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.
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
Magnetic resonance imaging (MRI) enables non-invasive and non-destructive, three-dimensional anatomical and functional imaging of plant tissues and the quantitative investigation of dynamic processes such as water transport. Despite these advantages, MRI remains underutilized in plant and biomimetic research. One major limitation is the difficulty of maintaining physiologically suitable and stable environmental conditions during prolonged measurements, particularly when using ultra-high-field preclinical MRI scanners that were originally developed for small-animal imaging.In this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners. The system integrates growth and imaging conditions into a single setup, allowing continuous control of temperature, humidity, and illumination by the same system and removing the need to maintain separate commercial growth chambers alongside custom in-bore extensions. The implementation was optimized for the horizontal bore of a small animal scanner (Bruker PharmaScan 70/16) with 16 cm bore diameter and 72 mm free access but is applicable to other ultra-high-field preclinical MRI systems with comparable dimensions.The performance of the climate chamber and the in-bore extension was characterized with respect to temperature, humidity, and illumination stability. In addition, the potential negative impact of the insert and its electronics on the MRI signal (B 0 homogeneity, RF attenuation as well as potential RF artefacts) were verified.Functional validation in form of sap flow measurements as well as anatomical validation was demonstrated in a naturally transpiring stem of Passiflora quadrangularis. Under controlled in-bore environmental conditions, changes in sap flow velocity were reliably detected using a pulsed field gradient spin-echo sequence. Specifically, increasing the light intensity in the extension resulted in a shift of the maximum flow velocity in individual vascular bundles from 0.21 mm/s and 0.39 mm/s to 1.37 mm/s and 1.17 mm/s, respectively. In addition, high-resolution anatomical imaging (1 mm slices with an in-plane resolution of 25 µm) of branching regions in Dracaena braunii was successfully performed without observable motion artifacts. The presented system provides a low-cost, open-source solution for conducting anatomical and functional MRI studies of intact plants using ultra-high field preclinical MRI scanners.
Why it matches plant phenotyping methods植物の解剖学的・機能的MRI計測を可能にする環境制御チャンバーとインボア拡張を開発し、性能および植物での機能・解剖学的計測を検証しており、フェノタイピング手法が中心である。
abstractIn this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners.
The core volume ratio (CVR) is a key indicator for evaluating the proportion of edible fraction in stone fruits. Traditionally, CVR is determined through destructive sampling by separately measuring the masses of the core and entire fruit. Recently, low-field nuclear magnetic resonance imaging (LF-NMRI) has been introduced as a non-destructive alternative, but its sparse sampling limits the ability to achieve accurate spatial and volumetric quantification of fruit quality. To address this limitation, we propose a novel method for high-precision three-dimensional (3D) modeling of stone fruits. The method acquires tomographic LF-NMRI sequences along three orthogonal axes. Each sequence is segmented into pulp and core regions using a SwinUNet deep learning model and converted into point clouds for each view. Point clouds from the three orthogonal views are registered via a genetic algorithm to align structural information from complementary perspectives and fused into a unified 3D model through Poisson surface reconstruction. Using prunes as a representative case, the method enables accurate quantification of core and entire fruit volumes, achieving a CVR estimation with a mean absolute error of 0.13% compared to manual measurements. The proposed three-view reconstruction strategy yields a volumetric error of only 0.73%, significantly outperforming single-view (4.57%) and dual-view (3.73%) approaches. This technology provides a robust and accurate non-destructive solution for 3D internal quality analysis of fruits.
Why it matches plant phenotyping methodsLF-NMRI、深層学習セグメンテーション、3D再構成を組み合わせ、果実内部の芯・可食部体積という植物器官形質を非破壊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中心である。
abstractwe propose a novel method for high-precision three-dimensional (3D) modeling of stone fruits.
Plant 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.
Abstract Magnetic resonance imaging (MRI) enables non-invasive and non-destructive, three-dimensional anatomical and functional imaging of plant tissues and the quantitative investigation of dynamic processes such as water transport. Despite these advantages, MRI remains underutilized in plant and biomimetic research. One major limitation is the difficulty of maintaining physiologically suitable and stable environmental conditions during prolonged measurements, particularly when using ultra-high-field preclinical MRI scanners that were originally developed for small-animal imaging. In this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners. The system integrates growth and imaging conditions into a single setup, allowing continuous control of temperature, humidity, and illumination by the same system and removing the need to maintain separate commercial growth chambers alongside custom in-bore extensions. The implementation was optimized for the horizontal bore of a small animal scanner (Bruker PharmaScan 70/16) with 16 cm bore diameter and 72 mm free access but is applicable to other ultra-high-field preclinical MRI systems with comparable dimensions. The performance of the climate chamber and the in-bore extension was characterized with respect to temperature, humidity, and illumination stability. In addition, the potential negative impact of the insert and its electronics on the MRI signal (B0 homogeneity, RF attenuation as well as potential RF artefacts) were verified. Functional validation in form of sap flow measurements as well as anatomical validation was demonstrated in a naturally transpiring stem of Passiflora quadrangularis. Under controlled in-bore environmental conditions, changes in sap flow velocity were reliably detected using a pulsed field gradient spin-echo sequence. Specifically, increasing the light intensity in the extension resulted in a shift of the maximum flow velocity in individual vascular bundles from 0.21 mm/s and 0.39 mm/s to 1.37 mm/s and 1.17 mm/s, respectively. In addition, high-resolution anatomical imaging (1 mm slices with an in-plane resolution of 25 µm) of branching regions in Dracaena braunii was successfully performed without observable motion artifacts. The presented system provides a low-cost, open-source solution for conducting anatomical and functional MRI studies of intact plants using ultra-high field preclinical MRI scanners.
Why it matches plant phenotyping methods植物のMRI計測を可能にする環境制御・MR互換チャンバーを開発し、性能および植物の解剖・通道機能計測で検証しており、フェノタイピング手法と基盤が研究の中心である。
abstractIn this work, we present a low cost, climate-controlled and MR-compatible growth chamber that includes an in-bore extension for preclinical MRI scanners.
Imaging carbon movements in the rhizosphere is fundamentally limited by high soil heterogeneity, low signal levels, and lack of methodology. We present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant–soil systems. The system achieved a global energy resolution of 11.93 ± 0.02% FWHM at 511 keV and maintained stable performance over 8 h of continuous acquisition, with a coincidence rate variation of only 0.7%. Spatial resolution reached 1.06 mm near the center of the field of view, establishing a high-fidelity region for root-scale analysis. Dynamic datasets were acquired from live Phaseolus vulgaris plants ( N = 3) over 180 min following 11 CO 2 pulse labeling and reconstructed into 3 min temporal frames. Quantitative analysis across 243 independent regions of interest (ROI) revealed that cumulative tracer accumulation decreases monotonically with radial distance from the root axis, while axial transport delays increase systematically in lower root segments ( p < 0.001). Hierarchical variability analysis showed that within-plant spatial organization ( CV TTP = 0.03) is significantly more stable than inter-plant variation ( CV TTP = 0.14), proving that the observed heterogeneity reflects biological spatial organization rather than experimental instability. These results establish Rhizo-PET as a robust, reproducible platform for the non-invasive, time-resolved analysis of carbon dynamics in the rhizosphere under realistic soil conditions.
Why it matches plant phenotyping methods植物根圏における炭素動態を非侵襲・時系列で測定する専用PETシステムを開発し、性能・再現性・空間解析能力を検証した研究であり、植物状態の取得方法が中心である。
abstractWe present Rhizo-PET, a dedicated positron emission tomography (PET) imaging and analysis framework designed to characterize the 4D spatiotemporal patterns of tracer distribution in intact plant–soil systems.
Background Phloem is the long-distance transport tissue of vascular plants in which photoassimilates are distributed from sources (e.g., leaves) to sinks (e.g., roots, fruits). Phloem transport occurs under pressure, making it very sensitive to manipulation and almost experimentally inaccessible. Therefore, functional data on phloem speed and dynamic distribution of photoassimilates along the transport pathway are still scarce, both in trees and herbaceous plants. This study presents a methodological pipeline to image phloem transport in very thin shoots of the model plant Arabidopsis using photosynthetic uptake of 11 CO 2 and state-of-the-art positron emission tomography (PET). Results Successful application of the latest generation preclinical PET scanners allowed in vivo visualization of internal movement of 11 C-labelled photoassimilates inside primary and secondary shoots of 1 to 2 mm diameter every 5 min. Using this data as input in a compartmental model enabled estimation of (i) phloem front speed, and (ii) radial carbon partitioning between leakage-retrieval phloem, carbon storage and respiratory efflux. The methodology shows that the phloem front speed of recently fixed carbon in primary shoots was almost two-fold the speed in secondary shoots (128 vs. 70 µm s -1 ). Furthermore, it was estimated that the fraction of recently fixed 11 CO 2 that was unloaded from the phloem to the surrounding storage cells and retrieved back into the phloem was higher in primary shoots than in secondary shoots, and that allocation to the storage compartment was higher in secondary shoots. Within the primary shoot, the fraction of unloading and retrieval of the 11 C-labelled photosynthates increased towards the inflorescence. Conclusion Here, we demonstrate the synergistic application of high-resolution PET scanning and compartmental modelling as a promising approach to advance our understanding of phloem dynamics in small-dimension plants, such as the model plant Arabidopsis. With this, an opportunity is created to explore the genetic basis of phloem dynamics.
Why it matches plant phenotyping methodsPET撮像とコンパートメントモデルを組み合わせ、植物体内の師部輸送速度や炭素分配という生理形質を推定する方法論が研究の中心であるため。
abstractThis study presents a methodological pipeline to image phloem transport in very thin shoots of the model plant Arabidopsis using photosynthetic uptake of 11 CO 2 and state-of-the-art positron emission tomography (PET).
In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk diseases (GTD) are a well-known example in viticulture that alter plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for high-resolution Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal host tissue degradation resulting from fungal pathogen colonization. The pipeline integrates spatio-temporal anatomical alignment and rigid registration; a generalized cylindrical-coordinate transformation; supervised segmentation of water-depleted regions; and population-level statistical analyses, including population mean images, probabilistic atlases, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal wood pathogen, our approach enables in vivo time-lapse comparisons between cultivars and treatments. The results reveal reproducible early degradation signals across individuals and cultivar-dependent differences in lesion progression. Overall, this methodological innovation provides a new paradigm for internal plant phenotyping, enabling non-invasive quantification of disease development and comparative spatio-temporal assessment of host responses in woody plants, with strong potential to advance early diagnosis and management of GTDs and internal diseases.
Why it matches plant phenotyping methodsMRI画像と計算パイプラインにより、ブドウ樹内部の病変・組織劣化を非破壊かつ時空間的に定量化する手法を開発・適用しており、植物表現型取得が中心である。
abstractwe present a novel non-destructive 3D + t pipeline for high-resolution Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal host tissue degradation resulting from fungal pathogen colonization.
Reproduction assets foundThe paper's MRI phenotyping data (~160 GB raw, 1.4 TB processed) is only available upon request, but the authors' processing pipeline (scripts and parameters) is publicly deposited on Zenodo with an explicit URL.Code · publicThe processing pipeline (including scripts and parameters required to reproduce the processed outputs from the raw data) is available at https://doi.org/10.5281/zenodo.17944369 .Open asset ↗Zenodo · 10.5281/zenodo.17944369lines:370-484Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Why it matches plant phenotyping methods発芽油種子の油含量・分布という植物器官の状態を、LF-NMR/MRIで非破壊測定する手法を開発・検証しており、表現型取得法が研究の中心である。
abstractThis study evaluated the feasibility of using low-field nuclear magnetic resonance (LF-NMR) coupled with magnetic resonance imaging (MRI) as a non-destructive approach for monitoring oil changes in germinating oilseeds.
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.
Abstract In perennial crops, inner wood degradation by pathogens often escapes detection until irreversible damage has occurred. Grapevine trunk disease (GTD) is a well-known example in viticulture that alters plants from within, years before foliar symptoms arise, making early assessment difficult. To overcome this limitation, we present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization. This pipeline integrates (i) anatomical alignment and rigid time-series registration of volumetric MRI scans, (ii) a generalized cylindrical coordinate transformation for cross-sectional trunk anatomy normalization, (iii) supervised classification to segment water-depleted (diseased/non-functional) regions, and (iv) population-level statistical analyses including construction of population mean images, probabilistic atlases of lesions, and 3D lesion descriptors. Applied to multiple Vitis vinifera cultivars inoculated with a fungal trunk pathogen, our approach enables time-lapse comparisons between cultivar and treatment in vivo. The results reveal consistent early degradation signals across individuals and cultivar-dependent lesion differences. By combining high-resolution MRI with advanced image processing and statistical atlas tools, this method provides a new paradigm for 3D plant phenotyping of internal disease progression. This methodological innovation allows non-invasive quantification of disease development and comparative assessment of host responses in woody plants, demonstrating its potential to advance understanding and management of GTDs.
Why it matches plant phenotyping methodsMRI画像と画像処理・統計アトラスを統合し、ブドウ樹内部の病変・組織劣化を3Dで定量化する植物フェノタイピング手法の開発が中心である。
abstractwe present a novel non-destructive 3D + t pipeline for Magnetic Resonance Imaging (MRI) spatial quantification and monitoring of early internal tissue degradation resulting from fungal colonization.
Reproduction assets foundThe paper's raw/processed MRI datasets are only available from the corresponding author upon reasonable request, but the authors' processing pipeline (scripts and parameters to reproduce processed outputs from raw data) is publicly deposited on Zenodo with an explicit DOI.Code · publiceer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
available under a CC-BY 4.0 International license.
1 reasonable request. The processing pipeline (including scripts and parameters required to reproduce the
2 processed outputs from the raw data) is available at https://doi.org/10.5281/zenodo.17944369.
3
Plant Phenomics Page 26 of 29Open asset ↗zenodo · 10.5281/zenodo.17944369pdf-layout-page:26 lines:1-14Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Lignocellulosic biomass (LB), which encompasses various plant samples, requires thorough characterization to optimize its use as a carbon resource. Chemical imaging simultaneously provides chemical and spatial information, offering significant benefits for LB analysis. This review presents an overview of the most advanced techniques for achieving this goal. By combining spectrometry and microscopy, microspectroscopy enables chemical imaging using various irradiation sources (IR, Raman, fluorescence, among others), allowing for the quantitative mapping of key LB components such as lignins, cellulose, and hemicelluloses. Mass Spectrometry Imaging (MSI) generates a mass spectrum for each spot of a sample thereby creating a chemical image pixel-by-pixel. MSI techniques like Matrix-Assisted Laser Desorption/Ionization (MALDI), down to 2-5 μm spatial resolution, and Secondary Ion Mass Spectrometry (SIMS), down to 300 nm for molecular analysis, effectively map small molecules in LB. In contrast, Desorption ElectroSpray Ionization (DESI) has been applied to plant extracts but remains largely unexplored for LB applications. Nuclear Magnetic Resonance (NMR) provides insight into various LB properties too. Solid-state NMR (ssNMR) and Dynamic Nuclear Polarization (DNP) help elucidate the structure of LB, sometimes aided by 3D atomistic modeling, whereas micro-Magnetic Resonance Imaging (micro-MRI) and Time-Domain (TD-NMR) probe the impact of water on LB properties.
Why it matches plant phenotyping methods植物バイオマスの化学成分を空間的に定量・マッピングする化学イメージング手法のレビューであり、植物試料の観察・形質抽出法が中心である。
abstractThis review presents an overview of the most advanced techniques for achieving this goal.
Abstract Magnetic resonance imaging (MRI), long established in medical diagnostics, offers powerful, non-invasive capabilities for visualizing physiological processes in intact plants. This review focuses on the principles, recent advances, and future prospects of MRI-based lipid analysis in plant science with a particular focus on seeds. Cutting-edge, spatially resolved MRI has uncovered a remarkable compartmentation of lipid metabolism and storage. Lipid distribution patterns reflect the tissue- and cell-specific functional roles of lipids and are shaped by local metabolite gradients and other regulatory factors, including biomechanical and environmental stimuli. Recent innovations in MRI methodology now allow comprehensive, non-invasive monitoring of lipid storage and degradation dynamics in vivo. Looking ahead, the integration of MRI with deep learning and multimodal approaches heralds a transformative era for seed biology, oilseed phenotyping, and breeding.
