Standardized extraction of quantitative phenotypes from images is increasingly important across plant biology, from ecological and evolutionary studies to genetics, breeding, and functional genomics. However, as large image datasets are increasingly used for trait analysis, many biologically relevant traits, including size, shape, color, and spatial patterning, are still measured manually or using fragmented semi-automated workflows. These limitations reduce throughput, reproducibility, and accessibility, especially for researchers without computational expertise. Here, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images. BioIMA integrates foundation model-based segmentation with automated trait computation, allowing users to extract quantitative measurements from images through an intuitive graphical interface and without model training. To validate its performance, we quantified a set of knot morphological traits in two Populus species, as these measurements are typically time-consuming to perform manually. Automatic measurements showed strong agreement with manual ImageJ-based measurements (R2 > 0.95), while reducing per-image processing time by approximately 75% (from ~15 s to ~4 s). BioIMA was further applied to diverse plant datasets, including Helianthus and Rhododendron images with varying morphologies and background conditions. Although developed for plant phenotyping, BioIMA may also be extended to other biological samples where region-based size, shape, or color traits are of interest. By combining accessibility and standardization in a lightweight local application, BioIMA provides a practical community resource for image-based phenotyping in ecological and evolutionary studies.
Why it matches plant phenotyping methods植物画像から形態形質を自動抽出するツールの開発と、手動測定との性能検証が中心であるため。
abstractHere, we present BioIMA, an open-source desktop tool for rapid and standardized phenotyping from biological images.
Reproduction assets foundThe paper's own phenotyping tool BioIMA (source code, documentation, example datasets, and user manual) is publicly available on the authors' GitHub repository, directly supporting the paper's image-based trait extraction and validation analyses.Code · publicis powered by embedded models
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currently including SAM (Kirillov et al., 2023) and mobile SAM (Zhang et al., 2023),
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which are executed locally through ONNX Runtime for efficient inference without
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internet connectivity. Source code, documentation, example datasets, and a user manual
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are publicly available on GitHub (https://github.com/jingwanglab/BioIMA).101
preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
The copyright holder for this
this version posted September 3, 2026.
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https://doi.org/10.64898/2026.08.30.747465
doi:
bioRxiv preprintOpen asset ↗jingwanglab/BioIMApdf-raw-page:4 lines:1-60Code / dataset availability confirmedarXiv · OpenAlex · checked 11 Sept 2026
Tomographic microscopy enables three-dimensional internal imaging but often requires expensive optical or X-ray instrumentation. Here we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples. The system uses a smartphone microscope, a white LED coupled into an optical fiber, 3D-printed micropositioners, and a physics-based forward model optimized with machine learning. We demonstrate full-color volumetric reconstructions from a tartrazine-cleared poplar section, a scattering phantom, fungal mycelium near an Arabidopsis root, and thick poplar branch imaging with an inserted side-emitting fiber. The current results are qualitative and exploratory, but they show that scanned fiber illumination and inexpensive hardware can produce useful three-dimensional reconstruction outputs for low-cost microscopy experiments.
Why it matches plant phenotyping methods低コスト三次元断層イメージング法そのものを開発し、ポプラ組織・枝やシロイヌナズナ根近傍を対象に植物の内部構造を可視化しているため、植物形態の取得法として中心的です。
abstractHere we present an ultra-low-cost continuous-wave diffusive tomography (CWDT) system for biological samples.
Reproduction assets foundThe paper's raw imaging inputs, configurations, and reconstruction outputs for Figures 2–5 are publicly deposited on Kaggle. The analysis code repository is only 'prepared for release' (no confirmed public deposit yet), so it is listed as request-only. Hardware CAD mirrors are public but are instrument designs, not theDataset · publicFigure-level raw inputs, model configurations, selected outputs, and manifests are available through the Kaggle dataset https://www.kaggle.com/datasets/alingold/continuous-wave-diffusive-tomography .Open asset ↗continuous-wave-diffusive-tomographylines:108-129Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Background Leaf-level biogenic volatile organic compounds (BVOCs) emissions represent a major source of organic gases in the atmosphere, influencing both climate and air quality. These emissions are strongly driven by environmental perturbations, which affect individual plant- to ecosystem-level processes. Uncovering all the BVOCs and understanding how their emissions respond to altered environmental conditions provide critical insights into vegetation-driven changes in atmospheric chemistry. We developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs. This novel system enables simultaneous, real-time monitoring of BVOC emissions and photosynthetic parameters at the leaf level, offering new opportunities to disentangle the physiological and environmental drivers of VOC release. Furthermore, we established the VOC Analysis and Processing Optimization Resource (VAPOR), an open-access software tool designed for rapid data post-processing and the analysis of the variability of hundreds of BVOCs. We assessed the performance of the tandem system under varying background conditions, using standard gas mixtures and a range of environmental factors. Results Blank emissions were substantially lower for major BVOCs (e.g., isoprene) compared to those observed in plant emissions. Despite this, the observation of background-level VOCs highlights the importance of routinely acquiring and accounting for blank measurements in analyses using the coupled instrumentation. Introduction of known VOC concentrations to the system demonstrated a linear response across different compounds with varying molecular compositions, indicating minimal gas loss regardless of chemical moieties within the coupled instrumentation. We applied the optimized system to investigate the physiological mechanisms driving BVOC emissions across different genotypes of poplar and pennycress. The high mass resolution capabilities of the PTR-ToF-MS, coupled with comprehensive VAPOR-driven data analysis, enabled the identification of several important BVOCs, including methanol and methanethiol; these BVOCs displayed substantial variation across pennycress genotypes and showed concentrations ~ 100-350% higher than the blank. Moreover, isoprene emissions varied significantly among poplar genotypes grown in different potting media. Conclusions Tandem instrumentation offers a powerful tool for profiling volatile molecular markers and elucidating their genetic and environmental underpinnings. This approach enhances our ability to predict BVOC emissions in response to genotype by environmental interactions and contributes to a deeper understanding of vegetation responses to environmental changes.
Why it matches plant phenotyping methods葉レベルの植物揮発性物質排出と光合成パラメータを取得するタンデム計測系を開発・検証し、解析ソフトウェアも提供しているため、植物表現型取得法が中心である。
abstractWe developed a tandem instrumentation setup that integrates a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) with parts-per-trillion detection limits and a photosynthetic infrared gas exchange system for the untargeted survey of all the BVOCs.
Reproduction assets foundThe paper's authors developed VAPOR, an open-access software tool used to post-process and analyze the paper's leaf VOC emission measurements, with explicit public availability at the authors' GitHub repository.Code · publicThe open-source code for VAPOR is accessible at https://github.com/INTERSECT-BESS/ORNL-VOC . In this study, VAPOR was used to post-process the VOC results generated from the offline collection of gases from poplars with different soil media.Open asset ↗INTERSECT-BESS/ORNL-VOClines:127-146Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
ABSTRACT Hyperspectral remote sensing is a powerful, high-throughput phenotyping tool that quantifies physiologically and structurally relevant wavelengths across diverse genotypes and over varying temporal scales. In this study, we combined tower-based continuous hyperspectral sensing with genome-wide association studies to analyze 1,423 wavebands (400-900 nm) and derivative vegetation indices across 505 genotypes and the genetic architecture of hyperspectral phenotypes over time in Populus trichocarpa Torr. & Gray grown under field conditions. Wavelengths related to chlorophyll and carotenoid absorption spectra exhibited the strongest genetic variation resulting in 98 significant SNP associations. Notably, we found substantial overlap in genetic association between the blue and red spectral regions, indicative of carotenoids and chlorophyll, respectively, and identified more than 10 candidate genes associated with chloroplast function, underpinning photosynthetic activity. Furthermore, fluctuations in associations for vegetative indices, such as the chlorophyll:carotenoid index (CCI), across the growing season reveal a temporally dynamic genetic architecture of physiological traits associated with fall senescence of this temperate tree species. Finally, we also observed correlations (⍴=0.3, p 0.5, p<1x10 -16 ), reinforcing the value of hyperspectral measurements for predicting traits linked to tree productivity. These findings highlight the potential of high-throughput, rapid, hyperspectral genome wide association studies GWAS to uncover physiologically meaningful genetic variation and offer promising insights for future acceleration for plant breeding.
