinate spatial attention (CSA) module, and class attention enhancement (CAE) module. MCB is the main component of the encoder to improve the feature extraction ability of the model for regions of different sizes in the internal structure. CSA is embedded into the UNet skip connections to enhance the expression of effective features and automatically locate the regions with different structures of rice seedling stems. CAE is designed to calculate the dependencies between image pixels and categories, which can enhance the feature expression from the perspective of categories and correct the category errors in the segmentation results. The experimental results show that MIOU, average dice coefficient and average precision of our proposed MCC-Net model on the self-built rice seedling stem CT image dataset are 92.56%, 96.33% and 96.59% respectively. Compared with several state of the art models, the proposed model achieves better segmentation performance on the rice seedling stem CT image dataset.
Why it matches plant phenotyping methodsイネ幼苗茎内部構造のCT画像から表現型形質を抽出する深層学習セグメンテーション手法を開発・評価しており、方法が研究の中心である。
abstractthis paper presents a non-destructive segmentation method for examining the internal structure of rice seedling stems based on deep learning.
Xylella fastidiosa is a xylem‐limited plant pathogen infecting many crops globally and is the cause of the recent olive disease epidemic in Italy. One strategy proposed to mitigate losses is to replant susceptible crops with resistant varieties. Several genetic, biochemical and biophysical traits are associated to X. fastidiosa disease resistance. However, mechanisms underpinning resistance are poorly understood. We hypothesize that the susceptibility of olive cultivars to infection will correlate to xylem vessel diameters, with narrower vessels being resistant to air embolisms and having slower flow rates limiting pathogen spread. To test this, we scanned stems from four olive cultivars of varying susceptibility to X. fastidiosa using X‐ray computed tomography. Scans were processed by a bespoke methodology that segmented vessels, facilitating diameter measurements. Though significant differences were not found comparing stem‐average vessel section diameters among cultivars, they were found when comparing diameter distributions. Moreover, the measurements indicated that although vessel diameter distributions may play a role regarding the resistance of Leccino, it is unlikely they do for FS17. Considering Young–Laplace and Hagen–Poiseuille equations, we inferred differences in embolism susceptibility and hydraulic conductivity of the vasculature. Our results suggest susceptible cultivars, having a greater proportion of larger vessels, are more vulnerable to air embolisms. In addition, results suggest that under certain pressure conditions, functional vasculature in susceptible cultivars could be subject to greater stresses than in resistant cultivars. These results support investigation into xylem morphological screening to help inform olive replanting. Furthermore, our framework could test the relevance of xylem geometry to disease resistance in other crops.
Why it matches plant phenotyping methodsX線CT画像から木部道管をセグメンテーションし、直径分布を測定する手法が研究の中心であり、病害抵抗性に関わる植物形態形質の取得・解析を実施している。
abstractScans were processed by a bespoke methodology that segmented vessels, facilitating diameter measurements.
Floral appendages display an array of shapes and sizes. Among these organs, staminodes are morphologically diverse structures that have lost the ability to produce pollen, but in some instances, they produce fertile pollen grains. In the family Cactaceae staminodes are uncommon and range from simple linear to flat to spatulate structures, but studies describing their structural attributes are scanty. This study highlights the advantages of synchrotron radiation for sample preparation and as a research tool for plant biology. It describes the internal morphology of floral parts, particularly stamen, tepal, and staminode in the Plains Prickly Pear Cactus, Opuntia polyacantha, using synchrotron radiation micro-computed tomography (SR-μCT). It also shows the different anatomical features in reconstructed three-dimensional imaging of reproductive parts and discuss the advantages of the segmentation method to detect and characterize the configuration and intricate patterns of vascular networks and associated structures of tepal and androecial parts applying SR-μCT. This powerful technology led to substantial improvements in terms of resolution allowing a more comprehensive understanding of the anatomical organization underlying the vasculature of floral parts and inception of staminodes in O. polyacantha. Tepal and androecial parts have uniseriate epidermis enclosing loose mesophyll with mucilage secretory ducts, lumen, and scattered vascular bundles. Cryptic underlying structural attributes provide evidence of a vascularized pseudo-anther conjoint with tepals. The undefined contours of staminodial appendages (pseudo-anther) amalgamated to the tepals' blurred boundaries suggest that staminodes originate from tepals, a developmental pattern supporting the fading border model of floral organ identity for angiosperms.
Why it matches plant phenotyping methodsSR-μCTと3次元再構成・セグメンテーションを中心に、花器官の内部形態と維管束構造を取得・可視化しており、植物形態計測法の実質的な適用研究である。
abstractThis study highlights the advantages of synchrotron radiation for sample preparation and as a research tool for plant biology.
Seeds play a critical role in ensuring food security for the earth's 8 billion people. There is great biodiversity in plant seed content traits worldwide. Consequently, the development of robust, rapid, and high-throughput methods is required for seed quality evaluation and acceleration of crop improvement. There has been considerable progress in the past 20 years in various non-destructive methods to uncover and understand plant seed phenomics. This review highlights recent advances in non-destructive seed phenomics techniques, including Fourier Transform near infrared (FT-NIR), Dispersive-Diode Array (DA-NIR), Single-Kernel (SKNIR), Micro-Electromechanical Systems (MEMS-NIR) spectroscopy, Hyperspectral Imaging (HSI), and Micro-Computed Tomography Imaging (micro-CT). The potential applications of NIR spectroscopy are expected to continue to rise as more seed researchers, breeders, and growers successfully adopt it as a powerful non-destructive method for seed quality phenomics. It will also discuss the advantages and limitations that need to be solved for each technique and how each method could help breeders and industry with trait identification, measurement, classification, and screening or sorting of seed nutritive traits. Finally, this review will focus on the future outlook for promoting and accelerating crop improvement and sustainability.
Why it matches plant phenotyping methods種子の非破壊的な表現型計測手法を中心に、複数の分光・画像技術の利点、限界、形質測定への応用をレビューしており、方法論が中心である。
abstractThis review highlights recent advances in non-destructive seed phenomics techniques, including Fourier Transform near infrared (FT-NIR), Dispersive-Diode Array (DA-NIR), Single-Kernel (SKNIR), Micro-Electromechanical Systems (MEMS-NIR) spectroscopy, Hyperspectral Imaging (HSI), and Micro-Computed Tomography Imaging (micro-CT).
Rice (Oryza sativa) is a staple food for over half of the global population. The rice grain has been targeted for biofortification to enhance its nutritional value and to prevent the accumulation of toxic elements. Therefore, understanding the regulatory mechanisms that control the amount and spatial distribution of essential and toxic elements in the seeds is essential. Several analytical techniques have been developed for in situ imaging of elements in plants, including histochemical staining, metal-sensitive fluorescent probes, and mass spectrometry or X-ray spectroscopy-based techniques (Conn and Gilliham, 2010; Lombi et al., 2011; Zhao et al., 2014; Kopittke et al., 2018). The method often depends on the questions being asked and the required level of throughput, sensitivity, and resolution. Inductively Coupled Plasma—Mass Spectrometry (ICP-MS), for instance, is an analytical technique that provides a snapshot of the bulk elemental status of a sample (Salt et al., 2008). ICP-MS has been used for high-throughput elemental analysis of Arabidopsis (Arabidopsis thaliana) seeds from a diverse set of natural accessions, identifying several accessions with interesting extreme elemental phenotypes (Campos et al., 2021). In addition to being destructive, ICP-MS also lacks the visual component and spatial detail, whereas elemental imaging enables associating the distribution of an element with its functions or alteration of these functions. High-throughput elemental imaging techniques that can non-destructively screen and quantify elemental distribution patterns in seeds are necessary. X-ray fluorescence microscopy (XRF) is an in vivo non-destructive imaging technique that allows quantitative mapping of elemental distribution in plants (Punshon et al., 2009; Donner et al., 2013; Kopittke et al., 2018). It has become a favorite method to quantify metals in seeds of hyperaccumulator plants (van der Ent et al., 2022), Arabidopsis (Kim et al., 2019), and rice (Takahashi et al., 2009). However, XRF-based techniques are often not synonymous with high-throughput. For instance, a previous study reported an acquisition time of 7.5 h for an elemental map of a longitudinal grain section with a step size of 25 µm (Lombi et al., 2009). The development of an advanced Maia fluorescence detector system that enables faster acquisition time has advanced the high-throughput of XRF-based techniques and opened-up new possibilities (Ryan et al., 2010). In this issue of Plant Physiology, Ren et al., (2022) present the use of synchrotron-based fast XRF (µ-XRF) as a high-throughput, non-destructive phenotyping tool for elemental distribution in rice seeds. The authors reported a scan time of ∼39 h for 4,190 mutagenized seeds at 30 µm resolution (average rice grain size from the varieties in the study was ∼5 mm). They quantified the concentration and elemental distribution in the endosperm, embryo, and aleurone layer of 4,190 EMS-mutagenized rice seeds and a panel of 533 diverse rice accessions (Figure 1A). They also used a laboratory-based µ-XRF system to image grain half-section for detail on the aleurone layer and presented a script to semi-automate the extraction of elemental data from the µ-XRF images using ImageJ. µ-XRF-based high-throughput elemental imaging of rice seeds. A, A representative image of a batch of rice seeds prepared for analysis and a µ-XRF image of the general elemental concentration in the seeds. B, A µ-XRF elemental map of copper (Cu) in the rice mutant oshma4 known for having increased Cu levels in the embryo and endosperm. OVT, ovular vascular trace. Modified from Ren et al. (2022). The authors first tested the pipeline on three previously characterized rice mutants: heavy metal atpase 2 (oshma2) (low Zn), metal transporter natural resistance-associated macrophage proteins (osnramp5) (low Mn), and heavy metal atpase 4 (oshma4) (high Cu). The µ-XRF images supported the known elemental phenotypes and provided spatial details on differences in their distribution in the seed. For instance, in oshma4 the reported higher Cu levels corresponded to significantly increased levels (172%–190%) in the embryo and endosperm of the mutant (Figure 1B), raising many interesting questions—Does the endosperm become the dumping site when there is excess Cu? Is this pattern due to defective nutrient transport? What is the functional relevance of the redistribution? Next, to screen for potential mutants with unusual elemental phenotypes, the authors performed a principle component analysis (PCA) on the µ-XRF elemental data of the two seed libraries. Among the EMS-mutagenized rice seeds, they identified individuals with outlier seed shapes (107) and elemental phenotypes (692). From the natural rice accessions, they distinguished individuals with strong elemental differences in the whole seed as well as selected tissues (embryo, aleurone layer, or endosperm). They found strong and expected correlations between the chemical analogs K and Rb, or Ca and Sr. They also reported strong associations between the elements Mn, Fe, Cu, and Zn in the seed. Interestingly, a similar pattern has been reported for these elements in Arabidopsis seeds (Campos et al., 2021), which shows a recurrent pattern across species. The results suggest these elements behave in an inter-dependent manner, and studying them in combination may uncover potential shared transport pathways or proteins that regulate their spatial distribution. The authors also performed a genome-wide association study on a sub-population of the natural rice accessions using seed shape and the spatial elemental details of the seeds from the µ-XRF analysis. They identified 11 significant SNPs linked to seed shape and 42 loci associated with elemental accumulation in the embryo (11 loci), endosperm (18), and aleurone layer (13). These findings constitute an interesting starting point for functional studies on elemental distribution in the seed and their substructures. This work demonstrates that synchrotron-based fast X-ray fluorescence microscopy (µ-XRF) can be an effective and powerful elemental screening tool for seeds. µ-XRF can be used to detect toxic metals (arsenic, silicon, or cadmium) in grains, study effects of grain polishing or test heritability of nutrient traits for breeding. The synchrotron-based imaging set-up is often part of advanced research facilities available at select locations in the world. Routine screening would be reliant on beamtime availability and the high costs of synchrotron light. The advanced detectors greatly increase the speed of data acquisition and the feasibility of the method for screening. With continued progress in the beam sources and speed of acquisition, there is even scope for in situ time-resolved imaging in the future. Access to synchrotron-based systems could limit researchers, but the lab-based XRF used in this study could present an alternate, intermediate accessible set-up that enables spatial details with niche applications. We may foresee that rice grains may not be the last agronomically important seed to be screened by µ-XRF!
Why it matches plant phenotyping methods米種子の元素分布をµ-XRFで高スループットに画像化・定量するフェノタイピング手法と、ImageJによるデータ抽出スクリプトを中心に扱っているため。
abstractsynchrotron-based fast XRF (µ-XRF) as a high-throughput, non-destructive phenotyping tool for elemental distribution in rice seeds
The concentration, chemical speciation, and spatial distribution of essential and toxic mineral elements in cereal seeds have important implications for human health. To identify genes responsible for element uptake, translocation, and storage, high-throughput phenotyping methods are needed to visualize element distribution and concentration in seeds. Here, we used X-ray fluorescence microscopy (μ-XRF) as a method for rapid and high-throughput phenotyping of seed libraries and developed an ImageJ-based pipeline to analyze the spatial distribution of elements. Using this method, we nondestructively scanned 4,190 ethyl methanesulfonate (EMS)-mutagenized M1 rice (Oryza sativa) seeds and 533 diverse rice accessions in a genome-wide association study (GWAS) panel to simultaneously measure concentrations and spatial distribution of elements in the embryo, endosperm, and aleurone layer. A total of 692 putative mutants and 65 loci associated with the spatial distribution of elements in rice seed were identified. This powerful method provides a basis for investigating the genetics and molecular mechanisms controlling the accumulation and spatial variations of mineral elements in plant seeds.
Why it matches plant phenotyping methodsμ-XRFによる種子内元素分布の高速・高スループット表現型計測法を開発し、ImageJ解析パイプラインも構築して大規模集団に適用しているため、フェノタイピング手法が中心である。
abstracthigh-throughput phenotyping methods are needed to visualize element distribution and concentration in seeds
Introduction Plant image datasets have the potential to greatly improve our understanding of the phenotypic response of plants to environmental and genetic factors. However, manual data extraction from such datasets are known to be time-consuming and resource intensive. Therefore, the development of efficient and reliable machine learning methods for extracting phenotype data from plant imagery is crucial. Methods In this paper, a current gold standard computed vision method for detecting and segmenting objects in three-dimensional imagery (StartDist-3D) is applied to X-ray micro-computed tomography scans of oilseed rape ( Brassica napus ) mature pods. Results With a relatively minimal training effort, this fine-tuned StarDist-3D model accurately detected (Validation F1-score = 96.3%,Testing F1-score = 99.3%) and predicted the shape (mean matched score = 90%) of seeds. Discussion This method then allowed rapid extraction of data on the number, size, shape, seed spacing and seed location in specific valves that can be integrated into models of plant development or crop yield. Additionally, the fine-tuned StarDist-3D provides an efficient way to create a dataset of segmented images of individual seeds that could be used to further explore the factors affecting seed development, abortion and maturation synchrony within the pod. There is also potential for the fine-tuned Stardist-3D method to be applied to imagery of seeds from other plant species, as well as imagery of similarly shaped plant structures such as beans or wheat grains, provided the structures targeted for detection and segmentation can be described as star-convex polygons.
Why it matches plant phenotyping methods植物のX線マイクロCT画像から種子を検出・分割し、個数・サイズ・形状・間隔・位置を抽出する機械学習手法の開発と検証が中心であるため。
abstractthe development of efficient and reliable machine learning methods for extracting phenotype data from plant imagery is crucial.
Wheat ( Triticum aestivum L.) is one of the most important crops as it provides 20% of calories and proteins to the human population. To overcome the increasing demand in wheat grain production, there is a need for a higher grain yield, and this can be achieved in particular through an increase in the grain weight. Moreover, grain shape is an important trait regarding the milling performance. Both the final grain weight and shape would benefit from a comprehensive knowledge of the morphological and anatomical determinism of wheat grain growth. Synchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages. Coupled with 3D reconstruction, this method revealed changes in the grain shape and new cellular features. The study focused on a particular tissue, the pericarp, which has been hypothesized to be involved in the control of grain development. We showed considerable spatio-temporal diversity in cell shape and orientations, and in tissue porosity associated with stomata detection. These results highlight the growth-related features rarely studied in cereal grains, which may contribute significantly to the final grain weight and shape.
Why it matches plant phenotyping methodsシンクロトロンX線マイクロCTと3D再構成を中核に、発達中コムギ粒の3D形状・細胞形態・組織空隙を抽出しており、植物器官の形態表現型取得が中心である。
abstractSynchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe development was integrated into the Imago software, which is
freely available at https://github.com/SciCompJ/Imago (accessed on 21 February 2023).Open asset ↗SciCompJ/Imagopdf-page:23 lines:1-59Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
To initiate its development into a plant, a small dark-grown seedling (prior to its emergence from the ground) must penetrate through the growth media. The path that the seedling takes during this journey has yet to be explained. As such, we conducted non-destructive tests using CT scans to observe the growth of dark-grown seedlings in soil over time; we also developed a model to simulate the dynamics of an emerging seedling, and to examine effects of various growth medium conditions, including Lunar soil. It was previously postulated that, with gravitropism in a terrestrial growth medium, a dark-grown seedling would grow directly upright. However, our CT scan results showed that dark-grown soybean seedlings departed from the vertical path in soil, as far as a lateral distance of approximately 10 mm. The phenomenon of the non-straight path was also demonstrated by the model results. Through simulations, we found that an emerging seedling naturally weaves through the particles of growth medium, in search for the path of least resistance. As a result, the seedling ends up travelling a longer distance. Compared with a seedling that was artificially forced to take a straight path in a growth media, the seedling taking the natural path encountered significantly lower resistances (20% lower) from the growth medium, while travelled 12% longer distance during the emergence process. A seedling encountered a much higher impedance in Lunar soil. Our results suggest that taking the path of least resistance, in addition to shaping and orientating itself for mechanical advantage, are strategies evolved by plant species that have contributed to its vast success. An understanding of plant behavior and survival strategies on Earth lay the foundation for future research in agriculture in novel environments, including on celestial bodies.
Why it matches plant phenotyping methodsCTスキャンを用いて土壌中の幼植物の成長経路を非破壊・経時的に観察し、モデルで経路や抵抗を解析しており、植物形態・成長状態の取得が研究の中心的手法となっている。
abstractwe conducted non-destructive tests using CT scans to observe the growth of dark-grown seedlings in soil over time
Leaves grown at different light intensities exhibit considerable differences in physiology, morphology and anatomy. Because plant leaves develop over three dimensions, analyses of the leaf structure should account for differences in lengths, surfaces, as well as volumes. In this manuscript, we set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components. This allowed us to estimate the contribution of each component to S m,LA , a whole-leaf trait known to link structure and function. We introduce the novel concept of a 'stomatal vaporshed,' i.e. the intercellular airspace unit most closely connected to a single stoma, and use it to describe the stomata-to-diffusive-surface pathway. To illustrate our new theoretical framework, we grew two cultivars of Vitis vinifera L. under high and low light, imaged 3D leaf anatomy using microcomputed tomography (microCT) and measured leaf gas exchange. Leaves grown under high light were less porous and thicker. Our analysis showed that these two traits and the lower S m per mesophyll cell volume ( S m,Vcl ) in sun leaves could almost completely explain the difference in S m,LA . Further, the studied cultivars exhibited different responses in carbon assimilation per photosynthesizing cell volume ( A Vcl ). While Cabernet Sauvignon maintained A Vcl constant between sun and shade leaves, it was lower in Blaufränkisch sun leaves. This difference may be related to genotype-specific strategies in building the stomata-to-diffusive-surface pathway.
Why it matches plant phenotyping methods3D葉解剖をmicroCTで画像化し、葉の拡散面積関連形質を分解・推定する新しい理論枠組みを提示しており、表現型取得・解析法が研究の中心である。
abstractwe set out to disentangle the mesophyll surface area available for diffusion per leaf area ( S m,LA ) into underlying one-, two- and three-dimensional components.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits all raw and segmented microCT imaging data plus extracted trait data on Zenodo, and the vaporshed-extraction analysis code in the public leaf-traits-microct GitHub repository. Both are paper-specific, public, and actionable.Dataset · publicAll imaging data (raw microCT scans and segmented scans) and data extracted from those images are available on Zenodo ( https://doi.org/10.5281/zenodo.5994663 ).Open asset ↗Zenodo · 10.5281/zenodo.5994663lines:219-265Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 7 Sept 2026
Passion fruit is a tropical liana of the Passiflora family that is commonly planted throughout the world due to its abundance of nutrients and industrial value. Researchers are committed to exploring the relationship between phenotype and genotype to promote the improvement of passion fruit varieties. However, the traditional manual phenotyping methods have shortcomings in accuracy, objectivity, and measurement efficiency when obtaining large quantities of personal data on passion fruit, especially internal organization data. This study selected samples of passion fruit from three widely grown cultivars, which differed significantly in fruit shape, size, and other morphological traits. A Micro-CT system was developed to perform fully automated nondestructive imaging of the samples to obtain 3D models of passion fruit. A designed label generation method and segmentation method based on U-Net model were used to distinguish different tissues in the samples. Finally, fourteen traits, including fruit volume, surface area, length and width, sarcocarp volume, pericarp thickness, and traits of fruit type, were automatically calculated. The experimental results show that the segmentation accuracy of the deep learning model reaches more than 0.95. Compared with the manual measurements, the mean absolute percentage error of the fruit width and length measurements by the Micro-CT system was 1.94% and 2.89%, respectively, and the squares of the correlation coefficients were 0.96 and 0.93. It shows that the measurement accuracy of external traits of passion fruit is comparable to manual operations, and the measurement of internal traits is more reliable because of the nondestructive characteristics of our method. According to the statistical data of the whole samples, the Pearson analysis method was used, and the results indicated specific correlations among fourteen phenotypic traits of passion fruit. At the same time, the results of the principal component analysis illustrated that the comprehensive quality of passion fruit could be scored using this method, which will help to screen for high-quality passion fruit samples with large sizes and high sarcocarp content. The results of this study will firstly provide a nondestructive method for more accurate and efficient automatic acquisition of comprehensive phenotypic traits of passion fruit and have the potential to be extended to more fruit crops. The preliminary study of the correlation between the characteristics of passion fruit can also provide a particular reference value for molecular breeding and comprehensive quality evaluation.
Why it matches plant phenotyping methodsX線マイクロCTと深層学習による果実の3D形質取得・組織分割・自動測定システムを開発し、手動測定との精度検証も行っており、植物フェノタイピング手法が研究の中心である。
abstractA Micro-CT system was developed to perform fully automated nondestructive imaging of the samples to obtain 3D models of passion fruit.
X-ray / CTRootMorphology / geometry measurementObject detection2D/3D reconstructionRoot system architecture
Currently, the three-dimensional detection of plant root structure is one of the core issues in studies on plant root phenotype. Manual measurement methods are not only cumbersome but also have poor reliability and damage the root. Among many solutions, X-ray computed tomography (X-ray CT) can help us observe the plant root structure in a three-dimensional and non-destructive form under the condition of underground soil in situ. Therefore, this paper proposes a high-throughput method and process for plant three-dimensional root phenotype and reconstruction based on X-ray CT technology. Firstly, this paper proposes a high-throughput transmission for the root phenotyping and utilizing the imaging technique to extract the root characteristics; then, the study adopts a moving cube algorithm to reconstruct the 3D (three-dimensional) root. Finally, this research simulates the proposed algorithm, and the simulation results show that the presented method in this paper works well.
Why it matches plant phenotyping methodsX線CT画像から根の形質を抽出し、3D再構成する高スループット手法と処理フローの開発が主題であり、植物フェノタイピング手法が中心である。
abstractthis paper proposes a high-throughput method and process for plant three-dimensional root phenotype and reconstruction based on X-ray CT technology.
RiceLaboratory / benchtopX-ray / CTRootImage / point-cloud registrationGrowth / time-series analysisTrackingGrowth / development / phenologyRoot system architecture
Background Root system architecture (RSA) is an essential characteristic for efficient water and nutrient absorption in terrestrial plants; its plasticity enables plants to respond to different soil environments. Better understanding of root plasticity is important in developing stress-tolerant crops. Non-invasive techniques that can measure roots in soils nondestructively, such as X-ray computed tomography (CT), are useful to evaluate RSA plasticity. However, although RSA plasticity can be measured by tracking individual root growth, only a few methods are available for tracking individual roots from time-series three-dimensional (3D) images. Results We developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps. The first step involves 3D alignment of the time-series RSA images by iterative closest point registration with point clouds generated by high-intensity particles in potted soils. This alignment ensures that the time-series RSA images overlap. The second step consists of backward prediction of vectorization, which is based on the phenomenon that the root length of the RSA vector at the earlier time point is shorter than that at the last time point. In other words, when CT scanning is performed at time point A and again at time point B for the same pot, the CT data and RSA vectors at time points A and B will almost overlap, but not where the roots have grown. We assumed that given a manually created RSA vector at the last time point of the time series, all RSA vectors except those at the last time point could be automatically predicted by referring to the corresponding RSA images. Using 21 time-series CT volumes of a potted plant of upland rice (Oryza sativa), this workflow revealed that the root elongation speed increased with age. Compared with a workflow that does not use backward prediction, the workflow with backward prediction reduced the manual labor time by 95%. Conclusions We developed a workflow to efficiently generate time-series RSA vectors from time-series X-ray CT volumes. We named this workflow 'RSAtrace4D' and are confident that it can be applied to the time-series analysis of RSA development and plasticity.
Why it matches plant phenotyping methods根系形態を時系列X線CT画像から抽出・追跡する半自動ワークフローを開発しており、植物フェノタイピング手法が研究の中心である。
abstractWe developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps.
Reproduction assets foundThe paper's authors publicly released RSAtrace4D, the software implementing the backward-prediction workflow for time-series X-ray CT root system architecture analysis, on GitHub, and state that the datasets used are available via their GitHub account and project homepage.Code · publicThe implementation of this workflow, which is specified for rice, was named RSAtrace4D and is available at the GitHub repository ( https://github.com/st707311g/RSAtrace4D ).Open asset ↗st707311g/RSAtrace4Dlines:116-124Dataset · publicThe datasets used in this study are available at the GitHub repository ( https://github.com/st707311g/ ) and the project homepage ( https://rootomics.dna.affrc.go.jp/en/ ).Open asset ↗st707311glines:136-191Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
With the completion of the coconut gene map and the gradual improvement of related molecular biology tools, molecular marker-assisted breeding of coconut has become the next focus of coconut breeding, and accurate coconut phenotypic traits measurement will provide technical support for screening and identifying the correspondence between genotype and phenotype. A Micro-CT system was developed to measure coconut fruits and seeds automatically and nondestructively to acquire the 3D model and phenotyping traits. A deeplabv3+ model with an Xception backbone was used to segment the sectional image of coconut fruits and seeds automatically. Compared with the structural-light system measurement, the mean absolute percentage error of the fruit volume and surface area measurements by the Micro-CT system was 1.87% and 2.24%, respectively, and the squares of the correlation coefficients were 0.977 and 0.964, respectively. In addition, compared with the manual measurements, the mean absolute percentage error of the automatic copra weight and total biomass measurements was 8.85% and 25.19%, respectively, and the adjusted squares of the correlation coefficients were 0.922 and 0.721, respectively. The Micro-CT system can nondestructively obtain up to 21 agronomic traits and 57 digital traits precisely.
Why it matches plant phenotyping methodsMicro-CTとDeepLabV3+によるココナッツ果実・種子の非破壊的な3D形質取得システムを開発し、他の測定法および手動測定と比較検証しているため、植物フェノタイピング手法が中心である。
abstractA Micro-CT system was developed to measure coconut fruits and seeds automatically and nondestructively to acquire the 3D model and phenotyping traits.
Flowers are intricate and integrated three-dimensional (3D) structures predominantly studied in 2D due to the difficulty in quantitatively characterising their morphology in 3D. Given the recent development of analytical methods for high-dimensional data, the reconstruction of flower models in three dimensions represents the limiting factor to studying flowers in 3D. We developed a floral photogrammetry protocol to reconstruct 3D models of flowers based on images taken with a digital single-lens reflex camera, a turntable and a portable lightbox. We demonstrate that photogrammetry allows a rapid and accurate reconstruction of 3D models of flowers from 2D images. It can reconstruct all visible parts of flowers and has the advantage of keeping colour information. We illustrated its use by studying the shape and colour of 18 Gesneriaceae species. Photogrammetry is an affordable alternative to micro-computed tomography (micro-CT) that requires minimal investment and equipment, allowing it to be used directly in the field. It has the potential to stimulate research on the evolution and ecology of flowers by providing a simple way to access 3D morphological data from a variety of flower types.
Why it matches plant phenotyping methods花の3D形態と色を画像から再構成するフォトグラメトリ法を開発し、精度評価と実例適用を行っており、植物形質取得が研究の中心である。
abstractWe developed a floral photogrammetry protocol to reconstruct 3D models of flowers based on images taken with a digital single-lens reflex camera, a turntable and a portable lightbox.