Why it matches plant phenotyping methods植物の脂質分布・貯蔵・分解動態をMRIで非侵襲的に可視化・追跡する方法を扱うレビューであり、種子フェノタイピングへの応用も明示されているため、方法論が中心です。
abstractThis review focuses on the principles, recent advances, and future prospects of MRI-based lipid analysis in plant science with a particular focus on seeds.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
ABSTRACT Roots are crucial for enhancing crop resilience to abiotic stresses, including drought, salinity, cold, nutrient deficiency, and metal toxicity. Root system architecture and morphological traits play a significant role in enabling plants to access water and nutrients under stress conditions. However, the study of roots is challenging due to their underground nature. Here, we review advancements in high‐throughput root phenotyping methodologies that enable the non‐destructive and large‐scale analysis of root traits in controlled conditions. These include soil‐less two‐dimensional platforms, such as hydroponics and gel‐based systems, and soil‐based systems like Rhizotrons and RhizoTubes. Additionally, cutting‐edge three‐dimensional soil‐less systems and soil‐based imaging technologies, such as x‐ray‐computed tomography and magnetic resonance imaging, have significantly improved the precision of root trait analysis. Computational tools, including machine learning algorithms, are also transforming root phenotyping by automating image segmentation, trait extraction, and data analysis. Case studies and examples described here demonstrate the successful application of these methods in identifying stress‐specific root traits that improve resilience to various abiotic stresses in monocots, dicots, and legumes. Despite these advancements, challenges such as high costs, scalability, and environmental variability persist. Integrating laboratory and field‐based phenotyping systems can address these limitations and lead the way for more effective breeding programs to improve crop resilience against climate change.
Why it matches plant phenotyping methods根系形質のハイスループット画像化・計測法と計算解析を体系的にレビューしており、植物フェノタイピング手法が中心である。
abstractHere, we review advancements in high‐throughput root phenotyping methodologies that enable the non‐destructive and large‐scale analysis of root traits in controlled conditions.
Objective . Plant Positron Emission Tomography (PET) is a new and efficient imaging technique which aims at providing a quantitative analysis of plant stress, enabling personalized crop management and maximizing productivity. However, a highly performant classification system for noisy dynamic plant PET images faces the challenge of retrieving noise-free datasets and encoding both spatial and temporal representations within a unified model. Approach . To overcome these limitations, we introduce an innovative hybrid model that combines denoising and classification for dynamic plant PET images. Initially, we compute a precise solution for the denoising problem of noisy dynamic plant PET images using a modified optimization method coupled with deep convolutional neural networks. Subsequently, this solution is unfolded into a deep network known as the predictor-corrector network (PCNet). To optimize the PCNet without requiring a noise-free dynamic training set, we propose a novel unsupervised learning method. Finally, the sequence of noise-reduced dynamic plant PET images is further fed into a unique classification system, encoding spatial representations of images and temporal representations of multivariate time series into a unified spatiotemporal representation and generating a prediction. Main results . The experimental results underscore the necessity of the denoising procedure and highlight the superiority of the proposed PCNet over existing competing denoising methods, demonstrating the effectiveness of the proposed classification system. Notably, the classification performance between the two classes achieves an averaged accuracy of 0.852, an averaged precision of 0.838, an averaged recall of 0.959, and an averaged F1-score of 0.880. Significance . The ability of the proposed method to reduce noise intensity and effectively encode spatiotemporal representations overcomes the limitations of existing methods. This advancement may have substantial implications for other noisy dynamic image classification.
Why it matches plant phenotyping methods植物PET画像のノイズ除去と時空間分類を統合した手法を開発・評価しており、植物ストレスの定量解析を目的とする画像ベースのフェノタイピング手法が中心である。
abstractwe introduce an innovative hybrid model that combines denoising and classification for dynamic plant PET images.
Positron Emission Tomography (PET) is a valuable tool for plant imaging, but its accuracy can be compromised by positron range effects. This study improves PET accuracy using the GATE Monte Carlo simulation tool to estimate and correct these effects. The GATE model was validated for the Siemens Biograph Vision system using the NEMA NU 2-2018 protocol, showing alignment with experimental data. Deviations were within 9% for sensitivity and 3% for peak Noise Equivalent Count Rate (NECR). Different isotopes ( 18 F, 11 C, 15 O, and 30 P) and plant phantom properties were analyzed for their impact on reconstructed images. A sixfold enhancement was observed for 15 O and a threefold improvement for 11 C when a magnetic field was applied to the plant phantom. Our findings suggest that integrating PET with magnetic resonance imaging can help address Positron range effects in plant imaging. This study provides valuable insights into PET imaging and offers refined methodologies for clinical and plant-centric research. Our research validates the use of GATE Monte Carlo simulation for Biograph Vision and advances our understanding of Positron range phenomena and potential mitigation strategies for precise PET Plant imaging.
Why it matches plant phenotyping methods植物PET画像の精度向上、モンテカルロ補正、装置検証を中心とする植物イメージング手法研究であり、植物状態の取得方法が中核です。
abstractThis study improves PET accuracy using the GATE Monte Carlo simulation tool to estimate and correct these effects.
The real-time and non-invasive visualization and quantification of symbiotic nitrogen fixation (SNF) in nodulated roots of soybean plants using Positron Emission Tomography (PET) imaging, coupled with the application of [ 13 N]N 2 gas as a PET radiotracer, has been explored in only a few studies. In these studies, [ 13 N]N 2 was delivered to nodulated soybean roots suspended in air within gas-tight acrylic boxes, followed by two-dimensional (2D) PET imaging to visualize the assimilated [ 13 N]N 2 in the air-suspended root nodules. In this paper, we introduce the In-Media Plant PET Root Imaging System (IMP 2 RIS), a novel gas delivery system designed and constructed in-house. Unlike the previous methods, IMP 2 RIS allows for non-intrusive delivery and exposure of [ 13 N]N 2 gas to the nodulated roots of soybean plants grown in a clay-rich, soil-like and visually opaque growth medium. This advancement enabled in-soil, three-dimensional (3D) visualization of SNF in soybean root nodules using Sofie, a preclinical PET scanner. Equipped with automated controls, IMP 2 RIS ensures ease of operation and operator safety during the [ 13 N]N 2 delivery process. We describe the components and functionalities of IMP 2 RIS, supported by experimental results showcasing its successful application in efficient delivery and exposure of [ 13 N]N 2 gas to nodulated roots of three soybean plant cultivars that vary in rates of N 2 fixation. The in-soil quantitative PET imaging of SNF, aided by IMP 2 RIS, holds promise for enhancing the integration of SNF as a functional phenotypic trait into breeding programs, aiming to enhance SNF efficiency by identifying breeding materials with high SNF capacities.
Why it matches plant phenotyping methods根圏内の窒素固定という植物生理形質をPETで可視化・定量するためのガス供給システムを開発し、実験的に適用しており、フェノタイピング手法が研究の中心である。
abstractwe introduce the In-Media Plant PET Root Imaging System (IMP 2 RIS), a novel gas delivery system designed and constructed in-house.
In the acquisition of Magnetic Resonance (MR) images shorter scan times lead to higher image noise. Therefore, automatic image denoising using deep learning methods is of high interest. In this work, we concentrate on image denoising of MR images containing line-like structures such as roots or vessels. In particular, we investigate if the special characteristics of these datasets (connectivity, sparsity) benefit from the use of special loss functions for network training. We hereby translate the Perceptual Loss to 3D data by comparing feature maps of untrained networks in the loss function. We tested the performance of untrained Perceptual Loss (uPL) on 3D image denoising of MR images displaying brain vessels (MR angiograms - MRA) and images of plant roots in soil. In this study, 536 MR images of plant roots in soil and 450 MRA images are included. The plant root dataset is split to 380, 80, and 76 images for training, validation, and testing. The MRA dataset is split to 300, 50, and 100 images for training, validation, and testing. We investigate the impact of various uPL characteristics such as weight initialization, network depth, kernel size, and pooling operations on the results. We tested the performance of the uPL loss on four Rician noise levels (1%, 5%, 10%, and 20%) using evaluation metrics such as the Structural Similarity Index Metric (SSIM). Our results are compared with the frequently used L1 loss for different network architectures. We observe, that our uPL outperforms conventional loss functions such as the L1 loss or a loss based on the Structural Similarity Index Metric (SSIM). For MRA images the uPL leads to SSIM values of 0.93 while L1 and SSIM loss led to SSIM values of 0.81 and 0.88, respectively. The uPL network's initialization is not important (e.g. for MR root images SSIM differences of 0.01 occur across initializations, while network depth and pooling operations impact denoising performance slightly more (SSIM of 0.83 for 5 convolutional layers and kernel size 3 vs. 0.86 for 5 convolutional layers and kernel size 5 for the root dataset). We also find that small uPL networks led to better or comparable results than using large networks such as VGG (e.g. SSIM values of 0.93 and 0.90 for a small and a VGG19 uPL network in the MRA dataset). In summary, we demonstrate superior performance of our loss for both datasets, all noise levels, and three network architectures. In conclusion, for images containing line-like structures, uPL is an alternative to other loss functions for 3D image denoising. We observe that small uPL networks have better or equal performance than very large network architectures while requiring lower computational costs and should therefore be preferred.
Why it matches plant phenotyping methods植物根のMR画像を対象に、3D画像デノイジング用の損失関数を開発・比較検証しており、根画像からの表現型取得を支える画像解析手法が中心である。
abstractWe tested the performance of untrained Perceptual Loss (uPL) on 3D image denoising of MR images displaying brain vessels (MR angiograms - MRA) and images of plant roots in soil.
Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programmes. There is a need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we introduce a novel tool, MRI-Seed-Wizard, which integrates deep learning algorithms with non-invasive magnetic resonance imaging (MRI) for use in a new domain-plant MRI. The tool enabled in vivo quantification of 23 grain traits, including volumetric parameters of inner seed structure. Several of these features cannot be assessed using conventional techniques, including X-ray computed tomography. MRI-Seed-Wizard was designed to automate the manual processes of identifying, labeling, and analysing digital MRI data. We further provide advanced MRI protocols that allow the evaluation of multiple seeds simultaneously to increase throughput. The versatility of MRI-Seed-Wizard in seed phenotyping is demonstrated for wheat (Triticum aestivum) and barley (Hordeum vulgare) grains, and it is applicable to a wide range of crop seeds. Thus, artificial intelligence, combined with the most versatile imaging modality, MRI, opens up new perspectives in seed phenotyping and crop improvement.
Why it matches plant phenotyping methodsMRIと深層学習を統合した種子表現型解析ツールを開発し、多数の種子形質を自動・非破壊・高スループットに定量化する中心的な方法論研究である。
abstractHere, we introduce a novel tool, MRI-Seed-Wizard, which integrates deep learning algorithms with non-invasive magnetic resonance imaging (MRI) for use in a new domain-plant MRI.
Reproduction assets foundThe paper's MRI-Seed-Wizard segmentation/phenotyping pipeline (Python/PyTorch scripts, nnU-Net/U-Net models) and demonstration data are explicitly published online by the authors at the GitHub repository akvilonBrown/mri-wizard, matching an allowed URL.Code · publicCode and demonstration data are available at: https://github.com/akvilonBrown/mri-wizard .Open asset ↗akvilonBrown/mri-wizardlines:227-303Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
This study introduces a non-destructive, quantitative method using low-field MRI to assess moisture mobility and content distribution in cherry tomatoes. This study developed an advanced 3D non-local mean denoising model to enhance tissue feature analysis and applied an optimized TransUNet model for structural segmentation, obtaining multi-echo data from six tissue types. The structural T2 relaxation inversion was refined by integrating an ACS-CIPSO algorithm. This approach addresses the challenge of low signal-to-noise ratios in multi-echo MRI images from low-field equipment by introducing an innovative solution that effectively reduces voxel noise while retaining structural relaxation variability. The study reveals that there are consistent patterns in the changes in moisture mobility and content across different structures of cherry tomatoes during their ripening process. Mono-exponential analysis reveals the patterns of changes in moisture mobility (T2) and content (A) across various structures. Furthermore, tri-exponential analysis elucidates the patterns of changes in bound water (T21), semi-bound water (T22), and free water (T23), along with their respective contents. These insights offer a novel perspective on the changes in moisture mobility throughout the ripening process of tomato fruit, thereby providing a research pathway for the precise assessment of moisture status and ripening expression in fruits.
Why it matches plant phenotyping methods低磁場MRIのノイズ低減、構造セグメンテーション、T2緩和反転を開発し、トマト果実の水分状態と成熟表現型を定量化する方法が研究の中心である。
abstractThis study introduces a non-destructive, quantitative method using low-field MRI to assess moisture mobility and content distribution in cherry tomatoes.
The early identification of pests and diseases in crops now presents a significant challenge. Different methods have been used to resolve this problem. Sticky traps and black light traps, used to identify diseases and for field monitoring, are examples of a manual procedure for analysing the diseases. A lot of time is required, and it is less effective to manually inspect larger crop fields manually. To serve requires a professional, so it is, therefore, costly. The use of sticky traps, where by bugs stick to the material upon contact, is one method of disease monitoring. A camera is used to take a picture of the sticky trap. From the picture using the average disease count, this image is then processed to ascertain the pet density for a specific time period. Such manual methods, as well as providing an effective outcome also pose a danger to the environment. This is because farmers spray pesticides in large quantities as a preventative measure. Various approaches have been used to identify diseases, including image processing and sophisticated algorithms. The most effective method of disease identification from crops is automatic detection using methods of image processing and classification algorithms for the diseases to be categorised based on different picture attributes. With a stacking stacking hybrid learning with scratch and transfer learning strategies, which is utilised in this work, a model that has already been trained is used to learn on images of diverse fruit plant leaves from the Plant Village dataset, spanning both safe samples and various illnesses. This reasearch paper used ensemble CNN and we achieved accuracy between 99.75% to 100%.
Why it matches plant phenotyping methods植物葉の画像から病害状態を自動分類する画像解析・機械学習手法が研究の中心であり、植物の病害表現型を直接推定しているため。
abstractThe most effective method of disease identification from crops is automatic detection using methods of image processing and classification algorithms for the diseases to be categorised based on different picture attributes.
Reproduction assets foundThe paper's plant-phenotyping input is the public New Plant Diseases Dataset (Kaggle), explicitly cited in the Data Availability statement as the data supporting the findings. No author analysis code or trained model checkpoints are disclosed.Dataset · publicThe data used to support the findings of this study is available at New Plant Diseases Dataset: " https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data " and Various fruits disease datasets is available at PlantDoc: A Dataset for Visual Plant Disease Detection [ 25 ].Open asset ↗Kaggle · vipoooool/new-plant-diseases-datasetlines:642-642Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published9 Oct 202430th ACM Symposium on Virtual Reality Software and TechnologyCited by 0 · OpenAlex ↗
Laboratory / benchtopMRI / PETRoot2D/3D reconstructionRoot system architecture
VRoot is an immersive extended reality reconstruction tool for root system architectures from 3D volumetric scans of soil columns. We have conducted a laboratory user study to assess the performance of new users with our software in comparison to established software. We utilize a plant model to derive a synthetic root architecture, providing a baseline for reconstruction. This demo showcases the processes and techniques contributing to exact and efficient manual root architecture reconstruction in Virtual Reality. The extraction task typically is the sparse graph-structure extraction from a 3D magnetic-resonance imaging (MRI) data set. We visualize the RSA directly within the MRI and offer selection-set-based methods of adapting and augmenting the root architecture. This application is in productive use at our partner institute, where it is used to analyze complex root images.
Why it matches plant phenotyping methods根系構造という植物形質をMRIから抽出・再構成するVRツールを開発し、既存ソフトウェアとの性能比較によるユーザー評価も行っているため、植物フェノタイピング手法が中心です。
abstractVRoot is an immersive extended reality reconstruction tool for root system architectures from 3D volumetric scans of soil columns.
Magnetic resonance imaging (MRI) is a versatile technique in the biomedical field, but its application to the study of plant metabolism in vivo remains challenging because of magnetic susceptibility problems. In this study, we report the establishment of chemical exchange saturation transfer (CEST) for plant MRI. This method enables noninvasive access to the metabolism of sugars and amino acids in complex sink organs (seeds, fruits, taproots, and tubers) of major crops (maize, barley, pea, potato, sugar beet, and sugarcane). Because of its high signal detection sensitivity and low susceptibility to magnetic field inhomogeneities, CEST analyzes heterogeneous botanical samples inaccessible to conventional magnetic resonance spectroscopy. The approach provides unprecedented insight into the dynamics and distribution of sugars and amino acids in intact, living plant tissue. The method is validated by chemical shift imaging, infrared microscopy, chromatography, and mass spectrometry. CEST is a versatile and promising tool for studying plant metabolism in vivo, with many applications in plant science and crop improvement.
Why it matches plant phenotyping methods植物の生体内代謝を非侵襲的に測定するCEST-MRI法を確立し、複数手法で検証しており、植物表現型取得法が研究の中心である。
abstractIn this study, we report the establishment of chemical exchange saturation transfer (CEST) for plant MRI.