Why it matches plant phenotyping methodsタワー型連続ハイパースペクトルセンシングを用いて多数の遺伝子型の生理・構造形質を時系列で取得し、表現型解析とGWASに substantively 適用しているため、フェノタイピング手法が中心的である。
abstractHyperspectral remote sensing is a powerful, high-throughput phenotyping tool that quantifies physiologically and structurally relevant wavelengths across diverse genotypes and over varying temporal scales.
Reproduction assets foundThe paper's hyperspectral phenotype dataset (tower-based hyperspectral traits for 505 Populus trichocarpa genotypes) is explicitly stated to be publicly available through the Oak Ridge National Laboratory LabKey data portal with DOI 10.25983/CBI/3012775. This is a paper-specific, public, actionable phenotype dataset. ADataset · publicHyperspectral phenotype data are publicly available through the Oak Ridge National Laboratory LabKey data portal (DOI: 10.25983/CBI/3012775).Oak Ridge National Laboratory LabKey data portal · 10.25983/CBI/3012775lines:163-201Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Poplars are essential to China's forestry, contributing to timber production, ecological restoration, and shelterbelt construction. Branch architecture critically influences tree growth, demanding scalable solutions beyond manual methods to assess phenotypic variation in large-scale poplar breeding programs. Unmanned aerial vehicle light detection and ranging (UAV LiDAR) provides an efficient alternative; however, existing methods focus on conifers, leaving a gap in approaches for the more complex morphology of poplar branches. This study proposes a poplar branch reconstruction algorithm utilizing material transport flux and object-level geometric features from low-cost UAV LiDAR data. First, a voxel-based near-centroid method is used to extract skeleton points from tree point clouds. Next, a material transport flux model identifies individual branches, and geometric features of transport paths, including path length and curvature, are used to reconstruct each branch. Finally, branch parameters are estimated based on reconstructed branches. Data from a 5-ha plot were collected using the DJI Zenmuse L1 UAV LiDAR at the Shishou National Poplar Breeding Station, Hubei Province, China. Results demonstrate the proposed algorithm achieves high accuracy in first-order branch identification (F1-score = 1), with second-order branches having an average F1-score of 0.69. Branch length estimation demonstrates an RMSE of 0.47 m, while branch angles show an RMSE of 7.06°. The study also reveals structural variability in branch traits, with the highest variability observed in the second-order branch length (coefficient of variation = 29.68%), and a moderate positive correlation between first- and second-order branch lengths (correlation coefficient = 0.34), providing insights into tree growth patterns. This approach offers a framework for high-throughput phenotyping, which provides an efficient solution towrads advanced tree breeding using UAV LiDAR.
Why it matches plant phenotyping methodsUAV LiDARによるポプラの枝構造再構成と枝長・枝角度などの形質推定アルゴリズムを開発し、精度検証まで行っており、フェノタイピング手法が研究の中心である。
abstractThis study proposes a poplar branch reconstruction algorithm utilizing material transport flux and object-level geometric features from low-cost UAV LiDAR data.
Reproduction assets foundThe paper's Data availability statement provides a public URL to the supporting UAV LiDAR point cloud data (the paper-specific phenotyping measurements) hosted on forestdata.cn, with a DOI. No author analysis code or trained models are mentioned.Dataset · publicThe data that support this study are available from https://www.forestdata.cn/dataDetail.html?id=6f6934f4-680e-4e18-b4e3-1e7a60b85b55 . The DOI is 10.12459.14.0320260116001.0000.V1.Open asset ↗forestdata.cn · 10.12459.14.0320260116001.0000.V1lines:192-218Code / dataset availability confirmedEurope PMC · OpenAlex · checked 13 Sept 2026
PoplarField / plotRoot2D/3D reconstructionRoot system architecture
Root system architecture (RSA) is pivotal for comprehending the ecological adaptation strategies and resource acquisition mechanisms of urban flora, playing a vital role in soil stability, carbon sequestration, and ecosystem sustainability. However, the non-destructive detection and precise three-dimensional (3D) reconstruction of RSA within urban environments remain challenging. In this study, a non-destructive reconstruction method utilizing ground-penetrating radar (GPR) technology was developed to achieve 3D reconstruction and visualization of RSA, with the goal of advancing the intelligent construction and precise ecological management of urban forest parks. Field-based GPR surveys of a 9-year-old triploid poplar were conducted using a square grid and concentric circular scanning scheme. A 3D data volume (C-scan) was constructed from two-dimensional (2D) profiles, and the spatial distribution of RSA was reconstructed using instantaneous amplitude analysis. The method was validated by comparing the results with actual root structures in sandy loam environments. The research results of the 1600 MHz GPR under the square grid scanning scheme show that extracting the instantaneous amplitude isosurface of GPR can effectively reflect the spatial distribution of roots with diameters greater than 1 cm within a depth of 0.4 m subsurface. The accuracy of RSA reconstruction can reach 89 %. The results demonstrate the applicability of the proposed method for non-destructive environmental monitoring in urban forest parks, showing significant potential for the large-scale detection and reconstruction of subsurface root systems. This research provides a novel approach for RSA reconstruction with significant implications for urban ecosystem management, soil conservation, and climate resilience research. The method enhances our capability to monitor the growth and adaptation of urban roots, laying the groundwork for the large-scale, non-destructive analysis of RSA.
Why it matches plant phenotyping methodsGPRと瞬時振幅解析を用いて樹木根系構造を3D再構成する方法を開発し、実際の根構造との比較で検証しており、根系形態の取得が中心的な研究目的である。
abstracta non-destructive reconstruction method utilizing ground-penetrating radar (GPR) technology was developed to achieve 3D reconstruction and visualization of RSA
Reproduction assets foundThe paper's Data Availability statement explicitly releases the GPR root scanning data on Zenodo and the RSA reconstruction analysis code on GitHub, both with public URLs matching allowed entries.Code · publicCode is available at https://github.com/Niceguoqiu/RSA-Reconstruction-Code.git .Open asset ↗GitHub · Niceguoqiu/RSA-Reconstruction-Codelines:268-286Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
The accurate point cloud completion of individual tree crowns is critical for quantifying crown complexity and advancing precision forestry, yet it remains challenging in dense plantations due to canopy occlusion and LiDAR limitations. In this study, we extended the scope of conventional point cloud completion techniques to artificial planted forests by introducing a novel approach called Multi−feature Fusion Completion of Populus (MFCPopulus). Specifically designed for Populus Tomentosa plantations with uniform spacing, this method utilized a dataset of 1050 manually segmented trees with expert−validated trunk−canopy separation. Key innovations include the following: (1) a hierarchical adversarial framework that integrates multi−scale feature extraction (via Farthest Point Sampling at varying rates) and biologically informed normalization to address trunk−canopy density disparities; (2) a structural characteristics split−collocation (SCS−SCC) strategy that prioritizes crown reconstruction through adaptive sampling ratios, achieving a 94.5% canopy coverage in outputs; (3) a cross−layer feature integration enabling the simultaneous recovery of global contours and a fine−grained branch topology. Compared to state−of−the−art methods, MFCPopulus reduced the Chamfer distance variance by 23% and structural complexity discrepancies (ΔDb) by 33% (mean, 0.12), while preserving species−specific morphological patterns. Octree analysis demonstrated an 89−94% spatial alignment with ground truth across height ratios (HR = 1.25−5.0). Although initially developed for artificial planted forests, the framework generalizes well to diverse species, accurately reconstructing 3D crown structures for both broadleaf (Fagus sylvatica, Acer campestre) and coniferous species (Pinus sylvestris) across public datasets, providing a precise and generalizable solution for cross−species trees’ phenotypic studies.