(1) Objective: The opacity of soils complicates studies of root infection. An example of this is the infection of Armillaria solidipes on poplar (Populus davidiana × Populus alba var. pyramidalis Louche) roots systems, which risks damaging trees. (2) Methods: Only one of the four tested substrates for tree species was shown to be suitable to perform X-ray computed tomography (CT). Three-dimensional (3D) imaging was used to reconstruct the root system of poplar seedlings and the changes caused by the infection. (3) Results: We developed a protocol to efficiently grow poplar on a synthetic matrix, vermiculite, that allows for monitoring the root system by X-ray CT. Poplar 3D reconstruction of the root system was automated using the software Win-RHIZO, and various infection parameters were identified. (4) Conclusions: Our procedure allows for monitoring the infection of root systems and provides new opportunities to characterize the complex Armillaria solidipes poplar interaction using X-ray CT.
Why it matches plant phenotyping methodsポプラ根系の感染状態をX線CTと3D再構成で取得・定量するプロトコルを開発しており、植物フェノタイピング手法が研究の中心である。
abstractPoplar 3D reconstruction of the root system was automated using the software Win-RHIZO, and various infection parameters were identified.
X-ray / CTLeafRootSegmentationRoot system architecture
Most current phenotype plant research focuses primarily on above-ground traits, like leaves and flowers. Roots often get comparatively less attention because they are challenging to examine and image. Minirhizotron (MR) systems are one of the imaging approaches to studying plant roots underground. In MR systems, a tube is inserted into the ground to allow a camera to be inserted to capture the images of root systems. Unlike minirhizotron imaging, X-ray computed tomography (CT) captures the three-dimensional (3D) information of soil cores extracted from the soil. For a better analysis of roots, the first step is always to segment the roots from the background in the images or image sequences. The results of root segmentation play an essential role in further analysis like root diameter and length estimation. Current fully-supervised segmentation methods mainly use pixel/point-level annotated labels, which require much manual effort and time. In this work, we propose a weakly supervised root segmentation approach with graph convolutional networks. Our model only requires image-level annotations to segment roots from the images or image sequences. In detail, our model first constructs graphs for the neighboring pixels/points and then learns the distinguishable features used as hints for segmentation by training a classifier based on the image-level annotations. Finally, post-processing procedures like principal component analysis (PCA) are applied to refine the final segmentation results. We conduct experiments on the challenging 2D PRMI minirhizotron benchmark and 3D switchgrass root X-ray CT datasets for evaluation.
Why it matches plant phenotyping methods植物根の画像からのセグメンテーション手法を開発し、ベンチマークおよびX線CTデータセットで評価しており、根形質抽出のための方法が中心です。
abstractIn this work, we propose a weakly supervised root segmentation approach with graph convolutional networks.
X-ray tomography (XRT) is a powerful and versatile tool for generating detailed non-destructive three-dimensional (3D) image data of large and complicated structures. In particular, excavated, cleaned and dried maize root crowns can be rapidly scanned, and the resulting 3D volumes processed in a computational feature extraction pipeline to provide a wide range of root trait measurements. These measurements provide rich data that give insights into how roots occupy 3D space in ways not possible with any 2D imaging/measurement systems. Hundreds of root crowns can be scanned in a moderate-throughput system, and multivariate statistical analyses can provide valuable insight into the role that genes and quantitative trait loci play in selected root traits. Research presented will describe XRT scan parameter optimization and its impact on root trait data generated by the feature extraction pipeline.
Why it matches plant phenotyping methodsX線CTのスキャン条件最適化と特徴抽出パイプラインによる根形質測定が研究の中心であり、植物表現型取得法の開発・評価に該当する。
abstractResearch presented will describe XRT scan parameter optimization and its impact on root trait data generated by the feature extraction pipeline.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 14 Sept 2026
X-ray / CTRootClassificationMorphology / geometry measurementPhysiological trait estimationSegmentationRoot system architecture
Critical factors that determine crop yields are located underground, making them difficult to analyze. Traditionally, these factors have been measured by growing plants in clear media and measuring traits with visible imaging. Modern phenomics technologies use one or several imaging modalities to capture traits that reflect plant physiology or performance. Analytical techniques for plant phenomics are a crucial part of approaches to achieving desirable agronomic and biological traits. Advances in sensor technologies have paved the way for faster and more efficient plant phenotyping, with methods adapted from disciplines like high-resolution 3D X-Ray computed tomography (CT). A crucial step in their analysis is segmentation-the identification and classification of the scan's voxels as "root" or "non-root". Unlike roots in transparent mediums, roots in non-transparent mediums are difficult to segment from their surrounding materials as root and non-root voxels have overlapping CT values. The challenge we address is the development of neural-driven approaches for volumetric semantic segmentation of plant roots in 3D CT scans, and discuss subsequent trait extraction methods that enable the quantification of root systems and their traits in several agriculturally
Why it matches plant phenotyping methods3D X線CT画像から根をセグメンテーションし、根系形質を抽出・定量化する手法開発が中心であり、植物フェノタイピング手法に該当する。
abstractThe challenge we address is the development of neural-driven approaches for volumetric semantic segmentation of plant roots in 3D CT scans, and discuss subsequent trait extraction methods that enable the quantification of root systems and their traits in several agriculturally
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
The architecture and structure of cereal spikes are key indicators of quality and yield and are determined by both genetic and environmental factors. Traditional phenotyping methods for cereal spike trait measurements rely largely on manual work and are often qualitative or destructive, which leads to loss of spatial information. In this study, by using the newly installed X-ray computed tomography (X-ray CT) system at the Plant Accelerator (Australian Plant Phenomics Facility), we developed a high-throughput and non-destructive method to analyse cereal spikes. The fully automated algorithm generates detailed grain traits measurement, including count, weight, size, surface area, sphericity and spatial position. The average scan time required per sample cassette is under 7 minutes for 30 cereal spikes. Various cultivars of wheat, barley, oat and sorghum have been tested, and the results have confirmed the high accuracy and efficiency of using the X-ray CT system in grain studies. To further utilise the obtained 3D information, a pipeline for analyse spike and spikelet morphological traits has been developed for wheat spikes and oat panicles. This method allows to study the spikelet morphological traits by categorising the spikelets base on grain count, volume and weight, which can potentially bring the insights of the relationship between the development of single or multiflorous spikelets and grain quality and yield. The innovative X-ray CT system can efficiently generate grain and spike traits in high-throughput and non-destructive way, which provides a new method to contribute to a better understanding of cereal screening and breeding directions.
Why it matches plant phenotyping methodsX線CTと自動解析アルゴリズムを開発し、穀類の穂・粒形質を高速かつ非破壊で抽出する手法が研究の中心で、精度・効率も検証している。
abstractwe developed a high-throughput and non-destructive method to analyse cereal spikes.
Visualizing the endogenous distribution of elements within plant organs affords key insights in the regulation of trace elements in plants. Hyperaccumulators have extreme metal(loid) concentrations in their tissues, which make them useful models for studying metal(loid) homeostasis in plants. X-ray-based methods allow for the nondestructive analysis of most macro and trace elements with low limits of detection. However, observing the internal distributions of elements within plant organs still typically requires destructive sample preparation methods, including sectioning, for synchrotron X-ray fluorescence microscopy (XFM). X-ray fluorescence microscopy-computed tomography (XFM-CT) enables "virtual sectioning" of a sample thereby entirely avoiding artefacts arising from destructive sample preparation. The method can be used on frozen-hydrated samples, as such preserving "life-like" conditions. Absorption and Compton scattering maps obtained from synchrotron XFM-CT offer exquisite detail on structural features that can be used in concert with elemental data to interpret the results. In this article we introduce the technique and use it to reveal the internal distribution of hyperaccumulated elements in hyperaccumulator plant species. XFM-CT can be used to effectively probe the distribution of a range of different elements in plant tissues/organs, which has wide ranging applications across the plant sciences.
Why it matches plant phenotyping methods植物組織内の元素分布と構造を非破壊・三次元で取得するXFM-CT手法を導入し、ハイパーアキュムレーター植物で実証しており、植物表現型取得法が中心である。
abstractIn this article we introduce the technique and use it to reveal the internal distribution of hyperaccumulated elements in hyperaccumulator plant species.
Flower morphologies shape the accessibility to nectar and pollen, two major traits that determine plant-pollinator interactions and reproductive success. Melon is an economically important crop whose reproduction is completely pollinator-dependent and, as such, is a valuable model for studying crop-ecological functions. High-resolution imaging techniques, such as micro-computed tomography (micro-CT), have recently become popular for phenotyping in plant science. Here, we implemented micro-CT to study floral morphology and honey bees in the context of nectar-related traits without a sample preparation to improve the phenotyping precision and quality. We generated high-quality 3D models of melon male and female flowers and compared the geometric measures. Micro-CT allowed for a relatively easy and rapid generation of 3D volumetric data on nectar, nectary, flower, and honey bee body sizes. A comparative analysis of male and female flowers showed a strong positive correlation between the nectar gland volume and the volume of the secreted nectar. We modeled the nectar level inside the flower and reconstructed a 3D model of the accessibility by honey bees. By combining data on flower morphology, the honey bee size and nectar volume, this protocol can be used to assess the flower accessibility to pollinators in a high resolution, and can readily carry out genotypes comparative analysis to identify nectar-pollination-related traits.
Why it matches plant phenotyping methodsマイクロCTを用いて花、蜜腺、蜜、ハナバチの3D形態・体積を取得し、花粉媒介関連形質を高精度に評価するプロトコルを実装・提示しており、表現型取得法が中心である。
abstractHere, we implemented micro-CT to study floral morphology and honey bees in the context of nectar-related traits without a sample preparation to improve the phenotyping precision and quality.
Reproduction assets foundThe paper deposits its Python image-processing/phenotyping pipeline on GitHub and provides a supplement containing raw nectar/nectary measurement data (Tables S1–S4). Both are paper-specific, public, and actionable.Supplement · publicThe following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cells11213452/s1 . Figure S1: pollen on Stamens in ♂ and ⚥ flower types at different magnifications; Table S1: nectar-related traits in male and female flowers; Table S2: correlation analysis between nectary volume, nectary cross-section area, nectary surface, flower width and nectar volume in the respective male, female and pooled melon flowerOpen asset ↗lines:83-224Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 8 Sept 2026
Non-invasive multi-scale and multimodal 3D characterization of heterogeneous or hierarchically structured intact mesoscale samples is of paramount importance in tackling challenging scientific problems. Scanning hard X-ray tomography techniques providing simultaneous complementary 3D information are ideally suited to such studies. However, the implementation of a robust on-site workflow remains the bottleneck for the widespread application of these powerful multimodal tomography methods. In this paper, we describe the development and implementation of such a robust, holistic workflow, including semi-automatic data reconstruction. Due to its flexibility, our approach is especially well suited for on-the-fly tuning of the experiments to study features of interest progressively at different length scales. To demonstrate the performance of the method, we studied, across multiple length scales, the elemental abundances and morphology of two complex biological systems, Arabidopsis plant seeds and mouse renal papilla samples. The proposed approach opens the way towards routine multimodal 3D characterization of intact samples by providing relevant information from pertinent sample regions in a wide range of scientific fields such as biology, geology, and material sciences.
Why it matches plant phenotyping methods植物種子の形態を取得するマルチスケールX線トモグラフィーと半自動再構成ワークフローの開発・実装が中心であり、単なる生物学的測定ではない。
abstractwe describe the development and implementation of such a robust, holistic workflow, including semi-automatic data reconstruction.
From preventing scurvy to being part of religious rituals, citrus are intrinsically connected to human health and perception. From tiny mandarins to head-sized pummelos, citrus capability of hybridization provides a vastly diverse array of fruit sizes and shapes, which in turn corresponds to a diversity of flavors and aromas. These sensory qualities are tightly linked to oil glands in the citrus skin. The oil glands are also key to understanding fruit development, and the essential oils contained by them are fundamental in the food and perfume industries. We study the shape of citrus based on 3D X-ray CT scan reconstruction of 163 different citrus samples comprising 58 different species and cultivars, including samples of all fundamental citrus species. First, using the power of X-rays and image processing, we are able to compare and contrast size ratios between different tissues, such as the size of the skin compared to the rind or the flesh. Second, we model the fruit shape as an ellipsoidal surface, and later we study and infer possible oil gland distributions on this surface using principles of directional statistics. We finally compare and contrast these overall fruit shape models along their gland distributions across different citrus species. This morphological modeling will allow us later to link genotype with phenotype, furthering our insight on how the physical shape is genetically specified in DNA.
Why it matches plant phenotyping methods3D X線CT、画像処理、形状モデリング、方向統計を中核として、柑橘果実の形態と油腺分布という植物形質を定量化しているため、フェノタイピング手法研究に該当します。
abstractWe study the shape of citrus based on 3D X-ray CT scan reconstruction of 163 different citrus samples comprising 58 different species and cultivars
Plant vascular bundles are responsible for water and material transportation, and their quantitative and functional evaluation is desirable in plant research. At the single-plant level, the number, size, and distribution of vascular bundles vary widely, posing a challenge to automatically and accurately identifying and quantifying them. In this study, a deep learning-integrated phenotyping pipeline was developed to robustly and accurately detect vascular bundles in Computed Tomography (CT) images of stem internodes. Two semantic indicators were used to evaluate and identify a suitable feature extraction network for semantic segmentation models. The epidermis thickness of maize stem was evaluated for the first time and adjacent vascular bundles were improved using an adaptive watershed-based approach. The counting accuracy (R2) of vascular bundles was 0.997 for all types of stem internodes, and the measured accuracy of size traits was over 0.98. Combining sap flow experiments, multiscale traits of vascular bundles were evaluated at the single-plant level, which provided an insight into the water use efficiency of the maize plant.
Why it matches plant phenotyping methodsCT画像からトウモロコシ茎の維管束形質を自動抽出する深層学習統合フェノタイピング手法を開発・検証しており、方法が研究の中心である。
abstracta deep learning-integrated phenotyping pipeline was developed to robustly and accurately detect vascular bundles in Computed Tomography (CT) images of stem internodes.
Currently, plant phenomics is considered the key to reducing the genotype-to-phenotype knowledge gap in plant breeding. In this context, breakthrough imaging technologies have demonstrated high accuracy and reliability. The X-ray computed tomography (CT) technology can noninvasively scan roots in 3D; however, it is urgently required to implement high-throughput phenotyping procedures and analyses to increase the amount of data to measure more complex root phenotypic traits. We have developed a spatial-temporal root architectural modeling software tool based on 4D data from temporal X-ray CT scans. Through a cylinder fitting, we automatically extract significant root architectural traits, distribution, and hierarchy. The open-source software tool is named 4DRoot and implemented in MATLAB. The source code is freely available at https://github.com/TIDOP-USAL/4DRoot. In this research, 3D root scans from the black walnut tree were analyzed, a punctual scan for the spatial study and a weekly time-slot series for the temporal one. 4DRoot provides breeders and root biologists an objective and useful tool to quantify carbon sequestration throw trait extraction. In addition, 4DRoot could help plant breeders to improve plants to meet the food, fuel, and fiber demands in the future, in order to increase crop yield while reducing farming inputs.
Why it matches plant phenotyping methodsX線CTの時系列3D画像から根系形態形質を自動抽出するソフトウェア開発が研究の中心であり、植物フェノタイピング手法に該当する。
abstractWe have developed a spatial-temporal root architectural modeling software tool based on 4D data from temporal X-ray CT scans.
Reproduction assets foundThe paper's authors explicitly state that the 4DRoot source code (the software performing the root phenotyping analysis) is freely available on GitHub. The X-ray CT scan data themselves are not deposited in a public repository; only the code is. TreeQSM is a cited prior-work dependency, not a paper-specific asset.Code · publicThe open-source software tool is named 4DRoot and implemented in MATLAB. The source code is freely available at https://github.com/TIDOP-USAL/4DRoot .Open asset ↗TIDOP-USAL/4DRootlines:225-297Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google's Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.
Why it matches plant phenotyping methods植物試料のX線μCT画像を対象に、CNNによるセグメンテーション workflow を開発・最適化しており、植物画像からの表現型情報抽出法が中心である。
abstractwe developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google's Colaboratory web application.
Reproduction assets foundThe paper's X-ray μCT training/annotation datasets (walnut leaf, almond flower bud, soil aggregate scans and annotations) are publicly deposited on USDA Ag Data Commons. The workflow code is stated to be on GitHub (Rippner et al., 2022b), but no authors' public URL for it appears in the supplied text or allowed URLs,soDataset · public; Théroux-Rancourt et al., 2020 ). This will allow researchers to gain novel insights into the role that 3d architecture of soil and plant samples plays in a variety of important processes.
Data availability statement
The datasets presented in this study can be found on the National Agricultural Library Ag Data Commons website https://doi.org/10.15482/USDA.ADC/1524793 .
Author contributions
AM, DR, JE, PR, EF, and DP contributed to the conception and design of the study. MM, FD, and KS annotated images. PR, DR, JE, JN, and AB wrote code for image segmentation and data extraction. DR wrote the first draft of the manuscript. MM helped write the “Materials and Methods” section of the manuscriptOpen asset ↗National Agricultural Library Ag Data Commons · 10.15482/USDA.ADC/1524793lines:106-157Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 14 Sept 2026
Sugar beet (Beta vulgaris L. ssp. vulgaris) accounts for roughly 20% of the global sugar production, with the remainder derived from sugar cane (Saccharum officinarum L.). To maximize sugar yield, high performing sugar beet varieties are needed, in combination with good agronomical practices. Delivering vigorous seeds to the market and meeting the highest quality standards is, therefore, essential. Seed vigor is highly determined by fruit morphology, with the main characteristics of interest being fruit and true seed size, pericarp morphology and fruit filling. Current methods for evaluating fruit morphology mostly rely on labor-intensive and destructive analyses. Here we present a high-throughput nondestructive method to quantitatively phenotype sugar beet fruit and true seeds using X-ray micro-CT imaging and deep learning. A 3D convolutional neural network was trained for the semantic segmentation of the pericarp, true seed and air in X-ray micro-CT scans. High average Dice scores of 0.996, 0.971 and 0.930 were found for the pericarp, true seed and air, respectively. Additionally, since farmers target single plants after emergence in the field, we present a method to identify whether sugar beet fruit are monogerm or contain more than one seed (bigerm). An excellent overall classification accuracy, false positive and false negative rate of respectively 98.6, 1.0and 1.8% were achieved. The presented methods have a high potential for integration into tools for breeding programs and the sample-wise monitoring and adjustment of production processes.
Why it matches plant phenotyping methodsX線マイクロCTと深層学習により、サトウダイコン果実・種子の形態を非破壊かつ高スループットに定量評価する手法を開発しており、表現型取得が研究の中心である。
abstractHere we present a high-throughput nondestructive method to quantitatively phenotype sugar beet fruit and true seeds using X-ray micro-CT imaging and deep learning.
Summary The study the effect of contrast on seeds, growth and the associated anatomy and physiology, with upgraded imaging systems. The use of phase information to explore new information at various stage of the growth. This work benefits, the use of Synchrotron-based DEI and DEI-CT systems to enhance the contrast in plant root architecture and contrast mechanisms, visibility of fine structures of root architecture growth and some aspects of physiology at acceptable level. These non-destructive, imaging systems available at the X-15A beamline, at NSLS, BNL, USA, are utilized. Noticed detailed anatomical and physiological observations, contrast mechanisms, with these upgraded systems, compared to other conventional techniques, equipped with tube source of X-rays. Examined the potential of these systems to quantify the plant roots in situ. The acquired images provided good contrast, anatomical structures and physiology of the plant root micro-architecture. We observed some of the complex plant traits, such as growth, development, root architecture and the associated physiology. The interior structure, root architecture, root morphology, growth of laterals and subsequent laterals can be visualized directly by synchrotron-based imaging techniques. Root architecture of the plant grown from seeds provides new information about the structure and enhancement of some desired property, for example, interior micro-structure of the root laterals and the subsequent laterals and the clear visibility of the leaves in detail. This way, it will be possible to differentiate the weakly and strongly attenuation of the signal traversing within the sample, clearly reflects the acceptable visibility in root laterals, subsequent laterals and the associated opaque matrix with enhanced contrast. The sample has a thin layer of hard structure outside and protein inside. Extinction properties of these samples will be characterized by Sy-DEI and Sy-DEI-CT. This way, we may be able to differentiate softly and weakly attenuation within the sample, to know more about the contrast mechanisms. The visibility, contrast and porosity, with finer details, can be noticed, with Sy-DEI-CT systems as distinguished from Sy-DEI. However, limited field of view, may limit the problems associated with Sy-DEI-CT.
Why it matches plant phenotyping methodsシンクロトロンX線DEI/DEI-CTによる植物根の解剖構造・根系形態・成長・生理の非破壊画像化と定量化可能性が研究の中心であり、植物フェノタイピング手法に該当する。
abstractThe use of phase information to explore new information at various stage of the growth.
Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo without harming the plant is extremely challenging. New solutions are needed in ligneous and perennial species, for which the sustainability of plantations is crucial. To tackle this challenge, we developed a novel approach based on multimodal 3D imaging and Artificial Intelligence (AI)-based image processing that allowed a noninvasive diagnosis of inner tissues in living plants. The method was successfully applied to the grapevine (Vitis vinifera L.) in vineyards where sustainability was threatened by trunk diseases, while the sanitary status of vines cannot be ascertained without injuring the plants. By combining MRI and X-ray CT 3D imaging with an automatic voxel classification, we could discriminate intact, degraded, and white rot tissues with a mean global accuracy of over 91%. Each imaging modality contribution to tissue detection was evaluated, and we identified quantitative structural and physiological markers characterizing wood degradation steps. The combined study of inner tissue distribution versus external foliar symptom history demonstrated that white rot and intact tissue contents are key measurements in evaluating vines sanitary status. We finally proposed a model for an accurate trunk disease diagnosis in grapevine. This work opens new routes for precision agriculture and in-situ monitoring of wood quality and plant health across plant species.
Why it matches plant phenotyping methodsブドウ樹内部組織と病害状態を、MRI・X線CT・自動ボクセル分類によって非破壊的に定量する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractwe developed a novel approach based on multimodal 3D imaging and Artificial Intelligence (AI)-based image processing that allowed a noninvasive diagnosis of inner tissues in living plants
Reproduction assets foundThe paper's imaging datasets (MRI, X-ray CT, photographic volumes, annotations) are only available 'upon reasonable request', but the authors' extended Trainable Segmentation plugin used for the machine-learning voxel classification is explicitly open-source on GitHub.Code · publicFernandez et al. 24
DATA AND CODE AVAILABILITY
The datasets generated and analyzed during the current study are available from the corresponding
author upon reasonable request.
The extension of the Trainable Segmentation plugin is open-source, and available as a fork of Trainable
Segmentation on GitHub: https://github.com/Rocsg/Trainable_Segmentation/tree/Hyperweka.
.
CC-BY-NC-ND 4.0 International license
perpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for this
this version posted February 3, 2023.