Background Wheat (Triticum aestivum L.) is an important grain crops in the world, and its growth and development in different stages is seriously affected by saline-alkali stress, especially in seedling stage. Therefore, nondestructive detection of wheat seedlings under saline-alkali stress can provide more comprehensive technical support for wheat breeding, cultivation and management. Results This research focused on moisture signal prediction and classification of saline-alkali stress in wheat seedlings using fusion techniques. After collecting and analyzing transverse relaxation time and Multispectral imaging (MSI) information of wheat seedlings, four regression models were used to predict the moisture signal. K-Nearest Neighbor (KNN) and Gaussian-Naïve Bayes (GNB) models were combined with fivefold cross validation to classify the prediction of wheat seedling stress. The results showed that wheat seedlings would increase the bound water content through a certain mechanism to enhance their saline-alkali stress. Under the same Na concentration, the effect of alkali stress on moisture, growth and spectrum of wheat seedlings is stronger than salt stress. The Gradient Boosting Decision Regression Tree model performs the best in predicting wheat moisture signals, with a coefficient of determination (R2P) of 0.98 and a root mean square error of 109.60. It also had a short training time (1.48 s) and an efficient prediction speed (1300 obs/s). The KNN and GNB demonstrated significantly enhanced predictive performance when classifying the fused dataset, compared to using single datasets individually. In particular, the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively. Conclusions Under the same Na concentration, the effects of alkali stress on water content, spectrum, and growth of wheat were stronger than that of salt stress, which was more unfavorable to the growth of wheat. The fusion of low-field nuclear magnetic resonance and MSI technology can improve the classification of wheat stress, and provide an effective technical method for rapid and accurate monitoring of wheat seedlings under saline-alkali stress.
Why it matches plant phenotyping methods低磁場NMRとマルチスペクトル画像を融合し、コムギ幼植物の水分状態と塩・アルカリストレスを非破壊推定・分類する手法が研究の中心であるため。
abstractThis research focused on moisture signal prediction and classification of saline-alkali stress in wheat seedlings using fusion techniques.
Rapeseed / canolaMRI / PETCell / cellular structureLeafTissueClassificationPhysiological trait estimationGrowth / development / phenologyStress response / toleranceWater status / transpiration
Background Characterisation of the structure and water status of leaf tissues is essential to the understanding of leaf hydraulic functioning under optimal and stressed conditions. Magnetic Resonance Imaging is unique in its capacity to access this information in a spatially resolved, non-invasive and non-destructive way. The purpose of this study was to develop an original approach based on transverse relaxation mapping by Magnetic Resonance Imaging for the detection of changes in water status and distribution at cell and tissue levels in Brassica napus leaves during blade development and dehydration. Results By combining transverse relaxation maps with a classification scheme, we were able to distinguish specific zones of areoles and veins. The tissue heterogeneity observed in young leaves still occurred in mature and senescent leaves, but with different distributions of T 2 values in accordance with the basipetal progression of leaf blade development, revealing changes in tissue structure. When subjected to severe water stress, all blade zones showed similar behaviours. Conclusion This study demonstrates the great potential of Magnetic Resonance Imaging in assessing information on the structure and water status of leaves. The feasibility of in planta leaf measurements was demonstrated, opening up many opportunities for the investigation of leaf structure and hydraulic functioning during development and/or in response to abiotic stresses.
Why it matches plant phenotyping methods葉の構造と水分状態を定量MRIで空間的に測定する新規手法を開発し、分類法と組み合わせて葉組織・葉脈を評価しており、植物フェノタイピング手法が中心である。
abstractThe purpose of this study was to develop an original approach based on transverse relaxation mapping by Magnetic Resonance Imaging for the detection of changes in water status and distribution at cell and tissue levels in Brassica napus leaves during blade development and dehydration.
Understanding the potato tuber development and effects of drought at key stages of sensitivity on yield is crucial, particularly when considering the increasing incidence of drought due to climate change. So far, few studies addressed the time course of tuber growth in soil, mainly due to difficulties in accessing underground plant organs in a non-destructive manner. This study aims to understand the tuber growth and quality and the complex long-term effects of realistic water stress on potato tuber yield. MRI was used to monitor the growth kinetics and spatialization of individual tubers in situ and the evolution of internal defects throughout the development period. The intermittent drought applied to plants reduced tuber yield by reducing tuber growth and increasing the number of aborted tubers. The reduction in the size of tubers depended on the vertical position of the tubers in the soil, indicating water exchanges between tubers and the mother plant during leaf dehydration events. The final size of tubers was linked with the growth rate at specific developmental periods. For plants experiencing stress, this corresponded to the days following rewatering, suggesting tuber growth plasticity. All internal defects occurred in large tubers and within a short time span immediately following a period of rapid growth of perimedullary tissues, probably due to high nutrient requirements. To conclude, the non-destructive 3D imaging by MRI allowed us to quantify and better understand the kinetics and spatialization of tuber growth and the appearance of internal defects under different soil water conditions.
Why it matches plant phenotyping methodsMRIによる非破壊3D画像で、土壌中のジャガイモ塊茎の成長速度・位置・内部欠陥を経時的に定量する手法が研究の中心であり、干ばつ影響の評価にも実質的に用いられている。
abstractMRI was used to monitor the growth kinetics and spatialization of individual tubers in situ and the evolution of internal defects throughout the development period.
CherryLaboratory / benchtopMRI / PETLeafPhysiological trait estimationWater status / transpiration
We report on a cross-species proton-relaxometry study in ex vivo tree leaves using nuclear magnetic resonance (NMR) at 7µT. Apart from the intrinsic interest of probing nuclear-spin relaxation in biological tissues at magnetic fields below Earth field, our setup enables comparative analysis of plant water dynamics without the use of expensive commercial spectrometers. In this work, we focus on leaves from common Eurasian evergreen and deciduous tree families: Pinaceae (pine, spruce), Taxaceae (yew), Betulaceae (hazel), Prunus (cherry), and Fagaceae (beech, oak). Using a nondestructive protocol, we measure their effective proton T 2 relaxation times as well as track the evolution of water content associated with leaf dehydration. Newly developed "gradiometric quadrature" detection and data-processing techniques are applied in order to increase the signal-to-noise ratio (SNR) of the relatively weak measured signals. We find that while measured relaxation times do not vary significantly among tree genera, they tend to increase as leaves dehydrate. Such experimental modalities may have particular relevance for future drought-stress research in ecology, agriculture, and space exploration.
Why it matches plant phenotyping methods植物葉の水分動態・脱水状態を測定するNMR法を適用し、低磁場での検出およびデータ処理技術も新規開発しているため、植物フェノタイピング手法が中心である。
abstractour setup enables comparative analysis of plant water dynamics without the use of expensive commercial spectrometers.
Plant root diseases threat plant growth and eventually cause plant death without proper treatment. It is difficult to diagnose root diseases without digging the roots from the soil, and it is late when the above-ground parts show symptoms under the stress of root diseases. This study used magnetic resonance imaging (MRI) for non-invasive root phenotyping to detect oilseed rape clubroot. MRI images of healthy oilseed rape roots and roots infected by clubroot were obtained. After image preprocessing, average sample grayscale histograms (Avg-SGH) were extracted to build classification models for disease identification using logistic regression (LR), support vector machine (SVM) and random forest (RF). Reconstruction of three-dimensional (3D) root architectures was also conducted. Root architecture parameters were extracted from the reconstructed roots. Analysis of variance (ANOVA) showed that the root architecture parameters differed significantly between healthy and infected roots. RF model using root architecture parameters showed good performances, and the feature importance for clubroot identification was also explored. The overall results showed that MRI could effectively detect clubroot diseases in a non-invasive manner, indicating significant potential for plant root phenotyping.
Why it matches plant phenotyping methodsMRIによる非侵襲的な根の表現型取得、3D根系再構成、根系形態パラメータ抽出、およびクラブルート識別モデルを中心に扱っており、植物病害状態のフェノタイピング手法として中心的です。
abstractThis study used magnetic resonance imaging (MRI) for non-invasive root phenotyping to detect oilseed rape clubroot.
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.
Objective. The pheno PET system is a plant dedicated positron emission tomography (PET) scanner consisting of fully digital photo multipliers with lutetium-yttrium oxyorthosilicate crystals and located inside a custom climate chamber. Here, we present the setup of pheno PET, its data processing and image reconstruction together with its performance. Approach. The performance characterization follows the national electrical manufacturers association (NEMA) standard for small animal PET systems with a number of adoptions due to the vertical oriented bore of a PET for plant sciences. In addition temperature stability and spatial resolution with a hot rod phantom are addressed. Main results. The spatial resolution for a 22 Na point source at a radial distance of 5 mm to the center of the field-of-view (FOV) is 1.45 mm, 0.82 mm and 1.88 mm with filtered back projection in radial, tangential and axial direction, respectively. A hot rod phantom with 18 F gives a spatial resolution of up to 1.6 mm. The peak noise-equivalent count rates are 550 kcps @ 35.08 MBq, 308 kcps @ 33 MBq and 45 kcps @ 40.60 MBq for the mouse, rat and monkey size scatter phantoms, respectively. The scatter fractions for these phantoms are 12.63%, 22.64% and 55.90%. We observe a peak sensitivity of up to 3.6% and a total sensitivity of up to S A , tot = 2.17%. For the NEMA image quality phantom we observe a uniformity of % STD = 4.22% with ordinary Poisson maximum likelihood expectation-maximization with 52 iterations. Here, recovery coefficients of 0.12, 0.64, 0.89, 0.93 and 0.91 for 1 mm, 2 mm, 3 mm, 4 mm and 5 mm rods are obtained and spill-over ratios of 0.08 and 0.14 for the water-filled and air-filled inserts, respectively. Significance. The pheno PET and its laboratory are now in routine operation for the administration of [ 11 C]CO 2 and non-invasive measurement of transport and allocation of 11 C-labelled photoassimilates in plants.
Why it matches plant phenotyping methods植物専用PETスキャナーの構築、データ処理・画像再構成、性能評価を中心に扱い、植物体内の光合成産物の輸送・配分を非侵襲測定するフェノタイピング基盤である。
abstractThe pheno PET system is a plant dedicated positron emission tomography (PET) scanner consisting of fully digital photo multipliers with lutetium-yttrium oxyorthosilicate crystals and located inside a custom climate chamber.
Plant physiology and structure are constantly changing according to internal and external factors. The study of plant water dynamics can give information on these changes, as they are linked to numerous plant functions. Currently, most of the methods used to study plant water dynamics are either invasive, destructive, or not easily accessible. Portable magnetic resonance imaging (MRI) is a field undergoing rapid expansion and which presents substantial advantages in the plant sciences. MRI permits the non-invasive study of plant water content, flow, structure, stress response, and other physiological processes, as a multitude of information can be obtained using the method, and portable devices make it possible to take these measurements in situ, in a plant's natural environment. In this work, we review the use of such devices applied to plants in climate chambers, greenhouses or in their natural environments. We also compare the use of portable MRI to other methods to obtain the same information and outline its advantages and disadvantages.
Why it matches plant phenotyping methods植物の水分動態・構造・ストレス応答を測定する携帯型MRIの利用法をレビューし、他手法との比較や利点・欠点を論じる方法論レビューである。
abstractIn this work, we review the use of such devices applied to plants in climate chambers, greenhouses or in their natural environments.
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.
Understanding the potato tuber development and effects of drought at key stages of sensitivity on yield is crucial, particularly when considering the increasing incidence of drought due to climate change. So far, few studies addressed the time course of tuber growth in soil, mainly due to difficulties in accessing underground plant organs in a non‐destructive manner. This study aims to understand the tuber growth and quality and the complex long‐term effects of realistic water stress on potato tuber yield. MRI was used to monitor the growth kinetics and spatialization of individual tubers in situ and the evolution of internal defects throughout the development period. The intermittent drought applied to plants reduced tuber yield by reducing tuber growth and increasing the number of aborted tubers. The reduction in the size of tubers depended on the vertical position of the tubers in the soil, indicating water exchanges between tubers and the mother plant during leaf dehydration events. The final size of tubers was linked with the growth rate at specific developmental periods. For plants experiencing stress, this corresponded to the days following rewatering, suggesting tuber growth plasticity. All internal defects occurred in large tubers and within a short time span immediately following a period of rapid growth of perimedullary tissues, probably due to high nutrient requirements. To conclude, the non‐destructive 3D imaging by MRI allowed us to quantify and better understand the kinetics and spatialization of tuber growth and the appearance of internal defects under different soil water conditions.
Why it matches plant phenotyping methodsMRIによる非破壊3D画像化が、塊茎の成長速度・空間分布・内部欠陥を経時的に定量する中心的手法であり、単なるルーチン測定ではない。
abstractMRI was used to monitor the growth kinetics and spatialization of individual tubers in situ and the evolution of internal defects throughout the development period.
Stomata are the gatekeepers of plant water use and must quickly respond to changes in plant water status to ensure plant survival under fluctuating environmental conditions. The mechanism for their closure is highly sensitive to disturbances in leaf water status, which makes isolating their response to declining water content difficult to characterise and to compare responses among species. Using a small-scale non-destructive nuclear magnetic resonance spectrometer as a leaf water content sensor, we measure the stomatal response to rapid induction of water deficit in the leaves of nine species of eucalypt from contrasting climates. We found a strong linear correlation between relative water content at 50% stomatal conductance (RWC gs50 ) and mean annual temperature at the climate of origin of each species. We also show evidence for stomata to maintain control over water loss well below turgor loss point in species adapted to warmer climates and secondary increases in stomatal conductance despite declining water content. We propose that RWC gs50 is a promising trait to guide future investigations comparing stomatal responses to water deficit. It may provide a useful phenotyping trait to delineate tolerance and adaption to hot temperatures and high leaf-to-air vapour pressure deficits.
Why it matches plant phenotyping methods非侵襲NMRを葉の水分状態センサーとして用い、乾燥応答を定量する新しい表現型RWC gs50を提案しており、測定手法と再利用可能な形質が研究の中心です。
abstractUsing a small-scale non-destructive nuclear magnetic resonance spectrometer as a leaf water content sensor, we measure the stomatal response to rapid induction of water deficit
During the past few years, milder autumn and winter seasons have caused severe problems to cauliflower harvest of Brittany region in France, mainly due to curd deformation. Consequently, cauliflower breeders are working on breeding new varieties that are more robust to climate change to stabilize the quality of cauliflower production. The aim of this study was to identify at which stage of the curd formation, significant difference can be detected between healthy and stressed cauliflower. A non-invasive classification based on Magnetic Resonance Imaging (MRI) images for cauliflower phenotyping was proposed. Plants exposed to vernalization stress were sampled at different times around primary meristem stage, then both MRI imaged and apex dissected. A work flow was developped to extract features from MRI images. A classification on phenotype was learned by LDA, QDA, PLSDA and CNN binary classification between two groups: healthy and stressed cauliflower. Promising F1 score and MCC up to 95% were achieved. Curd deformation is the main cause for cauliflower’s later physiological disorders when reaching maturity. Therefore, the cauliflowers with deformation could be removed at the earliest, e.g., screening for plant breeding. At the same time, the healthy cauliflowers are not destroyed and continue their life cycle.
Why it matches plant phenotyping methodsMRI画像からカリフラワーの健康・ストレス状態を非侵襲的に分類するワークフローを開発し、複数の分類器で性能評価しており、表現型取得・抽出法が研究の中心です。
abstractA non-invasive classification based on Magnetic Resonance Imaging (MRI) images for cauliflower phenotyping was proposed.
Abstract Background Increasing seed oil content is the most important breeding goal in Brassica napus , and phenotyping is crucial to dissect its genetic basis in crops. To date, QTL mapping for oil content has been based on whole seeds, and the lipid distribution is far from uniform in different tissues of seeds in B. napus . In this case, the phenotype based on whole seeds was unable to sufficiently reveal the complex genetic characteristics of seed oil content. Results Here, the three-dimensional (3D) distribution of lipid was determined for B. napus seeds by magnetic resonance imaging (MRI) and 3D quantitative analysis, and ten novel oil content-related traits were obtained by subdividing the seeds. Based on a high-density genetic linkage map, 35 QTLs were identified for 4 tissues, the outer cotyledon (OC), inner cotyledon (IC), radicle (R) and seed coat (SC), which explained up to 13.76% of the phenotypic variation. Notably, 14 tissue-specific QTLs were reported for the first time, 7 of which were novel. Moreover, haplotype analysis showed that the favorable alleles for different seed tissues exhibited cumulative effects on oil content. Furthermore, tissue-specific transcriptomes revealed that more active energy and pyruvate metabolism influenced carbon flow in the IC, OC and R than in the SC at the early and middle seed development stages, thus affecting the distribution difference in oil content. Combining tissue-specific QTL mapping and transcriptomics, 86 important candidate genes associated with lipid metabolism were identified that underlie 19 unique QTLs, including the fatty acid synthesis rate-limiting enzyme-related gene CAC2 , in the QTLs for OC and IC. Conclusions The present study provides further insight into the genetic basis of seed oil content at the tissue-specific level.