Why it matches plant phenotyping methods個体樹冠の3D点群補完・再構成手法を開発し、樹冠構造や形態形質の定量化に有効性を検証しているため、植物フェノタイピング手法が中心である。
abstractThe accurate point cloud completion of individual tree crowns is critical for quantifying crown complexity
Reproduction assets foundThe paper's Data Availability Statement points to a public Zenodo record (13255198) from which part of the tree point cloud data used in this study was sourced. This is a paper-specific, publicly accessible phenotyping input dataset (tree point clouds). No author analysis code, trained model checkpoints, or other paperDataset · publicData Availability Statement: The data presented in this study were partly sourced from the follow-
ing publicly available resource: https://zenodo.org/records/13255198 (accessed on 6 December
2024).Open asset ↗zenodo · 13255198pdf-page:23 lines:1-59Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Plants respond to rapid environmental change in ways that depend on both their genetic identity and their phenotypic plasticity, impacting their survival as well as associated ecosystems. However, genetic and environmental effects on phenotype are difficult to quantify across large spatial scales and through time. Leaf hyperspectral reflectance offers a potentially robust approach to map these effects from local to landscape levels. Using a handheld field spectrometer, we analyzed leaf-level hyperspectral reflectance of the foundation tree species Populus fremontii in wild populations and in three 6-year-old experimental common gardens spanning a steep climatic gradient. First, we show that genetic variation among populations and among clonal genotypes is detectable with leaf spectra, using both multivariate and univariate approaches. Spectra predicted population identity with 100% accuracy among trees in the wild, 87%-98% accuracy within a common garden, and 86% accuracy across different environments. Multiple spectral indices of plant health had significant heritability, with genotype accounting for 10%-23% of spectral variation within populations and 14%-48% of the variation across all populations. Second, we found gene by environment interactions leading to population-specific shifts in the spectral phenotype across common garden environments. Spectral indices indicate that genetically divergent populations made unique adjustments to their chlorophyll and water content in response to the same environmental stresses, so that detecting genetic identity is critical to predicting tree response to change. Third, spectral indicators of greenness and photosynthetic efficiency decreased when populations were transferred to growing environments with higher mean annual maximum temperatures relative to home conditions. This result suggests altered physiological strategies further from the conditions to which plants are locally adapted. Transfers to cooler environments had fewer negative effects, demonstrating that plant spectra show directionality in plant performance adjustments. Thus, leaf reflectance data can detect both local adaptation and plastic shifts in plant physiology, informing strategic restoration and conservation decisions by enabling high resolution tracking of genetic and phenotypic changes in response to climate change.
Why it matches plant phenotyping methods葉のハイパースペクトル反射を用いて遺伝型、クロロフィル、水分量、光合成効率などの植物形質・生理状態を推定し、精度評価と環境間比較を行っており、フェノタイピング手法の適用が中心です。
abstractLeaf hyperspectral reflectance offers a potentially robust approach to map these effects from local to landscape levels.
Reproduction assets foundThe paper's leaf hyperspectral reflectance data (the core phenotyping measurements) are publicly deposited in EcoSIS via an explicit data availability statement with DOI. No author analysis code repository is stated; R package references (vegan, prospectr) are generic libraries, not paper-specific assets.Dataset · publicThe data that support the findings of this study are available from EcoSIS at https://doi.org/10.21232/9bbY8fVJ .Open asset ↗EcoSIS · 10.21232/9bbY8fVJlines:291-351Code / dataset availability confirmedOpenAlex · bioRxiv · checked 15 Sept 2026
Abstract Globally, vegetation biodiversity is expected to decline as the rate of plant adaptation struggles to keep pace with rising temperatures. To support conservation efforts through remote sensing, we disentangled the nested effects of genetic and environmental influences on reflectance spectra, leveraging spectroscopy to assess plant adaptations to temperature. Specifically, we quantified the relative effect of plasticity and heritability on Populus fremontii (Fremont cottonwood) leaf reflectance using clonal replicates propagated from 16 populations and grown across three common gardens spanning a mean annual temperature gradient representing the thermal range of P. fremontii . We used variance partitioning to decompose phenotypic variation expressed in the leaf spectra into genotypic and environmental components to estimate broad-sense heritability. Heritability was strongly expressed in the spectral red edge (∼680-750nm) and shortwave infrared (∼1400-3000nm), though the heritability peak in the red edge was sensitive to extreme temperatures. By comparing distances of group centroids in principal component space, we determined that P. fremontii intraspecific spectral variation was shaped by the interaction between common garden site conditions and source population. Support vector machine models indicated pronounced environmental influence on spectral variation, as P. fremontii source population and garden location were classified at 71.8% and 92.6% accuracy, respectively. These findings emphasize the utility of reflectance data in separating genetic and environmental influences on plant phenotypes, offering a pathway to scale these insights across broader landscapes and aid in the conservation and management of vulnerable ecosystems in a warming climate.
Why it matches plant phenotyping methods葉の反射スペクトルを植物表現型として取得し、遺伝性・環境効果の分離、スペクトル変異の分類、温度適応評価に体系的に利用しており、単なる補助的な測定ではなく主要な解析基盤である。
abstractwe quantified the relative effect of plasticity and heritability on Populus fremontii (Fremont cottonwood) leaf reflectance
Reproduction assets foundThe paper's analysis code is explicitly stated to be publicly available on GitHub at the authors' repository (MegsSeeley/temperature_cottonwood). The phenotype/spectral data files are promised on Figshare only 'upon acceptance', so they are not yet publicly actionable and the Figshare DOI is not in the allowed URL listCode · publicAll authors reviewed
528 several drafts and agreed with the final version.
529 Availability of data: All data files will be made available on the Figshare database upon
530 acceptance of the manuscript at DOI: 10.6084/m9.figshare.25719585.
531 Code availability: Code is available on GitHub and is maintained by Seeley (2025)
532 https://github.com/MegsSeeley/temperature_cottonwood.
533 Conflict of interest: The authors have declared that no competing interests exist.
534
535 References
536 Ahmad, P., & Prasad, M. N. V. (2011). Environmental Adaptations and Stress Tolerance of
537 Plants in the Era of Climate Change. Springer Science & Business Media.
24Open asset ↗MegsSeeley/temperature_cottonwoodpdf-layout-page:24 lines:1-55Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
PoplarField / plotRootCalibration / preprocessingRoot system architecture
Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open-source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.
Why it matches plant phenotyping methods複数の根スキャン画像から根形質を統合する画像連結・統計集約法を開発し、比較検証した研究であり、根形質取得ワークフローが中心です。
abstractHere, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation.