;
https://doi.org/10.1101/2022.06.09.495457
doOpen asset ↗Rocsg/Trainable_Segmentation · Hyperwekapdf-raw-page:24 lines:1-16Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Field / plotX-ray / CTRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Natural ecosystems and agricultural production have been threatened by multifaceted global environmental changes. Soil degradation, extreme drought and flooding events, shifting climatic patterns and other challenges have prompted many disciplines within plant science to pivot to find solutions. Accordingly, root research has expanded from fundamental studies on roots, as providers of physical support, water and essential nutrients uptake, towards identification of beneficial traits for stress adaptation and control of key biological soil processes. Advances in trait identification, data acquisition, management and modelling are enabling root researchers to develop predictive models to support ecosystems in these changing environments. Through technical presentations, posters, industry exhibits and a root phenotyping workshop, the international, jointly presented, completely virtual International Society of Root Research (ISRR)11/Rooting2021 meeting provided a unique platform for researchers across disciplines to share recent advances in root biology, from molecular to ecosystem-level scales, in agricultural and natural ecosystems, addressing critical questions in response to climate change and its impact on crop productivity and ecosystem services. In this report, the 2021 ISRR Ambassador cohort provides an overview of the current root research landscape and reflection on the importance of frontier research for a more sustainable future. In response to global travel restrictions imposed by the COVID-19 pandemic, the 11th Symposium of the International Society of Root Research (ISRR11, https://www.rootresearch.org/) and the 9th International Symposium on Root Development (Rooting2021) merged into a single online event co-organised by the Interdisciplinary Plant Group at the University of Missouri (Columbia, MO, USA) and the University of Nottingham (UK). Over 700 participants representing academia, government and industry from more than 53 countries (Supporting Information Fig. S1) joined the virtual event held 24–28 May 2021. The schedule ran almost uninterrupted across international time zones, featuring 74 talks (10 plenaries, 16 keynotes, and 48 invited) and c. 300 posters, spanning a broad range of disciplines. In addition, the 2021 ISRR Lifetime Achievement Award was presented to Wendy Silk, Emeritus Professor at the University of California-Davis (USA). ISRR11/Rooting2021 hosted the 3rd ISRR Ambassador Program, a unique platform for early-career root researchers. The virtual ISRR11/Rooting2021 Ambassador Program provided networking activities, experience with conference organisation, interaction with professionals in diverse career areas, and opportunities to discuss advances in the field with a broadly multidisciplinary cohort (Notes S1). Ambassador tasks at the ISRR11/Rooting 2021 meeting included session note-taking, the production of this Meeting report, and a set of recommendations for diversity and inclusion in future scientific events (Notes S2). The ISRR11/Rooting2021 meeting concluded with a root phenotyping workshop with virtual tours of major root phenotyping facilities and demonstrations of methods. Organised by Larry York (Oak Ridge National Laboratory, TN, USA) and Darren Wells (University of Nottingham, UK), in collaboration with other experts and the ISRR Ambassadors, the workshop with a Q&A format was used to discuss the latest advances in root phenotyping techniques. The potential complementarity of image analysis software tools emerged as a key topic as depicted in Fig. S2. The availability of standardised protocols for root collection and trait measurement was also highlighted by the participants of the online survey, organised by the Ambassadors in addition to the Symposium (Delory et al., 2022) and the workshop as an important issue for future research. The Root Ecology Handbook recently published in New Phytologist provides a comprehensive guide on root sampling, processing and measuring for a wide variety of traits in a standardised manner (Freschet et al., 2021). Root phenotyping for traits related to crop performance or ecosystem services has been a main focus in the field of root biology since the 1970s (Hurd, 1974). However, quantitative analysis of plant phenotypes and their linkages to plant functions remains a major bottleneck. ISRR11/Rooting2021 highlighted the current emphasis on phenotyping root traits that will provide resilience to changing environmental conditions (Fig. 1), including traits related to root–microbial interactions (Kawasaki et al., 2021). Rhizosphere processes related to root stress responses are key for sustainable food production systems, as they impact soil functioning and resource use efficiency. The impact of drought and limited nutrient supply on plants under global climate change, and the mechanisms of root response from molecular to field scales, have prompted focussed advances on well established areas in the field of root research. Therefore, the role of auxin and cytokinin in molecular crosstalk has prompted the rise of the ‘hormonics’ to explore their functions in root development under drought stress (Rodriguez-Alonso et al., 2018), and to identify signalling pathways that link nutrient availability to root developmental parameters (Shahzad & Amtmann, 2017). Hormonal signalling also underlies ‘nutritropism’, an extension of ‘chemotropism’ (Newcombe & Rhodes, 1904), which can now be explored with advanced imaging and microscopy technology (T. Fujiwara, University of Tokyo, Japan). Root-related strategies to mitigate drought stress related to root hydraulic architecture and water transport were also discussed (Maurel & Nacry, 2020). Root-system-level traits linked with water and nutrient use efficiency such as wheat root axial conductance (Hendel et al., 2021), architectural traits in rice (Ruangsiri et al., 2021) and maize (Kistler et al., 2018) have been identified with a combination of shovelomics, phenotyping, functional genomics and modelling. The long-standing challenges of grafting for the introduction of root traits related to stress tolerance have been partially overcome by recent progress on our understanding of graft compatibility and cell-to-cell adhesion (Notaguchi et al., 2020). Advancing our understanding of grafting mechanisms will certainly provide new avenues to understand the effects of specific root genotypes and/or traits on other parts of the plant body (J. Cantillo, Donald Danforth Plant Science Center, MO, USA). Current trends in root research seek to integrate stress responses inside the root system with a better understanding of these root–soil–microbe interactions. ISRR11/Rooting2021 highlighted the role of the rhizosphere microbiome in nutrient homeostasis, for example, in root diffusion (Salas-González et al., 2021), or during nitrogen acquisition (Arsova et al., 2012). Root exudates were introduced as potential targets for rhizosphere engineering to promote beneficial microbiome functionalities (Kawasaki et al., 2021) or to control harmful species. Novel studies looking into root–microbiome interactions have become possible due to precision genome editing, production of knocked-down lines and reconstruction of biosynthetic metabolic pathways (Huang et al., 2019), and advanced imaging techniques such as positron emission tomography (Schmidt et al., 2020). Recent advances in imaging techniques and image analysis (Fig. 1) can support high-throughput root phenotyping of relevant structural features within the root architecture (Fig. 2). Detailed image-based root phenotyping techniques such as X-ray computed tomography (CT) scanning can improve our interpretation of in-field studies (C. Topp, Donald Danforth Plant Science Center, MO, USA). Current advances allow high-resolution and/or high-throughput phenotyping studies, even in mature crops and under field conditions (Gore et al., 2020; Rich et al., 2020), although methodological challenges remain (Delory et al., 2022). For example, root phenotyping of rooting depth and its significance for deep water or nitrate uptake is being addressed with large-scale field experiments using minirhizotrons or soil coring on maize (A. Leakey, University of Illinois, USA), wheat (J. Christopher, University of Queensland, Australia) and potatoes (O. Popovic, Copenhagen University, Denmark). Automated, high-resolution minirhizotrons are also used for visualising the dynamics of roots and fungi interaction in experimentally warmed peatlands (C. Iversen, Oak Ridge National Laboratory, TN, USA; Defrenne et al., 2020). These imaging advances are complemented by the development of free, open source and high-performance image analysis software (Fig. S2). Pairing 3D imaging techniques (e.g. X-ray CT) with mathematical modelling is a powerful way to study plant–soil interactions on different scales, from soil pores to growing root systems (Roose et al., 2016). This hybrid approach has resulted in key milestones by allowing the elucidation of how root architecture and exudation jointly affect P mobilisation and uptake (McKay Fletcher et al., 2020), and to quantify the extent to which the dissolution of N fertiliser granules affects soil microbial activity (Ruiz et al., 2020). Imaging techniques can also be used for traits related to root–microbe interactions (Fig. 1), complementing other multidisciplinary approaches that seek to better understand the complex dialogue between roots, the associated microbiome and soil processes. Mathematical modelling complements phenotyping advances by overcoming the challenges of experimental approaches and benefits from the emerging field of functional phenomics (York, 2019). Highlights from the diversity of modelling approaches presented at ISRR/Rooting2021, in both spatial and temporal scales, include: a micro-hydrological model that describes a new symplastic water pumping mechanism (Couvreur et al., 2021); the dynamics and regulation of a fast brassinosteroid response pathway in Arabidopsis root tips (Großeholz et al., 2021); functional–structural plant (FSP) models to identify optimal root phenotypes for nutrient capture in contrasting environments (Rangarajan, 2021); and field-scale simulations of plant populations and communities (Postma et al., 2017; Schnepf et al., 2018; Faverjon et al., 2019). Future mathematical models will draw on larger, more complex, datasets incorporating novel imaging technology, high-throughput phenotyping and availability of relevant environmental data. The positive feedback cycles between these models and continued advances in phenotyping are what will surely advance the field of root science. To meet the challenges imposed by the global COVID-19 pandemic, online communication has provided new opportunities for international multidisciplinary cooperation. The ISRR11/Rooting2021 online event brought the root research community together to share knowledge on the latest developments in root and rhizosphere research, present new technological advances and identify pressing research questions that still require answers. The adoption of a holistic approach to root research, that is, one that takes into account all categories of root traits, from anatomy to root morphology, physiology and architecture, as well as interactions with the rhizosphere microbiota, was emphasised as a crucial step in facing the challenges posed by global change. We encourage root researchers to actively take advantage of the plethora of online resources currently available for plant phenotyping (https://quantitative-plant.org/), and to join the ISRR (https://www.rootresearch.org/). Collaborations to share knowledge, along with new technological advances, will help us further understand roots and rhizosphere processes. The authors thank the New Phytologist Foundation for supporting the Ambassador Program, and John Kirkegaard and Hallie Thompson for initiating the ISRR Ambassador Program in 2015. LAG acknowledges support from the Plant Genome Research Program, National Science Foundation (IOS-1444448). We thank Michelle Watt, Bob Sharp, Malcolm Bennett and the organisers of the ISRR11/Rooting2021 Symposium from the Interdisciplinary Plant Group at the University of Missouri (Columbia, USA) and the University of Nottingham (UK). In particular, the authors would like to thank Victoria Bryan as well as Jennifer Hartwick and her team for their exceptional support. We thank Larry York and Darren Wells for organising an excellent virtual root phenotyping workshop during the ISRR11/Rooting2021 conference with generous financial support from the International Plant Phenotyping Network. The ISRR Ambassadors are also very grateful to Charlie Messina, Michelle Watt, Genevieve Croft and Ronald Vargas for sharing their professional experience and for taking the time to discuss career opportunities for root scientists. The authors thank Christopher Topp, Larry York and Abraham Smith for their contributions to the preparation of the figures. Finally, thanks to all ISRR11/Rooting2021 participants for making this conference a success! See you at the next ISRR (organised in 2024 in Leipzig, Germany) and/or Rooting (organised in 2023 in Ghent, Belgium) conference. None declared. AJM, CNT, LAG and AT coordinated the ‘ISRR11 Ambassador Program’ and provided valuable feedback on the manuscript. The ISRR11 Ambassadors group (CNC, GC, KKD, BMD, AD, YD, APG, QH, P-WH, MCH-S, ML, JLPN, LM, JM-M, AER, JS, TSW, PW, XW, LX, CZ) compiled and collated minutes of the sessions throughout the meeting and wrote the initial draft and the revised versions. Ambassador JS prepared Notes S2 addressing diversity and inclusion at ISRR11/Rooting2021. Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. Fig. S1 Map depicting the distribution and number of attendees to the joined Symposium ISRR11-Rooting2021. Fig. S2 Example of root image analysis pairing RootPainter and RhizoVision explorer. Notes S1 The ISRR11 3rd Graduate Student and Postdoc Ambassador Program. Notes S2 Diversity and inclusion at ISRR11/Rooting2021. Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Why it matches plant phenotyping methods根系フェノタイピングのワークショップ、画像解析ソフトウェア、X線CTやミニリゾトロンなどの技術進展を実質的に概説しており、方法レビューとして中心的です。
abstractRecent advances in imaging techniques and image analysis (Fig. 1) can support high-throughput root phenotyping of relevant structural features within the root architecture (Fig. 2).
Laboratory / benchtopMicroscopyX-ray / CTRoot2D/3D reconstructionSegmentationSkeletonization / topologyVisualization / data managementRoot system architecture
Land plants have two types of shoot-supporting systems, root system and rhizoid system, in vascular plants and bryophytes. However, since the evolutionary origin of the systems are different, how much they exploit common systems or distinct systems to architect their structures are largely unknown. To understand the regulatory mechanism how bryophytes architect rhizoid system responding to an environmental factor, such as gravity, and compare it with the root system of vascular plants, we have developed the methodology to visualize and quantitatively analyze the rhizoid system of the moss, Physcomitrium patens in 3D. The rhizoids having the diameter of 21.3 m on the average were visualized by refraction-contrast X-ray micro-CT using coherent X-ray optics available at synchrotron radiation facility SPring-8. Three types of shape (ring-shape, line, black circle) observed in tomographic slices of specimens embedded in paraffin were confirmed to be the rhizoids by optical and electron microscopy. Comprehensive automatic segmentation of the rhizoids which appeared in different three form types in tomograms was tested by a method using Canny edge detector or machine learning. Accuracy of output images was evaluated by comparing with the manually-segmented ground truth images using measures such as F1 score and IoU, revealing that the automatic segmentation using the machine learning was more effective than that using Canny edge detector. Thus, machine learning-based skeletonized 3D model revealed quite dense distribution of rhizoids, which was similar to root system architecture in vascular plants. We successfully visualized the moss rhizoid system in 3D for the first time.
Why it matches plant phenotyping methodsコケの根茎系を3D可視化・定量化するX線マイクロCTと自動セグメンテーション手法を開発し、教師データとの比較で精度検証しているため、植物表現型取得法が中心である。
abstractwe have developed the methodology to visualize and quantitatively analyze the rhizoid system of the moss, Physcomitrium patens in 3D
The first goal of this paper is to verify the accuracy of four calculation methods of log volume. The tool to achieve this goal is to compare the results of the calculation of the log volume with the real log volume obtained from the three-dimensional reconstruction obtained by computed tomography. The second goal of this paper is to determine the effectiveness of displaying the qualitative features of wood in three-dimensional models of selected pieces of logs of oak, beech, and spruce, which were obtained using computed tomography. It is possible to state that each of the tested calculation methods of wood log volume are applicable in practice. The tested methods achieve excellent accuracy in determining the volume of spruce logs with a small variance of values, and conversely, in the case of beech wood, the tested methods are the most inaccurate with the largest variance of values. When determining the volume of wood logs, we recommend using the calculation method STN 48 0009, because it achieves the best results. Qualitative analysis based on CT scans of internal features can be described as a completely new level of approach to the evaluation of log quality. The performed analysis showed great potential for automatic detection of internal qualitative features in the tested spruce log. In this wood, wood defects are distinguishable by computed tomography. In the case of deciduous oak and beech, the situation is more complicated. The internal structure of these trees overlaps the internal qualitative features of the wood. To accurately detect internal errors in these trees, it will be necessary to perform many comparative tests to achieve optimal results.
Why it matches plant phenotyping methodsCTによる丸太体積の定量と内部木質特徴・欠陥の自動検出可能性を検証しており、植物器官（木材）の形態・品質状態の取得方法が研究の中心である。
abstractThe tool to achieve this goal is to compare the results of the calculation of the log volume with the real log volume obtained from the three-dimensional reconstruction obtained by computed tomography.
X-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues. However, the potential X-ray exposure damages might affect the structure and elemental composition of living plant tissues leading to artefacts in the recorded data. Herein, we exposed soybean (Glycine max (L.) Merrill) leaves to several X-ray doses through a polychromatic benchtop microprobe X-ray fluorescence spectrometer, modulating the photon flux by adjusting either the beam size, focus, or exposure time. The structure, ultrastructure and physiological responses of the irradiated plant tissues were investigated through light and transmission electron microscopy (TEM). Depending on the dose, the X-ray exposure induced decreased K and X-ray scattering intensities, and increased Ca, P, and Mn signals on soybean leaves. Anatomical analysis indicated necrosis of the epidermal and mesophyll cells on the irradiated spots, where TEM images revealed the collapse of cytoplasm and cell-wall breaking. Furthermore, the histochemical analysis detected the production of reactive oxygen species, as well as inhibition of chlorophyll autofluorescence in these areas. Under certain X-ray exposure conditions, e.g., high photon flux and exposure time, XRF measurements may affect the soybean leaves structures, elemental composition, and cellular ultrastructure, and induce programmed cell death. These results shed light on the characterization of the radiation damage, and thus, help to assess the X-ray radiation limits and strategies for in vivo for XRF analysis. HighlightBy exposing soybean leaves to several X-ray doses, we show that the characteristic X-ray induced elemental changes stem from plants physiological signalling or responses rather than only sample dehydration.
Why it matches plant phenotyping methods植物組織のin vivo XRF測定における放射線損傷と測定アーティファクトを評価し、適用限界と測定条件を検証する研究であり、フェノタイピング手法の技術的妥当性が中心です。
abstractX-ray fluorescence spectroscopy (XRF) is a powerful technique for the in vivo assessment of plant tissues.
Reproduction assets foundThe paper's DATA AVAILABILITY section states the raw data (XRF spectra/maps and imaging measurements) are fully available on Figshare at the authors' public DOI, which matches an allowed URL.Dataset · publicThe raw data herein presented is fully available at Figshare
repository: https://doi.org/10.6084/m9.figshare.1858438Open asset ↗Figshare · 10.6084/m9.figshare.1858438pdf-page:6 lines:1-93Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
Background In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties 1 . Both methods predicted the kernel mass with R 2 > 0.93 (XRT: R 2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R 2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R 2 = 0.91, MAE = 0.09) compared to XRT (R 2 = 0.78; MAE = 0.08). Conclusion Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.
Why it matches plant phenotyping methodsX線画像と画像変換・CNNを用いて、落花生のカーネル重量・殻重量という器官形質を推定し、重量測定で性能検証しているため、植物形質取得法が中心です。
abstractwe investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation.
The vascular bundle of the shank is an important 'flow' organ for transforming maize biological yield to grain yield, and its microscopic phenotypic characteristics and genetic analysis are of great significance for promoting the breeding of new varieties with high yield and good quality. In this study, shank CT images were obtained using the standard process for stem micro-CT data acquisition at resolutions up to 13.5 μm. Moreover, five categories and 36 phenotypic traits of the shank including related to the cross-section, epidermis zone, periphery zone, inner zone and vascular bundle were analyzed through an automatic CT image process pipeline based on the functional zones. Next, we analyzed the phenotypic variations in vascular bundles at the base of the shank among a group of 202 inbred lines based on comprehensive phenotypic information for two environments. It was found that the number of vascular bundles in the inner zone (IZ_VB_N) and the area of the inner zone (IZ_A) varied the most among the different subgroups. Combined with genome-wide association studies (GWAS), 806 significant single nucleotide polymorphisms (SNPs) were identified, and 1245 unique candidate genes for 30 key traits were detected, including the total area of vascular bundles (VB_A), the total number of vascular bundles (VB_N), the density of the vascular bundles (VB_D), etc. These candidate genes encode proteins involved in lignin, cellulose synthesis, transcription factors, material transportation and plant development. The results presented here will improve the understanding of the phenotypic traits of maize shank and provide an important phenotypic basis for high-throughput identification of vascular bundle functional genes of maize shank and promoting the breeding of new varieties with high yield and good quality.
Why it matches plant phenotyping methods植物茎部のマイクロCT画像から36形質を自動抽出するパイプラインが研究の中心であり、ハイスループット表現型解析手法の実質的な適用に該当する。
abstractshank CT images were obtained using the standard process for stem micro-CT data acquisition at resolutions up to 13.5 μm.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Table S3: The BLUP values for 30-item phenotypic traits of the 202 inbred lines; Supplementary Table S4: The result data of GWASOpen asset ↗lines:712-726Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
O_LIAnnual rings from 30 year old vines in a California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions. Viticultural traits measuring vegetative growth, yield, berry quality, and nutrient uptake were collected at the beginning and end of the lifetime of the vineyard. C_LIO_LIX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines. Ring width was modeled as a function of ring number using a negative exponential model. Early and late wood ring widths, cambium width, and scion trunk radius were correlated with 27 traits. C_LIO_LIModeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay thereafter, consistently shortening ring width throughout the lifetime of the vine. The ratio of yield to vegetative growth, juice pH, photosynthetic assimilation and transpiration rates, and stomatal conductance are correlated with scion trunk radius. C_LIO_LIRootstocks modulate secondary growth over years, altering hydraulic conductance, physiology, and agronomic traits. Rootstocks act in similar but distinct ways from climate to modulate ring width, which borrowing techniques from dendrochronology, can be used to monitor both genetic and environmental effects in woody perennial crop species. C_LI
Why it matches plant phenotyping methodsX線CTによる年輪幅・形成層幅・幹半径の測定が研究の主要な表現型取得手段であり、樹体の二次成長を遺伝的・環境的影響のモニタリングに用いる方法として扱われている。
abstractX-ray Computed Tomography (CT) was used to measure ring widths in 103 vines.
Reproduction assets foundThe paper deposits its X-ray CT cross-section images with landmarks (the phenotyping inputs for ring-width measurement) on Dryad, and all data plus analysis code in a public GitHub repository/Jupyter notebook. Both are paper-specific, publicly available, and actionable.Dataset · publicBMG, IK, MRM, ELM, AWS, ALD, SS, and DHC analyzed data. ZM and DHC
510 coordinated research, data analysis, and manuscript writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
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Rootstock Parentage
775 Paulsen V. berlaOpen asset ↗Dryad · 10.5061/dryad.gqnk98sqfpdf-layout-page:13 lines:1-51Code · publict writing. DHC wrote a first draft of the
511 manuscript which all authors read, commented on, and edited.
512
513 Data Availability
514
515 X-ray CT cross-sections with landmarks are deposited on Dryad:
516 http://dx.doi.org/10.5061/dryad.gqnk98sqf. All data and code to reproduce results are posted on
517 the Github repository https://github.com/DanChitwood/grapevine_rings.
518
519 Supporting Information Table S1: Numbers of measured samples for each trait, for each
520 scion, for each year.
521
522 Table 1: Rootstock parentage
523
Rootstock Parentage
775 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
1103 Paulsen V. berlandieri Rességuier 2 × V. rupestris du Lot
3309 Couderc V. Open asset ↗GitHub · DanChitwood/grapevine_ringspdf-layout-page:13 lines:1-51Code / dataset availability confirmedbioRxiv · checked 13 Sept 2026
O_LICitrus come in diverse sizes and shapes, and play a key role in world culture and economy. Citrus oil glands in particular contain essential oils which include plant secondary metabolites associated with flavor and aroma. Capturing and analyzing nuanced information behind the citrus fruit shape and its oil gland distribution provides a morphology-driven path to further our insight into phenotype-genotype interactions. C_LIO_LIWe investigated the shape of citrus fruit of 51 accessions based on 3D X-ray CT scan reconstructions. Accessions include all three ancestral citrus species, accessions from related genera, and several interspecific hybrids. We digitally separate and compare the size of fruit endocarp, mesocarp, exocarp, and oil gland tissue. Based on the centers of the oil glands, overall fruit shape is approximated with an ellipsoid. Possible oil gland distributions on this ellipsoid surface are explored using directional statistics. C_LIO_LIThere is a strong allometry along fruit tissues; that is, we observe a strong linear relationship between the volume of any pair of major tissues. This suggests that the relative growth of fruit tissues with respect to each other follows a power law. We also observe that on average, glands distance themselves from their nearest neighbor following a square root relationship, which suggests normal diffusion dynamics at play. C_LIO_LIThe observed allometry and square root models point to the existence of biophysical developmental constraints that govern novel relationships between fruit dimensions from both evolutionary and breeding perspectives. Understanding these biophysical interactions prompt an exciting research path on fruit development and breeding. C_LI Societal Impact StatementCitrus are intrinsically connected to human health and culture, including preventing human diseases like scurvy, and inspiring sacred rituals. Citrus fruits come in a stunning number of different sizes and shapes, ranging from small clementines to oversized pummelos, and fruits display a vast diversity of flavors and aromas. These qualities are key in both traditional and modern medicine and the production of cleaning and perfume products. By quantifying and modeling overall fruit shape and oil gland distribution, we can gain further insight into citrus development and the impacts of domestication and improvement on multiple characteristics of the fruit.
Why it matches plant phenotyping methods3D X線CT再構成とデジタル分離により、柑橘果実の形状・組織体積・油腺分布を定量化しモデル化しており、植物表現型の取得・解析が研究の中心です。
abstractCapturing and analyzing nuanced information behind the citrus fruit shape and its oil gland distribution provides a morphology-driven path to further our insight into phenotype-genotype interactions.
Reproduction assets foundThe paper explicitly deposits its processed citrus X-ray CT 3D reconstructions, segmented tissues, oil gland point clouds, and ellipsoidal approximations in Dryad, and its full image-processing and analysis code on GitHub. Both are paper-specific, public, and actionable.Code · public357 All our code is available at the https://github.com/amezqui3/vitaminC_morphology repos-Open asset ↗GitHub · amezqui3/vitaminC_morphologypdf-page:19 lines:1-48Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
Efficient methodologies for automated seed quality evaluations are important for the seed industry. Advanced seed technology research requires the use of adequate methods to ensure good seed performance under adverse environmental conditions; thus, providing producers with detailed, quick, and accurate information on structural seed integrity and ensuring vigorous production. To address this problem, this study aimed to determine Brachiaria brizantha (Marandu cv., Piatã cv. and Xaraés cv.) seed quality through radiographic imaging analyses associated with vigor tests and anatomical characterizations. Brachiaria seed cultivars displaying different physical and physiological attributes were selected and subjected to the 1000-seed weight test, water content determinations, X-ray analyses, germination tests, and anatomical characterizations. The X-ray analyses made it possible to establish a relationship between the X-ray images and other determined variables. Furthermore, the X-ray images can indicate evidence of internal and external damage that could later compromise germination. The Marandu and Piatã cultivars presented the highest germination percentages, germination speed indices, normal seedling development, and cellular structure preservation compared to the Xaraés cultivar. To summarize, X-ray analyses are efficient methods used for the selection of higher physical quality cultivars and can aid in the decision-making processes of companies and seed producers worldwide.
Why it matches plant phenotyping methods種子の内部・外部構造と品質をX線画像から評価し、発芽や活力との関係を検証しているため、画像計測法が研究の中心です。
abstractThe X-ray analyses made it possible to establish a relationship between the X-ray images and other determined variables.
Fruits (follicles) of Hakea salicifolia and Hakea sericea (Proteaceae) are characterised by pronounced lignification and open via a ventral suture and the dorsal side. The opening along both sides is unique within the Proteaceae. Both serotinous species are obligate seeders, whose spreading benefits from bush fire events. The different tissues and the course of the vascular bundles must allow the opening mechanism. While their 2D-arrangements are known to some extent from light-microscopy images of cross-sections, this work presents their three-dimensional structures and discusses their contribution to the opening of Hakea fruits. For this purpose, 3D greyscale images, reconstructed from µCT-projection data of both fruits are segmented, assisted by a deep learning algorithm (AI algorithm). 3D renderings from these segmentations show strongly interconnected vascular bundles that build a double-dome shaped network in each valve of H. salicifolia and a dome shaped honeycomb-structure in each valve of H. sericea. However, the vascular bundles of both species show no interconnection between the two lateral valves of the fruit but leave gaps for predetermined fracture tissues on the ventral and dorsal side. The opening of the fruits after a fire or after separation from the mother plant can be explained by the anisotropic shrinkage in the two valves of the fruit.
Why it matches plant phenotyping methodsµCTと深層学習支援セグメンテーションにより、果実内の血管束の三次元形態を抽出・可視化しており、植物器官の形態計測が研究の中心的手法である。
abstractthis work presents their three-dimensional structures
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
Plant pathogens cause yield losses in crops worldwide. Breeding for improved disease resistance and management by precision agriculture are two approaches to limit such yield losses. Both rely on detecting and quantifying signs and symptoms of plant disease. To achieve this, the field of plant phenotyping makes use of non-invasive sensor technology. Compared to invasive methods, this can offer improved throughput and allow for repeated measurements on living plants. Abiotic stress responses and yield components have been successfully measured with phenotyping technologies, whereas phenotyping methods for biotic stresses are less developed, despite the relevance of plant disease in crop production. The interactions between plants and pathogens can lead to a variety of signs (when the pathogen itself can be detected) and diverse symptoms (detectable responses of the plant). Here, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots, including monochrome, RGB, hyperspectral, fluorescence, chlorophyll fluorescence and thermal sensors, as well as Raman spectroscopy, X-ray computed tomography, and optical coherence tomography. We argue that choosing and combining appropriate sensors for each plant-pathosystem and measuring with sufficient spatial resolution can enable specific and accurate measurements of above-ground signs and symptoms of plant disease.
Why it matches plant phenotyping methods植物病害の徴候・症状を非侵襲センサーで検出・定量するフェノタイピング手法を広範にレビューしており、方法論が中心である。
abstractHere, we review the strengths and weaknesses of a broad range of sensor technologies that are being used for sensing of signs and symptoms on plant shoots
Many plant leaves have two layers of photosynthetic tissue: the palisade and spongy mesophyll. Whereas palisade mesophyll consists of tightly packed columnar cells, the structure of spongy mesophyll is not well characterized and often treated as a random assemblage of irregularly shaped cells. Using micro-computed tomography imaging, topological analysis, and a comparative physiological framework, we examined the structure of the spongy mesophyll in 40 species from 30 genera with laminar leaves and reticulate venation. A spectrum of spongy mesophyll diversity encompassed two dominant phenotypes: first, an ordered, honeycomblike tissue structure that emerged from the spatial coordination of multilobed cells, conforming to the physical principles of Euler's law; and second, a less-ordered, isotropic network of cells. Phenotypic variation was associated with transitions in cell size, cell packing density, mesophyll surface-area-to-volume ratio, vein density, and maximum photosynthetic rate. These results show that simple principles may govern the organization and scaling of the spongy mesophyll in many plants and demonstrate the presence of structural patterns associated with leaf function. This improved understanding of mesophyll anatomy provides new opportunities for spatially explicit analyses of leaf development, physiology, and biomechanics.
Why it matches plant phenotyping methodsマイクロCT画像とトポロジー解析を用いて葉肉組織の構造・細胞形態・密度などの植物形質を定量化しており、画像取得・解析が比較研究の中心的手法である。
abstractUsing micro-computed tomography imaging, topological analysis, and a comparative physiological framework, we examined the structure of the spongy mesophyll in 40 species from 30 genera with laminar leaves and reticulate venation.
Capturing complete internal anatomies of plant organs and tissues within their relevant morphological context remains a key challenge in plant science. While plant growth and development are inherently multiscale, conventional light, fluorescence, and electron microscopy platforms are typically limited to imaging of plant microstructure from small flat samples that lack a direct spatial context to, and represent only a small portion of, the relevant plant macrostructures. We demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution three-dimensional (3D) volumes of intact plant samples from the cell to the whole plant level. Serial imaging of a single sample is shown to provide sub-micron 3D volumes co-registered with lower magnification scans for explicit contextual reference. High-quality 3D volume data from our enhanced methods facilitate sophisticated and effective computational segmentation. Advances in sample preparation make multimodal correlative imaging workflows possible, where a single resin-embedded plant sample is scanned via XRM to generate a 3D cell-level map, and then used to identify and zoom in on sub-cellular regions of interest for high-resolution scanning electron microscopy. In total, we present the methodologies for use of XRM in the multiscale and multimodal analysis of 3D plant features using numerous economically and scientifically important plant systems.
Why it matches plant phenotyping methods植物の細胞から個体レベルの3D形態を取得するX線顕微鏡法と試料調製・計算セグメンテーションを中心に開発・提示しており、植物表現型取得手法が明確に主題である。
abstractWe demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution three-dimensional (3D) volumes of intact plant samples from the cell to the whole plant level.
Reproduction assets foundThe authors deposited fly-through animations of 2D image stacks and 3D volume rendering animations of the XRM scans shown in the paper's figures on figshare, directly reproducing this paper's plant phenotyping imaging data. No author analysis code or trained model checkpoints were explicitly deposited.Dataset · publicCanada) was used for data integration, visualization, and animation of the scan data, and to export image data as 2D 16-bit Tag Image File Format (TIFF) stacks. Fly-through animations of 2D image stacks for scans shown in all Figures, as well as 3D volume rendering animations of selected scans, are available for download from ( https://figshare.com/s/944efc8832e47fd4f203 ).
Image analysis and segmentation
Data from XRM scans were segmented using Amira software and with the assistance of a Wacom tablet for manual segmentation, in addition to ORS Dragonfly Deep Learning Module 2021.1.0.977 which is free for noncommercial use. Segmentation for Figure 1D combined automated and manual methods in Open asset ↗figsharelines:87-114Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Plants modify the climate and provide natural cooling through transpiration. However, plant response is not only dependent on the atmospheric evaporative demand due to the combined effects of wind speed, air temperature, humidity, and solar radiation, but is also dependent on the water transport within the plant leaf-xylem-root system. These interactions result in a dynamic response of the plant where transpiration hysteresis can influence the cooling provided by the plant. Therefore, a detailed understanding of such dynamics is key to the development of appropriate mitigation strategies and numerical models. In this study, we unveil the diurnal dynamics of the microclimate of a Buxus sempervirens plant using multiple high-resolution non-intrusive imaging techniques. The wake flow field is measured using stereoscopic particle image velocimetry, the spatiotemporal leaf temperature history is obtained using infrared thermography, and additionally, the plant porosity is obtained using X-ray tomography. We find that the wake velocity statistics are not directly linked with the distribution of the porosity but depends mainly on the geometry of the plant foliage which generates the shear flow. The interaction between the shear regions and the upstream boundary layer profile is seen to have a dominant effect on the wake turbulent kinetic energy distribution. Furthermore, the leaf area density distribution has a direct impact on the short-wave radiative heat flux absorption inside the foliage where 50% of the radiation is absorbed in the top 20% of the foliage. This localized radiation absorption results in a high local leaf and air temperature. Furthermore, a comparison of the diurnal variation of leaf temperature and the net plant transpiration rate enabled us to quantify the diurnal hysteresis resulting from the stomatal response lag. The day of this plant is seen to comprise of four stages of climatic conditions: no-cooling, high-cooling, equilibrium, and decaying-cooling stages.
Why it matches plant phenotyping methods複数の高解像度非侵襲イメージング技術を中核として、葉温、植物空隙率、葉面積密度、蒸散応答などの植物形質・状態を取得・解析しており、単なるルーチン測定ではない。
abstractIn this study, we unveil the diurnal dynamics of the microclimate of a Buxus sempervirens plant using multiple high-resolution non-intrusive imaging techniques.