Why it matches plant phenotyping methodsMRIと3D定量解析を用いて種子組織別の脂質分布から複数の油含量形質を抽出しており、植物フェノタイピング手法の適用が研究の中心的要素です。
Individual plants vary in their ability to respond to environmental changes. The plastic response of a plant enhances its ability to avoid environmental constraints, and hence supports growth, reproduction, and evolutionary and agricultural success.Major progress in the analysis of above- and belowground processes on individual plants has been made by the application of non-invasive imaging methods including Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET).MRI allows for repetitive measurements of roots growing in soil and facilitates quantification of root system architecture traits in 3D. PET, on the other hand, opens a door to analyze dynamic physiological processes in plants such as long-distance carbon transport in a repeatable manner. Combining MRI with PET enables monitoring of short livedCarbon tracer (11C) allocation along the transport paths (i.e. roots visualized by MRI) into active sink structures.To analyse the link between root-internal C allocation patterns and C metabolism in the rhizosphere, we are combining 11CO2 with stable 13CO2 labelling of plants. Isotope ratio mass spectrometry (IRMS) analyses of rhizosphere soil is applied to link root-internal C allocation patterns with distribution of 13C in the rhizosphere soil. The metabolically active rhizosphere organisms are subsequently identified based on DNA 13C stable isotope probing.In our presentation we will highlight our approaches for gathering quantitative data from both image-based technologies in combination with destructive analysis that provides insights into the functioning and dynamics of C transport processes in the plant-soil system.
Why it matches plant phenotyping methodsMRIとPETを組み合わせ、根系形態と植物体内の炭素輸送を定量的に取得する画像基盤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応用に該当する。
abstractthe application of non-invasive imaging methods including Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET)
Verticillium wilt (VW) is often referred to as the cancer of cotton and it has a detrimental effect on cotton yield and quality. Since the root system is the first to be infested, it is feasible to detect VW by root analysis in the early stages of the disease. In recent years, with the update of computing equipment and the emergence of large-scale high-quality data sets, deep learning has achieved remarkable results in computer vision tasks. However, in some specific areas, such as cotton root MRI image task processing, it will bring some challenges. For example, the data imbalance problem (there is a serious imbalance between the cotton root and the background in the segmentation task) makes it difficult for existing algorithms to segment the target. In this paper, we proposed two new methods to solve these problems. The effectiveness of the algorithms was verified by experimental results. The results showed that the new segmentation model improved the Dice and mIoU by 46% and 44% compared with the original model. And this model could segment MRI images of rapeseed root cross-sections well with good robustness and scalability. The new classification model improved the accuracy by 34.9% over the original model. The recall score and F1 score increased by 59% and 42%, respectively. The results of this paper indicate that MRI and deep learning have the potential for non-destructive early detection of VW diseases in cotton.
Why it matches plant phenotyping methods綿花根のMRI画像から病害状態を検出・分節する画像解析手法を開発し、性能検証しており、植物フェノタイピング手法が中心である。
abstractIn this paper, we proposed two new methods to solve these problems.
PotatoMRI / PETTissueClassificationWater status / transpiration
Magnetic Resonance Imaging is a powerful non-destructive tool in the study of plant tissues. For potato tubers, it greatly assists the study of tissue defects and tissue evolution during storage. This paper describes the MRI analysis of potato tubers with internal defects in their flesh tissue at eight sampling dates from 14 to 33 weeks after harvest. Spatialized multi-exponential T2 relaxometry was used to generate bi-exponential T2 maps, coupled with a classification scheme to identify the different T2 homogeneous zones within the tubers. Six classes with statistically different relaxation parameters were identified at each sampling date, allowing the defects and the pith and cortex tissues to be detected. A further distinction could be made between three constitutive elements within the flesh, revealing the heterogeneity of this particular tissue. Relaxation parameters for each class and their evolution during storage were successfully analyzed. The work demonstrated the value of MRI for detailed non-invasive plant tissue characterization.
Why it matches plant phenotyping methodsMRIと空間化T2緩和解析を用いて、ジャガイモ塊茎の組織・内部欠陥を非破壊で分類・特性評価する方法が研究の中心であり、植物器官の状態を直接推定している。
abstractSpatialized multi-exponential T2 relaxometry was used to generate bi-exponential T2 maps, coupled with a classification scheme to identify the different T2 homogeneous zones within the tubers.
Reproduction assets foundThe paper's MRI T2 relaxometry data (potato tuber images and relaxation measurements) are openly deposited in a public repository (Recherche Data Gouv, DOI 10.57745/DR2GSS), as stated in the Data Availability Statement. The supplementary material contains only result figures, not datasets or code; no analysis code or模型Dataset · publicThe MRI data presented in this study are openly available at: https://doi.org/10.57745/DR2GSS (accessed on 29 January 2023).Open asset ↗10.57745/DR2GSSlines:388-401Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Occurrence of internal browning in sweet potato tuber has recently been confirmed, and its chemical and bacterial characteristics have been reported. However, the structural characteristics of such tissues are unknown. We investigated the tissue structural characteristics inside a browning sweet potato through magnetic resonance imaging (MRI) and bio-electrochemical impedance spectroscopy (BIS) and the relationship was discussed. The high-resolution proton density-weighted (PDW) and proton spin–spin relaxation time (T₂)-weighted (T2W) images of cutting out samples obtained from micro-imaging revealed changes in the physical structure surrounding the browning tissues. The T₂ distribution maps of the same browning samples assumed the changes in the water distribution and water mobility, which generally changes under the influence of solutes, such as metabolites, starch, protein, and metal ions. BIS further confirmed the variation in the distribution of electrolytes in the tissues. MRI may provide a non-destructive assessment of the internal browning of whole sweet potatoes.
Why it matches plant phenotyping methodsMRIとBISを用いてサツマイモ内部褐変の組織構造・水分/電解質分布を評価し、非破壊的な褐変判定への適用可能性を示すことが中心であり、植物状態の取得手法として該当する。
abstractWe investigated the tissue structural characteristics inside a browning sweet potato through magnetic resonance imaging (MRI) and bio-electrochemical impedance spectroscopy (BIS)
Laboratory / benchtopMRI / PETRootSegmentationRoot system architecture
Using magnetic resonance imaging (MRI), our established root phenotyping platform (van Dusschoten et al., 2016) can visualize and analyze plant roots in natural soil nondestructively (Pflugfelder et al., 2017). Using plant pots with 9 cm diameter and 30cm height, a root system can be scanned within 1h while roots down to diameters of 300µm can be detected and analyzed using our in-house root extraction software NMRooting (van Dusschoten et al., 2016). Thanks to automation with a pick-and-place robot the platform routinely achieves a throughput of 24 plants per day. All these values, however, are based on compromises between imaging speed and quality. In our system, the root detection limit is determined by the signal to noise ratio (SNR) of our images. The SNR can be increased by using smaller plant pots or by increasing the imaging time. In this contribution we investigate the potential gain in the root detection limit when sacrificing plant throughput in favor of image quality. We acquired low noise root images using repeated signal averaging during the measurement process. Using this approach, the root detection limit could be lowered, visualizing roots not detected by the standard imaging protocol.
Why it matches plant phenotyping methodsMRIによる非破壊的な根系画像化について、SNR向上と信号平均化により根の検出限界を改善する技術を検証しており、根フェノタイピング手法が中心である。
abstractUsing magnetic resonance imaging (MRI), our established root phenotyping platform
PeaMRI / PETRootMorphology / geometry measurementPhysiological trait estimationRoot system architecture
In natural ecosystems and low-input agriculture systems often the main source of nitrogen is biological nitrogen fixation by symbiotic coexistence with root colonizing microorganisms such as in root nodules in legumes. In return for this nutrient supply, plants allocate significant amount of photosynthetically fixed carbon (C) belowground, fueling activity and growth of the nodules. However, there is still a lack in understanding how plants modulate carbon allocation to a nodulated root system as a dynamic response to abiotic stimuli. Traditional approaches based on destructive sampling make investigations of localized carbon allocation dynamics difficult. Non-destructive 3D-imaging methods including Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) offers new perspective in analysing belowground processes on individual plants. MRI allows for repetitive measurements and quantification of root system architecture traits nodule structures while growing. PET was employed to follow the spatial distribution of leaf-supplied 11 C tracer to nodules and roots. Using Pisum sativum as model for legumes and applying nitrate as an additional N source we investigated short term C allocation dynamics in the root system. We found that the fraction of 11 C tracer arriving in the most active nodules decreased by almost 40% and remained stable between 16h and 42h after the N application. Our results highlight that the combination of MRI-PET enables deeper insights into short term C dynamics of roots and interactions with colonizing microbes. We expect that this modality has high potential for revealing mechanisms that relate to dynamic fitness traits supporting breedingprograms for future crops.
Why it matches plant phenotyping methodsMRI-PETによる非破壊3D画像化と定量化が研究の主要手法であり、根系構造・根粒構造および炭素分配という植物形質・状態を測定しているため。
abstractNon-destructive 3D-imaging methods including Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) offers new perspective in analysing belowground processes on individual plants.
TomatoMicroscopyMRI / PETLiDAR / point cloudTissueGrowth / time-series analysisTrackingArchitecture / morphology / geometryGrowth / development / phenologyStress response / tolerance
Grafting is a widely used agricultural technique that involves the physical joining of separate plant parts so they form a unified vascular system, enabling beneficial traits from independent genotypes to be captured in a single plant. This simple, yet powerful tool has been used for thousands of years to improve abiotic and biotic stress tolerance, enhance yield, and alter plant architecture in diverse crop systems. Despite the global importance and ancient history of grafting, our understanding of the fundamental biological processes that make this technique successful remains limited, making it difficult to efficiently expand on new genotypic graft combinations. One of the key determinants of successful grafting is the formation of the graft junction, an anatomically unique region where xylem and phloem strands connect between newly joined plant parts to form a unified vascular system. Here, we use an integrated imaging approach to establish a spatiotemporal framework for graft junction formation in the model crop Solanum lycopersicum (tomato), a plant that is commonly grafted worldwide to boost yield and improve abiotic and biotic stress resistance. By combining Positron Emission Tomography (PET), a technique that enables the spatio-temporal tracking of radiolabeled molecules, with high-resolution laser scanning confocal microscopy (LSCM), we are able to merge detailed, anatomical differentiation of the graft junction with a quantitative timeline for when xylem and phloem connections are functionally re-established. In this timeline, we identify a 72-hour window when anatomically connected xylem and phloem strands regain functional capacity, with phloem restoration typically preceding xylem restoration by about 24-hours. Furthermore, we identify heterogeneity in this developmental and physiological timeline that corresponds with microvariability in the physical contact between newly joined rootstock-scion tissues. Our integration of PET and confocal imaging technologies provides a spatio-temporal timeline that will enable future investigations into cellular and tissue patterning events that underlie successful versus failed vascular restoration across the graft junction.
Why it matches plant phenotyping methodsPETと共焦点顕微鏡を統合し、植物の接ぎ木接合部における血管再連結の時空間・機能状態を定量化する手法が研究の中心であるため、植物フェノタイピング手法として収録する。
abstractHere, we use an integrated imaging approach to establish a spatiotemporal framework for graft junction formation in the model crop Solanum lycopersicum (tomato)
Accurate estimation of regional-scale crop yield under drought conditions allows farmers and agricultural agencies to make well-informed decisions and guide agronomic management. However, few studies have focused on using the crop model data assimilation (CMDA) method for regional-scale winter wheat yield estimation under drought stress and partial-irrigation conditions. In this study, we developed a CMDA framework to integrate remotely sensed water stress factor (MOD16 ET PET−1) with the WOFOST model using an ensemble Kalman filter (EnKF) for winter wheat yield estimation at the regional scale in the North China Plain (NCP) during 2008–2018. According to our results, integration of MOD16 ET PET−1 with the WOFOST model produced more accurate estimates of regional winter wheat yield than open-loop simulation. The correlation coefficient of simulated yield with statistical yield increased for each year and error decreased in most years, with r ranging from 0.28 to 0.65 and RMSE ranging from 700.08 to 1966.12 kg ha−1. Yield estimation using the CMDA method was more suitable in drought years (r = 0.47, RMSE = 919.04 kg ha−1) than in normal years (r = 0.30, RMSE = 1215.51 kg ha−1). Our approach performed better in yield estimation under drought conditions than the conventional empirical correlation method using vegetation condition index (VCI). This research highlighted the potential of assimilating remotely sensed water stress factor, which can account for irrigation benefit, into crop model for improving the accuracy of winter wheat yield estimation at the regional scale especially under drought conditions, and this approach can be easily adapted to other regions and crops.
Why it matches plant phenotyping methodsリモートセンシング水ストレス情報を作物モデルに同化して冬小麦収量を推定する技術フレームワークを開発・比較検証しており、収量という植物形質の推定方法が中心である。
abstractwe developed a CMDA framework to integrate remotely sensed water stress factor (MOD16 ET PET−1) with the WOFOST model using an ensemble Kalman filter (EnKF) for winter wheat yield estimation at the regional scale
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 · UnverifiedEurope PMC · checked 15 Sept 2026
To detect maize seed vigor under salt-alkaline and drought stress conditions, transverse relaxation time, physiological index, and electron microscopy images of germinating seeds were studied under different stress conditions. The results showed that the water in germinating maize seeds exist in bound water (T₂₁), semi-bound water (T₂₂), and free water (T₂₃), in addition parabolic water (T₂₄). The Pearson correlation analysis was performed with T₂ relaxation parameters and seed vigor parameters, and this analysis yield nine optimized parameters. To predict vigor levels under different stress conditions, an error backpropagation artificial neural network model was developed, wherein the T₂ chirality parameter was used as the input value, and the seed germination indices of different stress levels were used as the output values. The model could predict the environmental stress level of maize seed growth. The optimized parameter set showed a prediction accuracy of 92.50%, thus outperforming the T₂ relaxation information model without parameter optimization (75.01%). The proposed method can collect data during the germination of maize seeds without any interference and achieve large-scale prediction of seed development status by small sample collection. With the stress environment was aggravated, the physiological structure of seed cells was changed, and the cell structure was destroyed and the ability of water absorption was disappeared. This analysis provides theoretical support and a reference basis for maize planting and production.
Why it matches plant phenotyping methods低磁場NMRによる種子の水分状態測定とニューラルネットワークによる活力・発育状態予測が研究の中心であり、植物状態の取得・推定手法として実質的に評価されている。
abstractTo predict vigor levels under different stress conditions, an error backpropagation artificial neural network model was developed
Time activity curve (TAC) signal processing in plant positron emission tomography (PET) is a frontier nuclear science technique to bring out the quantitative fluid dynamic (FD) flow parameters of the plant vascular system and generate knowledge on crops and their sustainable management, facing the accelerating global climate change. The sparse space-time sampling of the TAC signal impairs the extraction of the FD variables, which can be determined only as averaged values with existing techniques. A data-driven approach based on a reliable FD model has never been formulated. A novel sparse data assimilation digital signal processing method is proposed, with the unique capability of a direct computation of the dynamic evolution of noise correlations between estimated and measured variables, by taking into explicit account the numerical diffusion due to the sparse sampling. The sequential time-stepping procedure estimates the spatial profile of the velocity, the diffusion coefficient and the compartmental exchange rates along the plant stem from the TAC signals. To illustrate the performance of the method, we report an example of the measurement of transport mechanisms in zucchini sprouts.
Why it matches plant phenotyping methods植物PETの疎なTAC信号から、茎内の流速・拡散係数・交換速度を推定するデータ同化型信号処理法の開発が中心であり、植物の生理状態・輸送特性を定量化するフェノタイピング手法に該当する。
abstractA novel sparse data assimilation digital signal processing method is proposed
This study investigates the fibril nanostructure of fresh celery samples by modeling the anisotropic behavior of the transverse relaxation time (T 2 ) in nuclear magnetic resonance (NMR). Experimental results are interpreted within the framework of a previously developed theory, which was successfully used to model the nanostructures of several biological tissues as a set of water filled nanocavities, hence explaining the anisotropy the T 2 relaxation time in vivo. An important feature of this theory is to determine the degree of orientational ordering of the nanocavities, their characteristic volume, and their average direction with respect to the macroscopic sample. Results exhibit good agreement between theory and experimental data, which are, moreover, supported by optical microscopic resolution. The quantitative NMR approach presented herein can be potentially used to determine the internal ordering of biological tissues noninvasively.