Reproduction assets foundThe paper's Data availability statement deposits the root scan imageset, the Python image-concatenation script, and the R statistical-aggregation/figure code on Zenodo with explicit DOIs, making the paper-specific phenotyping images and analysis code publicly actionable. Since the Zenodo deposit URLs are not among the,Dataset · publicThe imageset is available at doi: 10.5281/zenodo.12667583Open asset ↗Zenodo · 10.5281/zenodo.12667583pdf-raw-page:7 lines:1-85Code · publicthe R code for statistical aggregation along
with the figures and statistics presented here are available at doi:
10.5281/zenodo.12668177Open asset ↗Zenodo · 10.5281/zenodo.12668177pdf-raw-page:7 lines:1-85Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Understanding lignocellulosic biomass resistance to enzymatic deconstruction is crucial for its sustainable conversion into bioproducts. Despite scientific advances, quantitative morphological analysis of plant deconstruction at cell and tissue scales remains under-explored. In this study, an original pipeline is devised, involving four-dimensional (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify deconstruction at cell and tissue scales. By applying this pipeline to poplar wood, dynamics of cellular parameters was computed and cellulose conversion during enzymatic deconstruction was measured. Results showed that enzymatic deconstruction predominantly impacts cell wall volume rather than surface area. Additionally, a negative correlation was observed between pre-hydrolysis compactness measures and volumetric cell wall deconstruction rate, whose strength was modulated by enzymatic activity. Results also revealed a strong positive correlation between average volumetric cell wall deconstruction rate and cellulose conversion rate. These findings link key deconstruction parameters across nano and micro scales.
Why it matches plant phenotyping methods植物細胞・組織の分解状態を定量する4次元蛍光共焦点イメージングと計算ツールが研究の中心であり、植物状態の形態的変化を抽出する方法を開発している。
abstractIn this study, an original pipeline is devised, involving four-dimensional (space + time) fluorescence confocal imaging, and a novel computational tool, to track and quantify deconstruction at cell and tissue scales.
Reproduction assets foundThe paper's WallTrack computational pipeline (used to track and quantify 4D confocal imaging of poplar cell wall deconstruction) is publicly available on the authors' FARE laboratory GitLab repository. The underlying imaging/phenotype data are not publicly deposited; the authors state data will be made available on.Code · publicnano and micro
scales.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
The WallTrack code is accessible through the FARE laboratory
GitLab repository at: https://gitlab.com/farelab/teamyr/publications/refahi_et_al_4d. Data will be made available on request.
Acknowledgments
The authors thank Anouck Habrant for her help in confocal imaging
and Grégoire Malandain, Solmaz Hossein Khani, Khadidja Ould Amer,
and Ali Faraj for their comments on the manuscript. This work was
supported by Agence Nationale de la Recherche (ANR) Open asset ↗https://gitlab.com/farelab/teamyr/publications/refahi_et_al_4d · refahi_et_al_4dpdf-raw-page:11 lines:1-66Code / dataset availability confirmedEurope PMC · OpenAlex · checked 14 Sept 2026
Drought stress is one of the main threats to poplar plant growth and has a negative impact on plant yield. Currently, high-throughput plant phenotyping has been widely studied as a rapid and nondestructive tool for analyzing the growth status of plants, such as water and nutrient content. In this study, a combination of computer vision and deep learning was used for drought-stressed poplar sapling phenotyping. Four varieties of poplar saplings were cultivated, and 5 different irrigation treatments were applied. Color images of the plant samples were captured for analysis. Two tasks, including leaf posture calculation and drought stress identification, were conducted. First, instance segmentation was used to extract the regions of the leaf, petiole, and midvein. A dataset augmentation method was created for reducing manual annotation costs. The horizontal angles of the fitted lines of the petiole and midvein were calculated for leaf posture digitization. Second, multitask learning models were proposed for simultaneously determining the stress level and poplar variety. The mean absolute errors of the angle calculations were 10.7° and 8.2° for the petiole and midvein, respectively. Drought stress increased the horizontal angle of leaves. Moreover, using raw images as the input, the multitask MobileNet achieved the highest accuracy (99% for variety identification and 76% for stress level classification), outperforming widely used single-task deep learning models (stress level classification accuracies of <70% on the prediction dataset). The plant phenotyping methods presented in this study could be further used for drought-stress-resistant poplar plant screening and precise irrigation decision-making.
Why it matches plant phenotyping methods画像解析と深層学習により、葉姿勢の定量化および干ばつストレス同定手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractIn this study, a combination of computer vision and deep learning was used for drought-stressed poplar sapling phenotyping.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe codes for conducting the proposed poplar plant image generation method and for annotation format conversion were uploaded to the GitHub platform ( https://github.com/L-Zhou17/Plant-Image-Generation ). Other codes and datasets are available upon request.Open asset ↗L-Zhou17/Plant-Image-Generationlines:269-294Code / dataset availability confirmedarXiv · checked 15 Sept 2026
High-throughput phenotyping refers to the non-destructive and efficient evaluation of plant phenotypes. In recent years, it has been coupled with machine learning in order to improve the process of phenotyping plants by increasing efficiency in handling large datasets and developing methods for the extraction of specific traits. Previous studies have developed methods to advance these challenges through the application of deep neural networks in tandem with automated cameras; however, the datasets being studied often excluded physical labels. In this study, we used a dataset provided by Oak Ridge National Laboratory with 1,672 images of Populus Trichocarpa with white labels displaying treatment (control or drought), block, row, position, and genotype. Optical character recognition (OCR) was used to read these labels on the plants, image segmentation techniques in conjunction with machine learning algorithms were used for morphological classifications, machine learning models were used to predict treatment based on those classifications, and analyzed encoded EXIF tags were used for the purpose of finding leaf size and correlations between phenotypes. We found that our OCR model had an accuracy of 94.31% for non-null text extractions, allowing for the information to be accurately placed in a spreadsheet. Our classification models identified leaf shape, color, and level of brown splotches with an average accuracy of 62.82%, and plant treatment with an accuracy of 60.08%. Finally, we identified a few crucial pieces of information absent from the EXIF tags that prevented the assessment of the leaf size. There was also missing information that prevented the assessment of correlations between phenotypes and conditions. However, future studies could improve upon this to allow for the assessment of these features.
Why it matches plant phenotyping methods植物画像からラベル情報を読み取り、画像分割・機械学習で葉形、色、斑点などの形態形質を抽出・分類する手法が研究の中心であり、植物フェノタイピング手法の開発・適用に該当する。
abstractimage segmentation techniques in conjunction with machine learning algorithms were used for morphological classifications
Reproduction assets foundThe paper's authors explicitly state that all analysis code (OCR label reading, leaf segmentation, morphology classification, treatment prediction) is publicly available under the MIT License on their GitHub repository. The underlying ORNL image dataset is not stated to be publicly available, so only the code asset is.Code · publicSince a pre-trained segmentation model (the SAM) was used in this study, researchers could attempt to build segmentation models fine-tuned to only recognize leaves, which could increase model efficiency and provide more consistent results.
6 Code Availability
All code is publicly available under the MIT License on GitHub here: https://github.com/vivaansinghvi07/smoky-mountain-data-comp .
Acknowledgements
We thank Dr. Ty Frazier at Oak Ridge National Laboratory for his helpful suggestions and mentoring throughout this project.
References
Arya et al. (2022)
Arya, S., Sandhu, K.S.,
Singh, J., Kumar, S.,
2022.