Abstract Background Micro-CT (X-ray computed tomographic) allows for 3D visualization of an entire structure, both internally and externally. The different materials are identified based on the differences in their ability to attenuate X-ray, with the differences being converted into a range of grey values. Low-density plant tissues with limited differences in grey values create a challenge in differentiating the cellular structures. In addition, internal movements due to autolysis, degradation, and shrinkage during dehydration of the tissues during scanning give rise to blurry images. Results In this study, oats and wheat were scanned using micro-CT to optimize the use of micro-CT in low-density plants. With the assistance of chemical fixing, phosphotunstate and a chemical drying agent, we were able to visualize microstructures of cereal stems. These preparation steps allow us to create 3D micrographs of low-density stem nodes suggesting key structural differences that are correlated with lodging resistance. Conclusion Micro-CT is a valuable tool to create 3D structural images of low-density material. Multiple steps to prepare the samples to stop autolysis, increase the contrast during scanning and eliminate internal movement are described in this paper. This process allowed for visualization of the stem nodal region suggesting morphology related to lodging resistance.
Why it matches plant phenotyping methods低密度植物茎の内部構造を取得するため、マイクロCT撮像と試料調製を最適化・開発しており、植物形態の3D表現型取得が中心です。
abstractIn this study, oats and wheat were scanned using micro-CT to optimize the use of micro-CT in low-density plants.
Background The structural characteristics of whole sorghum kernels are known to affect end-use quality, but traditional evaluation of this structure is two-dimensional (i.e., cross section of a kernel). Current technology offers the potential to consider three-dimensional structural characteristics of grain. X-ray computed tomography (CT) presents one such opportunity to nondestructively extract quantitative data from grain caryopses which can then be related to end-use quality. Results Phenotypic measurements were extracted from CT scans of grain sorghum caryopses. Extensive phenotypic variation was found for embryo volume, endosperm hardness, endosperm texture, endosperm volume, pericarp volume, and kernel volume. CT derived estimates were strongly correlated with ground truth measurements enabling the identification of genotypes with superior structural characteristics. Conclusions Presented herein is a phenotyping pipeline developed to quantify three-dimensional structural characteristics from grain sorghum caryopses which increases the throughput efficiency of previously difficult to measure traits. Adaptation of this workflow to other small-seeded crops is possible providing new and unique opportunities for scientists to study grain in a nondestructive manner which will ultimately lead to improvements end-use quality.
Why it matches plant phenotyping methodsX線CTを用いてソルガム穀粒の三次元形質を定量化するフェノタイピングパイプラインを開発・検証しており、形質取得手法が研究の中心である。
abstractPhenotypic measurements were extracted from CT scans of grain sorghum caryopses.
Phenotyping specific plant traits is difficult when the samples to be measured are architecturally complex. Inflorescence and root system traits are of great biological interest, but these structures present unique phenotyping challenges due to their often complicated and three-dimensional (3D) forms. We describe how a large industrial scale X-ray tomography (XRT) instrument can be used to scan architecturally complex plant structures for the goal of rapid and accurate measurement of traits that are otherwise cumbersome or not possible to capture by other means. The combination of a large imaging cabinet that can accommodate a wide range of sample size geometries and a variable microfocus reflection X-ray source allows noninvasive X-ray imaging and 3D volume generation of diverse sample types. Specific sample fixturing (mounting) and scanning conditions are presented. These techniques can be moderate to high throughput and still provide unprecedented levels of accuracy and information content in the 3D volume data they generate.
Why it matches plant phenotyping methods複雑な植物器官の形質を取得するための大規模X線トモグラフィー撮像法、固定法、走査条件を中心に開発・提示しているため、植物フェノタイピング手法として明確に該当する。
abstractWe describe how a large industrial scale X-ray tomography (XRT) instrument can be used to scan architecturally complex plant structures for the goal of rapid and accurate measurement of traits that are otherwise cumbersome or not possible to capture by other means.
In plants, the response to stress, such as salinity, pathogen attack, drought, high concentration of metals, hyperthermia, and hypothermia, is usually accompanied by potassium ion (K + ) leakage from the cytosol to the cell wall, mediated by plasma membrane cation conductivity. Stress-induced electrolyte leakage co-occurs with accumulation of reactive oxygen species (ROS) and calcium ions (Ca 2+ ) and often results in programmed cell death (PCD). The development of X-ray and mass spectrometry (MS) based imaging techniques has enabled insight into the spatial tissue and cell-specific redistribution of major and trace elements during the stress response. In this chapter a workflow for sample preparation, imaging, and image analysis by X-ray and MS based techniques is presented.
Why it matches plant phenotyping methods植物組織・細胞のストレス応答や細胞死に伴うイオン分布を可視化するX線/MS画像化について、試料調製・画像取得・画像解析のワークフローを中心に扱っており、植物状態の計測手法が中核です。
abstractThe development of X-ray and mass spectrometry (MS) based imaging techniques has enabled insight into the spatial tissue and cell-specific redistribution of major and trace elements during the stress response.
Roots are the prime organ for nutrient and water uptake and are therefore fundamental to the growth and development of plants. However, physical challenges of a heterogeneous environment and diverse edaphic stresses affect root growth in soil. Compacted soil is a serious global problem, causing inhibition of root elongation, which reduces surface area and impacts resource foraging. Visualisation and quantification of roots in soil is difficult due to this growth substrate's opaque nature; however, non-destructive imaging technologies are now becoming more widely available to plant and soil scientists working to address this challenge. We have recently developed an integrated approach, combining X-ray Computed Tomography (X-ray CT) and confocal microscopy to image roots grown in compacted soil conditions from a plant to a cellular scale. The method is suited to visualize cellular responses of root tips grown in both non-compacted and compacted soils. This protocol presents a fully integrated workflow, including soil column preparation, creation of compaction conditions, plant growth, imaging, and quantification of root adaptive responses at a cellular scale.
Why it matches plant phenotyping methodsX線CTと共焦点顕微鏡を統合し、土壌中のイネ根を非破壊で可視化・定量する手法とワークフローが中心であるため、植物表現型計測法に該当する。
abstractWe have recently developed an integrated approach, combining X-ray Computed Tomography (X-ray CT) and confocal microscopy to image roots grown in compacted soil conditions from a plant to a cellular scale.
The vascular bundle is an important structural unit that determines the growth and properties of bamboo. A high-resolution X-ray microtomography (μCT) was used to observe and reconstruct a three-dimensional (3D) morphometry model of the vascular bundle of the Qiongzhuea tumidinoda node due to its advantages of quick, nondestructive, and accurate testing of plant internal structure. The results showed that the morphology of vascular bundles varied significantly in the axial direction. In the cross-section, the number of axial vascular bundles reached a maximum at the lower end of the sheath scar, and the minimum of it was at the middle of the diaphragm. The frequency of axial vascular bundles decreased from the lower end of the node to the nodal ridge, and subsequently increased until the upper end of the bamboo node. The proportion of parenchyma, fibers, and conducting tissue was 65.7%, 30.5%, and 3.8%, respectively. The conducting tissues were intertwined to form a complex 3D network structure, with a connectivity of 94.77%. The conducting tissue with the largest volume accounted for 60.26% of the total volume of the conducting tissue. The 3D-distribution pattern of the conducting tissue of the node and that of the fibers were similar, but their thickness changed in the opposite pattern. This study revealed the 3D morphometry of the conducting tissue and fibers of the bamboo node, the reconstruction of the skeleton made the morphology more intuitive. Quantitative indicators such as the 3D volume, proportion, and connectivity of each type of tissue was obtained, the bamboo node was enlarged mainly caused by the particularly developed fibers. This work laid the foundation for a better understanding of the mechanical properties and water transportation of bamboo and revealed the mystery of bamboo node shedding of Q. tumidinoda.
Why it matches plant phenotyping methods植物ノード内部の維管束・繊維の3D形態をμCTで取得・再構築し、体積・割合・接続性などの定量形質を抽出することが研究の中心であり、単なる routine measurement ではない。
abstractA high-resolution X-ray microtomography (μCT) was used to observe and reconstruct a three-dimensional (3D) morphometry model of the vascular bundle
Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture. Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both CT scans of excavated field-grown root crowns and simulated images of root systems, and in both cases, it was shown to improve the accuracy of traits over existing methods. TopoRoot runs within a few minutes on a desktop workstation for images at the resolution range of 400^3, with minimal need for human intervention in the form of setting three intensity thresholds per image. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D imaging. The automation and efficiency make TopoRoot suitable for batch processing on large numbers of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.
Why it matches plant phenotyping methods3D画像からトウモロコシ根系の階層別形態形質を抽出する計算手法を開発し、既存法と精度比較・検証しており、植物表現型取得が中心です。
abstractWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D images of maize root crowns or root systems.
Reproduction assets foundThe paper's authors publicly distribute the TopoRoot analysis software (C++ pipeline with GUI) together with the 45 X-ray CT scans of maize root crowns, per-image threshold values, and hand-measured nodal root counts in a GitHub repository. The synthetic OpenSimRoot images and ground-truth traits are only available on.Code · publicto a Euclidean distance field (e.g., using [ 29 ]). Fig. 12
Hierarchies of sorghum roots computed by TopoRoot, showing one tiller ( A ), two tillers ( B ), and four tillers ( C ). Hierarchy levels 0, 1, 2, 3 and 4 are colored dark blue, light blue, green, orange, and red.
Software availability
TopoRoot is available for free at: https://github.com/danzeng8/TopoRoot .
Included in the page are instructions to run the software, and details on the formats of the input and output files. Currently, the accepted inputs are either image slices (suffixed with.png) or.raw files, with a.dat accompanying the.raw file to specify the dimensions. The output consists of a skeleton, a hierarchy annotationOpen asset ↗https://github.com/danzeng8/TopoRootlines:2051-2060Dataset · public\usepackage{amsfonts}
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\begin{document}$$t_{low} ,t_{mid} ,t_{high}$$\end{document} t low , t mid , t high ) and hand measurements of nodal roots for each sample, are available in the TopoRoot Github repository: https://github.com/danzeng8/TopoRoot . The synthetic images of simulated roots and associated ground truth trait measurements are available from the corresponding author upon request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing inOpen asset ↗https://github.com/danzeng8/TopoRootlines:2061-2116Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Abstract Background Breakthrough imaging technologies may challenge the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. In this context, X-Ray CT (computed tomography) technology can accurately obtain the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial–temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting to spatially quantify several traits. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). Results Roots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, several traits were computed. Two of them were accurately validated using the root digital twin as a ground truth against the cylindrical model: number of branches (RRMSE better than 9%) and volume, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. Conclusions The experimental results support the viability of the developed methodology, being able to provide scalability to a comprehensive analysis in order to perform high throughput root phenotyping.
Why it matches plant phenotyping methodsX線CTの4Dデータから根系構造形質を抽出する計算手法を開発し、精度検証とハイスループット根系フェノタイピングへの適用可能性を示した研究であり、方法が中心的です。
abstractWe develop a spatial–temporal root architectural modeling method based on 4D data from X-ray CT.
The internal structure of a seed plays a vital role during germination. Hence, before planting the seed internal quality inspection is advantageous to produce healthy seedlings. In this study, an X-ray CT scanner was used to generate CT images of five-year-old naturally aged pepper seeds. Several processing techniques such as reslicing, feature extraction, and classification were performed on these images. The reslicing process was applied to construct three different planes, namely, transaxial, sagittal, and coronal plane images from raw CT images. Then, three images were selected from each sample (one from each plane) for feature extraction. Using a pattern recognition algorithm, the gray-level co-occurrence matrix (GLCM) was created for each image, and twenty-two types of statistical derivations were performed to generate GLCM textural features. A supervised data matrix was constructed based on the germination results of the seed samples from the images, where the seeds were divided into two classes: normal viable seeds (class-1) and nonviable & abnormal viable seeds (class-2). Supervised classification methods, such as partial least-squares discriminant analysis (PLS-DA), support vector machine (SVM), and K-nearest neighbor (KNN) were used to evaluate the best outcome. Among the tested classifiers, PLS-DA provided the highest accuracy of 88.7% with five-fold cross validation, where seven important features extracted from different angles (θ) were found from the beta coefficient to be significant in the classification of the seed. Results from this study show that X-ray CT imaging incorporated with a pattern recognition system is a robust technique to classify seeds based on their internal quality attributes.
Why it matches plant phenotyping methodsX線CT画像と画像特徴抽出・分類を組み合わせ、種子の生存性・異常性という植物状態を推定する手法が研究の中心であり、交差検証による技術評価も行っている。
abstractSeveral processing techniques such as reslicing, feature extraction, and classification were performed on these images.
Maize seedlings are grown in Hostun sand with two different gradings and two different densities. The root-soil system is imaged daily for the first 8 days of plant growth with X-ray computed tomography. Segmentation, skeletonisation and digital image correlation techniques are used to analyse the evolution of the root system architecture, the displacement fields and the local strain fields due to plant growth in the soil. It is found that root thickness and root length density do not depend on the initial soil configuration. However, the depth of the root tip is strongly influenced by the initial soil density, and the number of laterals is impacted by grain size, which controls pore size, capillary rise and thus root access to water. Consequently, shorter root axes are observed in denser sand and fewer second order roots are observed in coarser sands. In all soil configurations tested, root growth induces shear strain in the soil around the root system, and locally, in the vicinity of the first order roots axis. Root-induced shear is accompanied by dilative volumetric strain close to the root body. Further away, the soil experiences dilation in denser sand and compaction in looser sand. These results suggest that the increase of porosity close to the roots can be caused by a mix of shear strain and steric exclusion.
Why it matches plant phenotyping methodsX線CT画像にセグメンテーション、骨格化、デジタル画像相関を適用し、根系構造や根の形態・成長を定量化する手法が研究の中心であるため。
abstractThe root-soil system is imaged daily for the first 8 days of plant growth with X-ray computed tomography.
MaizeX-ray / CTRootMorphology / geometry measurement2D/3D reconstructionRoot system architecture
The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies, one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root mass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts or scaled up as a stand-alone approach in large genetic studies of root system architecture.
Why it matches plant phenotyping methodsトウモロコシ根系のX線CT画像から3Dモデルを構築し、71形質を抽出する計算パイプラインを開発・適用しており、表現型取得手法が研究の中心です。
abstractHere, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample.
Reproduction assets foundThe paper's custom image-processing and feature-extraction scripts are publicly available in the Topp-Roots-Lab GitHub repository, explicitly linked by the authors for reproducing the work. The phenotype data (Data File S1) is in supplements without a direct URL, and image volumes are only available upon request.Code · publicA more extensive description of trait implementations, all scripts used for image processing and feature extraction, and links to repositories required to reproduce the work are available at https://github.com/Topp-Roots-Lab/3d-root-crown-analysis-pipeline/Open asset ↗Topp-Roots-Lab/3d-root-crown-analysis-pipelinelines:42-50Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The tropical forage species seed market, including signalgrass (Urochloa), is expanding. There is demand for fast, automated methods that can be implemented for quality control of the seeds produced, with the aim of evaluating physical purity. In this respect, seed morphological features obtained by processing radiographs with the Tomato Analyzer software and of red–green–blue obtained and processed on the Groundeye device were used to test differentiation of materials of the Urochloa genus. Seeds of Urochloa brizantha (A.Rich.) R.D.Webster cvv. BRS Piatã, Marandu, and Xaraés MG‐5; Urochloa ruziziensis (R.Germ. & C.M.Evrard) Crins cv. Ruziziensis; and Urochloa decumbens (Stapf) R.D.Webster cv. Basilisk were evaluated. Morphological features obtained by Tomato Analyzer allowed differentiation of Urochloa seeds at an accuracy level greater than 80% for all the materials evaluated. Seed area was one of the features that allowed this differentiation. The Groundeye system also showed high efficiency in distinguishing Urochloa seeds, except for Piatã and Xaraés, which exhibited morphological similarity, and lower accuracy levels in distinguishing their seeds (<50%). Additional studies should be conducted to evaluate Urochloa seed lots from other origins to validate the use of image analysis techniques for the purpose of obtaining morphological features and to assist in determining the physical purity of the materials.
Why it matches plant phenotyping methods種子の形態形質を画像解析で抽出し、品種・遺伝子型の識別精度を評価しており、画像ベースの植物表現型取得法が研究の中心である。
abstractfast, automated methods that can be implemented for quality control of the seeds produced
GreenhousePhotogrammetry / SfM / MVSX-ray / CTRootMorphology / geometry measurement2D/3D reconstructionVisualization / data managementBiomass / plant weightRoot system architectureWater status / transpiration
Current methods of root sampling typically only obtain small or incomplete sections of root systems and do not capture their true complexity. To facilitate the visualization and analysis of entire, full sized root systems of crop plants, mesocosm growth containers were developed with an internal volume of 45 ft3 (1.27 m3). Mesocosms allow for unconstrained root growth, excavation and preservation of 3-dimensional RSA, and modularity that facilitates the use of a variety of sensors. Sensors arrays monitoring matric potential, temperature and CO2 levels are buried in a grid formation at depths of 1.25, 2.75, & 4.25 ft to assess environmental fluxes at regular intervals. Additionally, 3-dimensional water availability can be measured using ERT inside of root mesocosms. Methods of 3D data visualization of fluxes were developed to allow for comparison with root architectural traits. Following harvest, the recovered root system can be digitally reconstructed through photogrammetry, which is an inexpensive method requiring only an appropriate studio space and a digital camera. Initial metrics inferred from the 3D models include root system biomass (occupied voxels), volume, flatness, convex hull volume and solidity with depth. Root systems are finally dissected and biomass measurements are made in a 3-dimensional matrix of the growth zone, while the crown is saved for X-ray CT analysis.
Why it matches plant phenotyping methods根系の3次元可視化・再構成と環境センシングを統合したメソコスムおよび表現型抽出手法の開発が中心であり、根系形態形質を定量化している。
abstractTo facilitate the visualization and analysis of entire, full sized root systems of crop plants, mesocosm growth containers were developed
Plant phenotyping relevance match · UnverifiedbioRxiv · Europe PMC · checked 15 Sept 2026
A novel deep learning algorithm is proposed for hollow heart detection which is an internal tuber defect. Hollow heart is one of many internal defects that decrease the market value of potatoes in the fresh market and food processing sectors. Susceptibility to internal defects like the hollow heart is influenced by genetic and environmental factors so elimination of defect-prone material in potato breeding programs is important. Current methods of evaluation utilize human scoring which is limiting (only collects binary data) relative to the data collection capacity afforded by computer vision or are based upon X-ray transmission techniques that are both expensive and can be hazardous. Automation of defect classification (e.g. hollow heart) from data sets collected using inexpensive, consumer-grade hardware has the potential to increase throughput and reduce bias in public breeding programs. The proposed algorithm consists of ResNet50 as the backbone of the model followed by a shallow fully connected network (FCN). A simple augmentation technique is performed to increase the number of images in the data set. The performance of the proposed algorithm is validated by investigating metrics such as precision and the area under the curve (AUC).
Why it matches plant phenotyping methodsジャガイモ塊茎の内部障害を画像から分類する深層学習手法を開発し、性能指標で検証しており、植物表現型取得が研究の中心である。
abstractA novel deep learning algorithm is proposed for hollow heart detection which is an internal tuber defect.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
In recent years, the plant morphology has been well studied by multiple approaches at cellular and subcellular levels. Two-dimensional (2D) microscopy techniques offer imaging of plant structures on a wide range of magnifications for researchers. However, subcellular imaging is still challenging in plant tissues like roots and seeds. Here we use a three-dimensional (3D) imaging technology based on the X-ray microscope (XRM) and analyze several plant tissues from different plant species. The XRM provides new insights into plant structures using non-destructive imaging at high-resolution and high contrast. We also utilized a workflow aiming to acquire accurate and high-quality images in the context of the whole specimen. Multiple plant samples including rice, tobacco, Arabidopsis and maize were used to display the differences of phenotypes. Our work indicates that the XRM is a powerful tool to investigate plant microstructure in high-resolution scale. Our work also provides evidence that evaluate and quantify tissue specific differences for a range of plant species. We also characterize novel plant tissue phenotypes by the XRM, such as seeds in Arabidopsis, and utilize them for novel observation measurement. Our work represents an evaluated spatial and temporal resolution solution on seed observation and screening.
Why it matches plant phenotyping methodsX線顕微鏡による非破壊3D画像取得とワークフローを用いて植物組織の表現型を定量・比較し、種子観察とスクリーニングへ応用しており、フェノタイピング手法が中心である。
abstractThe XRM provides new insights into plant structures using non-destructive imaging at high-resolution and high contrast.
The European mistletoe ( Viscum album ) is a dioecious epiphytic evergreen hemiparasite that develops an extensive endophyte enabling the absorption of water and mineral salts from the host tree, whereas the exophytic leaves are photosynthetically active. The attachment mode and host penetration are well studied, but little information is available about the effects of mistletoe age and sex on haustorium-host interactions. We harvested 130 plants of Viscum album ssp. album growing on host branches of Aesculus flava for morphological and anatomical investigations. Morphometric analyses of the mistletoe and the (hypertrophied) host interaction site were correlated with mistletoe age and sex. We recorded the morphology of the endophytic systems of various ages by using X-ray microtomography scans and corresponding stereomicroscopic images. For detailed anatomical studies, we examined thin stained sections of the mistletoe-host interface by light microscopy. The diameter and length of the branch hypertrophy showed a positive linear correlation with the age of the mistletoe. Correlations with their sex were only found for ratios between host branch and hypertrophy size. A female bias of about 76% was found. In a 4-year-old mistletoe, several small, almost equally sized sinkers and the connected cortical strands extend over more than 5 cm within the host branch. In older mistletoes, one main sinker was predominant and occupied an increasingly large proportion of the stem cross-section. Bands of vessels ran along the axis of the wedge-shaped haustoria and sinkers and bent sideways toward the mistletoe-host interface. At the interface, the vascular elements of the host wood changed their direction and formed vortices near the haustorium.
Why it matches plant phenotyping methodsマイクロCTとステレオ画像による植物内部構造の3D可視化・形態計測が研究の中心的手法であり、ミストルの内生系や宿主との相互作用部位という植物形態形質を取得している。
titleAdvances on the Visualization of the Internal Structures of the European Mistletoe: 3D Reconstruction Using Microtomography
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Table 1
Raw data of diameters and lengths of host branch (hypertrophy) and mistletoe.Open asset ↗lines:257-351Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Background 3D imaging, such as X-ray CT and MRI, has been widely deployed to study plant root structures. Many computational tools exist to extract coarse-grained features from 3D root images, such as total volume, root number and total root length. However, methods that can accurately and efficiently compute fine-grained root traits, such as root number and geometry at each hierarchy level, are still lacking. These traits would allow biologists to gain deeper insights into the root system architecture (RSA). Results We present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D X-ray CT images of field-excavated maize root crowns. These traits include the number, length, thickness, angle, tortuosity, and number of children for the roots at each level of the hierarchy. TopoRoot combines state-of-the-art algorithms in computer graphics, such as topological simplification and geometric skeletonization, with customized heuristics for robustly obtaining the branching structure and hierarchical information. TopoRoot is validated on both real and simulated root images, and in both cases it was shown to improve the accuracy of traits over existing methods. We also demonstrate TopoRoot in differentiating a maize root mutant from its wild type segregant using fine-grained traits. TopoRoot runs within a few minutes on a desktop workstation for volumes at the resolution range of 400^3, without need for human intervention. Conclusions TopoRoot improves the state-of-the-art methods in obtaining more accurate and comprehensive fine-grained traits of maize roots from 3D CT images. The automation and efficiency makes TopoRoot suitable for batch processing on a large number of root images. Our method is thus useful for phenomic studies aimed at finding the genetic basis behind root system architecture and the subsequent development of more productive crops.
Why it matches plant phenotyping methodsX線CT画像からトウモロコシ根系の階層的形態形質を抽出する計算手法を開発し、実画像・シミュレーション画像で検証しているため、植物フェノタイピング手法が中心である。
abstractWe present TopoRoot, a high-throughput computational method that computes fine-grained architectural traits from 3D X-ray CT images of field-excavated maize root crowns.
Reproduction assets foundThe paper's TopoRoot phenotyping software (C++ pipeline computing root hierarchy and fine-grained traits from X-ray CT volumes) and the datasets generated/analysed in the study (including the test dataset) are publicly released on the authors' GitHub repository.Code · publicduce a
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probability density field (e.g., deep learning). Since TopoRoot requires a gray-scale intensity
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volume with three thresholds (shape, kernel and neighborhood), a binary segmentation will first
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need to be converted into a Euclidean distance field.
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Software availability
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TopoRoot is available for free at: https://github.com/danzeng8/TopoRoot
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CC-BY 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
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this version posted August 28, 2021.
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https://doi.org/10.1101/2021.08.24.457522
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bOpen asset ↗danzeng8/TopoRootpdf-raw-page:37 lines:1-53Dataset · public39
CT: Computed Tomography
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Declarations
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Ethics approval and consent to participate
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Not applicable
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Consent for publication
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Not applicable
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Availability of data and materials
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The datasets generated and analysed during the current study are available in the TopoRoot
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Github repository: https://github.com/danzeng8/TopoRoot
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Competing interests
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The authors declare that they have no competing interests.
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Funding
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This material is based upon work supported by the National Science Foundation under award
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numbers DBI-1759836, DBI-1759807, DBI-1759796, EF-1971728, CCF-1907612, CCF-
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2106672, and IOS-1638507. DZ is funded in part by aOpen asset ↗danzeng8/TopoRootpdf-raw-page:39 lines:1-45Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
RiceX-ray / CTRootMorphology / geometry measurementSkeletonization / topologyRoot system architecture
Background The root distribution in the soil is one of the elements that comprise the root system architecture (RSA). In monocots, RSA comprises radicle and crown roots, each of which can be basically represented by a single curve with lateral root branches or approximated using a polyline. Moreover, RSA vectorization (polyline conversion) is useful for RSA phenotyping. However, a robust software that can enable RSA vectorization while using noisy three-dimensional (3D) volumes is unavailable. Results We developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping. It manages the single root (radicle or crown root) as a polyline (a vector), and the set of the polylines represents the entire RSA. RSAtrace3D vectorizes root segments between the two ends of a single root. By utilizing several base points on the root, RSAtrace3D suits noisy images if it is difficult to vectorize it using only two end nodes of the root. Additionally, by employing a simple tracking algorithm that uses the center of gravity (COG) of the root voxels to determine the tracking direction, RSAtrace3D efficiently vectorizes the roots. Thus, RSAtrace3D represents the single root shape more precisely than straight lines or spline curves. As a case study, rice (Oryza sativa) RSA was vectorized from X-ray computed tomography (CT) images, and RSA traits were calculated. In addition, varietal differences in RSA traits were observed. The vector data were 32,000 times more compact than raw X-ray CT images. Therefore, this makes it easier to share data and perform re-analyses. For example, using data from previously conducted studies. For monocot plants, the vectorization and phenotyping algorithm are extendable and suitable for numerous applications. Conclusions RSAtrace3D is an RSA vectorization software for 3D RSA phenotyping for monocots. Owing to the high expandability of the RSA vectorization and phenotyping algorithm, RSAtrace3D can be applied not only to rice in X-ray CT images but also to other monocots in various 3D images. Since this software is written in Python language, it can be easily modified and will be extensively applied by researchers in this field.
Why it matches plant phenotyping methods3D X線CT画像からイネ科根系構造をベクトル化し、根系形態形質を算出するソフトウェアとアルゴリズムの開発が中心である。
abstractWe developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
X-ray / CTTissue2D/3D reconstructionGrowth / development / phenology
X-ray computed tomography (CT) is a valuable tool for 3D imaging of plant tissues and organs. Applications include the study of plant development and organ morphogenesis, as well as modeling of transport processes in plants. Some challenges remain, however, including attaining higher contrast for easier quantification, increasing the resolution for imaging subcellular features, and decreasing image acquisition and processing time for high-throughput phenotyping. In addition, phase contrast, multispectral, dark-field, soft X-ray, and time-resolved imaging are emerging. At the same time, a large amount of 3D image data are becoming available, posing challenges for data management. We review recent advances in the area of X-ray CT for plant imaging, and describe opportunities for using such images for studying transport processes in plants.
Why it matches plant phenotyping methods植物組織・器官の3D画像化手法としてX線CTをレビューし、高スループット表現型解析や画像の定量化を主要課題として扱っているため、方法レビューとして中心的です。
abstractX-ray computed tomography (CT) is a valuable tool for 3D imaging of plant tissues and organs.