Why it matches plant phenotyping methods植物茎の内部配向・秩序度・ナノキャビティ体積という構造形質を、NMRで非侵襲的に定量する測定手法が研究の中心であり、単なる生物学的測定ではない。
abstractThe quantitative NMR approach presented herein can be potentially used to determine the internal ordering of biological tissues noninvasively.
In plants, water flows are the major driving force behind growth and play a crucial role in the life cycle. To study hydrodynamics, methods based on tracking small particles inside water flows attend a special place. Thanks to these tools, it is possible to obtain information about the dynamics of the spatial distribution of the flux characteristics. In this paper, using contrast-enhanced magnetic resonance imaging (MRI), we show that gadolinium chelate, used as an MRI contrast agent, marks the structural characteristics of the xylem bundles of maize stem nodes and internodes. Supplementing MRI data, the high-precision visualization of xylem vessels by laser scanning microscopy was used to reveal the structural and dimensional characteristics of the stem vascular system. In addition, we propose the concept of using prototype "Y-type xylem vascular connection" as a model of the elementary connection of vessels within the vascular system. A Reynolds number could match the microchannel model with the real xylem vessels.
Why it matches plant phenotyping methodsMRIとレーザー走査顕微鏡を用いてトウモロコシの木部構造・水流特性を可視化することが研究の中心であり、植物の構造・生理状態を取得する画像計測法の実質的な適用研究である。
titleParticle-Based Imaging Tools Revealing Water Flows in Maize Nodal Vascular Plexus.
MaizeMicroscopyMRI / PETStem / branchMorphology / geometry measurementVisualization / data managementWater status / transpiration
In plants, water flows are the major driving force behind the growth and play a crucial role in the life cycle. To study hydrodynamics, methods based on tracking small particles inside water flows occupy a special place. Due to these tools, it is possible to get information about the dynamics of the spatial distribution of the fluxes characteristics. In this paper, using contrast-enhanced MRI, we have shown that gadolinium chelate, used as an MRI contrast agent, marks the structural characteristics of xylem bundles of maize stem nodes and internodes. Supplementing MRI data, a high-precision visualization of xylem vessels by laser scanning microscopy was used to reveal structural and dimensional characteristics of the stem vascular system. In addition, we proposed the concept of using the prototype "Y-type xylem vascular bundles" as a model of the elementary connection of vessels within the vascular system. A Reynolds number can match the microchannel model with the real xylem vessels.
Why it matches plant phenotyping methodsMRIとレーザー走査顕微鏡を用いてトウモロコシの木部構造・寸法と水流動態を可視化する手法を提示しており、植物の生理・構造形質の取得が中心である。
abstractusing contrast-enhanced MRI, we have shown that gadolinium chelate, used as an MRI contrast agent, marks the structural characteristics of xylem bundles of maize stem nodes and internodes.
In this study, we aimed to clarify the distribution and dynamics of water in the Xudou 20 soybean cultivar post-germination after culturing plants with various concentrations of 6-benzylaminopurine (6-BA). Low-field nuclear magnetic resonance and magnetic resonance imaging (LF-NMR/MRI), as well as principal component analysis (PCA), were used for the investigation. Results showed that low concentrations of 6-BA promoted soybean germination and high concentrations inhibited soybean germination, with 5 mg/l of 6-BA producing the most optimal conditions for growth. Moreover, the T 22 determination of weakly bound water increased with increasing 6-BA concentration, and the PCA effectively distinguished soybeans cultured at different 6-BA concentrations. This study provides a method for the rapid detection of 6-BA concentration in bean sprouts and provides theoretical support and bean sprout quality assessment.
Why it matches plant phenotyping methodsLF-NMR/MRIとPCAを中核として、発芽大豆の水分分布・動態を測定し、6-BA濃度を迅速に識別する方法を提示しているため、植物の生理状態を対象とした実質的な表現型計測と判断します。
abstractLow-field nuclear magnetic resonance and magnetic resonance imaging (LF-NMR/MRI), as well as principal component analysis (PCA), were used for the investigation.
Abstract Background Studying dynamic processes in living organisms with MRI is one of the most promising research areas. The use of paramagnetic compounds as contrast agents (CA), has proven key to such studies, but so far, the lack of appropriate techniques limits the application of CA-technologies in experimental plant biology. The presented proof-of-principle aims to support method and knowledge transfer from medical research to plant science. Results In this study, we designed and tested a new approach for plant Dynamic Contrast Enhanced Magnetic Resonance Imaging (pDCE-MRI). The new approach has been applied in situ to a cereal crop ( Hordeum vulgare ). The pDCE-MRI allows non-invasive investigation of CA allocation within plant tissues. In our experiments, gadolinium-DTPA, the most commonly used contrast agent in medical MRI, was employed. By acquiring dynamic T 1 -maps, a new approach visualizes an alteration of a tissue-specific MRI parameter T 1 (longitudinal relaxation time) in response to the CA. Both, the measurement of local CA concentration and the monitoring of translocation in low velocity ranges (cm/h) was possible using this CA-enhanced method. Conclusions A novel pDCE-MRI method is presented for non-invasive investigation of paramagnetic CA allocation in living plants. The temporal resolution of the T 1 -mapping has been significantly improved to enable the dynamic in vivo analysis of transport processes at low-velocity ranges, which are common in plants. The newly developed procedure allows to identify vascular regions and to estimate their involvement in CA allocation. Therefore, the presented technique opens a perspective for further development of CA-aided MRI experiments in plant biology.
Why it matches plant phenotyping methods植物組織内の造影剤分布と輸送を非侵襲的に測定するpDCE-MRI手法を設計・試験した研究であり、植物の生理状態・輸送過程の取得方法が中心である。
abstractIn this study, we designed and tested a new approach for plant Dynamic Contrast Enhanced Magnetic Resonance Imaging (pDCE-MRI).
WheatMRI / PETRootMorphology / geometry measurementGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Seedling establishment is the first stage of crop productivity, and root phenotypes at seed emergence are critical to a successful start of shoot growth as well as for water and nutrient uptake. In this study, we investigate seedling establishment in winter wheat utilizing a newly developed workflow based on magnetic resonance imaging (MRI). Using the eight parents of the MAGIC (multi-parent advanced generation inter-cross) population we analysed the 4D root architecture of 288 individual seedlings grown in natural soils with plant neighbors over 3 d of development. Time of root and shoot emergence, total length, angle, and depth of the axile roots varied significantly among these genotypes. The temporal data resolved rates of elongation of primary roots and first and second seminal root pairs. Genotypes with slowly elongating primary roots had rapidly elongating first and second seminal root pairs and vice versa, resulting in variation in root system architecture mediated not only by root angle but also by initiation and relative elongation of axile roots. We demonstrated that our novel MRI workflow with a unique planting design and automated measurements allowed medium throughput phenotyping of wheat roots in 4D and could give new insights into regulation of root system architecture.
Why it matches plant phenotyping methods新規MRIワークフローと自動測定を開発し、4D根系形態を中スループットで表現型解析することが研究の中心である。
abstractutilizing a newly developed workflow based on magnetic resonance imaging (MRI)
Measuring plant-balance and -water dynamics is essential to gain better insight into plant health. In the Plantena research program (https://www.4tu.nl/plantenna/en/), new techniques have been developed for direct monitoring of plant traits. These include water status monitoring based on Ultrasound, magnetic resonance imaging (MRI) or radiofrequency (RF), volatile compound emission (“e-Nose”) and continuous stomatal aperture sensing (SAS). The SAS sensor enables real-time autonomous imaging of stomatal apertures inside the growth environment to asses dynamic behavior of individual stomata within the ensemble. For e-Nose, a new approach is being explored to utilize an electronic nose to smell insects for early detection of pests, thus safeguarding crop harvests while minimizing pesticide usage. RF sensing , Ultrasound and MRI are non-invasive techniques for real-time monitoring of internal plant parameters. RF is being investigated for monitoring of water and mineral content, Ultrasound technology enables the determination of internal plant parameters in a fast, non-contact, and non-destructive matter, thereby providing new ways for water monitoring, pest detection, and selective breeding. MRI enables monitoring of water content and flow in plants and offers the potential for non-invasive metabolite detection. Additionally, low-cost, autonomous sensor nodes are being developed for integration of novel and existing plant-sensors into a high density network (“internet of plants”). To this end, a smart and efficient power management scheme is being developed to adapt the sensor nodes to a wide range of environmental scenarios. A first demonstration of sensor innovations will be set up in spring 2022 in a commercial greenhouse environment. In this contribution we will present preliminary results of novel plant-sensors as well as the set-up of the Plantenna Demonstrator facility. Outlook: Results of the Plantenna Demonstrator will validate performance of the plant-sensor innovations in a real-life environment and by combining these with existing sensors will provide valuable datasets for assessing plant response to climate variability and stress conditions.
Why it matches plant phenotyping methods植物の水分状態、気孔開度、内部パラメータなどを測定する新規センサー群と統合プラットフォームの開発・実証が中心であり、植物表現型計測手法に該当する。
abstractnew techniques have been developed for direct monitoring of plant traits.
Legumes associate with root colonizing rhizobia that provide fixed nitrogen to its plant host in exchange for recently fixed carbon. There is a lack of understanding of how individual plants modulate carbon allocation to a nodulated root system as a dynamic response to abiotic stimuli. One reason is that most approaches are based on destructive sampling, making quantification of localised carbon allocation dynamics in the root system difficult. We established an experimental workflow for routinely using non-invasive Positron Emission Tomography (PET) to follow the allocation of leaf-supplied 11 C tracer towards individual nodules in a three-dimensional (3D) root system of pea ( Pisum sativum ). Nitrate was used for triggering a reduction of biological nitrogen fixation (BNF), which was expected to rapidly affect carbon allocation dynamics in the root-nodule system. The nitrate treatment led to a decrease in 11 C tracer allocation to nodules by 40% to 47% in 5 treated plants while the variation in control plants was less than 11%. The established experimental pipeline enabled for the first time that several plants could consistently be labelled and measured using 11 C tracers in a PET approach to quantify C-allocation to individual nodules following a BNF reduction. Our study demonstrates the strength of using 11 C tracers in a PET approach for non-invasive quantification of dynamic carbon allocation in several growing plants over several days. A major advantage of the approach is the possibility to investigate carbon dynamics in small regions of interest in a 3D system such as nodules in comparison to whole plant development.
Why it matches plant phenotyping methodsマメ科植物の根粒への炭素配分動態をPETで非侵襲・三次元定量する実験ワークフローを確立しており、植物生理状態の取得法が研究の中心である。
abstractWe established an experimental workflow for routinely using non-invasive Positron Emission Tomography (PET) to follow the allocation of leaf-supplied 11 C tracer towards individual nodules in a three-dimensional (3D) root system of pea ( Pisum sativum ).
MaizeMRI / PETSeed / grainPhysiological trait estimationGrowth / development / phenologyWater status / transpiration
Kernel moisture content (KMC) is important for maize grain development, harvest, and storage. Breeders always consider KMC as a necessary indicator when conducting breeding programs. The fact that a large number of samples are required for the typical breeding process necessitates the development of accurate, high-throughput, and non-destructive detection techniques for KMC. Here, we proposed an effective method based on nuclear magnetic resonance (NMR) spectroscopy to track moisture variation in maize kernels and select offspring with low KMC at harvest. Both moisture mass and content could be accurately measured via regression analysis (R² = 0.9924 and 0.9139, respectively). Magnetic resonance imaging technology was also applied to monitor the spatial distribution of moisture in individual kernels and ears. On the other hand, with two generations of KMC selection experiments, we demonstrated the effectiveness of the proposed method. KMC was measured for self-crossing kernels of two elite hybrids, namely Zhengdan958 and Xianyu335, and partial kernels were classified into a high-kernel-moisture group (HMG) or low-kernel-moisture group (LMG). The progenies were then selected for kernels having a high KMC in HMG and low KMC in LMG. After selection, KMCs in LMG were significantly lower than those in the HMG in both hybrids at generations F₂ and F₃. Finally, phenotypic response surveys revealed that KMC selection significantly affected the flowering time. Our results demonstrate the effectiveness of the NMR-based platform for optimizing KMC trait, with potential applications in maize breeding programs.
Why it matches plant phenotyping methodsトウモロコシ粒の水分含量という植物形質をNMR/MRIで非破壊・高 throughput に測定する手法を開発し、回帰精度と世代選抜で有効性を検証しており、表現型取得法が研究の中心である。
abstractnecessitates the development of accurate, high-throughput, and non-destructive detection techniques for KMC
Field / plotMRI / PETWhole plant / canopy / plot / field
Positron Emission Tomography is a non-disruptive and high-sensitive digital imaging technique which allows to measure in-vivo and non invasively the changes of metabolic and transport mechanisms in plants. When it comes to the early assessment of stress-induced alterations of plant functions, plant PET has the potential of a major breakthrough. The development of dedicated plant PET systems faces a series of technological and experimental difficulties, which make conventional clinical and preclinical PET systems not fully suitable to agronomy. First, the functional and metabolic mechanisms of plants depend on environmental conditions, which can be controlled during the experiment if the scanner is transported into the growing chamber. Second, plants need to be imaged vertically, thus requiring a proper Field Of View. Third, the transverse Field of View needs to adapt to the different plant shapes, according to the species and the experimental protocols. In this paper, we perform a simulation study, proposing a novel design of dedicated plant PET scanners specifically conceived to address these agronomic issues. We estimate their expected sensitivity, count rate performance and spatial resolution, and we identify these specific features, which need to be investigated when realizing a plant PET scanner. Finally, we propose a novel approach to the measurement and verification of the performance of plant PET systems, including the design of dedicated plant phantoms, in order to provide a standard evaluation procedure for this emerging digital imaging agronomic technology.
Why it matches plant phenotyping methods植物用PET撮像システムの設計、性能評価、専用ファントムを用いた標準検証手順を中心に扱う明確なフェノタイピング手法開発研究。
abstractIn this paper, we perform a simulation study, proposing a novel design of dedicated plant PET scanners specifically conceived to address these agronomic issues.
The fusion of multi-source data obtained by multimodal sensors has recently attracted attention. The manifold learning method has been applied to data fusion problems because of its ability to extract the underlying structure of data, and a new data fusion method has been developed called alternating diffusion maps. In practical application, some shortcomings of this method were discovered. Firstly, it is based on diffusion process, which is more suitable for clustering tasks than classification tasks because of its clustering characteristics; secondly, in possible data overlapping areas the diffusion process between different classes of samples makes the performance of the algorithm decline rapidly; finally, the lack of explicit mapping makes it difficult to extend to new samples. In response to these problems, this paper proposes a new kernel width selection method with discrimination effect, which can be applied to classification tasks and possible overlapping area. In addition, Nystrom method is extended to solve out-of-sample problem. The improved framework to the identification of maize haploids and proposed for the first time to carry out fusion analysis on the data obtained by NMR and NIRS measurement equipment. The experimental results show that our method has a significant improvement in the classification task of unclear class boundaries of maize kernel, up to about 9%, which confirmed the effectiveness of the fusion of NMR and NIRS data for classification and the superiority of our proposed framework.
Why it matches plant phenotyping methodsNMR・NIRSのマルチモーダルデータ融合と分類フレームワークを開発・検証し、トウモロコシ種子の半数体状態を識別する手法が研究の中心であるため、植物状態のセンシング型フェノタイピングに該当する。
abstractthis paper proposes a new kernel width selection method with discrimination effect
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
697
probability density field (e.g., deep learning). Since TopoRoot requires a gray-scale intensity
698
volume with three thresholds (shape, kernel and neighborhood), a binary segmentation will first
699
need to be converted into a Euclidean distance field.
700
Software availability
701
TopoRoot is available for free at: https://github.com/danzeng8/TopoRoot
702
.
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
The copyright holder for this preprint
this version posted August 28, 2021.
;
https://doi.org/10.1101/2021.08.24.457522
doi:
bOpen asset ↗danzeng8/TopoRootpdf-raw-page:37 lines:1-53Dataset · public39
CT: Computed Tomography
723
Declarations
724
Ethics approval and consent to participate
725
Not applicable
726
Consent for publication
727
Not applicable
728
Availability of data and materials
729
The datasets generated and analysed during the current study are available in the TopoRoot
730
Github repository: https://github.com/danzeng8/TopoRoot
731
Competing interests
732
The authors declare that they have no competing interests.