Deep learning: As the new frontier in high-throughput
plant phenotyping.
Euphytica 218Open asset ↗vivaansinghvi07/smoky-mountain-data-complines:272-401Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Published13 May 2024The Plant journal : for cell and molecular biologyCited by 6 · OpenAlex ↗
As a promising model, genome-based plant breeding has greatly promoted the improvement of agronomic traits. Traditional methods typically adopt linear regression models with clear assumptions, neither obtaining the linkage between phenotype and genotype nor providing good ideas for modification. Nonlinear models are well characterized in capturing complex nonadditive effects, filling this gap under traditional methods. Taking populus as the research object, this paper constructs a deep learning method, DCNGP, which can effectively predict the traits including 65 phenotypes. The method was trained on three datasets, and compared with other four classic models-Bayesian ridge regression (BRR), Elastic Net, support vector regression, and dualCNN. The results show that DCNGP has five typical advantages in performance: strong prediction ability on multiple experimental datasets; the incorporation of batch normalization layers and Early-Stopping technology enhancing the generalization capabilities and prediction stability on test data; learning potent features from the data and thus circumventing the tedious steps of manual production; the introduction of a Gaussian Noise layer enhancing predictive capabilities in the case of inherent uncertainties or perturbations; fewer hyperparameters aiding to reduce tuning time across datasets and improve auto-search efficiency. In this way, DCNGP shows powerful predictive ability from genotype to phenotype, which provide an important theoretical reference for building more robust populus breeding programs.
Why it matches plant phenotyping methodsポプラの65形質を遺伝子型から予測する深層学習手法DCNGPを開発し、複数データセットおよび既存モデルと比較評価しており、植物形質推定手法が研究の中心である。
abstractthis paper constructs a deep learning method, DCNGP, which can effectively predict the traits including 65 phenotypes.
Reproduction assets foundThe paper's authors publicly released the DCNGP analysis scripts on GitHub; the SRA deposits contain only raw sequencing reads (molecular omics), not phenotype datasets, so they are excluded.Code · publicILABILITY STATEMENT
All raw sequencing reads have been deposited in NCBI’s
Sequence Read Archive (SRA) under accession number
PRJNA297202 and PRJNA510671 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA297202/, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA510671/). The DCNGP scripts are
available in the release package on GitHub (https://github.com/xiangweidai/DCNGP).SUPPORTING INFORMATION
Additional Supporting Information may be found in the online ver-
sion of this article.
Figure S1. All 65 predicted phenotypes and their abbreviations in
this work.
Table S1. Predictions of 30 phenotypes from 94 samples by four
DL models in ablation experiment.
Table S2. Architecture details of the deep-Open asset ↗xiangweidai/DCNGPpdf-raw-page:10 lines:1-94Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
PoplarTissueSegmentationGrowth / time-series analysisGrowth / development / phenology
Plant regeneration is an important dimension of plant propagation and a key step in the production of transgenic plants. However, regeneration capacity varies widely among genotypes and species, the molecular basis of which is largely unknown. Association mapping methods such as genome-wide association studies (GWAS) have long demonstrated abilities to help uncover the genetic basis of trait variation in plants; however, the performance of these methods depends on the accuracy and scale of phenotyping. To enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time. We found that the resulting statistics were of highly non-normal distributions, and thus employed transformations or permutations to avoid violating assumptions of linear models used in GWAS. We report over 200 statistically supported quantitative trait loci (QTLs), with genes encompassing or near to top QTLs including regulators of cell adhesion, stress signaling, and hormone signaling pathways, as well as other diverse functions. Our results encourage models of hormonal signaling during plant regeneration to consider keystone roles of stress-related signaling (e.g. involving jasmonates and salicylic acid), in addition to the auxin and cytokinin pathways commonly considered. The putative regulatory genes and biological processes we identified provide new insights into the biological complexity of plant regeneration, and may serve as new reagents for improving regeneration and transformation of recalcitrant genotypes and species.
Why it matches plant phenotyping methods再生組織を時系列で定量するセマンティックセグメンテーションを中核としたフェノミクス・ワークフローを開発し、大規模GWASに適用しているため、植物表現型取得法が中心的です。
abstractTo enable a large-scale GWAS of in planta callus and shoot regeneration in the model tree Populus, we developed a phenomics workflow involving semantic segmentation to quantify regenerating plant tissues over time.
Reproduction assets foundThe authors publicly release their GWAS analysis code: the MTMC-SKAT R package and the inplantaGWAS repository containing phenotype data parsing, association mapping, and downstream analysis code used in this study. The SNP and image datasets are stated to be publicly available but only via a citation (Nagle et al. 202Code · publicThe MTMC-SKAT R package is available on GitHub ( https://github.com/naglemi/mtmcskat ), as is other R code used for this study, including phenotype data parsing, association mapping, and downstream interrogation of results ( https://github.com/naglemi/inplantaGWAS ).Open asset ↗naglemi/inplantaGWASlines:318-364Code · publicThe MTMC-SKAT R package is available on GitHub ( https://github.com/naglemi/mtmcskat ), as is other R code used for this study, including phenotype data parsing, association mapping, and downstream interrogation of results ( https://github.com/naglemi/inplantaGWAS ).Open asset ↗naglemi/mtmcskatlines:318-364Code / dataset availability confirmedEurope PMC · OpenAlex · checked 15 Sept 2026
PoplarLaboratory / benchtopRootMorphology / geometry measurement2D/3D reconstructionGrowth / time-series analysisGrowth / development / phenologyRoot system architecture
Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional-structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection ( P value = 0.0061, R 2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.
Why it matches plant phenotyping methods時系列画像フェノタイピングとFSPMを統合し、根系形態パラメータを自動抽出・評価する方法が中心である。
abstractwe applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM.
Reproduction assets foundThe paper's Data Availability statement deposits all data and R scripts (the paper's phenotyping measurements and analysis) on Zenodo, and the adapted CropRootBox.jl model code on GitHub. Only the Zenodo URL matches an allowed URL, so the Zenodo asset is reported; the GitHub repository is noted but its URL is not in anDataset · publicview and editing: S.P., D.B., K.Y., S.D., and S.-H.K.
Competing interests: The authors declare that there is no conflict of interest regarding the publication of this article.
Data Availability
The model is housed in Github at github.com/uwkimlab/CropRootBox.jl_propagation.jl . All data and and R scripts are housed in Zenodo at https://doi.org/10.5281/zenodo.8083525 .
Supplementary Materials
Supplementary 1
Figs. S1 to S7
Tables S1 to S2
Click here for additional data file.
References
1.
Seethepalli
A , Dhakal
K , Griffiths
M , Guo
H , Freschet
GT , York
LM
.
RhizoVision explorer: Open-source software for root image analysis and measurement standardization
. AoB PLANTS . 2021 ; 13 ( 6 ): pOpen asset ↗Zenodo · 10.5281/zenodo.8083525lines:196-345Code / dataset availability confirmedOpenAlex · arXiv · checked 15 Sept 2026
Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks (CNNs) to segment the leaf body and visible venation of 2,906 P. trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (i) does not require experimental or image pre-processing, (ii) uses the raw RGB images at full resolution, and (iii) requires very few samples for training (e.g., just eight images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (i) methods for fast and accurate image-based feature extraction that require minimal training data, and (ii) a new population-scale data set, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.