Background Hyperaccumulation of trace elements is a rare trait among plants which is being investigated to advance our understanding of the regulation of metal accumulation and applications in phytotechnologies. Noccaea caerulescens (Brassicaceae) is an intensively studied hyperaccumulator model plant capable of attaining extremely high tissue concentrations of zinc and nickel with substantial genetic variation at the population-level. Micro-X-ray Fluorescence spectroscopy (µXRF) mapping is a sensitive high-resolution technique to obtain information of the spatial distribution of the plant metallome in hydrated samples. We used laboratory-based µXRF to characterize a collection of 86 genetically diverse Noccaea caerulescens accessions from across Europe. We developed an image-processing method to segment different plant substructures in the µXRF images. We introduced the concentration quotient (CQ) to quantify spatial patterns of metal accumulation and linked that to genetic variation. Results Image processing resulted in automated segmentation of µXRF plant images into petiole, leaf margin, leaf interveinal and leaf vasculature substructures. The harmonic means of recall and precision (F1 score) were 0.79, 0.80, 0.67, and 0.68, respectively. Spatial metal accumulation as determined by CQ is highly heritable in Noccaea caerulescens for all substructures, with broad-sense heritability (H 2 ) ranging from 76 to 92%, and correlates only weakly with other heritable traits. Insertion of noise into the image segmentation algorithm barely decreases heritability scores of CQ for the segmented substructures, illustrating the robustness of the trait and the quantification method. Very low heritability was found for CQ if randomly generated substructures were compared, validating the approach. Conclusions A strategy for segmenting µXRF images of Noccaea caerulescens is proposed and the concentration quotient is developed to provide a quantitative measure of metal accumulation pattern, which can be used to determine genetic variation for such pattern. The metric is robust to segmentation error and provides reliable H 2 estimates. This strategy provides an avenue for quantifying XRF data for analysis of the genetics of metal distribution patterns in plants and the subsequent discovery of new genes that regulate metal homeostasis and sequestration in plants.
Why it matches plant phenotyping methodsµXRF画像の植物組織分割と、金属蓄積空間パターンを定量化する指標を開発・検証しており、植物表現型の取得・抽出手法が中心である。
abstractWe developed an image-processing method to segment different plant substructures in the µXRF images.
Reproduction assets foundThe authors explicitly state that the analysis code created for this study (µXRF image segmentation and CQ/heritability analysis) is publicly available in their GitHub repository. The phenotype datasets themselves are only available on request from the corresponding author, so they are noted as a request-only asset. NoCode · publicCode is available at https://github.com/LucasYEAST/noccaea .Open asset ↗LucasYEAST/noccaealines:211-272Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
ArabidopsisX-ray / CTRoot2D/3D reconstructionRoot system architecture
Abstract Plant roots change their morphological traits in order to adapt themselves to different environmental conditions, resulting in the alteration of the root system architecture. To understand this mechanism, it is essential to visualize the morphology of the entire root system. To reveal effects of long-term alteration of gravity environment on root system development, we have performed an experiment in the International Space Station using Arabidopsis plants and obtained dried root systems grown in rockwool slabs. The X-ray computed tomography (CT) technique using industrial X-ray scanners has been introduced to visualize the root system architecture of crop species grown in soil in 3D non-invasively. In the case of the present study, however, the root system of Arabidopsis is composed of finer roots compared with typical crop plants and rockwool is also composed of fibers having similar dimension to that of the roots. A higher spatial resolution imaging method is required for distinguishing roots from rockwool. Therefore, in the present study, we tested refraction-contrast X-ray micro-CT using coherent X-ray optics available at the beamline of the synchrotron radiation facility SPring-8 for bio-imaging. We have found that a wide field of view but with low resolution obtained at the experimental Hutch 3 of this beamline provided an overview map of the root systems, while a narrow field of view but with high resolution obtained at the experimental Hutch 1 provided an extended architecture of the secondary roots, by a clear distinction between roots and individual rockwool fibers, resulting in the successful tracing of these roots from their basal regions.
Why it matches plant phenotyping methods根系形態・アーキテクチャを3D可視化するX線マイクロCT手法の適用・評価が研究の中心であり、植物表現型の取得法に該当する。
abstractTherefore, in the present study, we tested refraction-contrast X-ray micro-CT using coherent X-ray optics available at the beamline of the synchrotron radiation facility SPring-8 for bio-imaging.
High-resolution X-ray computer tomography (microCT) is a well-established technique to analyse three-dimensional microstructures in 3D non-destructive imaging. The non-destructive three-dimensional analysis of lichens is interesting for many reasons. The examination of hidden structural characteristics can, e.g., provide information on internal structural features (form and distribution of fungal-supporting tissue/hypha), gas-filled spaces within the thallus (important for gas exchange and, thus, physiological processes), or yield information on the symbiont composition within the lichen, e.g., the localisation and amount of additional cyanobacteria in cephalodia. Here, we present the possibilities and current limitations for applying conventional laboratory-based high-resolution X-ray computer tomography to analyse lichens. MicroCT allows the virtual 3D reconstruction of a sample from 2D X-ray projections and is helpful for the non-destructive analysis of structural characters or the symbiont composition of lichens. By means of a quantitative 3D image analysis, the volume of internal cephalodia is determined for Lobaria pulmonaria and the external cephalodia of Peltigera leucophlebia. Nevertheless, the need for higher-resolution tomography for more detailed studies is emphasised. Particular challenges are the large sizes of datasets to be analysed and the high variability of the lichen microstructures.
Why it matches plant phenotyping methods地衣類の3D構造と内部セファロディア体積を、microCTと定量画像解析で取得する手法が中心であり、植物体の形態・構造形質を測定しているため含める。
abstractHere, we present the possibilities and current limitations for applying conventional laboratory-based high-resolution X-ray computer tomography to analyse lichens.
Published30 Jun 2021The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciencesCited by 3 · OpenAlex ↗
MaizeTomatoX-ray / CTRootImage / point-cloud registrationSkeletonization / topologyRoot system architecture
Abstract. Breakthrough imaging technologies are a potential solution to the plant phenotyping bottleneck in marker-assisted breeding and genetic mapping. X-Ray CT (computed tomography) technology is able to acquire the digital twin of root system architecture (RSA), however, advances in computational methods to digitally model spatial disposition of root system networks are urgently required.We extracted the root skeleton of the digital twin based on 3D data from X-ray CT, which is optimized for high-throughput and robust results. Significant root architectural traits such as number, length, growth angle, elongation rate and branching map can be easily extracted from the skeleton. The curve-skeleton extraction is computed based on a constrained Laplacian smoothing algorithm. This skeletal structure drives the registration procedure in temporal series. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University in West Lafayette (IN, USA). Three samples of tomato root at 2 different times and three samples of corn root at 3 different times were scanned. The skeleton is able to accurately match the shape of the RSA based on a visual inspection.The results based on a visual inspection confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis to high throughput root phenotyping.
Why it matches plant phenotyping methodsX線CT画像から根系骨格を抽出し、根の形態形質を高スループットに推定する計算手法の開発が中心であるため。
abstractWe extracted the root skeleton of the digital twin based on 3D data from X-ray CT, which is optimized for high-throughput and robust results.
Abstract Background Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown. Results Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A , which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding. Conclusion Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.
Why it matches plant phenotyping methods高スループット光学フェノタイピングシステムの開発と、画像から植物の外部・内部形質を抽出する解析パイプラインが研究の中心であるため含める。
abstractHere we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days
Reproduction assets foundThe paper publicly deposits its maize RGB/HSI/CT images, i-trait phenotypic data, and genotype data on Figshare, and the authors' CT/HSI/RGB image-analysis pipeline code on GitHub and Zenodo, plus figures/supplemental files on Figshare.Code · publicThe code of CT, HSI, and RGB image analysis pipelines could be downloaded via the link: https://github.com/fenghuifh2006/Maize-RGB-CT-HSI-programOpen asset ↗github · fenghuifh2006/Maize-RGB-CT-HSI-programlines:185-218Code · publicThe code of CT, HSI, and RGB image analysis pipelines could be downloaded via the link: https://github.com/fenghuifh2006/Maize-RGB-CT-HSI-program and https://doi.org/10.5281/zenodo.4690730Open asset ↗zenodo · 10.5281/zenodo.4690730lines:185-218Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract BackgroundBreakthrough imaging technologies are a potential solution to address the plant phenotyping bottleneck regarding marker-assisted breeding and genetic mapping. X-Ray CT (computed tomography) technology is able to acquire the digital twin of root system architecture (RSA) but computational methods to quantify RSA traits and analyze their changes over time are limited. RSA traits extremely affect agricultural productivity. We develop a spatial-temporal root architectural modeling method based on 4D data from X-ray CT. This novel approach is optimized for high-throughput phenotyping considering the cost-effective time to process the data and the accuracy and robustness of the results. Significant root architectural traits, including root elongation rate, number, length, growth angle, height, diameter, branching map, and volume of axial and lateral roots are extracted from the model based on the digital twin. Our pipeline is divided into two major steps: (i) first, we compute the curve-skeleton based on a constrained Laplacian smoothing algorithm. This skeletal structure determines the registration of the roots over time; (ii) subsequently, the RSA is robustly modeled by a cylindrical fitting. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) from Purdue University in West Lafayette (IN, USA). ResultsRoots from three samples of tomato plants at two different times and three samples of corn plants at three different times were scanned. Regarding the first step, the PCA analysis of the skeleton is able to accurately and robustly register temporal roots. From the second step, the volume from the cylindrical model was compared against the root digital twin, reaching a coefficient of determination (R2) of 0.84 and a P < 0.001. ConclusionsThe results confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis to high throughput root phenotyping.
Why it matches plant phenotyping methodsX線CTの4Dデータから根系構造形質を抽出する計算手法を開発・検証しており、植物フェノタイピング手法が研究の中心です。
abstractWe develop a spatial-temporal root architectural modeling method based on 4D data from X-ray CT.
Indeterminate growth of peanut (Arachis hypogaea L.) creates indecision for best digging date for maturity and economic return. The current standard to determine peanut maturity is the Hull Scrape method. This method uses human observations to place hull scraped peanuts on a color maturity profile board. Human observations may lack precision and repeatability from individual to individual. X-ray technology has the capability of viewing peanut kernels through the hull to possibly ascertain density and maturity. The objective was to determine if x-ray could be used as a quick, non-destructive, and repeatable method to determine peanut maturity of runner, spanish, and virginia market types. Fresh dug peanut pods had 25 percent greater peanut area and gray scale values compared with hull scraped pods (runner and virginia only) and showed no difference in x-ray value between immature and fully mature peanut. Dried peanut showed a linear response of x-ray value versus peanut maturity (hull color). Virginia market type had much higher x-ray values followed by runners, then spanish. The relationship between peanut maturity and x-ray value peaked at the Orange class for runners (Georgia-06G, Georgia-13M), and Spanish (AT9899) while virginia (Georgia-11J) tended to peak at the Brown class. This research demonstrated that x-ray technology may be used to measure peanut density and possible maturity but needs further examination past Orange and Brown maturity class. Final x-ray values determined by this proprietary x-ray equipment may not be transferable due to specific x-ray power, detector precision, background color/scatter, and other electronic nuances.
Why it matches plant phenotyping methodsX線で落花生の成熟度・密度を非破壊かつ反復的に測定する方法の有効性と限界を検証しており、植物形質の取得法が研究の中心です。
abstractThe objective was to determine if x-ray could be used as a quick, non-destructive, and repeatable method to determine peanut maturity
The rice panicle seed setting rate is extremely important for calculating rice yield and performing genetic analysis. Unlike machine vision, X-ray computed tomography (CT) imaging is a nondestructive technique that provides direct information on the internal and external structure of rice panicles. However, occlusion and adhesion of panicles and grains in a CT image sequence make these objects difficult to identify, which in turn hinders accurate determination of the seed setting rate of rice panicles. Therefore, this paper proposes a method based on a mask region convolutional neural network (Mask R-CNN) for feature extraction and three-dimensional (3-D) recognition of CT images of rice panicles. X-ray CT feature characterization was combined with the Mask R-CNN algorithm to perform feature extraction and classification of a panicle and grains in each layer of the CT sequence. The Euclidean distance between adjacent layers was minimized to extract the features of a 3-D panicle and grains. The results were used to calculate the rice panicle seed setting rate. The proposed method was experimentally verified using eight sets of different rice panicles. The results showed that the proposed method can efficiently identify and count plump grains and blighted grains to achieve an accuracy above 99% for the seed setting rate.
Why it matches plant phenotyping methodsX線CT画像とMask R-CNNを用いてイネ穂の粒を3次元抽出・認識し、登熟歩合という植物形質を推定する手法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractTherefore, this paper proposes a method based on a mask region convolutional neural network (Mask R-CNN) for feature extraction and three-dimensional (3-D) recognition of CT images of rice panicles.
ArabidopsisLaboratory / benchtopX-ray / CTRoot2D/3D reconstructionRoot system architecture
Abstract Plant roots change their morphological traits in order to adapt themselves to different environmental conditions, resulting in alteration of the root system architecture. To understand this mechanism, it is essential to visualize morphology of the entire root system. To reveal effects of long-term alteration of gravity environment on root system development, we have performed an experiment in the International Space Station using Arabidopsis ( Arabidopsis thaliana (L.) Heynh.) plants and obtained dried root systems grown in rockwool slabs (mineral wool substrate). X-ray computer tomography (CT) technique using an industrial X-ray scanner has been introduced for the purpose to visualize root system architecture of crop species grown in soil in 3D non-invasively. In the case of the present study, however, root system of Arabidopsis is composed of finer roots compared with typical crop plants and rockwool is also composed of fibers having similar dimension to that of the roots. A higher spatial resolution imaging method is required for distinguishing roots from rockwool. Therefore, in the present study, we tested refraction-contrast X-ray micro-CT using coherent X-ray optics available at the beamline BL20B2 of the synchrotron radiation facility SPring-8. Using this technique, both the primary and the secondary roots were successfully identified in the tomographic slices, clearly distinguished from the individual rockwool fibers and resulting in successful tracing of these roots from their basal regions. This newly-developed technique should contribute to elucidate the effect of microgravity on Arabidopsis root system architecture in space.
Why it matches plant phenotyping methods植物根系形態を3D可視化・追跡する高解像度X線マイクロCT法の開発が中心であり、根系構造という植物表現型を直接測定している。
abstractTherefore, in the present study, we tested refraction-contrast X-ray micro-CT using coherent X-ray optics available at the beamline BL20B2 of the synchrotron radiation facility SPring-8.
MaizeLaboratory / benchtopX-ray / CTRootObject detectionSegmentationRoot system architecture
Background X-ray computed tomography is acknowledged as a powerful tool for the study of root system architecture of plants growing in soil. In this paper, we improved the original root segmentation algorithm "Rootine" and present its succeeding version "Rootine v.2". In addition to gray value information, Rootine algorithms are based on shape detection of cylindrical roots. Both algorithms are macros for the ImageJ software and are made freely available to the public. New features in Rootine v.2 are (i) a pot wall detection and removal step to avoid segmentation artefacts for roots growing along the pot wall, (ii) a calculation of the root average gray value based on a histogram analysis, (iii) an automatic calculation of thresholds for hysteresis thresholding of the tubeness image to reduce the number of parameters and (iv) a false negatives recovery based on shape criteria to increase root recovery. We compare the segmentation results of Rootine v.1 and Rootine v.2 with the results of root washing and subsequent analysis with WinRhizo. We use a benchmark dataset of maize roots (Zea mays L. cv. B73) grown in repacked soil for two scenarios with differing soil heterogeneity and image quality. Results We demonstrate that Rootine v.2 outperforms its preceding version in terms of root recovery and enables to match better the root diameter distribution data obtained with root washing. Despite a longer processing time, Rootine v.2 comprises less user-defined parameters and shows an overall greater usability. Conclusion The proposed method facilitates higher root detection accuracy than its predecessor and has the potential for improving high-throughput root phenotyping procedures based on X-ray computed tomography data analysis.
Why it matches plant phenotyping methodsX線CT画像から根を自動セグメンテーションする手法を改良し、ベンチマークデータで既存手法および根洗浄法と比較検証しており、根系形態の取得が研究の中心です。
abstractIn this paper, we improved the original root segmentation algorithm "Rootine" and present its succeeding version "Rootine v.2".
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 8 Sept 2026
As a consequence of climate change, heat waves in combination with extended drought periods will be an increasing threat to crop yield. Therefore, breeding stress tolerant crop plants is an urgent need. Breeding for stress tolerance has benefited from large scale phenotyping, enabling non-invasive, continuous monitoring of plant growth. In case of potato, this is compromised by the fact that tubers grow belowground, making phenotyping of tuber development a challenging task. To determine the growth dynamics of tubers before, during and after stress treatment is nearly impossible with traditional destructive harvesting approaches. In contrast, X-ray Computed Tomography (CT) offers the opportunity to access belowground growth processes. In this study, potato tuber development from initiation until harvest was monitored by CT analysis for five different genotypes under stress conditions. Tuber growth was monitored three times per week via CT analysis. Stress treatment was started when all plants exhibited detectable tubers. Combined heat and drought stress was applied by increasing growth temperature for 2 weeks and simultaneously decreasing daily water supply. CT analysis revealed that tuber growth is inhibited under stress within a week and can resume after the stress has been terminated. After cessation of stress, tubers started growing again and were only slightly and insignificantly smaller than control tubers at the end of the experimental period. These growth characteristics were accompanied by corresponding changes in gene expression and activity of enzymes relevant for starch metabolism which is the driving force for tuber growth. Gene expression and activity of Sucrose Synthase (SuSy) reaffirmed the detrimental impact of the stress on starch biosynthesis. Perception of the stress treatment by the tubers was confirmed by gene expression analysis of potential stress marker genes whose applicability for potato tubers is further discussed. We established a semi-automatic imaging pipeline to analyze potato tuber delevopment in a medium thoughput (5 min per pot). The imaging pipeline presented here can be scaled up to be used in high-throughput phenotyping systems. However, the combination with automated data processing is the key to generate objective data accelerating breeding efforts to improve abiotic stress tolerance of potato genotypes.
Why it matches plant phenotyping methodsX線CTによる地下部塊茎の非破壊・反復計測と、半自動画像解析パイプラインの確立が研究の中心であり、植物形態・成長表現型を抽出する方法研究に該当する。
abstractIn contrast, X-ray Computed Tomography (CT) offers the opportunity to access belowground growth processes.
Plant pathogenic Ralstonia cause wilt diseases by colonizing xylem vessels and disrupting water transport. Due to the abundance of Ralstonia cells in vessels, the dogma is that bacterial biomass clogs vessels and reduces the flow of xylem sap. However, the physiological mechanism of xylem disruption during bacterial wilt disease is untested. Using a tomato and Ralstonia pseudosolanacearum GMI1000 model, we visualized and quantified the spatiotemporal dynamics of xylem disruption during bacterial wilt disease. First, we measured stomatal conductance of leaflets on mock-inoculated and wilt-symptomatic plants. Wilted leaflets had reduced stomatal conductance, as did turgid leaflets located on the same petiole as wilted leaflets. Next, we used X-ray microcomputed tomography (X-ray microCT) and light microscopy to differentiate between mechanisms of xylem disruption: blockage by bacterial biomass, blockage by vascular tyloses, or sap displacement by gas embolisms. We imaged stems on plants with intact roots and leaves to quantify embolized vessels. Embolized vessels were rare, but there was a slight trend of increased vessel embolisms in infected plants with low bacterial population sizes. To test the hypothesis that vessels are clogged during bacterial wilt, we imaged excised stems after allowing the sap to evaporate during a brief dehydration. Most xylem vessels in mock-infected plants emptied their contents after excision, but non-conductive clogged vessels were abundant in infected plants by 2 days post infection. At wilt onset when bacterial populations exceeded 5x108 cfu/g stem tissue, approximately half of the xylem vessels were clogged with electron-dense bacterial biomass. We found no evidence of tyloses in the X-ray microCT reconstructions or light microscopy on the preserved stems. Bacterial blockage of vessels appears to be the principal cause of vascular disruption during Ralstonia wilt.
Why it matches plant phenotyping methodsX線マイクロCTを用いて感染植物の木部塞栓・導管閉塞を可視化・定量し、病態に関わる植物の生理状態を測定している。病理学的機序の研究ではあるが、画像取得と定量が主要な技術的根拠である。
titleX-ray microcomputed tomography provides direct evidence that Ralstonia biomass clogs xylem vessels.
ABSTRACT The root system is critical for the survival of nearly all land plants and a key target for improving abiotic stress tolerance, nutrient accumulation, and yield in crop species. Although many methods of root phenotyping exist, within field studies one of the most popular methods is the extraction and measurement of the upper portion of the root system, known as the root crown, followed by trait quantification based on manual measurements or 2D imaging. However, 2D techniques are inherently limited by the information available from single points of view. Here, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample. This approach improves estimates of the genetic contribution to root system architecture, and is refined enough to detect various changes in global root system architecture over developmental time as well as more subtle changes in root distributions as a result of environmental differences. We demonstrate that root pulling force, a high-throughput method of root extraction that provides an estimate of root biomass, is associated with multiple 3D traits from our pipeline. Our combined methodology can therefore be used to calibrate and interpret root pulling force measurements across a range of experimental contexts, or scaled up as a stand-alone approach in large genetic studies of root system architecture.
Why it matches plant phenotyping methodsトウモロコシ根系を対象に、X線CTによる3Dモデル化と計算パイプラインで71形質を抽出し、根引抜き力との較正・解釈まで行う、中心的な表現型計測手法研究である。
abstractHere, we used X-ray computed tomography to generate highly accurate 3D models of maize root crowns and created computational pipelines capable of measuring 71 features from each sample.
Reproduction assets foundThe paper states that the authors' scripts for X-ray CT image processing and root feature extraction (batch-segmentation, batch-skeleton) are publicly available in the Topp-Roots-Lab GitHub repository. Raw phenotype data is said to be in Supplemental File 1, but no public URL for it is provided in the supplied blocks.Code · publicestimated by taking the 2D projection of the 3D volume, then
185 calculated using a similar approach to that described in Grift et al., 2011. DensityS features are
186 computationally similar to plant compactness traits described in Yang et al., 2014. Scripts used
187 for image processing and feature extraction are available at https://github.com/Topp-Roots-Lab/
188
189 Statistical Analysis
190
191 All downstream (i.e. post feature extraction) analysis was performed in the R statistical
192 computing environment. Initially, principal component analysis using all 71 3D roots traits was
193 used to identify large outliers, leading to the removal of 2 samples in the G2F 2017 data and 3
19Open asset ↗Topp-Roots-Labpdf-layout-page:5 lines:1-56Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Plant micro-phenotype mainly refers to the phenotypic information at the tissue, cell, and subcellular levels, which is an important part of plant phenomics research. In view of the problems of low efficiency, large error, and few traits of traditional methods for detecting kernel microscopic traits, Micro-CT scanning technology was used to carry out precise identification of micro-phenotype on 11 varieties of maize kernels. A total of 34 microscopic traits were obtained based on CT sequence images of 7 tissues, including seed, embryo, endosperm, cavity, subcutaneous cavity, endosperm cavity and embryo cavity. Among the 34 microscopic traits, 4 traits, including endosperm cavity surface area, kernel volume, endosperm volume ratio and endosperm cavity specific surface area, were significantly different among maize types (P-value<0.05). The surface area of endosperm cavity and kernel volume of common maize were significantly higher than those of other types of maize. The specific surface area of endosperm cavity of high oil maize was the largest. The endosperm cavity of sweet corn had the smallest specific surface area. The endosperm volume ration of popcorn was the largest. Furthermore, 34 traits were used for One-way ANOVA and cluster analysis, and 11 different maize varieties were divided into four categories, of which the first category was mainly common maize, the second category was mainly popcorn, the third category was sweet corn, and the fourth category was high oil maize. The results indicated that Micro-CT scanning technology could not only achieve precise identification of micro-phenotype of maize kernels, but also provide supports for kernel classification and variety detection, and so on.
Why it matches plant phenotyping methodsマイクロCT画像からトウモロコシ穀粒の微表現型形質を多数抽出する手法が研究の中心であり、従来法との課題を踏まえた技術適用と形質抽出・分類を行っている。
abstractMicro-CT scanning technology was used to carry out precise identification of micro-phenotype on 11 varieties of maize kernels.
Rice panicle phenotyping is required in rice breeding for high yield and grain quality. To fully evaluate spikelet and kernel traits without threshing and hulling, using X-ray and RGB scanning, we developed an integrated rice panicle phenotyping system and a corresponding image analysis pipeline. We compared five methods of counting spikelets and found that Faster R-CNN achieved high accuracy (R2 of 0.99) and speed. Faster R-CNN was also applied to indica and japonica classification and achieved 91% accuracy. The proposed integrated panicle phenotyping method offers benefit for rice functional genetics and breeding.
Why it matches plant phenotyping methodsX線・RGB画像と深層学習によるイネ穂の形質取得システムおよび解析パイプラインを開発し、計数精度も比較検証しており、フェノタイピング手法が研究の中心である。
abstractwe developed an integrated rice panicle phenotyping system and a corresponding image analysis pipeline.
Lodging is a common problem in rice, reducing its yield and mechanical harvesting efficiency. Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity. The ideal rice culm structure, including major_axis_culm, minor axis_culm, and wall thickness_culm, is critical for improving lodging resistance. However, the traditional method of measuring rice culms is destructive, time consuming, and labor intensive. In this study, we used a high-throughput micro-CT-RGB imaging system and deep learning (SegNet) to develop a high-throughput micro-CT image analysis pipeline that can extract 24 rice culm morphological traits and lodging resistance-related traits. When manual and automatic measurements were compared at the mature stage, the mean absolute percentage errors for major_axis_culm, minor_axis_culm, and wall_thickness_culm in 104 indica rice accessions were 6.03%, 5.60%, and 9.85%, respectively, and the R 2 values were 0.799, 0.818, and 0.623. We also built models of bending stress using culm traits at the mature and tillering stages, and the R 2 values were 0.722 and 0.544, respectively. The modeling results indicated that this method can quantify lodging resistance nondestructively, even at an early growth stage. In addition, we also evaluated the relationships of bending stress to shoot dry weight, culm density, and drought-related traits and found that plants with greater resistance to bending stress had slightly higher biomass, culm density, and culm area but poorer drought resistance. In conclusion, we developed a deep learning-integrated micro-CT image analysis pipeline to accurately quantify the phenotypic traits of rice culms in ∼4.6 min per plant; this pipeline will assist in future high-throughput screening of large rice populations for lodging resistance.
Why it matches plant phenotyping methods深層学習統合micro-CT画像解析パイプラインを開発し、イネ茎の形態・倒伏抵抗性関連形質を非破壊かつ高スループットに抽出・検証しており、植物フェノタイピング手法が研究の中心である。
abstractwe used a high-throughput micro-CT-RGB imaging system and deep learning (SegNet) to develop a high-throughput micro-CT image analysis pipeline that can extract 24 rice culm morphological traits and lodging resistance-related traits.
Reproduction assets foundThe paper's micro-CT rice culm phenotyping pipeline source code is explicitly stated to be publicly available on the authors' GitHub repository and their Crop Phenomics Group website; phenotypic data are in Supplemental Data 1 (not directly linked here).Code · publicThe source code and user guidelines are available at http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action and https://github.com/diwu861125/diwu123456 .Open asset ↗diwu861125/diwu123456lines:329-354Code · publicthe main source code is provided in Supplemental Video 1 , Supplemental Note 2 , our Crop Phenomics Group website ( http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action ), and a GitHub website ( https://github.com/diwu861125/diwu123456 ).Open asset ↗lines:145-217Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.
Why it matches plant phenotyping methods植物ストレス検出に用いるリモートセンシング、蛍光、サーモグラフィ、分光、CT、MRI、PETなどの技術 landscape をレビューしており、植物の状態を測定する方法論が中心です。
titlePast and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography
Background The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time. Results We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serves as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (> 0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds. Conclusions Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.
Why it matches plant phenotyping methodsマルチスペクトル画像とX線画像を組み合わせ、種子の生理的品質（正常・異常発芽種子、死種子）を自動推定する手法の開発が中心であり、植物フェノタイピング方法に該当する。
abstractWe present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies.
Quantification of anatomical and compositional features underpins both fundamental and applied studies of plant structure and function. Relatively few non-invasive techniques are available for aquatic plants. Traditional methods such as sectioning are low-throughput and provide 2-dimensional information. X-ray Computed Microtomography (μCT) offers a non-destructive method of three dimensional (3D) imaging in planta , but has not been widely used for aquatic species, due to the difficulties in sample preparation and handling. We present a novel sample handling protocol for aquatic plant material developed for μCT imaging, using duckweed plants and turions as exemplars, and compare the method against existing approaches. This technique allows for previously unseen 3D volume analysis of gaseous filled spaces, cell material, and sub-cellular features. The described embedding method, utilizing petrolatum gel for sample mounting, was shown to preserve sample quality during scanning, and to display sufficiently different X-ray attenuation to the plant material to be easily differentiated by image analysis pipelines. We present this technique as an improved method for anatomical structural analysis that provides novel cellular and developmental information.