733
Funding
734
This material is based upon work supported by the National Science Foundation under award
735
numbers DBI-1759836, DBI-1759807, DBI-1759796, EF-1971728, CCF-1907612, CCF-
736
2106672, and IOS-1638507. DZ is funded in part by aOpen asset ↗danzeng8/TopoRootpdf-raw-page:39 lines:1-45Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Abstract Background Drought is a major consequence of global heating that has negative impacts on agriculture. Potato is a drought-sensitive crop; tuber growth and dry matter content may both be impacted. Moreover, water deficit can induce physiological disorders such as glassy tubers and internal rust spots. The response of potato plants to drought is complex and can be affected by cultivar type, climatic and soil conditions, and the point at which water stress occurs during growth. The characterization of adaptive responses in plants presents a major phenotyping challenge. There is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping. Results This project aimed to take advantage of innovative approaches in MRI, phenotyping and molecular biology to evaluate the effects of water stress on potato plants during growth. Plants were cultivated in pots under different water conditions. A control group of plants were cultivated under optimal water uptake conditions. Other groups were cultivated under mild and severe water deficiency conditions (40 and 20% of field capacity, respectively) applied at different tuber growth phases (initiation, filling). Water stress was evaluated by monitoring soil water potential. Two fully-equipped imaging cabinets were set up to characterize plant morphology using high definition color cameras (top and side views) and to measure plant stress using RGB cameras. The response of potato plants to water stress depended on the intensity and duration of the stress. Three-dimensional morphological images of the underground organs of potato plants in pots were recorded using a 1.5 T MRI scanner. A significant difference in growth kinetics was observed at the early growth stages between the control and stressed plants. Quantitative PCR analysis was carried out at molecular level on the expression patterns of selected drought-responsive genes. Variations in stress levels were seen to modulate ABA and drought-responsive ABA-dependent and ABA-independent genes. Conclusions This methodology, when applied to the phenotyping of potato under water deficit conditions, provides a quantitative analysis of leaves and tubers properties at microstructural and molecular levels. The approaches thus developed could therefore be effective in the multi-scale characterization of plant response to water stress, from organ development to gene expression.
Why it matches plant phenotyping methodsジャガイモの水ストレス表現型を取得するための非侵襲的イメージング・生理計測手法と装置構成が研究の中心であり、単なる生物学的測定ではない。
abstractThere is therefore a demand for the development of non-invasive analytical techniques to improve phenotyping.
Reproduction assets foundThe paper's MRI phenotyping data (3D images of potato tubers in pots under water deficit) are openly deposited in Data INRAE with an explicit DOI, as stated in the Availability of data and materials section. No author analysis code or trained models are reported.Dataset · publicThe MRI data presented in this study are openly available in Data INRAE ( https://data.inrae.fr/ ) repository at: https://data.inrae.fr/dataset.xhtml?persistentId=doi:10.15454/SFAXAA ).Open asset ↗Data INRAE · doi:10.15454/SFAXAAlines:160-172Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 14 Sept 2026
During the past few years, milder autumn and winter seasons have caused severe problems to cauliflower harvest of Brittany region in France, mainly due to curd deformation. Consequently, cauliflower breeders are working on breeding new varieties that are more robust to climate change to stabilize the quality of cauliflower production. The aim of this study was to identify at which stage of the curd formation, significant difference can be detected between healthy and stressed cauliflower. A non-invasive classification based on Magnetic Resonance Imaging (MRI) images for cauliflower phenotyping was proposed. Plants exposed to vernalization stress were sampled at different times around primary meristem stage, then both MRI imaged and apex dissected. A work flow was developped to extract features from MRI images. A classification on phenotype was learned by LDA, QDA, PLSDA and CNN binary classification between two groups: healthy and stressed cauliflower. Promising F1 score and MCC up to 95% were achieved. Curd deformation is the main cause for cauliflower’s later physiological disorders when reaching maturity. Therefore, the cauliflowers with deformation could be removed at the earliest, e.g., screening for plant breeding. At the same time, the healthy cauliflowers are not destroyed and continue their life cycle.
Why it matches plant phenotyping methodsMRI画像からカリフラワーの健全・ストレス状態を分類する非侵襲的表現型解析ワークフローを開発し、複数の分類器で性能評価しており、表現型取得・抽出法が中心である。
abstractA non-invasive classification based on Magnetic Resonance Imaging (MRI) images for cauliflower phenotyping was proposed.
Legumes associate with root colonizing rhizobia that provide fixed nitrogen to its plant host in exchange for recently fixed carbon. There is a lack in understanding how individual plants modulate carbon allocation to a nodulated root system as a dynamic response to abiotic stimuli. One reason is that most approaches are based on destructive sampling, making quantification of localized carbon allocation dynamics in the root system difficult. We established an experimental workflow for routinely using non-invasive Positron Emission Tomography (PET) to follow the allocation of leaf-supplied 11 C tracer towards individual nodules in a three-dimensional (3D) root system of pea ( Pisum sativum ). Nitrate was used for triggering the shutdown of biological nitrogen fixation (BNF) expected to rapidly affect carbon allocation dynamics in the root-nodule system. This nitrate treatment lead to a reduction of 11 C tracer allocation to nodules by 40% – 47% in 5 treated plants while the variation in control plants was less than 11%. The established experimental pipeline enabled for the first time that several plants could consistently be labelled and measured using 11 C tracer in a PET approach to quantify C-allocation to individual nodules following a BNF shutdown. This demonstrates the strength of using 11 C tracers in a PET approach for non-invasive quantification of dynamic carbon allocation in several growing plants over several days. A major advantage of the approach is the possibility to investigate carbon dynamics in small regions of interest in a 3D system such as nodules in comparison to whole plant development. One sentence summary Positron Emission Tomography for quantification of carbon allocation dynamics in individual nodules within a 3D root system revealed strong effect of nitrate on carbon allocation.
Why it matches plant phenotyping methodsPETを用いた非侵襲的な根粒別炭素配分の3D定量ワークフローと測定パイプラインの確立が中心であり、植物の生理状態を抽出する方法研究に該当する。
abstractWe established an experimental workflow for routinely using non-invasive Positron Emission Tomography (PET) to follow the allocation of leaf-supplied 11 C tracer towards individual nodules in a three-dimensional (3D) root system of pea ( Pisum sativum ).
Due to its high sensitivity and specificity for tumor detection, positron emission tomography (PET) has become a standard and widely used molecular imaging technique. Given the popularity of PET, both clinically and preclinically, its use has been extended to study plants. However, only a limited number of research groups worldwide report PET-based studies, while we believe that this technique has much more potential and could contribute extensively to plant science. The limited application of PET may be related to the complexity of putting together methodological developments from multiple disciplines, such as radio-pharmacology, physics, mathematics and engineering, which may form an obstacle for some research groups. By means of this manuscript, we want to encourage researchers to study plants using PET. The main goal is to provide a clear description on how to design and execute PET scans, process the resulting data and fully explore its potential by quantification via compartmental modeling. The different steps that need to be taken will be discussed as well as the related challenges. Hereby, the main focus will be on, although not limited to, tracing 11 CO 2 to study plant carbon dynamics.
Why it matches plant phenotyping methods植物PET撮像の設計・実施、データ処理、コンパートメントモデルによる定量を体系的に扱う方法論的ガイドであり、植物の炭素動態という生理状態の取得・解析手法が中心である。
abstractThe main goal is to provide a clear description on how to design and execute PET scans, process the resulting data and fully explore its potential by quantification via compartmental modeling.
Roots are at the core of plant water dynamics. Nonetheless, root morphology and functioning are not easily assessable without destructive approaches. Nuclear Magnetic Resonance (NMR), and particularly low-field NMR (LF-NMR), is an interesting noninvasive method to study water in plants, as measurements can be performed outdoors and independent of sample size. However, as far as we know, there are no reported studies dealing with the water dynamics in plant roots using LF-NMR. Thus, the aim of this study is to assess the feasibility of using LF-NMR to characterize root water status and water dynamics non-invasively. To achieve this goal, a proof-of-concept study was designed using well-controlled environmental conditions. NMR and ecophysiological measurements were performed continuously over one week on three herbaceous species grown in rhizotrons. The NMR parameters measured were either the total signal or the transverse relaxation time T 2 . We observed circadian variations of the total NMR signal in roots and in soil and of the root slow relaxing T 2 value. These results were consistent with ecophysiological measurements, especially with the variation of fluxes between daytime and nighttime. This study assessed the feasibility of using LF-NMR to evaluate root water status in herbaceous species.
Why it matches plant phenotyping methodsLF-NMRによる根の水分状態・水動態の非破壊的測定可能性を中心に検証した proof-of-concept 研究であり、植物生理状態の取得手法が主要な貢献である。
abstractthe aim of this study is to assess the feasibility of using LF-NMR to characterize root water status and water dynamics non-invasively.
Reproduction assets foundThe authors deposited the paper's NMR and ecophysiological measurement data openly in Data INRAE (DOI 10.15454/NWRHDA), and supplementary materials at MDPI contain the CPMG decay curves and NNLS processing methods used for the T2 analysis.Dataset · publicThe data presented in this study are openly available in Data INRAE ( https://data.inrae.fr/ , accessed on 12 March 2021) repository at https://doi.org/10.15454/NWRHDA (accessed on 12 March 2021).Open asset ↗Data INRAE · 10.15454/NWRHDAlines:115-137Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
BlueberryLaboratory / benchtopMRI / PETTissueObject detectionCalibration / preprocessingStress / disease detectionVisualization / data managementStress response / toleranceWater status / transpiration
Abstract Background Investigating plant mechanisms to tolerate freezing temperatures is critical to developing crops with superior cold hardiness. However, the lack of imaging methods that allow the visualization of freezing events in complex plant tissues remains a key limitation. Magnetic resonance imaging (MRI) has been successfully used to study many different plant models, including the study of in vivo changes during freezing. However, despite its benefits and past successes, the use of MRI in plant sciences remains low, likely due to limited access, high costs, and associated engineering challenges, such as keeping samples frozen for cold hardiness studies. To address this latter need, a novel device for keeping plant specimens at freezing temperatures during MRI is described. Results The device consists of commercial and custom parts. All custom parts were 3D printed and made available as open source to increase accessibility to research groups who wish to reproduce or iterate on this work. Calibration tests documented that, upon temperature equilibration for a given experimental temperature, conditions between the circulating coolant bath and inside the device seated within the bore of the magnet varied by less than 0.1 °C. The device was tested on plant material by imaging buds from Vaccinium macrocarpon in a small animal MRI system, at four temperatures, 20 °C, − 7 °C, − 14 °C, and − 21 °C. Results were compared to those obtained by independent controlled freezing test (CFT) evaluations. Non-damaging freezing events in inner bud structures were detected from the imaging data collected using this device, phenomena that are undetectable using CFT. Conclusions The use of this novel cooling and freezing device in conjunction with MRI facilitated the detection of freezing events in intact plant tissues through the observation of the presence and absence of water in liquid state. The device represents an important addition to plant imaging tools currently available to researchers. Furthermore, its open-source and customizable design ensures that it will be accessible to a wide range of researchers and applications.
Why it matches plant phenotyping methods植物組織の凍結イベントをMRIで可視化するための冷却・凍結デバイスを開発し、温度校正と植物試料での検証を行った、中心的なフェノタイピング手法研究である。
abstracta novel device for keeping plant specimens at freezing temperatures during MRI is described.
MRI / PETRoot2D/3D reconstructionSegmentationSkeletonization / topologyRoot system architecture
Structural reconstruction of plant roots from MRI is challenging, because of low resolution and low signal-to-noise ratio of the 3D measurements which may lead to disconnectivities and wrongly connected roots. We propose a two-stage approach for this task. The first stage is based on semantic root vs. soil segmentation and finds lowest-cost paths from any root voxel to the shoot. The second stage takes the largest fully connected component generated in the first stage and uses 3D skeletonization to extract a graph structure. We evaluate our method on 22 MRI scans and compare to human expert reconstructions.
Why it matches plant phenotyping methodsMRI画像から植物根系を再構成し、セグメンテーションと3D骨格化で根構造を抽出する手法の開発・専門家比較検証が中心であるため。
abstractWe propose a two-stage approach for this task.
The development of a robust method to non-invasively visualize root morphology in natural soils has been hampered by the opaque, physical, and structural properties of soils. In this work we describe a novel technology, low field magnetic resonance imaging (LF-MRI), for imaging energy sorghum ( Sorghum bicolor (L.) Moench) root morphology and architecture in intact soils. The use of magnetic fields much weaker than those used with traditional MRI experiments reduces the distortion due to magnetic material naturally present in agricultural soils. A laboratory based LF-MRI operating at 47 mT magnetic field strength was evaluated using two sets of soil cores: 1) soil/root cores of Weswood silt loam (Udifluventic Haplustept) and a Belk clay (Entic Hapluderts) from a conventionally tilled field, and 2) soil/root cores from rhizotrons filled with either a Houston Black (Udic Haplusterts) clay or a sandy loam purchased from a turf company. The maximum soil water nuclear magnetic resonance (NMR) relaxation time T 2 (4 ms) and the typical root water relaxation time T 2 (100 ms) are far enough apart to provide a unique contrast mechanism such that the soil water signal has decayed to the point of no longer being detectable during the data collection time period. 2-D MRI projection images were produced of roots with a diameter range of 1.5-2.0 mm using an image acquisition time of 15 min with a pixel resolution of 1.74 mm in four soil types. Additionally, we demonstrate the use of a data-driven machine learning reconstruction approach, Automated Transform by Manifold Approximation (AUTOMAP) to reconstruct raw data and improve the quality of the final images. The application of AUTOMAP showed a SNR (Signal to Noise Ratio) improvement of two fold on average. The use of low field MRI presented here demonstrates the possibility of applying low field MRI through intact soils to root phenotyping and agronomy to aid in understanding of root morphology and the spatial arrangement of roots in situ .
Why it matches plant phenotyping methods根の形態・構造を非侵襲的に取得する低磁場MRI法を開発・評価し、機械学習再構成による画像品質改善も検証しているため、植物フェノタイピング手法が中心である。
abstractThe development of a robust method to non-invasively visualize root morphology in natural soils
Field / plotMRI / PETWhole plant / canopy / plot / fieldVisualization / data management
This Expert View provides an update on the recent development of new microsensors, and briefly summarizes some novel applications of existing microsensors, in plant biology research. Two major topics are covered: (i) sensors for gaseous analytes (O2, CO2, and H2S); and (ii) those for measuring concentrations and fluxes of ions (macro- and micronutrients and environmental pollutants such as heavy metals). We show that application of such microsensors may significantly advance understanding of mechanisms of plant-environmental interaction and regulation of plant developmental and adaptive responses under adverse environmental conditions via non-destructive visualization of key analytes with high spatial and/or temporal resolution. Examples included cover a broad range of environmental situations including hypoxia, salinity, and heavy metal toxicity. We highlight the power of combining microsensor technology with other advanced biophysical (patch-clamp, voltage-clamp, and single-cell pressure probe), imaging (MRI and fluorescent dyes), and genetic techniques and approaches. We conclude that future progress in the field may be achieved by applying existing microsensors for important signalling molecules such as NO and H2O2, by improving selectivity of existing microsensors for some key analytes (e.g. Na, Mg, and Zn), and by developing new microsensors for P.
Why it matches plant phenotyping methods植物内の無機分析物を高空間・時間分解能で可視化するマイクロセンサー技術の開発と応用を中心に扱うレビューであり、植物の生理状態を測定する方法論が主題である。
abstractThis Expert View provides an update on the recent development of new microsensors, and briefly summarizes some novel applications of existing microsensors, in plant biology research.
SoybeanMRI / PETLeafRootStem / branchWhole plant / canopy / plot / field2D/3D reconstructionVisualization / data managementWater status / transpiration
Abstract The observation of initial transport of fixed nitrogen in intact soybean plants in real-time was conducted by using the positron-emitting tracer imaging system (PETIS). Soybean root nodules were fed with [ 13 N]N 2 for 10 minutes, and the radioactivity of [ 13 N]N tracer was recorded for 60 minutes. The serial images of nitrogen fixation activity and translocation of fixed nitrogen in the soybean plant were reconstructed to estimate the fixed-N transport to the upper shoot. As a result, the signal of nitrogen radiotracer moving upward through the intact stem was successfully observed. This is the first report that the translocation of fixed-N is visualized in real-time in soybean plant by a moving image. The signal of nitrogen radiotracer appeared at the base stem at about 20 minutes after the feeding of tracer gas and it took 40 minutes to reach the upper stem. The velocity of fixed nitrogen translocation was estimated approximately at 1.63 cm min -1 . The autoradiography taken after PETIS experiment showed a clear picture of transport of fixed 13 N in the whole plant that the fixed-N moved not only via xylem system but also via the phloem system to the shoot after transferring from xylem to phloem in the stem although it has been generally considered that the fixed-N in nodule is transported dominantly via xylem by transpiration stream toward mature leaves. This result also suggests that the initial transport of fixed-N was mainly into the stem and subsequently translocated to young leaves and buds via the phloem system. These new findings in the initial transport of fixed nitrogen of soybean by PETIS observation will become the basis for future study of fixed-N transport in the whole legume plants.