Why it matches plant phenotyping methods葉画像から形態・葉脈形質を抽出する少数ショット学習手法を開発し、実測値で検証するとともに、大規模データセットを提供しており、植物フェノタイピング手法が中心である。
abstractwe address these challenges by leveraging few-shot learning with convolutional neural networks (CNNs) to segment the leaf body and visible venation of 2,906 P. trichocarpa leaf images obtained in the field.
Reproduction assets foundThe paper publicly releases its few-shot leaf/vein segmentation code and all phenotyping assets (2,906 leaf images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and SNPs) via two ORNL DOI repositories cited as [31] and [36].Code · publicIn addition to releasing all of the segmentation code on a public GitHub repository [ 31 ] , we are also releasing all of the images, manual segmentations, model predictions, 68 extracted leaf phenotypes, and a new set of SNPs called against the v4 P. trichocarpa genome for 1,419 genotypes on the Oak Ridge National Laboratory Constellation Portal (a public DOI data server) [ 36 ] .Open asset ↗lines:249-258Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
High-throughput phenotyping refers to the non-destructive and efficient evaluation of plant phenotypes. In recent years, it has been coupled with machine learning in order to improve the process of phenotyping plants by increasing efficiency in handling large datasets and developing methods for the extraction of specific traits. Previous studies have developed methods to advance these challenges through the application of deep neural networks in tandem with automated cameras; however, the datasets being studied often excluded physical labels. In this study, we used a dataset provided by Oak Ridge National Laboratory with 1,672 images of Populus Trichocarpa with white labels displaying treatment (control or drought), block, row, position, and genotype. Optical character recognition (OCR) was used to read these labels on the plants, image segmentation techniques in conjunction with machine learning algorithms were used for morphological classifications, machine learning models were used to predict treatment based on those classifications, and analyzed encoded EXIF tags were used for the purpose of finding leaf size and correlations between phenotypes. We found that our OCR model had an accuracy of 94.31% for non-null text extractions, allowing for the information to be accurately placed in a spreadsheet. Our classification models identified leaf shape, color, and level of brown splotches with an average accuracy of 62.82%, and plant treatment with an accuracy of 60.08%. Finally, we identified a few crucial pieces of information absent from the EXIF tags that prevented the assessment of the leaf size. There was also missing information that prevented the assessment of correlations between phenotypes and conditions. However, future studies could improve upon this to allow for the assessment of these features.
Why it matches plant phenotyping methods画像分割、機械学習、OCRを用いて植物の葉形・色・斑点などの形態形質を抽出・分類し、精度も評価しており、植物フェノタイピング手法が中心である。
titleHigh-Throughput Phenotyping using Computer Vision and Machine Learning
Reproduction assets foundThe paper's authors publicly release all analysis code (OCR label reading, leaf segmentation, morphology classification, treatment prediction) under the MIT License on GitHub; the underlying ORNL image dataset itself is not stated as publicly available.Code · publicSince a pre-trained segmentation model (the SAM) was used in this study, researchers could attempt to build segmentation models fine-tuned to only recognize leaves, which could increase model efficiency and provide more consistent results.
6 Code Availability
All code is publicly available under the MIT License on GitHub here: https://github.com/vivaansinghvi07/smoky-mountain-data-comp .
Acknowledgements
We thank Dr. Ty Frazier at Oak Ridge National Laboratory for his helpful suggestions and mentoring throughout this project.
References
Arya et al. (2022)
Arya, S., Sandhu, K.S.,
Singh, J., Kumar, S.,
2022.
Deep learning: As the new frontier in high-throughput
plant phenotyping.
Euphytica 218Open asset ↗vivaansinghvi07/smoky-mountain-data-complines:272-401Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Eastern cottonwood (Populus deltoides W. Bartram ex Marshall) and hybrid poplars are well-known bioenergy crops. With advances in tree breeding, it is increasingly necessary to find economical ways to identify high-performing Populus genotypes that can be planted under different environmental conditions. Photosynthesis and leaf nitrogen content are critical parameters for plant growth, however, measuring them is an expensive and time-consuming process. Instead, these parameters can be quickly estimated from hyperspectral leaf reflectance if robust statistical models can be developed. To this end, we measured photosynthetic capacity parameters (Rubisco-limited carboxylation rate (Vcmax), electron transport-limited carboxylation rate (Jmax), and triose phosphate utilization-limited carboxylation rate (TPU)), nitrogen per unit leaf area (Narea), and leaf reflectance of seven taxa and 62 genotypes of Populus from two study plantations in Mississippi. For statistical modeling, we used least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Our results showed that the predictive ability of LASSO and PCA models was comparable, except for Narea in which LASSO was superior. In terms of model interpretability, LASSO outperformed PCA because the LASSO models needed 2 to 4 spectral reflectance wavelengths to estimate parameters. The LASSO models used reflectance values at 758 and 935 nm for estimating Vcmax (R2 = 0.51 and RMSPE = 31%) and Jmax (R2 = 0.54 and RMSPE = 32%); 687, 746, and 757 nm for estimating TPU (R2 = 0.56 and RMSPE = 31%); and 304, 712, 921, and 1021 nm for estimating Narea (R2 = 0.29 and RMSPE = 21%). The PCA model also identified 935 nm as a significant wavelength for estimating Vcmax and Jmax. Therefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening of Populus genotypes because of its capacity to estimate these physicochemical parameters.
Why it matches plant phenotyping methodsハイパースペクトル葉反射から光合成能力と葉窒素含量を推定する統計モデルを開発・評価しており、植物形質取得手法が中心である。
abstractTherefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening of Populus genotypes
Reproduction assets foundThe authors deposited the paper's phenotype measurements (photosynthetic capacity parameters, leaf nitrogen, hyperspectral leaf reflectance of Populus taxa) in Mississippi State University's institutional repository, Scholars Junction, with an explicit public DOI. No author analysis code or trained models were shared.Dataset · publicData Availability: Our data can be accessed from Scholars Junction: Mississippi State University’s Institutional Repository at the following DOI: https://doi.org/10.54718/BACR5952 .Open asset ↗Scholars Junction · 10.54718/BACR5952lines:159-169Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Heterophylly, or leaf morphological changes along plant shoot axes, is an important indicator of plant eco-adaptation to heterogeneous microenvironments. Despite extensive studies on the genetic control of leaf shape, the genetic architecture of heterophylly remains elusive. To identify genes related to heterophylly and their associations with plant saline tolerance, we conducted a leaf shape mapping experiment using leaves from a natural population of Populus euphratica . We included 106 genotypes grown under salt stress and salt-free (control) conditions using clonal seedling replicates. We developed a shape tracking method to monitor and analyze the leaf shape using principal component (PC) analysis. PC1 explained 42.18% of the shape variation, indicating that shape variation is mainly determined by the leaf length. Using leaf length along shoot axes as a dynamic trait, we implemented a functional mapping-assisted genome-wide association study (GWAS) for heterophylly. We identified 171 and 134 significant quantitative trait loci (QTLs) in control and stressed plants, respectively, which were annotated as candidate genes for stress resistance, auxin, shape, and disease resistance. Functions of the stress resistance genes ABSCISIC ACIS-INSENSITIVE 5-like ( ABI5 ), WRKY72 , and MAPK3 were found to be related to many tolerance responses. The detection of AUXIN RESPONSE FACTOR17-LIKE ( ARF17 ) suggests a balance between auxin-regulated leaf growth and stress resistance within the genome, which led to the development of heterophylly via evolution. Differentially expressed genes between control and stressed plants included several factors with similar functions affecting stress-mediated heterophylly, such as the stress-related genes ABC transporter C family member 2 ( ABCC2 ) and ABC transporter F family member ( ABCF ), and the stomata-regulating and reactive oxygen species (ROS) signaling gene RESPIRATORY BURST OXIDASE HOMOLOG ( RBOH ). A comparison of the genetic architecture of control and salt-stressed plants revealed a potential link between heterophylly and saline tolerance in P. euphratica , which will provide new avenues for research on saline resistance-related genetic mechanisms.