Why it matches plant phenotyping methods水生植物のμCT撮像に向けた試料調製・ハンドリング法を開発し、3D画像から解剖学的・構造的形質を解析する手法を提示しており、植物フェノタイピング手法が中心である。
abstractWe present a novel sample handling protocol for aquatic plant material developed for μCT imaging, using duckweed plants and turions as exemplars, and compare the method against existing approaches.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 8 Sept 2026
X-ray / CTRootMorphology / geometry measurementSegmentationRoot system architecture
BACKGROUND: Computed X-ray tomography (CTX) is a high-end nondestructive approach for the visual assessment of root architecture in soil. Nevertheless, in order to evaluate high-resolution CTX data of root architectures, manual segmentation of the depicted root systems from large-scale volume data is currently necessary, which is both time consuming and error prone. The duration of such a segmentation is of importance, especially for time-resolved growth analysis, where several instances of a plant need to be segmented and evaluated. Specifically, in our application, the contrast between soil and root data varies due to different growth stages and watering situations at the time of scanning. Additionally, the root system itself is expanding in length and in the diameter of individual roots. OBJECTIVE: is not limited to the segmentation of small below-ground organs, but is also able to handle storage roots with a diameter larger than 40 voxels. RESULTS: tool can provide a higher efficiency for the semiautomatic high-throughput assessment of the root architectures of different types of plants from large-scale CTX. Furthermore, for all datasets within a growth experiment, only a single set of parameters is needed. Thus, the proposed tool can be used for a wide range of growth experiments in the field of plant phenotyping.
Why it matches plant phenotyping methods根系CT画像からの3Dセグメンテーションを半自動化するツール開発であり、植物表現型取得の中心的手法である。
abstractComputed X-ray tomography (CTX) is a high-end nondestructive approach for the visual assessment of root architecture in soil.
X-ray computed tomography (XCT) is a non-destructive 3D imaging technique now commonly employed for imaging plant and soil systems. In particular it is increasingly being utilised to capture plant root/ soil structures, in place of traditional destructive dissection techniques. One of the major challengesin the use of the technology is the similar X-ray attenuation characteristics of plant roots and soil-water which results in a low contrast to noise ratio between these materials - particularly when imaging larger soil samples. Where similar issues of poor contrast occur within biomedical imaging, these issues are overcome with the use of radiocontrast agents (solutions or suspensions containing elements with high X-ray attenuation characteristics). The aim of the research presented in this thesis is to investigate novel strategies for the application of various contrast agents in order to enhance the study of plant and soil structures and to then use contrast agents in innovative applications to capture the movement of solutes through soil and roots. I begin with a review of the current literature, determining the key requirements of contrast agents for imaging biological tissues: minimal toxicity, low reactivity, ... (continues)
Why it matches plant phenotyping methods植物根と土壌構造をX線CTで取得するための造影剤適用を中心に開発・検討しており、植物フェノタイピング画像取得法が本研究の中心である。
abstractThe aim of the research presented in this thesis is to investigate novel strategies for the application of various contrast agents in order to enhance the study of plant and soil structures
Capturing complete internal anatomies of plant organs and tissues within their relevant morphological context remains a key challenge in plant science. While plant growth and development are inherently multiscale, conventional light, fluorescence, and electron microscopy platforms are typically limited to imaging of plant microstructure from small flat samples that lack direct spatial context to, and represent only a small portion of, the relevant plant macrostructures. We demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution 3D volumes of intact plant samples from the cell to whole plant level. Serial imaging of a single sample is shown to provide sub-micron 3D volumes co-registered with lower magnification scans for explicit contextual reference. High quality 3D volume data from our enhanced methods facilitate more sophisticated and effective computational segmentation and analyses than have previously been employed for X-ray based imaging. Advances in sample preparation make multimodal correlative imaging workflows possible, where a single resin-embedded plant sample is scanned via XRM to generate a 3D cell-level map, and then used to identify and zoom in on sub-cellular regions of interest for high resolution scanning electron microscopy. In total, we present the methodologies for use of XRM in the multiscale and multimodal analysis of 3D plant features using numerous economically and scientifically important plant systems.
Why it matches plant phenotyping methods植物試料の細胞から個体までを対象に、X線顕微鏡によるマルチスケール3D画像取得、試料調製、計算セグメンテーション、相関イメージングの方法論を中心に提示しており、植物形態の取得・解析法が明確に中心です。
abstractWe demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution 3D volumes of intact plant samples from the cell to whole plant level.
Reproduction assets foundThe preprint points to a public figshare collection containing the paper's high-resolution XRM image stacks ('flythroughs') and videos of the 3D plant datasets, which directly reproduce the paper's phenotyping imaging measurements. No author analysis code or trained model checkpoint is explicitly deposited; the deep-seDataset · publicof these improved techniques will
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high-resolution image stacks (“flythroughs”) and videos portraying the 3D data sets can be found here:
https://figshare.com/s/944efc8832e47fd4f203
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CC-BY-NC 4.0 International license
available under a
(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
The copyright holder for this preprint
this version posted December 22, 2020.
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https://doi.org/10.1101/2020.12.18.423480
doiOpen asset ↗figsharepdf-raw-page:4 lines:1-64Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
SorghumLaboratory / benchtopX-ray / CTRoot2D/3D reconstructionSegmentationRoot system architecture
Plants establish their root system as a three-dimensional structure, which is then used to explore the soil to absorb resources and provide mechanical anchorage. Simplified two-dimensional growth systems, such as agar plates, have been used to study various aspects of plant root biology. However, it remains challenging to study the more realistic three-dimensional structure and function of roots hidden in opaque soil. Here, we optimized X-ray computer tomography (CT)-based visualization of an intact root system by using Toyoura sand, a standard silica sand used in geotechnology research, as a growth substrate. Distinct X-ray attenuation densities of root tissue and Toyoura sand enabled clear image segmentation of the CT data. Sorghum grew especially vigorously in Toyoura sand and it could be used as a model for analyzing root structure optimization in response to mechanical obstacles. The use of Toyoura sand has the potential to link plant root biology and geotechnology applications.
Why it matches plant phenotyping methods不透明な砂中の植物根系をX線CTで可視化・分割する手法を最適化しており、根系構造の取得が研究の中心であるため。
abstractwe optimized X-ray computer tomography (CT)-based visualization of an intact root system by using Toyoura sand
The relationship between seed physical characteristics and seed quality is widely investigated by using X-ray based imaging techniques. Recently the use of X-ray micro-tomography (micro-CT) is increasingly used for more accurate characterisation of the internal seed morphology. In this work a germination test was carried out along with the morphometric characterisation of tomato seed internal structure by means of X-ray micro-CT and 3D image analysis. The aim was to accurately evaluate the predictive potential of internal seed 3D morphology for germination outcomes. The visual assessment allowed the relationship between specific internal seed abnormalities and the different germination outcomes to be demonstrated experimentally. Univariate analysis of morphometric seed traits allowed 3D free space % and Sauter diameter, among the most discriminant parameters, to be identified as the most effective for the prediction of germination outcomes. Discriminant Analysis (DA) of 3D morphometric dataset correctly classified 96.3% of normal seedlings, 83.3% of ungerminated seeds and 63.6% of abnormal seedlings, providing a high overall prediction potential of 91.9%. The above analyses have also been performed referring to the germination at 5 days after sowing. As a side effect of the applied technique, an increase of abnormal seedlings was observed at increasing X-ray exposure level.Overall, X-ray micro-CT coupled with DA of internal morphometric traits has proved to be an effective tool to investigate the relationship between tomato seed 3D morphology and seed physiology, although attention has to be paid to possible consequences of X-ray exposure.
Why it matches plant phenotyping methodsX線マイクロCTと3D画像解析によりトマト種子内部形態を定量化し、形態特徴から発芽結果を予測する手法が研究の中心である。
abstractthe morphometric characterisation of tomato seed internal structure by means of X-ray micro-CT and 3D image analysis
Plant tissues are often heterogeneous. To accurately investigate these tissues, methods to spatially map these tissue stiffness values onto finite element models are required. The aim was to study the feasibility of using specimen-specific computed tomography data to inform the spatial mapping of Young's modulus values on finite element models.Specimen-specific finite element models with mapped elastic moduli values were developed. The validation models predicted the structural response of the specimen tests within 11.0% of the physical test data. The ability of the models to accurately predict the force-displacement response of the specimen in a different test configuration was considered to be positive validation of the mapping approach. The existence of a model with accurate spatial distribution of material stiffnesses allows for investigations into the stress patterns within the rind and pith tissues. Typically, structural failure in transverse compression manifests as a crack that propagates in the pith along the line of load. In building detailed FEM analyses, we are able to investigate in more detail how the stress is distributed through the pith, and further investigate the causes of the stress concentrations that ultimately lead to the structural failure of the specimen.A method was developed for determining the relationship between computed-tomography intensity and the transverse elastic modulus in maize stalks. The mapping was used to accurately predict the response of each specimen thus indicating that the mapping relationship is appropriate for modelling and stress analysis activities.
Why it matches plant phenotyping methodsトウモロコシ茎のCT強度から組織の弾性率を推定し、有限要素モデルで検証する植物組織特性の計測・推定法が研究の中心であるため。
abstractA method was developed for determining the relationship between computed-tomography intensity and the transverse elastic modulus in maize stalks.
Crucifer flowers have a stereotypical plan and much of the floral diversity in the family is revealed only by careful observation. This statement holds true for the flower of Stanleya elata, a relative of the model plant Arabidopsis thaliana, which exhibits a number of distinct features that highlight the value of crucifers in comparative studies. Such comparative approaches in combination with new imaging and genomic technologies provide novel insight into floral structure and diversity.
Why it matches plant phenotyping methodsコンピュータ断層撮影による花器官の三次元再構成が中心で、植物の形態・構造を画像から取得する方法として扱われているため含める。
titlecomputed tomographic reconstruction of a crucifer flower
Traditional seed and fruit phenotyping are mainly accomplished by manual measurement or extraction of morphological properties from two-dimensional images. These methods are not only in low-throughput but also unable to collect their three-dimensional (3D) characteristics and internal morphology. X-ray computed tomography (CT) scanning, which provides a convenient means of non-destructively recording the external and internal 3D structures of seeds and fruits, offers a potential to overcome these limitations. However, the current CT equipment cannot be adopted to scan seeds and fruits with high throughput. And there is no specialized software for automatic extraction of phenotypes from CT images. Here, we introduced a high-throughput image acquisition approach by mounting a specially-designed seed-fruit container onto the scanning bed. The corresponding 3D image analysis software, 3DPheno-Seed&Fruit, was created for automatic segmentation and rapid quantification of eight morphological phenotypes of internal and external compartments of seeds and fruits. 3DPheno-Seed&Fruit is a graphical user interface designed and user-friendly software with an excellent phenotype result visualization function. We described the software in detail and benchmarked it based upon CT image analyses in seeds of soybean, wheat, peanut, pine nut, pistacia nut and dwarf Russian almond fruit. R2 values between the extracted and manual measurements of seed length, width, thickness, and radius ranged from 0.80 to 0.96 for soybean and wheat. High correlations were found between the 2D (length, width, thickness, and radius) and 3D (volume and surface area) phenotypes for soybean. Overall, our methods provide robust and novel tools for phenotyping the morphological seed and fruit traits of various plant species, which could benefit crop breeding and functional genomics.
Why it matches plant phenotyping methodsCT画像取得法と3D解析ソフトウェアを開発し、種子・果実形態形質の自動抽出をベンチマークしており、フェノタイピング手法が中心である。
abstractHere, we introduced a high-throughput image acquisition approach by mounting a specially-designed seed-fruit container onto the scanning bed.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · public3DPheno-Seed&Fruit software and CT image datasets used in this manuscript are free for academic purpose and can be downloaded from http://www.wutbiolab.com/resources/39/info/29 and https://github.com/whut-biolab-liuchang/projectOpen asset ↗lines:304-314Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 15 Sept 2026
MaizeX-ray / CTRootObject detectionSegmentationRoot system architecture
Abstract Background X-ray computed tomography is acknowledged as a powerful tool for the study of root system architecture of plants growing in soil. In this paper, we improved the original root segmentation algorithm “Rootine” and present its succeeding version “Rootine v.2”. In addition to grey value information, Rootine algorithms are based on shape detection of cylindrical roots. Both algorithms are macros for the ImageJ software and are made freely available to the public. New features in Rootine v.2 are (1) a pot wall detection and removal step to avoid segmentation artefacts for roots growing along the pot wall, (2) a calculation of the root average grey value based on a histogram analysis, (3) an automatic calculation of thresholds for hysteresis thresholding of the tubeness image to reduce the number of parameters and (4) a false negatives recovery based on shape criteria to increase root recovery. We compare the segmentation results of Rootine v.1 and Rootine v.2 with the results of root washing and subsequent analysis with WinRhizo. We use a benchmark dataset of maize roots ( Zea mays L. cv. B73) grown in repacked soil for two scenarios with differing soil heterogeneity and image quality. Results We demonstrate that Rootine v.2 outperforms its preceding version in terms of root recovery and enables to match better the root diameter distribution data obtained with root washing. Despite a longer processing time, Rootine v.2 comprises less user-defined parameters and shows an overall greater usability. Conclusion The proposed method facilitates higher root detection accuracy than its predecessor and has the potential for improving high-throughput root phenotyping procedures based on X-ray CT data analysis.
Why it matches plant phenotyping methodsX線CT画像から植物根系を抽出する画像解析手法を開発・比較検証し、根径分布などの表現型取得への応用を明示しているため。
abstractIn this paper, we improved the original root segmentation algorithm “Rootine” and present its succeeding version “Rootine v.2”.
Abstract Background: The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time. Results: We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant ( Jatropha curcas ) was used as a model species, which also serve as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (>0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds. Conclusions: Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.
Why it matches plant phenotyping methodsマルチスペクトル画像とX線画像を組み合わせ、種子の生理的品質（正常・異常幼苗、死種子）を自動・非破壊推定する手法の開発が研究の中心である。
abstractWe present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies.
Background Rice quality research attracts attention worldwide. Rice chalkiness is one of the key indexes determining rice kernel quality. The traditional rice chalkiness measurement methods only use milled rice as materials and are mainly based on naked-eye observation or area-based two-dimensional (2D) image analysis and the results could not represent the three-dimensional (3D) characteristics of chalkiness in the rice kernel. These methods are neither in vivo thus are unable to analyze living rice seeds for high throughput screening of rice chalkiness phenotype. Results Here, we introduced a novel method for 3D visualization and accurate volume-based quantification of rice chalkiness in vivo by using X-ray microcomputed tomography (micro-CT). This approach not only develops a novel volume-based method to measure the 3D rice chalkiness index, but also provides a high throughput solution for rice chalkiness phenotype analysis by using living rice seeds. Conclusions Our method could be a new powerful tool for rice chalkiness measurement, especially for high throughput chalkiness phenotype screening using living rice seeds. This method could be used in chalkiness phenotype identification and screening, and would greatly promote the basic research in rice chalkiness regulation as well as the quality evaluation in rice production practice.
Why it matches plant phenotyping methodsイネ種子の胴割れ・白未熟（chalkiness）を生体のままマイクロCTで3D可視化し、体積ベースで定量する新規フェノタイピング手法を開発しており、方法自体が中心的です。
abstractwe introduced a novel method for 3D visualization and accurate volume-based quantification of rice chalkiness in vivo by using X-ray microcomputed tomography (micro-CT).
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 9 Sept 2026
Abstract In this work, several attributes of the internal morphology of drupaceous fruits found in the archaeological site Monte Castelo (Rondonia, Brazil) are analyzed by means of two different imaging methods. The aim is to explore similarities and differences in the visualization and analytical properties of the images obtained via High Resolution Light Microscopy and X-ray micro-computed tomography (X-ray MicroCT) methods. Both provide data about the three-layered pericarp (exo-, meso- and endocarp) of the studied exemplars, defined by cell differentiation, vascularisation, cellular contents, presence of sclerenchyma cells and secretory cavities. However, it is possible to identify a series of differences between the information that can be obtained through each of the methods. These variations are related to the definition of contours and fine details of some characteristics, their spatial distribution, size attributes, optical properties and material preservation. The results obtained from both imaging methods are complementary, contributing to a more exhaustive morphological study of the plant remains. X-ray MicroCT in phase-contrast mode represents a suitable non-destructive analytic technique when sample preservation is required.
Why it matches plant phenotyping methods植物果実の内部形態を対象に、光学顕微鏡とX線microCTを比較し、画像から得られる形態情報と各手法の特性を評価しているため、画像ベースの植物形態計測手法の検証として中心的です。
abstractThe aim is to explore similarities and differences in the visualization and analytical properties of the images obtained via High Resolution Light Microscopy and X-ray micro-computed tomography (X-ray MicroCT) methods.
Abstract In this paper we propose a motion-aware variational approach to reconstruct moving objects from sparse dynamic data. The motivation of this work stems from x-ray imaging of plants perfused with a liquid contrast agent, aimed at increasing the contrast of the images and studying the phloem transport in plants over time. The key idea of our approach is to deploy 3D shearlets as a space-temporal prior, treating time as the third dimension. The rationale behind this model is that a continuous evolution of a cartoon-like object is well suited for the use of 3D shearlets. We provide a basic mathematical analysis of the variational model for the image reconstruction. The numerical minimization is carried out with primal-dual scheme coupled with an automated choice of the regularization parameter. We test our model on different measurement setups: a simulated phantom especially designed to resemble a plant stem, with spreading points to simulate a spreading contrast agent; a measured agarose gel phantom to demonstrate iodide diffusion and geometry prior to imaging living sample; a measured living tree grown in vitro and perfused with a liquid sugar–iodine-mix. The results, compared against a 2D static model, show that our approach provides reconstructions that capture well the time dynamic of the contrast agent onset and are encouraging to develop microCT as a tool to study phloem transport using iodine tracer.
Why it matches plant phenotyping methods植物茎内のヨウ素トレーサー動態を推定するための動的X線トモグラフィ再構成法が論文の中心であり、植物の師部輸送という生理状態の計測に直接結びつく。
abstractwe propose a motion-aware variational approach to reconstruct moving objects from sparse dynamic data
Abstract The absorption of foliar fertilizer is a complex process and is poorly understood. The ability to visualize and quantify the pathway that elements take following their application to leaf surfaces is critical for understanding the science and for practical applications of foliar fertilizers. By the use of synchrotron-based X-ray fluorescence to analyze the in vivo localization of elements, our study aimed to investigate the penetration of foliar-applied Zn absorbed by apple ( Malus domestica Borkh.) leaves with different physiological surface properties, as well as the possible interactions between foliar Zn level and the mineral nutrient status of treated leaves. The results indicate that the absorption of foliar-applied Zn was largely dependent on plant leaf surface characteristics. High-resolution elemental maps revealed that the high binding capacity of the cell wall for Zn contributed to the observed limitation of Zn penetration across epidermal cells. Trichome density and stomatal aperture had opposite effects on Zn fertilizer penetration: a relatively high density of trichomes increased the hydrophobicity of leaves, whereas the presence of stomata facilitated foliar Zn penetration. Low levels of Zn promoted the accumulation of other mineral elements in treated leaves, and the complexation of Zn with phytic acid potentially occurred owing to exposure to high-Zn conditions. The present study provides direct visual evidence for the Zn penetration process across the leaf surface, which is important for the development of strategies for Zn biofortification in crop species.
Why it matches plant phenotyping methodsシンクロトロンX線蛍光による高解像度元素マッピングを用いて、リンゴ葉内のZn浸透経路と栄養状態を直接可視化・定量しており、植物の生理状態の取得法が研究の中心にある。
abstractThe ability to visualize and quantify the pathway that elements take following their application to leaf surfaces is critical
Abstract An essential prerequisite for the efficient biomechanical tailoring of crops is to accurately relate mechanical behavior to compositional and morphological properties across different length scales. In this article, we develop a multiscale approach to predict macroscale stiffness and strength properties of crop stem materials from their hierarchical microstructure. We first discuss the experimental multiscale characterization based on microimaging (micro-CT, light microscopy, transmission electron microscopy) and chemical analysis, with a particular focus on oat stems. We then derive in detail a general micromechanics-based model of macroscale stiffness and strength. We specify our model for oats and validate it against a series of bending experiments that we conducted with oat stem samples. In the context of biomechanical tailoring, we demonstrate that our model can predict the effects of genetic modifications of microscale composition and morphology on macroscale mechanical properties of thale cress that is available in the literature.
Why it matches plant phenotyping methods作物茎の微細構造を画像化・解析し、マクロな力学特性を予測するマルチスケール手法を開発し、オーツ麦の曲げ実験で検証しているため、植物形質取得・推定法が研究の中心である。
abstractwe develop a multiscale approach to predict macroscale stiffness and strength properties of crop stem materials from their hierarchical microstructure.
Background Walnuts are grown worldwide in temperate areas and producers are facing an increasing demand. In a climate change context, the industry also needs cultivars that provide fruits of quality. This quality includes satisfactory filling ratio, thicker shell, ease of cracking, smooth shell and round-shaped walnut, and larger nut size. These desirable traits have been analysed so far using calipers or micrometers, but it takes a lot of time and requires the destruction of the sample. A challenge to take up is to develop an accurate, fast and non-destructive method for quality-related and morphometric trait measurements of walnuts, that are used to characterize new cultivars or collections in any germplasm management process. Results In this study, we develop a method to measure different morphological traits on several walnuts simultaneously such as morphometric traits (nut length, nut face and profile diameters), traits that previously required opening the nut (shell thickness, kernel volume and filling kernel/nut ratio) and traits that previously were difficult to quantify (shell rugosity, nut sphericity, nut surface area and nut shape). These measurements were obtained from reconstructed 3D images acquired by X-ray computed tomography (CT). A workflow was created including several steps: noise elimination, walnut individualization, properties extraction and quantification of the different parts of the fruit. This method was applied to characterize 50 walnuts of a part of the INRAE walnut germplasm collection made of 161 unique accessions, obtained from the 2018 harvest. Our results indicate that 50 walnuts are sufficient to phenotype the fruit quality of one accession using X-ray CT and to find correlations between the morphometric traits. Our imaging workflow is suitable for any walnut size or shape and provides new and more accurate measurements. Conclusions The fast and accurate measurement of quantitative traits is of utmost importance to conduct quantitative genetic analyses or cultivar characterization. Our imaging workflow is well adapted for accurate phenotypic characterization of a various range of traits and could be easily applied to other important nut crops.
Why it matches plant phenotyping methodsX線CTの3D画像からクルミ果実の形態・品質形質を非破壊かつ自動的に抽出するワークフローを開発し、実 germplasm で適用・評価しており、植物フェノタイピング手法が中心である。
abstractA challenge to take up is to develop an accurate, fast and non-destructive method for quality-related and morphometric trait measurements of walnuts
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Background and Aims The endangered aquatic carnivorous waterwheel plant (Aldrovanda vesiculosa) catches prey with 3–5-mm-long underwater snap-traps. Trapping lasts 10–20 ms, which is 10-fold faster than in its famous sister, the terrestrial Venus flytrap (Dionaea muscipula). After successful capture, the trap narrows further and forms a ‘stomach’ for the digestion of prey, the so-called ‘sickle-shaped cavity’. To date, knowledge is very scarce regarding the deformation process during narrowing and consequent functional morphology of the trap. Methods We performed comparative analyses of virtual 3D histology using computed tomography (CT) and conventional 2D histology. For 3D histology we established a contrasting agent-based preparation protocol tailored for delicate underwater plant tissues. Key Results Our analyses reveal new structural insights into the adaptive architecture of the complex A. vesiculosa snap-trap. In particular, we discuss in detail the arrangement of sensitive trigger hairs inside the trap and present actual 3D representations of traps with prey. In addition, we provide trap volume calculations at different narrowing stages. Furthermore, the motile zone close to the trap midrib, which is thought to promote not only the fast trap closure by hydraulics but also the subsequent trap narrowing and trap reopening, is described and discussed for the first time in its entirety. Conclusions Our research contributes to the understanding of a complex, fast and reversible underwater plant movement and supplements preparation protocols for CT analyses of other non-lignified and sensitive plant structures.
Why it matches plant phenotyping methods水生植物の繊細な組織を対象に、CTによる3D画像解析と2D組織学を比較し、専用の試料調製プロトコルを確立した研究であり、トラップ形態・体積の定量が中心である。
abstractFor 3D histology we established a contrasting agent-based preparation protocol tailored for delicate underwater plant tissues.
Optical technologies that are able to analyze physical properties of biological samples are increasingly drawing interest in modern agriculture. The use of X-rays for analysis of internal properties of agricultural products, such as seeds, has proven its worth in providing information regarding their quality in a non-destructive manner. However, visual evaluations of radiographic images are time-consuming, subjective, and highly prone to error. Therefore, it is necessary to develop methods that allow these analyses to be performed in an efficient and assertive manner. To that end, a free-access, open-source, and easy-to-use tool called IJCropSeed has been developed for high-throughput analysis of radiographic images of seeds from several agricultural crops. In addition, an experiment was conducted in which machine learning models were developed from the information obtained from the tool to predict the seed germination capacity and seedling vigor of Crambeabyssinica. The results showed that IJCropSeed had a high performance for the analysis of digital radiographic images of the 24 agricultural crops evaluated, with high speed and high precision of segmentation of the images. The use of parameters obtained with the tool, in combination with the machine learning models, proved to be highly efficient in classifying the quality of C. abyssinica seeds. It is a non-destructive and highly effective method.
Why it matches plant phenotyping methods種子のX線画像から形態・内部特性を高スループットに抽出するオープンソースツールを開発し、セグメンテーション性能と発芽・幼苗 vigor 予測を評価しており、植物表現型取得法が中心である。
abstracta free-access, open-source, and easy-to-use tool called IJCropSeed has been developed for high-throughput analysis of radiographic images of seeds from several agricultural crops
Background The increasing number of novel approaches for large-scale, multi-dimensional imaging of cells has created an unprecedented opportunity to analyze plant morphogenesis. However, complex image processing, including identifying specific cells and quantitating parameters, and high running cost of some image analysis softwares remains challenging. Therefore, it is essential to develop an efficient method for identifying plant complex multicellularity in raw micrographs in plants. Results Here, we developed a high-efficiency procedure to characterize, segment, and quantify plant multicellularity in various raw images using the open-source software packages ImageJ and SR-Tesseler. This procedure allows for the rapid, accurate, automatic quantification of cell patterns and organization at different scales, from large tissues down to the cellular level. We validated our method using different images captured from Arabidopsis thaliana roots and seeds and Populus tremula stems, including fluorescently labeled images, Micro-CT scans, and dyed sections. Finally, we determined the area, centroid coordinate, perimeter, and Feret's diameter of the cells and harvested the cell distribution patterns from Voronoï diagrams by setting the threshold at localization density, mean distance, or area. Conclusions This procedure can be used to determine the character and organization of multicellular plant tissues at high efficiency, including precise parameter identification and polygon-based segmentation of plant cells.
Why it matches plant phenotyping methods植物画像から細胞形態・配置を自動抽出する画像解析手法を開発し、複数植物種・画像 modality で検証しており、表現型取得が研究の中心です。
abstractwe developed a high-efficiency procedure to characterize, segment, and quantify plant multicellularity in various raw images using the open-source software packages ImageJ and SR-Tesseler.
Abstract Background: Walnuts are grown worldwide in temperate areas and producers are facing an increasing demand. In a climate change context, the industry also needs cultivars that provide fruits of quality. This quality includes satisfactory filling ratio, thicker shell, ease of cracking, smooth shell and round-shaped walnut, and larger nut size. These desirable traits have been analysed so far using calipers or micrometers, but it takes a lot of time and requires the destruction of the sample. A challenge to take up is to develop an accurate, fast and non-destructive method for quality-related and morphometric trait measurements of walnuts, that are used to characterize new cultivars or collections in any germplasm management process. Results: In this study, we develop a method to measure different morphological traits on several walnuts simultaneously such as morphometric traits (nut length, nut face and profile diameters), traits that previously required opening the nut (shell thickness, kernel volume and filling kernel/nut ratio) and traits that previously were difficult to quantify (shell rugosity, nut sphericity, nut surface area and nut shape). These measurements were obtained from reconstructed 3D images acquired by X-ray computed tomography (CT). A workflow was created including several steps: noise elimination, walnut individualization, properties extraction and quantification of the different parts of the fruit. This method was applied to characterize 50 walnuts of a part of the INRAE walnut germplasm collection made of 161 unique accessions, obtained from the 2018 harvest. Our results indicate that 50 walnuts are sufficient to phenotype the fruit quality of one accession using X-ray CT and to find correlations between the morphometric traits. Our imaging workflow is suitable for any walnut size or shape and provides new and more accurate measurements. Conclusions: The fast and accurate measurement of quantitative traits is of utmost importance to conduct quantitative genetic analyses or cultivar characterization. Our imaging workflow is well adapted for accurate phenotypic characterization of a various range of traits and could be easily applied to other important nut crops.