Why it matches plant phenotyping methodsPETISを用いて、根粒で固定された窒素の植物体内輸送をリアルタイム画像化・定量推定しており、植物の生理状態の取得方法が研究の中心である。
abstractThe observation of initial transport of fixed nitrogen in intact soybean plants in real-time was conducted by using the positron-emitting tracer imaging system (PETIS).
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.
Why it matches plant phenotyping methods生きた茶植物内のフッ素輸送・分布を、18Fトレーサーと陽電子放出トレーサー画像化システムでリアルタイム取得しており、植物の生理状態を可視化する画像計測法の実質的な適用が中心です。
abstractF arrived at an aerial plant part about 1.5 h after absorption by roots, suggesting that tea roots had a retention effect on F, and then was transported upward mainly via the xylem and little via the phloem along the tea stem
Published10 Mar 2020Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicineCited by 6 · OpenAlex ↗
Manganese is one of the most important essential micronutrients for the plants. To monitor its uptake and transport by radioactive tracking is a powerful method due to the no carrier added 52 Mn in 10 -12 moldm -3 concentration range. The generally used method is to measure the radioactivity of cut parts of plants by gamma-spectrometry. Only few studies reported about noninvasive measurement, using pairs of detectors connected in coincidence. We use a full ring MiniPET machine for this purpose to dynamically visualize the uptake and distribution of the radionuclide in 4D. The results are controlled with the conventional gamma spectroscopy after chopping the plants into six parts. The study of stress tolerance initiated by PEG 6000 in different hybrids of maize is also presented as possible application for the phenotyping of plants by PET camera.
Why it matches plant phenotyping methodsMiniPETによる植物体内のマンガン吸収・輸送の4D可視化を開発し、ガンマ分光法で検証しており、植物フェノタイピングへの応用も中心的に扱っている。
abstractWe use a full ring MiniPET machine for this purpose to dynamically visualize the uptake and distribution of the radionuclide in 4D.
Abstract Sinojackia xylocarpa Hu is an endangered plant species endemic to China. In this study, we observed the permeability of Sinojackia xylocarpa Hu drupe in the imbibition phase by using magnetic resonance imaging (MRI) dynamics, and obtained the spatial representation of the water distribution in the drupe. At the same time, the structure of the drupe, the permeability of the seed coat and the endosperm were monitored through scanning electron microscopy (SEM), and paraffin section detection (PSD) .
Why it matches plant phenotyping methodsMRI、SEM、パラフィン切片を中核に、果実・種皮・胚乳の構造、透過性、水分分布を可視化・測定しており、植物器官の状態取得が研究の中心である。
abstractwe observed the permeability of Sinojackia xylocarpa Hu drupe in the imbibition phase by using magnetic resonance imaging (MRI) dynamics, and obtained the spatial representation of the water distribution in the drupe.
Soil salinity is a global environmental challenge for crop production. Understanding the uptake and transport properties of salt in plants is crucial to evaluate their potential for growth in high salinity soils and as a basis for engineering varieties with increased salt tolerance. Positron emission tomography (PET), traditionally used in medical and animal imaging applications for assessing and quantifying the dynamic bio-distribution of molecular species, has the potential to provide useful measurements of salt transport dynamics in an intact plant. Here we report on the feasibility of studying the dynamic transport of 22 Na in millet using PET. Twenty-four green foxtail (Setaria viridis L. Beauv.) plants, 12 of each of two different accessions, were incubated in a growth solution containing 22 Na + ions and imaged at 5 time points over a 2-week period using a high-resolution small animal PET scanner. The reconstructed PET images showed clear evidence of sodium transport throughout the whole plant over time. Quantitative region-of-interest analysis of the PET data confirmed a strong correlation between total 22 Na activity in the plants and time. Our results showed consistent salt transport dynamics within plants of the same variety and important differences between the accessions. These differences were corroborated by independent measurement of Na + content and expression of the NHX transcript, a gene implicated in sodium transport. Our results demonstrate that PET can be used to quantitatively evaluate the transport of sodium in plants over time and, potentially, to discern differing salt-tolerance properties between plant varieties. In this paper, we also address the practical radiation safety aspects of working with 22 Na in the context of plant imaging and describe a robust pipeline for handling and incubating plants. We conclude that PET is a promising and practical candidate technology to complement more traditional salt analysis methods and provide insights into systems-level salt transport mechanisms in intact plants.
Why it matches plant phenotyping methods植物体内のナトリウム輸送という生理形質を、PET画像と定量ROI解析で時系列測定する方法の実現可能性・定量性を検証しており、フェノタイピング手法が中心である。
abstractHere we report on the feasibility of studying the dynamic transport of 22 Na in millet using PET.
In many biomimetic approaches, a deep understanding of the form-structure-function relationships in living and functionally intact organisms, which act as biological role models, is essential. This knowledge is a prerequisite for the identification of parameters that are relevant for the desired technical transfer of working principles. Hence, non-invasive and non-destructive techniques for static (3D) and dynamic (4D) high-resolution plant imaging and analysis on multiple hierarchical levels become increasingly important. In this study we demonstrate that magnetic resonance imaging (MRI) can be used to resolve the plants inner tissue structuring and functioning on the example of four plant concept generators with sizes larger than 5 mm used in current biomimetic research projects: Dragon tree (Dracaena reflexa var. angustifolia), Venus flytrap (Dionaea muscipula), Sugar pine (Pinus lambertiana) and Chinese witch hazel (Hamamelis mollis). Two different MRI sequences were applied for high-resolution 3D imaging of the differing material composition (amount, distribution, and density of various tissues) and condition (hydrated, desiccated, and mechanically stressed) of the four model organisms. Main aim is to better understand their biomechanics, development, and kinematics. The results are used as inspiration for developing novel design and fabrication concepts for bio-inspired technical fiber-reinforced branchings and smart biomimetic actuators.
Why it matches plant phenotyping methodsMRIによる植物の非侵襲的な高解像度3D/4D画像化と組織構造・状態の抽出が研究の中心であり、単なる生物学的測定ではない。
abstractnon-invasive and non-destructive techniques for static (3D) and dynamic (4D) high-resolution plant imaging and analysis on multiple hierarchical levels become increasingly important.
Background Epicormic branches arise from dormant buds patterned during the growth of previous years. Dormant epicormic buds remain just below the surface of trees, pushed outward from the pith during secondary growth, but maintain vascular connections. Epicormic buds can be activated to elongate into a new shoot, either through natural processes or horticultural intervention, to potentially rejuvenate orchards and restructure tree architecture. Because epicormic structures are embedded within secondary growth, tomographic approaches are a useful method to study them and understand their development. Results We apply techniques from image processing to determine the locations of epicormic vascular traces embedded within secondary growth of sweet cherry ( Prunus avium L.), revealing the juvenile phyllotactic pattern in the trunk of an adult tree. Techniques include the flood fill algorithm to find the pith of the tree, edge detection to approximate the radius, and a conversion to polar coordinates to threshold and segment phyllotactic features. Intensity values from magnetic resonance imaging (MRI) of the trunk are projected onto the surface of a perfect cylinder to find the locations of traces in the "boundary image". Mathematical phyllotaxy provides a means to capture the patterns in the boundary image by modeling phyllotactic parameters. Our cherry tree specimen has the conspicuous parastichy pair (2,3), phyllotactic fraction 2/5, and divergence angle of approximately 143°. Conclusions The methods described provide a framework not only for studying phyllotaxy, but also for processing of volumetric image data in plants. Our results have practical implications for orchard rejuvenation and directed approaches to influence tree architecture. The study of epicormic structures, which are hidden within secondary growth, using tomographic methods also opens the possibility of studying genetic and environmental influences such structures.
Why it matches plant phenotyping methodsMRI画像と画像処理を組み合わせ、樹幹内部の維管束痕と葉序パターンを抽出・定量する方法が研究の中心であり、植物形態のフェノタイピング手法に該当する。
abstractWe apply techniques from image processing to determine the locations of epicormic vascular traces embedded within secondary growth of sweet cherry ( Prunus avium L.), revealing the juvenile phyllotactic pattern in the trunk of an adult tree.
Reproduction assets foundThe paper's availability statement explicitly provides authors' analysis code on GitHub and the raw MRI data on figshare, both paper-specific and publicly actionable.Code · publicCodes are available on Github ( https://github.com/eithun/cherry-phyllotaxy ), and raw data are available on the figshare repository ( https://doi.org/10.6084/m9.figshare.7409843 ).Open asset ↗eithun/cherry-phyllotaxylines:174-195Dataset · publicCodes are available on Github ( https://github.com/eithun/cherry-phyllotaxy ), and raw data are available on the figshare repository ( https://doi.org/10.6084/m9.figshare.7409843 ).Open asset ↗10.6084/m9.figshare.7409843lines:174-195Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · OpenAlex · checked 15 Sept 2026
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.
In protected strawberry ( Fragaria × ananassa Duch.) cultivation, environmental control based on the process of photosynthate translocation is essential for optimizing fruit quality and yield, because the process of photosynthate translocation directly affects dry matter partitioning. We visualized photosynthate translocation to strawberry fruits non-invasively with 11 CO 2 and a positron-emitting tracer imaging system (PETIS). We used PETIS to evaluate real-time dynamics of 11 C-labeled photosynthate translocation from a 11 CO 2 -fed leaf, which was immediately below the inflorescence, to individual fruits on an inflorescence in intact plant. Serial photosynthate translocation images and animations obtained by PETIS verified that the 11 C-photosynthates from the source leaf reached the sink fruit within 1 h but did not accumulate homogeneously within a fruit. The quantity of photosynthate translocation as represented by 11 C radioactivity varied among individual fruits and their positions on the inflorescence. Photosynthate translocation rates to secondary fruit were faster than those to primary or tertiary fruits, even though the translocation pathway from leaf to fruit was the longest for the secondary fruit. Moreover, the secondary fruit was 25% smaller than the primary fruit. Sink activity ( 11 C radioactivity/dry weight [DW]) of the secondary fruit was higher than those of the primary and tertiary fruits. These relative differences in sink activity levels among the three fruit positions were also confirmed by 13 C tracer measurement. Photosynthate translocation rates in the pedicels might be dependent on the sink strength of the adjoining fruits. The present study established 11 C-photosynthate arrival times to the sink fruits and demonstrated that the translocated material does not uniformly accumulate within a fruit. The actual quantities of translocated photosynthates from a specific leaf differed among individual fruits on the same inflorescence. To the best of our knowledge, this is the first reported observation of real-time translocation to individual fruits in an intact strawberry plant using 11 C-radioactive- and 13 C-stable-isotope analyses.
Why it matches plant phenotyping methodsPETISによる非侵襲的な光合成産物移行のリアルタイム画像化が研究の中心で、果実への移行速度・蓄積という植物生理形質を定量・評価している。
abstractWe visualized photosynthate translocation to strawberry fruits non-invasively with 11 CO 2 and a positron-emitting tracer imaging system (PETIS).
The effects of high voltage pulsed electric fields (PEF) applied to potato tubers were examined by the contrast enhanced, low field Magnetic Resonance Imaging (MRI). This is a non-destructive, and relatively inexpensive method that allows to monitor the spatial distribution of damages caused by the pulses and their evolution in time. The MRI results confirmed the irreversible damage of the potato tuber cell membranes caused by the PEF treatment, leading to non-selective flow of ions. The extent of electroporation was also evaluated by electrical conductivity measurements, as well as by compression tests and compared with the MRI. On the basis of these results, the PEF method can be optimized in applications aiming at the increase of the permeability of potato cell membranes.
Why it matches plant phenotyping methods低磁場MRIによるジャガイモ塊茎の電気穿孔・損傷の空間分布と経時変化の非破壊計測が中心で、電気伝導度・圧縮試験との比較検証も行っているため、植物状態の画像計測手法として採用。
abstractThis is a non-destructive, and relatively inexpensive method that allows to monitor the spatial distribution of damages caused by the pulses and their evolution in time.
Source: Adobe Stock. While there are great advances in crop genotyping, many research programs still depend upon the selection of plants or seeds based on the phenotype. This can involve sorting through thousands of samples by hand. Finding ways to automate sorting based on phenotype can increase the productivity of plant breeders, freeing up time to do other tasks. Albrecht Melchinger, ASA and CSSA member and Professor of Applied Genetics and Plant Breeding at the University of Hohenheim in Germany, uses seed phenotyping in his work. One example is the use of a color marker, “where you can see from the embryo coloration whether it's a haploid seed or a diploid seed. The haploid seeds are, in this case, white and the other ones are purple.” However, some germplasm is naturally purple and could not be used in the breeding program. To solve this problem, Melchinger and colleagues developed an inducer with high oil content. Haploid seeds would have normal oil content while the diploid seed had higher oil content. While color was no longer limiting the germplasm that could be used, researchers still had to perform the time-consuming task of analyzing individual seeds to determine oil content. To speed this process, these researchers have created a platform for determining oil content. Although they were working with maize, they realized this automated, high-throughput system had the potential to benefit breeders working with other oil crops. Oil crop breeders are often trying to increase oil content, “and it would be very nice if you had measurements of individual seeds in a nondestructive manner,” Melchinger says. An article recently published in Crop Science (http://bit.ly/2FLCqAX) describes this phenotyping platform for measuring the oil content of seeds and tests accuracy across a range of oil crops. Researchers used the platform to measure the oil content of canola, castor bean, cotton, jatropha, maize, soy, and sunflower. The platform has four modules (Fig. 1). The first separates individual seeds from a larger sample using suction. Depending upon the size and shape of the seed, the pneumatic pressure required to select a single seed needs to be adjusted. The second module determines seed mass. Mass is measured on a balance, and it is key to keep this clean and free from debris. Oil mass is measured in the third module using commercial TD-NMR (time domain nuclear magnetic resonance) equipment. A computer then calculates oil content from oil mass and seed mass data. This step is also one that needs to be adjusted based on seed size. Flow chart of the seeds through the modules of the high-throughput platform. NMR, nuclear magnetic resonance. The final module sorts seeds based on oil content, which can be done in two different ways. One approach is to set categories. For example, when sorting based on oil content to separate haploid and diploid seeds, a user can sort seeds into two categories. Alternatively, the module will measure each seed and set them on a tray in a grid pattern. The computer tracks the placement of each seed. A researcher can then query the dataset, for example identifying the top 10% of seeds based on oil content. Seeds meeting the selected criteria are identified by LED lights, which are located under each seed. In testing the system with these seven oil crops, the researchers report that their high-throughput phenotyping platform has high accuracy. Because the process is fully automated, a user can load seeds for analysis and walk away. “We do it very often overnight,” Melchinger says. He explains the pneumatic system that moves seeds through the modules is the key development, and the researchers have applied for a patent on this technology. They are also developing a manual that will outline how settings should be adjusted based on seed size and shape when using this platform for different crops. Melchinger sees this platform being useful beyond measuring oil content. “Our system makes use of NMR, but it is not confined to NMR,” he says. As technology is developed to analyze other traits, there is potential to switch the components while maintaining the high-throughput functionality of the platform. Slide 1: Module 1: A feeder (or hopper) is filled with seeds and the separator uses pneumatic pressure to select a single seed at a time for processing. Slide 2: Module 2: Seeds are weighed on a mass balance. Slide 3: Module 3: The NMR machine, where oil mass is determined. Seeds are transported into and out of the NMR machine using pneumatic pressure. Slide 4: Module 4a: Seed sorting based on pre-established categories. Seeds can be sorted into as many as six defined categories. Slide 5: Module 4b: Seed sorting onto a tray. Data for each seed are stored in the computer, and a user can define criteria for selection. LED lights under tray identify seeds that meet the criteria. Slide 6: LED selection grid in Module 4b: Seeds with light shining below meet user-defined selection criteria. Check out the Crop Science article, “High-Throughput Precision Phenotyping of the Oil Content of Single Seeds of Various Oilseed Crops” at: http://bit.ly/2FLCqAX.
Why it matches plant phenotyping methods種子油含量を個別・非破壊・高スループットで測定および選別するプラットフォームの開発、精度評価、複数作物への適用が中心であり、植物フェノタイピング手法に該当する。
abstractTo speed this process, these researchers have created a platform for determining oil content.