Why it matches plant phenotyping methods葉形を追跡・解析する方法を開発し、葉形状を動的な表現型として定量化しているため、植物フェノタイピング手法が研究の中心です。
abstractWe developed a shape tracking method to monitor and analyze the leaf shape using principal component (PC) analysis.
Reproduction assets foundThe paper's data availability statement explicitly deposits the authors' analysis code and shell scripts (supporting the functional mapping/GWAS of leaf heterophylly phenotypes) in a public GitHub repository. No public phenotype dataset or image deposit is stated; supplementary material link exists but its contents areCode · publicThe code and shell script that support the findings of this study are available from https://github.com/YaruFu01/leafQTL or can be requested from the corresponding author.Open asset ↗YaruFu01/leafQTLlines:523-535Code / dataset availability confirmedEurope PMC · bioRxiv · checked 15 Sept 2026
This article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL” for REP orter R atiometrics I ntegrating S egmentation for A nalyzing L ignification. REPRISAL consists of the combination of three separate approaches. In the first approach, H*, G* and S* monolignol chemical reporters, corresponding to p -coumaryl alcohol, coniferyl alcohol and sinapyl alcohol, are used to label the growing lignin polymer in a fluorescent triple labelling strategy based on the sequential use of 3 main bioorthogonal chemical reactions. In the second step, an automatic parametric and/or artificial intelligence (AI) segmentation algorithm is developed that assigns fluorescent image pixels to 3 distinct cell wall zones corresponding to cell corners (CC), compound middle lamella (CML) and secondary cell walls (SCW). The last step corresponds to the exploitation of a ratiometric approach enabling statistical analyses of differences in monolignol reporter distribution (ratiometric method 1) and proportions (ratiometric method 2) within the different cell wall zones. In order to demonstrate the potential of REPRISAL for investigating lignin formation we firstly describe its use to map developmentally-related changes in the lignification capacity of WT Arabidopsis interfascicular fiber cells. We then show how it can be used to reveal subtle phenotypical differences in lignification by analyzing the Arabidopsis prx64 peroxidase mutant and provide further evidence for the implication of the AtPRX64 protein in floral stem lignification. Finally, we demonstrate the general applicability of REPRISAL by using it to map lignification capacity in poplar, flax and maize.
Why it matches plant phenotyping methodsREPRISALという蛍光画像・自動セグメンテーション・比率解析を統合した、細胞壁リグニン形成状態の植物フェノタイピング手法を開発し、複数種・変異体で適用している。
abstractThis article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL”
Reproduction assets foundThe authors publicly deposited their Fiji/ImageJ segmentation plugin (GUI, parametric macro, WEKA classifier and training data) plus representative confocal sample images in a Zenodo repository, explicitly referenced in the methods and supplementary data as containing the paper's lignification ratiometric analysis toolDataset · publicThe binary mask of each region was applied to each fluorescence channel and
569
fluorescence mean values were extracted for the 9 newly-created images. A recapitulative
570
montage image was then created to quickly estimate segmentation quality. The imageJ macro
571
and sample images are available in the Zenodo repository,
572
http://doi.org/10.5281/zenodo.4809980.573
574
AI Segmentation
575
The Machine learning approach is based on the “Waikato Environment for Knowledge
576
Analysis” (WEKA) implemented in ImageJ (Witten et al., 2016). We first defined a
577
classification based on four categories: i) secondary cell wall, ii) cell corners, iii) compound
578
middle lamella and iv) backgroOpen asset ↗zenodo · 10.5281/zenodo.4809980pdf-raw-page:21 lines:1-63Code / dataset availability confirmedCrossref · checked 9 Sept 2026
Stomata are microscopic pores on the plant epidermis that regulate the water content and CO2 levels in leaves. Thus, they play an important role in plant growth and development. Currently, most of the common methods for the measurement of pore anatomy parameters involve manual measurement or semi-automatic analysis technology, which makes it difficult to achieve high-throughput and automated processing. This paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks. The proposed method uses a type of convolutional neural network model (Mask R-CNN (region-based convolutional neural network)) to obtain the contour coordinates of the pore regions in microscope images of leaves. The anatomy parameters of pores are then obtained by ellipse fitting technology, and the quantitative analysis of pore parameters is implemented. Stomatal microscope image datasets for black poplar leaves were obtained using a large depth-of-field microscope observation system, the VHX-2000, from Keyence Corporation. The images used in the training, validation, and test sets were taken randomly from the datasets (562, 188, and 188 images, respectively). After 10-fold cross validation, the 188 test images were found to contain an average of 2278 pores (pore widths smaller than 0.34 μm (1.65 pixels) were considered to be closed stomata), and an average of 2201 pores were detected by our network with a detection accuracy of 96.6%, and the intersection of union (IoU) of the pores was 0.82. The segmentation results of 2201 stomatal pores of black poplar leaves showed that the average measurement accuracies of the (a) pore length, (b) pore width, (c) area, (d) eccentricity, and (e) degree of stomatal opening, with a ratio of width-to-maximum length of a stomatal pore, were (a) 94.66%, (b) 93.54%, (c) 90.73%, (d) 99.09%, and (e) 92.95%, respectively. The proposed stomatal pore detection and measurement method based on the Mask R-CNN can automatically measure the anatomy parameters of pores in plants, thus helping researchers to obtain accurate stomatal pore information for leaves in an efficient and simple way.
Why it matches plant phenotyping methods葉の気孔画像から形態・開口状態を自動抽出・定量する画像解析手法を開発し、精度検証しており、植物フェノタイピング手法が研究の中心です。
abstractThis paper presents a method for the automatic segmentation and parameter calculation of stomatal pores in microscope images of plant leaves based on deep convolutional neural networks.
Reproduction assets foundThe paper's authors explicitly state that the complete project code for the Mask R-CNN-based stomatal pore detection and measurement method is publicly available on GitHub. The image datasets themselves are not stated as deposited by the authors (the generalization datasets are cited prior work, Stomatacounter [39]).Code · publicThe complete code for the project can be accessed at
https://github.com/lijunyu159/stomatal_pore_measurement-MaskRCNN (accessed on 15 July 2020).Open asset ↗lijunyu159/stomatal_pore_measurement-MaskRCNNpdf-page:9 lines:1-57Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Lignocellulosic biomass (LB) is recalcitrant to enzymatic hydrolysis due to its compact and complex cell wall structure. To identify the parameters behind LB recalcitrance, experimental data over hydrolysis time must be collected. Here, we describe a novel method to collect time-lapse images during cell wall deconstruction by enzymatic hydrolysis. The protocol includes instructions for sample preparation, layout of a custom designed incubation chamber and instructions for confocal time lapse acquisition. The protocol sets out a detailed plan where cross-sections of untreated and pretreated poplar samples are mounted in a sealed frame containing a buffer and an enzymatic cocktail. The sealed frame is then placed into an incubator to maintain the sample at a constant temperature of 50 °C, which is optimal for enzymatic reaction while avoiding enzymatic cocktail evaporation. Using lignin natural autofluorescence, confocal z-stacks of untreated and pretreated samples were acquired at regular time intervals during enzymatic hydrolysis for 24 h. Acquisition parameters were optimized to compromise between image resolution and reduced photo-bleaching. The acquired image might then be processed by further development of algorithms to extract precise quantitative information on cell wall deconstruction. This protocol is an important first step towards elucidating the underlying parameters of LB recalcitrance by allowing the acquisition of high-quality images of LB hydrolysis for extracting quantitative data on LB deconstruction.