Why it matches plant phenotyping methodsX線CT画像からクルミの形態・品質形質を非破壊かつ高精度に抽出するワークフローを開発し、実際の遺伝資源で適用・評価しており、フェノタイピング手法が中心です。
abstractA challenge to take up is to develop an accurate, fast and non-destructive method for quality-related and morphometric trait measurements of walnuts
New approaches based on image analysis can assist in phenotyping of biological characteristics, serving as support for decision-making in modern agribusiness. The aim of this study was to propose a method of high-throughput phenotyping of free access for processing of 2D X-ray images of brachiaria grass (Brachiaria ruziziensis cv. Ruziziensis) seeds, as well as correlate the parameters linked to the physiological potential of the seeds. The study was carried out by means of automated analysis of X-ray images of seeds in which a macro, called PhenoXray, was developed, responsible for digital image processing, for which a series of descriptors were obtained. After the X-ray analysis, a germination test was performed on the seeds and, from this, variables related to the physiological quality of the seeds were obtained. The use of the macro PhenoXray allowed large-scale phenotyping of seed X-rays in a simple, rapid, robust, and totally free manner. This study confirmed that the methodology is efficient for obtaining morphometric data and tissue integrity data in Brachiaria ruziziensis seeds and that parameters such as relative density, integrated density, and seed filling are closely related to the physiological attributes of seed quality.
Why it matches plant phenotyping methods種子X線画像から形態・組織完全性を抽出するPhenoXrayマクロを開発し、高スループット測定法として検証・適用しているため、表現型取得手法が中心である。
abstractThe aim of this study was to propose a method of high-throughput phenotyping of free access for processing of 2D X-ray images of brachiaria grass (Brachiaria ruziziensis cv. Ruziziensis) seeds
PREMISE: X-ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organization. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small data sets, restricting its utility for phenotyping experiments and limiting our confidence in the inferences of these studies due to low replication numbers. METHODS AND RESULTS: We present a Python codebase for random forest machine learning segmentation and 3D leaf anatomical trait quantification that dramatically reduces the time required to process single-leaf microCT scans into detailed segmentations. By training the model on each scan using six hand-segmented image slices out of >1500 in the full leaf scan, it achieves >90% accuracy in background and tissue segmentation. CONCLUSIONS: Overall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high-throughput plant phenotyping.
Why it matches plant phenotyping methods3DマイクロCT画像から葉の内部解剖形質を抽出する機械学習セグメンテーションと定量化パイプラインの開発が中心であり、植物フェノタイピングへの適用性も明示されている。
abstractWe present a Python codebase for random forest machine learning segmentation and 3D leaf anatomical trait quantification
Reproduction assets foundThe paper's authors publicly released their random forest segmentation/leaf-traits analysis code on GitHub and the microCT image dataset, hand-labeled training slices, and segmentation outputs on Zenodo.Code · publicThe code and an in-depth user manual are available at https://Open asset ↗pdf-raw-page:8 lines:1-78Dataset · publicgithub.com/plant-microct-tools/leaf-traits-microct. Future updates
will be integrated to this repository. The microCT data set, training
hand-labeled slices, and all image outputs of the program including
one full stack segmentation are available on Zenodo at https://doi.org/10.5281/zenodo.3694973 (Théroux-Rancourt et al., 2020b).
SUPPORTING INFORMATION
Additional Supporting Information may be found online in the
supporting information tab for this article.
APPENDIX S1. Average proportion of pixels per tissue in the 24
slices of the training data set.
APPENDIX S2. Standard deviation of thickness estimates pre-
sented inOpen asset ↗Zenodo · 10.5281/zenodo.3694973pdf-raw-page:8 lines:79-106Plant phenotyping relevance match · UnverifiedEurope PMC · checked 9 Sept 2026
Background and aims Synchrotron- and laboratory-based micro-X-ray fluorescence (µ-XRF) is a powerful technique to quantify the distribution of elements in physically large intact samples, including live plants, at room temperature and atmospheric pressure. However, analysis of light elements with atomic number (Z) less than that of phosphorus is challenging due to the need for a vacuum, which of course is not compatible with live plant material, or the availability of a helium environment. Method A new laboratory µ-XRF instrument was used to examine the effects of silicon (Si) on the manganese (Mn) status of soybean (Glycine max) and sunflower (Helianthus annuus) grown at elevated Mn in solution. The use of a helium environment allowed for highly sensitive detection of both Si and Mn to determine their distribution. Key results The µ-XRF analysis revealed that when Si was added to the nutrient solution, the Si also accumulated in the base of the trichomes, being co-located with the Mn and reducing the darkening of the trichomes. The addition of Si did not reduce the concentrations of Mn in accumulations despite seeming to reduce its adverse effects. Conclusions The ability to gain information on the dynamics of the metallome or ionome within living plants or excised hydrated tissues can offer valuable insights into their ecophysiology, and laboratory µ-XRF is likely to become available to more plant scientists for use in their research.
Why it matches plant phenotyping methods生体植物中の元素分布を取得する実験室µ-XRFの技術適用・適応が中心で、SiとMnの植物体内分布という生理状態を測定している。
abstractA new laboratory µ-XRF instrument was used to examine the effects of silicon (Si) on the manganese (Mn) status of soybean (Glycine max) and sunflower (Helianthus annuus) grown at elevated Mn in solution.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
High-throughput phenotyping is increasingly becoming an important tool for rapid advancement of genetic gain in breeding programmes. Manual phenotyping of vascular bundles is tedious and time-consuming, which lags behind the rapid development of functional genomics in maize. More robust and automated techniques of phenotyping vascular bundles traits at high-throughput are urgently needed for large crop populations. In this study, we developed a standard process for stem micro-CT data acquisition and an automatic CT image process pipeline to obtain vascular bundle traits of stems including geometry-related, morphology-related and distribution-related traits. Next, we analysed the phenotypic variation of stem vascular bundles between natural population subgroup (480 inbred lines) based on 48 comprehensively phenotypic information. Also, the first database for stem micro-phenotypes, MaizeSPD, was established, storing 554 pieces of basic information of maize inbred lines, 523 pieces of experimental information, 1008 pieces of CT scanning images and processed images, and 24 192 pieces of phenotypic data. Combined with genome-wide association studies (GWASs), a total of 1562 significant single nucleotide polymorphism (SNPs) were identified for 30 stem micro-phenotypic traits, and 84 unique genes of 20 traits such as VBNum, VBAvArea and PZVBDensity were detected. Candidate genes identified by GWAS mainly encode enzymes involved in cell wall metabolism, transcription factors, protein kinase and protein related to plant signal transduction and stress response. The results presented here will advance our knowledge about phenotypic trait components of stem vascular bundles and provide useful information for understanding the genetic controls of vascular bundle formation and development.
Why it matches plant phenotyping methodsトウモロコシ茎の維管束形質を取得するマイクロCT撮像標準化と自動画像処理パイプラインを開発し、データベースも構築しており、フェノタイピング手法が研究の中心である。
abstractwe developed a standard process for stem micro-CT data acquisition and an automatic CT image process pipeline to obtain vascular bundle traits of stems
MaizeX-ray / CTRoot2D/3D reconstructionSegmentationRoot system architecture
No-tillage seeding has become an important approach to improve crop productivity, which needs colters of high performance to cut the root-stubble-soil composite. However, the difficulty of maize root-stubbles three-dimensional (3D) modeling hinders finite element (FE) simulation to improve development efficiency of such colters because of maize root system complexity and opaque nature of the soil. Fortunately, the non-destructive 3D geometric model of the maize root-stubble in-situ can be established via X-ray computed tomography (CT) following by a systematic procedure. The whole procedure includes CT scanning of the maize root-stubble-soil composite sample, image reconstruction via filtered back-projection (FBP) with the Hanning filter, segmentation of root-stubble via a variational level set method, and post-processing via morphological operations. The 3D reconstruction model of the maize root-stubble in-situ presents a complete, complex and in-situ geometrical morphology, which cannot be realized via other methods, including the destructive modelling after washing via CT. This study is the first to build a 3D geometric model of a maize root-stubble in-situ via CT, which opens up new possibilities for simulation of root-stubble-soil cutting using FEM, and much other research related to plant root-stubbles. Keywords: maize root-stubble, non-destructive modeling, X-ray computed tomography, variational level set method DOI: 10.25165/j.ijabe.20201303.5268 Citation: Zhao X, Xing L Y, Shen S F, Liu J M, Zhang D X. Non-destructive 3D geometric modeling of maize root-stubble in-situ via X-ray computed tomography. Int J Agric & Biol Eng, 2020; 13(3): 174–179.
Why it matches plant phenotyping methodsX線CT、画像再構成、根・残株のセグメンテーション、形態学的後処理を組み合わせ、トウモロコシ根・残株の非破壊3D形態を抽出する手法開発が中心である。
abstractThe whole procedure includes CT scanning of the maize root-stubble-soil composite sample, image reconstruction via filtered back-projection (FBP) with the Hanning filter, segmentation of root-stubble via a variational level set method, and post-processing via morphological operations.
Background Many metals are essential for plants and humans. Knowledge of metal distribution in plant tissues in vivo contributes to the understanding of physiological mechanisms of metal uptake, accumulation and sequestration. For those studies, X-rays are a non-destructive tool, especially suited to study metals in plants. Results We present microfluorescence imaging of trace elements in living plants using a customized benchtop X-ray fluorescence machine. The system was optimized by additional detector shielding to minimize stray counts, and by a custom-made measuring chamber to ensure sample integrity. Protocols of data recording and analysis were optimised to minimise artefacts. We show that Zn distribution maps of whole leaves in high resolution are easily attainable in the hyperaccumulator Noccaea caerulescens . The sensitivity of the method was further shown by analysis of micro- (Cu, Ni, Fe, Zn) and macronutrients (Ca, K) in non-hyperaccumulating crop plants (soybean roots and pepper leaves), which could be obtained in high resolution for scan areas of several millimetres. This allows to study trace metal distribution in shoots and roots with a wide overview of the object, and thus avoids making conclusions based on singular features of tiny spots. The custom-made measuring chamber with continuous humidity and air supply coupled to devices for imaging chlorophyll fluorescence kinetic measurements enabled direct correlation of element distribution with photosynthesis. Leaf samples remained vital even after 20 h of X-ray measurements. Subtle changes in some of photosynthetic parameters in response to the X-ray radiation are discussed. Conclusions We show that using an optimized benchtop machine, with protocols for measurement and quantification tailored for plant analyses, trace metal distribution can be investigated in a reliable manner in intact, living plant leaves and roots. Zinc distribution maps showed higher accumulation in the tips and the veins of young leaves compared to the mesophyll tissue, while in the older leaves the distribution was more homogeneous.
Why it matches plant phenotyping methods生きた植物の元素分布を定量・可視化するX線蛍光イメージング装置と測定・解析プロトコルを最適化し、植物試料で性能を実証しており、フェノタイピング手法が中心である。
abstractWe present microfluorescence imaging of trace elements in living plants using a customized benchtop X-ray fluorescence machine.
Abstract Background: X-ray computed tomography (CT) allows us to visualize root system architecture (RSA) beneath the soil, non-destructively and in a three-dimensional (3-D) form. However, CT scanning, reconstruction processes, and root isolation from X-ray CT volumes, take considerable time. For genetic analyses, such as quantitative trait locus mapping, which require a large population size, a high-throughput RSA visualization method is required. Results: We have developed a high-throughput process flow for the 3-D visualization of rice ( Oryza sativa ) RSA (consisting of radicle and crown roots), using X-ray CT. The process flow includes use of a uniform particle size, calcined clay to reduce the possibility of visualizing non-root segments, use of a higher tube voltage and current in the X-ray CT scanning to increase root-to-soil contrast, and use of a 3-D median filter and edge detection algorithm to isolate root segments. Using high-performance computing technology, this analysis flow requires only 10 min (33 s, if a rough image is acceptable) for CT scanning and reconstruction, and 2 min for image processing, to visualize rice RSA. This reduced time allowed us to conduct the genetic analysis associated with 3-D RSA phenotyping. In 2-week-old seedlings, 85% and 100% of radicle and crown roots were detected, when 16 cm and 20 cm diameter pots were used, respectively. The X-ray dose per scan was estimated at i.e. , 4-D RSA development, of an upland rice variety, over three weeks. Conclusions: We developed a high-throughput process flow for 3-D rice RSA visualization by X-ray CT. The X-ray dose assay on plant growth has shown that this methodology could be applicable for 4-D RSA phenotyping. We named the RSA visualization method ‘RSAvis3D’ and are confident that it represents a potentially efficient application for 3-D RSA phenotyping of various plant species.
Why it matches plant phenotyping methodsイネ根系構造をX線CTで高スループットに可視化・抽出する画像ベースの表現型計測フローを開発しており、方法自体が研究の中心である。
abstractWe have developed a high-throughput process flow for the 3-D visualization of rice ( Oryza sativa ) RSA
RiceX-ray / CTRootMorphology / geometry measurement2D/3D reconstructionSegmentationRoot system architecture
Background X-ray computed tomography (CT) allows us to visualize root system architecture (RSA) beneath the soil, non-destructively and in a three-dimensional (3-D) form. However, CT scanning, reconstruction processes, and root isolation from X-ray CT volumes, take considerable time. For genetic analyses, such as quantitative trait locus mapping, which require a large population size, a high-throughput RSA visualization method is required. Results We have developed a high-throughput process flow for the 3-D visualization of rice ( Oryza sativa ) RSA (consisting of radicle and crown roots), using X-ray CT. The process flow includes use of a uniform particle size, calcined clay to reduce the possibility of visualizing non-root segments, use of a higher tube voltage and current in the X-ray CT scanning to increase root-to-soil contrast, and use of a 3-D median filter and edge detection algorithm to isolate root segments. Using high-performance computing technology, this analysis flow requires only 10 min (33 s, if a rough image is acceptable) for CT scanning and reconstruction, and 2 min for image processing, to visualize rice RSA. This reduced time allowed us to conduct the genetic analysis associated with 3-D RSA phenotyping. In 2-week-old seedlings, 85% and 100% of radicle and crown roots were detected, when 16 cm and 20 cm diameter pots were used, respectively. The X-ray dose per scan was estimated at Conclusions We developed a high-throughput process flow for 3-D rice RSA visualization by X-ray CT. The X-ray dose assay on plant growth has shown that this methodology could be applicable for 4-D RSA phenotyping. We named the RSA visualization method 'RSAvis3D' and are confident that it represents a potentially efficient application for 3-D RSA phenotyping of various plant species.
Why it matches plant phenotyping methodsX線CTによるイネ根系構造の3D取得・分離・高速処理フローを開発し、検出性能と高速性を評価した、明確な植物フェノタイピング手法研究。
abstractWe have developed a high-throughput process flow for the 3-D visualization of rice ( Oryza sativa ) RSA
The traits of rice panicles play important roles in yield assessment, variety classification, rice breeding, and cultivation management. Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive; moreover, these methods cannot obtain 3D grain traits. In this work, based on X-ray computed tomography, we proposed an image analysis method to extract twenty-two 3D grain traits. After 104 samples were tested, the R 2 values between the extracted and manual measurements of the grain number and grain length were 0.980 and 0.960, respectively. We also found a high correlation between the total grain volume and weight. In addition, the extracted 3D grain traits were used to classify the rice varieties, and the support vector machine classifier had a higher recognition accuracy than the stepwise discriminant analysis and random forest classifiers. In conclusion, we developed a 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography that can provide more 3D grain information and could benefit future research on rice functional genomics and rice breeding.
Why it matches plant phenotyping methodsX線CTを用いてイネ穀粒の22種類の3D形質を抽出する画像解析パイプラインを開発し、手動測定との一致性で検証しているため、植物フェノタイピング手法が研究の中心です。
abstractIn this work, based on X-ray computed tomography, we proposed an image analysis method to extract twenty-two 3D grain traits.
Reproduction assets foundThe paper's MATLAB 3D image analysis pipeline for extracting rice grain traits from X-ray CT is explicitly stated to be publicly available, with an authors' GitHub repository URL matching an allowed URL. The phenotypic data (Supplementary File 1) is only available as a PMC supplement with no allowed URL, so it is not aCode · publicthe source codes of all the scripts are available online in Supplementary File 3 or at the following link: http://plantphenomics.hzau.edu.cn/download_checkiflogin_en.action , or https://github.com/cancanzc/ricePanicle_grainTraits_ProcessingOpen asset ↗cancanzc/ricePanicle_grainTraits_Processinglines:109-117Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 15 Sept 2026
Field / plotGreenhouseLaboratory / benchtopMultispectral / hyperspectralX-ray / CT
Abstract In recent years, a new branch of plant physiology, plant phenomics, which focuses on identifying patterns of organization and changes in plant Phenomes, i.e., physical and biochemical characteristics, considered as a set of phenotypes of a plant organism, has emerged. Phenomics is a postgenomic discipline that actively uses the achievements of the genomic era and bioinformatics. It supplements them with standardized and statistically significant factual material on phenotypes with a high degree of detail. The technique of obtaining and analyzing information about phenotypes in phenomics is called phenotyping. High-performance phenotyping, providing digital automated analysis of large data samples, has become widespread. Recent progress in high-performance phenotyping has been associated with the development of image registration systems in various spectral regions, approaches to cultivating plant objects under standardized conditions, sensory technologies, robotics, and methods for data processing and analysis, such as computer vision and machine learning (artificial neural network). Phenomics technologies have a high information content analysis, surpassing human capabilities, performing measurements in the hyperspectral range using X-ray tomography and ultra-precise “thermal” images, and have a number of other low-invasive and precision approaches. Arrays of data obtained using phenomics technologies are recorded and processed automatically and are free from the problems of subjective assessment and inadequate statistical processing. It is assumed that phenotyping will allow for the creation of digital models of the vital activity processes and the “formation” of plant productivity at the organism level in connection with the dynamics of transcriptomes, proteomes, and metabolomes. Phenomics helps researchers transform a large amount of information received from modern sensors into new knowledge using computer data processing and modeling, reducing the distance from basic science to the practical application of results in crop production and breeding. Phenotyping is actively developing both in laboratory and in greenhouse conditions as well as on open agricultural sites, forests, and in real natural phytocenoses. The review analyzes the current state of plant phenomics with a focus on technical aspects, in particular, the design of hardware-software phenotyping complexes, i.e., phenomics platforms, as well as the use of neural networks in phenotyping of plant organisms.
Why it matches plant phenotyping methods植物フェノミクスの技術的側面、ハードウェア・ソフトウェア基盤、センサー、画像処理、機械学習を中心に扱うレビューであり、フェノタイピング手法が中核である。
abstractThe review analyzes the current state of plant phenomics with a focus on technical aspects, in particular, the design of hardware-software phenotyping complexes, i.e., phenomics platforms, as well as the use of neural networks in phenotyping of plant organisms.
Summary Inflorescence architecture in plants is often complex and challenging to quantify, particularly for inflorescences of cereal grasses. Methods for capturing inflorescence architecture and for analyzing the resulting data are limited to a few easily captured parameters that may miss the rich underlying diversity. Here, we apply X‐ray computed tomography combined with detailed morphometrics, offering new imaging and computational tools to analyze three‐dimensional inflorescence architecture. To show the power of this approach, we focus on the panicles of Sorghum bicolor , which vary extensively in numbers, lengths, and angles of primary branches, as well as the three‐dimensional shape, size, and distribution of the seed. We imaged and comprehensively evaluated the panicle morphology of 55 sorghum accessions that represent the five botanical races in the most common classification system of the species, defined by genetic data. We used our data to determine the reliability of the morphological characters for assigning specimens to race and found that seed features were particularly informative. However, the extensive overlap between botanical races in multivariate trait space indicates that the phenotypic range of each group extends well beyond its overall genetic background, indicating unexpectedly weak correlation between morphology, genetic identity, and domestication history.
Why it matches plant phenotyping methodsX線CTと詳細な形態計測を組み合わせ、ソルガム穂の3次元形態を取得・解析する画像ベース表現型手法が研究の中心であるため。
abstractHere, we apply X‐ray computed tomography combined with detailed morphometrics, offering new imaging and computational tools to analyze three‐dimensional inflorescence architecture.
Reproduction assets foundThe paper explicitly states that the full 3D X-ray imaging dataset of sorghum panicles is publicly downloadable from the Topp lab resources page, and that all image processing, feature extraction, and statistical analysis code is available in a public GitHub repository (Topp-Roots-Lab/3D-Sorghum-Inflorescence). Both URDataset · publicThe full 3D imaging dataset for this work can be downloaded from: https://www.danforthcenter.org/scientists‐research/principal‐investigators/chris‐topp/resourcesOpen asset ↗lines:51-62Code · publicAll code used for image processing, digital feature extraction, and statistical analysis from this study can be found at the following GitHub repository: https://github.com/Topp‐Roots‐Lab/3D‐Sorghum‐InflorescenceOpen asset ↗Topp‐Roots‐Lab/3D‐Sorghum‐Inflorescencelines:51-62Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Published24 Mar 2020IEEE Transactions on Radiation and Plasma Medical SciencesCited by 29 · OpenAlex ↗
The RAYCAN Discoverist 180 (RAYCAN D180) is a novel preclinical positron emission tomography (PET) and computed tomography (CT) integrated system for the imaging of small- and medium-sized animals and plants. We measured a system resolution of (2.56 ± 0.04) mm (radial), (2.46 ± 0.05) mm (transverse), and (1.67 ± 0.03) mm (axial) at the center of the field of view using a filtered backprojection reconstruction algorithm. An OSEM-PSF-3D reconstruction algorithm improves the spatial resolution to (1.34 ± 0.04) mm (radial), (1.35 ± 0.05) mm (transverse), and (1.64±0.04) mm (axial). The peak noise equivalent count rate of the system is (713 ± 2) kcps, (207 ± 2) kcps, and (47 ± 1) kcps reached at approximately 97 MBq for the mouse-, rat- and monkey-like phantoms, respectively. This result is approximately 2x higher and is reached at approximately 3x higher activity in comparison with most of the existing commercial and research scanners. The peak absolute system sensitivity is (4.00±0.02)% and is competitive with state-of-the-art preclinical PET scanners. We found an image uniformity of (4.86±0.04)%. Finally, we report the static PET/CT scan of a mouse and of a leaf of Epipremnum aureum, and the time-activity curves of the [18F]-FDG metabolism in mouse left myocardial ventricle and kidneys obtained with a PET/CT scan in dynamic modality.
Why it matches plant phenotyping methods植物を対象に含む新規PET/CTシステムの性能評価であり、植物画像取得基盤の開発・検証が中心です。葉のPET/CT撮像も報告されています。
abstractThe RAYCAN Discoverist 180 (RAYCAN D180) is a novel preclinical positron emission tomography (PET) and computed tomography (CT) integrated system for the imaging of small- and medium-sized animals and plants.
X-ray / CTRootMorphology / geometry measurementSegmentationRoot system architecture
X-ray computed tomography (CT) is acknowledged as a powerful tool for the study of root system architecture (RSA) of plants grown in soil. The study of the root system properties is however only possible after performing root segmentation, i.e. the binarization of all root voxels. Root segmentation is often regarded as a tedious and difficult task as its success depends on several factors such as the image resolution, the signal to noise during image acquisition and the gray value contrast between the roots and all other surrounding features. Here, we present an improved method for the segmentation of roots from X-Ray computed tomography 3D images. The algorithm Rootine (Gao et al. 2019) does not detect roots by their gray values but by their characteristic tubular shape. This algorithm was further developed in order to improve the root recovery rate and to reduce the number of parameters involved during the segmentation process. This was achieved by adding two key steps: (1) an absolute difference transform and (2) an automatic calculation of the parameters used during the Gaussian smoothing. The first step allows for targeting specific features based on a gray value criteria contained within a user-defined gray value range in order to better distinguish roots from pores whereas the second step allows for targeting root segments of specific diameters. On the benchmark dataset of Gao et al. 2019, the newly called “Rootine v.2” was able to recover 34 % more roots as compared to its preceding version. Moreover, the number of parameters was reduced from 10 down to 5 which allows for a faster calibration and an overall better usability of the algorithm. The presented method also allows for a more reliable estimation of root diameter derived from X-Ray CT images. This work was carried out in the framework of the priority programme 2089 “Rhizosphere spatiotemporal organization - a key to rhizosphere functions” funded by DFG (project number 403640293).
Why it matches plant phenotyping methods植物根系画像からのセグメンテーションと根径推定アルゴリズムを開発・ベンチマーク検証しており、植物フェノタイピング手法が研究の中心です。
abstractHere, we present an improved method for the segmentation of roots from X-Ray computed tomography 3D images.
MaizeField / plotX-ray / CTRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
A better understanding of how roots explore soil is crucial for plant breeding, yield increase and sustainable agriculture. This requires detailed knowledge about the temporal dynamics of root system architecture under field conditions, which is hard to achieve as sampling of roots with unconstrained growth (no root windows) is very laborious. Here we present the results of a major undertaking to sample maize roots in a field experiments at four growth stages in various depths (0-20, 20-40, 40-60 cm) with two different methods: a) destructive sampling with a root corer and root washing vs. b) undisturbed sampling combined with root detection in X-ray CT images. The first method results in root length data with a higher number of technical replicates per depth and plot, whereas the second provides more details of small-scale rooting patterns and plant-soil interactions in intact soil for a smaller number of samples. The aim of the study was to explore differences in spatio-temporal root growth patterns between two different maize genotypes (wild type vs. root hairless mutant) growing in two different homogenized substrates (sand vs. loam). For disturbed sampling we found that for both genotypes root growth was more vigorous in sand during the entire growing season. This was remarkable since shoot biomass was larger on the loam plot. As drought developed during the growing season, root length density profiles reversed in loam, but not on sandy substrate. For intact cores we find the same trends so that they can now be analyzed towards inter-root distances at shorter scales. In loam the absence of root hairs and the associated reduction of available surface for water and nutrient uptake resulted in a 50% reduction in shoot biomass, whereas root length profiles did not differ in the root corer data. In sand differences in shoot biomass between genotypes were comparable, but here root length densities were lower for the root hairless mutant in the root corer data. Unexpectedly there was no compensation of lacking root hairs by enhanced root growth. The root length data in intact samples showed higher variation due to smaller sampled volumes which disguised possible trends between genotypes. In summary, the different hydraulic properties of the substrates had a strong effect on root growth and root distribution with depth, whereas the genotype governed shoot biomass supposedly through differences in nutrient and/or water uptake efficiency mediated by the presence or absence of root hairs. As the next steps, these observations will be underpinned by transpiration and soil moisture monitoring data as well as plant nutrient uptake data.
Why it matches plant phenotyping methods根系形態を定量するためのX線CT画像による根検出と、根コア法との比較が研究の主要部分であり、根長・根系構造という植物形質を取得する手法を実質的に評価している。
abstractundisturbed sampling combined with root detection in X-ray CT images
Why it matches plant phenotyping methodsX線CTを用いて作物の根系構造を非破壊に可視化・定量し、根と土壌中の掘孔の相互作用を評価する手法適用が中心である。
abstractIn this study, we apply X-ray CT to visualise and quantify wireworms, their burrow systems and the root architecture of two contrasting crop species
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
BACKGROUND: The production and availability of annotated data sets are indispensable for training and evaluation of automatic phenotyping methods. The need for complete 3D models of real plants with organ-level labeling is even more pronounced due to the advances in 3D vision-based phenotyping techniques and the difficulty of full annotation of the intricate 3D plant structure. RESULTS: We introduce the ROSE-X data set of 11 annotated 3D models of real rosebush plants acquired through X-ray tomography and presented both in volumetric form and as point clouds. The annotation is performed manually to provide ground truth data in the form of organ labels for the voxels corresponding to the plant shoot. This data set is constructed to serve both as training data for supervised learning methods performing organ-level segmentation and as a benchmark to evaluate their performance. The rosebush models in the data set are of high quality and complex architecture with organs frequently touching each other posing a challenge for the current plant organ segmentation methods. We report leaf/stem segmentation results obtained using four baseline methods. The best performance is achieved by the volumetric approach where local features are trained with a random forest classifier, giving Intersection of Union (IoU) values of 97.93% and 86.23% for leaf and stem classes, respectively. CONCLUSION: We provided an annotated 3D data set of 11 rosebush plants for training and evaluation of organ segmentation methods. We also reported leaf/stem segmentation results of baseline methods, which are open to improvement. The data set, together with the baseline results, has the potential of becoming a significant resource for future studies on automatic plant phenotyping.
Why it matches plant phenotyping methods植物器官セグメンテーション手法の訓練・評価用データセットとベンチマークを提供しており、植物フェノタイピング手法が中心である。
abstractThe production and availability of annotated data sets are indispensable for training and evaluation of automatic phenotyping methods.