BarleyLaboratory / benchtopMRI / PETRootMorphology / geometry measurementRoot system architecture
Background Root systems are highly plastic and adapt according to their soil environment. Studying the particular influence of soils on root development necessitates the adaptation and evaluation of imaging methods for multiple substrates. Non-invasive 3D root images in soil can be obtained using magnetic resonance imaging (MRI). Not all substrates, however, are suitable for MRI. Using barley as a model plant we investigated the achievable image quality and the suitability for root phenotyping of six commercially available natural soil substrates of commonly occurring soil textures. The results are compared with two artificially composed substrates previously documented for MRI root imaging. Results In five out of the eight tested substrates, barley lateral roots with diameters below 300 µm could still be resolved. In two other soils, only the thicker barley seminal roots were detectable. For these two substrates the minimal detectable root diameter was between 400 and 500 µm. Only one soil did not allow imaging of the roots with MRI. In the artificially composed substrates, soil moisture above 70% of the maximal water holding capacity (WHC max ) impeded root imaging. For the natural soil substrates, soil moisture had no effect on MRI root image quality in the investigated range of 50-80% WHC max . Conclusions Almost all tested natural soil substrates allowed for root imaging using MRI. Half of these substrates resulted in root images comparable to our current lab standard substrate, allowing root detection down to a diameter of 300 µm. These soils were used as supplied by the vendor and, in particular, removal of ferromagnetic particles was not necessary. With the characterization of different soils, investigations such as trait stability across substrates are now possible using noninvasive MRI.
Why it matches plant phenotyping methodsMRIによる土壌中の根の非侵襲的画像化について、異なる土壌基質への適用性と画像品質を評価し、根径の検出性能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ある。
abstractStudying the particular influence of soils on root development necessitates the adaptation and evaluation of imaging methods for multiple substrates.
Reproduction assets foundThe authors state that the 3D MRI root images and excavated root images from this study are publicly available under a DOI (IPK repository), directly reproducing the paper's root phenotyping measurements.Dataset · public3D root images and excavated root images are available at: http://dx.doi.org/10.5447/IPK/2017/10 .Open asset ↗IPK · 10.5447/IPK/2017/10lines:196-230Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Engineered nanoparticles (NPs) are increasingly used in commercial products including automotive lubricants, clothing, deodorants, sunscreens, and cosmetics and can potentially accumulate in our food supply. Given their size it is difficult to detect and visualize the presence of NPs in environmental samples, including crop plants. New analytical tools are needed to fill the void for detection and visualization of NPs in complex biological and environmental matrices. We aimed to determine whether radiolabeled NPs could be used as a noninvasive, highly sensitive analytical tool to quantitatively track and visualize NP transport and accumulation in vivo in lettuce (Lactuca sativa) and to investigate the effect of NP size on transport and distribution over time using a combination of autoradiography, positron emission tomography (PET)/computed tomography (CT), scanning electron microscopy (SEM), and transition electron microscopy (TEM). Azide functionalized NPs were radiolabeled via a "click" reaction with copper-64 ( 64 Cu)-1,4,7-triazacyclononane triacetic acid (NOTA) azadibenzocyclooctyne (ADIBO) conjugate ([ 64 Cu]-ADIBO-NOTA) via copper-free Huisgen-1,3-dipolar cycloaddition reaction. This yielded radiolabeled [ 64 Cu]-NPs of uniform shape and size with a high radiochemical purity (>99%), specific activity of 2.2 mCi/mg of NP, and high stability (i.e., no detectable dissolution) over 24 h across a pH range of 5-9. Both PET/CT and autoradiography showed that [ 64 Cu]-NPs entered the lettuce seedling roots and were rapidly transported to the cotyledons with the majority of the accumulation inside the roots. Uptake and transport of intact NPs was size-dependent, and in combination with the accumulation within the roots suggests a filtering effect of the plant cell walls at various points along the water transport pathway.
Why it matches plant phenotyping methods植物体内のナノ粒子輸送・蓄積という状態を、放射標識とPET/CT・オートラジオグラフィー等で非侵襲的かつ定量的に追跡・可視化する分析手法が研究の中心であり、植物フェノタイピング手法として適格です。
abstractNew analytical tools are needed to fill the void for detection and visualization of NPs in complex biological and environmental matrices.
Abstract The current findings show that positron emission tomography (PET), primarily developed for medical diagnostic imaging, can be applied in plant studies to analyze the transport and allocation of wide range of compounds labelled with positronemitting radioisotopes. This work is focused on PET analysis of the uptake and transport of 2-deoxy-2-fluoro[ 18 F]-D-glucose (2-[ 18 F]FDG), as a model of photoassimilates, in tissues of giant reed (Arundo donax L. var. versicolor) as a potential energy crop. The absorption of 2-[ 18 F]FDG and its subsequent transport in plant tissues were evaluated in both acropetal and basipetal direction as well. Visualization and quantification of the uptake and transport of 2-[ 18 F]FDG in plants immersed with the root system into a 2-[ 18 F]FDG solution revealed a significant accumulation of 18F radioactivity in the roots. The transport rate in plants was increased in the order of plant exposure through: stem > mechanically damaged root system > intact root system. PET analysis in basipetal direction, when the plant was immersed into the 2-[ 18 F]FDG solution with the cut area of the leaf of whole plant, showed minimal translocation of 2-[ 18 F]FDG into the other plant parts. The PET results were verified by measuring the accumulated radioactivity of 18 F by direct gamma-spectrometry.
Why it matches plant phenotyping methods植物組織内の光合成産物の吸収・輸送をPETで可視化・定量し、ガンマ線測定でも検証している。植物の生理状態を取得する画像計測法の実質的な適用であり、単なる routine measurement ではない。
abstractVisualization and quantification of the uptake and transport of 2-[ 18 F]FDG in plants
Background Positron imaging can be used to non-destructively visualize the dynamics of a positron-emitting radionuclide in vivo, and is therefore a tool for understanding the mechanisms of nutrient transport in intact plants. The transport of zinc, which is one of the most important nutrient elements for plants, has so far been visualized by positron imaging using 62 Zn (half-life: 9.2 h), which is manufactured in the limited number of facilities that have a cyclotron. In contrast, the positron-emitting radionuclide 65 Zn (half-life: 244 days) is commercially available worldwide. In this study, we examined the possibility of conducting positron imaging of zinc in intact plants using 65 Zn. Results By administering 65 Zn and imaging over a long time, clear serial images of 65 Zn distributions from the root to the panicle of dwarf rice plants were successfully obtained. Conclusions Non-destructive visualization of zinc dynamics in plants was achieved using commercially available 65 Zn and a positron imaging system, demonstrating that zinc dynamics can be visualized even in facilities without a cyclotron.
Why it matches plant phenotyping methods市販65Znと陽電子イメージングを用いて、植物体内の亜鉛動態を非破壊可視化する手法の実現可能性を検証しており、植物生理状態の取得法が研究の中心である。
abstractIn this study, we examined the possibility of conducting positron imaging of zinc in intact plants using 65 Zn.
Handling and processing of potatoes is performed in increasingly large and more automated facilities, and the industry calls for more automated machinery for quality assessment and sorting by concentration of starch, soluble sugars, protein, amino acids etc. of the potato tubers. The present study was designed to evaluate five different scanning methods for their potential use in potato assessment and sorting. Two methods were based on hyperspectral imaging, two were based on dielectric/bio-impedance and one was based on low-field nuclear magnetic resonance. A set of 60 potatoes of 10 different cultivars were simultaneously sampled for analyses of content and scanned by the five different scanning methods. The resulting multivariate dataset was used to estimate the prediction ability of the individual scanning methods on starch-related parameters, selected simple sugars, selected amino acids, conductivity of pressed cell sap and cell sizes. Results showed that most types of spectral analyses had relatively high potential for predicting the starch-related parameters and medium potential for predicting the concentration of the reducing sugars fructose and glucose. Most methods showed medium potential for prediction of several amino acids, including asparagine, which showed particularly promising predictions in the hyperspectral analyses of intact potatoes. The presented screening study enabled us to perform robust choices for the further development and optimization of the methods and instruments for industrial implementation.
Why it matches plant phenotyping methodsジャガイモ塊茎の化学・物理的形質を推定する5種類の走査法を比較評価し、工業的な選別用手法の開発・最適化に直接つなげているため、フェノタイピング手法が中心である。
abstractThe present study was designed to evaluate five different scanning methods for their potential use in potato assessment and sorting.
Magnetic resonance imaging (MRI) was used to gain in vivo insight into load-induced displacements of inner plant tissues making a non-invasive and non-destructive stress and strain analysis possible. The central aim of this study was the identification of a possible load-adapted orientation of the vascular bundles and their fibre caps as the mechanically relevant tissue in branch-stem-attachments of Dracaena marginata. The complex three-dimensional deformations that occur during mechanical loading can be analysed on the basis of quasi-three-dimensional data representations of the outer surface, the inner tissue arrangement (meristem and vascular system), and the course of single vascular bundles within the branch-stem-attachment region. In addition, deformations of vascular bundles could be quantified manually and by using digital image correlation software. This combination of qualitative and quantitative stress and strain analysis leads to an improved understanding of the functional morphology and biomechanics of D. marginata, a plant that is used as a model organism for optimizing branched technical fibre-reinforced lightweight trusses in order to increase their load bearing capacity.
Why it matches plant phenotyping methodsMRIとデジタル画像相関を用いて、生体植物の内部組織配置および荷重下の変形・応力・ひずみを定量化することが研究の中心であり、植物の形態・力学的状態を取得する手法の実質的応用である。
abstractMagnetic resonance imaging (MRI) was used to gain in vivo insight into load-induced displacements of inner plant tissues making a non-invasive and non-destructive stress and strain analysis possible.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Positron emitting isotopes, such as 11 C, 13 N, and 18 F, can be used to label molecules. The tracers, such as 11 CO 2 , are delivered to plants to study their biological processes, particularly metabolism and photosynthesis, which may contribute to the development of plants that have a higher yield of crops and biomass. Measurements and resulting images from PET scanners are not quantitative in young plant structures or in plant leaves due to poor positron annihilation in thin objects. To address this problem we have designed, assembled, modeled, and tested a nuclear imaging system (simultaneous beta–gamma imager). The imager can simultaneously detect positrons ( β + ) and coincidence-gamma rays ( γ ). The imaging system employs two planar detectors; one is a regular gamma detector which has a LYSO crystal array, and the other is a phoswich detector which has an additional BC-404 plastic scintillator for beta detection. A forward model for positrons is proposed along with a joint image reconstruction formulation to utilize the beta and coincidence-gamma measurements for estimating radioactivity distribution in plant leaves. The joint reconstruction algorithm first reconstructs beta and gamma images independently to estimate the thickness component of the beta forward model and afterward jointly estimates the radioactivity distribution in the object. We have validated the physics model and reconstruction framework through a phantom imaging study and imaging a tomato leaf that has absorbed 11 CO 2 . The results demonstrate that the simultaneously acquired beta and coincidence-gamma data, combined with our proposed joint reconstruction algorithm, improved the quantitative accuracy of estimating radioactivity distribution in thin objects such as leaves. We used the structural similarity (SSIM) index for comparing the leaf images from the simultaneous beta–gamma imager with the ground truth image. The jointly reconstructed images yield SSIM indices of 0.69 and 0.63, whereas the separately reconstructed beta alone and gamma alone images had indices of 0.33 and 0.52, respectively.
Why it matches plant phenotyping methods植物葉の放射能分布を定量推定する画像取得・再構成システムを開発し、ファントムとトマト葉で検証しており、植物の生理状態を測定する方法が研究の中心です。
abstractwe have designed, assembled, modeled, and tested a nuclear imaging system (simultaneous beta–gamma imager).
The 'hydraulic vulnerability segmentation' hypothesis predicts that expendable distal organs are more susceptible to water stress-induced embolism than the main stem of the plant. In the current work, we present the first in vivo visualization of this phenomenon. In two separate experiments, using magnetic resonance imaging or synchrotron-based microcomputed tomography, grapevines (Vitis vinifera) were dehydrated while simultaneously scanning the main stems and petioles for the occurrence of emboli at different xylem pressures (Ψx ). Magnetic resonance imaging revealed that 50% of the conductive xylem area of the petioles was embolized at a Ψx of -1.54 MPa, whereas the stems did not reach similar losses until -1.9 MPa. Microcomputed tomography confirmed these findings, showing that approximately half the vessels in the petioles were embolized at a Ψx of -1.6 MPa, whereas only few were embolized in the stems. Petioles were shown to be more resistant to water stress-induced embolism than previously measured with invasive hydraulic methods. The results provide the first direct evidence for the hydraulic vulnerability segmentation hypothesis and highlight its importance in grapevine responses to severe water stress. Additionally, these data suggest that air entry through the petiole into the stem is unlikely in grapevines during drought.
Why it matches plant phenotyping methodsMRIとマイクロCTによる生体内画像化を用いてブドウの茎・葉柄の木部塞栓を定量し、植物の水ストレス状態を評価する手法が研究の中心であるため。
abstractusing magnetic resonance imaging or synchrotron-based microcomputed tomography, grapevines (Vitis vinifera) were dehydrated while simultaneously scanning the main stems and petioles for the occurrence of emboli at different xylem pressures
Precise measurements of root system architecture traits are an important requirement for plant phenotyping. Most of the current methods for analyzing root growth require either artificial growing conditions (e.g. hydroponics), are severely restricted in the fraction of roots detectable (e.g. rhizotrons), or are destructive (e.g. soil coring). On the other hand, modalities such as magnetic resonance imaging (MRI) are noninvasive and allow high-quality three-dimensional imaging of roots in soil. Here, we present a plant root imaging and analysis pipeline using MRI together with an advanced image visualization and analysis software toolbox named NMRooting. Pots up to 117 mm in diameter and 800 mm in height can be measured with the 4.7 T MRI instrument used here. For 1.5 l pots (81 mm diameter, 300 mm high), a fully automated system was developed enabling measurement of up to 18 pots per day. The most important root traits that can be nondestructively monitored over time are root mass, length, diameter, tip number, and growth angles (in two-dimensional polar coordinates) and spatial distribution. Various validation measurements for these traits were performed, showing that roots down to a diameter range between 200 μm and 300 μm can be quantitatively measured. Root fresh weight correlates linearly with root mass determined by MRI. We demonstrate the capabilities of MRI and the dedicated imaging pipeline in experimental series performed on soil-grown maize (Zea mays) and barley (Hordeum vulgare) plants.
Why it matches plant phenotyping methodsMRIによる土壌中根系の3D画像取得・解析パイプラインと専用ソフトウェアを開発し、根形態形質を検証しており、植物フェノタイピング手法が研究の中心である。
abstractHere, we present a plant root imaging and analysis pipeline using MRI together with an advanced image visualization and analysis software toolbox named NMRooting.
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 · publicAutomated image analysis was performed using an in-house developed software tool, named NMRooting (available at http://www.nmrooting.de ), which was written in the programming language PythonOpen asset ↗NMRootinglines:169-172Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jan 2016Ying yong sheng tai xue bao = The journal of applied ecology
MRI / PETRootStem / branchTissuePhysiological trait estimationWater status / transpiration
Status and transport of water in plant body are the main contents of study of soil-plant-atmosphere continuum (SPAC), as well as the base for use and regulation of agricultural water. The process of water transport in plant can be deeply influenced by the environments. Thus, plant needs to adjust its water status to accommodate the environmental change to sustain its own growth and development. Traditional methods for plant water monitoring, such as evaporation flux, pressure chamber, high pressure flow meter, heat pulse, and so on, usually cause damage or even destruction of plant body and disturb the original water status. Thus, they are not able to truly and precisely detect and reflect the real water status of plant. Nuclear magnetic resonance (NMR) is a non-destructive and non-invasive technique which can be used for the measurement of water molecular displacement, and transportation. This study aimed to provide an overview of the applications of NMR technique in the study of water distribution and transport in plant roots and stems, as well as the water content in plant cells and tissues. In addition, the existing main problems and possible solutions were analyzed for the applications of NMR in SPAC studies. Several important issues were proposed for the acquisition of more precise and reliable detection signals. It was suggested that the NMR technique would probably make important progress in the relevant fields such as plant water physiology, plantenvironment interactions, and water metabolism. In general, the application of NMR in SPAC system study was still in its infancy in China. The deeper application and expansion of NMR in SPAC study would depend on the development of portable and open NMR equipment that could be easily applied for different plants in field.
Why it matches plant phenotyping methods植物の水分分布・輸送・含水量をNMRで非破壊測定する方法を中心に扱う、植物フェノタイピング手法のレビューである。
abstractNuclear magnetic resonance (NMR) is a non-destructive and non-invasive technique which can be used for the measurement of water molecular displacement, and transportation.