Why it matches plant phenotyping methodsポプラ細胞壁の分解状態を時系列の共焦点3D画像で取得するプロトコル自体が中心であり、植物組織状態の定量的表現型抽出を可能にするため。
abstractHere, we describe a novel method to collect time-lapse images during cell wall deconstruction by enzymatic hydrolysis.
Reproduction assets foundThe paper's authors state that the scripts for computing photobleaching signal loss and image registration/analysis are publicly available in the FARE Laboratory GitLab repository, with an explicit URL matching an allowed URL.Code · publicels’ intensity
reduction in confocal image) between successive z-stacks. The signal loss was computed by subtracting
the voxels’ intensities between the registered floating image, It ◦ T It ←It+∆t , and the reference image It+∆t
and summing up the subtracted values (Scripts are available at the FARE Laboratory Gitlab Repository
https://gitlab.com/farelab/teamyr/publications/zoghlami_et_al_sus_chem_2020).
4. Results
Using the protocol, we acquired confocal images of pretreated poplar samples during hydrolysis
(Figure 7). We could visually observe that the cell walls gradually degraded over time. To illustrate
the advantages offered by using this protocol to achieve a quantitative characterizaOpen asset ↗gitlab.com/farelab/teamyr/publications/zoghlami_et_al_sus_chem_2020pdf-layout-page:8 lines:1-42Code / dataset availability confirmedEurope PMC · checked 10 Sept 2026
Genomic selection - the prediction of breeding values using DNA polymorphisms - is a disruptive method that has widely been adopted by animal and plant breeders to increase productivity. It was recently shown that other sources of molecular variations such as those resulting from transcripts or metabolites could be used to accurately predict complex traits. These endophenotypes have the advantage of capturing the expressed genotypes and consequently the complex regulatory networks that occur in the different layers between the genome and the phenotype. However, obtaining such omics data at very large scales, such as those typically experienced in breeding, remains challenging. As an alternative, we proposed using near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits, and coined this new approach "phenomic selection" (PS). We tested PS on two species of economic interest ( Triticum aestivum L. and Populus nigra L.) using NIRS on various tissues (grains, leaves, wood). We showed that one could reach predictions as accurate as with molecular markers, for developmental, tolerance and productivity traits, even in environments radically different from the one in which NIRS were collected. Our work constitutes a proof of concept and provides new perspectives for the breeding community, as PS is theoretically applicable to any organism at low cost and does not require any molecular information.
Why it matches plant phenotyping methodsNIRSを用いて植物組織から表現型関連情報を非破壊・高スループットに取得し、複雑形質を予測する手法自体が研究の中心である。
abstractusing near-infrared spectroscopy (NIRS) as a high-throughput, low cost and non-destructive tool to indirectly capture endophenotypic variants and compute relationship matrices for predicting complex traits
Reproduction assets foundThe paper's NIRS spectra, phenotypic and SNP datasets are publicly deposited in the INRA Dataverse repository (DOI 10.15454/MB4G3T), and the authors' R functions for cross-validation prediction comparisons are on GitHub (visegura/PS). Supplemental material (including File S1 with variance-partition results) is on FigshDataset · publicThe datasets generated during and/or analyzed during the current study are available in the INRA Dataverse repository ( https://data.inra.fr/ ). They can be accessed with the following link http://dx.doi.org/10.15454/MB4G3T .Open asset ↗INRA Dataverse · 10.15454/MB4G3Tlines:66-74Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Summary Spectroscopy has recently emerged as an effective method to accurately characterize leaf biochemistry in living tissue through the application of chemometric approaches to foliar optical data, but this approach has not been widely used for plant secondary metabolites. Here, we examine the ability of reflectance spectroscopy to quantify specific phenolic compounds in trembling aspen ( Populus tremuloides ) and paper birch ( Betula papyrifera ) that play influential roles in ecosystem functioning related to trophic‐level interactions and nutrient cycling. Spectral measurements on live aspen and birch leaves were collected, after which concentrations of condensed tannins (aspen and birch) and salicinoids (aspen only) were determined using standard analytical approaches in the laboratory. Predictive models were then constructed using jackknifed, partial least squares regression ( PLSR ). Model performance was evaluated using coefficient of determination ( R 2 ), root‐mean‐square error ( RMSE ) and the per cent RMSE of the data range (% RMSE ). Condensed tannins of aspen and birch were well predicted from both combined ( R 2 = 0·86, RMSE = 2·4, % RMSE = 7%)‐ and individual‐species models (aspen: R 2 = 0·86, RMSE = 2·4, % RMSE = 6%; birch: R 2 = 0·81, RMSE = 1·9, % RMSE = 10%). Aspen total salicinoids were better predicted than individual salicinoids (total: R 2 = 0·76, RMSE = 2·4, % RMSE = 8%; salicortin: R 2 = 0·57, RMSE = 1·9, % RMSE = 11%; tremulacin: R 2 = 0·72, RMSE = 1·1, % RMSE = 11%), and spectra collected from dry leaves produced better models for both aspen tannins ( R 2 = 0·92, RMSE = 1·7, % RMSE = 5%) and salicinoids ( R 2 = 0·84, RMSE = 1·4, % RMSE = 5%) compared with spectra from fresh leaves. The decline in prediction performance from total to individual salicinoids and from dry to fresh measurements was marginal, however, given the increase in detailed salicinoid information acquired and the time saved by avoiding drying and grinding leaf samples. Reflectance spectroscopy can successfully characterize specific secondary metabolites in living plant tissue and provide detailed information on individual compounds within a constituent group. The ability to simultaneously measure multiple plant traits is a powerful attribute of reflectance spectroscopy because of its potential for in situ – in vivo field deployment using portable spectrometers. The suite of traits currently estimable, however, needs to expand to include specific secondary metabolites that play influential roles in ecosystem functioning if we are to advance the integration of chemical, landscape and ecosystem ecology.
Why it matches plant phenotyping methods生葉の反射分光とPLSRにより二次代謝産物を定量する測定・予測手法を構築し、モデル性能を評価しており、植物形質取得法が研究の中心である。
abstractSpectroscopy has recently emerged as an effective method to accurately characterize leaf biochemistry in living tissue through the application of chemometric approaches to foliar optical data
Reproduction assets foundThe paper's Data Accessibility statement explicitly archives both the spectral data used in the study and the PLSR model-building code in EcoSIS, with a public URL matching an allowed URL.Dataset · publico PAT and RLL, and USDA NIFA McIntire-Stennis projects
WIS01651 to RLL and WIS01531 and WIS01599 to PAT.
Data Accessibility
Spectral data used in this study and the partial least squares regression code used for model
building are archived in the Ecosystem Spectral Information System (EcoSIS;
www.ecosis.org) and can be found at https://ecosis.org/#result/d5445eb9-f334-4ee7-90a9-1fe07e67a20c.Open asset ↗EcoSIS · d5445eb9-f334-4ee7-90a9-1fe07e67a20cpdf-raw-page:23 lines:1-25