Background Improving abiotic stress tolerance in wheat requires large scale screening of yield components such as seed weight, seed number and single seed weight, all of which is very laborious, and a detailed analysis of seed morphology is time-consuming and visually often impossible. Computed tomography offers the opportunity for much faster and more accurate assessment of yield components. Results An X-ray computed tomographic analysis was carried out on 203 very diverse wheat accessions which have been exposed to either drought or combined drought and heat stress. Results demonstrated that our computed tomography pipeline was capable of evaluating grain set with an accuracy of 95-99%. Most accessions exposed to combined drought and heat stress developed smaller, shrivelled seeds with an increased seed surface. As expected, seed weight and seed number per ear as well as single seed size were significantly reduced under combined drought and heat compared to drought alone. Seed weight along the ear was significantly reduced at the top and bottom of the wheat spike. Conclusions We were able to establish a pipeline with a higher throughput with scanning times of 7 min per ear and accuracy than previous pipelines predicting a set of agronomical important seed traits and to visualize even more complex traits such as seed deformations. The pipeline presented here could be scaled up to use for high throughput, high resolution phenotyping of tens of thousands of heads, greatly accelerating breeding efforts to improve abiotic stress tolerance.
Why it matches plant phenotyping methodsX線CTによる穀粒形態・収量関連形質の高速抽出パイプラインを開発・精度検証し、高スループット表現型解析への展開を示した研究であり、方法が中心です。
abstractComputed tomography offers the opportunity for much faster and more accurate assessment of yield components.
Background The visualization of internal 3D-structure of tissues at micron resolutions without staining by contrast reagents is desirable in plant researches, and it can be achieved by an X-ray computed tomography (CT) with a phase-retrieval technique. Recently, a laboratory-based X-ray microscope adopting the phase contrast CT was developed as a powerful tool for the observation of weakly absorbing biological samples. Here we report the observation of unstained pansy seeds using the laboratory-based X-ray phase-contrast CT. Results A live pansy seed within 2 mm in size was simply mounted inside a plastic tube and irradiated by in-house X-rays to collect projection images using a laboratory-based X-ray microscope. The phase-retrieval technique was applied to enhance contrasts in the projection images. In addition to a dry seed, wet seeds on germination with the poorer contrasts were tried. The phase-retrieved tomograms from both the dry and the wet seeds revealed a cellular level of spatial resolutions that were enough to resolve cells in the seeds, and provided enough contrasts to delineate the boundary of embryos manually. The manual segmentation allowed a 3D rendering of embryos at three different stages in the germination, which visualized an overall morphological change of the embryo upon germination as well as a spatial arrangement of cells inside the embryo. Conclusions Our results confirmed an availability of the laboratory-based X-ray phase-contrast CT for a 3D-structural study on the development of small seeds. The present method may provide a unique way to observe live plant tissues at micron resolutions without structural perturbations due to the sample preparation.
Why it matches plant phenotyping methods生きた種子内部の3D形態を非破壊・高解像度で取得するX線位相コントラストCT法が研究の中心であり、胚の形態変化と細胞配置という植物形質を可視化している。
abstractHere we report the observation of unstained pansy seeds using the laboratory-based X-ray phase-contrast CT.
X-ray fluorescence spectroscopy (XRF) is an analytical tool used to determine the elemental composition in a myriad of sample matrices. Due to the XRF non-destructive feature, this technique may allow time-resolved plant tissue analyses under in vivo conditions, and additionally, the combination with other non-destructive techniques. In this study, we employed handheld and benchtop XRF to evaluate the elemental distribution changes in living plant tissues exposed to X-rays, as well as real-time uptake kinetics of Zn(aq) and Mn(aq) in soybean (Glycine max (L.) Merrill) stem and leaves, for 48 hours, combined with transpiration rate assessment on leaves by an infrared gas analyzer (IRGA). We found higher Zn content than Mn in stems. The latter micronutrient, in turn, presented higher concentration in leaf veins. Besides, both micronutrients were more concentrated in the first trifolium (i.e., youngest leaf) of soybean plants. Moreover, the transpiration rate was more influenced by circadian cycles than Zn and Mn uptake. Thus, XRF represents a convenient tool for in vivo nutritional studies in plants, and it can be coupled successfully to other analytical techniques.
Why it matches plant phenotyping methods生体植物組織の元素分布・吸収 kinetics を非破壊XRFで時系列測定する方法を中心に扱い、植物の栄養状態という表現型を取得しているため。
abstractIn this study, we employed handheld and benchtop XRF to evaluate the elemental distribution changes in living plant tissues exposed to X-rays, as well as real-time uptake kinetics of Zn(aq) and Mn(aq) in soybean (Glycine max (L.) Merrill) stem and leaves, for 48 hours
Abstract Background: Rice quality research attracts attention worldwide. Rice chalkiness is one of the key indexes determining rice kernel quality. The traditional rice chalkiness measurement methods are mainly based on naked-eye observation or two-dimensional (2D) image analysis and the results could not represent the three-dimensional (3D) characteristics of chalkiness in the rice kernel. These methods are neither in vivo thus are unable to provide technical support for high throughput screening of rice chalkiness phenotype. Results: Here, we introduced a novel method for 3D visualization and accurate volume-based quantification of rice chalkiness in vivo by using X-ray microcomputed tomography (micro-CT). This approach not only develops a novel method to measure the rice chalkiness index, but also provides a high throughput solution for rice chalkiness phenotype analysis. Conclusions: Our method could be a new powerful tool for rice chalkiness measurement, which would greatly help the research of rice chalkiness traits as well as the quality evaluation in rice production practice.
Why it matches plant phenotyping methodsイネ玄米の胴割れではなく白未熟粒（chalkiness）を、マイクロCTで3D可視化・体積定量する植物表現型取得法を開発しており、方法自体が中心的である。
abstractHere, we introduced a novel method for 3D visualization and accurate volume-based quantification of rice chalkiness in vivo by using X-ray microcomputed tomography (micro-CT).
X-ray / CTPose / keypoint estimationGrowth / development / phenology
In agricultural robotics, a unique challenge exists in the automated planting of bulbous plants: the estimation of the bulb's growth direction. To date, no existing work addresses this challenge. Therefore, we propose the first robotic vision framework for the estimation of a plant bulb's growth direction. The framework takes as input three x-ray images of the bulb and extracts shape, edge, and texture features from each image. These features are then fed into a machine learning regression algorithm in order to predict the 2D projection of the bulb's growth direction. Using the x-ray system's geometry, these 2D estimates are then mapped to the 3D world coordinate space, where a filtering on the estimate's variance is used to determine whether the estimate is reliable. We applied our algorithm on 27,200 x-ray simulations from T. Apeldoorn bulbs on a standard desktop workstation. Results indicate that our machine learning framework is fast enough to meet industry standards (<0.1 seconds per bulb) while providing acceptable accuracy (e.g. error < 30° in 98.40% of cases using an artificial 3-layer neural network). The high success rates of the proposed framework indicate that it is worthwhile to proceed with the development and testing of a physical prototype of a robotic bulb planting system.
Why it matches plant phenotyping methodsX線画像と機械学習により球根の成長方向という植物器官の形態特性を推定する手法を開発・評価しており、フェノタイピング手法が中心である。
abstractwe propose the first robotic vision framework for the estimation of a plant bulb's growth direction.
Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · Europe PMC · checked 9 Sept 2026
Abstract Background Micro-computed tomography (μCT) bring a new opportunity to accurately quantify micro phenotypic traits of maize stem, also provide comparable benchmark to evaluate its dynamic development at the different growth stages. The progressive accumulation of stem biomass brings manifest structure changes of maize stem and vascular bundles, which are closely related with maize varietal characteristics and growth stages. Thus, micro-phenotyping (μPhenotyping) of maize stems is not only valuable to evaluate bio-mechanics and water-transport performance of maize, but also yield growth-based traits for quantitative traits loci (QTL) and functional genes location in molecular breeding. Result In this study, maize stems of 20 maize cultivars and two growth stages were imaged using μCT scanning technology. According to the observable differences of maize stems from the elongation and tasseling stages, function zones of maize stem were firstly defined to describe the substance accumulation of maize stems. And then a set of image-based μPhenotyping pipelines were implemented to quantify maize stem and vascular bundles at the two stages. The coefficient of determination (R 2 ) of counting vascular bundles was higher than 0.95. Based on the uniform contour representation, intensity-related, geometry-related and distribution-related traits of vascular bundles were respectively evaluated in function zones and structure layers. And growth-related traits of the slice, epidermis, periphery and inner zones were also used to describe the dynamic growth of maize stem. Statistical analysis demonstrated the presented method was suitable to the phenotyping analysis of maize stem for multiple growth stages. Conclusions The novel descriptors of function zones provide effective phenotypic references to quantify the differences between growth stages; and the detection and identification of vascular bundles based on function zones are more robust to determine the adaptive image analysis pipeline. Developing robust and effective image-based phenotyping method to assess the traits of stem and vascular bundles, is highly relevant for understanding the relationship between maize phenomics and genomics.
Why it matches plant phenotyping methodsμCT画像と画像解析パイプラインを用いて、トウモロコシ茎および維管束の形態・成長形質を定量化するフェノタイピング手法の開発と評価が中心である。
abstracta set of image-based μPhenotyping pipelines were implemented to quantify maize stem and vascular bundles at the two stages.
MaizeX-ray / CTRootMorphology / geometry measurement2D/3D reconstructionSegmentationRoot system architecture
No-tillage seeding has become an important approach to improve crop productivity, which needs colters of high performance to cut the root-stubble-soil composite. However, the difficulty of maize root-stubbles three-dimensional (3D) modeling hinders finite element (FE) simulation to improve development efficiency of such colters because of maize root system complexity and opaque nature of the soil. Fortunately, the non-destructive 3D geometric model of the maize root-stubble in-situ can be established via X-ray computed tomography (CT) following by a systematic procedure. The whole procedure includes CT scanning of the maize root-stubble-soil composite sample, image reconstruction via filtered back-projection (FBP) with the Hanning filter, segmentation of root-stubble via a variational level set method, and post-processing via morphological operations. The 3D reconstruction model of the maize root-stubble in-situ presents a complete, complex and in-situ geometrical morphology, which cannot be realized via other methods, including the destructive modelling after washing via CT. This study is the first to build a 3D geometric model of a maize root-stubble in-situ via CT, which opens up new possibilities for simulation of root-stubble-soil cutting using FEM, and much other research related to plant root-stubbles. Keywords: maize root-stubble, non-destructive modeling, X-ray computed tomography, variational level set method DOI: 10.25165/j.ijabe.20201303.5268 Citation: Zhao X, Xing L Y, Shen S F, Liu J M, Zhang D X. Non-destructive 3D geometric modeling of maize root-stubble in-situ via X-ray computed tomography. Int J Agric & Biol Eng, 2020; 13(3): 174–179.
Why it matches plant phenotyping methodsX線CT、画像再構成、セグメンテーション、形態学的後処理を組み合わせ、土壌中のトウモロコシ根・残株の3D形態を非破壊抽出する手法が研究の中心である。
abstractThe whole procedure includes CT scanning of the maize root-stubble-soil composite sample, image reconstruction via filtered back-projection (FBP) with the Hanning filter, segmentation of root-stubble via a variational level set method, and post-processing via morphological operations.
The improvement of existing analyses that access the physiological quality of seeds and the inclusion of nondestructive techniques represent significant progress to the seed sector. Despite being prominent, the use of x‐ray is hindered by the fact that radiographic images are, in general, analyzed subjectively. Therefore, this study aimed at investigating the relationship between the seed internal morphology, accessed via x‐ray images, and the physiological quality of habanero pepper seeds. The seeds were harvested from fruits at three maturity stages and then kept in post‐harvest storage for different periods. Initially, radiographs were generated and subjected to automated image analysis, using the ImageJ software. The parameters area, perimeter, circularity, relative density, integrated density, and percentage of seed filling were evaluated. After the x‐ray testing, the seeds were tested for germination and vigor. It was observed that postharvest storage increased the relative density of seed tissues, as well as seed filling and germination for all stages of maturity. Positive and significant correlations were found between tissue density parameters, evaluated by image analysis, with seed germination, germination speed and seed viability, while negative correlations were observed with seed dormancy. In general, the automated radiograph analysis of habanero pepper seeds is a promising method to obtain physical variables of seeds, such as relative density, integrated density, and seed filling. Habanero pepper seeds obtained from yellow and orange fruits exhibit higher physiological quality. The storage of these fruits after harvested before seed extraction is a good alternative to improve the seed physiological quality.
Why it matches plant phenotyping methodsX線画像をImageJで自動解析し、種子の内部形態・密度・充填率などの物理形質を抽出する手法が研究の中心であり、発芽・活力との関連も検証している。
abstractradiographic images are, in general, analyzed subjectively. Therefore, this study aimed at investigating the relationship between the seed internal morphology, accessed via x‐ray images, and the physiological quality of habanero pepper seeds.
Plant-microbe interactions can be utilized in bio-based processes such as bioremediation and biofortification, either to remove hazardous radionuclides and heavy metals from the soil, or to increase the accumulation of desired elements into crops to improve their quality. Optimizing such elegant plant-microbe interactions requires detailed understanding of the chemical element compositions of fresh plant tissues at cellular organelle resolution. However, such analyses remain challenging because conventional methods lack the required spatial resolution, contrast or sensitivity. Using a novel combination of nanoscaleresolution 3D cryogenic synchrotron-light ptychography, holotomography and fluorescence tomography, we show how soil bacteria interact with Arabidopsis thaliana and promote the uptake of various metals. Co-cultivation with Pseudomonas sp. strain T5-6-I alters root anatomy and increases levels of selenium (Se), iron (Fe) and other micronutrients in roots. Our approach highlights the interaction of plants and microbes in bioremediation and biofortification on the subcellular level.
Why it matches plant phenotyping methods植物組織の元素分布・根形態を高分解能で取得する新規イメージング手法が研究の中心であり、植物状態の定量的解析に直接用いられているため。
abstractUsing a novel combination of nanoscaleresolution 3D cryogenic synchrotron-light ptychography, holotomography and fluorescence tomography
Abstract Background and Aims X-ray fluorescence microscopy (XFM) is a powerful technique to elucidate the distribution of elements within plants. However, accumulated radiation exposure during analysis can lead to structural damage and experimental artefacts including elemental redistribution. To date, acceptable dose limits have not been systematically established for hydrated plant specimens. Methods Here we systematically explore acceptable dose rate limits for investigating fresh sunflower (Helianthus annuus) leaf and root samples and investigate the time–dose damage in leaves attached to live plants. Key Results We find that dose limits in fresh roots and leaves are comparatively low (4.1 kGy), based on localized disintegration of structures and element-specific redistribution. In contrast, frozen-hydrated samples did not incur any apparent damage even at doses as high as 587 kGy. Furthermore, we find that for living plants subjected to XFM measurement in vivo and grown for a further 9 d before being reimaged with XFM, the leaves display elemental redistribution at doses as low as 0.9 kGy and they continue to develop bleaching and necrosis in the days after exposure. Conclusions The suggested radiation dose limits for studies using XFM to examine plants are important for the increasing number of plant scientists undertaking multidimensional measurements such as tomography and repeated imaging using XFM.
Why it matches plant phenotyping methods植物試料のX線蛍光顕微鏡（XFM）について、測定による損傷や元素再分布を系統的に評価し、許容線量限界を検証・設定している。植物の元素分布・構造状態を取得する測定法の技術的妥当性が中心である。
abstractThe suggested radiation dose limits for studies using XFM to examine plants are important for the increasing number of plant scientists undertaking multidimensional measurements such as tomography and repeated imaging using XFM.
Contradictory evidence exists regarding whether and to which extend roots change soil structure in their vicinity. Here we attempt to reconcile disparate views allowing for the two-way interaction between soil structure and root traits, i.e. changes in soil structure due to plants and changes in root growth due to soil structure. Porosity gradients extending from the root/biopore surface into the bulk soil were investigated with X-ray µCT for undisturbed soil samples from a field chronosequence as well as for a laboratory experiment with Zea mays growing into three different bulk densities. An image analysis protocol was developed, which enabled a fast analysis of the large sample pool (n > 300) at a resolution of 19 µm. Lab experiment showed that growing roots only compact the surrounding soil if macroporosity is low and dominated by isolated pores. When roots can grow into a highly connected macropore system showing high connectivity the rhizosphere is more porous compared to the bulk soil. A compaction around roots/biopores in the field chronosequence was only observed in combination with high root/biopore length densities. We conclude that roots compact the rhizosphere only if the initial soil structure does not offer a sufficient volume of well-connected macropores.
Why it matches plant phenotyping methodsX線µCT画像から根周辺の土壌構造・空隙率を定量化する画像解析プロトコルを開発しており、植物形態と周辺状態の取得手法が研究の中心である。
abstractPorosity gradients extending from the root/biopore surface into the bulk soil were investigated with X-ray µCT
Inflorescence architecture provides the scaffold on which flowers and fruits develop, and consequently is a primary trait under investigation in many crop systems. Yet the challenge remains to analyse these complex 3D branching structures with appropriate tools. High information content datasets are required to represent the actual structure and facilitate full analysis of both the geometric and the topological features relevant to phenotypic variation in order to clarify evolutionary and developmental inflorescence patterns. We combined advanced imaging (X-ray tomography) and computational approaches (topological and geometric data analysis and structural simulations) to comprehensively characterize grapevine inflorescence architecture (the rachis and all branches without berries) among 10 wild Vitis species. Clustering and correlation analyses revealed unexpected relationships, for example pedicel branch angles were largely independent of other traits. We identified multivariate traits that typified species, which allowed us to classify species with 78.3% accuracy, versus 10% by chance. Twelve traits had strong signals across phylogenetic clades, providing insight into the evolution of inflorescence architecture. We provide an advanced framework to quantify 3D inflorescence and other branched plant structures that can be used to tease apart subtle, heritable features for a better understanding of genetic and environmental effects on plant phenotypes.
Why it matches plant phenotyping methodsX線CT画像と計算解析を組み合わせ、ブドウの3D花序構造を定量化する再利用可能な表現型解析フレームワークを開発・適用しており、手法が研究の中心である。
abstractWe combined advanced imaging (X-ray tomography) and computational approaches (topological and geometric data analysis and structural simulations) to comprehensively characterize grapevine inflorescence architecture
Reproduction assets foundThe paper explicitly deposits two paper-specific public assets: the full X-ray tomography PLY dataset (7.85 GB) of 392 scanned grapevine inflorescences hosted on the Danforth Center Topp lab resources page, and the authors' Matlab analysis code (persistence barcodes, bottleneck distances, berry potential simulation,几何/Dataset · publicThe full PLY dataset for this work is 7.85 GB, and can be downloaded from: https://www.danforthcenter.org/scientists-research/principal-investigators/chris-topp/resources .Open asset ↗lines:39-133Code · publicAll Matlab functions used to calculate persistence barcodes, bottleneck distances, simulation for berry potential, other geometric features used in this study, and the script for extracting phylogenetic information can be found at the following GitHub repository: https://github.com/Topp-Roots-Lab/Grapevine-inflorescence-architecture .Open asset ↗Topp-Roots-Lab/Grapevine-inflorescence-architecturelines:174-184Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 15 Sept 2026
ABSTRACT Premise of the study X-ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organisation. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small datasets, restricting its utility for phenotyping experiments and limiting our confidence in the conclusion of these studies due to low replication numbers. Methods and Results We present a Python codebase for random-forest machine learning segmentation and 3D leaf anatomical trait quantification which dramatically reduces the time required to process single leaf microCT scans into detailed segmentations. By training the model on each scan using 6 hand segmented image slices out of >1500 in the full leaf scan, it achieves >90% accuracy in background and tissue segmentation. Conclusion Overall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high-throughput plant phenotyping.
Why it matches plant phenotyping methods植物葉の3D内部解剖形質をmicroCT画像から抽出する機械学習セグメンテーション・定量化パイプラインの開発であり、植物フェノタイピング手法が中心です。
abstractWe present a Python codebase for random-forest machine learning segmentation and 3D leaf anatomical trait quantification
Apple is a seasonal product that is stored for long periods of time, during which fungal‐caused decay can occur. Previous infection experiments of intact Jonagored apple with Botrytis cinerea displayed apparent differences in lesion expansion rate with respect to inoculation position on the fruit (on, above or below the equator). The goal of this study was to investigate whether these differences are consistent or not and if so, to relate them to fruit characteristics. The study involved measuring the hue angle of the intended inoculation spots prior to inoculation, and firmness and total soluble solids content of fruit from the same batch. Results showed that firmness correlated somewhat (−0.55 and −0.72 for shadow and sun side, respectively) with lesion diameter expansion rate. In a subsequent step, X‐ray imaging was carried out for samples from each position. Analysis of 3D reconstructions by microcomputed tomography of these diffraction images showed that cell size was strongly correlated (0.996) to lesion diameter expansion rate. Finally, it was investigated if cell size could also be used to rank different apple cultivars for their susceptibility to B. cinerea . The result shows that there is a clear distinction between Jonagold and Golden Delicious (non‐blushing variety), which have a smaller cell size, and Braeburn and Kanzi, which have a larger cell size (overall correlation of 0.87). This indicates that cell size may also be an important factor in determining susceptibility across cultivars.
Why it matches plant phenotyping methodsマイクロCTの3D再構成によりリンゴ果実の細胞サイズという形態形質を定量し、病斑拡大や品種間感受性との関連を評価しており、画像ベースの形質取得が研究の主要な技術要素です。
abstractAnalysis of 3D reconstructions by microcomputed tomography of these diffraction images showed that cell size was strongly correlated (0.996) to lesion diameter expansion rate.
ABSTRACT Background In recent years, the plant morphology has been well studied by multiple approaches at cellular and subcellular levels. Two-dimensional (2D) microscopy techniques offer imaging of plant structures on a wide range of magnifications for researchers. However, subcellular imaging is still challenging in plant tissues like roots and seeds. Results Here we use a three-dimensional (3D) imaging technology based on the ZEISS X-ray microscope (XRM) Versa and analyze several plant tissues from different plant species. The XRM provides new insights into plant structures using non-destructive imaging at high-resolution and high contrast. We also developed a workflow aiming to acquire accurate and high-quality images in the context of the whole specimen. Multiple plant samples including rice, tobacco, Arabidopsis and maize were used to display the differences of phenotypes, which indicates that the XRM is a powerful tool to investigate plant microstructure. Conclusions Our work provides a novel observation method to evaluate and quantify tissue specific differences for a range of plant species. This new tool is suitable for non-destructive seed observation and screening.
Why it matches plant phenotyping methods植物組織・種子の微細構造をX線顕微鏡で非破壊撮像し、ワークフロー開発と組織差の定量評価・スクリーニングを行うことが中心であり、植物フェノタイピング手法に該当する。
abstractWe also developed a workflow aiming to acquire accurate and high-quality images in the context of the whole specimen.
Characterizing panicle traits is essential for measuring rice production. Because these traits can be used to analyze the effects of different genes on the rice. The spikelet number and the seed setting rate are usually considered to be more crucial. In existing characterizing methods, each grain of the panicle must be spread out manually to reduce the overlapping areas and made existing 2D imaging for the unfolded panicle. However, these methods, which maybe influence the panicle structure, are inefficient and complicated for operating, and cannot determine the seed setting rate. For the problems mentioned above, X-ray CT, as a non-destructive technique, can be used to examine and characterize the internal structure of the panicle. Therefore, in this paper, the panicle was scanned and reconstructed by Microfocus CT System. The panicle traits were detected by combination of distance transform watershed algorithm and 3D connected domains. The effectiveness of this method was verified by accuracy calculation of eight different rice panicle samples. This method is of great significance in practical application.
Why it matches plant phenotyping methodsイネ穂の形質を3D X線CTで取得・再構成し、画像アルゴリズムで抽出する手法を開発・精度検証しており、フェノタイピング手法が中心である。
abstractTherefore, in this paper, the panicle was scanned and reconstructed by Microfocus CT System. The panicle traits were detected by combination of distance transform watershed algorithm and 3D connected domains.
Background Wheat is one of the most important staple source in the world for human consumption, animal feed and industrial raw materials. To deal with the global and increasing population demand, enhancing crop yield by increasing the final weight of individual grain is considered as a feasible solution. Morphometric analysis of wheat grain plays an important role in tracking and understanding developmental processes by assessing potential impacts on grains properties, size and shape that are major determinants of final grain weight. X-ray micro computed tomography (μCT) is a very powerful non-invasive imaging tool that is able to acquire 3D images of an individual grain, enabling to assess the morphology of wheat grain and of its different compartments. Our objective is to quantify changes of morphology during growth stages of wheat grain from 3D μCT images. Methods 3D μCT images of wheat grains were acquired at various development stages ranging from 60 to 310 degree days after anthesis. We developed robust methods for the identification of outer and inner tissues within the grains, and the extraction of morphometric features using 3D μCT images. We also developed a specific workflow for the quantification of the shape of the grain crease. Results The different compartments of the grain could be semi-automatically segmented. Variations of volumes of the compartments adequately describe the different stages of grain developments. The evolution of voids within wheat grain reflects lysis of outer tissues and growth of inner tissues. The crease shape could be quantified for each grain and averaged for each stage of development, helping us understand the genesis of the grain shape. Conclusion This work shows that μCT acquisitions and image processing methodologies are powerful tools to extract morphometric parameters of developing wheat grain. The results of quantitative analysis revealed remarkable features of wheat grain growth. Further work will focus on building a computational model of wheat grain growth based on real 3D imaging data.
Why it matches plant phenotyping methods小麦粒の3D形態形質を抽出するμCT撮像・画像処理手法の開発が中心であり、形態計測ワークフローを明示的に提示している。
abstractWe developed robust methods for the identification of outer and inner tissues within the grains, and the extraction of morphometric features using 3D μCT images.
Three- and four-dimensional imaging techniques are a prerequisite for spatially resolving the form-structure-function relationships in plants. However, choosing the right imaging method is a difficult and time-consuming process as the imaging principles, advantages and limitations, as well as the appropriate fields of application first need to be compared. The present study aims to provide an overview of three imaging methods that allow for imaging opaque, large and thick (>5 mm, up to several centimeters), hierarchically organized plant samples that can have complex geometries. We compare light microscopy of serial thin sections followed by 3D reconstruction (LMTS3D) as an optical imaging technique, micro-computed tomography (µ-CT) based on ionizing radiation, and magnetic resonance imaging (MRI) which uses the natural magnetic properties of a sample for image acquisition. We discuss the most important imaging principles, advantages, and limitations, and suggest fields of application for each imaging technique (LMTS, µ-CT, and MRI) with regard to static (at a given time; 3D) and dynamic (at different time points; quasi 4D) structural and functional plant imaging.
Why it matches plant phenotyping methods植物の構造・機能を3D/4D画像化する複数手法を比較し、原理・利点・限界・適用分野を整理した方法論レビューであり、植物フェノタイピング手法が中心です。
abstractWe compare light microscopy of serial thin sections followed by 3D reconstruction (LMTS3D) as an optical imaging technique, micro-computed tomography (µ-CT) based on ionizing radiation, and magnetic resonance imaging (MRI) which uses the natural magnetic properties of a sample for image acquisition.
Technologies that increase safety and efficiency, while facilitating and streamlining the work of seed analysts, are increasingly required by the seed industry. X-ray image analysis is a technique that has been used in the analysis of grain and seeds because it is fast, accurate and non-destructive. The traditional method to verify the presence of insect damage in seeds involves manual cutting of the seeds, which endangers the safety of the analyst and is time-consuming and repetitive work that leads to visual fatigue. The objective of this study was to compared the efficiency of radiographic analysis with and without contrast in the determination of infestation by Sitophilus zeamais Motschulsky (Coleoptera: Curculionidae), at different stages of development, in maize seeds, compared to the traditional method required by seed legislation, which consists of cutting and visual evaluation. Seeds were evaluated regarding the presence of eggs/oviposition signs, larvae, pupae, adult insects, insect damage in five infestation periods (5, 18, 33 and 35 days after infestation), while evaluating the total number of seeds infested, comparing the three methods. For characterization of the oviposition stage, the use of contrast was best at all times of infestation. For the larval stage, there was no difference between the evaluation methods; however, at 18 days, larger infestations were observed by the traditional method. At 5 days, the identification of pupae was better by the traditional method and radiography without contrast, while for the identification of adult insects the best method was the use of radiography without contrast. The characterization of the level of infestation with maize weevil damage was best verified using contrast radiography. Radiographic analysis is efficient in the detection of damage caused by S. zeamais in maize seeds. This method of radiographic analysis (with or without contrast) is thus an auxiliary tool to assess the damage and presence of S. zeamais in maize seeds.
Why it matches plant phenotyping methodsトウモロコシ種子の害虫被害・ infestation という植物器官の状態を対象に、X線（造影あり・なし）と従来法を比較評価しており、表現型取得法の技術的検証が中心である。
abstractThe objective of this study was to compared the efficiency of radiographic analysis with and without contrast in the determination of infestation by Sitophilus zeamais